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cb1739c50d15ab590cbf99159ef00e1f5a3570cd | 1,510 | ipynb | Jupyter Notebook | speech_recoginition_challenge.ipynb | xrick/Kaggle_TensorFlow_Speech_Recognition_Challenge | 7f4a59d0545166255af216fff8b439dea74f4a65 | [
"MIT"
] | null | null | null | speech_recoginition_challenge.ipynb | xrick/Kaggle_TensorFlow_Speech_Recognition_Challenge | 7f4a59d0545166255af216fff8b439dea74f4a65 | [
"MIT"
] | null | null | null | speech_recoginition_challenge.ipynb | xrick/Kaggle_TensorFlow_Speech_Recognition_Challenge | 7f4a59d0545166255af216fff8b439dea74f4a65 | [
"MIT"
] | null | null | null | 40.810811 | 719 | 0.619868 | [
[
[
"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code"
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cb173e314f2a854065b15366e6cb44a38398759b | 8,100 | ipynb | Jupyter Notebook | classification/notebooks/06 - Hyperparameter and Grid Search CV.ipynb | pshn111/Machine-Learning-Package | fbbaa44daf5f0701ea77e5b62eb57ef822e40ab2 | [
"MIT"
] | null | null | null | classification/notebooks/06 - Hyperparameter and Grid Search CV.ipynb | pshn111/Machine-Learning-Package | fbbaa44daf5f0701ea77e5b62eb57ef822e40ab2 | [
"MIT"
] | null | null | null | classification/notebooks/06 - Hyperparameter and Grid Search CV.ipynb | pshn111/Machine-Learning-Package | fbbaa44daf5f0701ea77e5b62eb57ef822e40ab2 | [
"MIT"
] | null | null | null | 27 | 121 | 0.408272 | [
[
[
"# Hyperparameter Tuning and Grid Search CV",
"_____no_output_____"
],
[
"### Read data from pickle file",
"_____no_output_____"
]
],
[
[
"import pickle as pkl\n\nwith open('../data/titanic_tansformed.pkl', 'rb') as f:\n df_data = pkl.load(f)",
"_____no_output_____"
],
[
"df_data.head()",
"_____no_output_____"
],
[
"data = df_data.drop(\"Survived\",axis=1)\nlabel = df_data[\"Survived\"]",
"_____no_output_____"
]
],
[
[
"#### Divide into train and test split",
"_____no_output_____"
]
],
[
[
"from sklearn.model_selection import train_test_split\ndata_train, data_test, label_train, label_test = train_test_split(data, label, test_size = 0.2, random_state = 101)",
"_____no_output_____"
],
[
"from sklearn.model_selection import StratifiedKFold\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import GridSearchCV\n\nC_param_range = [0.001,0.01,0.1,0.5,1,10,100]\npenalties = ['l1','l2']\nscore_func = 'accuracy'\n\nlog_regr = LogisticRegression()\nlog_grid = GridSearchCV(estimator=log_regr, \n param_grid=[{'C':C_param_range, 'penalty': penalties}], \n cv=5, \n scoring=score_func)\nlog_grid.fit(data_train, label_train)\nprint('Best Score', log_grid.best_score_)\nprint('Best Value of C', log_grid.best_estimator_.C)\nprint('Best penalty', log_grid.best_estimator_.penalty)",
"Best Score 0.8045007032348804\nBest Value of C 0.5\nBest penalty l1\n"
]
],
[
[
"### Optimal Model after a GridCV Search",
"_____no_output_____"
]
],
[
[
"optimal_model = LogisticRegression(C=log_grid.best_estimator_.C, penalty=log_grid.best_estimator_.penalty)\noptimal_model.fit(data_train, label_train)\npredictions = optimal_model.predict(data_test)\n\nprint('Accuracy', optimal_model.score(data_test, label_test))\nprint('Coefficients', optimal_model.coef_)\nprint('Intercept', optimal_model.intercept_)",
"Accuracy 0.8146067415730337\nCoefficients [[-2.88708422e-02 -2.72763004e-01 -1.09620072e-01 2.39262062e-03\n 7.90405016e-01 0.00000000e+00 -1.08574363e+00 2.56316901e+00\n 0.00000000e+00 0.00000000e+00 0.00000000e+00 -3.09675215e-01]]\nIntercept [0.]\n"
]
]
] | [
"markdown",
"code",
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"markdown"
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[
"code"
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] |
cb174a32ff2168fa1b8f7fcad5b4b49b946bd05d | 385,801 | ipynb | Jupyter Notebook | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline | 23526d30fef1ac415caf1573164e1705d95d8692 | [
"MIT"
] | 2 | 2022-02-01T19:24:09.000Z | 2022-02-15T14:26:08.000Z | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline | 23526d30fef1ac415caf1573164e1705d95d8692 | [
"MIT"
] | 113 | 2021-12-20T15:17:51.000Z | 2022-03-30T15:04:54.000Z | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline | 23526d30fef1ac415caf1573164e1705d95d8692 | [
"MIT"
] | 10 | 2021-12-17T19:45:33.000Z | 2022-02-24T21:38:05.000Z | 380.849951 | 347,365 | 0.923043 | [
[
[
"# Bulk RNA-seq eQTL analysis\n\nThis notebook provide a command generator on the XQTL workflow so it can automate the work for data preprocessing and association testing on multiple data collection as proposed.",
"_____no_output_____"
]
],
[
[
"%preview ../images/eqtl_command.png",
"_____no_output_____"
]
],
[
[
"This master control notebook is mainly to serve the 8 tissues snuc_bulk_expression analysis, but should be functional on all analysis where expression data are are a tsv table in a bed.gz like format.\n\nInput:\n\n A recipe file,each row is a data collection and with the following column:\n \n Theme\n name of dataset, must be different, each uni_study analysis will be performed in a folder named after each, meta analysis will be performed in a folder named as {study1}_{study2}\n \n The column name must contain the # and be the first column\n \n genotype_file\n {Path to a whole genome genotype file}\n \n molecular_pheno\n {Path to file}\n \n covariate_file\n {Path to file}\n \n ### note: Only data collection from the same Populations and conditions will me merged to perform Fix effect meta analysis\n \n A genotype list, with two column, `#chr` and `path`\n \n This can be generated by the genotype session of this command generator.\n \n \nOutput:\n \n 1 set of association_scan result for each tissue (each row in the recipe)",
"_____no_output_____"
]
],
[
[
"pd.DataFrame({\"Theme\":\"MWE\",\"molecular_pheno\":\"MWE.log2cpm.tsv\",\"genotype_file\":\"MWE.bed\",\"covariate_file\":\"MWE.covariate.cov.gz\"}).to_csv(\"/mnt/vast/hpc/csg/snuc_pseudo_bulk/eight_tissue_analysis/MWE/command_generator\",sep = \"\\t\",index = 0)",
"_____no_output_____"
]
],
[
[
"| Theme | molecular_pheno | genotype_file |covariate_file|\n| ----------- | ----------- |-----------||\n| MWE | MWE.log2cpm.tsv | /data/genotype_data/GRCh38_liftedover_sorted_all.add_chr.leftnorm.filtered.bed |MWE.covariate.cov.gz|",
"_____no_output_____"
],
[
"## Minimal Working Example",
"_____no_output_____"
],
[
"### Genotype\nThe MWE for the genotype session can be ran with the following commands, please be noted that a [seperated MWE genoFile]( https://drive.google.com/file/d/1zaacRlZ63Nf_oEUv2nIiqekpQmt2EDch/view?usp=sharing) was needed.",
"_____no_output_____"
]
],
[
[
"sos run pipeline/eQTL_analysis_commands.ipynb plink_per_chrom \\\n --ref_fasta reference_data/GRCh38_full_analysis_set_plus_decoy_hla.noALT_noHLA_noDecoy_ERCC.fasta \\\n --genoFile mwe_genotype.vcf.gz \\\n --dbSNP_vcf reference_data/00-All.vcf.gz \\\n --sample_participant_lookup reference_data/sampleSheetAfterQC.txt -n ",
"_____no_output_____"
]
],
[
[
"### Per tissue analysis\nA MWE for the core per tissue analysis can be ran with the following commands, a complete collection of input file as well as intermediate output of the analysis can be found at [here](https://drive.google.com/drive/folders/16ZUsciZHqCeeEWwZQR46Hvh5OtS8lFtA?usp=sharing). ",
"_____no_output_____"
]
],
[
[
"sos run pipeline/eQTL_analysis_commands.ipynb sumstat_merge \\\n --recipe MWE.recipe \\\n --genotype_list plink_files_list.txt \\\n --annotation_gtf reference_data/genes.reformatted.gene.gtf \\\n --sample_participant_lookup reference_data/sampleSheetAfterQC.txt \\\n --Association_option \"TensorQTL\" -n ",
"_____no_output_____"
],
[
"sos run pipeline/eQTL_analysis_commands.ipynb sumstat_merge \\\n --recipe MWE.recipe \\\n --genotype_list plink_files_list.txt \\\n --annotation_gtf /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/reference_data/genes.reformatted.gene.gtf \\\n --sample_participant_lookup /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/reference_data/sampleSheetAfterQC.txt \\\n --Association_option \"APEX\" -n ",
"_____no_output_____"
]
],
[
[
"## Example for running the workflow\nThis will run the workflow from via several submission",
"_____no_output_____"
]
],
[
[
"sos run ~/GIT/xqtl-pipeline/pipeline/eQTL_analysis_commands.ipynb sumstat_merge \\\n --recipe /mnt/vast/hpc/csg/snuc_pseudo_bulk//data/recipe_8tissue_new \\\n --genotype_list /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/genotype_qced/plink_files_list.txt \\\n --annotation_gtf /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/reference_data/genes.reformatted.gene.gtf \\\n --sample_participant_lookup /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/reference_data/sampleSheetAfterQC.txt \\\n --Association_option \"TensorQTL\" --run &",
"_____no_output_____"
],
[
"sos run ~/GIT/xqtl-pipeline/pipeline/eQTL_analysis_commands.ipynb sumstat_merge \\\n --recipe <(cat /mnt/vast/hpc/csg/snuc_pseudo_bulk//data/recipe_8tissue_new | head -2) \\\n --genotype_list /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/genotype_qced/plink_files_list.txt \\\n --annotation_gtf /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/reference_data/genes.reformatted.gene.gtf \\\n --sample_participant_lookup /mnt/vast/hpc/csg/snuc_pseudo_bulk/data/reference_data/sampleSheetAfterQC.txt \\\n --factor_option \"PEER\" --Association_option \"TensorQTL\" -n",
"_____no_output_____"
],
[
"[global]\n## The aforementioned input recipe\nparameter: recipe = path(\".\") # Added option to run genotype part without the recipe input, which was not used.\n## Overall wd, the file structure of analysis is wd/[steps]/[sub_dir for each steps]\nparameter: cwd = path(\"output\")\n## Diretory to the excutable\nparameter: exe_dir = path(\"~/GIT/xqtl-pipeline/\")\nparameter: container_base_bioinfo = 'containers/bioinfo.sif'\nparameter: container_apex = 'containers/apex.sif'\nparameter: container_PEER = 'containers/PEER.sif'\nparameter: container_TensorQTL = 'containers//TensorQTL.sif'\nparameter: container_rnaquant = 'containers/rna_quantification.sif'\nparameter: container_flashpca = 'containers/flashpcaR.sif'\nparameter: container_susie = 'containers/stephenslab.sif'\nparameter: sample_participant_lookup = path\nparameter: phenotype_id_type = \"gene_name\" \nparameter: yml = path(\"csg.yml\")\nparameter: run = False\ninterpreter = 'cat' if not run else 'bash'\nimport pandas as pd\nif recipe.is_file():\n input_inv = pd.read_csv(recipe, sep = \"\\t\").to_dict(\"records\")\nimport os\nparameter: jobs = 50 # Number of jobs that are submitted to the cluster\nparameter: queue = \"csg\" # The queue that jobs are submitted to\nsubmission = f'-J {jobs} -c {yml} -q {queue}'\n\n\n## Control of the workflow\n### Factor option (PEER vs BiCV)\nparameter: factor_option = \"PEER\"\n### Association scan option (APEX vs TensorQTL)\nparameter: Association_option = \"TensorQTL\"",
"_____no_output_____"
]
],
[
[
"## Data Preprocessing\n### Genotype Preprocessing (Once for all tissues)",
"_____no_output_____"
]
],
[
[
"[dbSNP]\nparameter: dbSNP_vcf = path\ninput: dbSNP_vcf\nparameter: add_chr = True\noutput: f'{cwd}/reference_data/{_input:bnn}.add_chr.variants.gz'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]/pipeline//VCF_QC.ipynb dbsnp_annotate \\\n --genoFile $[_input] \\\n --cwd $[_output:d] \\\n --container $[container_base_bioinfo] \\\n $[submission if yml.is_file() else \"\" ] $[\"--add_chr\" if add_chr else \"--no-add_chr\" ]",
"_____no_output_____"
],
[
"[VCF_QC]\nparameter: genoFile = path\nparameter: ref_fasta = path\nparameter: add_chr = True\ninput: genoFile, output_from(\"dbSNP\")\noutput: f'{cwd}/data_preprocessing/{_input[0]:bnn}.{\"add_chr.\" if add_chr else False}leftnorm.filtered.bed'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]//pipeline/VCF_QC.ipynb qc \\\n --genoFile $[_input[0]] \\\n --dbsnp-variants $[_input[1]] \\\n --reference-genome $[ref_fasta] \\\n --cwd $[_output:d] \\\n --container $[container_base_bioinfo] \\\n --walltime \"24h\" \\\n $[submission if yml.is_file() else \"\" ] $[\"--add_chr\" if add_chr else \"--no-add_chr\" ]",
"_____no_output_____"
],
[
"[plink_QC]\n# minimum MAF filter to use. 0 means do not apply this filter.\nparameter: maf_filter = 0.05\n# maximum MAF filter to use. 0 means do not apply this filter.\nparameter: maf_max_filter = 0.0\n# Maximum missingess per-variant\nparameter: geno_filter = 0.1\n# Maximum missingness per-sample\nparameter: mind_filter = 0.1\n# HWE filter \nparameter: hwe_filter = 1e-06\ninput: output_from(\"VCF_QC\")\noutput: f'{_input:n}.filtered.bed'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]//pipeline/GWAS_QC.ipynb qc_no_prune \\\n --cwd $[_output:d] \\\n --genoFile $[_input] \\\n --maf-filter $[maf_filter] \\\n --geno-filter $[geno_filter] \\\n --mind-filter $[mind_filter] \\\n --hwe-filter $[hwe_filter] \\\n --mem 40G \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[plink_per_chrom]\ninput: output_from(\"plink_QC\")\noutput: f'{cwd:a}/data_preprocessing/{_input:bn}.plink_files_list.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]//pipeline/genotype_formatting.ipynb plink_by_chrom \\\n --genoFile $[_input] \\\n --cwd $[_output:d] \\\n --chrom `cut -f 1 $[_input:n].bim | uniq | sed \"s/chr//g\"` \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[plink_to_vcf]\nparameter: genotype_list = path\ninput: genotype_list\nimport pandas as pd\nparameter: genotype_file_name = pd.read_csv(_input,\"\\t\",nrows = 1).values.tolist()[0][1]\noutput: f'{cwd:a}/data_preprocessing/{path(genotype_file_name):bnn}.vcf_files_list.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]//pipeline/genotype_formatting.ipynb plink_to_vcf \\\n --genoFile $[_input] \\\n --cwd $[_output:d] \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[plink_per_gene]\n# The plink genotype file\nparameter: genoFile = path\ninput: output_from(\"region_list_concat\"),genoFile\noutput: f'{cwd:a}/{_input[1]:bn}.plink_files_list.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]/pipeline//genotype_formatting.ipynb plink_by_gene \\\n --genoFile $[_input[1]] \\\n --cwd $[_output:d] \\\n --region_list $[_input[0]] \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
]
],
[
[
"### Molecular Phenotype Processing",
"_____no_output_____"
]
],
[
[
"[annotation]\nstop_if(not recipe.is_file(), msg = \"Please specify a valid recipe as input\")\nimport os\nparameter: annotation_gtf = path\ninput: for_each = \"input_inv\"\noutput: f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/phenotype_data/{path(_input_inv[\"molecular_pheno\"]):bn}.bed.gz'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]/pipeline/gene_annotation.ipynb annotate_coord \\\n --cwd $[_output:d] \\\n --phenoFile $[_input_inv[\"molecular_pheno\"]] \\\n --annotation-gtf $[annotation_gtf] \\\n --sample-participant-lookup $[sample_participant_lookup] \\\n --container $[container_rnaquant] \\\n --phenotype-id-type $[phenotype_id_type] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[region_list_generation]\nparameter: annotation_gtf = path\ninput: output_from(\"annotation\"), group_with = \"input_inv\"\noutput: pheno_mod = f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/phenotype_data/{_input:bnn}.region_list'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/gene_annotation.ipynb region_list_generation \\\n --cwd $[_output:d] \\\n --phenoFile $[_input]\\\n --annotation-gtf $[annotation_gtf] \\\n --sample-participant-lookup $[sample_participant_lookup] \\\n --container $[container_rnaquant] \\\n --phenotype-id-type $[phenotype_id_type] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[region_list_concat]\ninput: output_from(\"region_list_generation\"), group_by = \"all\"\noutput: f'{cwd:a}/data_preprocessing/phenotype_data/concat.region_list'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n cat $[_input:a] | sort | uniq > $[_output:a] ",
"_____no_output_____"
],
[
"[phenotype_partition_by_chrom]\ninput: output_from(\"annotation\"),output_from(\"region_list_generation\"), group_with = \"input_inv\"\noutput: per_chrom_pheno_list = f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/phenotype_data/{_input[0]:bn}.processed_phenotype.per_chrom.recipe' \nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/phenotype_formatting.ipynb partition_by_chrom \\\n --cwd $[_output:d] \\\n --phenoFile $[_input[0]:a] \\\n --region-list $[_input[1]:a] \\\n --container $[container_rnaquant] \\\n --mem 4G $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
]
],
[
[
"### Genotype Processing\nSince genotype is shared among the eight tissue, the QC of whole genome file is not needed. Only pca needed to be run again.",
"_____no_output_____"
]
],
[
[
"[sample_match]\ninput: for_each = \"input_inv\"\noutput: f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/{sample_participant_lookup:bn}.filtered.txt',\n geno = f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/{sample_participant_lookup:bn}.filtered_geno.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/sample_matcher.ipynb filtered_sample_list \\\n --cwd $[_output[0]:d] \\\n --phenoFile $[_input_inv[\"molecular_pheno\"]] \\\n --genoFile $[path(_input_inv[\"genotype_file\"]):n].fam \\\n --sample-participant-lookup $[sample_participant_lookup] \\\n --container $[container_rnaquant] \\\n --translated_phenoFile $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[king]\nparameter: maximize_unrelated = False\ninput:output_from(\"sample_match\")[\"geno\"], group_with = \"input_inv\"\noutput: related = f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/genotype_data/{path(_input_inv[\"genotype_file\"]):bn}.{_input_inv[\"Theme\"]}.related.bed',\n unrelated = f'{cwd:a}/data_preprocessing/{_input_inv[\"Theme\"]}/genotype_data/{path(_input_inv[\"genotype_file\"]):bn}.{_input_inv[\"Theme\"]}.unrelated.bed'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/GWAS_QC.ipynb king \\\n --cwd $[_output[0]:d] \\\n --genoFile $[_input_inv[\"genotype_file\"]] \\\n --name $[_input_inv[\"Theme\"]] \\\n --keep-samples $[_input] \\\n --container $[container_base_bioinfo] \\\n --walltime 48h $[submission if yml.is_file() else \"\" ] $[\"--maximize_unrelated\" if maximize_unrelated else \"--no-maximize_unrelated\"]",
"_____no_output_____"
],
[
"[unrelated_QC]\ninput: output_from(\"king\")[\"unrelated\"]\noutput: unrelated_bed = f'{_input:n}.filtered.prune.bed', \n prune = f'{_input:n}.filtered.prune.in'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/GWAS_QC.ipynb qc \\\n --cwd $[_output[0]:d] \\\n --genoFile $[_input] \\\n --exclude-variants /mnt/vast/hpc/csg/snuc_pseudo_bulk/Ast/genotype/dupe_snp_to_exclude \\\n --maf-filter 0.05 \\\n --container $[container_base_bioinfo] \\\n --mem 40G $[submission if yml.is_file() else \"\" ] ",
"_____no_output_____"
],
[
"[related_QC]\ninput: output_from(\"king\")[\"related\"],output_from(\"unrelated_QC\")[\"prune\"]\noutput: f'{_input[0]:n}.filtered.extracted.bed'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/GWAS_QC.ipynb qc_no_prune \\\n --cwd $[_output[0]:d] \\\n --genoFile $[_input[0]] \\\n --maf-filter 0 \\\n --geno-filter 0 \\\n --mind-filter 0.1 \\\n --hwe-filter 0 \\\n --keep-variants $[_input[1]] \\\n --container $[container_base_bioinfo] \\\n --mem 40G $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
]
],
[
[
"## Factor Analysis",
"_____no_output_____"
]
],
[
[
"[pca]\ninput: output_from(\"unrelated_QC\")[\"unrelated_bed\"],group_with = \"input_inv\"\noutput: f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/pca/{_input:bn}.pca.rds',\n f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/pca/{_input:bn}.pca.scree.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/PCA.ipynb flashpca \\\n --cwd $[_output:d] \\\n --genoFile $[_input] \\\n --container $[container_flashpca] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[projected_sample]\n# The percentage of PVE explained\nparameter: PVE_treshold = 0.7\ninput: output_from(\"related_QC\"),output_from(\"pca\"), group_with = \"input_inv\"\noutput: f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/pca/{_input[0]:bn}.pca.projected.rds',\n f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/pca/{_input[0]:bn}.pca.projected.scree.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/PCA.ipynb project_samples \\\n --cwd $[_output:d] \\\n --genoFile $[_input[0]] \\\n --pca-model $[_input[1]] \\\n --maha-k `awk '$3 < $[PVE_treshold]' $[_input[2]] | tail -1 | cut -f 1 ` \\\n --container $[container_flashpca] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[merge_pca_covariate]\n# The percentage of PVE explained\nparameter: PVE_treshold = 0.7\ninput: output_from(\"projected_sample\"),group_with = \"input_inv\"\noutput: f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/covariates/{path(_input_inv[\"covariate_file\"]):bn}.pca.gz'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/covariate_formatting.ipynb merge_pca_covariate \\\n --cwd $[_output:d] \\\n --pcaFile $[_input[0]:a] \\\n --covFile $[path(_input_inv[\"covariate_file\"])] \\\n --tol_cov 0.3 \\\n --k `awk '$3 < $[PVE_treshold]' $[_input[1]] | tail -1 | cut -f 1 ` \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ] --name $[_output:bn] --outliersFile $[_input[0]:an].outliers",
"_____no_output_____"
],
[
"[resid_exp]\ninput: output_from(\"merge_pca_covariate\"),output_from(\"annotation\"),group_with = \"input_inv\"\noutput: f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/resid_phenotype/{_input[1]:bnn}.{_input[0]:bn}.resid.bed.gz'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/covariate_formatting.ipynb compute_residual \\\n --cwd $[_output:d] \\\n --phenoFile $[_input[1]:a] \\\n --covFile $[_input[0]:a] \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[factor]\nparameter: N = 0\ninput: output_from(\"resid_exp\"),group_with = \"input_inv\"\noutput: f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/covariates/{_input[0]:bnn}.{factor_option}.gz'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]/pipeline/$[factor_option]_factor.ipynb $[factor_option] \\\n --cwd $[_output:d] \\\n --phenoFile $[_input[0]:a] \\\n --container $[container_apex if factor_option == \"BiCV\" else container_PEER] \\\n --walltime 24h \\\n --numThreads 8 \\\n --iteration 1000 \\\n --N $[N] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
],
[
"[merge_factor_covariate]\n# The percentage of PVE explained\nparameter: PVE_treshold = 0.7\ninput: output_from(\"factor\"),output_from(\"merge_pca_covariate\"),group_with = \"input_inv\"\noutput: f'{cwd}/data_preprocessing/{_input_inv[\"Theme\"]}/covariates/{_input[0]:bn}.cov.gz'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n sos run $[exe_dir]/pipeline/covariate_formatting.ipynb merge_factor_covariate \\\n --cwd $[_output:d] \\\n --factorFile $[_input[0]:a] \\\n --covFile $[_input[1]:a] \\\n --container $[container_base_bioinfo] $[submission if yml.is_file() else \"\" ] --name $[_output:bn]",
"_____no_output_____"
]
],
[
[
"## Association Scan",
"_____no_output_____"
]
],
[
[
"[TensorQTL]\n# The number of minor allele count as treshold for the analysis\nparameter: MAC = 0\n# The minor allele frequency as treshold for the analysis, overwrite MAC\nparameter: maf_threshold = 0\nparameter: genotype_list = path\ninput: genotype_list, output_from(\"phenotype_partition_by_chrom\"),output_from(\"merge_factor_covariate\"),group_with = \"input_inv\"\noutput: f'{cwd:a}/association_scan/{_input_inv[\"Theme\"]}/TensorQTL/TensorQTL.cis._recipe.tsv'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/TensorQTL.ipynb cis \\\n --genotype-list $[_input[0]] \\\n --phenotype-list $[_input[1]] \\\n --covariate-file $[_input[2]] \\\n --cwd $[_output:d] \\\n --container $[container_TensorQTL] $[submission if yml.is_file() else \"\" ] $[f'--MAC {MAC}' if MAC else \"\"] $[f'--maf_threshold {maf_threshold}' if maf_threshold else \"\"] ",
"_____no_output_____"
],
[
"[APEX]\nparameter: genotype_list = path\ninput: output_from(\"plink_to_vcf\"), output_from(\"phenotype_partition_by_chrom\"),output_from(\"merge_factor_covariate\"),group_with = \"input_inv\"\noutput: f'{cwd:a}/association_scan/{_input_inv[\"Theme\"]}/APEX/APEX_QTL_recipe.tsv'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/APEX.ipynb cis \\\n --genotype-list $[_input[0]] \\\n --phenotype-list $[_input[1]] \\\n --covariate-file $[_input[2]] \\\n --cwd $[_output:d] \\\n --container $[container_apex] $[submission if yml.is_file() else \"\" ] --name $[_input[1]:bnn]",
"_____no_output_____"
]
],
[
[
"## Trans Association Scan",
"_____no_output_____"
]
],
[
[
"[TensorQTL_Trans]\nparameter: MAC = 0\n# The minor allele frequency as treshold for the analysis, overwrite MAC\nparameter: maf_threshold = 0\nparameter: genotype_list = path\nparameter: region_list = path\ninput: genotype_list, output_from(\"phenotype_partition_by_chrom\"),output_from(\"merge_factor_covariate\"),group_with = \"input_inv\"\noutput: f'{cwd:a}/association_scan/{_input_inv[\"Theme\"]}/Trans/TensorQTL.trans._recipe.tsv'\nscript: interpreter = interpreter, expand = \"$[ ]\", stderr = f'{_output[0]}.stderr', stdout = f'{_output[0]}.stdout'\n sos run $[exe_dir]/pipeline/TensorQTL.ipynb trans \\\n --genotype-list $[_input[0]] \\\n --phenotype-list $[_input[1]] \\\n --covariate-file $[_input[2]] \\\n --cwd $[_output:d] \\\n --region_list $[region_list] \\\n --container $[container_TensorQTL] $[submission if yml.is_file() else \"\" ] $[f'--MAC {MAC}' if MAC else \"\"] $[f'--maf_threshold {maf_threshold}' if maf_threshold else \"\"]",
"_____no_output_____"
]
],
[
[
"## SuSiE\n",
"_____no_output_____"
]
],
[
[
"[UniSuSiE]\ninput: output_from(\"plink_per_gene\"), output_from(\"annotation\"),output_from(\"factor\"), output_from(\"region_list_concat\"), group_by = \"all\"\noutput: f'{cwd:a}/Fine_mapping/UniSuSiE/UniSuSiE_recipe.tsv'\nscript: interpreter = interpreter, expand = \"$[ ]\"\n sos run $[exe_dir]/pipeline/SuSiE.ipynb uni_susie \\\n --genoFile $[_input[0]] \\\n --phenoFile $[\" \".join([str(x) for x in _input[1:len(input_inv)+1]])] \\\n --covFile $[\" \".join([str(x) for x in _input[len(input_inv)+1:len(input_inv)*2+1]])] \\\n --cwd $[_output:d] \\\n --tissues $[\" \".join([x[\"Theme\"] for x in input_inv])] \\\n --region-list $[_input[3]] \\\n --container $[container_susie] $[submission if yml.is_file() else \"\" ]",
"_____no_output_____"
]
],
[
[
"## Sumstat Merger",
"_____no_output_____"
]
],
[
[
"[yml_generation]\nparameter: TARGET_list = path(\"./\")\ninput: output_from(Association_option), group_by = \"all\"\noutput: f'{cwd:a}/data_intergration/{Association_option}/qced_sumstat_list.txt',f'{cwd:a}/data_intergration/{Association_option}/yml_list.txt'\nscript: interpreter = interpreter, expand = \"$[ ]\"\n sos run $[exe_dir]/pipeline/yml_generator.ipynb yml_list \\\n --sumstat-list $[_input] \\\n --cwd $[_output[1]:d] --name $[\" \".join([str(x).split(\"/\")[-3] for x in _input])] --TARGET_list $[TARGET_list]",
"_____no_output_____"
],
[
"[sumstat_merge]\ninput: output_from(\"yml_generation\")\nscript: interpreter = interpreter, expand = \"$[ ]\"\n sos run $[exe_dir]/pipeline/summary_stats_merger.ipynb \\\n --sumstat-list $[_input[0]] \\\n --yml-list $[_input[1]] \\\n --cwd $[_input[0]:d] $[submission if yml.is_file() else \"\" ] --mem 50G --walltime 48h \n ",
"_____no_output_____"
]
]
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cb174d800ecdbec2b31fce0dcc39331702afeef1 | 73,917 | ipynb | Jupyter Notebook | data_process/simtime_prediction/.ipynb_checkpoints/process_data-checkpoint.ipynb | ribuild/delphin_6_automation | 12024381fc1042b46314c55d88b6349229ea33b7 | [
"MIT"
] | 2 | 2017-11-08T18:37:36.000Z | 2018-01-09T12:10:58.000Z | data_process/simtime_prediction/.ipynb_checkpoints/process_data-checkpoint.ipynb | ribuild/delphin_6_automation | 12024381fc1042b46314c55d88b6349229ea33b7 | [
"MIT"
] | 111 | 2018-02-26T08:25:44.000Z | 2021-03-31T19:17:19.000Z | data_process/simtime_prediction/.ipynb_checkpoints/process_data-checkpoint.ipynb | thp44/delphin_6_automation | 12024381fc1042b46314c55d88b6349229ea33b7 | [
"MIT"
] | 3 | 2017-11-06T10:01:25.000Z | 2018-02-14T09:45:28.000Z | 78.053854 | 23,900 | 0.734635 | [
[
[
"import os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport plotly.plotly as py\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\nimport plotly.graph_objs as go\ninit_notebook_mode(connected=True)\n%matplotlib inline\n",
"_____no_output_____"
],
[
"data_folder = r'C:\\Users\\ocni\\PycharmProjects\\delphin_6_automation\\data_process\\simtime_prediction\\data'\nexcel_file = os.path.join(data_folder, 'sim_time.xlsx')\n\ndata = pd.read_excel(excel_file)\ndata.shape",
"_____no_output_____"
],
[
"plt.figure(figsize=(16, 8), dpi= 80, facecolor='w', edgecolor='k')\n(data['time'][data['time'] < 1500 * 60] / 60).plot('hist', bins=50, color='#003399')\nplt.xlabel('Simulation Time in minutes')\n#plt.savefig('simulation_time_histogram.pdf')",
"_____no_output_____"
],
[
"(data['time'][data['time'] < 1500 * 60] / 60).describe()",
"_____no_output_____"
],
[
"hist, edges = np.histogram((data['time'][data['time'] < 1500 * 60] / 60), density=True, bins=50)\ndx = edges[1] - edges[0]\ncdf = np.cumsum(hist) * dx\n\nplt.figure(figsize=(16, 8), dpi= 80, facecolor='w', edgecolor='k')\nplt.plot(edges[:-1], cdf)",
"_____no_output_____"
],
[
"from sklearn.model_selection import train_test_split\nfrom sklearn import linear_model\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.neighbors import KNeighborsRegressor",
"_____no_output_____"
],
[
"y_data = data['time']\n\nx_data = data.loc[:, data.columns != 'time']\nx_data.loc[:, 'exterior_climate'] = np.ones(len(x_data['exterior_climate']))\nx_data = x_data.fillna(0.0)\nx_data.loc[x_data.loc[:, 'interior_climate'] == 'a', 'interior_climate'] = 0.0\nx_data.loc[x_data.loc[:, 'interior_climate'] == 'b', 'interior_climate'] = 1.0\nx_data.loc[x_data.loc[:, 'system_name'] == 'ClimateBoard', 'system_name'] = 1.0\nx_data.head()",
"C:\\Users\\ocni\\AppData\\Local\\Continuum\\anaconda3\\envs\\ribuild\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning:\n\n\nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n\n"
],
[
"x_data.columns",
"_____no_output_____"
],
[
"processed_data = x_data.assign(time=y_data/60)\n\nplt_data = [\n go.Parcoords(\n line = dict(color = processed_data['time'],\n colorscale = 'Jet',\n showscale = True,\n cmin = 0,\n cmax = 1500),\n \n dimensions = list([\n dict(range = [0,1440],\n label = 'Time', values = processed_data['time'],\n tickformat='r'),\n \n dict(range = [0, 5],\n label = 'Ext. Heat\\nTransfer Coef. Slope', \n values = processed_data['exterior_heat_transfer_coefficient_slope']),\n \n dict(range = [4 * 10 ** -9, 10 ** -8],\n label = 'Ext. Moisture Transfer Coef.', \n values = processed_data['exterior_moisture_transfer_coefficient'],\n tickformat='e'),\n \n dict(range = [0.4, 0.8],\n label = 'Solar Absorption', values = processed_data['solar_absorption'],\n tickformat='.1f'),\n \n dict(range = [0.0, 2.0],\n label = 'Rain Scale Factor', values = processed_data['rain_scale_factor']),\n \n dict(range = [0.0, 1.0],\n label = 'Int. Climate', values = processed_data['interior_climate']),\n \n dict(range = [4.0, 11.0],\n label = 'Int. Heat Transfer Coef.', \n values = processed_data['interior_heat_transfer_coefficient']),\n \n dict(range = [4 * 10 ** -9, 10 ** -8],\n label = 'Int. Moisture Transfer Coef.', \n values = processed_data['interior_moisture_transfer_coefficient'],\n tickformat='e'),\n \n dict(range = [0.0, 0.6],\n label = 'Int. Sd Value', values = processed_data['interior_sd_value'],\n tickformat='.1f'),\n \n dict(range = [0.0, 360.0],\n label = 'Wall Orientation', values = processed_data['wall_orientation']),\n \n dict(range = [0.0, 1.0],\n label = 'Wall Core Width', values = processed_data['wall_core_width']),\n \n dict(range = [0.0, 1000],\n label = 'Wall Core Material', values = processed_data['wall_core_material'],\n tickformat='r'),\n \n dict(range = [0.01, 0.02],\n label = 'Plaster Width', values = processed_data['plaster_width'],\n tickformat='.2f'),\n \n dict(range = [0.0, 1000],\n label = 'Plaster Material', values = processed_data['plaster_material'],\n tickformat='r'),\n \n dict(range = [0.0, 1.0],\n label = 'Ext. Plaster', values = processed_data['exterior_plaster']),\n \n dict(range = [0.0, 1.0],\n label = 'System', values = processed_data['system_name']),\n \n dict(range = [0.0, 1000],\n label = 'Insulation Material', values = processed_data['insulation_material'],\n tickformat='r'),\n \n dict(range = [0.0, 1000],\n label = 'Finish Material', values = processed_data['finish_material'],\n tickformat='r'),\n \n dict(range = [0.0, 1000],\n label = 'Detail Material', values = processed_data['detail_material'],\n tickformat='r'),\n \n dict(range = [0.0, 200],\n label = 'Insulation Thickness', values = processed_data['insulation_thickness']),\n ])\n )\n]\n\nlayout = go.Layout(\n plot_bgcolor = '#E5E5E5',\n paper_bgcolor = '#E5E5E5'\n)\n\nfig = go.Figure(data = plt_data, layout = layout)\nplot(fig, filename = 'sim_time.html')",
"_____no_output_____"
],
[
"X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, random_state=0)",
"_____no_output_____"
],
[
"# Linear Model\nlinreg = linear_model.LinearRegression(normalize=True)\nlinreg.fit(X_train, y_train)\n\nprint('linear model intercept: {}'.format(linreg.intercept_))\nprint('linear model coeff:\\n{}'.format(linreg.coef_))\nprint('R-squared score (training): {:.3f}'.format(linreg.score(X_train, y_train)))\nprint('R-squared score (test): {:.3f}'.format(linreg.score(X_test, y_test)))\nprint('Number of non-zero features: {}'.format(np.sum(linreg.coef_ != 0)))",
"linear model intercept: 32482.880955415232\nlinear model coeff:\n[ 0.00000000e+00 -2.76486389e-08 -8.00355338e-09 -1.31168318e+03\n -1.83765671e+12 -4.63605291e+04 3.36024330e+04 3.74389268e+03\n 2.10530945e+03 -4.71320133e+11 2.25374546e+04 5.26862731e+01\n -1.51557448e+04 2.70673274e+01 -4.04535215e+05 -4.49497329e+04\n -1.80159510e+04 7.74066617e+01 -2.49528407e+01 -2.55545404e+01\n 2.26605303e+01]\nR-squared score (training): 0.519\nR-squared score (test): 0.609\nNumber of non-zero features: 20\n"
],
[
"# Ridge Model\n\nlinridge = linear_model.Ridge(alpha=20.0).fit(X_train, y_train)\n\nprint('ridge regression linear model intercept: {}'.format(linridge.intercept_))\nprint('ridge regression linear model coeff:\\n{}'.format(linridge.coef_))\nprint('R-squared score (training): {:.3f}'.format(linridge.score(X_train, y_train)))\nprint('R-squared score (test): {:.3f}'.format(linridge.score(X_test, y_test)))\nprint('Number of non-zero features: {}'.format(np.sum(linridge.coef_ != 0)))\n",
"ridge regression linear model intercept: -6451.791983746429\nridge regression linear model coeff:\n[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.07723215e+03\n -1.99175622e-04 -1.42138054e+04 2.88601245e+04 3.22149102e+03\n 1.22629834e+03 -9.54586335e-05 8.05623512e+03 5.01136286e+01\n -5.99029750e+03 2.58811209e+01 -1.72981623e+02 -4.03231381e+04\n 1.09631461e-01 -1.56022060e+02 7.91539158e+01 7.72901810e+01\n 1.15837561e+01]\nR-squared score (training): 0.495\nR-squared score (test): 0.592\nNumber of non-zero features: 18\n"
],
[
"# Ridge Model Normalized\nscaler = MinMaxScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n\nlinridge_normal = linear_model.Ridge(alpha=20.0).fit(X_train_scaled, y_train)\n\nprint('ridge regression linear model intercept: {}'.format(linridge_normal.intercept_))\nprint('ridge regression linear model coeff:\\n{}'.format(linridge_normal.coef_))\nprint('R-squared score (training): {:.3f}'.format(linridge_normal.score(X_train_scaled, y_train)))\nprint('R-squared score (test): {:.3f}'.format(linridge_normal.score(X_test_scaled, y_test)))\nprint('Number of non-zero features: {}'.format(np.sum(linridge_normal.coef_ != 0)))",
"ridge regression linear model intercept: 19076.462274881553\nridge regression linear model coeff:\n[ 0. 0. 0. 1871.25540089\n -6492.23680267 -12720.9425476 44003.73600111 6669.12976491\n 4613.80204355 -2098.88266525 9115.05353437 15167.14558948\n -6810.87813483 8198.79843907 -1173.99558604 -39770.31875001\n 403.29861856 401.33706576 403.29861856 403.29861856\n 386.9231038 ]\nR-squared score (training): 0.496\nR-squared score (test): 0.600\nNumber of non-zero features: 18\n"
],
[
"# K-nearest regression - 5 neighbors\nscaler = MinMaxScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\nknn_reg5_uni = KNeighborsRegressor(n_neighbors=5).fit(X_train_scaled, y_train)\n\n#print(knn_reg5_uni.predict(X_test_scaled))\nprint('R-squared train score: {:.5f}'.format(knn_reg5_uni.score(X_train_scaled, y_train)))\nprint('R-squared test score: {:.5f}'.format(knn_reg5_uni.score(X_test_scaled, y_test)))",
"R-squared train score: 0.90101\nR-squared test score: 0.90546\n"
],
[
"# K-nearest regression - 3 neighbors\nscaler = MinMaxScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\nknn_reg5_uni = KNeighborsRegressor(n_neighbors=3).fit(X_train_scaled, y_train)\n\n#print(knn_reg5_uni.predict(X_test_scaled))\nprint('R-squared train score: {:.5f}'.format(knn_reg5_uni.score(X_train_scaled, y_train)))\nprint('R-squared test score: {:.5f}'.format(knn_reg5_uni.score(X_test_scaled, y_test)))",
"R-squared train score: 0.94225\nR-squared test score: 0.91910\n"
],
[
"# K-nearest regression - 5 neighbors, weights = distance\nscaler = MinMaxScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\nknn_reg5 = KNeighborsRegressor(n_neighbors=3, weights='distance').fit(X_train_scaled, y_train)\n\n#print(knn_reg5.predict(X_test_scaled))\nprint('R-squared train score: {:.5f}'.format(knn_reg5.score(X_train_scaled, y_train)))\nprint('R-squared test score: {:.5f}'.format(knn_reg5.score(X_test_scaled, y_test)))",
"R-squared train score: 1.00000\nR-squared test score: 0.91603\n"
],
[
"from sklearn.model_selection import ShuffleSplit\n\nss = ShuffleSplit(n_splits=5, test_size=0.25, random_state=47)\nscaler = MinMaxScaler()\n\ntest_scores = []\nfor train_index, test_index in ss.split(x_data):\n x_train = scaler.fit_transform(x_data.iloc[train_index, :])\n x_test = scaler.transform(x_data.iloc[test_index, :])\n y_train = y_data.iloc[train_index]\n y_test = y_data.iloc[test_index]\n knn_reg = KNeighborsRegressor(n_neighbors=5, weights='distance').fit(x_train, y_train)\n #knn_reg = KNeighborsRegressor(n_neighbors=5).fit(x_train, y_train)\n test_scores.append(knn_reg.score(x_test, y_test))\n\nmean_score = np.mean(test_scores)\nprint(f'Average R-squared test score: {mean_score:.5f}')",
"Average R-squared test score: 0.91219\n"
],
[
"# Cross Validation Score\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn.model_selection import cross_val_score\n\nss = ShuffleSplit(n_splits=5, test_size=0.25, random_state=47)\nscaler = MinMaxScaler()\nknn_reg = KNeighborsRegressor(n_neighbors=5, weights='distance')\n#knn_reg = KNeighborsRegressor(n_neighbors=5)\nvalidated_test_scores = cross_val_score(knn_reg, scaler.fit_transform(x_data), y_data, cv=ss)\n\nprint(f'Accuracy: {validated_test_scores.mean():.5f} (+/- {validated_test_scores.std()*2:.5f})')",
"Accuracy: 0.91220 (+/- 0.04608)\n"
],
[
"# Feature Importance\nfeatures = x_data.columns\n\ncol_del = []\nfeature_scores = []\nfor feat in features:\n feature_less_data = x_data.loc[:, x_data.columns != feat]\n test_scores = cross_val_score(knn_reg, scaler.fit_transform(feature_less_data), y_data, cv=ss, scoring='r2')\n feature_scores.append((feat, test_scores.mean()))\n if test_scores.mean() >= validated_test_scores.mean():\n col_del.append(feat)\nfeature_scores = sorted(feature_scores, key=lambda x: x[1])\nwidth = len('exterior heat transfer coefficient slope')\nprint('Feature'.ljust(width, ' ') + ' Accuracy') \nfor i in feature_scores:\n print(f'{i[0].ljust(width, \" \")} - {i[1]:.5f}')",
"Feature Accuracy\nexterior_plaster - -0.83341\ninsulation_thickness - 0.71738\ninterior_climate - 0.88941\nsolar_absorption - 0.89634\nrain_scale_factor - 0.89654\nplaster_width - 0.90016\nwall_core_width - 0.90911\nexterior_moisture_transfer_coefficient - 0.90921\nexterior_heat_transfer_coefficient_slope - 0.90928\ninterior_heat_transfer_coefficient - 0.91014\ninterior_sd_value - 0.91079\ninterior_moisture_transfer_coefficient - 0.91127\nplaster_material - 0.91138\ninsulation_material - 0.91187\ndesign_option - 0.91220\nsequence - 0.91220\nexterior_climate - 0.91220\nsystem_name - 0.91220\nfinish_material - 0.91220\ndetail_material - 0.91220\nwall_orientation - 0.91264\nwall_core_material - 0.91327\n"
],
[
"print('Columns to delete:\\n')\nfor col in col_del:\n print(f'\\t{col}')",
"Columns to delete:\n\n\tdesign_option\n\tsequence\n\texterior_climate\n\twall_orientation\n\twall_core_material\n\tsystem_name\n\tfinish_material\n\tdetail_material\n"
],
[
"clean_col = x_data.columns[[c not in col_del for c in x_data.columns.tolist()]]\ncleaned_data = x_data.loc[:, clean_col]\nclean_scores = cross_val_score(knn_reg, scaler.fit_transform(cleaned_data), y_data, cv=ss, scoring='r2')\nprint(f'Accuracy: {clean_scores.mean():.5f} (+/- {clean_scores.std()*2:.5f})')",
"Accuracy: 0.93525 (+/- 0.04236)\n"
]
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cb17557a318ce64c082002c7657236e734a28c3f | 384,218 | ipynb | Jupyter Notebook | Deep.Learning/3.Convulutional-Networks/2.Convolutional-Neural-Networks/conv_visualization.ipynb | Scrier/udacity | 1326441aa2104a641b555676ec2429d8b6eb539f | [
"MIT"
] | 1 | 2021-09-08T02:55:34.000Z | 2021-09-08T02:55:34.000Z | Deep.Learning/3.Convulutional-Networks/2.Convolutional-Neural-Networks/conv_visualization.ipynb | Scrier/udacity | 1326441aa2104a641b555676ec2429d8b6eb539f | [
"MIT"
] | 1 | 2018-01-14T16:34:49.000Z | 2018-01-14T16:34:49.000Z | Deep.Learning/3.Convulutional-Networks/2.Convolutional-Neural-Networks/conv_visualization.ipynb | Scrier/udacity | 1326441aa2104a641b555676ec2429d8b6eb539f | [
"MIT"
] | null | null | null | 1,561.861789 | 166,184 | 0.945687 | [
[
[
"# Artificial Intelligence Nanodegree\n\n## Convolutional Neural Networks\n\n---\n\nIn this notebook, we visualize four activation maps in a CNN layer.\n\n\n### 1. Import the Image",
"_____no_output_____"
]
],
[
[
"import cv2\nimport scipy.misc\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n# TODO: Feel free to try out your own images here by changing img_path\n# to a file path to another image on your computer!\nimg_path = 'part12/udacity_sdc.png'\n\n# load color image \nbgr_img = cv2.imread(img_path)\n# convert to grayscale\ngray_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)\n# resize to smaller\nsmall_img = scipy.misc.imresize(gray_img, 0.3)\n\n# rescale entries to lie in [0,1]\nsmall_img = small_img.astype(\"float32\")/255\n\n# plot image\nplt.imshow(small_img, cmap='gray')\nplt.show()",
"/Users/scrier/anaconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:482: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n if issubdtype(ts, int):\n/Users/scrier/anaconda3/lib/python3.6/site-packages/scipy/misc/pilutil.py:485: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n elif issubdtype(type(size), float):\n"
]
],
[
[
"### 2. Specify the Filters",
"_____no_output_____"
]
],
[
[
"import numpy as np\n\n# TODO: Feel free to modify the numbers here, to try out another filter!\n# Please don't change the size of the array ~ :D\nfilter_vals = np.array([[-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1]])\n\n### do not modify the code below this line ###\n\n# define four filters\nfilter_1 = filter_vals\nfilter_2 = -filter_1\nfilter_3 = filter_1.T\nfilter_4 = -filter_3\nfilters = [filter_1, filter_2, filter_3, filter_4]\n\n# visualize all filters\nfig = plt.figure(figsize=(10, 5))\nfor i in range(4):\n ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])\n ax.imshow(filters[i], cmap='gray')\n ax.set_title('Filter %s' % str(i+1))\n width, height = filters[i].shape\n for x in range(width):\n for y in range(height):\n ax.annotate(str(filters[i][x][y]), xy=(y,x),\n horizontalalignment='center',\n verticalalignment='center',\n color='white' if filters[i][x][y]<0 else 'black')",
"_____no_output_____"
]
],
[
[
"### 3. Visualize the Activation Maps for Each Filter",
"_____no_output_____"
]
],
[
[
"from keras.models import Sequential\nfrom keras.layers.convolutional import Convolution2D\nimport matplotlib.cm as cm\n\n# plot image\nplt.imshow(small_img, cmap='gray')\n\n# define a neural network with a single convolutional layer with one filter\nmodel = Sequential()\nmodel.add(Convolution2D(1, (4, 4), activation='relu', input_shape=(small_img.shape[0], small_img.shape[1], 1)))\n\n# apply convolutional filter and return output\ndef apply_filter(img, index, filter_list, ax):\n # set the weights of the filter in the convolutional layer to filter_list[i]\n model.layers[0].set_weights([np.reshape(filter_list[i], (4,4,1,1)), np.array([0])])\n # plot the corresponding activation map\n ax.imshow(np.squeeze(model.predict(np.reshape(img, (1, img.shape[0], img.shape[1], 1)))), cmap='gray')\n\n# visualize all filters\nfig = plt.figure(figsize=(12, 6))\nfig.subplots_adjust(left=0, right=1.5, bottom=0.8, top=1, hspace=0.05, wspace=0.05)\nfor i in range(4):\n ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])\n ax.imshow(filters[i], cmap='gray')\n ax.set_title('Filter %s' % str(i+1))\n\n# visualize all activation maps\nfig = plt.figure(figsize=(20, 20))\nfor i in range(4):\n ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])\n apply_filter(small_img, i, filters, ax)\n ax.set_title('Activation Map for Filter %s' % str(i+1))",
"/Users/scrier/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\nUsing TensorFlow backend.\n/Users/scrier/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n return f(*args, **kwds)\n"
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cb17786427bfd6a43b73daa75576c5333c2f990d | 112,949 | ipynb | Jupyter Notebook | Sentiment Analysis About Corona DeepLearning.ipynb | hafidh561/Sentiment-Analysis-About-Corona-DeepLearning | 92101490cc5ddc0d73b56f3ddf465eabf1074eef | [
"MIT"
] | null | null | null | Sentiment Analysis About Corona DeepLearning.ipynb | hafidh561/Sentiment-Analysis-About-Corona-DeepLearning | 92101490cc5ddc0d73b56f3ddf465eabf1074eef | [
"MIT"
] | null | null | null | Sentiment Analysis About Corona DeepLearning.ipynb | hafidh561/Sentiment-Analysis-About-Corona-DeepLearning | 92101490cc5ddc0d73b56f3ddf465eabf1074eef | [
"MIT"
] | null | null | null | 187.935108 | 46,603 | 0.867507 | [
[
[
"# Import All Libraries\r\nimport pandas as pd\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nimport nltk\r\nimport re\r\nfrom tensorflow.keras.preprocessing.text import Tokenizer\r\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\r\nfrom sklearn.model_selection import train_test_split\r\nfrom nltk.corpus import stopwords",
"_____no_output_____"
],
[
"# Import Data\r\ndata = pd.read_csv(\"./Corona_NLP.csv\", encoding='ISO-8859-1')\r\ndata.head()",
"_____no_output_____"
],
[
"# Check Data Type\r\ndata.info(memory_usage=False)",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 41157 entries, 0 to 41156\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 UserName 41157 non-null int64 \n 1 ScreenName 41157 non-null int64 \n 2 Location 32567 non-null object\n 3 TweetAt 41157 non-null object\n 4 OriginalTweet 41157 non-null object\n 5 Sentiment 41157 non-null object\ndtypes: int64(2), object(4)"
],
[
"# Check Data Null\r\nprint(\"Data Empty:\", data.isnull().sum().sum())",
"Data Empty: 8590\n"
],
[
"# Drop Data Null\r\ndata.dropna(inplace=True)\r\nprint(\"Data Empty:\", data.isnull().sum().sum())",
"Data Empty: 0\n"
],
[
"# Drop column\r\ndata.drop(columns=['UserName', 'ScreenName', \r\n 'Location', 'TweetAt'], inplace=True)",
"_____no_output_____"
],
[
"# Simplify Data\r\ndata['Sentiment'] = data['Sentiment'].map({\r\n \"Neutral\": \"Neutral\",\r\n \"Positive\": \"Positive\",\r\n \"Negative\": \"Negative\",\r\n \"Extremely Positive\": \"Positive\",\r\n \"Extremely Negative\": \"Negative\"\r\n})\r\nsentiment = pd.get_dummies(data['Sentiment'])\r\ndf = pd.concat([data, sentiment], axis=1)\r\ndf.drop(columns='Sentiment', inplace=True)",
"_____no_output_____"
],
[
"# Data Cleaning with regex\r\ndef data_cleaner(tweet):\r\n # hapus link\r\n tweet = re.sub(r'http\\S+', ' ', tweet)\r\n # hapus html tags\r\n tweet = re.sub(r'<.*?>',' ', tweet)\r\n # hapus angka\r\n tweet = re.sub(r'\\d+',' ', tweet)\r\n # hapus hashtags\r\n tweet = re.sub(r'#\\w+',' ', tweet)\r\n # hapus tag\r\n tweet = re.sub(r'@\\w+',' ', tweet)\r\n # hapus kata tambahan\r\n tweet = tweet.split()\r\n tweet = \" \".join([word for word in tweet if not word in stop_words])\r\n return tweet\r\n\r\nnltk.download('stopwords')\r\nstop_words = stopwords.words('english')\r\ndf['OriginalTweet'] = df['OriginalTweet'].apply(lambda x: x.lower()).apply(data_cleaner)\r\ndf.head()",
"[nltk_data] Downloading package stopwords to C:\\Users\\Hafidh\n[nltk_data] Soekma\\AppData\\Roaming\\nltk_data...\n[nltk_data] Package stopwords is already up-to-date!\n"
],
[
"# Split Data\r\ntweet = df['OriginalTweet'].values\r\nsentiment = df[['Negative', 'Neutral', 'Positive']].values\r\ntweet_train, tweet_test, sentiment_train, sentiment_test = train_test_split(\r\n tweet, sentiment, test_size=0.2, random_state=69\r\n)",
"_____no_output_____"
],
[
"# Tokenizer Data\r\ntokenizer = Tokenizer(num_words=36000, oov_token='-')\r\ntokenizer.fit_on_texts(tweet_train)\r\ntokenizer.fit_on_texts(tweet_test)\r\n\r\nsequens_train = tokenizer.texts_to_sequences(tweet_train)\r\nsequens_test = tokenizer.texts_to_sequences(tweet_test)\r\n\r\npadded_train = pad_sequences(sequens_train, padding='post')\r\npadded_test = pad_sequences(sequens_test, padding='post')",
"_____no_output_____"
],
[
"# Create Model NN\r\nmodel = tf.keras.models.Sequential([\r\n tf.keras.layers.Embedding(input_dim=36000, output_dim=16),\r\n tf.keras.layers.LSTM(128),\r\n tf.keras.layers.Dense(32, activation='relu'),\r\n tf.keras.layers.Dense(3, activation='softmax')\r\n])\r\n\r\nmodel.compile(\r\n optimizer='adam',\r\n loss='categorical_crossentropy',\r\n metrics='accuracy'\r\n)",
"_____no_output_____"
],
[
"# Create Callback\r\nclass Callback(tf.keras.callbacks.Callback):\r\n def on_epoch_end(self, epoch, logs={}):\r\n if logs.get('val_accuracy') > 0.9:\r\n print(\"\\nValidasi Akurasi telah mencapai > 90%!\")\r\n self.model.stop_training = True\r\n\r\ncallback0 = Callback()\r\ncallback1 = tf.keras.callbacks.EarlyStopping(\r\n min_delta=0.001,\r\n patience=20,\r\n restore_best_weights=True\r\n)",
"_____no_output_____"
],
[
"# Train Model\r\nhistory = model.fit(\r\n padded_train,\r\n sentiment_train,\r\n validation_data=(padded_test, sentiment_test),\r\n epochs=5,\r\n callbacks=[callback0, callback1],\r\n)",
"Epoch 1/5\n815/815 [==============================] - ETA: 0s - loss: 0.9786 - accuracy: 0.4789"
],
[
"# Plot Accuracy Model\r\nplt.figure(figsize=(20, 10))\r\nplt.plot(history.history['accuracy'])\r\nplt.plot(history.history['val_accuracy'])\r\nplt.title('Akurasi Model', fontsize=20)\r\nplt.xlabel('Epoch', fontsize=20)\r\nplt.ylabel('Accuracy', fontsize=20)\r\nplt.legend(['train', 'test'], loc='upper left')\r\nplt.show()",
"_____no_output_____"
],
[
"# Plot Loss Model\r\nplt.figure(figsize=(20, 10))\r\nplt.plot(history.history['loss'])\r\nplt.plot(history.history['val_loss'])\r\nplt.title('Loss Model', fontsize=20)\r\nplt.ylabel('loss', fontsize=20)\r\nplt.xlabel('epoch', fontsize=20)\r\nplt.legend(['train', 'test'], loc='upper left')\r\nplt.show()",
"_____no_output_____"
]
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cb17931b334a45c27f4ae3903c089a3875de8523 | 47,203 | ipynb | Jupyter Notebook | part-2/Part-2-COVID-19-mlnet-prediction.ipynb | praveenraghuvanshi1512/covid-19 | 7707339924ee522da6b231a68a5b5b48a82b90d9 | [
"MIT"
] | 1 | 2020-05-31T22:36:34.000Z | 2020-05-31T22:36:34.000Z | part-2/Part-2-COVID-19-mlnet-prediction.ipynb | praveenraghuvanshi1512/covid-19 | 7707339924ee522da6b231a68a5b5b48a82b90d9 | [
"MIT"
] | null | null | null | part-2/Part-2-COVID-19-mlnet-prediction.ipynb | praveenraghuvanshi1512/covid-19 | 7707339924ee522da6b231a68a5b5b48a82b90d9 | [
"MIT"
] | 1 | 2020-06-19T16:12:03.000Z | 2020-06-19T16:12:03.000Z | 44.531132 | 5,514 | 0.592166 | [
[
[
"# Part - 2: COVID-19 Time Series Analysis and Prediction using ML.Net framework",
"_____no_output_____"
],
[
"## COVID-19\n- As per [Wiki](https://en.wikipedia.org/wiki/Coronavirus_disease_2019) **Coronavirus disease 2019** (**COVID-19**) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally, resulting in the ongoing 2019–20 coronavirus pandemic.\n- The virus had caused a pandemic across the globe and spreading/affecting most of the nations. \n- The purpose of notebook is to visualize the trends of virus spread in various countries and explore features present in ML.Net such as DataFrame.",
"_____no_output_____"
],
[
"### Acknowledgement\n- [Johns Hopkins CSSE](https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data) for dataset\n- [COVID-19 data visualization](https://www.kaggle.com/akshaysb/covid-19-data-visualization) by Akshay Sb",
"_____no_output_____"
],
[
"### Dataset\n\n- [2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE - Time Series](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series).",
"_____no_output_____"
],
[
"### Introduction \n\nThis is **Part-2** of our analysis on the COVID-19 dataset provided by Johns Hopkins CSSE. In [**Part-1**](https://github.com/praveenraghuvanshi1512/TechnicalSessions/tree/31052020-virtualmlnet/31052020-virtualmlnet/src/part-1), I did data analysis on the dataset and created some tables and plots for getting insights from it. In Part-2, I'll focus on applying machine learning for making a prediction using time-series API's provided by ML.Net framework. I'll be building a model from scratch on the number of confirmed cases and predicting for the next 7 days. Later on, I'll plot these numbers for better visualization.\n\n[**ML.Net**](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet) is a cross-platform framework from Microsoft for developing Machine learning models in the .Net ecosystem. It allows .Net developers to solve business problems using machine learning algorithms leveraging their preferred language such as C#/F#. It's highly scalable and used within Microsoft in many of its products such as Bing, Powerpoint, etc.\n\n**Disclaimer**: This is an exercise to explore different features present in ML.Net. The actual and predicted numbers might vary due to several factors such as size and features in a dataset.",
"_____no_output_____"
],
[
"### Summary\n\nBelow is the summary of steps we'll be performing\n\n1. Define application level items\n - Nuget packages\n - Namespaces\n - Constants\n \n2. Utility Functions\n - Formatters \n\n3. Dataset and Transformations\n - Actual from [Johns Hopkins CSSE](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series)\n - Transformed [time_series_covid19_confirmed_global_transposed.csv](time_series_covid19_confirmed_global_transposed.csv)\n \n4. Data Classes\n - ConfirmedData : Provides a map between columns in a dataset\n - ConfirmedForecast : Holds predicted values\n\n5. Data Analysis\n - Visualize Data using DataFrame API\n - Display Top 10 Rows - dataframe.Head(10)\n - Display Last 10 Rows - dataframe.Tail(10)\n - Display Dataset Statistics - dataframe.Description()\n - Plot of TotalConfimed cases vs Date\n\n6. Load Data - MLContext\n7. ML Pipeline\n8. Train Model\n9. Prediction/Forecasting\n10. Prediction Visualization\n11. Prediction Analysis\n12. Conclusion\n\n**Note** : Graphs/Plots may not render in GitHub due to secutiry reasons, however if you run this notebook locally/binder they will render.",
"_____no_output_____"
]
],
[
[
"#!about",
"_____no_output_____"
]
],
[
[
"### 1. Define Application wide Items",
"_____no_output_____"
],
[
"#### Nuget Packages\n",
"_____no_output_____"
]
],
[
[
"// ML.NET Nuget packages installation\n#r \"nuget:Microsoft.ML\"\n#r \"nuget:Microsoft.ML.TimeSeries\"\n#r \"nuget:Microsoft.Data.Analysis\"\n\n// Install XPlot package\n#r \"nuget:XPlot.Plotly\"",
"_____no_output_____"
]
],
[
[
"#### Namespaces",
"_____no_output_____"
]
],
[
[
"using System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing Microsoft.ML;\nusing Microsoft.ML.Data;\nusing Microsoft.Data.Analysis;\nusing Microsoft.ML.Transforms.TimeSeries;\nusing Microsoft.AspNetCore.Html;\nusing XPlot.Plotly;",
"_____no_output_____"
]
],
[
[
"#### Constants",
"_____no_output_____"
]
],
[
[
"const string CONFIRMED_DATASET_FILE = \"time_series_covid19_confirmed_global_transposed.csv\";\n\n// Forecast API\nconst int WINDOW_SIZE = 5;\nconst int SERIES_LENGTH = 10;\nconst int TRAIN_SIZE = 100;\nconst int HORIZON = 7;\n\n// Dataset\nconst int DEFAULT_ROW_COUNT = 10;\nconst string TOTAL_CONFIRMED_COLUMN = \"TotalConfirmed\";\nconst string DATE_COLUMN = \"Date\";",
"_____no_output_____"
]
],
[
[
"### 2. Utility Functions - TBR",
"_____no_output_____"
],
[
"#### Formatters\n\nBy default the output of DataFrame is not proper and in order to display it as a table, we need to have a custom formatter implemented as shown in next cell. ",
"_____no_output_____"
]
],
[
[
"Formatter<DataFrame>.Register((df, writer) =>\n{\n var headers = new List<IHtmlContent>();\n headers.Add(th(i(\"index\")));\n headers.AddRange(df.Columns.Select(c => (IHtmlContent) th(c.Name)));\n var rows = new List<List<IHtmlContent>>();\n var take = DEFAULT_ROW_COUNT;\n for (var i = 0; i < Math.Min(take, df.Rows.Count); i++)\n {\n var cells = new List<IHtmlContent>();\n cells.Add(td(i));\n foreach (var obj in df.Rows[i])\n {\n cells.Add(td(obj));\n }\n rows.Add(cells);\n }\n\n var t = table(\n thead(\n headers),\n tbody(\n rows.Select(\n r => tr(r))));\n\n writer.Write(t);\n}, \"text/html\");",
"_____no_output_____"
]
],
[
[
"### 3. Dataset and Transformations",
"_____no_output_____"
],
[
"#### Download Dataset\n- Actual Dataset: [Johns Hopkins CSSE](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series)\n- Transformed Dataset: [time_series_covid19_confirmed_global_transposed.csv](time_series_covid19_confirmed_global_transposed.csv)\n\n\nI'll be using COVID-19 time series dataset from [Johns Hopkins CSSE](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series) and will be performing predictions using **time_series_covid19_confirmed_global.csv** file.\n\nThe data present in these files have name of the countries as Rows and dates as columns which makes it difficult to map to our classes while loading data from csv. Also, it contains data per country wise. In order to keep things simple I'll work with global count of COVID-19 cases and not specific country.\n\nI have done few transformations to the dataset as below and created transformed csv's\n- Sum cases from all the countries for a specific date\n- Just have two rows with Date and Total \n- Applied transformation to the csv for converting Rows into Columns and vice-versa. [Refer](https://support.office.com/en-us/article/transpose-rotate-data-from-rows-to-columns-or-vice-versa-3419f2e3-beab-4318-aae5-d0f862209744) for transformation.\n- Below transposed files have been saved in the current github directory. There is no change in dataset. The files have data till 05-27-2020\n - [time_series_covid19_confirmed_global_transposed.csv](time_series_covid19_confirmed_global_transposed.csv) : Columns - **Date, TotalConfirmed**",
"_____no_output_____"
],
[
"##### Before transformation\n\n<img src=\".\\assets\\time-series-before-transformation.png\" alt=\"Time Series data before transofmation\" style=\"zoom: 80%;\" />",
"_____no_output_____"
],
[
"#### After transformation\n\n<img src=\".\\assets\\time-series-after-transformation.png\" alt=\"Time Series data after transofmation\" style=\"zoom: 80%;\" />",
"_____no_output_____"
],
[
"### 4. Data Classes",
"_____no_output_____"
],
[
"Now, we need to create few data structures to map to columns within our dataset.",
"_____no_output_____"
],
[
"#### Confirmed cases",
"_____no_output_____"
]
],
[
[
"/// <summary>\n/// Represent data for confirmed cases with a mapping to columns in a dataset\n/// </summary>\npublic class ConfirmedData\n{\n /// <summary>\n /// Date of confirmed case\n /// </summary>\n [LoadColumn(0)]\n public DateTime Date;\n\n /// <summary>\n /// Total no of confirmed cases on a particular date\n /// </summary>\n [LoadColumn(1)]\n public float TotalConfirmed;\n}",
"_____no_output_____"
],
[
"/// <summary>\n/// Prediction/Forecast for Confirmed cases\n/// </summary>\ninternal class ConfirmedForecast\n{\n /// <summary>\n /// No of predicted confirmed cases for multiple days\n /// </summary>\n public float[] Forecast { get; set; }\n}",
"_____no_output_____"
]
],
[
[
"### 5. Data Analysis",
"_____no_output_____"
],
[
"For loading data from csv, first we need to create MLContext that acts as a starting point for creating a machine learning model in ML.Net. Few things to note\n- Set hasHeader as true as our dataset has header\n- Add separatorChar to ',' as its a csv",
"_____no_output_____"
],
[
"#### Visualize Data - DataFrame",
"_____no_output_____"
]
],
[
[
"var predictedDf = DataFrame.LoadCsv(CONFIRMED_DATASET_FILE);",
"_____no_output_____"
],
[
"predictedDf.Head(DEFAULT_ROW_COUNT)",
"_____no_output_____"
],
[
"predictedDf.Tail(DEFAULT_ROW_COUNT)",
"_____no_output_____"
],
[
"predictedDf.Description()",
"_____no_output_____"
]
],
[
[
"##### Number of Confirmed cases over Time",
"_____no_output_____"
]
],
[
[
"// Number of confirmed cases over time\nvar totalConfirmedDateColumn = predictedDf.Columns[DATE_COLUMN];\nvar totalConfirmedColumn = predictedDf.Columns[TOTAL_CONFIRMED_COLUMN];\n\nvar dates = new List<string>();\nvar totalConfirmedCases = new List<string>();\nfor (int index = 0; index < totalConfirmedDateColumn.Length; index++)\n{\n dates.Add(totalConfirmedDateColumn[index].ToString());\n totalConfirmedCases.Add(totalConfirmedColumn[index].ToString());\n}",
"_____no_output_____"
],
[
"var title = \"Number of Confirmed Cases over Time\";\nvar confirmedTimeGraph = new Graph.Scattergl()\n {\n x = dates.ToArray(),\n y = totalConfirmedCases.ToArray(),\n mode = \"lines+markers\"\n };\n \n\n\nvar chart = Chart.Plot(confirmedTimeGraph);\nchart.WithTitle(title);\ndisplay(chart);",
"_____no_output_____"
]
],
[
[
"**Analysis**\n- Duration: 1/22/2020 through 5/27/2020\n- Total records: 127\n- Case on first day: 555\n- Case on last day: 5691790\n- No of confirmed cases was low in the beginning, there was first jump around 2/12/2020 and an exponential jump around 3/22/2020.\n- Cases have been increasing at an alarming rate in the past two months.",
"_____no_output_____"
],
[
"### 6. Load Data - MLContext",
"_____no_output_____"
]
],
[
[
"var context = new MLContext();",
"_____no_output_____"
],
[
"var data = context.Data.LoadFromTextFile<ConfirmedData>(CONFIRMED_DATASET_FILE, hasHeader: true, separatorChar: ',');",
"_____no_output_____"
]
],
[
[
"### 7. ML Pipeline",
"_____no_output_____"
],
[
"For creating ML Pipeline for a time-series analysis, we'll use [Single Spectrum Analysis](https://en.wikipedia.org/wiki/Singular_spectrum_analysis). ML.Net provides built in API for same, more details could be found at [TimeSeriesCatalog.ForecastBySsa](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.timeseriescatalog.forecastbyssa?view=ml-dotnet) ",
"_____no_output_____"
]
],
[
[
"var pipeline = context.Forecasting.ForecastBySsa(\n nameof(ConfirmedForecast.Forecast),\n nameof(ConfirmedData.TotalConfirmed),\n WINDOW_SIZE, \n SERIES_LENGTH,\n TRAIN_SIZE,\n HORIZON);",
"_____no_output_____"
]
],
[
[
"### 8. Train Model",
"_____no_output_____"
],
[
"We are ready with our pipeline and ready to train the model",
"_____no_output_____"
]
],
[
[
"var model = pipeline.Fit(data);",
"_____no_output_____"
]
],
[
[
"### 9. Prediction/Forecasting - 7 days",
"_____no_output_____"
],
[
"Our model is trained and we need to do prediction for next 7(Horizon) days.\nTime-series provides its own engine for making prediction which is similar to PredictionEngine present in ML.Net. Predicted values show an increasing trend which is in alignment with recent past values.",
"_____no_output_____"
]
],
[
[
"var forecastingEngine = model.CreateTimeSeriesEngine<ConfirmedData, ConfirmedForecast>(context);\nvar forecasts = forecastingEngine.Predict();\ndisplay(forecasts.Forecast.Select(x => (int) x))",
"_____no_output_____"
]
],
[
[
"### 10. Prediction Visualization",
"_____no_output_____"
]
],
[
[
"var lastDate = DateTime.Parse(dates.LastOrDefault());\nvar predictionStartDate = lastDate.AddDays(1);\n\nfor (int index = 0; index < HORIZON; index++)\n{\n dates.Add(lastDate.AddDays(index + 1).ToShortDateString());\n totalConfirmedCases.Add(forecasts.Forecast[index].ToString());\n}",
"_____no_output_____"
],
[
"var title = \"Number of Confirmed Cases over Time\";\nvar layout = new Layout.Layout();\nlayout.shapes = new List<Graph.Shape>\n{\n new Graph.Shape\n {\n x0 = predictionStartDate.ToShortDateString(),\n x1 = predictionStartDate.ToShortDateString(),\n y0 = \"0\",\n y1 = \"1\",\n xref = 'x',\n yref = \"paper\",\n line = new Graph.Line() {color = \"red\", width = 2}\n }\n};\n\nvar chart1 = Chart.Plot(\nnew [] \n {\n new Graph.Scattergl()\n {\n x = dates.ToArray(),\n y = totalConfirmedCases.ToArray(),\n mode = \"lines+markers\"\n }\n },\n layout\n);\n\nchart1.WithTitle(title);\ndisplay(chart1);",
"_____no_output_____"
]
],
[
[
"### 11. Analysis",
"_____no_output_____"
],
[
"Comparing the plots before and after prediction, it seems our ML model has performed reasonably well. The red line represents the data on future date(5/8/2020). Beyond this, we predicted for 7 days. Looking at the plot, there is a sudden drop on 5/8/2020 which could be accounted due to insufficient data as we have only 127 records. However we see an increasing trend for next 7 days in alignment with previous confirmed cases. We can extend this model for predicting confirmed cases for any number of days by changing HORIZON constant value. This plot is helpful in analysing the increased number of cases and allow authorities to take precautionary measures to keep the numbers low.",
"_____no_output_____"
],
[
"## Conclusion",
"_____no_output_____"
],
[
"I hope you have enjoyed reading the notebook, and might have got some idea on the powerful framework ML.Net. ML.Net is a very fast emerging framework for .Net developers which abstracts lot of complexity present in the field of Data science and Machine Learning. The focus of Part-2 notebook is leverage ML.Net for making predictions using time-series API. The model generated can be saved as a zip file and used in different applications.\n\nFeedback/Suggestion are welcome. Please reach out to me through below channels\n\n**Contact**\n\n**Email :** [email protected] \n**LinkedIn :** https://in.linkedin.com/in/praveenraghuvanshi \n**Github :** https://github.com/praveenraghuvanshi1512 \n**Twitter :** @praveenraghuvan\n\n",
"_____no_output_____"
],
[
"## References\n- [Tutorial: Forecast bike rental service demand with time series analysis and ML.NET](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/time-series-demand-forecasting#evaluate-the-model)\n- [Time Series Forecasting in ML.NET and Azure ML notebooks](https://github.com/gvashishtha/time-series-mlnet/blob/master/time-series-forecast.ipynb) by Gopal Vashishtha",
"_____no_output_____"
],
[
"# ******************** Be Safe **********************",
"_____no_output_____"
]
]
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cb17ac3d2c5bc867d100240b0a4ddd567d2cccc1 | 4,386 | ipynb | Jupyter Notebook | chapter2/homework/localization/4-5/201611680311 .ipynb | hpishacker/python_tutorial | 9005f0db9dae10bdc1d1c3e9e5cf2268036cd5bd | [
"MIT"
] | 76 | 2017-09-26T01:07:26.000Z | 2021-02-23T03:06:25.000Z | chapter2/homework/localization/4-5/201611680311 .ipynb | hpishacker/python_tutorial | 9005f0db9dae10bdc1d1c3e9e5cf2268036cd5bd | [
"MIT"
] | 5 | 2017-12-10T08:40:11.000Z | 2020-01-10T03:39:21.000Z | chapter2/homework/localization/4-5/201611680311 .ipynb | hacker-14/python_tutorial | 4a110b12aaab1313ded253f5207ff263d85e1b56 | [
"MIT"
] | 112 | 2017-09-26T01:07:30.000Z | 2021-11-25T19:46:51.000Z | 19.493333 | 67 | 0.425217 | [
[
[
"#练习1\nimport random , math\n\nn=int(input('请输入随机整数的个数'))\nm=int(input('请输入随机整数的下界'))\nk=int(input('请输入随机整数的上界'))\n\ni=0\ntotal=0\nwhile i<n:\n num=random.randint(m,k)\n print(num)\n i+=1\n total+=num\nprint(total)\n\ndef average(total,n):\n result=total/n\n return result\n\naverage(total,n)\n\n\n ",
"请输入一个整数作为随机整数的个数。3\n下界1\n上界9\n5\n9\n1\n15\n"
],
[
"#练习2\nimport random , math\n\nn=int(input('请输入随机整数的个数'))\nm=int(input('请输入随机整数的下界'))\nk=int(input('请输入随机整数的上界'))\n\ni=0\ntotal=0\n\nwhile i<n:\n num=random.randint(m,k)\n print(num)\n i+=1\n total_1=0\n total_2=0\n total_1+=math.log(num)\n total_2+=1/math.log(num)\n\nprint(total_1)\nprint(total_2)\n ",
"请输入随机整数的个数3\n请输入随机整数的下界1\n请输入随机整数的上界9\n4\n5\n5\n1.6094379124341003\n0.6213349345596119\n"
],
[
"#挑战性练习\n\nimport random , math\n\ndef guess_game():\n n = int(input('请输入一个大于0的整数作为上界。'))\n number=int(input('请输入一个整数作为结果'))\n max_times = math.ceil(math.log2(n))\n guess_times = 0\n while guess_times < max_times:\n guess = random.randint(1,n)\n print(guess)\n guess_times += 1\n print('一共可以猜', max_times, '次')\n print('计算机已经猜了', guess_times, '次')\n\n if guess == number:\n print('神秘数字是：', guess)\n print('计算机比标准次数少', max_times-guess_times, '次')\n break\n elif guess > number:\n print('猜大了')\n else:\n print('猜小了')\n else:\n print('数字是：', number)\n \nguess_game()",
"请输入一个大于0的整数作为上界。99\n请输入一个整数作为结果87\n26\n一共可以猜 7 次\n计算机已经猜了 1 次\n猜小了\n91\n一共可以猜 7 次\n计算机已经猜了 2 次\n猜大了\n43\n一共可以猜 7 次\n计算机已经猜了 3 次\n猜小了\n62\n一共可以猜 7 次\n计算机已经猜了 4 次\n猜小了\n57\n一共可以猜 7 次\n计算机已经猜了 5 次\n猜小了\n91\n一共可以猜 7 次\n计算机已经猜了 6 次\n猜大了\n99\n一共可以猜 7 次\n计算机已经猜了 7 次\n猜大了\n数字是： 87\n"
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cb17b3ff8141515491f21d9c686dedb91ca60abf | 28,688 | ipynb | Jupyter Notebook | gapminder_plotly_murat.ipynb | mmuratardag/mmuratardag-DS_SpA_W01_Animated_Plotting | 459758ee8bea293fb9b428ed379279d12e965be6 | [
"MIT"
] | null | null | null | gapminder_plotly_murat.ipynb | mmuratardag/mmuratardag-DS_SpA_W01_Animated_Plotting | 459758ee8bea293fb9b428ed379279d12e965be6 | [
"MIT"
] | null | null | null | gapminder_plotly_murat.ipynb | mmuratardag/mmuratardag-DS_SpA_W01_Animated_Plotting | 459758ee8bea293fb9b428ed379279d12e965be6 | [
"MIT"
] | null | null | null | 31.21654 | 130 | 0.356769 | [
[
[
"import pandas as pd\ngm_3v = pd.read_excel('gapminder_lf_1800_2015.xlsx', index_col=0).sort_values(by=['country', 'continent', 'year'])\ngm_3v.head()",
"_____no_output_____"
],
[
"gm_3v.tail()",
"_____no_output_____"
],
[
"gm_1v = pd.read_excel('gapminder_plotly_lf_1957_2007.xlsx', index_col=0).sort_values(by=['country', 'continent', 'year'])\ngm_1v.head()",
"_____no_output_____"
],
[
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"_____no_output_____"
],
[
"df = pd.merge(left = gm_1v, right = gm_3v, on = ['country', 'continent', 'year'], how = 'inner')\ndf.head()",
"_____no_output_____"
],
[
"df = df[['country','continent','year','gdpPercap','fertility_rate','total_population','life_expectancy']]\ndf.head()",
"_____no_output_____"
],
[
"round(df.describe(), 3)",
"_____no_output_____"
],
[
"import plotly.express as px",
"_____no_output_____"
],
[
"fig_life = px.scatter(df, x=\"gdpPercap\", y=\"life_expectancy\", animation_frame=\"year\", animation_group=\"country\",\n size=\"total_population\", color=\"continent\", hover_name=\"country\", \n log_x = True, \n size_max=45, range_x=[100,100000], range_y=[10,90])",
"_____no_output_____"
],
[
"fig_life.write_html('gm_px_life.html')",
"_____no_output_____"
],
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"_____no_output_____"
],
[
"fig_fert.write_html('gm_px_fert.html')",
"_____no_output_____"
]
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cb17c493b48109760ffd180b54d7c99a76d359bd | 80,968 | ipynb | Jupyter Notebook | jupyter/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb | hatrungduc/spark-nlp-workshop | 4a4ec0195d1d3d847261df9ef2df7aa5f95bbaec | [
"Apache-2.0"
] | 687 | 2018-09-07T03:45:39.000Z | 2022-03-20T17:11:20.000Z | jupyter/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb | hatrungduc/spark-nlp-workshop | 4a4ec0195d1d3d847261df9ef2df7aa5f95bbaec | [
"Apache-2.0"
] | 89 | 2018-09-18T02:04:42.000Z | 2022-02-24T18:22:27.000Z | jupyter/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb | hatrungduc/spark-nlp-workshop | 4a4ec0195d1d3d847261df9ef2df7aa5f95bbaec | [
"Apache-2.0"
] | 407 | 2018-09-07T03:45:44.000Z | 2022-03-20T05:12:25.000Z | 40,484 | 80,967 | 0.76881 | [
[
[
"[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBertaForTokenClassification.ipynb)",
"_____no_output_____"
],
[
"## Import RoBertaForTokenClassification models from HuggingFace 🤗 into Spark NLP 🚀 \n\nLet's keep in mind a few things before we start 😊 \n\n- This feature is only in `Spark NLP 3.3.x` and after. So please make sure you have upgraded to the latest Spark NLP release\n- You can import RoBERTa models trained/fine-tuned for token classification via `RobertaForTokenClassification` or `TFRobertaForTokenClassification`. These models are usually under `Token Classification` category and have `roberta` in their labels\n- Reference: [TFRobertaForTokenClassification](https://huggingface.co/transformers/model_doc/roberta.html#tfrobertafortokenclassification)\n- Some [example models](https://huggingface.co/models?filter=roberta&pipeline_tag=token-classification)",
"_____no_output_____"
],
[
"## Export and Save HuggingFace model",
"_____no_output_____"
],
[
"- Let's install `HuggingFace` and `TensorFlow`. You don't need `TensorFlow` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n- We lock TensorFlow on `2.4.1` version and Transformers on `4.10.0`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully.",
"_____no_output_____"
]
],
[
[
"!pip install -q transformers==4.10.0 tensorflow==2.4.1",
"\u001b[K |████████████████████████████████| 2.8 MB 7.0 MB/s \n\u001b[K |████████████████████████████████| 394.3 MB 14 kB/s \n\u001b[K |████████████████████████████████| 895 kB 73.7 MB/s \n\u001b[K |████████████████████████████████| 636 kB 73.2 MB/s \n\u001b[K |████████████████████████████████| 3.3 MB 37.9 MB/s \n\u001b[K |████████████████████████████████| 52 kB 1.7 MB/s \n\u001b[K |████████████████████████████████| 2.9 MB 45.1 MB/s \n\u001b[K |████████████████████████████████| 462 kB 59.1 MB/s \n\u001b[K |████████████████████████████████| 3.8 MB 48.9 MB/s \n\u001b[?25h"
]
],
[
[
"- HuggingFace comes with a native `saved_model` feature inside `save_pretrained` function for TensorFlow based models. We will use that to save it as TF `SavedModel`.\n- We'll use [philschmid/distilroberta-base-ner-wikiann-conll2003-3-class](https://huggingface.co/philschmid/distilroberta-base-ner-wikiann-conll2003-3-class) model from HuggingFace as an example\n- In addition to `TFRobertaForTokenClassification` we also need to save the `RobertaTokenizer`. This is the same for every model, these are assets needed for tokenization inside Spark NLP.",
"_____no_output_____"
]
],
[
[
"from transformers import TFRobertaForTokenClassification, RobertaTokenizer \n\nMODEL_NAME = 'philschmid/distilroberta-base-ner-wikiann-conll2003-3-class'\n\ntokenizer = RobertaTokenizer.from_pretrained(MODEL_NAME)\ntokenizer.save_pretrained('./{}_tokenizer/'.format(MODEL_NAME))\n\n# just in case if there is no TF/Keras file provided in the model\n# we can just use `from_pt` and convert PyTorch to TensorFlow\ntry:\n print('try downloading TF weights')\n model = TFRobertaForTokenClassification.from_pretrained(MODEL_NAME)\nexcept:\n print('try downloading PyTorch weights')\n model = TFRobertaForTokenClassification.from_pretrained(MODEL_NAME, from_pt=True)\n\nmodel.save_pretrained(\"./{}\".format(MODEL_NAME), saved_model=True)",
"_____no_output_____"
]
],
[
[
"Let's have a look inside these two directories and see what we are dealing with:",
"_____no_output_____"
]
],
[
[
"!ls -l {MODEL_NAME}",
"total 318644\n-rw-r--r-- 1 root root 1034 Sep 20 11:57 config.json\ndrwxr-xr-x 3 root root 4096 Sep 20 11:57 saved_model\n-rw-r--r-- 1 root root 326280904 Sep 20 11:57 tf_model.h5\n"
],
[
"!ls -l {MODEL_NAME}/saved_model/1",
"total 4120\ndrwxr-xr-x 2 root root 4096 Sep 20 11:57 assets\n-rw-r--r-- 1 root root 4206967 Sep 20 11:57 saved_model.pb\ndrwxr-xr-x 2 root root 4096 Sep 20 11:57 variables\n"
],
[
"!ls -l {MODEL_NAME}_tokenizer",
"total 1336\n-rw-r--r-- 1 root root 456318 Sep 20 11:56 merges.txt\n-rw-r--r-- 1 root root 239 Sep 20 11:56 special_tokens_map.json\n-rw-r--r-- 1 root root 1352 Sep 20 11:56 tokenizer_config.json\n-rw-r--r-- 1 root root 898822 Sep 20 11:56 vocab.json\n"
]
],
[
[
"- as you can see, we need the SavedModel from `saved_model/1/` path\n- we also be needing `vocab.json` and `merges.txt` files from the tokenizer\n- all we need is to first convert `vocab.json` to `vocab.txt` and copy both `vocab.txt` and `merges.txt` into `saved_model/1/assets` which Spark NLP will look for\n- in addition to vocabs, we also need `labels` and their `ids` which is saved inside the model's config. We will save this inside `labels.txt`",
"_____no_output_____"
]
],
[
[
"asset_path = '{}/saved_model/1/assets'.format(MODEL_NAME)\n\n# let's save the vocab as txt file\nwith open('{}_tokenizer/vocab.txt'.format(MODEL_NAME), 'w') as f:\n for item in tokenizer.get_vocab().keys():\n f.write(\"%s\\n\" % item)\n\n# let's copy both vocab.txt and merges.txt files to saved_model/1/assets\n!cp {MODEL_NAME}_tokenizer/vocab.txt {asset_path}\n!cp {MODEL_NAME}_tokenizer/merges.txt {asset_path}",
"_____no_output_____"
],
[
"# get label2id dictionary \nlabels = model.config.label2id\n# sort the dictionary based on the id\nlabels = sorted(labels, key=labels.get)\n\nwith open(asset_path+'/labels.txt', 'w') as f:\n f.write('\\n'.join(labels))",
"_____no_output_____"
]
],
[
[
"Voila! We have our `vocab.txt` and `labels.txt` inside assets directory",
"_____no_output_____"
]
],
[
[
"!ls -l {MODEL_NAME}/saved_model/1/assets",
"total 852\n-rw-r--r-- 1 root root 37 Sep 20 11:59 labels.txt\n-rw-r--r-- 1 root root 456318 Sep 20 11:59 merges.txt\n-rw-r--r-- 1 root root 407065 Sep 20 11:59 vocab.txt\n"
]
],
[
[
"## Import and Save RobertaForTokenClassification in Spark NLP\n",
"_____no_output_____"
],
[
"- Let's install and setup Spark NLP in Google Colab\n- This part is pretty easy via our simple script",
"_____no_output_____"
]
],
[
[
"! wget http://setup.johnsnowlabs.com/colab.sh -O - | bash",
"_____no_output_____"
]
],
[
[
"Let's start Spark with Spark NLP included via our simple `start()` function",
"_____no_output_____"
]
],
[
[
"import sparknlp\n# let's start Spark with Spark NLP\nspark = sparknlp.start()",
"_____no_output_____"
]
],
[
[
"- Let's use `loadSavedModel` functon in `RoBertaForTokenClassification` which allows us to load TensorFlow model in SavedModel format\n- Most params can be set later when you are loading this model in `RoBertaForTokenClassification` in runtime like `setMaxSentenceLength`, so don't worry what you are setting them now\n- `loadSavedModel` accepts two params, first is the path to the TF SavedModel. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n- NOTE: `loadSavedModel` only accepts local paths and not distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. That is why we use `write.save` so we can use `.load()` from any file systems\n\n",
"_____no_output_____"
]
],
[
[
"from sparknlp.annotator import *\n\ntokenClassifier = RoBertaForTokenClassification\\\n .loadSavedModel('{}/saved_model/1'.format(MODEL_NAME), spark)\\\n .setInputCols([\"sentence\",'token'])\\\n .setOutputCol(\"ner\")\\\n .setCaseSensitive(True)\\\n .setMaxSentenceLength(128)",
"_____no_output_____"
]
],
[
[
"- Let's save it on disk so it is easier to be moved around and also be used later via `.load` function",
"_____no_output_____"
]
],
[
[
"tokenClassifier.write().overwrite().save(\"./{}_spark_nlp\".format(MODEL_NAME))",
"_____no_output_____"
]
],
[
[
"Let's clean up stuff we don't need anymore",
"_____no_output_____"
]
],
[
[
"!rm -rf {MODEL_NAME}_tokenizer {MODEL_NAME}",
"_____no_output_____"
]
],
[
[
"Awesome 😎 !\n\nThis is your RoBertaForTokenClassification model from HuggingFace 🤗 loaded and saved by Spark NLP 🚀 ",
"_____no_output_____"
]
],
[
[
"! ls -l {MODEL_NAME}_spark_nlp",
"total 322504\ndrwxr-xr-x 6 root root 4096 Sep 20 12:01 fields\ndrwxr-xr-x 2 root root 4096 Sep 20 12:01 metadata\n-rw-r--r-- 1 root root 330232386 Sep 20 12:01 roberta_classification_tensorflow\n"
]
],
[
[
"Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny RoBertaForTokenClassification model 😊 ",
"_____no_output_____"
]
],
[
[
"tokenClassifier_loaded = RoBertaForTokenClassification.load(\"./{}_spark_nlp\".format(MODEL_NAME))\\\n .setInputCols([\"sentence\",'token'])\\\n .setOutputCol(\"ner\")",
"_____no_output_____"
],
[
"tokenClassifier_loaded.getCaseSensitive()",
"_____no_output_____"
]
],
[
[
"That's it! You can now go wild and use hundreds of `RoBertaForTokenClassification` models from HuggingFace 🤗 in Spark NLP 🚀 \n",
"_____no_output_____"
]
]
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cb17d56871365cc0bf7f647ba1af7d7a5b109216 | 94,358 | ipynb | Jupyter Notebook | notebooks/2_DataStructures/1_Ins_DataStructures.ipynb | fabriquant/bayesian-model-evaluation | 6c68899699db7119ca709e533b7c3edf8b3a3e4f | [
"Apache-2.0"
] | 108 | 2019-06-11T21:49:35.000Z | 2021-12-24T00:53:22.000Z | notebooks/2_DataStructures/1_Ins_DataStructures.ipynb | fabriquant/bayesian-model-evaluation | 6c68899699db7119ca709e533b7c3edf8b3a3e4f | [
"Apache-2.0"
] | 51 | 2019-03-30T22:36:14.000Z | 2019-05-27T13:59:20.000Z | notebooks/2_DataStructures/1_Ins_DataStructures.ipynb | fabriquant/bayesian-model-evaluation | 6c68899699db7119ca709e533b7c3edf8b3a3e4f | [
"Apache-2.0"
] | 41 | 2019-06-22T20:46:54.000Z | 2022-02-12T05:36:13.000Z | 105.310268 | 23,692 | 0.841603 | [
[
[
"# Section 2.1 `xarray`, `az.InferenceData`, and NetCDF for Markov Chain Monte Carlo\n\n_How do we generate, store, and save Markov chain Monte Carlo results_",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport pandas as pd\nimport scipy.stats as stats\nimport matplotlib.pyplot as plt\nimport arviz as az\nimport pystan\nimport xarray as xr\nfrom IPython.display import Video\n\nnp.random.seed(0)",
"_____no_output_____"
],
[
"plt.style.use('arviz-white')",
"_____no_output_____"
]
],
[
[
"## Learning Objectives\n\n* Understand Markov chain Monte Carlo fundamentals\n* Recognize the meaning of sample, draws, and chains in MCMC context\n* Understand relationship between Xarray, az.InferenceData, and NetCDF\n* Gain profiency with Xarray, NetCDF, and az.InferenceData objects",
"_____no_output_____"
],
[
"## Markov Chain Monte Carlo\n**Pop quiz**: Why do we use Markov chain Monte Carlo in Bayesian inference?\n\n**Highlight for answer:** C<span style=\"color:white\">alculating the posterior distribution is hard</span>!\n\n**Example:** If a flight has cancellation rate $r$, alternate tickets cost you $c$, and these distributions are modelled by $p(r, c)$, then expected cost of insuring a flight is\n\n$$\n\\text{risk} = \\int_{r=0}^{1}\\int_{c=0}^{\\infty} r\\cdot c~dp(r, c)\n$$\n\nThis can be hard to calculate for any number of reasons! If, instead, we have samples \n$$\n\\{r_j, c_j\\}_{j=1}^N \\sim p(r, c)\n$$\n\nthen \n\n$$\n\\text{risk} \\approx \\frac{1}{N}\\sum_{j=1}^N r_j \\cdot c_j\n$$\n\nIn python code, this would just be\n\n```\nrisk = np.dot(r, c) / N\n```",
"_____no_output_____"
],
[
"## Markov Chain Monte Carlo algorithm (greatly simplified)\n\nStep 1: Start at a random spot \nStep 2: Propose a new spot, possibly based on the previous spot \nStep 3: Accept or reject this proposal based on some mathematical book keeping \nStep 4: If accepted, move to proposed spot, if rejected, stay where you are \nStep 5: Write down where you're standing \nStep 6: Go back to step 2 \n\nThe accepted proposals are called draws (or samples).\n\nWhen animated this algorithm looks like this:",
"_____no_output_____"
]
],
[
[
"Video(\"../../img/medium_steps.mp4\")",
"_____no_output_____"
]
],
[
[
"In MCMC Step 2 and Step 4 is where most MCMC variants differentiate themselves. Algorithms like Hamiltonian Monte Carlo and Sequential Monte Carlo are better at picking that next step for certain tasks. Richard McElreath has a great visual explainer [on his blog]([http://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/)\n\nChain: A Markov chain\nSample/Draw: A single element of that chain\n\nRegardless of algorithm in MCMC we end up with the same thing, a chain of accepted proposals with a fixed size. There is a rich literature to show that these algorithms produce samples that are eventually distributed according to the distribution we care about.",
"_____no_output_____"
],
[
"## Markov chain Monte Carlo with Metropolis-Hastings\n\nBelow is a working Metropolis-Hastings sampler, taken from [Thomas Wiecki's blog](https://twiecki.io/blog/2015/11/10/mcmc-sampling/). For the purposes of this tutorial focus more on the return value than the algorithm details.\n\nIt is important to note that this for simplicity's sake we have also hard coded the likelihood and prior in the sampler below. In mathematical notation our model looks like this. We are adding 20 to the estimation of mu to make it easier to recognize the distribution of **parameters** from the distribution of **observed data**\n\n$$\n\\mu \\sim \\mathcal{N}(0, 1) \\\\\ny \\sim \\mathcal{N}(\\mu+20, 1)\n$$",
"_____no_output_____"
]
],
[
[
"def mh_sampler(data, samples=4, mu_init=.5):\n mu_current = mu_init\n posterior = []\n prior_logpdf = stats.norm(0, 1).logpdf\n \n for i in range(samples):\n # suggest new position\n mu_proposal = stats.norm(mu_current, 0.5).rvs()\n\n # Compute likelihood by multiplying probabilities of each data point\n likelihood_current = stats.norm(mu_current + 20, 1).logpdf(data).sum()\n likelihood_proposal = stats.norm(mu_proposal + 20, 1).logpdf(data).sum()\n \n # Compute prior probability of current and proposed mu \n prior_current = prior_logpdf(mu_current)\n prior_proposal = prior_logpdf(mu_proposal)\n \n # log(p(x|θ) p(θ)) = log(p(x|θ)) + log(p(θ))\n p_current = likelihood_current + prior_current\n p_proposal = likelihood_proposal + prior_proposal\n \n # Accept proposal?\n p_accept = np.exp(p_proposal - p_current)\n accept = np.random.rand() < p_accept\n \n if accept:\n # Update position\n mu_current = mu_proposal\n else:\n # don't move\n pass\n \n posterior.append(mu_current)\n \n return np.array(posterior)",
"_____no_output_____"
]
],
[
[
"## Setup\nBefore using the sampler let's generate some data to test our Metropolis Hasting Implementation. In the code block below we are generating a bimodal distribution for the sampler. ",
"_____no_output_____"
]
],
[
[
"data = stats.norm.rvs(loc=30, scale=1, size=1000).flatten()",
"_____no_output_____"
]
],
[
[
"We'll also plot our samples to get a sense of what the distribution of data looks like. Note how the histogram centers around 30. This should intuitively make sense as we're specified a mean of 30 when generating random values.",
"_____no_output_____"
]
],
[
[
"fig, ax = plt.subplots()\nax.hist(data)\nfig.suptitle(\"Histogram of observed data\");",
"_____no_output_____"
]
],
[
[
"As humans we can intuit *data mean* of **30** + an offset of **20** will lead to a parameter mean for *mu* of **10**. We want to see if our inference algorithm can recover our parameters.",
"_____no_output_____"
],
[
"## Single Variable Single Chain Inference Run",
"_____no_output_____"
],
[
"The simplest MCMC run we can perform is with a single variable and a single chain. We'll do so by putting our sampler function and data to use.",
"_____no_output_____"
]
],
[
[
"samples = 200\nchain = mh_sampler(data=data, samples=samples)\nchain[:100]",
"_____no_output_____"
]
],
[
[
"And just like that we've performed an inference run! We can generate a traceplot",
"_____no_output_____"
]
],
[
[
"fig, ax = plt.subplots(figsize=(10, 7))\nx = np.arange(samples)\nax.plot(x, chain);",
"_____no_output_____"
]
],
[
[
"In terms of data structures, for a **single** variable **single** chain inference run, an array suffices for storing samples.",
"_____no_output_____"
],
[
"## Single Variable Multiple Chain Inference Run",
"_____no_output_____"
],
[
"As Bayesian modelers, life would be relatively easy if a single chain worked well every time, but unfortunately this is not the case. To understand why look at the above inference run. While the sampler started at *mu=8*, it took a 50 or so steps before the sampler honed in on the \"correct\" value of 10.\n\nMCMC algorithms are sensitive to their starting points and in finite runs it's **not** guaranteed that the Markov Chain will approach the true underlying distribution. A common method to get around this is to sample from many chains in parallel and see if we get to the same place. We will discuss this further when we get to single model diagnostics.",
"_____no_output_____"
]
],
[
[
"chain_0 = mh_sampler(data=data, samples=samples)\nchain_1 = mh_sampler(data=data, samples=samples, mu_init=13)\ndata_df = pd.DataFrame({\"x_0\":chain_0, \"x_1\":chain_1})",
"_____no_output_____"
],
[
"fig, ax = plt.subplots()\nx = np.arange(samples)\nax.plot(x, data_df[\"x_0\"], c=\"g\")\nax.plot(x, data_df[\"x_1\"])",
"_____no_output_____"
]
],
[
[
"With two chains converging to approximately a single value we can be more confident that the sampler reached the true underlying parameter. We can also store the results in a 2D data structures, such as Pandas Dataframes in python memory, and csvs or sql tables for persistent on disk storage.",
"_____no_output_____"
],
[
"## Multiple Variable Multiple Chain Inference Runs",
"_____no_output_____"
],
[
"A Bayesian modelers, life would be relatively easy if all models only had one variable (univariate models in math speak). Unfortunately many types of models require 2 or more variables. For example in a linear regression we are interested in estimating both <b>m</b> and <b>b</b>:\n\n$$ y \\sim mx+b$$\n\nWith at least 3 things to track (chains, samples, and variables) a 2d data structures become limiting. This problem exists in many domains and is the focus of the *xarray* project.\n\nA motivating example comes from climate sciences. In this image from the xarray documentation the researcher might want to measure the temperature and humidity, across a 2D region at a point in time. Or they may want to plot the temperature over a time interval. xarray simplifies the data handling in cases like these.\n\n\n\n\n",
"_____no_output_____"
],
[
"### Xarray\nIn ArviZ an xarray DataSet object would look like the one below, where the variables are the Inference run variables, and the coordinates are at a minimum chains, draws.",
"_____no_output_____"
]
],
[
[
"posterior = xr.Dataset(\n {\"mu\": ([\"chain\", \"draw\"], [[11,12,13],[22,23,24]]), \"sd\": ([\"chain\", \"draw\"], [[33,34,35],[44,45,46]])},\n coords={\"draw\": [1,2,3], \"chain\": [0,1]},\n )\nposterior",
"_____no_output_____"
]
],
[
[
"## Multiple Variable Multiple Chain Inference runs and associated datasets\n\nAs a Bayesian modelers, life would be relatively easy if we were only concerned about posterior distributions. Looking back at the full end to end workflow, recall that there are other datasets, such as prior predictive samples, posterior predictive samples, among others. To aid the ArviZ user we present `az.InferenceData`.\n\n### az.InferenceData\n\naz.InferenceData serves as a data container for the various xarray datasets that are generated from an end-to-end Bayesian workflow. Consider our earlier simple model, and this time let's use `stan` to run a full analysis with multiple chains, multiple runs, and generate all sorts of datasets common in Bayesian analysis.",
"_____no_output_____"
],
[
"### Calculating prior",
"_____no_output_____"
]
],
[
[
"stan_code_prior = \"\"\"\ndata {\n int<lower=1> N;\n}\nparameters {\n real mu; // Estimated parameter\n}\n\nmodel {\n mu ~ normal(0, 1);\n}\ngenerated quantities {\n real y_hat[N]; // prior prediction\n for (n in 1:N) {\n y_hat[n] = normal_rng(mu+20, 1);\n }\n}\n\"\"\"\nstan_prior = pystan.StanModel(model_code=stan_code_prior)",
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9e2c867eeb58fa5e5ed2bc37cd00496e NOW.\n"
],
[
"stan_data_prior = {\"N\" : len(data)}\nstan_fit_prior = stan_prior.sampling(data=stan_data_prior)",
"WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\nTo run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
],
[
"stan_code_posterior = \"\"\"\ndata {\n int N;\n real y[N]; // Observed data\n}\nparameters {\n real mu; // Estimated parameter\n}\nmodel {\n mu ~ normal(0, 1);\n y ~ normal(mu+20, 1);\n}\ngenerated quantities {\n real y_hat[N]; // posterior prediction\n real log_lik[N]; // log_likelihood\n \n for (n in 1:N) {\n // Stan normal functions https://mc-stan.org/docs/2_19/functions-reference/normal-distribution.html\n y_hat[n] = normal_rng(mu, 1);\n log_lik[n] = normal_lpdf(y[n] | mu, 1);\n }\n}\n\"\"\"\nstan_model_posterior = pystan.StanModel(model_code=stan_code_posterior)",
"INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_804203b3f322a673e7cacf6457ff2d3c NOW.\n"
],
[
"stan_data_posterior = dict(\n y=data,\n N=len(data)\n)\nstan_fit_posterior = stan_model_posterior.sampling(data=stan_data_posterior)",
"WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\nTo run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
],
[
"stan_inference_data = az.from_pystan(posterior=stan_fit_posterior,\n observed_data=\"y\",\n \n # Other Bayesian Datasets that we have not discussed yet!\n posterior_predictive=\"y_hat\", \n prior=stan_fit_prior, \n prior_predictive=\"y_hat\",\n log_likelihood=\"log_lik\",\n )",
"_____no_output_____"
]
],
[
[
"### NetCDF\nCalculating the various datasets is usually not trivial. Network Common Data Form (NetCDF) is an open standard for storing multidimensional datasets, and `xarray` is a library for doing high performance analysis on those datasets. NetCDF even comes with \"group\" support, making it easy to serialize az.InferenceData straight to disk. ArviZ uses NetCDF to save the results to disk, allowing reproducible analyses, multiple experiments, and sharing with others.\n \nArviZ even ships with sample datasets, serialized in NetCDF\nhttps://github.com/arviz-devs/arviz/tree/master/arviz/data/_datasets\n\nIn short: like SQL is to Pandas DataFrame, NetCDF is to az.InferenceData.",
"_____no_output_____"
]
],
[
[
"data = az.load_arviz_data(\"centered_eight\")\ndata",
"_____no_output_____"
]
],
[
[
"## The benefits of az.InferenceData\nOne of the goals for the ArviZ developers is to ensure that Bayesian practioners can share and reproduce analyses regardless of PPl, regardless of language and az.InferenceData was the implementation of this idea.\n\nIn summary az.InferenceData \n\n* provides a consistent format for Bayesian datasets.\n* makes it easy to save results\n* makes use of ArviZ plotting and statistics functions simpler\n* stores metadata for ease of reproducibility",
"_____no_output_____"
],
[
"## InferenceData in practice\n\nIn practice it's rare to ever generate a xarray manually for use in ArviZ. Instead ArviZ provides methods for instantiating InferenceData from plain Python objects, mappings to various PPLs, as well as methods to save and load NetCDF files.\n\nFor further references consider the ArviZ cookbook, and data structure tutorial.\nhttps://arviz-devs.github.io/arviz/notebooks/InferenceDataCookbook.html\nhttps://arviz-devs.github.io/arviz/notebooks/XarrayforArviZ.html\n\n## Examples\n\nSee below for some useful methods of interacting with az.InferenceData, Xarray, and NetCDF\n\nFor Xarray methods we only demo a subset of the available API. For a much more comprehensive explanation view the indexing and selection page from the xarray docs\nhttp://xarray.pydata.org/en/stable/indexing.html",
"_____no_output_____"
],
[
"### Creating InferenceData objects\nWe can create an InferenceData objects from our \"home built\" chain, not just from the output of supported PPLs",
"_____no_output_____"
]
],
[
[
"data_dict = {\"mu\": [chain_0, chain_1]}\nhome_built_data = az.from_dict(data_dict)\nhome_built_data",
"_____no_output_____"
],
[
"# Load NetCDF from disk into memory\n## Replace with NetCDF that's \"visible\"\ndata = az.load_arviz_data(\"centered_eight\")",
"_____no_output_____"
],
[
"# Reference posterior directly\nposterior = data.posterior\nposterior",
"_____no_output_____"
],
[
"# Select specific variables\nposterior[[\"mu\", \"tau\"]]",
"_____no_output_____"
],
[
"# Select specific chains and draws\nposterior.sel(chain=[0,2], draw=slice(0,5))",
"_____no_output_____"
],
[
"# Get first 10 samples of mu from chain 0\nposterior[\"mu\"].sel(chain=0, draw=slice(0,10)).values",
"_____no_output_____"
]
],
[
[
"## Extra Credit\n* xarray supports numpy \"ufuncs\" (https://docs.scipy.org/doc/numpy/reference/ufuncs.html). ArviZ uses these under the hood for efficient calculations. ",
"_____no_output_____"
]
]
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cb17dc686300c84b6eae23215552b82cbacc492c | 48,152 | ipynb | Jupyter Notebook | tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb | JulioLarrea/course-content | ab5bf2ecdae024857ab1456fce262ed83526dc40 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 1 | 2021-06-29T06:11:47.000Z | 2021-06-29T06:11:47.000Z | tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb | NiloofarT/course-content | ba1293fe831dc9b6ea9c0edac046767381ffafc6 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | null | null | null | tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb | NiloofarT/course-content | ba1293fe831dc9b6ea9c0edac046767381ffafc6 | [
"CC-BY-4.0",
"BSD-3-Clause"
] | 1 | 2021-11-26T17:23:48.000Z | 2021-11-26T17:23:48.000Z | 35.589061 | 588 | 0.602841 | [
[
[
"<a href=\"https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# Tutorial 1: \"What\" models\n**Week 1, Day 1: Model Types**\n\n**By Neuromatch Academy**\n\n__Content creators:__ Matt Laporte, Byron Galbraith, Konrad Kording\n\n__Content reviewers:__ Dalin Guo, Aishwarya Balwani, Madineh Sarvestani, Maryam Vaziri-Pashkam, Michael Waskom\n\nWe would like to acknowledge [Steinmetz _et al._ (2019)](https://www.nature.com/articles/s41586-019-1787-x) for sharing their data, a subset of which is used here.\n",
"_____no_output_____"
],
[
"**Our 2021 Sponsors, including Presenting Sponsor Facebook Reality Labs**\n\n<p align='center'><img src='https://github.com/NeuromatchAcademy/widgets/blob/master/sponsors.png?raw=True'/></p>",
"_____no_output_____"
],
[
"___\n# Tutorial Objectives\nThis is tutorial 1 of a 3-part series on different flavors of models used to understand neural data. In this tutorial we will explore 'What' models, used to describe the data. To understand what our data looks like, we will visualize it in different ways. Then we will compare it to simple mathematical models. Specifically, we will:\n\n- Load a dataset with spiking activity from hundreds of neurons and understand how it is organized\n- Make plots to visualize characteristics of the spiking activity across the population\n- Compute the distribution of \"inter-spike intervals\" (ISIs) for a single neuron\n- Consider several formal models of this distribution's shape and fit them to the data \"by hand\"",
"_____no_output_____"
]
],
[
[
"# @title Video 1: \"What\" Models\nfrom ipywidgets import widgets\n\nout2 = widgets.Output()\nwith out2:\n from IPython.display import IFrame\n class BiliVideo(IFrame):\n def __init__(self, id, page=1, width=400, height=300, **kwargs):\n self.id=id\n src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n super(BiliVideo, self).__init__(src, width, height, **kwargs)\n\n video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))\n display(video)\n\nout1 = widgets.Output()\nwith out1:\n from IPython.display import YouTubeVideo\n video = YouTubeVideo(id=\"KgqR_jbjMQg\", width=854, height=480, fs=1, rel=0)\n print('Video available at https://youtube.com/watch?v=' + video.id)\n display(video)\n\nout = widgets.Tab([out1, out2])\nout.set_title(0, 'Youtube')\nout.set_title(1, 'Bilibili')\n\ndisplay(out)",
"_____no_output_____"
]
],
[
[
"# Setup\n\n",
"_____no_output_____"
],
[
"Python requires you to explictly \"import\" libraries before their functions are available to use. We will always specify our imports at the beginning of each notebook or script.",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport matplotlib.pyplot as plt",
"_____no_output_____"
]
],
[
[
"Tutorial notebooks typically begin with several set-up steps that are hidden from view by default.\n\n**Important:** Even though the code is hidden, you still need to run it so that the rest of the notebook can work properly. Step through each cell, either by pressing the play button in the upper-left-hand corner or with a keyboard shortcut (`Cmd-Return` on a Mac, `Ctrl-Enter` otherwise). A number will appear inside the brackets (e.g. `[3]`) to tell you that the cell was executed and what order that happened in.\n\nIf you are curious to see what is going on inside each cell, you can double click to expand. Once expanded, double-click the white space to the right of the editor to collapse again.",
"_____no_output_____"
]
],
[
[
"#@title Figure Settings\nimport ipywidgets as widgets #interactive display\n\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\nplt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content/master/nma.mplstyle\")",
"_____no_output_____"
],
[
"#@title Helper functions\n\n#@markdown Most of the tutorials make use of helper functions\n#@markdown to simplify the code that you need to write. They are defined here.\n\n# Please don't edit these, or worry about understanding them now!\n\ndef restrict_spike_times(spike_times, interval):\n \"\"\"Given a spike_time dataset, restrict to spikes within given interval.\n\n Args:\n spike_times (sequence of np.ndarray): List or array of arrays,\n each inner array has spike times for a single neuron.\n interval (tuple): Min, max time values; keep min <= t < max.\n\n Returns:\n np.ndarray: like `spike_times`, but only within `interval`\n \"\"\"\n interval_spike_times = []\n for spikes in spike_times:\n interval_mask = (spikes >= interval[0]) & (spikes < interval[1])\n interval_spike_times.append(spikes[interval_mask])\n return np.array(interval_spike_times, object)",
"_____no_output_____"
],
[
"#@title Data retrieval\n#@markdown This cell downloads the example dataset that we will use in this tutorial.\nimport io\nimport requests\nr = requests.get('https://osf.io/sy5xt/download')\nif r.status_code != 200:\n print('Failed to download data')\nelse:\n spike_times = np.load(io.BytesIO(r.content), allow_pickle=True)['spike_times']",
"_____no_output_____"
]
],
[
[
"---\n\n# Section 1: Exploring the Steinmetz dataset\n\nIn this tutorial we will explore the structure of a neuroscience dataset.\n\nWe consider a subset of data from a study of [Steinmetz _et al._ (2019)](https://www.nature.com/articles/s41586-019-1787-x). In this study, Neuropixels probes were implanted in the brains of mice. Electrical potentials were measured by hundreds of electrodes along the length of each probe. Each electrode's measurements captured local variations in the electric field due to nearby spiking neurons. A spike sorting algorithm was used to infer spike times and cluster spikes according to common origin: a single cluster of sorted spikes is causally attributed to a single neuron.\n\nIn particular, a single recording session of spike times and neuron assignments was loaded and assigned to `spike_times` in the preceding setup.\n\nTypically a dataset comes with some information about its structure. However, this information may be incomplete. You might also apply some transformations or \"pre-processing\" to create a working representation of the data of interest, which might go partly undocumented depending on the circumstances. In any case it is important to be able to use the available tools to investigate unfamiliar aspects of a data structure.\n\nLet's see what our data looks like...",
"_____no_output_____"
],
[
"## Section 1.1: Warming up with `spike_times`",
"_____no_output_____"
],
[
"What is the Python type of our variable?",
"_____no_output_____"
]
],
[
[
"type(spike_times)",
"_____no_output_____"
]
],
[
[
"You should see `numpy.ndarray`, which means that it's a normal NumPy array.\n\nIf you see an error message, it probably means that you did not execute the set-up cells at the top of the notebook. So go ahead and make sure to do that.\n\nOnce everything is running properly, we can ask the next question about the dataset: what's its shape?",
"_____no_output_____"
]
],
[
[
"spike_times.shape",
"_____no_output_____"
]
],
[
[
"There are 734 entries in one dimension, and no other dimensions. What is the Python type of the first entry, and what is *its* shape?",
"_____no_output_____"
]
],
[
[
"idx = 0\nprint(\n type(spike_times[idx]),\n spike_times[idx].shape,\n sep=\"\\n\",\n)",
"_____no_output_____"
]
],
[
[
"It's also a NumPy array with a 1D shape! Why didn't this show up as a second dimension in the shape of `spike_times`? That is, why not `spike_times.shape == (734, 826)`?\n\nTo investigate, let's check another entry.",
"_____no_output_____"
]
],
[
[
"idx = 321\nprint(\n type(spike_times[idx]),\n spike_times[idx].shape,\n sep=\"\\n\",\n)",
"_____no_output_____"
]
],
[
[
"It's also a 1D NumPy array, but it has a different shape. Checking the NumPy types of the values in these arrays, and their first few elements, we see they are composed of floating point numbers (not another level of `np.ndarray`):",
"_____no_output_____"
]
],
[
[
"i_neurons = [0, 321]\ni_print = slice(0, 5)\n\nfor i in i_neurons:\n print(\n \"Neuron {}:\".format(i),\n spike_times[i].dtype,\n spike_times[i][i_print],\n \"\\n\",\n sep=\"\\n\"\n )",
"_____no_output_____"
]
],
[
[
"Note that this time we've checked the NumPy `dtype` rather than the Python variable type. These two arrays contain floating point numbers (\"floats\") with 32 bits of precision.\n\nThe basic picture is coming together:\n- `spike_times` is 1D, its entries are NumPy arrays, and its length is the number of neurons (734): by indexing it, we select a subset of neurons.\n- An array in `spike_times` is also 1D and corresponds to a single neuron; its entries are floating point numbers, and its length is the number of spikes attributed to that neuron. By indexing it, we select a subset of spike times for that neuron.\n\nVisually, you can think of the data structure as looking something like this:\n\n```\n| . . . . . |\n| . . . . . . . . |\n| . . . |\n| . . . . . . . |\n```\n\nBefore moving on, we'll calculate and store the number of neurons in the dataset and the number of spikes per neuron:",
"_____no_output_____"
]
],
[
[
"n_neurons = len(spike_times)\ntotal_spikes_per_neuron = [len(spike_times_i) for spike_times_i in spike_times]\n\nprint(f\"Number of neurons: {n_neurons}\")\nprint(f\"Number of spikes for first five neurons: {total_spikes_per_neuron[:5]}\")",
"_____no_output_____"
],
[
"# @title Video 2: Exploring the dataset\nfrom ipywidgets import widgets\n\nout2 = widgets.Output()\nwith out2:\n from IPython.display import IFrame\n class BiliVideo(IFrame):\n def __init__(self, id, page=1, width=400, height=300, **kwargs):\n self.id=id\n src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n super(BiliVideo, self).__init__(src, width, height, **kwargs)\n\n video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))\n display(video)\n\nout1 = widgets.Output()\nwith out1:\n from IPython.display import YouTubeVideo\n video = YouTubeVideo(id=\"oHwYWUI_o1U\", width=854, height=480, fs=1, rel=0)\n print('Video available at https://youtube.com/watch?v=' + video.id)\n display(video)\n\nout = widgets.Tab([out1, out2])\nout.set_title(0, 'Youtube')\nout.set_title(1, 'Bilibili')\n\ndisplay(out)",
"_____no_output_____"
]
],
[
[
"## Section 1.2: Getting warmer: counting and plotting total spike counts\n\nAs we've seen, the number of spikes over the entire recording is variable between neurons. More generally, some neurons tend to spike more than others in a given period. Lets explore what the distribution of spiking looks like across all the neurons in the dataset.",
"_____no_output_____"
],
[
"Are most neurons \"loud\" or \"quiet\", compared to the average? To see, we'll define bins of constant width in terms of total spikes and count the neurons that fall in each bin. This is known as a \"histogram\".\n\nYou can plot a histogram with the matplotlib function `plt.hist`. If you just need to compute it, you can use the numpy function `np.histogram` instead.",
"_____no_output_____"
]
],
[
[
"plt.hist(total_spikes_per_neuron, bins=50, histtype=\"stepfilled\")\nplt.xlabel(\"Total spikes per neuron\")\nplt.ylabel(\"Number of neurons\");",
"_____no_output_____"
]
],
[
[
"Let's see what percentage of neurons have a below-average spike count:",
"_____no_output_____"
]
],
[
[
"mean_spike_count = np.mean(total_spikes_per_neuron)\nfrac_below_mean = (total_spikes_per_neuron < mean_spike_count).mean()\nprint(f\"{frac_below_mean:2.1%} of neurons are below the mean\")",
"_____no_output_____"
]
],
[
[
"We can also see this by adding the average spike count to the histogram plot:",
"_____no_output_____"
]
],
[
[
"plt.hist(total_spikes_per_neuron, bins=50, histtype=\"stepfilled\")\nplt.xlabel(\"Total spikes per neuron\")\nplt.ylabel(\"Number of neurons\")\nplt.axvline(mean_spike_count, color=\"orange\", label=\"Mean neuron\")\nplt.legend();",
"_____no_output_____"
]
],
[
[
"This shows that the majority of neurons are relatively \"quiet\" compared to the mean, while a small number of neurons are exceptionally \"loud\": they must have spiked more often to reach a large count.\n\n### Exercise 1: Comparing mean and median neurons\n\nIf the mean neuron is more active than 68% of the population, what does that imply about the relationship between the mean neuron and the median neuron?\n\n*Exercise objective:* Reproduce the plot above, but add the median neuron.\n",
"_____no_output_____"
]
],
[
[
"# To complete the exercise, fill in the missing parts (...) and uncomment the code\n\nmedian_spike_count = ... # Hint: Try the function np.median\n\n# plt.hist(..., bins=50, histtype=\"stepfilled\")\n# plt.axvline(..., color=\"limegreen\", label=\"Median neuron\")\n# plt.axvline(mean_spike_count, color=\"orange\", label=\"Mean neuron\")\n# plt.xlabel(\"Total spikes per neuron\")\n# plt.ylabel(\"Number of neurons\")\n# plt.legend()",
"_____no_output_____"
]
],
[
[
"[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/master//tutorials/W1D1_ModelTypes/solutions/W1D1_Tutorial1_Solution_b3411d5d.py)\n\n*Example output:*\n\n<img alt='Solution hint' align='left' width=558 height=414 src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/master/tutorials/W1D1_ModelTypes/static/W1D1_Tutorial1_Solution_b3411d5d_0.png>\n\n",
"_____no_output_____"
],
[
"\n*Bonus:* The median is the 50th percentile. What about other percentiles? Can you show the interquartile range on the histogram?",
"_____no_output_____"
],
[
"---\n\n# Section 2: Visualizing neuronal spiking activity",
"_____no_output_____"
],
[
"## Section 2.1: Getting a subset of the data\n\nNow we'll visualize trains of spikes. Because the recordings are long, we will first define a short time interval and restrict the visualization to only the spikes in this interval. We defined a utility function, `restrict_spike_times`, to do this for you. If you call `help()` on the function, it will tell you a little bit about itself:",
"_____no_output_____"
]
],
[
[
"help(restrict_spike_times)",
"_____no_output_____"
],
[
"t_interval = (5, 15) # units are seconds after start of recording\ninterval_spike_times = restrict_spike_times(spike_times, t_interval)",
"_____no_output_____"
]
],
[
[
"Is this a representative interval? What fraction of the total spikes fall in this interval?",
"_____no_output_____"
]
],
[
[
"original_counts = sum([len(spikes) for spikes in spike_times])\ninterval_counts = sum([len(spikes) for spikes in interval_spike_times])\nfrac_interval_spikes = interval_counts / original_counts\nprint(f\"{frac_interval_spikes:.2%} of the total spikes are in the interval\")",
"_____no_output_____"
]
],
[
[
"How does this compare to the ratio between the interval duration and the experiment duration? (What fraction of the total time is in this interval?)\n\nWe can approximate the experiment duration by taking the minimum and maximum spike time in the whole dataset. To do that, we \"concatenate\" all of the neurons into one array and then use `np.ptp` (\"peak-to-peak\") to get the difference between the maximum and minimum value:",
"_____no_output_____"
]
],
[
[
"spike_times_flat = np.concatenate(spike_times)\nexperiment_duration = np.ptp(spike_times_flat)\ninterval_duration = t_interval[1] - t_interval[0]\n\nfrac_interval_time = interval_duration / experiment_duration\nprint(f\"{frac_interval_time:.2%} of the total time is in the interval\")",
"_____no_output_____"
]
],
[
[
"These two values—the fraction of total spikes and the fraction of total time—are similar. This suggests the average spike rate of the neuronal population is not very different in this interval compared to the entire recording.\n\n## Section 2.2: Plotting spike trains and rasters\n\nNow that we have a representative subset, we're ready to plot the spikes, using the matplotlib `plt.eventplot` function. Let's look at a single neuron first:",
"_____no_output_____"
]
],
[
[
"neuron_idx = 1\nplt.eventplot(interval_spike_times[neuron_idx], color=\".2\")\nplt.xlabel(\"Time (s)\")\nplt.yticks([]);",
"_____no_output_____"
]
],
[
[
"We can also plot multiple neurons. Here are three:",
"_____no_output_____"
]
],
[
[
"neuron_idx = [1, 11, 51]\nplt.eventplot(interval_spike_times[neuron_idx], color=\".2\")\nplt.xlabel(\"Time (s)\")\nplt.yticks([]);",
"_____no_output_____"
]
],
[
[
"This makes a \"raster\" plot, where the spikes from each neuron appear in a different row.\n\nPlotting a large number of neurons can give you a sense for the characteristics in the population. Let's show every 5th neuron that was recorded:",
"_____no_output_____"
]
],
[
[
"neuron_idx = np.arange(0, len(spike_times), 5)\nplt.eventplot(interval_spike_times[neuron_idx], color=\".2\")\nplt.xlabel(\"Time (s)\")\nplt.yticks([]);",
"_____no_output_____"
]
],
[
[
"*Question*: How does the information in this plot relate to the histogram of total spike counts that you saw above?",
"_____no_output_____"
]
],
[
[
"# @title Video 3: Visualizing activity\nfrom ipywidgets import widgets\n\nout2 = widgets.Output()\nwith out2:\n from IPython.display import IFrame\n class BiliVideo(IFrame):\n def __init__(self, id, page=1, width=400, height=300, **kwargs):\n self.id=id\n src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n super(BiliVideo, self).__init__(src, width, height, **kwargs)\n\n video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))\n display(video)\n\nout1 = widgets.Output()\nwith out1:\n from IPython.display import YouTubeVideo\n video = YouTubeVideo(id=\"QGA5FCW7kkA\", width=854, height=480, fs=1, rel=0)\n print('Video available at https://youtube.com/watch?v=' + video.id)\n display(video)\n\nout = widgets.Tab([out1, out2])\nout.set_title(0, 'Youtube')\nout.set_title(1, 'Bilibili')\n\ndisplay(out)",
"_____no_output_____"
]
],
[
[
"---\n\n# Section 3: Inter-spike intervals and their distributions",
"_____no_output_____"
],
[
"Given the ordered arrays of spike times for each neuron in `spike_times`, which we've just visualized, what can we ask next?\n\nScientific questions are informed by existing models. So, what knowledge do we already have that can inform questions about this data?\n\nWe know that there are physical constraints on neuron spiking. Spiking costs energy, which the neuron's cellular machinery can only obtain at a finite rate. Therefore neurons should have a refractory period: they can only fire as quickly as their metabolic processes can support, and there is a minimum delay between consecutive spikes of the same neuron.\n\nMore generally, we can ask \"how long does a neuron wait to spike again?\" or \"what is the longest a neuron will wait?\" Can we transform spike times into something else, to address questions like these more directly?\n\nWe can consider the inter-spike times (or interspike intervals: ISIs). These are simply the time differences between consecutive spikes of the same neuron.\n\n### Exercise 2: Plot the distribution of ISIs for a single neuron\n\n*Exercise objective:* make a histogram, like we did for spike counts, to show the distribution of ISIs for one of the neurons in the dataset.\n\nDo this in three steps:\n\n1. Extract the spike times for one of the neurons\n2. Compute the ISIs (the amount of time between spikes, or equivalently, the difference between adjacent spike times)\n3. Plot a histogram with the array of individual ISIs",
"_____no_output_____"
]
],
[
[
"def compute_single_neuron_isis(spike_times, neuron_idx):\n \"\"\"Compute a vector of ISIs for a single neuron given spike times.\n\n Args:\n spike_times (list of 1D arrays): Spike time dataset, with the first\n dimension corresponding to different neurons.\n neuron_idx (int): Index of the unit to compute ISIs for.\n\n Returns:\n isis (1D array): Duration of time between each spike from one neuron.\n \"\"\"\n #############################################################################\n # Students: Fill in missing code (...) and comment or remove the next line\n raise NotImplementedError(\"Exercise: compute single neuron ISIs\")\n #############################################################################\n\n # Extract the spike times for the specified neuron\n single_neuron_spikes = ...\n\n # Compute the ISIs for this set of spikes\n # Hint: the function np.diff computes discrete differences along an array\n isis = ...\n\n return isis\n\n# Uncomment the following lines when you are ready to test your function\n# single_neuron_isis = compute_single_neuron_isis(spike_times, neuron_idx=283)\n# plt.hist(single_neuron_isis, bins=50, histtype=\"stepfilled\")\n# plt.axvline(single_neuron_isis.mean(), color=\"orange\", label=\"Mean ISI\")\n# plt.xlabel(\"ISI duration (s)\")\n# plt.ylabel(\"Number of spikes\")\n# plt.legend()",
"_____no_output_____"
]
],
[
[
"[*Click for solution*](https://github.com/NeuromatchAcademy/course-content/tree/master//tutorials/W1D1_ModelTypes/solutions/W1D1_Tutorial1_Solution_4792dbfa.py)\n\n*Example output:*\n\n<img alt='Solution hint' align='left' width=558 height=414 src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/master/tutorials/W1D1_ModelTypes/static/W1D1_Tutorial1_Solution_4792dbfa_0.png>\n\n",
"_____no_output_____"
],
[
"---\n\nIn general, the shorter ISIs are predominant, with counts decreasing rapidly (and smoothly, more or less) with increasing ISI. However, counts also rapidly decrease to zero with _decreasing_ ISI, below the maximum of the distribution (8-11 ms). The absence of these very low ISIs agrees with the refractory period hypothesis: the neuron cannot fire quickly enough to populate this region of the ISI distribution.\n\nCheck the distributions of some other neurons. To resolve various features of the distributions, you might need to play with the value of `n_bins`. Using too few bins might smooth over interesting details, but if you use too many bins, the random variability will start to dominate.\n\nYou might also want to restrict the range to see the shape of the distribution when focusing on relatively short or long ISIs. *Hint:* `plt.hist` takes a `range` argument",
"_____no_output_____"
],
[
"---\n\n# Section 4: What is the functional form of an ISI distribution?",
"_____no_output_____"
]
],
[
[
"# @title Video 4: ISI distribution\nfrom ipywidgets import widgets\n\nout2 = widgets.Output()\nwith out2:\n from IPython.display import IFrame\n class BiliVideo(IFrame):\n def __init__(self, id, page=1, width=400, height=300, **kwargs):\n self.id=id\n src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n super(BiliVideo, self).__init__(src, width, height, **kwargs)\n\n video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))\n display(video)\n\nout1 = widgets.Output()\nwith out1:\n from IPython.display import YouTubeVideo\n video = YouTubeVideo(id=\"DHhM80MOTe8\", width=854, height=480, fs=1, rel=0)\n print('Video available at https://youtube.com/watch?v=' + video.id)\n display(video)\n\nout = widgets.Tab([out1, out2])\nout.set_title(0, 'Youtube')\nout.set_title(1, 'Bilibili')\n\ndisplay(out)",
"_____no_output_____"
]
],
[
[
"The ISI histograms seem to follow continuous, monotonically decreasing functions above their maxima. The function is clearly non-linear. Could it belong to a single family of functions?\n\nTo motivate the idea of using a mathematical function to explain physiological phenomena, let's define a few different function forms that we might expect the relationship to follow: exponential, inverse, and linear.",
"_____no_output_____"
]
],
[
[
"def exponential(xs, scale, rate, x0):\n \"\"\"A simple parametrized exponential function, applied element-wise.\n\n Args:\n xs (np.ndarray or float): Input(s) to the function.\n scale (float): Linear scaling factor.\n rate (float): Exponential growth (positive) or decay (negative) rate.\n x0 (float): Horizontal offset.\n\n \"\"\"\n ys = scale * np.exp(rate * (xs - x0))\n return ys\n\ndef inverse(xs, scale, x0):\n \"\"\"A simple parametrized inverse function (`1/x`), applied element-wise.\n\n Args:\n xs (np.ndarray or float): Input(s) to the function.\n scale (float): Linear scaling factor.\n x0 (float): Horizontal offset.\n\n \"\"\"\n ys = scale / (xs - x0)\n return ys\n\ndef linear(xs, slope, y0):\n \"\"\"A simple linear function, applied element-wise.\n\n Args:\n xs (np.ndarray or float): Input(s) to the function.\n slope (float): Slope of the line.\n y0 (float): y-intercept of the line.\n\n \"\"\"\n ys = slope * xs + y0\n return ys",
"_____no_output_____"
]
],
[
[
"### Interactive Demo: ISI functions explorer\n\nHere is an interactive demo where you can vary the parameters of these functions and see how well the resulting outputs correspond to the data. Adjust the parameters by moving the sliders and see how close you can get the lines to follow the falling curve of the histogram. This will give you a taste of what you're trying to do when you *fit a model* to data.\n\n\"Interactive demo\" cells have hidden code that defines an interface where you can play with the parameters of some function using sliders. You don't need to worry about how the code works – but you do need to **run the cell** to enable the sliders.\n",
"_____no_output_____"
]
],
[
[
"#@title\n\n#@markdown Be sure to run this cell to enable the demo\n# Don't worry about understanding this code! It's to setup an interactive plot.\nsingle_neuron_idx = 283\nsingle_neuron_spikes = spike_times[single_neuron_idx]\nsingle_neuron_isis = np.diff(single_neuron_spikes)\n\ncounts, edges = np.histogram(\n single_neuron_isis,\n bins=50,\n range=(0, single_neuron_isis.max())\n)\n\nfunctions = dict(\n exponential=exponential,\n inverse=inverse,\n linear=linear,\n)\n\ncolors = dict(\n exponential=\"C1\",\n inverse=\"C2\",\n linear=\"C4\",\n)\n\[email protected](\n exp_scale=widgets.FloatSlider(1000, min=0, max=20000, step=250),\n exp_rate=widgets.FloatSlider(-10, min=-200, max=50, step=1),\n exp_x0=widgets.FloatSlider(0.1, min=-0.5, max=0.5, step=0.005),\n inv_scale=widgets.FloatSlider(1000, min=0, max=3e2, step=10),\n inv_x0=widgets.FloatSlider(0, min=-0.2, max=0.2, step=0.01),\n lin_slope=widgets.FloatSlider(-1e5, min=-6e5, max=1e5, step=10000),\n lin_y0=widgets.FloatSlider(10000, min=0, max=4e4, step=1000),\n)\ndef fit_plot(\n exp_scale=1000, exp_rate=-10, exp_x0=0.1,\n inv_scale=1000, inv_x0=0,\n lin_slope=-1e5, lin_y0=2000,\n):\n \"\"\"Helper function for plotting function fits with interactive sliders.\"\"\"\n func_params = dict(\n exponential=(exp_scale, exp_rate, exp_x0),\n inverse=(inv_scale, inv_x0),\n linear=(lin_slope, lin_y0),\n )\n f, ax = plt.subplots()\n ax.fill_between(edges[:-1], counts, step=\"post\", alpha=.5)\n xs = np.linspace(1e-10, edges.max())\n for name, function in functions.items():\n ys = function(xs, *func_params[name])\n ax.plot(xs, ys, lw=3, color=colors[name], label=name);\n ax.set(\n xlim=(edges.min(), edges.max()),\n ylim=(0, counts.max() * 1.1),\n xlabel=\"ISI (s)\",\n ylabel=\"Number of spikes\",\n )\n ax.legend()",
"_____no_output_____"
],
[
"# @title Video 5: Fitting models by hand\nfrom ipywidgets import widgets\n\nout2 = widgets.Output()\nwith out2:\n from IPython.display import IFrame\n class BiliVideo(IFrame):\n def __init__(self, id, page=1, width=400, height=300, **kwargs):\n self.id=id\n src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)\n super(BiliVideo, self).__init__(src, width, height, **kwargs)\n\n video = BiliVideo(id=\"\", width=854, height=480, fs=1)\n print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))\n display(video)\n\nout1 = widgets.Output()\nwith out1:\n from IPython.display import YouTubeVideo\n video = YouTubeVideo(id=\"uW2HDk_4-wk\", width=854, height=480, fs=1, rel=0)\n print('Video available at https://youtube.com/watch?v=' + video.id)\n display(video)\n\nout = widgets.Tab([out1, out2])\nout.set_title(0, 'Youtube')\nout.set_title(1, 'Bilibili')\n\ndisplay(out)",
"_____no_output_____"
]
],
[
[
"# Summary\n\nIn this tutorial, we loaded some neural data and poked at it to understand how the dataset is organized. Then we made some basic plots to visualize (1) the average level of activity across the population and (2) the distribution of ISIs for an individual neuron. In the very last bit, we started to think about using mathematical formalisms to understand or explain some physiological phenomenon. All of this only allowed us to understand \"What\" the data looks like.\n\nThis is the first step towards developing models that can tell us something about the brain. That's what we'll focus on in the next two tutorials.",
"_____no_output_____"
]
]
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cb17e83e3b0584f82beadff800827dd8cac324f6 | 132,788 | ipynb | Jupyter Notebook | SwingDoorTrending.ipynb | randomseed42/SwingDoorTrending | 655459c99a87afef8949f5ccab8594b1be673158 | [
"MIT"
] | null | null | null | SwingDoorTrending.ipynb | randomseed42/SwingDoorTrending | 655459c99a87afef8949f5ccab8594b1be673158 | [
"MIT"
] | null | null | null | SwingDoorTrending.ipynb | randomseed42/SwingDoorTrending | 655459c99a87afef8949f5ccab8594b1be673158 | [
"MIT"
] | null | null | null | 1,079.577236 | 88,024 | 0.95849 | [
[
[
"import numpy as np\nimport matplotlib.pyplot as plt",
"_____no_output_____"
],
[
"from SwingDoorTrending import swing_door_trending",
"_____no_output_____"
],
[
"np.random.seed(0)\nN = 100\nstep = 1\nx = np.arange(0, N*step, step)\ny = np.random.randint(0, 10, size=(N))\n\n_, x_new, y_new = swing_door_trending(x, y, 0.01, m=(15,85))\n\nfig, ax = plt.subplots(figsize=(15,3))\nax.scatter(x, y)\nax.plot(x, y)\nax.scatter(x_new, y_new)\nax.plot(x_new, y_new)\nplt.tight_layout()\nplt.show()",
"_____no_output_____"
],
[
"N = 1000\nstep = 1\nx = np.arange(0, N*step, step)\ny = np.sin(x*np.pi/180)\n\n_, x_new, y_new = swing_door_trending(x, y, 0.001, m=(15,85))\n\nfig, ax = plt.subplots(nrows=2, figsize=(15,6))\nax[0].scatter(x, y)\nax[0].plot(x, y)\nax[1].scatter(x_new, y_new)\nax[1].plot(x_new, y_new)\nplt.tight_layout()\nplt.show()",
"_____no_output_____"
]
]
] | [
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cb17ed524c0528db9cefa2a8338ae2b61633ecf8 | 5,996 | ipynb | Jupyter Notebook | notebooks/sql/raw/ex2.ipynb | Mattjez914/Blackjack_Microchallenge | c4f60b62a3ada14663eb30ce72563af994e1eda4 | [
"Apache-2.0"
] | 1 | 2019-12-09T04:45:42.000Z | 2019-12-09T04:45:42.000Z | notebooks/sql/raw/ex2.ipynb | NeuroLaunch/learntools | 60f8929f3526b7469d3da236a13748fde8584153 | [
"Apache-2.0"
] | null | null | null | notebooks/sql/raw/ex2.ipynb | NeuroLaunch/learntools | 60f8929f3526b7469d3da236a13748fde8584153 | [
"Apache-2.0"
] | 1 | 2019-04-17T06:12:23.000Z | 2019-04-17T06:12:23.000Z | 28.283019 | 226 | 0.586391 | [
[
[
"# Introduction\n\nTry writing some **SELECT** statements of your own to explore a large dataset of air pollution measurements.\n\nRun the cell below to set up the feedback system.",
"_____no_output_____"
]
],
[
[
"# Set up feedback system\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.sql.ex2 import *\nprint(\"Setup Complete\")",
"_____no_output_____"
]
],
[
[
"The code cell below fetches the `global_air_quality` table from the `openaq` dataset. We also preview the first five rows of the table.",
"_____no_output_____"
]
],
[
[
"from google.cloud import bigquery\n\n# Create a \"Client\" object\nclient = bigquery.Client()\n\n# Construct a reference to the \"openaq\" dataset\ndataset_ref = client.dataset(\"openaq\", project=\"bigquery-public-data\")\n\n# API request - fetch the dataset\ndataset = client.get_dataset(dataset_ref)\n\n# Construct a reference to the \"global_air_quality\" table\ntable_ref = dataset_ref.table(\"global_air_quality\")\n\n# API request - fetch the table\ntable = client.get_table(table_ref)\n\n# Preview the first five lines of the \"global_air_quality\" table\nclient.list_rows(table, max_results=5).to_dataframe()",
"_____no_output_____"
]
],
[
[
"# Exercises\n\n### 1) Units of measurement\n\nWhich countries have reported pollution levels in units of \"ppm\"? In the code cell below, set `first_query` to an SQL query that pulls the appropriate entries from the `country` column.\n\nIn case it's useful to see an example query, here's some code from the tutorial:\n\n```\nquery = \"\"\"\n SELECT city\n FROM `bigquery-public-data.openaq.global_air_quality`\n WHERE country = 'US'\n \"\"\"\n```",
"_____no_output_____"
]
],
[
[
"# Query to select countries with units of \"ppm\"\nfirst_query = ____ # Your code goes here\n\n# Set up the query (cancel the query if it would use too much of \n# your quota, with the limit set to 1 GB)\nsafe_config = bigquery.QueryJobConfig(maximum_bytes_billed=1e9)\nfirst_query_job = client.query(first_query, job_config=safe_config)\n\n# API request - run the query, and return a pandas DataFrame\nfirst_results = first_query_job.to_dataframe()\n\n# View top few rows of results\nprint(first_results.head())\n\n# Check your answer\nq_1.check()",
"_____no_output_____"
]
],
[
[
"For the solution, uncomment the line below.",
"_____no_output_____"
]
],
[
[
"#q_1.solution()",
"_____no_output_____"
]
],
[
[
"### 2) High air quality\n\nWhich pollution levels were reported to be exactly 0? \n- Set `zero_pollution_query` to select **all columns** of the rows where the `value` column is 0.\n- Set `zero_pollution_results` to a pandas DataFrame containing the query results.",
"_____no_output_____"
]
],
[
[
"# Query to select all columns where pollution levels are exactly 0\nzero_pollution_query = ____ # Your code goes here\n\n# Set up the query\nquery_job = client.query(zero_pollution_query, job_config=safe_config)\n\n# API request - run the query and return a pandas DataFrame\nzero_pollution_results = ____ # Your code goes here\n\nprint(zero_pollution_results.head())\n\n# Check your answer\nq_2.check()",
"_____no_output_____"
]
],
[
[
"For the solution, uncomment the line below.",
"_____no_output_____"
]
],
[
[
"#q_2.solution()",
"_____no_output_____"
]
],
[
[
"That query wasn't too complicated, and it got the data you want. But these **SELECT** queries don't organizing data in a way that answers the most interesting questions. For that, we'll need the **GROUP BY** command. \n\nIf you know how to use [`groupby()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) in pandas, this is similar. But BigQuery works quickly with far larger datasets.\n\nFortunately, that's next.",
"_____no_output_____"
],
[
"# Keep going\n**[GROUP BY](#$NEXT_NOTEBOOK_URL$)** clauses and their extensions give you the power to pull interesting statistics out of data, rather than receiving it in just its raw format.",
"_____no_output_____"
]
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cb17ede9d6b8c989cbdc3b9b9a6b4227619424c8 | 31,246 | ipynb | Jupyter Notebook | notebooks/ETL Pipeline Preparation.ipynb | felixnext/disaster_pipeline | 8355cd264698a370ade0ab21a10b3da44d353e26 | [
"MIT"
] | null | null | null | notebooks/ETL Pipeline Preparation.ipynb | felixnext/disaster_pipeline | 8355cd264698a370ade0ab21a10b3da44d353e26 | [
"MIT"
] | 4 | 2020-04-10T05:42:01.000Z | 2022-03-11T23:57:33.000Z | notebooks/ETL Pipeline Preparation.ipynb | felixnext/disaster_pipeline | 8355cd264698a370ade0ab21a10b3da44d353e26 | [
"MIT"
] | null | null | null | 36.080831 | 468 | 0.440888 | [
[
[
"# ETL Pipeline Preparation\nFollow the instructions below to help you create your ETL pipeline.\n### 1. Import libraries and load datasets.\n- Import Python libraries\n- Load `messages.csv` into a dataframe and inspect the first few lines.\n- Load `categories.csv` into a dataframe and inspect the first few lines.",
"_____no_output_____"
]
],
[
[
"# import libraries\nimport pandas as pd\nimport numpy as np",
"_____no_output_____"
],
[
"# load messages dataset\nmessages = pd.read_csv(\"../data/disaster_messages.csv\")\nmessages.head()",
"_____no_output_____"
],
[
"# load categories dataset\ncategories = pd.read_csv(\"../data/disaster_categories.csv\")\ncategories.head()",
"_____no_output_____"
]
],
[
[
"### 2. Merge datasets.\n- Merge the messages and categories datasets using the common id\n- Assign this combined dataset to `df`, which will be cleaned in the following steps",
"_____no_output_____"
]
],
[
[
"# merge datasets\ndf = pd.merge(messages, categories, on=\"id\")\ndf.head()",
"_____no_output_____"
],
[
"df['categories'].unique()",
"_____no_output_____"
]
],
[
[
"### 3. Split `categories` into separate category columns.\n- Split the values in the `categories` column on the `;` character so that each value becomes a separate column. You'll find [this method](https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.Series.str.split.html) very helpful! Make sure to set `expand=True`.\n- Use the first row of categories dataframe to create column names for the categories data.\n- Rename columns of `categories` with new column names.\n- Update the total value of the items to be 0 or 1",
"_____no_output_____"
]
],
[
[
"# create a dataframe of the 36 individual category columns\ndf_tmp = df['categories'].str.split(\";\", expand=True)\ncols = [w.split(\"-\")[0] for w in df_tmp.iloc[0, :].tolist()]\ndf_tmp.columns = cols\ndf_tmp = df_tmp.apply(lambda x: x.str.replace(\".*?-([0-9]+)\", r\"\\g<1>\").astype(int)).clip(0, 1)\ndf = pd.concat([df.iloc[:, :-1], df_tmp], axis=1)",
"_____no_output_____"
]
],
[
[
"### 4. Remove duplicates.\n- Check how many duplicates are in this dataset.\n- Drop the duplicates.\n- Confirm duplicates were removed.",
"_____no_output_____"
]
],
[
[
"# check number of duplicates\ndf_dub = df[df.duplicated(subset=None)]\nprint(\"Duplicates: {}\".format(df_dub.shape[0]))\ndf_dub.head()",
"Duplicates: 171\n"
],
[
"# drop duplicates\ndf = df.drop_duplicates()",
"_____no_output_____"
],
[
"# check number of duplicates\ndf[df.duplicated(subset=None)]",
"_____no_output_____"
]
],
[
[
"Check for items with a very low mean",
"_____no_output_____"
]
],
[
[
"desc = df.describe()\ndesc[desc.columns[desc.loc['std', :] < 0.01]]",
"_____no_output_____"
],
[
"# since child_alone holds no relevant information, remove:\ndf = df.drop('child_alone', axis=1)",
"_____no_output_____"
]
],
[
[
"### 5. Save the clean dataset into an sqlite database.\nYou can do this with pandas [`to_sql` method](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html) combined with the SQLAlchemy library. Remember to import SQLAlchemy's `create_engine` in the first cell of this notebook to use it below.",
"_____no_output_____"
]
],
[
[
"from sqlalchemy import create_engine\nengine = create_engine('sqlite:///../data/disaster_data.db')\ndf.to_sql('texts', engine, index=False)",
"_____no_output_____"
]
],
[
[
"### 6. Use this notebook to complete `etl_pipeline.py`\nUse the template file attached in the Resources folder to write a script that runs the steps above to create a database based on new datasets specified by the user. Alternatively, you can complete `etl_pipeline.py` in the classroom on the `Project Workspace IDE` coming later.",
"_____no_output_____"
]
]
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cb17fe15dca8154f91190e9f03fd577cbed10c35 | 14,706 | ipynb | Jupyter Notebook | 7.18.ipynb | dimong/dimongg | 2de53028a4252e8c3ae416746b66abbbcb4f35fc | [
"Apache-2.0"
] | null | null | null | 7.18.ipynb | dimong/dimongg | 2de53028a4252e8c3ae416746b66abbbcb4f35fc | [
"Apache-2.0"
] | null | null | null | 7.18.ipynb | dimong/dimongg | 2de53028a4252e8c3ae416746b66abbbcb4f35fc | [
"Apache-2.0"
] | null | null | null | 19.846154 | 126 | 0.425881 | [
[
[
"# 选择\n## 布尔类型、数值和表达式\n\n- 注意：比较运算符的相等是两个等到，一个等到代表赋值\n- 在Python中可以用整型0来代表False，其他数字来代表True\n- 后面还会讲到 is 在判断语句中的用发",
"_____no_output_____"
]
],
[
[
"b=100",
"_____no_output_____"
],
[
"aa = eval(input('请输入密码： '))\nbb = 123456\nif aa == bb:\n a = eval(input('请输入取多少钱： '))\n if a <= b:\n c = b-a\n print('取钱成功')\n b=c\n print('余额',c)\n # send_message()\n else:\n print('失败')\nelse:\n print('密码错误')",
"_____no_output_____"
]
],
[
[
"## 字符串的比较使用ASCII值",
"_____no_output_____"
],
[
"## Markdown \n- https://github.com/younghz/Markdown",
"_____no_output_____"
],
[
"## EP:\n- <img src=\"../Photo/34.png\"></img>\n- 输入一个数字，判断其实奇数还是偶数",
"_____no_output_____"
],
[
"## 产生随机数字\n- 函数random.randint(a,b) 可以用来产生一个a和b之间且包括a和b的随机整数",
"_____no_output_____"
],
[
"## 其他random方法\n- random.random 返回0.0到1.0之间前闭后开区间的随机浮点\n- random.randrange(a,b) 前闭后开",
"_____no_output_____"
],
[
"## EP：\n- 产生两个随机整数number1和number2，然后显示给用户，使用户输入数字的和，并判定其是否正确\n- 进阶:写一个随机序号点名程序",
"_____no_output_____"
]
],
[
[
"import random\na = random.randint(1,10)\nb = random.randint(1,6)\nif b < 5:\n print('请第',a,'行','第',b,'个同学回答问题')\nelse:\n print('请第 9 行','第',b,'个同学回答问题')",
"_____no_output_____"
]
],
[
[
"## if语句\n- 如果条件正确就执行一个单向if语句，亦即当条件为真的时候才执行if内部的语句\n- Python有很多选择语句:\n> - 单向if \n - 双向if-else\n - 嵌套if\n - 多向if-elif-else\n \n- 注意：当语句含有子语句的时候，那么一定至少要有一个缩进，也就是说如果有儿子存在，那么一定要缩进\n- 切记不可tab键和space混用，单用tab 或者 space\n- 当你输出的结果是无论if是否为真时都需要显示时，语句应该与if对齐",
"_____no_output_____"
],
[
"## EP：\n- 用户输入一个数字，判断其实奇数还是偶数\n- 进阶：可以查看下4.5实例研究猜生日",
"_____no_output_____"
],
[
"## 双向if-else 语句\n- 如果条件为真，那么走if内部语句，否则走else内部语句",
"_____no_output_____"
],
[
"## EP：\n- 产生两个随机整数number1和number2，然后显示给用户，使用户输入数字，并判定其是否正确,如果正确打印“you‘re correct”，否则打印正确错误",
"_____no_output_____"
],
[
"## 嵌套if 和多向if-elif-else\n",
"_____no_output_____"
],
[
"## EP：\n- 提示用户输入一个年份，然后显示表示这一年的动物\n\n- 计算身体质量指数的程序\n- BMI = 以千克为单位的体重除以以米为单位的身高\n",
"_____no_output_____"
]
],
[
[
"tz = eval(input('请输入体重（KG）： '))\nsg = eval(input('身高（m）： '))\naa = tz / sg\nif aa<18.5:\n print('超轻')\nif 18.5<=aa<25.0:\n print('标准')\nif 25.0<=aa<30.0:\n print('超重')\nif 30.0<=aa:\n print('吃肥')",
"_____no_output_____"
],
[
"year = eval(input('请输入年份： '))\nb = year % 12\nif b == 0:\n print('猴')\nelif b== 1:\n print('鸡')\nelif b== 2:\n print('狗')\nelif b== 3:\n print('猪')\nelif b== 4:\n print('鼠')\nelif b== 5:\n print('牛')\nelif b== 6:\n print('虎')\nelif b== 7:\n print('兔')\nelif b== 8:\n print('龙')\nelif b== 9:\n print('蛇')\nelif b== 10:\n print('马')\nelif b== 11:\n print('羊')",
"_____no_output_____"
]
],
[
[
"## 逻辑运算符\n",
"_____no_output_____"
],
[
"\n",
"_____no_output_____"
],
[
"## EP：\n- 判定闰年：一个年份如果能被4整除但不能被100整除，或者能被400整除，那么这个年份就是闰年\n- 提示用户输入一个年份，并返回是否是闰年\n- 提示用户输入一个数字，判断其是否为水仙花数",
"_____no_output_____"
]
],
[
[
"for i in range(100,1000):\n a = i % 10\n b = i // 100\n c = (i // 10) % 10\n e = a ** 3 + b ** 3 + c ** 3\n if e == i:\n print(e)",
"_____no_output_____"
],
[
"stri = 'asdsad'",
"_____no_output_____"
],
[
"stri[1]",
"_____no_output_____"
],
[
"year = eval(input('nianfen: '))\nif (year % 4 ==0 and year % 100 != 0) or year % 400 ==0:\n print('闰年')\nelse:\n print('非')",
"_____no_output_____"
]
],
[
[
"## 实例研究：彩票\n",
"_____no_output_____"
]
],
[
[
"import random\na = input('请输入一个两位数： ')\nb = str(random.randint(10,99))\nprint('随机生成： ',b)\nif a == (b[0]+b[1]):\n print('恭喜获得10000$')\nelif a == (b[1]+b[0]):\n print('恭喜获得3000$')\nelif a[0] == b[0] or a[0] == b[1] or a[1] == b[0] or a[1] == b[1]:\n print('恭喜获得1000$')\nelse:\n print('抱歉什么也没有！')",
"_____no_output_____"
]
],
[
[
"# Homework\n- 1\n",
"_____no_output_____"
]
],
[
[
"import math\na,b,c = eval(input('Enter a,b,c'))\ne = b**2 -4*a*c\nif e > 0:\n r1=(-b+math.sqrt(e))/2*a\n r2=(-b-math.sqrt(e))/2*a\n print('r1=',round(r1,6),'\\nr2',round(r2,6))\nelif e == 0:\n r =-b / 2*a\n print('r=',int(r))\nelse:\n print('没有实根！')",
"_____no_output_____"
]
],
[
[
"- 2\n",
"_____no_output_____"
]
],
[
[
"import random\na = random.randint(0,100)\nb = random.randint(0,100)\nprint('生成2个数为： ',a,' ',b)\nc = eval(input('请输入整和： '))\nif b + a == c:\n print('真')\nelse:\n print('假')",
"_____no_output_____"
]
],
[
[
"- 3\n",
"_____no_output_____"
]
],
[
[
"a = eval(input('今天星期几： '))\nb = eval(input('未来几天后： '))\nc = (a+b)%7\nif c==0:\n print('今天是星期日',b,'天后星期日')\nelse:\n print('今天是星期',a,b,'天后星期',c)",
"今天星期几： 0\n未来几天后： 31\n今天是星期 0 31 天后星期 3\n"
]
],
[
[
"- 4\n",
"_____no_output_____"
]
],
[
[
"a,b,c = eval(input('请输入3个数： '))\nd=max(a,b,c)\ne=min(a,b,c)\nf=a+b+c-d-e\nprint(e,f,d)",
"_____no_output_____"
]
],
[
[
"- 5\n",
"_____no_output_____"
]
],
[
[
"a,b = eval(input('请输入1号重量和价钱： '))\nc,d = eval(input('请输入2号重量和价钱： '))\ne=a/b\nf=c/d\nif e>f:\n print('1号食品性价比更好！')\nelse:\n print('2号食品性价比更好！')",
"_____no_output_____"
]
],
[
[
"- 6\n",
"_____no_output_____"
]
],
[
[
"yue = eval(input('月份： '))\nyear = eval(input('年份: '))\nif yue == 2:\n if ((year % 4 ==0 and year % 100 != 0) or year % 400 ==0):\n print(year,'年的2月有29天！')\n else:\n print(year,'年的2月有28天！')\nelif yue == 1 or yue ==3 or yue ==5 or yue ==7 or yue ==8 or yue ==10 or yue ==12:\n print(year,'年的',yue,'月有31天！')\nelif yue == 4 or yue ==6 or yue ==9 or yue ==11:\n print(year,'年的',yue,'月有30天！')\n\n",
"_____no_output_____"
]
],
[
[
"- 7\n",
"_____no_output_____"
]
],
[
[
"import random\na = random.randint(2,3)\nprint('(测试程序是否正确2为正，3为反)',a)\n#2为正面，3为反面\nb = input('请猜测硬币正反面： ')\nif b == '正面' and a == 2:\n print('猜对了！')\nelif b == '反面' and a==3:\n print('猜对了！')\nelse:\n print('猜错了！')",
"(测试程序是否正确2为正，3为反) 3\n请猜测硬币正反面： 正面\n猜错了！\n"
]
],
[
[
"- 8\n",
"_____no_output_____"
]
],
[
[
"import random\na = eval(input('请输入你要出什么（0=剪刀，1=石头，2=布）： '))\nb = random.randint(0,2)\nprint(b)\nif a == 0 and b == 2:\n print('玩家获胜!')\nelif a == 1 and b == 0:\n print('玩家获胜!')\nelif a == 2 and b == 1:\n print('玩家获胜!')\nelif a == b:\n print('平局！')\nelse:\n print('电脑获胜!')",
"请输入你要出什么（0=剪刀，1=石头，2=布）： 0\n2\n玩家获胜!\n"
],
[
"h =(q+(26*(m+1))/10+k+k/4+j/4+5*j)%7",
"_____no_output_____"
]
],
[
[
"- 9\n",
"_____no_output_____"
]
],
[
[
"year = eval(input('输入年份：'))\nmonths = eval(input('输入月份：'))\ndays = eval(input('输入当前月份的某天：'))\nhdayDict = {0:'星期六',1:'星期日', 2: '星期一', 3: '星期二', 4: '星期三', 5: '星期四', 6: '星期五'}\nmonDict = {1: '13', 2: '14', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: '10', \\\n 11: '11', 12: '12'}\nif months == 1 or months == 2:\n year = year - 1 \nshijishu = year // 100\nshijiyear = year % 100\nh = (days + (26 * (eval(monDict[months]) + 1)) // 10 + shijiyear + shijiyear // 4 + shijishu // 4 + 5 * shijishu) % 7\nprint('Day of the week is ' + hdayDict[h])\n ",
"输入年份：2013\n输入月份：1\n输入当前月份的某天：25\nDay of the week is 星期五\n"
]
],
[
[
"- 10\n",
"_____no_output_____"
]
],
[
[
"import random\na = random.randint(1,13)\nhuase ={1:'梅花',2:'红桃',3:'方块',4:'黑桃'}\nc = random.randint(1,4)\nd = huase[c]+str(a)\nprint(d)",
"黑桃1\n"
]
],
[
[
"- 11\n",
"_____no_output_____"
]
],
[
[
"a = input('请输入一个3位数： ')\nb = a[2]+a[1]+a[0]\nif a == b:\n print('是回文数')\nelse:\n print('不是回文数')",
"请输入一个3位数： 123\n不是回文数\n"
]
],
[
[
"- 12\n",
"_____no_output_____"
]
],
[
[
"a,b,c =eval(input('输入三边：'))\nif a+b>c and a-b<c:\n d=a+b+c\n print(d)\nelse:\n print('输入非法')",
"输入三边：4,6,10\n输入非法\n"
]
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cb180ccb7674f9bc8600501b06e3dbc2501dd1d3 | 10,180 | ipynb | Jupyter Notebook | notebooks/logistic_regression_mnist.ipynb | smwgf/Tensorflow-101 | d5bc262378a70c72242debda4d5198f429e1102d | [
"MIT"
] | null | null | null | notebooks/logistic_regression_mnist.ipynb | smwgf/Tensorflow-101 | d5bc262378a70c72242debda4d5198f429e1102d | [
"MIT"
] | null | null | null | notebooks/logistic_regression_mnist.ipynb | smwgf/Tensorflow-101 | d5bc262378a70c72242debda4d5198f429e1102d | [
"MIT"
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[
[
"# LOGISTIC REGRESSION WITH MNIST",
"_____no_output_____"
]
],
[
[
"import numpy as np\n# import tensorflow as tf\nimport tensorflow.compat.v1 as tf\nimport matplotlib.pyplot as plt\n# tf.disable_eager_execution()\n# tf.enable_eager_execution()\nprint (\"PACKAGES LOADED\")",
"PACKAGES LOADED\n"
]
],
[
[
"# DOWNLOAD AND EXTRACT MNIST DATASET",
"_____no_output_____"
]
],
[
[
"def OnehotEncoding(target):\n from sklearn.preprocessing import OneHotEncoder\n target_re = target.reshape(-1,1)\n enc = OneHotEncoder()\n enc.fit(target_re)\n return enc.transform(target_re).toarray()",
"_____no_output_____"
],
[
"def SuffleWithNumpy(data_x, data_y):\n idx = np.random.permutation(len(data_x))\n x,y = data_x[idx], data_y[idx]\n return x,y",
"_____no_output_____"
]
],
[
[
"## download with keras dataset",
"_____no_output_____"
]
],
[
[
"print (\"Download and Extract MNIST dataset\")\n# mnist = input_data.read_data_sets('data/', one_hot=True)\nmnist = tf.keras.datasets.mnist\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nx_train, x_test = x_train / 255.0, x_test / 255.0\n\nprint\nprint (\" tpye of 'mnist' is %s\" % (type(mnist)))\nprint (\" number of train data is %d\" % (len(x_train)))\nprint (\" number of test data is %d\" % (len(x_test)))\nnum_train_data = len(x_train)\ntrainimg = x_train\ntrainimg = trainimg.reshape(len(trainimg),784)\ntrainlabel = OnehotEncoding(y_train)\ntestimg = x_test\ntestimg = testimg.reshape(len(testimg),784)\ntestlabel = OnehotEncoding(y_test)\nprint (\"MNIST loaded\")\ntf.disable_eager_execution()",
"Download and Extract MNIST dataset\n tpye of 'mnist' is <class 'tensorflow.python.util.module_wrapper.TFModuleWrapper'>\n number of train data is 60000\n number of test data is 10000\nMNIST loaded\n"
]
],
[
[
"## Download with tfds",
"_____no_output_____"
]
],
[
[
"import tensorflow_datasets as tfds\nprint (\"Batch Learning? \")\ntf.enable_eager_execution()\ndataset, metadata = tfds.load('mnist', as_supervised=True, with_info=True)\ntrain_dataset, test_dataset = dataset['train'], dataset['test']\n\nnum_train_examples = metadata.splits['train'].num_examples\nnum_test_examples = metadata.splits['test'].num_examples\nprint(\"Number of training examples: {}\".format(num_train_examples))\nprint(\"Number of test examples: {}\".format(num_test_examples))\n\ndef normalize(images, labels):\n images = tf.cast(images, tf.float32)\n images /= 255\n return images, labels\n\n# The map function applies the normalize function to each element in the train\n# and test datasets\ntrain_dataset = train_dataset.map(normalize)\ntest_dataset = test_dataset.map(normalize)\n\n# The first time you use the dataset, the images will be loaded from disk\n# Caching will keep them in memory, making training faster\ntrain_dataset = train_dataset.cache()\ntest_dataset = test_dataset.cache()\n\ntrain_dataset=train_dataset.shuffle(num_train_examples,reshuffle_each_iteration=True)\n\ntrain_to_np=tf.compat.v1.data.make_one_shot_iterator(train_dataset.batch(num_train_examples)).get_next()\nx_train=train_to_np[0].numpy()\ny_train=train_to_np[1].numpy()\ntest_to_np=tf.compat.v1.data.make_one_shot_iterator(test_dataset.batch(num_test_examples)).get_next()\nx_test = test_to_np[0].numpy()\ny_test = test_to_np[1].numpy()\n\nnum_train_data = len(x_train)\ntrainimg = x_train\ntrainlabel = OnehotEncoding(y_train)\ntrainimg = trainimg.reshape(len(trainimg),784)\ntestimg = x_test\ntestimg = testimg.reshape(len(testimg),784)\ntestlabel = OnehotEncoding(y_test)\n\ntf.disable_eager_execution()\ntrainimg.shape, trainlabel.shape",
"Batch Learning? \nNumber of training examples: 60000\nNumber of test examples: 10000\n"
]
],
[
[
"## CREATE TENSOR GRAPH FOR LOGISTIC REGRESSION",
"_____no_output_____"
]
],
[
[
"x = tf.placeholder(tf.float32, shape = (None, 784))\ny = tf.placeholder(tf.float32, shape = (None, 10)) # None is for infinite \nW = tf.Variable(tf.zeros([784, 10]))\nb = tf.Variable(tf.zeros([10]))\n# LOGISTIC REGRESSION MODEL\nactv = tf.nn.softmax(tf.matmul(x, W) + b) \n# COST FUNCTION\ncost = tf.reduce_mean(-tf.reduce_sum(y*tf.math.log(actv), axis=1)) \n# OPTIMIZER\nlearning_rate = 0.01\noptm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)",
"_____no_output_____"
]
],
[
[
"## PREDICTION AND ACCURACY",
"_____no_output_____"
]
],
[
[
"# PREDICTION\npred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1)) \n# ACCURACY\naccr = tf.reduce_mean(tf.cast(pred, \"float\"))\n# INITIALIZER\ninitializer = tf.global_variables_initializer()",
"_____no_output_____"
]
],
[
[
"# TRAIN MODEL",
"_____no_output_____"
]
],
[
[
"training_epochs = 50\nbatch_size = 100\ndisplay_step = 5\n# SESSION\nsess = tf.Session()\nsess.run(initializer)\n# MINI-BATCH LEARNING\nfor epoch in range(training_epochs):\n avg_cost = 0.\n num_batch = int(num_train_data/batch_size)\n for i in range(num_batch): \n batch_xs=trainimg[i*batch_size:(i+1)*batch_size]\n batch_ys=trainlabel[i*batch_size:(i+1)*batch_size]\n\n sess.run(optm, feed_dict={x: batch_xs, y: batch_ys})\n feeds = {x: batch_xs, y: batch_ys}\n avg_cost += sess.run(cost, feed_dict=feeds)/num_batch \n # DISPLAY\n if epoch % display_step == 0:\n feeds_train = {x: batch_xs, y: batch_ys}\n feeds_test = {x: testimg, y: testlabel}\n train_acc = sess.run(accr, feed_dict=feeds_train)\n test_acc = sess.run(accr, feed_dict=feeds_test)\n print (\"Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f\" \n % (epoch, training_epochs, avg_cost, train_acc, test_acc))\n #shuffle in each epoch\n trainimg,trainlabel = SuffleWithNumpy(trainimg,trainlabel)\n \nprint (\"DONE\")",
"Epoch: 000/050 cost: 1.137559623 train_acc: 0.830 test_acc: 0.856\nEpoch: 005/050 cost: 0.429877024 train_acc: 0.870 test_acc: 0.896\nEpoch: 010/050 cost: 0.375152144 train_acc: 0.950 test_acc: 0.905\nEpoch: 015/050 cost: 0.350435106 train_acc: 0.920 test_acc: 0.909\nEpoch: 020/050 cost: 0.335372693 train_acc: 0.890 test_acc: 0.913\nEpoch: 025/050 cost: 0.324955257 train_acc: 0.910 test_acc: 0.915\nEpoch: 030/050 cost: 0.317174830 train_acc: 0.930 test_acc: 0.916\nEpoch: 035/050 cost: 0.311087956 train_acc: 0.910 test_acc: 0.918\nEpoch: 040/050 cost: 0.306114847 train_acc: 0.870 test_acc: 0.918\nEpoch: 045/050 cost: 0.301994932 train_acc: 0.930 test_acc: 0.919\nDONE\n"
]
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cb181b160badcc04d6d539458838d2c90086358f | 157,634 | ipynb | Jupyter Notebook | WineClassification.ipynb | anaurora/WineClassification | fbf9dbcc56dd754e1bbcdf6634cdc5235d8687c1 | [
"MIT"
] | null | null | null | WineClassification.ipynb | anaurora/WineClassification | fbf9dbcc56dd754e1bbcdf6634cdc5235d8687c1 | [
"MIT"
] | null | null | null | WineClassification.ipynb | anaurora/WineClassification | fbf9dbcc56dd754e1bbcdf6634cdc5235d8687c1 | [
"MIT"
] | null | null | null | 81.004111 | 37,226 | 0.713177 | [
[
[
"[View in Colaboratory](https://colab.research.google.com/github/anaurora/WineClassification/blob/master/WineClassification.ipynb)",
"_____no_output_____"
],
[
"#Wine Type Prediction (Multi-class Classification)\n\nThis data set is taken from the UCI repository (link [here](https://archive.ics.uci.edu/ml/datasets/wine)). I have done some basic pre-processing of the data in Excel, like adding headers based on the dataset description and converting the dataset to the CSV format. As usual, I've ingested this data from my personal Drive, and this project's GitHub repository has the pre-processed dataset (in CSV format).\n\n###Dataset Information:\n\n**Winelab.csv**: To quote the UCI dataset description:\n\n\n> *These data are the results of a chemical analysis of\n wines grown in the same region in Italy but derived from three\n different cultivars.\n The analysis determined the quantities of 13 constituents\n found in each of the three types of wines. *\n \nBasically, there are 3 different types of wines and their attributes are their chemical compositions (Alcohol, Malic Acid, Ash etc.). Fortunately, this is a dataset with continuous quantitative features only. This makes our life a lot easier!\n\n",
"_____no_output_____"
],
[
"## Install and import necessary libraries",
"_____no_output_____"
]
],
[
[
"!pip3 install -U -q PyDrive #Only if you are loading your data from Google Drive",
"_____no_output_____"
],
[
"from pydrive.auth import GoogleAuth\nfrom pydrive.drive import GoogleDrive\nfrom google.colab import auth\nfrom oauth2client.client import GoogleCredentials\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.model_selection import train_test_split,GridSearchCV,KFold,cross_val_score\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import f1_score, accuracy_score, classification_report\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import LinearSVC\nfrom sklearn.calibration import CalibratedClassifierCV",
"_____no_output_____"
]
],
[
[
"## Authorize Google Drive (if your data is stored in Drive)",
"_____no_output_____"
]
],
[
[
"%%capture\nauth.authenticate_user()\ngauth = GoogleAuth()\ngauth.credentials = GoogleCredentials.get_application_default()\ndrive = GoogleDrive(gauth)",
"_____no_output_____"
]
],
[
[
"## Data Ingestion\n\nI have saved the file in my personal drive storage and read it from there into a pandas data frame. Please modify the following cells to read the CSV files into a Pandas dataframe as per your storage location.",
"_____no_output_____"
]
],
[
[
"%%capture\ndownloaded = drive.CreateFile({'id':'1Tghlhn7nTZv_WOV7typp9fFZil4TjZ_9'}) # replace the id with id of file you want to access\ndownloaded.GetContentFile('Winelab.csv') \nwinedata = pd.read_csv('Winelab.csv') ",
"_____no_output_____"
]
],
[
[
"Let's take a sneak peek at the data",
"_____no_output_____"
]
],
[
[
"winedata.head()",
"_____no_output_____"
],
[
"winedata.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 178 entries, 0 to 177\nData columns (total 14 columns):\nWine_Class 178 non-null int64\nAlcohol 178 non-null float64\nMalic_acid 178 non-null float64\nAsh 178 non-null float64\nAlkalinity_ash 178 non-null float64\nMagnesium 178 non-null int64\nTotal_phenols 178 non-null float64\nFlavanoids 178 non-null float64\nNonflavanoid_phenols 178 non-null float64\nProanthocyanins 178 non-null float64\nColor_intensity 178 non-null float64\nHue 178 non-null float64\nOD280_OD315 178 non-null float64\nProline 178 non-null int64\ndtypes: float64(11), int64(3)\nmemory usage: 19.5 KB\n"
]
],
[
[
"As we can see, we have 13 attributes for each wine with one class label (*Wine_Class*).",
"_____no_output_____"
],
[
"##Data Preparation",
"_____no_output_____"
],
[
"To prepare the data for analysis, we need to convert our Wine Class feature to a categorical one and scale the features, to prepare for dimensionality reduction using Principal Component Analysis (PCA).",
"_____no_output_____"
]
],
[
[
"winedata['Wine_Class']=winedata['Wine_Class'].astype('category')",
"_____no_output_____"
]
],
[
[
"In the previous section, we noticed that all 13 attributes of each wine are measured on completely different scales. To run PCA, and most machine learning algorithms, we need to focus on keeping the data around the same scale, so that no one feature can overpower the others with pure magnitude.",
"_____no_output_____"
]
],
[
[
"scaler = StandardScaler()\nwinedatapc=winedata.copy()\nscaler.fit(winedatapc.iloc[:,1:])\nwinedatapc.iloc[:,1:]=scaler.transform(winedatapc.iloc[:,1:])",
"_____no_output_____"
]
],
[
[
"Now, one can see that all data has been scaled i.e. each feature (except the class label, of course) has been centered and scaled to unit variance (Z scores).",
"_____no_output_____"
]
],
[
[
"winedatapc.head()",
"_____no_output_____"
]
],
[
[
"##Dimension Reduction (using PCA)",
"_____no_output_____"
],
[
"We are going to generate all possible Principal Components for our dataset (13), plot them to see how well segregated the classes are, and decide on the number of components we are going to use for our prediction algorithm.",
"_____no_output_____"
],
[
"The following lines of code generate the Principal Component scores of all the observations, for all 13 principal components (PC0 through PC12).",
"_____no_output_____"
]
],
[
[
"pca = PCA(n_components=13,random_state=0)\nPCAscores=pd.DataFrame(pca.fit_transform(winedatapc.iloc[:,1:]))\nPCAscores=PCAscores.rename('PC{}'.format, axis='columns')\nPCAscores['Wine_Class']=winedatapc.Wine_Class\nPCAscores.head()",
"_____no_output_____"
]
],
[
[
"To see what these Principal Components mean, we create a loadings dataframe for each attribute of the dataframe (columns), for each Principal Component (index)",
"_____no_output_____"
]
],
[
[
"load=pd.DataFrame(pca.components_,columns=winedatapc.iloc[:,1:].columns)\nload=load.rename('PC{}'.format, axis='index')\nload",
"_____no_output_____"
]
],
[
[
"To interpret the above table, we take an example of the first row (PC0 i.e. the first Principal Component). The highest dependence of this PC is on *Flavanoids* and the dependence is positive. This implies that the higher the amount of Flavanoids in this wine, the higher the score of the first principal component. The highest negative dependence is on *Nonflavanoid_phenols*, which implies the higher the *Nonflavanoid_phenols* attribute for a wine, the lower the PC score will be. This can be extended to all other PCs.",
"_____no_output_____"
],
[
"We now visualize all the wines (observations) using the first 2 Principal Components to see if the class distributions are well separated.",
"_____no_output_____"
]
],
[
[
"PC=sns.pairplot(x_vars=['PC0'], y_vars=['PC1'], data=PCAscores, hue=\"Wine_Class\", size=8)\nplt.xlabel('Principal Component 0')\nplt.ylabel('Principal Component 1')\nplt.title('Visual Representation of all observations')\nplt.show()",
"_____no_output_____"
]
],
[
[
"We can see that the 3 categories of wine, are pretty well segregated. This is good, as we have distinct features for each type of wine. This will improve the accuracy of our prediction algorithm.",
"_____no_output_____"
],
[
"The next task is to use these Principal Components to reduce the dimension of the problem. To see how many Principal Components we need, we are going to try 2 approaches:\n\n\n1) Scree Plot \n\n2) Percentage of Variance Explained",
"_____no_output_____"
]
],
[
[
"scree=plt.plot(pca.explained_variance_,'o-')\nplt.title('Scree Plot')\nplt.ylabel('Eigen Values')\nplt.xlabel('Component/Dimension')\nplt.xticks(range(0,13))\nplt.show()",
"_____no_output_____"
],
[
"var1=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)\ncumsum=plt.plot(var1,'o-')\nplt.xlabel('Component/Dimension')\nplt.ylabel('Percentage of Explained Variance')\nplt.title('Cumulative Percentage of Variance Explained')\nplt.xticks(range(0,13))\nplt.show()",
"_____no_output_____"
]
],
[
[
"According to Kaiser's Rule, we should select the number of components that have eigen values greater than 1, and according to the variance rule, we should select the number of components that explain 95% of the variance in the data.\n\nFrom Kaiser's Rule, we should select approximately 3 components (scree plot), and from the Variance Rule, we should select approximately 8 components. For this case, I'm going to be leaning towards the Variance Rule, but not exactly. I'm going to select the number of components that explain approximately 80% of the variation of the data.\n\n\n**This implies that I'm going to choose 5 components.**\n\nThe code below selects only the first 5 principal component scores.",
"_____no_output_____"
]
],
[
[
"X=PCAscores.iloc[:,:5].copy()\nX=X.values\ny=PCAscores.Wine_Class.astype(np.int64).values",
"_____no_output_____"
]
],
[
[
"##Prediction",
"_____no_output_____"
],
[
"###Split Data: Training and Testing",
"_____no_output_____"
],
[
"**We split the data to use 70% of it for training our machine learning models, and the remaining 30% for testing.**",
"_____no_output_____"
]
],
[
[
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)",
"_____no_output_____"
]
],
[
[
"###Class distribution",
"_____no_output_____"
],
[
"**A large part of how we decide which Machine Learning model to choose will depend on the class distribution. **",
"_____no_output_____"
]
],
[
[
"unique, counts = np.unique(y, return_counts=True)\nprint(np.asarray((unique, counts)).T)",
"[[ 1 59]\n [ 2 71]\n [ 3 48]]\n"
]
],
[
[
"**So we have a noticeably uneven class distribution. This skew can introduce a bias in our model, and hence we need to be careful with our scoring method.**\n\n**We chose the F1 micro score as a measure of model performance as we have an uneven class distribution (we have more ones than zeros in our class labels). For a more detailed explanation (and formulae), please see [this link.](https://blogs.msdn.microsoft.com/andreasderuiter/2015/02/09/performance-measures-in-azure-ml-accuracy-precision-recall-and-f1-score/)**",
"_____no_output_____"
],
[
"###Method 1: K-Nearest Neighbours (KNN)",
"_____no_output_____"
],
[
"####Hyperparameter Tuning",
"_____no_output_____"
],
[
"**In the case of KNN, we tune the type of distance metric used and the number of nearest neigbours considered in classification.**",
"_____no_output_____"
]
],
[
[
"def knn_param_selection(X, y, nfolds):\n n_neighborss = [3,4,5,6,7,8,9,10,11,12,13,14,15]\n metrics=['minkowski','euclidean','manhattan']\n param_grid = {'n_neighbors': n_neighborss,'metric':metrics}\n grid_search = GridSearchCV(KNeighborsClassifier(), param_grid, cv=nfolds,scoring='f1_micro')\n grid_search.fit(X, y)\n grid_search.best_params_\n return grid_search.best_params_",
"_____no_output_____"
],
[
"knn_param_selection(X_train, y_train, 10)",
"_____no_output_____"
]
],
[
[
"**The grid search reveals that we should use the *minkowski* distance metric and the *5 nearest neighbours* to maximize the F1 (micro) score. Since we have a class imbalance, we have used the F1 Micro score.**",
"_____no_output_____"
],
[
"####Model Fitting and Analysis",
"_____no_output_____"
]
],
[
[
"knnclass = KNeighborsClassifier(n_neighbors=5,metric='minkowski') \nknnclass.fit(X_train, y_train) \ny_pred = knnclass.predict(X_test) \nprint('K-Nearest Neighbors predicts the correct class label with a',str(round(accuracy_score(y_test, y_pred)*100,2)),'% accuracy.')",
"K-Nearest Neighbors predicts the correct class label with a 98.15 % accuracy.\n"
],
[
"kfold=KFold(n_splits=10, random_state=0)\nmodelCV = KNeighborsClassifier(n_neighbors=5,metric='minkowski')\nscoring = 'f1_micro'\nresults = cross_val_score(modelCV, X, y, cv=kfold,scoring=scoring)\nprint(\"10-fold average cross validation average F1 micro score: %.3f\" % (results.mean()))",
"10-fold average cross validation average F1 micro score: 0.950\n"
]
],
[
[
"###Method 2: Logistic Regression",
"_____no_output_____"
],
[
"\n####Hyperparameter Tuning",
"_____no_output_____"
],
[
"**We tune the regularization parameter (*C*) for logistic regression using a 10 fold cross validated Grid Search, using our training set. Notice, that we have set the grid search to maximize the F1 micro score for our model. **",
"_____no_output_____"
]
],
[
[
"def lgr_param_selection(X, y, nfolds):\n Cs = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.5,2,2.5,3]\n param_grid = {'C': Cs}\n grid_search = GridSearchCV(LogisticRegression(max_iter=1000,solver='sag',penalty='l2',random_state=0), param_grid, cv=nfolds,scoring='f1_micro')\n grid_search.fit(X, y)\n grid_search.best_params_\n return grid_search.best_params_",
"_____no_output_____"
],
[
"lgr_param_selection(X_train, y_train, 10)",
"_____no_output_____"
]
],
[
[
"**The Grid Search has yielded a regularization parameter (C) of 0.5. Note that we have used the L2 norm penalty, as L1/Elastic Net would perform automatic feature selection for us, which we have already done in the previous section.**\n\n**We now apply this parameter to create a logistic regression model for analysis, and then run a 10 fold cross validation to check if we have a good model that is not overfitted.**",
"_____no_output_____"
],
[
"####Model Fitting and Analysis",
"_____no_output_____"
]
],
[
[
"logreg = LogisticRegression(max_iter=1000,solver='sag',penalty='l2',C=0.5,random_state=0)\nlogreg.fit(X_train, y_train)\ny_pred = logreg.predict(X_test)\nprint('Logistic Regression predicts the correct class label with a',str(round(accuracy_score(y_test, y_pred)*100,2)),'% accuracy.')",
"Logistic Regression predicts the correct class label with a 100.0 % accuracy.\n"
],
[
"kfold=KFold(n_splits=10, random_state=0)\nmodelCV = LogisticRegression(max_iter=1000,solver='sag',penalty='l2',C=0.5,random_state=0)\nscoring = 'f1_micro'\nresults = cross_val_score(modelCV, X, y, cv=kfold,scoring=scoring)\nprint(\"10-fold average cross validation average F1 micro score: %.3f\" % (results.mean()))",
"10-fold average cross validation average F1 micro score: 0.933\n"
]
],
[
[
"###Method 3: Support Vector Classifier with Linear Kernel",
"_____no_output_____"
],
[
"####Hyperparameter Tuning",
"_____no_output_____"
]
],
[
[
"def linsvc_param_selection(X, y, nfolds):\n Cs = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.5,2,2.5,3]\n param_grid = {'C': Cs}\n grid_search = GridSearchCV(LinearSVC(random_state=0), param_grid, cv=nfolds,scoring='f1_micro')\n grid_search.fit(X, y)\n grid_search.best_params_\n return grid_search.best_params_",
"_____no_output_____"
],
[
"linsvc_param_selection(X_train, y_train, 10)",
"_____no_output_____"
]
],
[
[
"####Model Fitting and Analysis",
"_____no_output_____"
]
],
[
[
"clf = LinearSVC(C=0.1,random_state=0)\nlinsvc = CalibratedClassifierCV(clf) \nlinsvc.fit(X_train, y_train)\ny_pred=linsvc.predict(X_test) \nprint('Linear SVC predicts the correct class label with a',str(round(accuracy_score(y_test, y_pred)*100,2)),'% accuracy.')",
"Linear SVC predicts the correct class label with a 100.0 % accuracy.\n"
],
[
"kfold=KFold(n_splits=10, random_state=0)\nmodelCV = CalibratedClassifierCV(LinearSVC(C=0.1,random_state=0)) \nscoring = 'f1_micro'\nresults = cross_val_score(modelCV, X, y, cv=kfold,scoring=scoring)\nprint(\"10-fold average cross validation average F1 micro score: %.3f\" % (results.mean()))",
"10-fold average cross validation average F1 micro score: 0.944\n"
]
],
[
[
"###Summary",
"_____no_output_____"
],
[
"We have seen a multi-class dataset, and have more than halved the number of features using Principal Component Analysis. As we can see, PCA is an extremely powerful tool for feature selection (or feature engineering, in a way!), and one major advantage it has is that it produces orthogonal, independent components. We used the Principal Components in 3 machine learning algorithms, to predict our class labels. Since we had a class imbalance, we used the F1 Micro score as our primary measure of effectiveness. The average scores of a 10 fold cross validation set (on the whole dataset) for each algorithm are tabulated below:\n\n\n\n| Classifier | F1 Micro (10 fold CV) |\n| --- | --- |\n| K-Nearest Neighbours | 0.950\n| Logistic Regression | 0.933\n| Linear SVC | 0.944\n\n\n\nIt can be seen that the highest score is when we used the K-Nearest Neighbours algorithm, while the worst (not bad by any measure) was Logistic Regression. The power of simplicity really shines in this dataset. KNN is one of the simplest classifiers and yet was able to outperform far more complex classifiers. This can be attributed to the fact that our classes are very well segregated (see wine class scatter plot), and while the other two algorithms were able to predict the classes with 100% accuracy, they generalize worse, over the whole dataset. This is why KNN comes out on top. Simple, elegant and fast!",
"_____no_output_____"
]
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cb18371cdffc455e87ab65932fae0f1b1edf07e8 | 25,127 | ipynb | Jupyter Notebook | Notebooks/KappaLambda.ipynb | alasfar-lina/Interpretable-ML-hh | aacb83294b370d91f9d63db144624ab31a1478b6 | [
"MIT"
] | 1 | 2020-12-04T22:31:38.000Z | 2020-12-04T22:31:38.000Z | Notebooks/KappaLambda.ipynb | alasfar-lina/Interpretable-ML-hh | aacb83294b370d91f9d63db144624ab31a1478b6 | [
"MIT"
] | null | null | null | Notebooks/KappaLambda.ipynb | alasfar-lina/Interpretable-ML-hh | aacb83294b370d91f9d63db144624ab31a1478b6 | [
"MIT"
] | null | null | null | 123.171569 | 16,196 | 0.819875 | [
[
[
"import numpy as np\nimport pandas as pd\nimport json\nimport shap\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc\nfrom colour import Color\nfrom matplotlib.colors import ListedColormap, LinearSegmentedColormap\nimport collections\nimport pickle\n\ncolors = ['#3f7f93','#da3b46','#F6AE2D', '#98b83b', '#825FC3']\ncmp_5 = LinearSegmentedColormap.from_list('my_list', [Color(c1).rgb for c1 in colors], N=len(colors))\n\nseed = 42",
"_____no_output_____"
],
[
"def abs_shap(df_shap, df, shap_plot, names, class_names, cmp):\n ''' A function to plot the bar plot for the mean abs SHAP values\n arguments:\n df_shap: the dataframe of the SHAP values\n df: the dataframe for the feature values for which the SHAP values have been determined\n shap_plot: The name of the output file for the plot\n names: The names of the variables\n class_names: names of the classes\n cmp: the colour map\n '''\n rc('text', usetex=True)\n plt.rcParams['text.latex.preamble'] = r\"\\usepackage{amsmath}\"\n plt.figure(figsize=(5,5))\n shap.summary_plot(df_shap, df, color=cmp, class_names=class_names, class_inds='original', plot_size=(5,5), show=False)#, feature_names=names)\n ax = plt.gca()\n handles, labels = ax.get_legend_handles_labels()\n ax.legend(reversed(handles), reversed(labels), loc='lower right', fontsize=15)\n plt.xlabel(r'$\\overline{|S_v|}$', fontsize=15)\n ax = plt.gca()\n ax.spines[\"top\"].set_visible(True)\n ax.spines[\"right\"].set_visible(True)\n ax.spines[\"left\"].set_visible(True)\n vals = ax.get_xticks()\n ax.tick_params(axis='both', which='major', labelsize=15)\n for tick in vals:\n ax.axvline(x=tick, linestyle='dashed', alpha=0.7, color='#808080', zorder=0, linewidth=0.5)\n plt.tight_layout()\n plt.savefig(shap_plot, dpi=300)\n rc('text', usetex=False)\n \ndef get_mclass(i, df_array, weight_array, ps_exp_class, seed=seed):\n \"\"\" This function is used to create the confusion matrix\n arguments:\n i: integer corresponding to the class number\n df_array: the array of the dataframes of the different classes\n weight_array: the array of the weights for the different classes\n ps_exp_class: the collection of the pseudo experiment events\n seed: the seed for the random number generator\n returns:\n nevents: the number of events\n sif: the significance\n \"\"\"\n mclass = []\n nchannels = len(df_array)\n for j in range(nchannels):\n mclass.append(collections.Counter(classifier.predict(df_array[j].iloc[:,:-2].values))[i]/len(df_array[j])*weight_array[j]/weight_array[i])\n\n sig = np.sqrt(ps_exp_class[i])*mclass[i]/np.sum(mclass)\n nevents = np.round(ps_exp_class[i]/np.sum(mclass)*np.array(mclass)).astype(int)\n if nchannels == 5: print('sig: {:2.2f}, klam events: {}, hhsm events: {}, tth events: {}, bbh events: {}, bbxaa events: {}'.format(sig, nevents[4], nevents[3], nevents[2], nevents[1], nevents[0]))\n if nchannels == 4: print('sig: {:2.2f}, hhsm events: {}, tth events: {}, bbh events: {}, bbxaa events: {}'.format(sig, nevents[3], nevents[2], nevents[1], nevents[0]))\n if nchannels == 2: print('sig: {:2.2f}, ku events: {}, hhsm events: {}'.format(sig, nevents[1], nevents[0]))\n return nevents, sig ",
"_____no_output_____"
],
[
"prefix = '../WORK/klm1/'\n\ndf_sig_test = pd.read_json(prefix+'test_files/sig_test.json')\ndf_bkg_test = pd.read_json(prefix+'test_files/bkg_test.json')\ndf_bbh_test = pd.read_json(prefix+'test_files/bbh_test.json')\ndf_tth_test = pd.read_json(prefix+'test_files/tth_test.json')\ndf_bbxaa_test = pd.read_json(prefix+'test_files/bbxaa_test.json')\n\nX_shap = pd.read_json(prefix+'shapley_files/shapley_X.json')\n\nwith open(prefix+'shapley_files/shapley_values.json', 'r') as f:\n shapley_values = json.load(f)['shap_values']\nshapley_values = [np.array(elem) for elem in shapley_values]\n\nweight_sig = df_sig_test['weight'].sum()\nweight_bkg = df_bkg_test['weight'].mean()\nweight_bbh = df_bbh_test['weight'].mean()\nweight_tth = df_tth_test['weight'].mean()\nweight_bbxaa = df_bbxaa_test['weight'].mean()\n\nclassifier = pickle.load(open(prefix+'hbb-BDT-5class-hhsm-klm1.csv.pickle.dat', 'rb'))\n\nwith open(prefix+'test_files/weights.json', 'r') as f:\n weights = json.load(f)",
"_____no_output_____"
],
[
"class_names = [r'$bb\\gamma\\gamma$', r'$b\\bar{b}h$', r'$t\\bar{t}h$', r'$hh^{SM}$', r'$hh^{\\kappa_u}$']\nnames = list(df_bbxaa_test.columns)[:-2]\nshap_plot = '../plots/shap-klm1.pdf'\n\nabs_shap(shapley_values, X_shap, shap_plot, names, class_names, cmp=cmp_5)",
"_____no_output_____"
],
[
"df_array = [df_bbxaa_test, df_bbh_test, df_tth_test, df_bkg_test, df_sig_test]\nweight_array = [weights['weight_bbxaa']*1.5, weights['weight_bbh'], \n weights['weight_tth']*1.2, weights['weight_bkg']*1.72, weights['weight_sig']*1.28]\n\nps_exp_class = collections.Counter(classifier.predict(pd.concat([df_array[4].iloc[:,:-2].sample(n=round(weight_array[4]), random_state=seed, replace=True),\n df_array[3].iloc[:,:-2].sample(n=round(weight_array[3]), random_state=seed, replace=True),\n df_array[2].iloc[:,:-2].sample(n=round(weight_array[2]), random_state=seed, replace=True),\n df_array[1].iloc[:,:-2].sample(n=round(weight_array[1]), random_state=seed, replace=True),\n df_array[0].iloc[:,:-2].sample(n=round(weight_array[0]), random_state=seed, replace=True)]).values))\n\nnevents_ku, sig_ku = get_mclass(4, df_array, weight_array, ps_exp_class)\nnevents_hhsm, sig_hhsm = get_mclass(3, df_array, weight_array, ps_exp_class)\nnevents_tth, sig_tth = get_mclass(2, df_array, weight_array, ps_exp_class)\nnevents_bbh, sig_bbh = get_mclass(1, df_array, weight_array, ps_exp_class)\nnevents_bbxaa, sig_bbxaa = get_mclass(0, df_array, weight_array, ps_exp_class)\nconfusion = np.column_stack((nevents_ku, nevents_hhsm, nevents_tth, nevents_bbh, nevents_bbxaa))",
"sig: 8.76, ku events: 340, hhsm events: 97, tth events: 434, bbh events: 75, bbxaa events: 566\nsig: 2.74, ku events: 372, hhsm events: 109, tth events: 408, bbh events: 83, bbxaa events: 617\nsig: 55.58, ku events: 64, hhsm events: 18, tth events: 3535, bbh events: 39, bbxaa events: 389\nsig: 14.01, ku events: 185, hhsm events: 54, tth events: 249, bbh events: 2333, bbxaa events: 24914\nsig: 297.95, ku events: 21, hhsm events: 5, tth events: 44, bbh events: 442, bbxaa events: 89282\n"
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cb1839c18164963b42f199a3126c00fbc090bc62 | 7,209 | ipynb | Jupyter Notebook | benchmarking_FRESA-CAD.ipynb | 88vikram/jupyter | 4ed85b2bd7a597ad946d48a1475e41286134ebb2 | [
"Apache-2.0"
] | null | null | null | benchmarking_FRESA-CAD.ipynb | 88vikram/jupyter | 4ed85b2bd7a597ad946d48a1475e41286134ebb2 | [
"Apache-2.0"
] | null | null | null | benchmarking_FRESA-CAD.ipynb | 88vikram/jupyter | 4ed85b2bd7a597ad946d48a1475e41286134ebb2 | [
"Apache-2.0"
] | null | null | null | 39.179348 | 1,453 | 0.658482 | [
[
[
"# Benchmark FRESA.CAD BSWIMS final Script\n",
"_____no_output_____"
],
[
"This algorithm implementation uses R code and a Python library (rpy2) to connect with it, in order to run the following it is necesary to have installed both on your computer:\n\n- R (you can download in https://www.r-project.org/) <br>\n- install rpy2 by <code> pip install rpy2 </code>",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport pandas as pd\nimport sys\nfrom pathlib import Path\nimport tadpole_algorithms\nfrom tadpole_algorithms.models import BenchmarkSVM_R\nfrom tadpole_algorithms.preprocessing.split import split_test_train_tadpole\n#rpy2 libs and funcs\nimport rpy2.robjects.packages as rpackages\nfrom rpy2.robjects.vectors import StrVector\nfrom rpy2.robjects import r, pandas2ri \nfrom rpy2 import robjects",
"_____no_output_____"
],
[
"# Load D1_D2 train and possible test data set\ndata_path_train_test = Path(\"data/TADPOLE_D1_D2.csv\")\ndata_df_train_test = pd.read_csv(data_path_train_test)\n\n# Load D4 evaluation data set \ndata_path_eval = Path(\"data/TADPOLE_D4_corr.csv\")\ndata_df_eval = pd.read_csv(data_path_eval)\n\n# Split data in test, train and evaluation data\ntrain_df, test_df, eval_df = split_test_train_tadpole(data_df_train_test, data_df_eval)\n\n#instanciate the model to get the functions\nmodel = BenchmarkSVM_R()\n#set the flag to true to use a preprocessed data\nUSE_PREPROC = True\n#preprocess the data\nAdjustedTrainFrame,testingFrame,Train_Imputed,Test_Imputed = model.preproc_tadpole_D1_D2(train_df,USE_PREPROC)\n#train and predit \nForecast_D2 = model.Forecast_D2(AdjustedTrainFrame,testingFrame,Train_Imputed,Test_Imputed,USE_PREPROC)",
"/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (471,473,474,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,569,570,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,599,601,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,624,625,626,627,628,629,630,631,632,633,634,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,745,746,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,770,771,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,794,795,797,798,799,800,801,802,803,804,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831) have mixed types.Specify dtype option on import or set low_memory=False.\n interactivity=interactivity, compiler=compiler, result=result)\n"
],
[
"AdjustedTrainFrame,testingFrame,Train_Imputed,Test_Imputed = model.preproc_tadpole_D3(train_df,USE_PREPROC)\n#train and predit \nForecast_D3 = model.Forecast_D3(AdjustedTrainFrame,testingFrame,Train_Imputed,Test_Imputed,USE_PREPROC)",
"_____no_output_____"
],
[
"from tadpole_algorithms.evaluation import evaluate_forecast\nfrom tadpole_algorithms.evaluation import print_metrics\n\n# Evaluate the model \ndictionary = evaluate_forecast(eval_df,Forecast_D3)\n\n# Print metrics\nprint_metrics(dictionary)",
"[[74 10 2]\n [19 57 16]\n [ 2 6 24]]\nmAUC (multiclass Area Under Curve): 0.8667411601289393\nbca (balanced classification accuracy): 0.8413280386255071\nadasMAE (ADAS13 Mean Aboslute Error): 5.478656872750637\nventsMAE (Ventricles Mean Aboslute Error): 0.011137489424899361\nadasWES (ADAS13 Weighted Error Score): 5.486078100298777\nventsWES (Ventricles Weighted Error Score ): 0.01115330631499541\nadasCPA (ADAS13 Coverage Probability Accuracy for 50% Confidence Interval: 0.2442922374429224\nventsCPA (Ventricles Coverage Probability Accuracy for 50% Confidence Interval: 0.4066666666666667\n"
],
[
"\nfrom tadpole_algorithms.evaluation import evaluate_forecast\nfrom tadpole_algorithms.evaluation import print_metrics\n# Evaluate the model \ndictionary = evaluate_forecast(eval_df, Forecast_D2)\n# Print metrics\nprint_metrics(dictionary)",
"[[76 10 0]\n [20 69 3]\n [ 2 11 19]]\nmAUC (multiclass Area Under Curve): 0.8905512475700299\nbca (balanced classification accuracy): 0.894263287696832\nadasMAE (ADAS13 Mean Aboslute Error): 9.700607599015592\nventsMAE (Ventricles Mean Aboslute Error): 0.014373537870395907\nadasWES (ADAS13 Weighted Error Score): 8.243242488487729\nventsWES (Ventricles Weighted Error Score ): 0.013643624974610162\nadasCPA (ADAS13 Coverage Probability Accuracy for 50% Confidence Interval: 0.3949771689497717\nventsCPA (Ventricles Coverage Probability Accuracy for 50% Confidence Interval: 0.44666666666666666\n"
]
]
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cb1849ca2c66ec443a5794483daf4da76d210248 | 10,275 | ipynb | Jupyter Notebook | docs/examples.ipynb | asterist/pandarallel | a3cc960e6878393d03ab5916870bf7237fc1ff15 | [
"BSD-3-Clause"
] | null | null | null | docs/examples.ipynb | asterist/pandarallel | a3cc960e6878393d03ab5916870bf7237fc1ff15 | [
"BSD-3-Clause"
] | null | null | null | docs/examples.ipynb | asterist/pandarallel | a3cc960e6878393d03ab5916870bf7237fc1ff15 | [
"BSD-3-Clause"
] | null | null | null | 18.887868 | 83 | 0.488273 | [
[
[
"%load_ext autoreload\n%autoreload 2\nimport pandas as pd\nimport time\nfrom pandarallel import pandarallel\nimport math\nimport numpy as np",
"_____no_output_____"
]
],
[
[
"# Initialize pandarallel",
"_____no_output_____"
]
],
[
[
"pandarallel.initialize()",
"_____no_output_____"
]
],
[
[
"# DataFrame.apply",
"_____no_output_____"
]
],
[
[
"df_size = int(5e6)\ndf = pd.DataFrame(dict(a=np.random.randint(1, 8, df_size),\n b=np.random.rand(df_size)))",
"_____no_output_____"
],
[
"def func(x):\n return math.sin(x.a**2) + math.sin(x.b**2)",
"_____no_output_____"
],
[
"%%time\nres = df.apply(func, axis=1)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.parallel_apply(func, axis=1)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
],
[
[
"# DataFrame.applymap",
"_____no_output_____"
]
],
[
[
"df_size = int(1e7)\ndf = pd.DataFrame(dict(a=np.random.randint(1, 8, df_size),\n b=np.random.rand(df_size)))",
"_____no_output_____"
],
[
"def func(x):\n return math.sin(x**2) - math.cos(x**2)",
"_____no_output_____"
],
[
"%%time\nres = df.applymap(func)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.parallel_applymap(func)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
],
[
[
"# DataFrame.groupby.apply",
"_____no_output_____"
]
],
[
[
"df_size = int(3e7)\ndf = pd.DataFrame(dict(a=np.random.randint(1, 1000, df_size),\n b=np.random.rand(df_size)))",
"_____no_output_____"
],
[
"def func(df):\n dum = 0\n for item in df.b:\n dum += math.log10(math.sqrt(math.exp(item**2)))\n \n return dum / len(df.b)",
"_____no_output_____"
],
[
"%%time\nres = df.groupby(\"a\").apply(func)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.groupby(\"a\").parallel_apply(func)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
],
[
[
"# DataFrame.groupby.rolling.apply",
"_____no_output_____"
]
],
[
[
"df_size = int(1e6)\ndf = pd.DataFrame(dict(a=np.random.randint(1, 300, df_size),\n b=np.random.rand(df_size)))",
"_____no_output_____"
],
[
"def func(x):\n return x.iloc[0] + x.iloc[1] ** 2 + x.iloc[2] ** 3 + x.iloc[3] ** 4",
"_____no_output_____"
],
[
"%%time\nres = df.groupby('a').b.rolling(4).apply(func, raw=False)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.groupby('a').b.rolling(4).parallel_apply(func, raw=False)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
],
[
[
"# DataFrame.groupby.expanding.apply",
"_____no_output_____"
]
],
[
[
"df_size = int(1e6)\ndf = pd.DataFrame(dict(a=np.random.randint(1, 300, df_size),\n b=np.random.rand(df_size)))",
"_____no_output_____"
],
[
"def func(x):\n return x.iloc[0] + x.iloc[1] ** 2 + x.iloc[2] ** 3 + x.iloc[3] ** 4",
"_____no_output_____"
],
[
"%%time\nres = df.groupby('a').b.expanding(4).apply(func, raw=False)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.groupby('a').b.expanding(4).parallel_apply(func, raw=False)",
"_____no_output_____"
]
],
[
[
"# Series.map",
"_____no_output_____"
]
],
[
[
"df_size = int(5e7)\ndf = pd.DataFrame(dict(a=np.random.rand(df_size) + 1))",
"_____no_output_____"
],
[
"def func(x):\n return math.log10(math.sqrt(math.exp(x**2)))",
"_____no_output_____"
],
[
"%%time\nres = df.a.map(func)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.a.parallel_map(func)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
],
[
[
"# Series.apply",
"_____no_output_____"
]
],
[
[
"df_size = int(3.5e7)\ndf = pd.DataFrame(dict(a=np.random.rand(df_size) + 1))",
"_____no_output_____"
],
[
"def func(x, power, bias=0):\n return math.log10(math.sqrt(math.exp(x**power))) + bias",
"_____no_output_____"
],
[
"%%time\nres = df.a.apply(func, args=(2,), bias=3)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.a.parallel_apply(func, args=(2,), bias=3)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
],
[
[
"# Series.rolling.apply",
"_____no_output_____"
]
],
[
[
"df_size = int(1e6)\ndf = pd.DataFrame(dict(a=np.random.randint(1, 8, df_size),\n b=list(range(df_size))))",
"_____no_output_____"
],
[
"def func(x):\n return x.iloc[0] + x.iloc[1] ** 2 + x.iloc[2] ** 3 + x.iloc[3] ** 4",
"_____no_output_____"
],
[
"%%time\nres = df.b.rolling(4).apply(func, raw=False)",
"_____no_output_____"
],
[
"%%time\nres_parallel = df.b.rolling(4).parallel_apply(func, raw=False)",
"_____no_output_____"
],
[
"res.equals(res_parallel)",
"_____no_output_____"
]
]
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cb1854904f2cabc9f4f1657067d04e8d3bab1ef1 | 6,759 | ipynb | Jupyter Notebook | other/popular_station.ipynb | chrisdmell/Project_Bikeshare | 208594d75e06bee3d8bb78eccdea6757cf55a4be | [
"MIT"
] | null | null | null | other/popular_station.ipynb | chrisdmell/Project_Bikeshare | 208594d75e06bee3d8bb78eccdea6757cf55a4be | [
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] | null | null | null | other/popular_station.ipynb | chrisdmell/Project_Bikeshare | 208594d75e06bee3d8bb78eccdea6757cf55a4be | [
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] | null | null | null | 30.445946 | 125 | 0.474035 | [
[
[
"# What is the most popular start station and most popular end station?\n",
"_____no_output_____"
]
],
[
[
"#one\nimport csv\nfrom pprint import pprint\n\"\"\"This takes the file and returns dict of values. \"\"\"\nwith open('chicago.csv', newline='') as csv_file:\n reader = [{key: value for key, value in row.items()} #list comprehimsion or one liners {} its s dictionary\n \tfor row in csv.DictReader(csv_file, skipinitialspace=True)]\n \n\"\"\"creates a mini chicogo file called testing\"\"\"\ntesting=(reader[0:15])\npprint(testing[0:1])\n",
"[{'Birth Year': '',\n 'End Station': 'Canal St & Monroe St (*)',\n 'End Time': '2017-01-01 00:06:32',\n 'Gender': '',\n 'Start Station': 'Canal St & Taylor St',\n 'Start Time': '2017-01-01 00:00:36',\n 'Trip Duration': '356',\n 'User Type': 'Customer'}]\n"
],
[
"from collections import Counter\n\n\"\"\"start/end station popular\"\"\"\nstation_start = []\nstation_end = []\nfor x in testing:\n station_start.append(x['Start Station'])\n station_end.append(x['End Station'])\n\ny=Counter(station_start)\nd= max(y,key=y.get)\n\nz=Counter(station_end)\ne= max(z,key=z.get)\nprint('popular start station is {} and popular end station is {}'.format(d,e) )\n",
"popular start station is Daley Center Plaza and popular end station is Canal St & Monroe St (*)\n"
],
[
"#one\nimport csv\nfrom pprint import pprint\nfrom collections import Counter\n\n\"\"\"This takes the file and returns dict of values. \"\"\"\nwith open('chicago.csv', newline='') as csv_file:\n reader = [{key: value for key, value in row.items()} #list comprehimsion or one liners {} its s dictionary\n \tfor row in csv.DictReader(csv_file, skipinitialspace=True)]\n\n\"\"\"start/end station popular\"\"\"\nstation_start = []\nstation_end = []\nfor x in reader:\n station_start.append(x['Start Station'])\n station_end.append(x['End Station'])\n\ny=Counter(station_start)\nd= max(y,key=y.get)\n\nz=Counter(station_end)\ne= max(z,key=z.get)\nprint('popular start station is {} and popular end station is {}'.format(d,e) )\n \n",
"popular start station is Streeter Dr & Grand Ave and popular end station is Streeter Dr & Grand Ave\n"
],
[
"station_start = []\n station_end = [] \n pop_start = []\n pop_end = []\n\nelif time_period == 'month' :\n \"\"\"filter month wise\"\"\"\n for x in city_file:\n a=(calendar.month_name[int(x['Start Time'][5:7])]) #month name\n b=x['Start Station']\n c=x['End Station']\n popular_start += [(a,b)]\n popular_end += [(a,b)]\n \n x= Counter (pop_start)\n y= max(x, key=x.get) \n xx= Counter (pop_end)\n yy= max(xx, key=x.get) \n return (y,yy)\n \n else: \n \"\"\"filter by day\"\"\"\n for x in city_file:\n a=parser.parse(x['Start Time']).strftime(\"%a\") # day name\n b=x['Start Station']\n c=x['End Station']\n popular_start += [(a,b)]\n popular_end += [(a,b)]\n \n x= Counter (pop_start)\n y= max(x, key=x.get) \n xx= Counter (pop_end)\n yy= max(xx, key=x.get) \n return (y,yy)\n \n popular_hour_day = []\n for x in file_st:\n a=parser.parse(x['Start Time']).strftime(\"%a\") # day name\n b=hour(x)\n popular_day += [(a,b)]\n \n xx= Counter(popular_hour)\n yy= max(xx, key=xx.get) #retund the filtered month and popular day example june:friday\n return (yy)",
"_____no_output_____"
],
[
"elif time_period == 'month' :\n \"\"\"filter month wise\"\"\"\n for x in file_st:\n a=(calendar.month_name[int(x['Start Time'][5:7])]) #month name\n b=hour(x)\n popular_day += [(a,b)]\n \n x= Counter (popular_hour)\n y= max(x, key=x.get) #retund the filtered month and popular day example june:friday\n return (y)\n \n else: \n \"\"\"filter by day\"\"\"\n popular_hour_day = []\n for x in file_st:\n a=parser.parse(x['Start Time']).strftime(\"%a\") # day name\n b=hour(x)\n popular_day += [(a,b)]\n \n xx= Counter(popular_hour)\n yy= max(xx, key=xx.get) #retund the filtered month and popular day example june:friday\n return (yy)\n",
"_____no_output_____"
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cb18573cc3334776857be17c38252b27c532c643 | 54,487 | ipynb | Jupyter Notebook | python/coursera_python/ML_WITH_PYTHON_IBM/.ipynb_checkpoints/ML0101EN-Clas-SVM-cancer-py-v1-checkpoint.ipynb | SayanGhoshBDA/code-backup | 8b6135facc0e598e9686b2e8eb2d69dd68198b80 | [
"MIT"
] | 16 | 2018-11-26T08:39:42.000Z | 2019-05-08T10:09:52.000Z | python/coursera_python/ML_WITH_PYTHON_IBM/.ipynb_checkpoints/ML0101EN-Clas-SVM-cancer-py-v1-checkpoint.ipynb | SayanGhoshBDA/code-backup | 8b6135facc0e598e9686b2e8eb2d69dd68198b80 | [
"MIT"
] | 8 | 2020-05-04T06:29:26.000Z | 2022-02-12T05:33:16.000Z | python/coursera_python/ML_WITH_PYTHON_IBM/.ipynb_checkpoints/ML0101EN-Clas-SVM-cancer-py-v1-checkpoint.ipynb | SayanGhoshBDA/code-backup | 8b6135facc0e598e9686b2e8eb2d69dd68198b80 | [
"MIT"
] | 5 | 2020-02-11T16:02:21.000Z | 2021-02-05T07:48:30.000Z | 62.199772 | 15,220 | 0.723641 | [
[
[
"<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n\n<h1 align=center><font size=\"5\"> SVM (Support Vector Machines)</font></h1>",
"_____no_output_____"
],
[
"In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.\n\nSVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.",
"_____no_output_____"
],
[
"<h1>Table of contents</h1>\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n <ol>\n <li><a href=\"#load_dataset\">Load the Cancer data</a></li>\n <li><a href=\"#modeling\">Modeling</a></li>\n <li><a href=\"#evaluation\">Evaluation</a></li>\n <li><a href=\"#practice\">Practice</a></li>\n </ol>\n</div>\n<br>\n<hr>",
"_____no_output_____"
]
],
[
[
"import pandas as pd\nimport pylab as pl\nimport numpy as np\nimport scipy.optimize as opt\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\n%matplotlib inline \nimport matplotlib.pyplot as plt",
"_____no_output_____"
]
],
[
[
"<h2 id=\"load_dataset\">Load the Cancer data</h2>\nThe example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepository.html]. The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n\n|Field name|Description|\n|--- |--- |\n|ID|Clump thickness|\n|Clump|Clump thickness|\n|UnifSize|Uniformity of cell size|\n|UnifShape|Uniformity of cell shape|\n|MargAdh|Marginal adhesion|\n|SingEpiSize|Single epithelial cell size|\n|BareNuc|Bare nuclei|\n|BlandChrom|Bland chromatin|\n|NormNucl|Normal nucleoli|\n|Mit|Mitoses|\n|Class|Benign or malignant|\n\n<br>\n<br>\n\nFor the purposes of this example, we're using a dataset that has a relatively small number of predictors in each record. To download the data, we will use `!wget` to download it from IBM Object Storage. \n__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)",
"_____no_output_____"
]
],
[
[
"#Click here and press Shift+Enter\n!wget -O cell_samples.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv",
"--2019-12-25 19:08:27-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 20675 (20K) [text/csv]\nSaving to: ‘cell_samples.csv’\n\ncell_samples.csv 100%[===================>] 20.19K --.-KB/s in 0.02s \n\n2019-12-25 19:08:27 (975 KB/s) - ‘cell_samples.csv’ saved [20675/20675]\n\n"
]
],
[
[
"### Load Data From CSV File ",
"_____no_output_____"
]
],
[
[
"cell_df = pd.read_csv(\"cell_samples.csv\")\ncell_df.head()",
"_____no_output_____"
],
[
"cell_df.describe",
"_____no_output_____"
]
],
[
[
"The ID field contains the patient identifiers. The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign.\n\nThe Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4).\n\nLets look at the distribution of the classes based on Clump thickness and Uniformity of cell size:",
"_____no_output_____"
]
],
[
[
"ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\ncell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\nplt.show()",
"_____no_output_____"
]
],
[
[
"## Data pre-processing and selection",
"_____no_output_____"
],
[
"Lets first look at columns data types:",
"_____no_output_____"
]
],
[
[
"cell_df.dtypes",
"_____no_output_____"
]
],
[
[
"It looks like the __BareNuc__ column includes some values that are not numerical. We can drop those rows:",
"_____no_output_____"
]
],
[
[
"cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\ncell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\ncell_df.dtypes",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n"
],
[
"feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\nX = np.asarray(feature_df)\nX[0:5]",
"_____no_output_____"
]
],
[
[
"We want the model to predict the value of Class (that is, benign (=2) or malignant (=4)). As this field can have one of only two possible values, we need to change its measurement level to reflect this.",
"_____no_output_____"
]
],
[
[
"cell_df['Class'] = cell_df['Class'].astype('int')\ny = np.asarray(cell_df['Class'])\ny [0:5]",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \"\"\"Entry point for launching an IPython kernel.\n"
]
],
[
[
"## Train/Test dataset",
"_____no_output_____"
],
[
"Okay, we split our dataset into train and test set:",
"_____no_output_____"
]
],
[
[
"X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape, y_train.shape)\nprint ('Test set:', X_test.shape, y_test.shape)",
"Train set: (546, 9) (546,)\nTest set: (137, 9) (137,)\n"
]
],
[
[
"<h2 id=\"modeling\">Modeling (SVM with Scikit-learn)</h2>",
"_____no_output_____"
],
[
"The SVM algorithm offers a choice of kernel functions for performing its processing. Basically, mapping data into a higher dimensional space is called kernelling. The mathematical function used for the transformation is known as the kernel function, and can be of different types, such as:\n\n 1.Linear\n 2.Polynomial\n 3.Radial basis function (RBF)\n 4.Sigmoid\nEach of these functions has its characteristics, its pros and cons, and its equation, but as there's no easy way of knowing which function performs best with any given dataset, we usually choose different functions in turn and compare the results. Let's just use the default, RBF (Radial Basis Function) for this lab.",
"_____no_output_____"
]
],
[
[
"from sklearn import svm\nclf = svm.SVC(kernel='rbf')\nclf.fit(X_train, y_train) ",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n \"avoid this warning.\", FutureWarning)\n"
]
],
[
[
"After being fitted, the model can then be used to predict new values:",
"_____no_output_____"
]
],
[
[
"yhat = clf.predict(X_test)\nyhat [0:5]",
"_____no_output_____"
]
],
[
[
"<h2 id=\"evaluation\">Evaluation</h2>",
"_____no_output_____"
]
],
[
[
"from sklearn.metrics import classification_report, confusion_matrix\nimport itertools",
"_____no_output_____"
],
[
"def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')",
"_____no_output_____"
],
[
"# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhat))\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False, title='Confusion matrix')",
" precision recall f1-score support\n\n 2 1.00 0.94 0.97 90\n 4 0.90 1.00 0.95 47\n\n micro avg 0.96 0.96 0.96 137\n macro avg 0.95 0.97 0.96 137\nweighted avg 0.97 0.96 0.96 137\n\nConfusion matrix, without normalization\n[[85 5]\n [ 0 47]]\n"
]
],
[
[
"You can also easily use the __f1_score__ from sklearn library:",
"_____no_output_____"
]
],
[
[
"from sklearn.metrics import f1_score\nf1_score(y_test, yhat, average='weighted') ",
"_____no_output_____"
]
],
[
[
"Lets try jaccard index for accuracy:",
"_____no_output_____"
]
],
[
[
"from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat)",
"_____no_output_____"
]
],
[
[
"<h2 id=\"practice\">Practice</h2>\nCan you rebuild the model, but this time with a __linear__ kernel? You can use __kernel='linear'__ option, when you define the svm. How the accuracy changes with the new kernel function?",
"_____no_output_____"
]
],
[
[
"# write your code here\n",
"_____no_output_____"
]
],
[
[
"Double-click __here__ for the solution.\n\n<!-- Your answer is below:\n \nclf2 = svm.SVC(kernel='linear')\nclf2.fit(X_train, y_train) \nyhat2 = clf2.predict(X_test)\nprint(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\nprint(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))\n\n-->",
"_____no_output_____"
],
[
"<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n\n<h3>Thanks for completing this lesson!</h3>\n\n<h4>Author: <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n\n<hr>\n\n<p>Copyright © 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>",
"_____no_output_____"
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cb185c09802dcd772ec4ec4f414b3d53d96608d4 | 32,204 | ipynb | Jupyter Notebook | Lecture3/Iteration.ipynb | ccha23/CS1302 | b5d55a9844c3e6b80ec9029509b5d572b24b6be3 | [
"MIT"
] | null | null | null | Lecture3/Iteration.ipynb | ccha23/CS1302 | b5d55a9844c3e6b80ec9029509b5d572b24b6be3 | [
"MIT"
] | null | null | null | Lecture3/Iteration.ipynb | ccha23/CS1302 | b5d55a9844c3e6b80ec9029509b5d572b24b6be3 | [
"MIT"
] | 4 | 2021-01-22T06:54:05.000Z | 2022-01-20T06:05:01.000Z | 21.613423 | 205 | 0.5177 | [
[
[
"# Iteration",
"_____no_output_____"
],
[
"**CS1302 Introduction to Computer Programming**\n___",
"_____no_output_____"
]
],
[
[
"%reload_ext mytutor\nfrom ipywidgets import interact",
"_____no_output_____"
]
],
[
[
"## Motivation",
"_____no_output_____"
],
[
"Many tasks are repetitive:\n- To print from 1 up to a user-specified number *arbitrarily large*.\n- To compute the maximum of a sequence of numbers *arbitrarily long*.\n- To get user input *repeatedly until* it is within a certain range.",
"_____no_output_____"
],
[
"**How to write code to perform repetitive tasks?**",
"_____no_output_____"
],
[
"E.g., can you complete the following code to print from 1 up to a user-specified number?",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 400\nnum = int(input(\">\"))\nif 1 <= num:\n print(1)\nif 2 <= num:\n print(2)\nif 3 <= num:\n print(3)\n# YOUR CODE HERE",
"_____no_output_____"
]
],
[
[
"*Code duplication* is not good because: \n- Duplicate code is hard to read/write/maintain. \n (Imagine what you need to do to change some code.)\n- The number of repetitions may not be known before runtime.",
"_____no_output_____"
],
[
"Instead, programmers write a *loop* which specifies a piece of code to be executed iteratively.",
"_____no_output_____"
],
[
"## For Loop",
"_____no_output_____"
],
[
"### Iterate over a sequence",
"_____no_output_____"
],
[
"**How to print from 1 up to 4?**",
"_____no_output_____"
],
[
"We can use a [`for` statement](https://docs.python.org/3.3/tutorial/controlflow.html#for-statements) as follows:",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nfor i in 1, 2, 3, 4:\n print(i)",
"_____no_output_____"
]
],
[
[
"- `i` is automatically assigned to each element in the sequence `1, 2, 3, 4` one-by-one from left to right.\n- After each assignment, the body `print(i)` is executed. \n\nN.b., if `i` is defined before the for loop, its value will be overwritten. ",
"_____no_output_____"
],
[
"The assignment is not restricted to integers and can also be a tuple assignment. The expression list can also be an [iterable object](https://docs.python.org/3.3/glossary.html#term-iterable) instead.",
"_____no_output_____"
]
],
[
[
"tuples = (0, \"l\"), (1, \"o\"), (2, \"o\"), (3, \"p\")\nfor i, c in tuples:\n print(i, c)",
"_____no_output_____"
]
],
[
[
"An even shorter code...",
"_____no_output_____"
]
],
[
[
"for i, c in enumerate(\"loop\"):\n print(i, c)",
"_____no_output_____"
]
],
[
[
"### Iterate over a range",
"_____no_output_____"
],
[
"**How to print up to a user-specified number?**",
"_____no_output_____"
],
[
"We can use [`range`](https://docs.python.org/3/library/stdtypes.html#range):",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nstop = int(input(\">\")) + 1\nfor i in range(stop):\n print(i)",
"_____no_output_____"
]
],
[
[
"**Why add 1 to the user input number?**",
"_____no_output_____"
],
[
"`range(stop)` generates a sequence of integers from `0` up to *but excluding* `stop`.",
"_____no_output_____"
],
[
"**How to start from a number different from `0`?**",
"_____no_output_____"
]
],
[
[
"for i in range(1, 5):\n print(i)",
"_____no_output_____"
]
],
[
[
"**What about a step size different from `1`?**",
"_____no_output_____"
]
],
[
[
"for i in range(0, 5, 2): print(i) # starting number must also be specified. Why?",
"_____no_output_____"
]
],
[
[
"**Exercise** How to count down from 4 to 0? Try doing it without addition or subtraction.",
"_____no_output_____"
]
],
[
[
"# YOUR CODE HERE\nraise NotImplementedError()",
"_____no_output_____"
]
],
[
[
"**Exercise** Print from `0` to a user-specified number but in steps of `0.5`. \nE.g., if the user inputs `2`, the program should print:\n```\n0.0\n0.5\n1.0\n1.5\n2.0\n```\n\n*Note:* `range` only accepts integer arguments.",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nnum = int(input(\">\"))\n# YOUR CODE HERE\nraise NotImplementedError()",
"_____no_output_____"
]
],
[
[
"**Exercise** How to print the character `'*'` repeatedly for `m` rows and `n` columns? \nTry using a *nested for loop*: Write a for loop (*inner loop*) inside the body of another for loop (*outer loop*).",
"_____no_output_____"
]
],
[
[
"@interact(m=(0, 10), n=(0, 10))\ndef draw_rectangle(m=5, n=5):\n # YOUR CODE HERE\n raise NotImplementedError()",
"_____no_output_____"
]
],
[
[
"### Iterate over a string",
"_____no_output_____"
],
[
"**What does the following do?**",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nfor character in \"loop\":\n print(character)",
"_____no_output_____"
]
],
[
[
"`str` is a [sequence type](https://docs.python.org/3/library/stdtypes.html#textseq) because a string is regarded as a sequence of characters.\n- The function [`len`](https://docs.python.org/3/library/functions.html#len) can return the length of a string.\n- The indexing operator `[]` can return the character of a string at a specified location.",
"_____no_output_____"
]
],
[
[
"message = \"loop\"\nprint(\"length:\", len(message))\nprint(\"characters:\", message[0], message[1], message[2], message[3])",
"_____no_output_____"
]
],
[
[
"We can also iterate over a string as follows although it is less elegant:",
"_____no_output_____"
]
],
[
[
"for i in range(len(\"loop\")):\n print(\"loop\"[i])",
"_____no_output_____"
]
],
[
[
"**Exercise** Print a string assigned to `message` in reverse. \nE.g., `'loop'` should be printed as `'pool'`. Try using the for loop and indexing operator.",
"_____no_output_____"
]
],
[
[
"@interact(message=\"loop\")\ndef reverse_print(message):\n # YOUR CODE HERE\n raise NotImplementedError()",
"_____no_output_____"
]
],
[
[
"## While Loop",
"_____no_output_____"
],
[
"**How to ensure user input is non-empty?**",
"_____no_output_____"
],
[
"Python provides the [`while` statement](https://docs.python.org/3/reference/compound_stmts.html#while) to loop until a specified condition is false.",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nwhile not input(\"Input something please:\"):\n pass",
"_____no_output_____"
]
],
[
[
"As long as the condition after `while` is true, the body gets executed repeatedly. In the above example,\n- if user inputs nothing, \n- `input` returns an empty string `''`, which is [regarded as `False`](https://docs.python.org/3/reference/expressions.html#booleans), and so\n- the looping condition `not input('...')` is `True`.",
"_____no_output_____"
],
[
"**Is it possible to use a for loop instead of a while loop?**",
"_____no_output_____"
],
[
"- Not without hacks because the for loop is a *definite loop* which has a definite number of iterations before the execution of the loop.\n- `while` statement is useful for an *indefinite loop* where the number of iterations is unknown before the execution of the loop.",
"_____no_output_____"
],
[
"It is possible, however, to replace a for loop by a while loop. \nE.g., the following code prints from `0` to `4` using a while loop instead of a for loop.",
"_____no_output_____"
]
],
[
[
"i = 0\nwhile i <= 4:\n print(i)\n i += 1",
"_____no_output_____"
]
],
[
[
"- A while loop may not be as elegant, c.f., \n ```Python\n for i in range(5): print(i)\n ```\n- but it can be as efficient.",
"_____no_output_____"
],
[
"**Should we just use while loop?**",
"_____no_output_____"
],
[
"Consider using the following while loop to print from `0` to a user-specified value.",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 310\nnum = int(input(\">\"))\ni = 0\nwhile i != num + 1:\n print(i)\n i += 1",
"_____no_output_____"
]
],
[
[
"**Exercise** Is the above while loop doing the same thing as the for loop below?",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nfor i in range(int(input(\">\")) + 1):\n print(i)",
"_____no_output_____"
]
],
[
[
"YOUR ANSWER HERE",
"_____no_output_____"
],
[
"We have to be careful not to create unintended *infinite loops*. \nThe computer can't always detect whether there is an infinite loop. ([Why not?](https://en.wikipedia.org/wiki/Halting_problem))",
"_____no_output_____"
],
[
"## Break/Continue/Else Constructs of a Loop",
"_____no_output_____"
],
[
"### Breaking out of a loop",
"_____no_output_____"
],
[
"**Is the following an infinite loop?**",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 310\nwhile True:\n message = input(\"Input something please:\")\n if message:\n break\nprint(\"You entered:\", message)",
"_____no_output_____"
]
],
[
[
"The loop is terminated by the [`break` statement](https://docs.python.org/3/tutorial/controlflow.html#break-and-continue-statements-and-else-clauses-on-loops) when user input is non-empty.",
"_____no_output_____"
],
[
"**Why is the `break` statement useful?**",
"_____no_output_____"
],
[
" Recall the earlier `while` loop:",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 300\nwhile not input(\"Input something please:\"):\n pass",
"_____no_output_____"
]
],
[
[
"This while loop is not useful because it does not store the user input.",
"_____no_output_____"
],
[
"**Is the `break` statement strictly necessary?** ",
"_____no_output_____"
],
[
"- We can use the assignment expression but it is not supported by Python version <3.8.",
"_____no_output_____"
],
[
"- We can avoid `break` statement by using *flags*, which are boolean variables for flow control:",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 350\nhas_no_input = True\nwhile has_no_input:\n message = input(\"Input something please:\")\n if message:\n has_no_input = False\nprint(\"You entered:\", message)",
"_____no_output_____"
]
],
[
[
"Using flags makes the program more readable, and we can use multiple flags for more complicated behavior. \nThe variable names for flags are often `is_...`, `has_...`, etc.",
"_____no_output_____"
],
[
"### Continue to Next Iteration",
"_____no_output_____"
],
[
"**What does the following program do? \nIs it an infinite loop?**",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 310\nwhile True:\n message = input(\"Input something please:\")\n if not message:\n continue\n print(\"You entered:\", message)",
"_____no_output_____"
]
],
[
[
"- The program repeatedly asks the user for input.\n- If the input is empty, the `continue` statement will skip to the next iteration.\n- The loop can only be terminated by interrupting the kernel.\n- Such an infinite loop can be useful. E.g., your computer clock continuously updates the current time.",
"_____no_output_____"
],
[
"**Exercise** Is the `continue` statement strictly necessary? Can you rewrite the above program without the `continue` statement? ",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 350\nwhile True:\n message = input(\"Input something please:\")\n # YOUR CODE HERE\n raise NotImplementedError()",
"_____no_output_____"
]
],
[
[
"### Else construct for a loop",
"_____no_output_____"
],
[
"The following program checks whether a number is composite, namely, \n- a positive integer that is\n- a product of two strictly smaller positive integers.",
"_____no_output_____"
]
],
[
[
"@interact(num=\"1\")\ndef check_composite(num):\n if num.isdigit():\n num = int(num)\n for divisor in range(2, num): # why starts from 2 instead of 1\n if num % divisor:\n continue # where will this go?\n else:\n print(\"It is composite.\")\n break # where will this go?\n else:\n print(\"It is not composite.\") # how to get here?\n else:\n print(\"Not a positive integer.\") # how to get here?",
"_____no_output_____"
]
],
[
[
"**Exercise** There are three else claues in the earlier code. Which one is for the loop?",
"_____no_output_____"
],
[
"- The second else clause that `print('It is not composite.')`.\n- The clause is called when there is no divisor found in the range from `2` to `num`.",
"_____no_output_____"
],
[
"If program flow is confusing, try stepping through executation:",
"_____no_output_____"
]
],
[
[
"%%mytutor -h 520\ndef check_composite(num):\n if num.isdigit():\n num = int(num)\n for divisor in range(2, num):\n if num % divisor:\n continue\n else:\n print(\"It is composite.\")\n break\n else:\n print(\"It is not composite.\")\n else:\n print(\"Not a positive integer.\")\n\n\ncheck_composite(\"1\")\ncheck_composite(\"2\")\ncheck_composite(\"3\")\ncheck_composite(\"4\")",
"_____no_output_____"
]
],
[
[
"- In addition to using `continue` and `break` in an elegant way, \n- the code also uses an else clause that is executed only when the loop terminates *normally* not by `break`.",
"_____no_output_____"
],
[
"**Exercise** Convert the for loop to a while loop. Try to make the code as efficient as possible with less computation and storage.",
"_____no_output_____"
]
],
[
[
"@interact(num=\"1\")\ndef check_composite(num):\n if num.isdigit():\n num = int(num)\n # YOUR CODE HERE\n raise NotImplementedError()\n else:\n print(\"Not a positive integer.\")",
"_____no_output_____"
]
]
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cb1873116644bb2d7c6de4be62df69f1a52d0fc5 | 60,838 | ipynb | Jupyter Notebook | cpd3.5/notebooks/python_sdk/deployments/spark/Use Spark to predict credit risk.ipynb | kwright15/watson-machine-learning-samples | fed00fa740d2a8f0e653a98c49d3d63a4788c472 | [
"Apache-2.0"
] | 27 | 2020-09-09T20:46:03.000Z | 2021-11-29T20:13:35.000Z | cpd3.5/notebooks/python_sdk/deployments/spark/Use Spark to predict credit risk.ipynb | kwright15/watson-machine-learning-samples | fed00fa740d2a8f0e653a98c49d3d63a4788c472 | [
"Apache-2.0"
] | 6 | 2020-09-21T12:50:05.000Z | 2021-01-09T14:06:41.000Z | cpd3.5/notebooks/python_sdk/deployments/spark/Use Spark to predict credit risk.ipynb | kwright15/watson-machine-learning-samples | fed00fa740d2a8f0e653a98c49d3d63a4788c472 | [
"Apache-2.0"
] | 55 | 2020-09-14T12:38:44.000Z | 2022-03-18T13:28:34.000Z | 35.850324 | 593 | 0.386716 | [
[
[
"# Use Spark to predict credit risk with `ibm-watson-machine-learning`",
"_____no_output_____"
],
[
"This notebook introduces commands for model persistance to Watson Machine Learning repository, model deployment, and scoring.\n\nSome familiarity with Python is helpful. This notebook uses Python 3.6 and Apache® Spark 2.4.\n\nYou will use **German Credit Risk** dataset.\n\n\n## Learning goals\n\nThe learning goals of this notebook are:\n\n- Load a CSV file into an Apache® Spark DataFrame.\n- Explore data.\n- Prepare data for training and evaluation.\n- Persist a pipeline and model in Watson Machine Learning repository from tar.gz files.\n- Deploy a model for online scoring using Wastson Machine Learning API.\n- Score sample scoring data using the Watson Machine Learning API.\n- Explore and visualize prediction result using the plotly package.\n\n\n## Contents\n\nThis notebook contains the following parts:\n\n1.\t[Set up](#setup)\n2.\t[Load and explore data](#load)\n3.\t[Persist model](#persistence)\n4.\t[Predict locally](#visualization)\n5.\t[Deploy and score](#scoring)\n6.\t[Clean up](#cleanup)\n7.\t[Summary and next steps](#summary)",
"_____no_output_____"
],
[
"<a id=\"setup\"></a>\n## 1. Set up the environment\n\nBefore you use the sample code in this notebook, you must perform the following setup tasks:\n\n- Contact with your Cloud Pack for Data administrator and ask him for your account credentials",
"_____no_output_____"
],
[
"### Connection to WML\n\nAuthenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform `url`, your `username` and `password`.",
"_____no_output_____"
]
],
[
[
"username = 'PASTE YOUR USERNAME HERE'\npassword = 'PASTE YOUR PASSWORD HERE'\nurl = 'PASTE THE PLATFORM URL HERE'",
"_____no_output_____"
],
[
"wml_credentials = {\n \"username\": username,\n \"password\": password,\n \"url\": url,\n \"instance_id\": 'openshift',\n \"version\": '3.5'\n}",
"_____no_output_____"
]
],
[
[
"### Install and import the `ibm-watson-machine-learning` package\n**Note:** `ibm-watson-machine-learning` documentation can be found <a href=\"http://ibm-wml-api-pyclient.mybluemix.net/\" target=\"_blank\" rel=\"noopener no referrer\">here</a>.",
"_____no_output_____"
]
],
[
[
"!pip install -U ibm-watson-machine-learning",
"_____no_output_____"
],
[
"from ibm_watson_machine_learning import APIClient\n\nclient = APIClient(wml_credentials)",
"_____no_output_____"
]
],
[
[
"### Working with spaces\n\nFirst of all, you need to create a space that will be used for your work. If you do not have space already created, you can use `{PLATFORM_URL}/ml-runtime/spaces?context=icp4data` to create one.\n\n- Click New Deployment Space\n- Create an empty space\n- Go to space `Settings` tab\n- Copy `space_id` and paste it below\n\n**Tip**: You can also use SDK to prepare the space for your work. More information can be found [here](https://github.com/IBM/watson-machine-learning-samples/blob/master/cpd3.5/notebooks/python_sdk/instance-management/Space%20management.ipynb).\n\n**Action**: Assign space ID below",
"_____no_output_____"
]
],
[
[
"space_id = 'PASTE YOUR SPACE ID HERE'",
"_____no_output_____"
]
],
[
[
"You can use `list` method to print all existing spaces.",
"_____no_output_____"
]
],
[
[
"client.spaces.list(limit=10)",
"_____no_output_____"
]
],
[
[
"To be able to interact with all resources available in Watson Machine Learning, you need to set **space** which you will be using.",
"_____no_output_____"
]
],
[
[
"client.set.default_space(space_id)",
"_____no_output_____"
]
],
[
[
"### Test Spark",
"_____no_output_____"
]
],
[
[
"try:\n from pyspark.sql import SparkSession\nexcept:\n print('Error: Spark runtime is missing. If you are using Watson Studio change the notebook runtime to Spark.')\n raise",
"_____no_output_____"
]
],
[
[
"<a id=\"load\"></a>\n## 2. Load and explore data",
"_____no_output_____"
],
[
"In this section you will load the data as an Apache® Spark DataFrame and perform a basic exploration.\n ",
"_____no_output_____"
],
[
"The csv file for German Credit Risk is available on the same repository as this notebook. Load the file to Apache® Spark DataFrame using code below.",
"_____no_output_____"
]
],
[
[
"import os\nfrom wget import download\n\nsample_dir = 'spark_sample_model'\nif not os.path.isdir(sample_dir):\n os.mkdir(sample_dir)\n \nfilename = os.path.join(sample_dir, 'credit_risk_training.csv')\nif not os.path.isfile(filename):\n filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd3.5/data/credit_risk/credit_risk_training.csv', out=sample_dir)",
"_____no_output_____"
],
[
"spark = SparkSession.builder.getOrCreate()\n\ndf_data = spark.read\\\n .format('org.apache.spark.sql.execution.datasources.csv.CSVFileFormat')\\\n .option('header', 'true')\\\n .option('inferSchema', 'true')\\\n .load(filename)",
"_____no_output_____"
]
],
[
[
"Explore the loaded data by using the following Apache® Spark DataFrame methods:\n- print schema\n- print top ten records\n- count all records",
"_____no_output_____"
]
],
[
[
"df_data.printSchema()",
"root\n |-- CheckingStatus: string (nullable = true)\n |-- LoanDuration: integer (nullable = true)\n |-- CreditHistory: string (nullable = true)\n |-- LoanPurpose: string (nullable = true)\n |-- LoanAmount: integer (nullable = true)\n |-- ExistingSavings: string (nullable = true)\n |-- EmploymentDuration: string (nullable = true)\n |-- InstallmentPercent: integer (nullable = true)\n |-- Sex: string (nullable = true)\n |-- OthersOnLoan: string (nullable = true)\n |-- CurrentResidenceDuration: integer (nullable = true)\n |-- OwnsProperty: string (nullable = true)\n |-- Age: integer (nullable = true)\n |-- InstallmentPlans: string (nullable = true)\n |-- Housing: string (nullable = true)\n |-- ExistingCreditsCount: integer (nullable = true)\n |-- Job: string (nullable = true)\n |-- Dependents: integer (nullable = true)\n |-- Telephone: string (nullable = true)\n |-- ForeignWorker: string (nullable = true)\n |-- Risk: string (nullable = true)\n\n"
]
],
[
[
"As you can see, the data contains 21 fields. Risk field is the one we would like to predict (label).",
"_____no_output_____"
]
],
[
[
"df_data.show(n=5, truncate=False, vertical=True)",
"-RECORD 0------------------------------------------\n CheckingStatus | 0_to_200 \n LoanDuration | 31 \n CreditHistory | credits_paid_to_date \n LoanPurpose | other \n LoanAmount | 1889 \n ExistingSavings | 100_to_500 \n EmploymentDuration | less_1 \n InstallmentPercent | 3 \n Sex | female \n OthersOnLoan | none \n CurrentResidenceDuration | 3 \n OwnsProperty | savings_insurance \n Age | 32 \n InstallmentPlans | none \n Housing | own \n ExistingCreditsCount | 1 \n Job | skilled \n Dependents | 1 \n Telephone | none \n ForeignWorker | yes \n Risk | No Risk \n-RECORD 1------------------------------------------\n CheckingStatus | less_0 \n LoanDuration | 18 \n CreditHistory | credits_paid_to_date \n LoanPurpose | car_new \n LoanAmount | 462 \n ExistingSavings | less_100 \n EmploymentDuration | 1_to_4 \n InstallmentPercent | 2 \n Sex | female \n OthersOnLoan | none \n CurrentResidenceDuration | 2 \n OwnsProperty | savings_insurance \n Age | 37 \n InstallmentPlans | stores \n Housing | own \n ExistingCreditsCount | 2 \n Job | skilled \n Dependents | 1 \n Telephone | none \n ForeignWorker | yes \n Risk | No Risk \n-RECORD 2------------------------------------------\n CheckingStatus | less_0 \n LoanDuration | 15 \n CreditHistory | prior_payments_delayed \n LoanPurpose | furniture \n LoanAmount | 250 \n ExistingSavings | less_100 \n EmploymentDuration | 1_to_4 \n InstallmentPercent | 2 \n Sex | male \n OthersOnLoan | none \n CurrentResidenceDuration | 3 \n OwnsProperty | real_estate \n Age | 28 \n InstallmentPlans | none \n Housing | own \n ExistingCreditsCount | 2 \n Job | skilled \n Dependents | 1 \n Telephone | yes \n ForeignWorker | no \n Risk | No Risk \n-RECORD 3------------------------------------------\n CheckingStatus | 0_to_200 \n LoanDuration | 28 \n CreditHistory | credits_paid_to_date \n LoanPurpose | retraining \n LoanAmount | 3693 \n ExistingSavings | less_100 \n EmploymentDuration | greater_7 \n InstallmentPercent | 3 \n Sex | male \n OthersOnLoan | none \n CurrentResidenceDuration | 2 \n OwnsProperty | savings_insurance \n Age | 32 \n InstallmentPlans | none \n Housing | own \n ExistingCreditsCount | 1 \n Job | skilled \n Dependents | 1 \n Telephone | none \n ForeignWorker | yes \n Risk | No Risk \n-RECORD 4------------------------------------------\n CheckingStatus | no_checking \n LoanDuration | 28 \n CreditHistory | prior_payments_delayed \n LoanPurpose | education \n LoanAmount | 6235 \n ExistingSavings | 500_to_1000 \n EmploymentDuration | greater_7 \n InstallmentPercent | 3 \n Sex | male \n OthersOnLoan | none \n CurrentResidenceDuration | 3 \n OwnsProperty | unknown \n Age | 57 \n InstallmentPlans | none \n Housing | own \n ExistingCreditsCount | 2 \n Job | skilled \n Dependents | 1 \n Telephone | none \n ForeignWorker | yes \n Risk | Risk \nonly showing top 5 rows\n\n"
],
[
"print(\"Number of records: \" + str(df_data.count()))",
"Number of records: 5000\n"
]
],
[
[
"As you can see, the data set contains 5000 records.",
"_____no_output_____"
],
[
"### 2.1 Prepare data\n\nIn this subsection you will split your data into: train, test and predict datasets.",
"_____no_output_____"
]
],
[
[
"splitted_data = df_data.randomSplit([0.8, 0.18, 0.02], 24)\ntrain_data = splitted_data[0]\ntest_data = splitted_data[1]\npredict_data = splitted_data[2]\n\nprint(\"Number of training records: \" + str(train_data.count()))\nprint(\"Number of testing records : \" + str(test_data.count()))\nprint(\"Number of prediction records : \" + str(predict_data.count()))",
"Number of training records: 4016\nNumber of testing records : 881\nNumber of prediction records : 103\n"
]
],
[
[
"As you can see our data has been successfully split into three datasets: \n\n- The train data set, which is the largest group, is used for training.\n- The test data set will be used for model evaluation and is used to test the assumptions of the model.\n- The predict data set will be used for prediction.",
"_____no_output_____"
],
[
"<a id=\"persistence\"></a>\n## 3. Persist model",
"_____no_output_____"
],
[
"In this section you will learn how to store your pipeline and model in Watson Machine Learning repository by using python client libraries.",
"_____no_output_____"
],
[
"**Note**: Apache® Spark 2.4 is required.",
"_____no_output_____"
],
[
"### 3.1: Save pipeline and model",
"_____no_output_____"
],
[
"In this subsection you will learn how to save pipeline and model artifacts to your Watson Machine Learning instance.",
"_____no_output_____"
],
[
"**Download pipeline and model archives**",
"_____no_output_____"
]
],
[
[
"import os\nfrom wget import download\n\nsample_dir = 'spark_sample_model'\nif not os.path.isdir(sample_dir):\n os.mkdir(sample_dir)\n \npipeline_filename = os.path.join(sample_dir, 'credit_risk_spark_pipeline.tar.gz')\nif not os.path.isfile(pipeline_filename):\n pipeline_filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd3.5/models/spark/credit-risk/model/credit_risk_spark_pipeline.tar.gz', out=sample_dir)\nmodel_filename = os.path.join(sample_dir, 'credit_risk_spark_model.gz')\nif not os.path.isfile(model_filename):\n model_filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd3.5/models/spark/credit-risk/model/credit_risk_spark_model.gz', out=sample_dir)",
"_____no_output_____"
]
],
[
[
"**Store piepline and model**",
"_____no_output_____"
],
[
"To be able to store your Spark model, you need to provide a training data reference, this will allow to read the model schema automatically.",
"_____no_output_____"
]
],
[
[
"training_data_references = [\n {\n \"type\": \"fs\",\n \"connection\": {},\n \"location\": {},\n \"schema\": {\n \"id\": \"training_schema\",\n \"fields\": [\n {\n \"metadata\": {},\n \"name\": \"CheckingStatus\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"LoanDuration\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"CreditHistory\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"LoanPurpose\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"LoanAmount\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"ExistingSavings\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"EmploymentDuration\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"InstallmentPercent\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"Sex\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"OthersOnLoan\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"CurrentResidenceDuration\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"OwnsProperty\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"Age\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"InstallmentPlans\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"Housing\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"ExistingCreditsCount\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"Job\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"Dependents\",\n \"nullable\": True,\n \"type\": \"integer\"\n },\n {\n \"metadata\": {},\n \"name\": \"Telephone\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {},\n \"name\": \"ForeignWorker\",\n \"nullable\": True,\n \"type\": \"string\"\n },\n {\n \"metadata\": {\n \"modeling_role\": \"target\"\n },\n \"name\": \"Risk\",\n \"nullable\": True,\n \"type\": \"string\"\n }\n ]\n }\n }\n]",
"_____no_output_____"
],
[
"published_model_details = client.repository.store_model(\n model=model_filename, \n meta_props={\n client.repository.ModelMetaNames.NAME:'Credit Risk model',\n client.repository.ModelMetaNames.TYPE: \"mllib_2.4\",\n client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: client.software_specifications.get_id_by_name('spark-mllib_2.4'),\n client.repository.ModelMetaNames.TRAINING_DATA_REFERENCES: training_data_references,\n client.repository.ModelMetaNames.LABEL_FIELD: \"Risk\",\n }, \n training_data=train_data, \n pipeline=pipeline_filename)",
"_____no_output_____"
],
[
"model_uid = client.repository.get_model_uid(published_model_details)\nprint(model_uid)",
"e96692a9-cc64-408d-9b1c-05da622c508a\n"
],
[
"client.repository.get_model_details(model_uid)",
"_____no_output_____"
]
],
[
[
"Get saved model metadata from Watson Machine Learning.",
"_____no_output_____"
],
[
"**Tip**: Use `client.repository.ModelMetaNames.show()` to get the list of available props.",
"_____no_output_____"
]
],
[
[
"client.repository.ModelMetaNames.show()",
"------------------------ ---- -------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\nMETA_PROP NAME TYPE REQUIRED SCHEMA\nNAME str Y\nDESCRIPTION str N\nINPUT_DATA_SCHEMA list N {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}\nTRAINING_DATA_REFERENCES list N [{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}]\nOUTPUT_DATA_SCHEMA dict N {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}\nLABEL_FIELD str N\nTRANSFORMED_LABEL_FIELD str N\nTAGS list N ['string', 'string']\nSIZE dict N {'in_memory(optional)': 'string', 'content(optional)': 'string'}\nSPACE_UID str N\nPIPELINE_UID str N\nRUNTIME_UID str N\nTYPE str Y\nCUSTOM dict N\nDOMAIN str N\nHYPER_PARAMETERS dict N\nMETRICS list N\nIMPORT dict N {'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}}\nTRAINING_LIB_UID str N\nMODEL_DEFINITION_UID str N\nSOFTWARE_SPEC_UID str N\nTF_MODEL_PARAMS dict N\n------------------------ ---- -------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n"
]
],
[
[
"### 3.2: Load model",
"_____no_output_____"
],
[
"In this subsection you will learn how to load back saved model from specified instance of Watson Machine Learning.",
"_____no_output_____"
]
],
[
[
"loaded_model = client.repository.load(model_uid)",
"_____no_output_____"
]
],
[
[
"You can print for example model name to make sure that model has been loaded correctly.",
"_____no_output_____"
]
],
[
[
"print(type(loaded_model))",
"<class 'pyspark.ml.pipeline.PipelineModel'>\n"
]
],
[
[
"<a id=\"visualization\"></a>\n## 4. Predict locally",
"_____no_output_____"
],
[
"In this section you will learn how to score test data using loaded model.",
"_____no_output_____"
],
[
"### 4.1: Make local prediction using previously loaded model and test data",
"_____no_output_____"
],
[
"In this subsection you will score *predict_data* data set.",
"_____no_output_____"
]
],
[
[
"predictions = loaded_model.transform(predict_data)",
"_____no_output_____"
]
],
[
[
"Preview the results by calling the *show()* method on the predictions DataFrame.",
"_____no_output_____"
]
],
[
[
"predictions.show(5)",
"+--------------+------------+--------------------+-----------+----------+---------------+------------------+------------------+------+------------+------------------------+------------+---+----------------+-------+--------------------+----------+----------+---------+-------------+-------+-----------------+----------------+--------------+------------------+---------------------+------+---------------+---------------+-------------------+----------+------+------------+----------------+-----+--------------------+--------------------+--------------------+----------+--------------+\n|CheckingStatus|LoanDuration| CreditHistory|LoanPurpose|LoanAmount|ExistingSavings|EmploymentDuration|InstallmentPercent| Sex|OthersOnLoan|CurrentResidenceDuration|OwnsProperty|Age|InstallmentPlans|Housing|ExistingCreditsCount| Job|Dependents|Telephone|ForeignWorker| Risk|CheckingStatus_IX|CreditHistory_IX|LoanPurpose_IX|ExistingSavings_IX|EmploymentDuration_IX|Sex_IX|OthersOnLoan_IX|OwnsProperty_IX|InstallmentPlans_IX|Housing_IX|Job_IX|Telephone_IX|ForeignWorker_IX|label| features| rawPrediction| probability|prediction|predictedLabel|\n+--------------+------------+--------------------+-----------+----------+---------------+------------------+------------------+------+------------+------------------------+------------+---+----------------+-------+--------------------+----------+----------+---------+-------------+-------+-----------------+----------------+--------------+------------------+---------------------+------+---------------+---------------+-------------------+----------+------+------------+----------------+-----+--------------------+--------------------+--------------------+----------+--------------+\n| 0_to_200| 10|credits_paid_to_date| repairs| 2224| less_100| 4_to_7| 3| male| none| 3| car_other| 29| none| own| 1| skilled| 1| none| yes| Risk| 2.0| 1.0| 5.0| 0.0| 1.0| 0.0| 0.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 1.0|(20,[0,1,2,4,7,13...|[16.7095445704993...|[0.83547722852496...| 0.0| No Risk|\n| 0_to_200| 10| no_credits| car_new| 250| less_100| less_1| 2|female| none| 2| real_estate| 22| none| own| 1| skilled| 1| none| yes|No Risk| 2.0| 4.0| 0.0| 0.0| 3.0| 1.0| 0.0| 2.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|(20,[0,1,4,5,7,13...|[18.7487477403696...|[0.93743738701848...| 0.0| No Risk|\n| 0_to_200| 12|credits_paid_to_date| car_used| 250| greater_1000| 1_to_4| 2|female| none| 2| real_estate| 24| bank| rent| 1|unemployed| 1| none| yes|No Risk| 2.0| 1.0| 2.0| 3.0| 0.0| 1.0| 0.0| 2.0| 2.0| 1.0| 3.0| 0.0| 0.0| 0.0|[2.0,1.0,2.0,3.0,...|[17.8941841848564...|[0.89470920924282...| 0.0| No Risk|\n| 0_to_200| 13|all_credits_paid_...| car_new| 250| less_100| less_1| 1| male| none| 2| real_estate| 25| stores| rent| 1| skilled| 1| none| yes|No Risk| 2.0| 3.0| 0.0| 0.0| 3.0| 0.0| 0.0| 2.0| 1.0| 1.0| 0.0| 0.0| 0.0| 0.0|[2.0,3.0,0.0,0.0,...|[18.7487477403696...|[0.93743738701848...| 0.0| No Risk|\n| 0_to_200| 13|prior_payments_de...| radio_tv| 2453| less_100| 4_to_7| 3| male| none| 2| car_other| 39| none| own| 2| skilled| 1| none| yes|No Risk| 2.0| 0.0| 3.0| 0.0| 1.0| 0.0| 0.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|(20,[0,2,4,7,13,1...|[16.1402858904161...|[0.80701429452080...| 0.0| No Risk|\n+--------------+------------+--------------------+-----------+----------+---------------+------------------+------------------+------+------------+------------------------+------------+---+----------------+-------+--------------------+----------+----------+---------+-------------+-------+-----------------+----------------+--------------+------------------+---------------------+------+---------------+---------------+-------------------+----------+------+------------+----------------+-----+--------------------+--------------------+--------------------+----------+--------------+\nonly showing top 5 rows\n\n"
]
],
[
[
"By tabulating a count, you can see which product line is the most popular.",
"_____no_output_____"
]
],
[
[
"predictions.select(\"predictedLabel\").groupBy(\"predictedLabel\").count().show(truncate=False)",
"+--------------+-----+\n|predictedLabel|count|\n+--------------+-----+\n|No Risk |82 |\n|Risk |21 |\n+--------------+-----+\n\n"
]
],
[
[
"<a id=\"scoring\"></a>\n## 5. Deploy and score",
"_____no_output_____"
],
[
"In this section you will learn how to create online scoring and to score a new data record using `ibm-watson-machine-learning`.",
"_____no_output_____"
],
[
"**Note:** You can also use REST API to deploy and score.\nFor more information about REST APIs, see the [Swagger Documentation](https://watson-ml-v4-api.mybluemix.net/wml-restapi-cloud.html#/Deployments/deployments_create).",
"_____no_output_____"
],
[
"### 5.1: Create online scoring endpoint",
"_____no_output_____"
],
[
"Now you can create an online scoring endpoint. ",
"_____no_output_____"
],
[
"#### Create online deployment for published model",
"_____no_output_____"
]
],
[
[
"deployment_details = client.deployments.create(\n model_uid, \n meta_props={\n client.deployments.ConfigurationMetaNames.NAME: \"Credit Risk model deployment\",\n client.deployments.ConfigurationMetaNames.ONLINE: {}\n }\n)",
"\n\n#######################################################################################\n\nSynchronous deployment creation for uid: 'e96692a9-cc64-408d-9b1c-05da622c508a' started\n\n#######################################################################################\n\n\ninitializing.\nready\n\n\n------------------------------------------------------------------------------------------------\nSuccessfully finished deployment creation, deployment_uid='3f22f536-f395-4d03-a08f-6311765a0e42'\n------------------------------------------------------------------------------------------------\n\n\n"
],
[
"deployment_details",
"_____no_output_____"
]
],
[
[
"Now, you can send new scoring records (new data) for which you would like to get predictions. To do that, execute the following sample code: ",
"_____no_output_____"
]
],
[
[
"fields = [\"CheckingStatus\", \"LoanDuration\", \"CreditHistory\", \"LoanPurpose\", \"LoanAmount\", \"ExistingSavings\",\n \"EmploymentDuration\", \"InstallmentPercent\", \"Sex\", \"OthersOnLoan\", \"CurrentResidenceDuration\",\n \"OwnsProperty\", \"Age\", \"InstallmentPlans\", \"Housing\", \"ExistingCreditsCount\", \"Job\", \"Dependents\",\n \"Telephone\", \"ForeignWorker\"]\nvalues = [\n [\"no_checking\", 13, \"credits_paid_to_date\", \"car_new\", 1343, \"100_to_500\", \"1_to_4\", 2, \"female\", \"none\", 3,\n \"savings_insurance\", 46, \"none\", \"own\", 2, \"skilled\", 1, \"none\", \"yes\"],\n [\"no_checking\", 24, \"prior_payments_delayed\", \"furniture\", 4567, \"500_to_1000\", \"1_to_4\", 4, \"male\", \"none\",\n 4, \"savings_insurance\", 36, \"none\", \"free\", 2, \"management_self-employed\", 1, \"none\", \"yes\"],\n [\"0_to_200\", 26, \"all_credits_paid_back\", \"car_new\", 863, \"less_100\", \"less_1\", 2, \"female\", \"co-applicant\",\n 2, \"real_estate\", 38, \"none\", \"own\", 1, \"skilled\", 1, \"none\", \"yes\"],\n [\"0_to_200\", 14, \"no_credits\", \"car_new\", 2368, \"less_100\", \"1_to_4\", 3, \"female\", \"none\", 3, \"real_estate\",\n 29, \"none\", \"own\", 1, \"skilled\", 1, \"none\", \"yes\"],\n [\"0_to_200\", 4, \"no_credits\", \"car_new\", 250, \"less_100\", \"unemployed\", 2, \"female\", \"none\", 3,\n \"real_estate\", 23, \"none\", \"rent\", 1, \"management_self-employed\", 1, \"none\", \"yes\"],\n [\"no_checking\", 17, \"credits_paid_to_date\", \"car_new\", 832, \"100_to_500\", \"1_to_4\", 2, \"male\", \"none\", 2,\n \"real_estate\", 42, \"none\", \"own\", 1, \"skilled\", 1, \"none\", \"yes\"],\n [\"no_checking\", 33, \"outstanding_credit\", \"appliances\", 5696, \"unknown\", \"greater_7\", 4, \"male\",\n \"co-applicant\", 4, \"unknown\", 54, \"none\", \"free\", 2, \"skilled\", 1, \"yes\", \"yes\"],\n [\"0_to_200\", 13, \"prior_payments_delayed\", \"retraining\", 1375, \"100_to_500\", \"4_to_7\", 3, \"male\", \"none\", 3,\n \"real_estate\", 37, \"none\", \"own\", 2, \"management_self-employed\", 1, \"none\", \"yes\"]\n]\n\npayload_scoring = {\"input_data\": [{\"fields\": fields, \"values\": values}]}\ndeployment_id = client.deployments.get_id(deployment_details)\n\nclient.deployments.score(deployment_id, payload_scoring)",
"_____no_output_____"
]
],
[
[
"<a id=\"cleanup\"></a>\n## 6. Clean up ",
"_____no_output_____"
],
[
"If you want to clean up all created assets:\n- experiments\n- trainings\n- pipelines\n- model definitions\n- models\n- functions\n- deployments\n\nplease follow up this sample [notebook](https://github.com/IBM/watson-machine-learning-samples/blob/master/cpd3.5/notebooks/python_sdk/instance-management/Machine%20Learning%20artifacts%20management.ipynb).",
"_____no_output_____"
],
[
"<a id=\"summary\"></a>\n## 7. Summary and next steps ",
"_____no_output_____"
],
[
" You successfully completed this notebook! You learned how to use Apache Spark machine learning as well as Watson Machine Learning for model creation and deployment. \n \n Check out our [Online Documentation](https://dataplatform.cloud.ibm.com/docs/content/analyze-data/wml-setup.html) for more samples, tutorials, documentation, how-tos, and blog posts. ",
"_____no_output_____"
],
[
"### Authors\n\n**Amadeusz Masny**, Python Software Developer in Watson Machine Learning at IBM",
"_____no_output_____"
],
[
"Copyright © 2020, 2021 IBM. This notebook and its source code are released under the terms of the MIT License.",
"_____no_output_____"
]
]
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cb18741718c19c51c92cffcf7722aeef6a7f2e0e | 214,582 | ipynb | Jupyter Notebook | CdSe/5. Single-output.ipynb | Cossairt-Lab/Indium-Phosphide | 77c3c02a1d6ff0b5a0c298f0d5de827a89a6d154 | [
"MIT"
] | null | null | null | CdSe/5. Single-output.ipynb | Cossairt-Lab/Indium-Phosphide | 77c3c02a1d6ff0b5a0c298f0d5de827a89a6d154 | [
"MIT"
] | null | null | null | CdSe/5. Single-output.ipynb | Cossairt-Lab/Indium-Phosphide | 77c3c02a1d6ff0b5a0c298f0d5de827a89a6d154 | [
"MIT"
] | null | null | null | 86.350905 | 46,920 | 0.769678 | [
[
[
"import numpy as np \nimport pandas as pd\nimport os\nimport joblib\nimport sklearn \nimport matplotlib\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\n\n#Regressions:\n\nfrom sklearn.multioutput import MultiOutputRegressor\n\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.linear_model import Ridge\nfrom sklearn.linear_model import Lasso\nfrom sklearn.linear_model import ElasticNet\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import RidgeCV\nfrom sklearn.ensemble import ExtraTreesRegressor\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.ensemble import AdaBoostRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n#Metric\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import r2_score\n\n\nfrom pandas import DataFrame\n\n\n# Show progress bar\nfrom tqdm import tqdm",
"_____no_output_____"
],
[
"df = pd.read_csv('dataset_CdSe_augmented_adjusted.csv')\ndf",
"_____no_output_____"
],
[
"input_col = ['Growth Temp (Celsius)', 'Metal_mmol (mmol)', 'Chalcogen_mmol (mmol)',\n 'Amines_mmol (mmol)', 'CA_mmol (mmol)', 'Phosphines_mmol (mmol)', \n 'S_I_amount (g)', 'S_II_amount (g)', 'Time_min (min)', \n 'x0_cadmium acetate', 'x0_cadmium acetate dihydrate', \n 'x0_cadmium oxide', 'x0_cadmium stearate', 'x0_dimethylcadmium', \n 'x1_None', 'x1_benzoic acid', 'x1_dodecylphosphonic acid', \n 'x1_ethylphosphonic acid', 'x1_lauric acid', \n 'x1_myrstic acid', 'x1_oleic acid', 'x1_stearic acid',\n 'x2_2-6-dimethylpyridine', 'x2_None', 'x2_aniline', \n 'x2_benzylamine', 'x2_dioctylamine/hexadecylamine',\n 'x2_dodecylamine', 'x2_heptylamine', 'x2_hexadecylamine', \n 'x2_octadecylamine', 'x2_octylamine', 'x2_oleylamine', \n 'x2_pyridine', 'x2_trioctylamine', 'x3_None', 'x3_diphenylphosphine', \n 'x3_tributylphosphine', 'x3_trioctylphosphine', \n 'x3_triphenylphosphine', 'x4_None', 'x4_liquid parafin', \n 'x4_octadecene', 'x4_phenyl ether', 'x4_trioctylphosphine oxide', \n 'x5_None', 'x5_phosphinic acid', 'x5_trioctylphosphine oxide'\n ]\n\n\n#Three individual outputs:\ndiameter = ['diameter_nm']\nemission = ['emission_nm']\nabsorbance = ['abs_nm']\n\n#Splitting dataset\n\nX = df[input_col]\n\nY_d = df[diameter]\nY_e = df[emission]\nY_a = df[absorbance]\n\n\nX_train_d, X_test_d, Y_train_d, Y_test_d = train_test_split(X, Y_d, test_size=0.15, random_state=45, shuffle=True)\nX_train_e, X_test_e, Y_train_e, Y_test_e = train_test_split(X, Y_e, test_size=0.15, random_state=45, shuffle=True)\nX_train_a, X_test_a, Y_train_a, Y_test_a = train_test_split(X, Y_a, test_size=0.15, random_state=45, shuffle=True)",
"_____no_output_____"
]
],
[
[
"## D - Optimizing diameter model\n",
"_____no_output_____"
],
[
"### 1D. Extra Trees",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Extra Trees algorithm. \n# Parameters are: random_state, n_estimators, max_features.\n\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\nmin_i, min_j, min_k = 0, 0, 0\nfor i in tqdm(range(1, 25)):\n for j in range(1, 25):\n for k in range(2, 50, 1):\n ET_regr = ExtraTreesRegressor(n_estimators=i, \n max_features=j,\n random_state=k)\n \n ET_regr.fit(X_train_d, np.ravel(Y_train_d))\n ET_Y_pred_d = pd.DataFrame(ET_regr.predict(X_test_d))\n\n mae = mean_absolute_error(Y_test_d, ET_Y_pred_d)\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 24/24 [09:04<00:00, 22.67s/it]"
],
[
"ET_regr_d = ExtraTreesRegressor(n_estimators=2, \n max_features=23,\n random_state=20)\n \nET_regr_d.fit(X_train_d, np.ravel(Y_train_d))\nET_Y_pred_d = pd.DataFrame(ET_regr_d.predict(X_test_d))\nD_mae = mean_absolute_error(Y_test_d, ET_Y_pred_d)\nD_r_2 = r2_score(Y_test_d, ET_Y_pred_d)\nD_mse = mean_squared_error(Y_test_d, ET_Y_pred_d)\nD_rmse = mean_squared_error(Y_test_d, ET_Y_pred_d, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n \n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))\n\n",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n--------- -------- --------------------- -------------------- -------------------------\nDiameter 0.928258 0.175193 0.066874 0.2586\n"
]
],
[
[
"### 2D. Decision Tree ",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Decision Trees algorithm. \n# Parameters are: max_depth, max_features, random_state.\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\n\nmin_i, min_j, min_k = 0, 0, 0\n\nfor i in tqdm(range(1, 30)):\n for j in range(1, 30):\n for k in range(4, 80, 2):\n \n DT_regr = DecisionTreeRegressor(max_depth=i,\n max_features=j,\n random_state=k)\n \n DT_regr.fit(X_train_d, np.ravel(Y_train_d))\n DT_Y_pred_d = pd.DataFrame(DT_regr.predict(X_test_d))\n\n mae = mean_absolute_error(Y_test_d, DT_Y_pred_d)\n \n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 29/29 [03:24<00:00, 7.04s/it]"
],
[
"DT_regr_d = DecisionTreeRegressor(max_depth=12,\n max_features=25,\n random_state=62)\n \nDT_regr_d.fit(X_train_d, np.ravel(Y_train_d))\nDT_Y_pred_d = pd.DataFrame(DT_regr_d.predict(X_test_d))\n \nDT_regr_d.fit(X_train_d, np.ravel(Y_train_d))\nDT_Y_pred_d = pd.DataFrame(DT_regr_d.predict(X_test_d))\nD_mae = mean_absolute_error(Y_test_d, DT_Y_pred_d)\nD_r_2 = r2_score(Y_test_d, DT_Y_pred_d)\nD_mse = mean_squared_error(Y_test_d, DT_Y_pred_d)\nD_rmse = mean_squared_error(Y_test_d, DT_Y_pred_d, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n \n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))\n",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n--------- -------- --------------------- -------------------- -------------------------\nDiameter 0.920359 0.214379 0.0742371 0.272465\n"
]
],
[
[
"### 3D. Random Forest",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Random Forest algorithm. \n# Parameters are: max_depth, n_estimators, max_features.\n# Random_state is set to 45.\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\nmin_i, min_j, min_k = 0, 0, 0\nfor i in tqdm(range(1, 21)):\n for j in range(1, 21):\n for k in range(2, 40, 1):\n RF_regr = RandomForestRegressor(max_depth=i, \n n_estimators=j, \n max_features=k,\n random_state=45)\n RF_regr.fit(X_train_d, np.ravel(Y_train_d))\n RF_Y_pred_d = pd.DataFrame(RF_regr.predict(X_test_d))\n\n mae = mean_absolute_error(Y_test_d, RF_Y_pred_d)\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 20/20 [05:10<00:00, 15.54s/it]"
]
],
[
[
"### 4D. K Neighbors",
"_____no_output_____"
]
],
[
[
"min_mae = 99999\nmin_i, min_j = 0, 0\n\nfor i in tqdm(range(1, 40)):\n for j in range(1, 40):\n\n KNN_reg_d = KNeighborsRegressor(n_neighbors=i, \n p=j).fit(X_train_d, np.ravel(Y_train_d))\n\n KNN_Y_pred_d = KNN_reg_d.predict(X_test_d)\n\n mae = mean_absolute_error(Y_test_d, KNN_Y_pred_d)\n\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n\nprint(min_mae, min_i, min_j)",
"100%|██████████| 39/39 [00:16<00:00, 2.31it/s]"
]
],
[
[
"### Saving Decision Tree model",
"_____no_output_____"
]
],
[
[
"ET_regr_d = ExtraTreesRegressor(n_estimators=2, \n max_features=23,\n random_state=20)\n \nET_regr_d.fit(X_train_d, np.ravel(Y_train_d))\nET_Y_pred_d = pd.DataFrame(ET_regr.predict(X_test_d))\n\njoblib.dump(ET_regr_d, \"./model_CdSe_SO_diameter_ExtraTrees.joblib\")",
"_____no_output_____"
]
],
[
[
"## E - Optimizing emission model\n",
"_____no_output_____"
],
[
"### 1E. Extra Trees",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Extra Trees algorithm. \n# Parameters are: random_state, n_estimators, max_features.\n\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\nmin_i, min_j, min_k = 0, 0, 0\nfor i in tqdm(range(1, 25)):\n for j in range(1, 25):\n for k in range(2, 50, 1):\n ET_regr_e = ExtraTreesRegressor(n_estimators=i, \n max_features=j,\n random_state=k)\n \n ET_regr_e.fit(X_train_e, np.ravel(Y_train_e))\n ET_Y_pred_e = pd.DataFrame(ET_regr_e.predict(X_test_e))\n\n mae = mean_absolute_error(Y_test_e, ET_Y_pred_e)\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 24/24 [09:13<00:00, 23.04s/it]"
],
[
"ET_regr_e = ExtraTreesRegressor(n_estimators=8, \n max_features=2,\n random_state=13)\n \nET_regr_e.fit(X_train_e, np.ravel(Y_train_e))\nET_Y_pred_e = pd.DataFrame(ET_regr_e.predict(X_test_e))\nD_mae = mean_absolute_error(Y_test_e, ET_Y_pred_e)\nD_r_2 = r2_score(Y_test_e, ET_Y_pred_e)\nD_mse = mean_squared_error(Y_test_e, ET_Y_pred_e)\nD_rmse = mean_squared_error(Y_test_e, ET_Y_pred_e, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n \n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))\n",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n--------- -------- --------------------- -------------------- -------------------------\nDiameter 0.712964 11.9488 459.214 21.4293\n"
]
],
[
[
"### 2E. Decision Trees",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Decision Trees algorithm. \n\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\n\nmin_i, min_j, min_k = 0, 0, 0\n\nfor i in tqdm(range(1, 30)):\n for j in range(1, 30):\n for k in range(4, 70, 2):\n \n DT_regr_e = DecisionTreeRegressor(max_depth=i,\n max_features=j,\n random_state=k)\n \n DT_regr_e.fit(X_train_e, np.ravel(Y_train_e))\n DT_Y_pred_e = pd.DataFrame(DT_regr_e.predict(X_test_e))\n\n mae = mean_absolute_error(Y_test_e, DT_Y_pred_e)\n \n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 29/29 [02:55<00:00, 6.04s/it]"
],
[
"DT_regr_e = DecisionTreeRegressor(max_depth=13,\n max_features=5,\n random_state=32)\n \nDT_regr_e.fit(X_train_e, np.ravel(Y_train_e))\nDT_Y_pred_e = pd.DataFrame(DT_regr_e.predict(X_test_e))\n \nDT_regr_e.fit(X_train_e, np.ravel(Y_train_e))\nDT_Y_pred_e = pd.DataFrame(DT_regr_e.predict(X_test_e))\nD_mae = mean_absolute_error(Y_test_e, DT_Y_pred_e)\nD_r_2 = r2_score(Y_test_e, DT_Y_pred_e)\nD_mse = mean_squared_error(Y_test_e, DT_Y_pred_e)\nD_rmse = mean_squared_error(Y_test_e, DT_Y_pred_e, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n \n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n--------- -------- --------------------- -------------------- -------------------------\nDiameter 0.912234 8.37353 140.412 11.8496\n"
]
],
[
[
"### 3E. Random Forest",
"_____no_output_____"
]
],
[
[
"min_mae = 99999\nmin_i, min_j, min_k = 0, 0, 0\nfor i in tqdm(range(1, 21)):\n for j in range(1, 21):\n for k in range(2, 30, 1):\n RF_regr_e = RandomForestRegressor(max_depth=i, \n n_estimators=j, \n max_features=k,\n random_state=45)\n RF_regr_e.fit(X_train_e, np.ravel(Y_train_e))\n RF_Y_pred_e = pd.DataFrame(RF_regr_e.predict(X_test_e))\n\n mae = mean_absolute_error(Y_test_e, RF_Y_pred_e)\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 20/20 [03:02<00:00, 9.12s/it]"
]
],
[
[
"### 4E. K Neighbors",
"_____no_output_____"
]
],
[
[
"min_mae = 99999\nmin_i, min_j = 0, 0\n\nfor i in tqdm(range(1, 40)):\n for j in range(1, 40):\n\n KNN_reg_e = KNeighborsRegressor(n_neighbors=i, \n p=j).fit(X_train_e, np.ravel(Y_train_e))\n\n KNN_Y_pred_e = KNN_reg_e.predict(X_test_e)\n\n mae = mean_absolute_error(Y_test_e, KNN_Y_pred_e)\n\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n\nprint(min_mae, min_i, min_j)",
"100%|██████████| 39/39 [00:15<00:00, 2.47it/s]"
]
],
[
[
"### Saving Extra Trees model",
"_____no_output_____"
]
],
[
[
"DT_regr_e = DecisionTreeRegressor(max_depth=13,\n max_features=5,\n random_state=32)\nDT_regr_e.fit(X_train_e, np.ravel(Y_train_e))\nDT_Y_pred_e = pd.DataFrame(DT_regr_e.predict(X_test_e))\n\njoblib.dump(ET_regr_e, \"./model_CdSe_SO_emission_DecisionTree.joblib\")",
"_____no_output_____"
]
],
[
[
"## A - Optimizing absorption model\n",
"_____no_output_____"
],
[
"### 1A: Extra Trees",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Extra Trees algorithm. \n# Parameters are: random_state, n_estimators, max_features.\n\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\nmin_i, min_j, min_k = 0, 0, 0\nfor i in tqdm(range(1, 30)):\n for j in range(1, 30):\n for k in range(2, 50, 1):\n ET_regr_a = ExtraTreesRegressor(n_estimators=i, \n max_features=j,\n random_state=k)\n \n ET_regr_a.fit(X_train_a, np.ravel(Y_train_a))\n ET_Y_pred_a = pd.DataFrame(ET_regr_a.predict(X_test_a))\n\n mae = mean_absolute_error(Y_test_a, ET_Y_pred_a)\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 29/29 [12:49<00:00, 26.54s/it]"
],
[
"ET_regr_a = ExtraTreesRegressor(n_estimators=3, \n max_features=28,\n random_state=12)\n \nET_regr_a.fit(X_train_a, np.ravel(Y_train_a))\nET_Y_pred_a = pd.DataFrame(ET_regr_a.predict(X_test_a))\nD_mae = mean_absolute_error(Y_test_a, ET_Y_pred_a)\nD_r_2 = r2_score(Y_test_a, ET_Y_pred_a)\nD_mse = mean_squared_error(Y_test_a, ET_Y_pred_a)\nD_rmse = mean_squared_error(Y_test_a, ET_Y_pred_a, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n \n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))\n",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n--------- -------- --------------------- -------------------- -------------------------\nDiameter 0.872221 15.0212 448.564 21.1793\n"
]
],
[
[
"### 2A. Decision Trees\n",
"_____no_output_____"
]
],
[
[
"# This is a grid search for three parameters in the Decision Trees algorithm. \n\n# This gives the best combination of the three parameters for the smallest mean squared error.\n\nmin_mae = 99999\n\nmin_i, min_j, min_k = 0, 0, 0\n\nfor i in tqdm(range(1, 30)):\n for j in range(1, 30):\n for k in range(4, 60, 2):\n \n DT_regr_a = DecisionTreeRegressor(max_depth=i,\n max_features=j,\n random_state=k)\n \n DT_regr_a.fit(X_train_a, np.ravel(Y_train_a))\n DT_Y_pred_a = pd.DataFrame(DT_regr_a.predict(X_test_a))\n\n mae = mean_absolute_error(Y_test_a, DT_Y_pred_a)\n \n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 29/29 [03:13<00:00, 6.68s/it]"
],
[
"DT_regr_a = DecisionTreeRegressor(max_depth=11,\n max_features=11,\n random_state=38)\n \nDT_regr_a.fit(X_train_a, np.ravel(Y_train_a))\nDT_Y_pred_a = pd.DataFrame(DT_regr_a.predict(X_test_a))\n \nDT_regr_a.fit(X_train_a, np.ravel(Y_train_a))\nDT_Y_pred_a = pd.DataFrame(DT_regr_a.predict(X_test_a))\nD_mae = mean_absolute_error(Y_test_a, DT_Y_pred_a)\nD_r_2 = r2_score(Y_test_a, DT_Y_pred_a)\nD_mse = mean_squared_error(Y_test_a, DT_Y_pred_a)\nD_rmse = mean_squared_error(Y_test_a, DT_Y_pred_a, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n \n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n--------- -------- --------------------- -------------------- -------------------------\nDiameter 0.888377 14.6697 391.85 19.7952\n"
]
],
[
[
"### 3A. Random Forest",
"_____no_output_____"
]
],
[
[
"min_mae = 99999\nmin_i, min_j, min_k = 0, 0, 0\nfor i in tqdm(range(1, 21)):\n for j in range(1, 21):\n for k in range(2, 31, 1):\n RF_regr_a = RandomForestRegressor(max_depth=i, \n n_estimators=j, \n max_features=k,\n random_state=45)\n RF_regr_a.fit(X_train_a, np.ravel(Y_train_a))\n RF_Y_pred_a = pd.DataFrame(RF_regr_a.predict(X_test_a))\n\n mae = mean_absolute_error(Y_test_a, RF_Y_pred_a)\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n min_k = k\n \nprint(min_mae, min_i, min_j, min_k)",
"100%|██████████| 20/20 [03:44<00:00, 11.25s/it]"
]
],
[
[
"### 4A. K Neighbors",
"_____no_output_____"
]
],
[
[
"min_mae = 99999\nmin_i, min_j = 0, 0\n\nfor i in tqdm(range(1, 40)):\n for j in range(1, 40):\n\n KNN_reg_a = KNeighborsRegressor(n_neighbors=i, \n p=j).fit(X_train_a, np.ravel(Y_train_a))\n\n KNN_Y_pred_a = KNN_reg_a.predict(X_test_a)\n\n mae = mean_absolute_error(Y_test_a, KNN_Y_pred_a)\n\n if (min_mae > mae):\n min_mae = mae\n min_i = i\n min_j = j\n\nprint(min_mae, min_i, min_j)",
"100%|██████████| 39/39 [00:13<00:00, 2.84it/s]"
]
],
[
[
"### Saving model",
"_____no_output_____"
]
],
[
[
"DT_regr_a = DecisionTreeRegressor(max_depth=11,\n max_features=11,\n random_state=38)\nDT_regr_a.fit(X_train_a, np.ravel(Y_train_a))\nDT_Y_pred_a = pd.DataFrame(DT_regr_a.predict(X_test_a))\n\njoblib.dump(DT_regr_a, \"./model_CdSe_SO_abs_DecisionTree.joblib\")",
"_____no_output_____"
]
],
[
[
"## Analyzing",
"_____no_output_____"
]
],
[
[
"## Diameter\n\nET_regr_d = ExtraTreesRegressor(n_estimators=2, \n max_features=23,\n random_state=20)\n \nET_regr_d.fit(X_train_d, np.ravel(Y_train_d))\nET_Y_pred_d = pd.DataFrame(ET_regr_d.predict(X_test_d))\n\nD_mae = mean_absolute_error(Y_test_d, ET_Y_pred_d)\nD_r_2 = r2_score(Y_test_d, ET_Y_pred_d)\nD_mse = mean_squared_error(Y_test_d, ET_Y_pred_d)\nD_rmse = mean_squared_error(Y_test_d, ET_Y_pred_d, squared=False)\n\n## Emission\n\nDT_regr_e = DecisionTreeRegressor(max_depth=13,\n max_features=5,\n random_state=32)\nDT_regr_e.fit(X_train_e, np.ravel(Y_train_e))\nDT_Y_pred_e = pd.DataFrame(DT_regr_e.predict(X_test_e))\n\nE_mae = mean_absolute_error(Y_test_e, DT_Y_pred_e)\nE_r_2 = r2_score(Y_test_e, DT_Y_pred_e)\nE_mse = mean_squared_error(Y_test_e, DT_Y_pred_e)\nE_rmse = mean_squared_error(Y_test_e, DT_Y_pred_e, squared=False)\n\n\n### Absorption\n\nDT_regr_a = DecisionTreeRegressor(max_depth=11,\n max_features=11,\n random_state=38).fit(X_train_a, np.ravel(Y_train_a))\nDT_Y_pred_a = DT_regr_a.predict(X_test_a)\n\nA_mae = mean_absolute_error(Y_test_a, DT_Y_pred_a)\nA_r_2 = r2_score(Y_test_a, DT_Y_pred_a)\nA_mse = mean_squared_error(Y_test_a, DT_Y_pred_a)\nA_rmse = mean_squared_error(Y_test_a, DT_Y_pred_a, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [ [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n [\"Absorption\", A_r_2, A_mae, A_mse, A_rmse],\n [\"Emission\", E_r_2, E_mae, E_mse, E_rmse]]\n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n---------- -------- --------------------- -------------------- -------------------------\nDiameter 0.928258 0.175193 0.066874 0.2586\nAbsorption 0.888377 14.6697 391.85 19.7952\nEmission 0.912234 8.37353 140.412 11.8496\n"
],
[
"## Diameter\n\nET_regr_d = ExtraTreesRegressor(n_estimators=2, \n max_features=23,\n random_state=20)\n \nET_regr_d.fit(X_train_d, np.ravel(Y_train_d))\nET_Y_pred_d = ET_regr_d.predict(X_test_d)\n\nD_mae = mean_absolute_error(Y_test_d, ET_Y_pred_d)\nD_r_2 = r2_score(Y_test_d, ET_Y_pred_d)\nD_mse = mean_squared_error(Y_test_d, ET_Y_pred_d)\nD_rmse = mean_squared_error(Y_test_d, ET_Y_pred_d, squared=False)\n\n## Emission\n\nDT_regr_e = DecisionTreeRegressor(max_depth=13,\n max_features=5,\n random_state=32)\nDT_regr_e.fit(X_train_e, np.ravel(Y_train_e))\nDT_Y_pred_e =DT_regr_e.predict(X_test_e)\n\nE_mae = mean_absolute_error(Y_test_e, DT_Y_pred_e)\nE_r_2 = r2_score(Y_test_e, DT_Y_pred_e)\nE_mse = mean_squared_error(Y_test_e, DT_Y_pred_e)\nE_rmse = mean_squared_error(Y_test_e, DT_Y_pred_e, squared=False)\n\n\n### Absorption\n\nDT_regr_a = DecisionTreeRegressor(max_depth=11,\n max_features=11,\n random_state=38).fit(X_train_a, np.ravel(Y_train_a))\nDT_Y_pred_a = DT_regr_a.predict(X_test_a)\n\nA_mae = mean_absolute_error(Y_test_a, DT_Y_pred_a)\nA_r_2 = r2_score(Y_test_a, DT_Y_pred_a)\nA_mse = mean_squared_error(Y_test_a, DT_Y_pred_a)\nA_rmse = mean_squared_error(Y_test_a, DT_Y_pred_a, squared=False)\n\n\nfrom tabulate import tabulate\n\nd = [ [\"Diameter\", D_r_2, D_mae, D_mse, D_rmse],\n [\"Absorption\", A_r_2, A_mae, A_mse, A_rmse],\n [\"Emission\", E_r_2, E_mae, E_mse, E_rmse]]\n\nprint(tabulate(d, headers=[\"Outputs\", \"R2\", \"Mean absolute error\", \"Mean squared error\", \"Root mean squared error\"]))",
"Outputs R2 Mean absolute error Mean squared error Root mean squared error\n---------- -------- --------------------- -------------------- -------------------------\nDiameter 0.928258 0.175193 0.066874 0.2586\nAbsorption 0.888377 14.6697 391.85 19.7952\nEmission 0.912234 8.37353 140.412 11.8496\n"
],
[
"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,5))\nfig.suptitle('Single Outputs', fontsize=25)\n\nax1.plot(Y_test_d, ET_Y_pred_d,'o')\nax1.plot([1,6],[1,6], color = 'r')\nax1.set_title('Diameter')\nax1.set(xlabel='Observed Values (nm)', ylabel='Predicted Values (nm)')\n\nax2.plot(Y_test_a, DT_Y_pred_a,'o')\nax2.plot([350,650],[350,650], color = 'r')\nax2.set_title('Absorption')\nax2.set(xlabel='Observed Values (nm)', ylabel='Predicted Values (nm)')\n\nax3.plot(Y_test_e, DT_Y_pred_e,'o')\nax3.plot([450,650],[450,650], color = 'r')\nax3.set_title('Emission')\nax3.set(xlabel='Observed Values (nm)', ylabel='Predicted Values (nm)')\n\n\nfig.tight_layout()",
"_____no_output_____"
]
],
[
[
"## Feature importance",
"_____no_output_____"
],
[
"### For diameter prediction",
"_____no_output_____"
]
],
[
[
"importance_dict_d = dict()\nfor i in range(0,48):\n importance_dict_d[input_col[i]] = ET_regr_d.feature_importances_[i]\n\nsorted_importance_d = sorted(importance_dict_d.items(), key=lambda x: x[1], reverse=True)\n\nsorted_importance_d\n",
"_____no_output_____"
],
[
"top7_d = DataFrame(sorted_importance_d[0:7], columns=['features', 'importance score'])\n\nothers_d = DataFrame(sorted_importance_d[7:], columns=['features', 'importance score'])\n\n\nimport seaborn as sns\n\na4_dims = (20.7, 8.27)\nfig, ax = plt.subplots(figsize=a4_dims)\nsns.set_theme(style=\"whitegrid\")\n\nax = sns.barplot(x=\"features\", y=\"importance score\", data=top7_d)",
"_____no_output_____"
]
],
[
[
"### Emission prediction",
"_____no_output_____"
]
],
[
[
"importance_dict_e = dict()\nfor i in range(0,48):\n importance_dict_e[input_col[i]] = DT_regr_e.feature_importances_[i]\n\nsorted_importance_e = sorted(importance_dict_e.items(), key=lambda x: x[1], reverse=True)\n\n\nsorted_importance_e\n",
"_____no_output_____"
],
[
"top7_e = DataFrame(sorted_importance_e[0:7], columns=['features', 'importance score'])\n\nothers_e = DataFrame(sorted_importance_e[7:], columns=['features', 'importance score'])\n\n# combined_others2 = pd.DataFrame(data = {\n# 'features' : ['others'],\n# 'importance score' : [others2['importance score'].sum()]\n# })\n\n# #combining top 10 with others\n# imp_score2 = pd.concat([top7, combined_others2])\n\nimport seaborn as sns\n\na4_dims = (20.7, 8.27)\nfig, ax = plt.subplots(figsize=a4_dims)\nsns.set_theme(style=\"whitegrid\")\n\nax = sns.barplot(x=\"features\", y=\"importance score\", data=top7_e)",
"_____no_output_____"
]
],
[
[
"### Absorption prediction",
"_____no_output_____"
]
],
[
[
"importance_dict_a = dict()\nfor i in range(0,48):\n importance_dict_a[input_col[i]] = DT_regr_a.feature_importances_[i]\n\nsorted_importance_a = sorted(importance_dict_a.items(), key=lambda x: x[1], reverse=True)\n\nsorted_importance_a",
"_____no_output_____"
],
[
"top7_a = DataFrame(sorted_importance_a[0:7], columns=['features', 'importance score'])\n\nothers_a = DataFrame(sorted_importance_a[7:], columns=['features', 'importance score'])\n\nimport seaborn as sns\n\na4_dims = (20.7, 8.27)\nfig, ax = plt.subplots(figsize=a4_dims)\nsns.set_theme(style=\"whitegrid\")\n\nax = sns.barplot(x=\"features\", y=\"importance score\", data=top7_a)",
"_____no_output_____"
],
[
"importance_dict_a",
"_____no_output_____"
]
],
[
[
"### Combine\n",
"_____no_output_____"
]
],
[
[
"sorted_a = sorted(importance_dict_a.items(), key=lambda x: x[0], reverse=False)\nsorted_d = sorted(importance_dict_d.items(), key=lambda x: x[0], reverse=False)\nsorted_e = sorted(importance_dict_e.items(), key=lambda x: x[0], reverse=False)\n",
"_____no_output_____"
],
[
"sorted_d ",
"_____no_output_____"
],
[
"combined_importance = dict()",
"_____no_output_____"
],
[
"for i in range(0,48):\n combined_importance[sorted_e[i][0]] = sorted_e[i][1] + sorted_a[i][1] + sorted_d[i][1]\ncombined_importance\n",
"_____no_output_____"
],
[
"sorted_combined_importance = sorted(combined_importance.items(), key=lambda x: x[1], reverse=True)\n\nsorted_combined_importance ",
"_____no_output_____"
],
[
"top7_combined = DataFrame(sorted_combined_importance[0:7], columns=['features', 'importance score'])\n\nothers_combined = DataFrame(sorted_combined_importance [7:], columns=['features', 'importance score'])\n\nimport seaborn as sns\n\na4_dims = (20.7, 8.27)\nfig, ax = plt.subplots(figsize=a4_dims)\nsns.set_theme(style=\"whitegrid\")\n\nax = sns.barplot(x=\"features\", y=\"importance score\", data=top7_combined)",
"_____no_output_____"
]
]
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cb1878342dd4b951046b5811a66b40f834782b79 | 29,812 | ipynb | Jupyter Notebook | code/transformer_201102/esm_transformer_mlm_balanced.ipynb | steveyu323/motor_embedding | 65b05e024ca5a0aa339330eff6b63927af5ce4aa | [
"MIT"
] | null | null | null | code/transformer_201102/esm_transformer_mlm_balanced.ipynb | steveyu323/motor_embedding | 65b05e024ca5a0aa339330eff6b63927af5ce4aa | [
"MIT"
] | null | null | null | code/transformer_201102/esm_transformer_mlm_balanced.ipynb | steveyu323/motor_embedding | 65b05e024ca5a0aa339330eff6b63927af5ce4aa | [
"MIT"
] | null | null | null | 40.450475 | 177 | 0.561519 | [
[
[
"# import esm\nimport torch\nfrom argparse import Namespace\nfrom esm.constants import proteinseq_toks\nimport math\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom esm.modules import TransformerLayer, PositionalEmbedding # noqa\nfrom esm.model import ProteinBertModel\n\n# model, alphabet = torch.hub.load(\"facebookresearch/esm\", \"esm1_t34_670M_UR50S\")\nimport esm",
"_____no_output_____"
],
[
"from ych_util import prepare_mlm_mask\nimport pandas as pd\nimport time",
"_____no_output_____"
],
[
"pfamA_balanced = pd.read_csv(\"../../data/esm/pfamA_motors_balanced.csv\")\npfamA_balanced = pfamA_balanced.sample(frac = 1)\npfamA_balanced.head()",
"_____no_output_____"
],
[
"alphabet = esm.Alphabet.from_dict(proteinseq_toks)\n# model_name = \"esm1_t34_670M_UR50S\"\nmodel_name = \"esm1_t12_85M_UR50S\"\nurl = f\"https://dl.fbaipublicfiles.com/fair-esm/models/{model_name}.pt\"",
"_____no_output_____"
],
[
"if torch.cuda.is_available():\n print(\"cuda\")\n model_data = torch.hub.load_state_dict_from_url(url, progress=False)\nelse:\n model_data = torch.hub.load_state_dict_from_url(url, progress=False, map_location=torch.device('cpu'))",
"cuda\n"
],
[
"pra = lambda s: ''.join(s.split('decoder_')[1:] if 'decoder' in s else s)\nprs = lambda s: ''.join(s.split('decoder.')[1:] if 'decoder' in s else s)\nmodel_args = {pra(arg[0]): arg[1] for arg in vars(model_data[\"args\"]).items()}\nmodel_state = {prs(arg[0]): arg[1] for arg in model_data[\"model\"].items()}",
"_____no_output_____"
],
[
"model = esm.ProteinBertModel(\n Namespace(**model_args), len(alphabet), padding_idx=alphabet.padding_idx\n )\n\nmodel.load_state_dict(model_state)",
"_____no_output_____"
],
[
"# model.load_state_dict(torch.load(\"../../data/esm1_t12_85M_UR50S_balanced_201102.pt\"))",
"_____no_output_____"
],
[
"model.cuda()\nmodel.train()",
"_____no_output_____"
],
[
"batch_converter = alphabet.get_batch_converter()",
"_____no_output_____"
],
[
"criterion = nn.CrossEntropyLoss()\nlr = 0.0001 # learning rate\noptimizer = torch.optim.Adam(model.parameters(), lr=lr)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)\n",
"_____no_output_____"
],
[
"start_time = time.time()\nprint_every = 10000\nfor j in range(10):\n for i in range(pfamA_balanced.shape[0]):\n if len(pfamA_balanced.iloc[i,3])>1024:\n continue\n data = [(pfamA_balanced.iloc[i,1], pfamA_balanced.iloc[i,3])]\n batch_labels, batch_strs, batch_tokens = batch_converter(data)\n true_aa,target_ind,masked_batch_tokens = prepare_mlm_mask(alphabet,batch_tokens)\n optimizer.zero_grad()\n results = model(masked_batch_tokens.to('cuda'), repr_layers=[34]) \n pred = results[\"logits\"].squeeze(0)[target_ind,:] \n target = true_aa.squeeze(0)\n loss = criterion(pred.cpu(),target)\n loss.backward()\n optimizer.step()\n\n if i % print_every == 0:\n print(batch_labels)\n print(batch_strs)\n print(batch_tokens.size())\n print(masked_batch_tokens.size())\n print(results[\"logits\"].size())\n print(pred.size())\n print(target.size())\n print(f\"At Epoch: %.1f\"% i)\n print(f\"Loss %.4f\"% loss)\n elapsed = time.time() - start_time\n print(f\"time elapsed %.4f\"% elapsed)\n torch.save(model.state_dict(), \"../../data/esm1_t12_85M_UR50S_balanced_201102.pt\")\n # loss_vector.append(loss)\n # break",
"['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.1437\ntime elapsed 0.2020\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 1.3429\ntime elapsed 2207.5310\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.5217\ntime elapsed 4261.9109\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.9721\ntime elapsed 6814.2369\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.6199\ntime elapsed 8863.0887\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 1.1343\ntime elapsed 11410.7000\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.8044\ntime elapsed 13242.9931\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.6637\ntime elapsed 15054.1381\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.6110\ntime elapsed 16505.1196\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.9589\ntime elapsed 18313.3232\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.6960\ntime elapsed 19672.2145\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.9120\ntime elapsed 21116.4287\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.7479\ntime elapsed 22247.4822\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.6681\ntime elapsed 23726.1994\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.6873\ntime elapsed 24897.5002\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.8364\ntime elapsed 26305.9779\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.6363\ntime elapsed 27432.8445\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.7154\ntime elapsed 28834.1162\n['A0A1V0A8E6_9ACTN/194-355']\n['TPHPHHRTACSTMRWFERGPGGDQLSLSDLEEDRSFEADRAQALALLRLADLGIDDVLIDQCEVAHSDGPRTQRRIRLVHQTAHEKAPLDFAAESAGTRTWFHLIGPVLAALKAGSLLLFDELDASLHPTLCVQLLRLFQDPAMNPKGAQLVFTSHDTSLLN']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 0.0\nLoss 2.7733\ntime elapsed 30001.5070\n['A0A2N5Y3G4_9GAMM/13-174']\n['VIKVIGVGGGGGNAVKHMIENAVEGVDFICANTDAQALSDISSKTVLQLGGDITKGLGAGANPEIGRAAALEDRERIADALRGADMVFITAGMGGGTGTGGAPVVAEVAREMGILTVAVVTRPFAFEGKKRLAIAQEGVRELQQHVDSLITIPNEKLLEVLG']\ntorch.Size([1, 163])\ntorch.Size([1, 163])\ntorch.Size([1, 163, 35])\ntorch.Size([24, 35])\ntorch.Size([24])\nAt Epoch: 10000.0\nLoss 0.7854\ntime elapsed 31420.8056\n"
],
[
"torch.save(model.state_dict(), \"../../data/esm1_t12_85M_UR50S_balanced_201102.pt\")",
"_____no_output_____"
],
[
"print(\"done\")",
"done\n"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code"
]
] |
cb18b1d0edb7d11c8580bebc2606f9bb7373cff6 | 74,539 | ipynb | Jupyter Notebook | example/Example 3 - Genetic/Tree-GP.ipynb | wangjiehui11235/ultron | ade46fdcff7eaf01187cdf9b9fb1d6a04ae972b7 | [
"Apache-2.0"
] | 4 | 2019-06-06T09:38:49.000Z | 2022-01-29T00:02:11.000Z | example/Example 3 - Genetic/Tree-GP.ipynb | wangjiehui11235/ultron | ade46fdcff7eaf01187cdf9b9fb1d6a04ae972b7 | [
"Apache-2.0"
] | 1 | 2022-02-11T03:43:10.000Z | 2022-02-11T03:43:10.000Z | example/Example 3 - Genetic/Tree-GP.ipynb | wangjiehui11235/ultron | ade46fdcff7eaf01187cdf9b9fb1d6a04ae972b7 | [
"Apache-2.0"
] | 8 | 2019-06-02T13:11:00.000Z | 2021-11-11T01:06:22.000Z | 46.068603 | 261 | 0.482352 | [
[
[
"%pylab inline\nimport pandas as pd\nimport numpy as np\nimport pickle,itertools,sys,pdb\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport graphviz\nfrom ultron.factor.genetic.accumulators import mutated_pool, cross_pool\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityCombinedValueHolder",
"Populating the interactive namespace from numpy and matplotlib\n"
],
[
"from ultron.sentry.Analysis.SecurityValueHolders import SecurityLatestValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityCurrentValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityDiffValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecuritySignValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityExpValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityLogValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecuritySqrtValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityAbsValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityNormInvValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityCeilValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityFloorValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityRoundValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecuritySigmoidValueHolder\nfrom ultron.sentry.Analysis.TechnicalAnalysis.StatelessTechnicalAnalysers import SecurityTanhValueHolder\nfrom ultron.sentry.Analysis.CrossSectionValueHolders import CSRankedSecurityValueHolder\nfrom ultron.sentry.Analysis.CrossSectionValueHolders import CSZScoreSecurityValueHolder\nfrom ultron.sentry.Analysis.CrossSectionValueHolders import CSPercentileSecurityValueHolder\n\n\nfrom ultron.sentry.Analysis.CrossSectionValueHolders import CSResidueSecurityValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityAddedValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecuritySubbedValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityMultipliedValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityDividedValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityLtOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityLeOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityGtOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityGeOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityEqOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityNeOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityAndOperatorValueHolder\nfrom ultron.sentry.Analysis.SecurityValueHolders import SecurityOrOperatorValueHolder\n",
"_____no_output_____"
],
[
"# 读取算子\nmutated_list = list(mutated_pool.values())\ncross_list = list(cross_pool.values())",
"_____no_output_____"
],
[
"with open('factor_data.pkl','rb') as file2:\n total_data = pickle.load(file2)",
"_____no_output_____"
],
[
"facotr_sets = [i for i in list(set(total_data.columns)) if i not in ['trade_date','code','ret']]",
"_____no_output_____"
],
[
"#合并函数\nmutated_sets = [{'activy':1,'function': f} for f in mutated_list]\ncross_sets = [{'activy':2,'function': f} for f in cross_list]\nfunction_sets = mutated_sets + cross_sets",
"_____no_output_____"
],
[
"def calcu_program(max_depth=4):\n n_features = 2\n function_obj = function_sets[np.random.randint(0,len(function_sets)-1)] # 随机选择函数\n program = [function_obj]\n terminal_stack = [function_obj['activy']]\n while terminal_stack:\n depth = len(terminal_stack)\n choice = n_features + len(function_sets)\n choice = np.random.randint(0,choice)\n if depth < max_depth and choice <= len(function_sets):\n function_obj = function_sets[np.random.randint(0,len(function_sets)-1)] \n program.append(function_obj)\n terminal_stack.append(function_obj['activy'])\n else:\n factor = facotr_sets[np.random.randint(0,len(facotr_sets)-1)]\n program.append(factor)\n terminal_stack[-1] -= 1\n while terminal_stack[-1] == 0:\n terminal_stack.pop()\n if not terminal_stack:\n return program\n terminal_stack[-1] -= 1",
"_____no_output_____"
],
[
"def draw_program(program):\n fade_nodes = None\n terminals = []\n if fade_nodes is None:\n fade_nodes = []\n output = 'digraph program {\\nnode [style=filled]\\n'\n for i, node in enumerate(program):\n fill = '#cecece'\n if node in function_sets:\n if i not in fade_nodes:\n fill = '#2a5caa'\n terminals.append([node['activy'], i])\n output += ('%d [label=\"%s\", fillcolor=\"%s\"] ;\\n'\n % (i, node['function'].__name__, fill))\n else:\n if i not in fade_nodes:\n fill = '#60a6f6'\n if node in facotr_sets:\n feature_name = node\n else:\n feature_name = 'X%s' % node\n output += ('%d [label=\"%s\", fillcolor=\"%s\"] ;\\n'\n % (i, feature_name, fill))\n \n if i == 0 :\n output += '}'\n return output\n terminals[-1][0] -= 1\n terminals[-1].append(i)\n while terminals[-1][0] == 0:\n output += '%d -> %d ;\\n' % (terminals[-1][1],\n terminals[-1][-1])\n terminals[-1].pop()\n if len(terminals[-1]) == 2:\n parent = terminals[-1][-1]\n terminals.pop()\n if not terminals:\n output += '}'\n return output\n terminals[-1].append(parent)\n terminals[-1][0] -= 1",
"_____no_output_____"
],
[
"graph = graphviz.Source(draw_program(calcu_program()))\ngraph",
"_____no_output_____"
],
[
"graph.render('test-table3.gv', view=True)",
"_____no_output_____"
]
],
[
[
"# 繁衍计算",
"_____no_output_____"
]
],
[
[
"program = calcu_program()\ngraphviz.Source(draw_program(program))",
"_____no_output_____"
],
[
"def get_subtree(program):\n probs = np.array([0.9 if node in function_sets else 0.1 for node in program])\n probs = np.cumsum(probs / probs.sum())\n start = np.searchsorted(probs, np.random.uniform(0, 1))\n stack = 1\n end = start\n while stack > end - start:\n node = program[end]\n if node in function_sets:\n stack += node['activy']\n end += 1\n return start, end",
"_____no_output_____"
]
],
[
[
"## 交叉计算",
"_____no_output_____"
]
],
[
[
"copy_program = program\ndonor_program = calcu_program()\nstart, end = get_subtree(copy_program)\nremoved = range(start, end)\n\ndonor_start, donor_end = get_subtree(donor_program)\ndonor_removed = list(set(range(len(donor_program))) -\n set(range(donor_start, donor_end)))\ncrossover_program = copy_program[:start] + donor_program[donor_start:donor_end] + copy_program[end:]",
"_____no_output_____"
],
[
"graphviz.Source(draw_program(crossover_program))",
"_____no_output_____"
]
],
[
[
"## 变异计算",
"_____no_output_____"
],
[
"#### 树突变 - 等同于交叉计算",
"_____no_output_____"
]
],
[
[
"copy_program = program\nchicken_program = calcu_program()\nstart, end = get_subtree(copy_program)\nremoved = range(start, end)\n\nchicken_start, chicken_end = get_subtree(chicken_program)\nchicken_removed = list(set(range(len(chicken_program))) -\n set(range(chicken_start, chicken_end)))\ncrossover_program = copy_program[:start] + chicken_program[chicken_start:chicken_end] + copy_program[end:]\ngraphviz.Source(draw_program(crossover_program))",
"_____no_output_____"
]
],
[
[
"#### 提升突变",
"_____no_output_____"
]
],
[
[
"copy_program = program\nstart, end = get_subtree(copy_program)\nsubtree = program[start:end]\nsub_start, sub_end = get_subtree(subtree)\nhoist = subtree[sub_start:sub_end]\nhosit_program = copy_program[:start] + hoist + copy_program[end:]\ngraphviz.Source(draw_program(hosit_program))",
"_____no_output_____"
]
],
[
[
"#### 节点突变",
"_____no_output_____"
]
],
[
[
"copy_program = copy(program)\nmutate = np.where(np.random.uniform(size=len(copy_program)) < 0.5)[0]\nfor node in mutate:\n if copy_program[node] in function_sets:\n activy = copy_program[node]['activy']\n #找到参数个数替换\n if activy == 1:\n replace_node = mutated_sets[np.random.randint(0,len(mutated_sets)-1)]\n else:\n replace_node = cross_sets[np.random.randint(0,len(cross_sets)-1)]\n copy_program[node] = replace_node\n else:\n factor = facotr_sets[np.random.randint(0,len(facotr_sets)-1)]\n copy_program[node] = factor",
"_____no_output_____"
],
[
"graphviz.Source(draw_program(copy_program))",
"_____no_output_____"
]
],
[
[
"## 计算因子值",
"_____no_output_____"
]
],
[
[
"def create_formual(apply_formual):\n function = apply_formual[0]\n formula = function['function'].__name__\n formula +='('\n for i in range(0,function['activy']):\n if i != 0:\n formula += ','\n if apply_formual[i+1] in facotr_sets:\n formula += '\\'' + apply_formual[i+1] + '\\''\n else:\n formula += apply_formual[i+1]\n formula += ')'\n return formula",
"_____no_output_____"
],
[
"apply_stack = []\nfor node in program:\n if node in function_sets:\n apply_stack.append([node])\n else:\n apply_stack[-1].append(node)\n while len(apply_stack[-1]) == apply_stack[-1][0]['activy'] + 1:\n result = create_formual(apply_stack[-1])\n if len(apply_stack) != 1:\n apply_stack.pop()\n apply_stack[-1].append(result)\n else:\n print(result)\n break",
"SecuritySignValueHolder(SecurityDividedValueHolder(SecurityEqOperatorValueHolder(SecurityEqOperatorValueHolder('alpha_7','alpha_81'),SecuritySqrtValueHolder('alpha_66')),SecurityNormInvValueHolder(SecurityLtOperatorValueHolder('alpha_88','alpha_8'))))\n"
],
[
"%%time\nrt = eval(result).transform(total_data.set_index(['trade_date']), category_field='code', dropna=False)",
"/home/kerry/work/workenv/alpha_mind/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in less\n \"\"\"Entry point for launching an IPython kernel.\n"
],
[
"rt",
"_____no_output_____"
]
]
] | [
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"markdown",
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cb18b755bb720172225858c5b40c595103c9ffc7 | 100,837 | ipynb | Jupyter Notebook | SESSION_20_(Decision_trees_and_Random_Forests).ipynb | madhavjk/DataScience-ML_and_DL | 3bdf6211c9ea5aa5d4ec0668ed9a1ff8a7f7d13f | [
"Apache-2.0"
] | null | null | null | SESSION_20_(Decision_trees_and_Random_Forests).ipynb | madhavjk/DataScience-ML_and_DL | 3bdf6211c9ea5aa5d4ec0668ed9a1ff8a7f7d13f | [
"Apache-2.0"
] | null | null | null | SESSION_20_(Decision_trees_and_Random_Forests).ipynb | madhavjk/DataScience-ML_and_DL | 3bdf6211c9ea5aa5d4ec0668ed9a1ff8a7f7d13f | [
"Apache-2.0"
] | null | null | null | 233.418981 | 18,674 | 0.905283 | [
[
[
"<a href=\"https://colab.research.google.com/github/madhavjk/DataScience-ML_and_DL/blob/main/SESSION_20_(Decision_trees_and_Random_Forests).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\nfrom PIL import Image\nfrom scipy import ndimage",
"_____no_output_____"
],
[
"with h5py.File('train_signs.h5', 'r') as hdf:\n ls = list(hdf.keys())\n print(ls)\n train_set_x = np.array(hdf.get('train_set_x'))\n train_set_y = np.array(hdf.get('train_set_y'))\n\nprint(train_set_x.shape)\nprint(train_set_y.shape)",
"['list_classes', 'train_set_x', 'train_set_y']\n(1080, 64, 64, 3)\n(1080,)\n"
],
[
"with h5py.File('test_signs.h5', 'r') as hdf:\n ls = list(hdf.keys())\n print(ls)\n test_set_x = np.array(hdf.get('test_set_x'))\n test_set_y = np.array(hdf.get('test_set_y'))\n \nprint(test_set_x.shape)\nprint(test_set_y.shape)",
"['list_classes', 'test_set_x', 'test_set_y']\n(120, 64, 64, 3)\n(120,)\n"
],
[
"plt.figure()\nplt.imshow(train_set_x[0])\nplt.figure()\nplt.imshow(train_set_x[5])\nplt.figure()\nplt.imshow(train_set_x[14])\nplt.figure()\nplt.imshow(train_set_x[2])\nplt.figure()\nplt.imshow(train_set_x[9])",
"_____no_output_____"
],
[
"print(train_set_y[0])\nprint(train_set_y[5])\nprint(train_set_y[14])\nprint(train_set_y[2])\nprint(train_set_y[9])",
"5\n4\n2\n2\n4\n"
],
[
"m_train = train_set_x.shape[0]\nm_test = test_set_x.shape[0]\nnum_px = train_set_x.shape[1]\n\ntrain_set_y.shape = (1,m_train)\ntest_set_y.shape = (1,m_test)\n\nprint (\"Number of training examples: m_train = \" + str(m_train))\nprint (\"Number of testing examples: m_test = \" + str(m_test))\nprint (\"Height/Width of each image: num_px = \" + str(num_px))\nprint (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\nprint (\"train_set_x shape: \" + str(train_set_x.shape))\nprint (\"train_set_y shape: \" + str(train_set_y.shape))\nprint (\"test_set_x shape: \" + str(test_set_x.shape))\nprint (\"test_set_y shape: \" + str(test_set_y.shape))",
"Number of training examples: m_train = 1080\nNumber of testing examples: m_test = 120\nHeight/Width of each image: num_px = 64\nEach image is of size: (64, 64, 3)\ntrain_set_x shape: (1080, 64, 64, 3)\ntrain_set_y shape: (1, 1080)\ntest_set_x shape: (120, 64, 64, 3)\ntest_set_y shape: (1, 120)\n"
],
[
"# Reshape the training and test examples\n# Each column represents a flattened image\n# There are total m columns for m images\n\ntrain_set_x_flatten = train_set_x.reshape(num_px*num_px*3, m_train)\ntest_set_x_flatten = test_set_x.reshape(num_px*num_px*3, m_test)\n\nprint (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\nprint (\"train_set_y shape: \" + str(train_set_y.shape))\nprint (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\nprint (\"test_set_y shape: \" + str(test_set_y.shape))",
"train_set_x_flatten shape: (12288, 1080)\ntrain_set_y shape: (1, 1080)\ntest_set_x_flatten shape: (12288, 120)\ntest_set_y shape: (1, 120)\n"
],
[
"x_train = train_set_x_flatten/255.\nx_test = test_set_x_flatten/255.",
"_____no_output_____"
],
[
"from sklearn.tree import DecisionTreeClassifier\nclassifier1 = DecisionTreeClassifier(criterion = 'gini', random_state = 0,max_depth = 300,min_samples_split = 10,min_samples_leaf = 5)\nclassifier1.fit(x_train.T,train_set_y.T)",
"_____no_output_____"
],
[
"y_pred1 = classifier1.predict(x_train.T)\ny_pred2 = classifier1.predict(x_test.T)",
"_____no_output_____"
],
[
"from sklearn.metrics import accuracy_score\naccuracy_score(train_set_y.T, y_pred1)",
"_____no_output_____"
],
[
"accuracy_score(test_set_y.T, y_pred2)",
"_____no_output_____"
],
[
"",
"_____no_output_____"
]
]
] | [
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]
] |
cb18be6ade2995922ca3d96734031a79615b6696 | 132,857 | ipynb | Jupyter Notebook | word_completion_prediction.ipynb | Shahid1993/colab-notebooks | c6235b4e7af68ea1c441fb7ce6af3a298b655714 | [
"MIT"
] | null | null | null | word_completion_prediction.ipynb | Shahid1993/colab-notebooks | c6235b4e7af68ea1c441fb7ce6af3a298b655714 | [
"MIT"
] | null | null | null | word_completion_prediction.ipynb | Shahid1993/colab-notebooks | c6235b4e7af68ea1c441fb7ce6af3a298b655714 | [
"MIT"
] | null | null | null | 89.828938 | 43,824 | 0.767886 | [
[
[
"<a href=\"https://colab.research.google.com/github/Shahid1993/colab-notebooks/blob/master/word_completion_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# [Making a Predictive Keyboard using Recurrent Neural Networks](https://medium.com/@curiousily/making-a-predictive-keyboard-using-recurrent-neural-networks-tensorflow-for-hackers-part-v-3f238d824218)\n\n# Recurrent Neural Networks\n\nIn short, `RNN` models provide a way to not only examine the current input but the one that was provided one step back, as well. If we turn that around, we can say that the decision reached at time step t-1 directly affects the future at step t.\n\n\n\n## Definition\nRNNs define a recurrence relation over time steps which is given by:\n\n\n\nWhere St is the state at time step t, Xt an exogenous input at time t, Wrec and Wx are weights parameters. The feedback loops gives memory to the model because it can remember information between time steps.\n\n`RNNs` can compute the current state St from the current input Xt and previous state St−1 or predict the next state from St+1 from the current St and current input Xt. Concretely, we will pass a sequence of 40 characters and ask the model to predict the next one. We will append the new character and drop the first one and predict again. This will continue until we complete a whole word.\n\n# LSTMs\nTwo major problems torment the `RNNs` — **vanishing** and **exploding gradients**. In traditional `RNNs` the gradient signal can be multiplied a large number of times by the weight matrix. Thus, the magnitude of the weights of the transition matrix can play an important role.\n\nIf the weights in the matrix are small, the gradient signal becomes smaller at every training step, thus making learning very slow or completely stops it. This is called vanishing gradient. Let’s have a look at applying the sigmoid function multiple times, thus simulating the effect of vanishing gradient:\n\n\n\nConversely, the exploding gradient refers to the weights in this matrix being so large that it can cause learning to diverge.\n\n\n`LSTM` model is a special kind of `RNN` that learns long-term dependencies. It introduces new structure — the memory cell that is composed of four elements: input, forget and output gates and a neuron that connects to itself:\n\n\n\n`LSTMs` *fight the gradient vanishing problem by preserving the error that can be backpropagated through time and layers*. By maintaining a more constant error, they allow for learning long-term dependencies. On another hand, *exploding is controlled with **gradient clipping***, that is the gradient is not allowed to go above some predefined value.\n\n\n# Setup\nLet’s properly seed our random number generator and import all required modules:",
"_____no_output_____"
]
],
[
[
"# Mounting Google Drive to Load Data\nfrom google.colab import drive\ndrive.mount('/content/drive')",
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n\nEnter your authorization code:\n··········\nMounted at /content/drive\n"
],
[
"import numpy as np\nnp.random.seed(42)\nimport tensorflow as tf\ntf.set_random_seed(42)\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense, Activation\nfrom keras.layers import LSTM, Dropout, CuDNNLSTM\nfrom keras.layers import TimeDistributed\nfrom keras.layers.core import Dense, Activation, Dropout, RepeatVector\nfrom keras.optimizers import RMSprop\nimport matplotlib.pyplot as plt\nimport pickle\nimport sys\nimport heapq\nimport seaborn as sns\nfrom pylab import rcParams\n\n%matplotlib inline\n\nsns.set(style='whitegrid', palette='muted', font_scale=1.5)\n\nrcParams['figure.figsize'] = 12, 5",
"_____no_output_____"
]
],
[
[
"# Loading the data",
"_____no_output_____"
]
],
[
[
"#path = 'nietzsche.txt'\n\npath = \"./drive/My Drive/ML/data/nietzsche.txt\"\n\n#path = \"./drive/My Drive/ML/data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00001-of-00100\"\n\ntext = open(path).read().lower()\nprint('corpus length:', len(text))",
"corpus length: 600902\n"
]
],
[
[
"# Preprocessing\n\nLet’s find all unique chars in the corpus and create char to index and index to char maps:",
"_____no_output_____"
]
],
[
[
"chars = sorted(list(set(text)))\nchar_indices = dict((c, i) for i, c in enumerate(chars))\nindices_char = dict((i, c) for i, c in enumerate(chars))\n\nprint(f'unique chars: {len(chars)}')\n\nprint(chars)\n\nprint(''.join(map(str, chars)))",
"unique chars: 59\n['\\n', ' ', '!', '\"', \"'\", '(', ')', ',', '-', '.', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '=', '?', '[', ']', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '¤', '¦', '©', '«', 'ã', '†']\n\n !\"'(),-.0123456789:;=?[]_abcdefghijklmnopqrstuvwxyz¤¦©«ã†\n"
],
[
"# def clean_special_chars(text, punct):\n# for p in punct:\n# text = text.replace(p, '')\n# return text\n\n \n# def preprocess(data):\n# output = []\n# punct = '\\n#$<=>[\\\\]@^{|}~¡¢£¤¥©«¬®°²´µ¶·º»¼½¾¿×àáâãäåæçèéêëíîïñóôõöøùúüþąćĕěœšŵžʼ˚а‐‑‚‟†•′₤€∆④●♥fi（）￡�'\n# for line in data:\n# pline= clean_special_chars(line.lower(), punct)\n# output.append(pline)\n# return output \n \n# text = preprocess(text) \n\n \ndef preprocess(data):\n punct = '\\n#$<=>[\\\\]@^{|}~¡¢£¤¥©«¬®°²´µ¶·º»¼½¾¿×àáâãäåæçèéêëíîïñóôõöøùúüþąćĕěœšŵžʼ˚а‐‑‚‟†•′₤€∆④●♥fi（）￡�'\n \n for p in punct:\n data = data.replace(p, '')\n \n return data\n \ntext = preprocess(text)",
"_____no_output_____"
],
[
"chars = sorted(list(set(text)))\nchar_indices = dict((c, i) for i, c in enumerate(chars))\nindices_char = dict((i, c) for i, c in enumerate(chars))\n\nprint(f'unique chars: {len(chars)}')\n\nprint(chars)\n\nprint(''.join(map(str, chars)))\n\nprint('corpus length:', len(text))",
"unique chars: 50\n[' ', '!', '\"', \"'\", '(', ')', ',', '-', '.', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '?', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '¦']\n !\"'(),-.0123456789:;?_abcdefghijklmnopqrstuvwxyz¦\ncorpus length: 590491\n"
]
],
[
[
"Next, let’s cut the corpus into chunks of `40` characters, spacing the sequences by `3` characters. Additionally, we will store the next character (the one we need to predict) for every sequence: ",
"_____no_output_____"
]
],
[
[
"SEQUENCE_LENGTH = 40\nstep = 3\nsentences = []\nnext_chars = []\nfor i in range(0, len(text) - SEQUENCE_LENGTH, step):\n sentences.append(text[i: i + SEQUENCE_LENGTH])\n next_chars.append(text[i + SEQUENCE_LENGTH])\nprint(f'num training examples: {len(sentences)}')",
"num training examples: 196817\n"
]
],
[
[
"It is time for generating our features and labels. We will use the previously generated sequences and characters that need to be predicted to create one-hot encoded vectors using the `char_indices` map:",
"_____no_output_____"
]
],
[
[
"X = np.zeros((len(sentences), SEQUENCE_LENGTH, len(chars)), dtype=np.bool)\ny = np.zeros((len(sentences), len(chars)), dtype=np.bool)\nfor i, sentence in enumerate(sentences):\n for t, char in enumerate(sentence):\n X[i, t, char_indices[char]] = 1\n y[i, char_indices[next_chars[i]]] = 1",
"_____no_output_____"
]
],
[
[
"Let’s have a look at a single training sequence:",
"_____no_output_____"
]
],
[
[
"sentences[1091]",
"_____no_output_____"
]
],
[
[
"The character that needs to be predicted for it is:",
"_____no_output_____"
]
],
[
[
"next_chars[1091]",
"_____no_output_____"
]
],
[
[
"The encoded (one-hot) data looks like this:",
"_____no_output_____"
]
],
[
[
"X[0]",
"_____no_output_____"
],
[
"y[0]",
"_____no_output_____"
]
],
[
[
"And for the dimensions:",
"_____no_output_____"
]
],
[
[
"X.shape",
"_____no_output_____"
],
[
"y.shape",
"_____no_output_____"
]
],
[
[
"We have `200285` training examples, each sequence has length of `40` with `57` unique chars.\n\n# Building the model\n\nThe model we’re going to train is pretty straight forward. Single `LSTM` layer with `128` neurons which accepts input of shape (`40` — the length of a sequence, `57` — the number of unique characters in our dataset). A fully connected layer (for our output) is added after that. It has `57` neurons and softmax for activation function:",
"_____no_output_____"
]
],
[
[
"model = Sequential()\n#model.add(LSTM(128, input_shape=(SEQUENCE_LENGTH, len(chars))))\n\n#model.add(CuDNNLSTM(128, input_shape=(None, len(chars))))\n\nmodel.add(CuDNNLSTM(128, input_shape=(None, len(chars)), return_sequences=True))\nmodel.add(CuDNNLSTM(128, return_sequences=True))\nmodel.add(CuDNNLSTM(128))\n\n#Dropout added to avoid overfitting\nmodel.add(Dropout(rate = 0.2))\n\n# build model using keras documentation recommended optimizer initialization\noptimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n\nmodel.add(Dense(len(chars)))\nmodel.add(Activation('softmax'))",
"_____no_output_____"
],
[
"model.summary()",
"Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\ncu_dnnlstm_4 (CuDNNLSTM) (None, None, 128) 92160 \n_________________________________________________________________\ncu_dnnlstm_5 (CuDNNLSTM) (None, None, 128) 132096 \n_________________________________________________________________\ncu_dnnlstm_6 (CuDNNLSTM) (None, 128) 132096 \n_________________________________________________________________\ndropout_2 (Dropout) (None, 128) 0 \n_________________________________________________________________\ndense_2 (Dense) (None, 50) 6450 \n_________________________________________________________________\nactivation_2 (Activation) (None, 50) 0 \n=================================================================\nTotal params: 362,802\nTrainable params: 362,802\nNon-trainable params: 0\n_________________________________________________________________\n"
]
],
[
[
"# Training\n\nOur model is trained for `20` epochs using `RMSProp` optimizer and uses `5%` of the data for validation:",
"_____no_output_____"
]
],
[
[
"#optimizer = RMSprop(lr=0.01)\nmodel.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\nhistory = model.fit(X, y, validation_split=0.05, batch_size=128, epochs=15, shuffle=True).history",
"Train on 186976 samples, validate on 9841 samples\nEpoch 1/15\n186976/186976 [==============================] - 46s 247us/step - loss: 2.5242 - acc: 0.2785 - val_loss: 2.1670 - val_acc: 0.3651\nEpoch 2/15\n186976/186976 [==============================] - 45s 242us/step - loss: 2.0713 - acc: 0.4002 - val_loss: 1.9043 - val_acc: 0.4421\nEpoch 3/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.8946 - acc: 0.4526 - val_loss: 1.7707 - val_acc: 0.4844\nEpoch 4/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.7827 - acc: 0.4826 - val_loss: 1.6902 - val_acc: 0.5071\nEpoch 5/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.7021 - acc: 0.5046 - val_loss: 1.6463 - val_acc: 0.5199\nEpoch 6/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.6405 - acc: 0.5223 - val_loss: 1.5858 - val_acc: 0.5368\nEpoch 7/15\n186976/186976 [==============================] - 45s 241us/step - loss: 1.5883 - acc: 0.5363 - val_loss: 1.5634 - val_acc: 0.5515\nEpoch 8/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.5465 - acc: 0.5480 - val_loss: 1.5387 - val_acc: 0.5553\nEpoch 9/15\n186976/186976 [==============================] - 46s 245us/step - loss: 1.5087 - acc: 0.5582 - val_loss: 1.5311 - val_acc: 0.5586\nEpoch 10/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.4742 - acc: 0.5679 - val_loss: 1.5128 - val_acc: 0.5592\nEpoch 11/15\n186976/186976 [==============================] - 45s 243us/step - loss: 1.4444 - acc: 0.5749 - val_loss: 1.5125 - val_acc: 0.5604\nEpoch 12/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.4178 - acc: 0.5832 - val_loss: 1.5049 - val_acc: 0.5682\nEpoch 13/15\n186976/186976 [==============================] - 45s 243us/step - loss: 1.3925 - acc: 0.5891 - val_loss: 1.4941 - val_acc: 0.5673\nEpoch 14/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.3663 - acc: 0.5959 - val_loss: 1.5018 - val_acc: 0.5686\nEpoch 15/15\n186976/186976 [==============================] - 45s 242us/step - loss: 1.3396 - acc: 0.6035 - val_loss: 1.4971 - val_acc: 0.5694\n"
]
],
[
[
"# Saving",
"_____no_output_____"
]
],
[
[
"model.save('predictive_keyboard_keras_model.h5')\npickle.dump(history, open(\"predictive_keyboard_history.p\", \"wb\"))",
"_____no_output_____"
]
],
[
[
"And load it back, just to make sure it works:",
"_____no_output_____"
]
],
[
[
"model = load_model('predictive_keyboard_keras_model.h5')\nhistory = pickle.load(open(\"predictive_keyboard_history.p\", \"rb\"))",
"_____no_output_____"
]
],
[
[
"# Evaluation\n\nLet’s have a look at how our accuracy and loss change over training epochs:",
"_____no_output_____"
]
],
[
[
"plt.plot(history['acc'])\nplt.plot(history['val_acc'])\nplt.title('model accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left');",
"_____no_output_____"
],
[
"plt.plot(history['loss'])\nplt.plot(history['val_loss'])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left');",
"_____no_output_____"
]
],
[
[
"# Let’s put our model to the test",
"_____no_output_____"
]
],
[
[
"# def prepare_input(text):\n# x = np.zeros((1, SEQUENCE_LENGTH, len(chars)))\n# for t, char in enumerate(text):\n# x[0, t, char_indices[char]] = 1.\n \n# return x\n \ndef prepare_input(text):\n x = np.zeros((1, len(text), len(chars)))\n for t, char in enumerate(text):\n x[0, t, char_indices[char]] = 1.\n \n return x",
"_____no_output_____"
]
],
[
[
"Remember that our sequences must be `40` characters long. So we make a tensor with shape `(1, 40, 59)`, initialized with zeros. Then, a value of 1 is placed for each character in the passed text. We must not forget to use the lowercase version of the text:",
"_____no_output_____"
]
],
[
[
"prepare_input(\"This is an example of input for our LSTM\".lower())\n#prepare_input(\"Tests\".lower())",
"_____no_output_____"
]
],
[
[
"#### Next up, the sample function:\n\nThis function allows us to ask our model what are the next `n` most probable characters.",
"_____no_output_____"
]
],
[
[
"def sample(preds, top_n=3):\n preds = np.asarray(preds).astype('float64')\n preds = np.log(preds)\n exp_preds = np.exp(preds)\n preds = exp_preds / np.sum(exp_preds)\n \n return heapq.nlargest(top_n, range(len(preds)), preds.take)",
"_____no_output_____"
]
],
[
[
"Now for the **prediction functions** themselves:\n\nThis function predicts next character until space is predicted (you can extend that to punctuation symbols, right?). It does so by repeatedly preparing input, asking our model for predictions and sampling from them.",
"_____no_output_____"
]
],
[
[
"def predict_completion(text):\n original_text = text\n generated = text\n completion = ''\n while True:\n x = prepare_input(text)\n preds = model.predict(x, verbose=0)[0]\n next_index = sample(preds, top_n=1)[0]\n next_char = indices_char[next_index]\n text = text[1:] + next_char\n completion += next_char\n \n if len(original_text + completion) + 2 > len(original_text) and next_char == ' ':\n return completion",
"_____no_output_____"
]
],
[
[
"The final piece of the puzzle — `predict_completions` wraps everything and allow us to predict multiple completions:",
"_____no_output_____"
]
],
[
[
"def predict_completions(text, n=3):\n x = prepare_input(text)\n preds = model.predict(x, verbose=0)[0]\n next_indices = sample(preds, n)\n return [indices_char[idx] + predict_completion(text[1:] + indices_char[idx]) for idx in next_indices]",
"_____no_output_____"
]
],
[
[
"Let’s use sequences of 40 characters that we will use as seed for our completions. All of these are quotes from Friedrich Nietzsche himself:",
"_____no_output_____"
]
],
[
[
"# actual_text = [\n# \"It is not a lack of love, but a lack of friendship that makes unhappy marriages.\",\n# \"That which does not kill us makes us stronger.\",\n# \"I'm not upset that you lied to me, I'm upset that from now on I can't believe you.\",\n# \"And those who were seen dancing were thought to be insane by those who could not hear the music.\",\n# \"It is hard enough to remember my opinions, without also remembering my reasons for them!\",\n# \"A man lying on a comfortable sofa is listening to his wi\",\n# \"Assuming the predictions are probabilistic, novel sequences can be generated from a trai\",\n# \"The networks performance is competitive with state-of-the-art language models, and it works almost\",\n# \"This document is the initial part of a study to predict next words from a text dataset\"\n# ]\n\ninput = [\n \"It is not a lack of lov\",\n \"That which does not kill us makes us stro\",\n \"I'm not upset that you lied to me, I'm upset that from now on I can't bel\",\n \"And those who were seen dan\",\n \"It is hard enough to remember my opini\",\n \"A man lying on a comfortable ch\",\n \"Assuming the pre\",\n \"The networks performance is competi\",\n \"The networks performance is competitive with state-of-the-art lan\",\n \"This document is the initial part of a study to pre\",\n \"This document is the initial part of a study to pred\",\n \"Assuming the prediction\",\n \"Assuming the predictions are probabilistic, novel sequences can be gene\",\n \"Assuming the predictions are probabilistic, novel sequences can be generat\"\n]",
"_____no_output_____"
],
[
"for i in input:\n seq = i.lower()\n print(seq)\n print(predict_completions(seq, 5))\n print()",
"it is not a lack of lov\n['e ', 'ical ', 'ality ', 'oure ', 'uling ']\n\nthat which does not kill us makes us stro\n['ng ', 'dger ', 've ', 'gget ', 'w ']\n\ni'm not upset that you lied to me, i'm upset that from now on i can't bel\n['ieve ', 'ong ', 'aes ', 'ess ', 'low ']\n\nand those who were seen dan\n['gerous ', 'king ', 'ders ', 'y ', 'ce ']\n\nit is hard enough to remember my opini\n['on ', 'an ', 'ty ', 'fic ', 's ']\n\na man lying on a comfortable ch\n['ild ', 'aracteristic ', 'ristian ', 'erristic ', 'omes ']\n\nassuming the pre\n['sent ', 'dication ', 'vious ', 'cisely ', 'jucial ']\n\nthe networks performance is competi\n['tion ', 'ce, ', 'ences ', 'sion ', 'ons ']\n\nthe networks performance is competitive with state-of-the-art lan\n['ger ', 'ds ', 'k ', 'ce ', 'ture ']\n\nthis document is the initial part of a study to pre\n['sent ', 'dicate ', 'cisely ', 'vail ', 'juce ']\n\nthis document is the initial part of a study to pred\n['icate ', 'ention ', 'ucate ', 'action ', 'ocation ']\n\nassuming the prediction\n[' of ', ', ', 's ', '. ', 'al ']\n\nassuming the predictions are probabilistic, novel sequences can be gene\n['rally ', 'ution ', 'man ', 'st ', 'dations, ']\n\nassuming the predictions are probabilistic, novel sequences can be generat\n['ions, ', 'ed ', 'on, ', 'y ', 'ure ']\n\n"
]
],
[
[
"Apart from the fact that the completions look like proper words (remember, we are training our model on characters, not words), they look pretty reasonable as well! Perhaps better model and/or more training will provide even better results?",
"_____no_output_____"
],
[
"# Conclusion\n\nWe’ve built a model using just a few lines of code in `Keras` that performs reasonably well after just 20 training epochs. Can you try it with your own text? Why not predict whole sentences? Will it work that well in other languages?",
"_____no_output_____"
],
[
"# Testing Already Created Models",
"_____no_output_____"
],
[
"### Load Model from Google Drive",
"_____no_output_____"
]
],
[
[
"# Mounting Google Drive to Load Data\nfrom google.colab import drive\ndrive.mount('/content/drive')",
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
],
[
"model = load_model('./drive/My Drive/ML/Models/word_completion_prediction/word_completion_prediction_keras_model.h5')\nhistory = pickle.load(open(\"./drive/My Drive/ML/Models/word_completion_prediction/word_completion_prediction_history.p\", \"rb\"))",
"_____no_output_____"
],
[
"def prepare_input(text):\n x = np.zeros((1, len(text), len(chars)))\n for t, char in enumerate(text):\n x[0, t, char_indices[char]] = 1.\n \n return x",
"_____no_output_____"
],
[
"def sample(preds, top_n=3):\n preds = np.asarray(preds).astype('float64')\n preds = np.log(preds)\n exp_preds = np.exp(preds)\n preds = exp_preds / np.sum(exp_preds)\n \n return heapq.nlargest(top_n, range(len(preds)), preds.take)",
"_____no_output_____"
],
[
"def predict_completion(text):\n original_text = text\n generated = text\n completion = ''\n while True:\n x = prepare_input(text)\n preds = model.predict(x, verbose=0)[0]\n next_index = sample(preds, top_n=1)[0]\n next_char = indices_char[next_index]\n text = text[1:] + next_char\n completion += next_char\n \n if len(original_text + completion) + 2 > len(original_text) and next_char == ' ':\n return completion",
"_____no_output_____"
],
[
"def predict_completions(text, n=3):\n x = prepare_input(text)\n preds = model.predict(x, verbose=0)[0]\n next_indices = sample(preds, n)\n return [indices_char[idx] + predict_completion(text[1:] + indices_char[idx]) for idx in next_indices]",
"_____no_output_____"
],
[
"# actual_text = [\n# \"It is not a lack of love, but a lack of friendship that makes unhappy marriages.\",\n# \"That which does not kill us makes us stronger.\",\n# \"I'm not upset that you lied to me, I'm upset that from now on I can't believe you.\",\n# \"And those who were seen dancing were thought to be insane by those who could not hear the music.\",\n# \"It is hard enough to remember my opinions, without also remembering my reasons for them!\",\n# \"A man lying on a comfortable sofa is listening to his wi\",\n# \"Assuming the predictions are probabilistic, novel sequences can be generated from a trai\",\n# \"The networks performance is competitive with state-of-the-art language models, and it works almost\",\n# \"This document is the initial part of a study to predict next words from a text dataset\"\n# ]\n\ninput = [\n \"It is not a lack of lov\",\n \"That which does not kill us makes us stro\",\n \"I'm not upset that you lied to me, I'm upset that from now on I can't bel\",\n \"And those who were seen dan\",\n \"It is hard enough to remember my opini\",\n \"A man lying on a comfortable ch\",\n \"The networks perf\",\n \"The networks performance is competi\",\n \"The networks performance is competitive with state-of-the-art lan\",\n \"This document is the initial part of a study to pre\",\n \"This document is the initial part of a study to pred\",\n \"Assuming the prediction\",\n \"Assuming the predictions are probabilistic, novel sequences can be gene\",\n \"Assuming the predictions are probabilistic, novel sequences can be generat\"\n]",
"_____no_output_____"
],
[
"for i in input:\n seq = i.lower()\n print(seq)\n print(predict_completions(seq, 5))\n print()",
"it is not a lack of lov\n['e ', 'ical ', 'ality ', 'oure ', 'uling ']\n\nthat which does not kill us makes us stro\n['ng ', 'dger ', 've ', 'gget ', 'w ']\n\ni'm not upset that you lied to me, i'm upset that from now on i can't bel\n['ieve ', 'ong ', 'aes ', 'ess ', 'low ']\n\nand those who were seen dan\n['gerous ', 'king ', 'ders ', 'y ', 'ce ']\n\nit is hard enough to remember my opini\n['on ', 'an ', 'ty ', 'fic ', 's ']\n\na man lying on a comfortable ch\n['ild ', 'aracteristic ', 'ristian ', 'erristic ', 'omes ']\n\nthe networks perf\n['ectly ', 'ord ', 'aind, ', 'iced ', 'uch ']\n\nthe networks performance is competi\n['tion ', 'ce, ', 'ences ', 'sion ', 'ons ']\n\nthe networks performance is competitive with state-of-the-art lan\n['ger ', 'ds ', 'k ', 'ce ', 'ture ']\n\nthis document is the initial part of a study to pre\n['sent ', 'dicate ', 'cisely ', 'vail ', 'juce ']\n\nthis document is the initial part of a study to pred\n['icate ', 'ention ', 'ucate ', 'action ', 'ocation ']\n\nassuming the prediction\n[' of ', ', ', 's ', '. ', 'al ']\n\nassuming the predictions are probabilistic, novel sequences can be gene\n['rally ', 'ution ', 'man ', 'st ', 'dations, ']\n\nassuming the predictions are probabilistic, novel sequences can be generat\n['ions, ', 'ed ', 'on, ', 'y ', 'ure ']\n\n"
],
[
"",
"_____no_output_____"
]
]
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cb18c36d9f29c2020b0f49b309e16d2eedc4b2df | 6,215 | ipynb | Jupyter Notebook | module_3/your_first_model.ipynb | stuit/I-am-data-scientist | a7db01a1e9210fd3b5cd5d6aac603c090b73c738 | [
"MIT"
] | 1 | 2022-02-01T14:36:41.000Z | 2022-02-01T14:36:41.000Z | module_3/your_first_model.ipynb | stuit/I-am-data-scientist | a7db01a1e9210fd3b5cd5d6aac603c090b73c738 | [
"MIT"
] | null | null | null | module_3/your_first_model.ipynb | stuit/I-am-data-scientist | a7db01a1e9210fd3b5cd5d6aac603c090b73c738 | [
"MIT"
] | null | null | null | 32.036082 | 762 | 0.584232 | [
[
[
"# Загрузка Pandas и очистка данных",
"_____no_output_____"
]
],
[
[
"import pandas as pd",
"_____no_output_____"
],
[
"df = pd.read_csv('main_task.csv')",
"_____no_output_____"
],
[
"# Ваш код по очистке данных и генерации новых признаков\n# При необходимости добавьте ячейки\n# df.Restaurant_id = df.Restaurant_id.apply(lambda x: int(x[3:]))\ndf.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 40000 entries, 0 to 39999\nData columns (total 10 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Restaurant_id 40000 non-null object \n 1 City 40000 non-null object \n 2 Cuisine Style 30717 non-null object \n 3 Ranking 40000 non-null float64\n 4 Rating 40000 non-null float64\n 5 Price Range 26114 non-null object \n 6 Number of Reviews 37457 non-null float64\n 7 Reviews 40000 non-null object \n 8 URL_TA 40000 non-null object \n 9 ID_TA 40000 non-null object \ndtypes: float64(3), object(7)\nmemory usage: 3.1+ MB\n"
],
[
"newdf = df.drop(columns=['Restaurant_id','City','Cuisine Style','Price Range','Reviews','URL_TA','ID_TA'])\nnewdf.rename(columns={'Number of Reviews': 'review_num'}, inplace=True)\n# newdf[newdf.review_num.isna() == False]\nIQR = newdf.review_num.quantile(0.75) - newdf.review_num.quantile(0.25)\nperc25 = newdf.review_num.quantile(0.25)\nperc75 = newdf.review_num.quantile(0.75)\nreal_mean = newdf.review_num.loc[newdf.review_num.between(perc25 - 1.5*IQR, perc75 + 1.5*IQR)].mean()\ndf['Number of Reviews'] = newdf.review_num.fillna(real_mean)\ndf['Restaurant_id'] = df.Restaurant_id.str[3:]\ndf = df.drop(columns=['City','Cuisine Style','Price Range','Reviews','URL_TA','ID_TA'])\ndf.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 40000 entries, 0 to 39999\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Restaurant_id 40000 non-null object \n 1 Ranking 40000 non-null float64\n 2 Rating 40000 non-null float64\n 3 Number of Reviews 40000 non-null float64\ndtypes: float64(3), object(1)\nmemory usage: 1.2+ MB\n"
]
],
[
[
"# Разбиваем датафрейм на части, необходимые для обучения и тестирования модели",
"_____no_output_____"
]
],
[
[
"# Х - данные с информацией о ресторанах, у - целевая переменная (рейтинги ресторанов)\nX = df.drop(['Restaurant_id', 'Rating'], axis = 1)\ny = df['Rating']",
"_____no_output_____"
],
[
"# Загружаем специальный инструмент для разбивки:\nfrom sklearn.model_selection import train_test_split",
"_____no_output_____"
],
[
"# Наборы данных с меткой \"train\" будут использоваться для обучения модели, \"test\" - для тестирования.\n# Для тестирования мы будем использовать 25% от исходного датасета.\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)",
"_____no_output_____"
]
],
[
[
"# Создаём, обучаем и тестируем модель",
"_____no_output_____"
]
],
[
[
"# Импортируем необходимые библиотеки:\nfrom sklearn.ensemble import RandomForestRegressor # инструмент для создания и обучения модели\nfrom sklearn import metrics # инструменты для оценки точности модели",
"_____no_output_____"
],
[
"# Создаём модель\nregr = RandomForestRegressor(n_estimators=100)\n\n# Обучаем модель на тестовом наборе данных\nregr.fit(X_train, y_train)\n\n# Используем обученную модель для предсказания рейтинга ресторанов в тестовой выборке.\n# Предсказанные значения записываем в переменную y_pred\ny_pred = regr.predict(X_test)",
"_____no_output_____"
],
[
"# Сравниваем предсказанные значения (y_pred) с реальными (y_test), и смотрим насколько они в среднем отличаются\n# Метрика называется Mean Absolute Error (MAE) и показывает среднее отклонение предсказанных значений от фактических.\nprint('MAE:', metrics.mean_absolute_error(y_test, y_pred))",
"MAE: 0.43638770734126986\n"
]
]
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cb18c724908a5a0fe1643d60d3f33a9c265071ea | 354,090 | ipynb | Jupyter Notebook | vol2/extractor_met14.ipynb | esturdivant-usgs/geomorph-working-files | bd8d5391714ad0d8243580b6278c21ba419d83c5 | [
"CC0-1.0"
] | null | null | null | vol2/extractor_met14.ipynb | esturdivant-usgs/geomorph-working-files | bd8d5391714ad0d8243580b6278c21ba419d83c5 | [
"CC0-1.0"
] | 1 | 2018-12-26T18:11:51.000Z | 2018-12-26T18:12:02.000Z | vol2/extractor_met14.ipynb | esturdivant-usgs/geomorph-working-files | bd8d5391714ad0d8243580b6278c21ba419d83c5 | [
"CC0-1.0"
] | null | null | null | 104.85342 | 74,780 | 0.78946 | [
[
[
"# Extract barrier island metrics along transects\n\nAuthor: Emily Sturdivant, [email protected]\n\n***\n\nExtract barrier island metrics along transects for Barrier Island Geomorphology Bayesian Network. See the project [README](https://github.com/esturdivant-usgs/BI-geomorph-extraction/blob/master/README.md) and the Methods Report (Zeigler et al., in review). \n\n\n## Pre-requisites:\n- All the input layers (transects, shoreline, etc.) must be ready. This is performed with the notebook file prepper.ipynb.\n- The files servars.py and configmap.py may need to be updated for the current dataset.\n\n## Notes:\n- This notebook includes interactive quality checking, which requires the user's attention. For thorough QC'ing, we recommend displaying the layers in ArcGIS, especially to confirm the integrity of values for variables such as distance to inlet (__Dist2Inlet__) and widths of the landmass (__WidthPart__, etc.). \n\n\n***\n\n## Import modules",
"_____no_output_____"
]
],
[
[
"import os\nimport sys\nimport pandas as pd\nimport numpy as np\nimport io\nimport arcpy\nimport pyproj\nimport datetime\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.style.use('ggplot')\nimport core.functions_warcpy as fwa\nimport core.functions as fun",
"_____no_output_____"
],
[
"print(\"Date: {}\".format(datetime.date.today()))\n# print(os.__version__)\n# print(sys.__version__)\nprint('pandas version: {}'.format(pd.__version__))\nprint('numpy version: {}'.format(np.__version__))\nprint('matplotlib version: {}'.format(matplotlib.__version__))\n# print(io.__version__)\n# print(arcpy.__version__)\nprint('pyproj version: {}'.format(pyproj.__version__))\n\n# print(fwa.__version__)",
"Date: 2019-06-28\npandas version: 0.20.1\nnumpy version: 1.11.2\nmatplotlib version: 1.5.3\npyproj version: 1.9.5.1\n"
]
],
[
[
"### Initialize variables\n\nThis cell prompts you for the site, year, and project directory path. `setvars.py` retrieves the pre-determined values for that site in that year from `configmap.py`. The project directory will be used to set up your workspace. It's hidden for security – sorry! I recommend that you type the path somewhere and paste it in.",
"_____no_output_____"
]
],
[
[
"from core.setvars import *",
"site (options: Assawoman, Fisherman, Cedar, RhodeIsland, FireIsland, Parramore, Cobb, Monomoy, Smith, CoastGuard, Metompkin, Forsythe, Assateague, CapeLookout, CapeHatteras, Rockaway, ParkerRiver): Metompkin\nyear (options: 2010, 2012, 2014): 2014\nPath to project directory (e.g. \\\\Mac\u000bolume\\dir\\FireIsland2014): ··················································\n"
]
],
[
[
"Change the filename variables to match your local files. They should be in an Esri file geodatabase named site+year.gdb in your project directory, which you input above and is the value of the variable `home`. ",
"_____no_output_____"
]
],
[
[
"# Extended transects: NASC transects extended and sorted, ready to be the base geometry for processing\nextendedTrans = os.path.join(home, 'Monomoy2014_extTrans_null')\n\n# Tidied transects: Extended transects without overlapping transects\nextTrans_tidy = os.path.join(home, 'Monomoy_tidyTrans') \n\n# Geomorphology points: positions of indicated geomorphic features\nShorelinePts = os.path.join(home, 'Monomoy2014_SLpts') # shoreline\ndlPts = os.path.join(home, 'Monomoy2014_DLpts') # dune toe\ndhPts = os.path.join(home, 'Monomoy2014_DHpts') # dune crest\n\n# Inlet lines: polyline feature classes delimiting inlet position. Must intersect the full island shoreline\ninletLines = os.path.join(home, 'Monomoy2014_inletLines')\n\n# Full island shoreline: polygon that outlines the island shoreline, MHW on oceanside and MTL on bayside\nbarrierBoundary = os.path.join(home, 'Monomoy2014_bndpoly_2sl') \n\n# Elevation grid: DEM of island elevation at either 5 m or 1 m resolution\nelevGrid = os.path.join(home, 'Monomoy2014_DEM_5m')\n\n# ---\n# OPTIONAL - comment out each one that is not available\n# ---\n# \n# morphdata_prefix = '14CNT01'\n\n# Study area boundary; manually digitize if the barrier island study area does not end at an inlet.\n# SA_bounds = os.path.join(home, 'SA_bounds')\n\n# Armoring lines: digitize lines of shorefront armoring to be used if dune toe points are not available.\n# armorLines = os.path.join(home, 'armorLines')\n\n# Extended transects with Construction, Development, and Nourishment coding\n# tr_w_anthro = os.path.join(home, 'extTrans_wAnthro')\n\n# Piping Plover Habitat BN raster layers\nSubType = os.path.join(home, 'SubType') # substrate type\nVegType = os.path.join(home, 'VegType') # vegetation type\nVegDens = os.path.join(home, 'VegDens') # vegetation density\nGeoSet = os.path.join(home, 'GeoSet') # geomorphic setting\n\n# Derivatives of inputs: They will be generated during process if they are not found. \nshoreline = os.path.join(home, 'ShoreBetweenInlets') # oceanside shoreline between inlets; generated from shoreline polygon, inlet lines, and SA bounds\nslopeGrid = os.path.join(home, 'slope_5m') # Slope at 5 m resolution; generated from DEM",
"_____no_output_____"
]
],
[
[
"## Transect-averaged values\nWe work with the shapefile/feature class as a pandas DataFrame as much as possible to speed processing and minimize reliance on the ArcGIS GUI display.\n\n1. Add the bearing of each transect line to the attribute table from the LINE_BEARING geometry attribute.\n1. Create a pandas dataframe from the transects feature class. In the process, remove some of the unnecessary fields. The resulting dataframe is indexed by __sort_ID__ with columns corresponding to the attribute fields in the transects feature class. \n2. Add __DD_ID__.\n3. Join the values from the transect file that includes the three anthropologic development fields, __Construction__, __Development__, and __Nourishment__. ",
"_____no_output_____"
]
],
[
[
"# Add BEARING field to extendedTrans feature class\narcpy.AddGeometryAttributes_management (extendedTrans, 'LINE_BEARING')\nprint(\"Adding line bearing field to transects.\")\n\n# Copy feature class to dataframe.\ntrans_df = fwa.FCtoDF(extendedTrans, id_fld=tID_fld, extra_fields=extra_fields)\ntrans_df['DD_ID'] = trans_df[tID_fld] + sitevals['id_init_val']\ntrans_df.drop('Azimuth', axis=1, inplace=True)\ntrans_df.rename_axis({\"BEARING\": \"Azimuth\"}, axis=1, inplace=True)\n\n# Get anthro fields and join to DF\nif 'tr_w_anthro' in locals():\n trdf_anthro = fwa.FCtoDF(tr_w_anthro, id_fld=tID_fld, dffields=['Development', 'Nourishment','Construction'])\n trans_df = fun.join_columns(trans_df, trdf_anthro) \n\n# Save\ntrans_df.to_pickle(os.path.join(scratch_dir, 'trans_df.pkl'))\n\n# Display\nprint(\"\\nHeader of transects dataframe (rows 1-5 out of {}): \".format(len(trans_df)))\ntrans_df.head()",
"Adding\n...converting feature class to array...\n...converting array to dataframe...\n\nHeader of transects dataframe (rows 1-5 out of 209): \n"
],
[
"print(extra_fields)",
"['StartX', 'StartY', 'ORIG_FID', 'Autogen', 'ProcTime', 'SHAPE_Leng', 'OBJECTID_1', 'Shape_Length', 'EndX', 'EndY', 'BaselineID', 'OBJECTID', 'orig_oid', 'TransOrder_1', 'lr2', 'lse', 'lci90', 'STARTX', 'STARTY', 'ORIG_FID', 'AUTOGEN', 'PROCTIME', 'SHAPE_LENG', 'OBJECTID_1', 'SHAPE_LENGTH', 'ENDX', 'ENDY', 'BASELINEID', 'OBJECTID', 'ORIG_OID', 'TRANSORDER_1', 'LR2', 'LSE', 'LCI90', 'STARTX', 'STARTY', 'ORIG_FID', 'AUTOGEN', 'PROCTIME', 'SHAPE_LENG', 'OBJECTID_1', 'SHAPE_LENGTH', 'ENDX', 'ENDY', 'BASELINEID', 'OBJECTID', 'ORIG_OID', 'TRANSORDER_1', 'LR2', 'LSE', 'LCI90', 'STARTX', 'STARTY', 'ORIG_FID', 'AUTOGEN', 'PROCTIME', 'SHAPE_LENG', 'OBJECTID_1', 'SHAPE_LENGTH', 'ENDX', 'ENDY', 'BASELINEID', 'OBJECTID', 'ORIG_OID', 'TRANSORDER_1', 'LR2', 'LSE', 'LCI90']\n"
]
],
[
[
"### Get XY and Z/slope from SL, DH, DL points within 25 m of transects\nAdd to each transect row the positions of the nearest pre-created beach geomorphic features (shoreline, dune toe, and dune crest).\n\n#### If needed, convert morphology points stored locally to feature classes for use.\nAfter which, view the new feature classes in a GIS. Isolate the points to the region of interest. Quality check them. Then copy them for use with this code, which will require setting the filenames to match those included here or changing the values included here to match the final filenames.",
"_____no_output_____"
]
],
[
[
"if \"morphdata_prefix\" in locals():\n csvpath = os.path.join(proj_dir, 'Input_Data', '{}_morphology'.format(morphdata_prefix), \n '{}_morphology.csv'.format(morphdata_prefix))\n dt_fc, dc_fc, sl_fc = fwa.MorphologyCSV_to_FCsByFeature(csvpath, state, proj_code, \n csv_fill = 999, fc_fill = -99999, csv_epsg=4326)\n print(\"OUTPUT: morphology point feature classes in the scratch gdb. We recommend QC before proceeding.\")",
"_____no_output_____"
]
],
[
[
"#### Shoreline\n\nThe MHW shoreline easting and northing (__SL_x__, __SL_y__) are the coordinates of the intersection of the oceanside shoreline with the transect. Each transect is assigned the foreshore slope (__Bslope__) from the nearest shoreline point within 25 m. These values are populated for each transect as follows: \n1. get __SL_x__ and __SL_y__ at the point where the transect crosses the oceanside shoreline; \n2. find the closest shoreline point to the intersection point (must be within 25 m) and copy the slope value from the point to the transect in the field __Bslope__.",
"_____no_output_____"
]
],
[
[
"if not arcpy.Exists(inletLines):\n # manually create lines that correspond to end of land and cross the MHW line (refer to shoreline polygon)\n arcpy.CreateFeatureclass_management(home, os.path.basename(inletLines), 'POLYLINE', spatial_reference=utmSR)\n print(\"OUTPUT: {}. Interrupt execution to manually create lines at each inlet.\".format(inletLines))\n\nif not arcpy.Exists(shoreline):\n if not 'SA_bounds' in locals(): \n SA_bounds = ''\n shoreline = fwa.CreateShoreBetweenInlets(barrierBoundary, inletLines, shoreline, ShorelinePts, proj_code, SA_bounds)\n\n# Get the XY position where transect crosses the oceanside shoreline\nsl2trans_df, ShorelinePts = fwa.add_shorelinePts2Trans(extendedTrans, ShorelinePts, shoreline, \n tID_fld, proximity=pt2trans_disttolerance)\n\n# Save and print sample\nsl2trans_df.to_pickle(os.path.join(scratch_dir, 'sl2trans.pkl'))\nsl2trans_df.sample(5)",
"\nMatching shoreline points to transects...\nUsing field 'slope' as slope.\nThe projection of Metompkin2014_SLpts was changed. The new file is Metompkin2014_SLpts_utm.\n...duration at transect 100: 0:1:18.9 seconds\n...duration at transect 200: 0:2:19.8 seconds\nDuration: 0:2:25.4 seconds\n"
],
[
"# Export the inlet delineation and shoreline polygons to the scratch directory ultimately for publication\narcpy.FeatureClassToFeatureClass_conversion(inletLines, scratch_dir, pts_name.split('_')[0] + '_inletLines.shp')\narcpy.FeatureClassToFeatureClass_conversion(barrierBoundary, scratch_dir, pts_name.split('_')[0] + '_shoreline.shp')\nprint('OUTPUT: Saved inletLines and shoreline shapefiles in the scratch directory.')",
"OUTPUT: Saved inletLines and shoreline shapefiles in the scratch directory.\n"
],
[
"# fun.AddGeographicCoordinates(ShorelinePts)\n\n# Convert to pandas DF\nslpts_df = fwa.FCtoDF(ShorelinePts)\nslpts_df.head()\n\n# Report values\nxmlfile = os.path.join(scratch_dir, pts_name.split('_')[0] + '_SLpts_eainfo.xml')\nsl_extra_flds = fun.report_fc_values(slpts_df, field_defs, xmlfile)\n\n# Delete extra fields from points feature class and dataframe (which will become CSV)\nif len(sl_extra_flds) > 0:\n for fld in sl_extra_flds:\n try:\n arcpy.DeleteField_management(ShorelinePts, fld)\n print('Deleted field \"{}\"'.format(fld))\n except:\n print('WARNING: Failed to delete field \"{}\"'.format(fld))\n pass\narcpy.Delete_management(pts_name.split('_')[0] + '_SLpts.shp')\narcpy.FeatureClassToFeatureClass_conversion(ShorelinePts, scratch_dir, pts_name.split('_')[0] + '_SLpts.shp')\nprint(\"\\nOUTPUT: {} in specified scratch_dir.\".format(os.path.basename(pts_name.split('_')[0] + '_SLpts.shp')))\n\n# Save CSV in scratch_dir\nslpts_df.drop(sl_extra_flds, axis=1, inplace=True)\ncsv_fname = os.path.join(scratch_dir, pts_name.split('_')[0] + '_SLpts.csv')\nslpts_df.to_csv(csv_fname, na_rep=fill, index=False)\nprint(\"\\nOUTPUT: {} in specified scratch_dir.\".format(os.path.basename(csv_fname)))",
"...converting feature class to array...\n...converting array to dataframe...\nNumber of points in dataset: (967, 13)\n\nOBJECTID_______________________________1 | 970_________________ No fills_________No nulls\nShape............... nan\nstate............... 12\nseg___________________________________74 | 77__________________ No fills_________No nulls\nprofile________________________________1 | 346_________________ No fills_________No nulls\nsl_x__________________________-117.82688 | 344.18396___________ No fills_________No nulls\nci95_slx_________________________8.4e-05 | 0.290865____________ No fills_________No nulls\nslope__________________________-0.100573 | -0.013588___________ No fills_________No nulls\neasting____________________448909.511521 | 452799.77442________ No fills_________No nulls\nnorthing__________________4171997.070568 | 4180920.86076_______ No fills_________No nulls\n\nWARNING: Field(s) ['MHW', 'start_date', 'end_date'] in dataframe not included in field_defs.\nDeleted field \"MHW\"\nDeleted field \"start_date\"\nDeleted field \"end_date\"\n\nOUTPUT: met14_SLpts.shp in specified scratch_dir.\n\nOUTPUT: met14_SLpts.csv in specified scratch_dir.\n"
]
],
[
[
"#### Dune positions along transects\n\n__DL_x__, __DL_y__, and __DL_z__ are the easting, northing, and elevation, respectively, of the nearest dune toe point within 25 meters of the transect. __DH_x__, __DH_y__, and __DH_z__ are the easting, northing, and elevation, respectively, of the nearest dune crest point within 25 meters. \n\n__DL_snapX__, __DL_snapY__, __DH_snapX__, and __DH_snapY__ are the eastings and northings of the points \"snapped\" to the transect. \"Snapping\" finds the position along the transect nearest to the point, i.e. orthogonal to the transect. These values are used to find the beach width. The elevation values are not snapped; we use the elevation values straight from the original points. \n\nThese values are populated as follows: \n\n1. Find the nearest dune crest/toe point to the transect and proceed if the distance is less than 25 m. If there are no points within 25 m of the transect, populate the row with Null values.\n2. Get the X, Y, and Z values of the point. \n3. Find the position along the transect of an orthogonal line drawn to the dune point (__DL_snapX__, __DL_snapY__, __DH_snapX__, and __DH_snapY__). This is considered the 'snapped' XY position and is calculated using the arcpy geometry method. ",
"_____no_output_____"
]
],
[
[
"# Create dataframe for both dune crest and dune toe positions\ndune2trans_df, dhPts, dlPts = fwa.find_ClosestPt2Trans_snap(extendedTrans, dhPts, dlPts, trans_df, \n tID_fld, proximity=pt2trans_disttolerance)\n\n# Save and print sample\ndune2trans_df.to_pickle(os.path.join(scratch_dir, 'dune2trans.pkl'))\ndune2trans_df.sample(5)",
"\nMatching dune points with transects:\nUsing field 'dhigh_z' as DH Z field...\nUsing field 'dlow_z' as DL Z field...\nLooping through transects and dune points to find nearest point within 25 m...\n...duration at transect 100: 0:1:24.8 seconds\n...duration at transect 200: 0:2:35.5 seconds\nDuration: 0:2:42.0 seconds\n"
],
[
"# Convert to pandas DF\ndlpts_df = fwa.FCtoDF(dlPts)\n\n# Report values\nxmlfile = os.path.join(scratch_dir, pts_name.split('_')[0] + '_DTpts_eainfo.xml')\ndl_extra_flds = fun.report_fc_values(dlpts_df, field_defs, xmlfile)\n\n# Delete extra fields from points feature class and dataframe (which will become CSV)\nfor fld in dl_extra_flds:\n try:\n arcpy.DeleteField_management(dlPts, fld)\n print('Deleted field \"{}\"'.format(fld))\n except:\n print('WARNING: Failed to delete field \"{}\"'.format(fld))\n pass\narcpy.FeatureClassToFeatureClass_conversion(dlPts, scratch_dir, pts_name.split('_')[0] + '_DTpts.shp')\n\n# Save CSV in scratch_dir\ndlpts_df.drop(dl_extra_flds, axis=1, inplace=True)\ncsv_fname = os.path.join(scratch_dir, pts_name.split('_')[0] + '_DTpts.csv')\ndlpts_df.to_csv(csv_fname, na_rep=fill, index=False)\nprint(\"\\nOUTPUT: {} in specified scratch_dir.\\n\".format(os.path.basename(csv_fname)))",
"...converting feature class to array...\n...converting array to dataframe...\nNumber of points in dataset: (643, 14)\n\nOBJECTID_______________________________2 | 644_________________ No fills_________No nulls\nShape............... nan\nstate............... 12\nseg___________________________________74 | 77__________________ No fills_________No nulls\nprofile________________________________1 | 343_________________ No fills_________No nulls\nlon___________________________-75.581208 | -75.536269__________ No fills_________No nulls\nlat____________________________37.694257 | 37.774509___________ No fills_________No nulls\neasting____________________448760.169617 | 452772.940858_______ No fills_________No nulls\nnorthing__________________4172052.001045 | 4180932.197076______ No fills_________No nulls\ndlow_x_______________________-165.761263 | 269.469281__________ No fills_________No nulls\ndlow_z__________________________1.000163 | 3.954317____________ No fills_________No nulls\nz_error_________________________0.008295 | 0.719522____________ No fills_________No nulls\n\nWARNING: Field(s) ['start_date', 'end_date'] in dataframe not included in field_defs.\nDeleted field \"start_date\"\nDeleted field \"end_date\"\n\nOUTPUT: met14_DTpts.csv in specified scratch_dir.\n\n"
],
[
"# Convert to pandas DF\ndhpts_df = fwa.FCtoDF(dhPts)\n\n# Report values\nxmlfile = os.path.join(scratch_dir, pts_name.split('_')[0] + '_DCpts_eainfo.xml')\ndh_extra_flds = fun.report_fc_values(dhpts_df, field_defs, xmlfile)\n\n# Delete extra fields from points feature class and dataframe (which will become CSV)\nfor fld in dh_extra_flds:\n try:\n arcpy.DeleteField_management(dhPts, fld)\n print('Deleted field \"{}\"'.format(fld))\n except:\n print('WARNING: Failed to delete field \"{}\"'.format(fld))\n pass\narcpy.FeatureClassToFeatureClass_conversion(dhPts, scratch_dir, pts_name.split('_')[0] + '_DCpts.shp')\n\n# Save CSV in scratch_dir\ndhpts_df.drop(dh_extra_flds, axis=1, inplace=True)\ncsv_fname = os.path.join(scratch_dir, pts_name.split('_')[0] + '_DCpts.csv')\ndhpts_df.to_csv(csv_fname, na_rep=fill, index=False)\nprint(\"\\nOUTPUT: {} in specified scratch_dir.\".format(os.path.basename(csv_fname)))",
"...converting feature class to array...\n...converting array to dataframe...\nNumber of points in dataset: (913, 14)\n\nOBJECTID_______________________________2 | 914_________________ No fills_________No nulls\nShape............... nan\nstate............... 12\nseg___________________________________74 | 77__________________ No fills_________No nulls\nprofile________________________________1 | 343_________________ No fills_________No nulls\nlon___________________________-75.581381 | -75.536405__________ No fills_________No nulls\nlat____________________________37.694307 | 37.77497____________ No fills_________No nulls\neasting____________________448744.918554 | 452760.868412_______ No fills_________No nulls\nnorthing__________________4172057.610644 | 4180983.585245______ No fills_________No nulls\ndhigh_x______________________-200.761263 | 235.719281__________ No fills_________No nulls\ndhigh_z_________________________0.895413 | 5.825841____________ No fills_________No nulls\nz_error_________________________0.006649 | 0.499434____________ No fills_________No nulls\n\nWARNING: Field(s) ['start_date', 'end_date'] in dataframe not included in field_defs.\nDeleted field \"start_date\"\nDeleted field \"end_date\"\n\nOUTPUT: met14_DCpts.csv in specified scratch_dir.\n"
]
],
[
[
"#### Armoring\n__Arm_x__, __Arm_y__, and __Arm_z__ are the easting, northing, and elevation, respectively, where an artificial structure crosses the transect in the vicinity of the beach. These features are meant to supplement the dune toe data set by providing an upper limit to the beach in areas where dune toe extraction was confounded by the presence of an artificial structure. Values are populated for each transect as follows: \n\n1. Get the positions of intersection between the digitized armoring lines and the transects (Intersect tool from the Overlay toolset); \n2. Extract the elevation value at each intersection point from the DEM (Extract Multi Values to Points tool from Spatial Analyst); ",
"_____no_output_____"
]
],
[
[
"# Create elevation raster at 5-m resolution if not already\nelevGrid = fwa.ProcessDEM_2(elevGrid, utmSR)\n\n# Armoring line\nif not arcpy.Exists(armorLines):\n arcpy.CreateFeatureclass_management(home, os.path.basename(armorLines), 'POLYLINE', spatial_reference=utmSR)\n print(\"{} created. If shorefront armoring exists, interrupt execution to manually digitize.\".format(armorLines))\n\narm2trans_df = fwa.ArmorLineToTrans_PD(extendedTrans, armorLines, sl2trans_df, tID_fld, proj_code, elevGrid)\n\n# Save and print sample\narm2trans_df.to_pickle(os.path.join(scratch_dir, 'arm2trans.pkl'))\ntry:\n arm2trans_df.sample(5)\nexcept:\n pass",
"OUTPUT: Metompkin2014_dem_5m at 5x5 resolution.\n\nArmoring file either missing or empty so we will proceed without armoring data. If shorefront tampering is present at this site, cancel the operations to digitize.\n"
]
],
[
[
"### Add all the positions to the trans_df\nJoin the new dataframes to the transect dataframe. Before it performs the join, `join_columns_id_check()` checks the index and the ID field for potential errors such as whether they are the equal and whether there are duplicated IDs or null values in either.",
"_____no_output_____"
]
],
[
[
"# Load saved dataframes\ntrans_df = pd.read_pickle(os.path.join(scratch_dir, 'trans_df.pkl'))\nsl2trans_df = pd.read_pickle(os.path.join(scratch_dir, 'sl2trans.pkl'))\ndune2trans_df = pd.read_pickle(os.path.join(scratch_dir, 'dune2trans.pkl'))\narm2trans_df = pd.read_pickle(os.path.join(scratch_dir, 'arm2trans.pkl'))",
"_____no_output_____"
],
[
"# Join positions of shoreline, dune crest, dune toe, armoring\ntrans_df = fun.join_columns_id_check(trans_df, sl2trans_df, tID_fld)\ntrans_df = fun.join_columns_id_check(trans_df, dune2trans_df, tID_fld)\ntrans_df = fun.join_columns_id_check(trans_df, arm2trans_df, tID_fld)\n\n# Save and print sample\ntrans_df.to_pickle(os.path.join(scratch_dir, 'trans_df_beachmetrics.pkl'))\ntrans_df.sample(5)",
"_____no_output_____"
]
],
[
[
"### Check for errors\n*Optional*\n\nDisplay summary stats / histograms and create feature classes. The feature classes display the locations that will be used to calculate beach width. Review the output feature classes in a GIS to validate. ",
"_____no_output_____"
]
],
[
[
"plots = trans_df.hist(['DH_z', 'DL_z', 'Arm_z'])\n\n# Subplot Labels\nplots[0][0].set_xlabel(\"Elevation (m in NAVD88)\")\nplots[0][0].set_ylabel(\"Frequency\")\nplots[0][1].set_xlabel(\"Elevation (m in NAVD88)\")\nplots[0][1].set_ylabel(\"Frequency\")\ntry:\n plots[0][2].set_xlabel(\"Elevation (m in NAVD88)\")\n plots[0][2].set_ylabel(\"Frequency\")\nexcept:\n pass\n\nplt.show()\nplt.close()",
"_____no_output_____"
],
[
"# Convert dataframe to feature class - shoreline points with slope\nfwa.DFtoFC(sl2trans_df, os.path.join(arcpy.env.workspace, 'pts2trans_SL'), \n spatial_ref=utmSR, id_fld=tID_fld, xy=[\"SL_x\", \"SL_y\"], keep_fields=['Bslope'])\nprint('OUTPUT: pts2trans_SL in designated scratch geodatabase.')\n\n# Dune crests\ntry:\n fwa.DFtoFC(dune2trans_df, os.path.join(arcpy.env.workspace, 'ptSnap2trans_DH'), \n spatial_ref=utmSR, id_fld=tID_fld, xy=[\"DH_snapX\", \"DH_snapY\"], keep_fields=['DH_z'])\n print('OUTPUT: ptSnap2trans_DH in designated scratch geodatabase.')\nexcept Exception as err:\n print(err)\n pass\n\n# Dune toes\ntry:\n fwa.DFtoFC(dune2trans_df, os.path.join(arcpy.env.workspace, 'ptSnap2trans_DL'), \n spatial_ref=utmSR, id_fld=tID_fld, xy=[\"DL_snapX\", \"DL_snapY\"], keep_fields=['DL_z'])\n print('OUTPUT: ptSnap2trans_DL in designated scratch geodatabase.')\nexcept Exception as err:\n print(err)\n pass",
"... converting dataframe to array... \n... converting array to feature class... \n\nOUTPUT: pts2trans_SL in designated scratch geodatabase.\n... converting dataframe to array... \n... converting array to feature class... \n\nOUTPUT: ptSnap2trans_DH in designated scratch geodatabase.\n... converting dataframe to array... \n... converting array to feature class... \n\nOUTPUT: ptSnap2trans_DL in designated scratch geodatabase.\n"
]
],
[
[
"### Calculate upper beach width and height\nUpper beach width (__uBW__) and upper beach height (__uBH__) are calculated based on the difference in position between two points: the position of MHW along the transect (__SL_x__, __SL_y__) and the dune toe position or equivalent (usually __DL_snapX__, __DL_snapY__). In some cases, the dune toe is not appropriate to designate the \"top of beach\" so beach width and height are calculated from either the position of the dune toe, the dune crest, or the base of an armoring structure. The dune crest was only considered a possibility if the dune crest elevation (__DH_zMHW__) was less than or equal to `maxDH`. \n\nThey are calculated as follows: \n2. Calculate distances from MHW to the position along the transect of the dune toe (__DistDL__), dune crest (__DistDH__), and armoring (__DistArm__). \n2. Adjust the elevations to MHW, populating fields __DH_zmhw__, __DL_zmhw__, and __Arm_zmhw__. \n3. Conditionally select the appropriate feature to represent \"top of beach.\" Dune toe is prioritized. If it is not available and __DH_zmhw__ is less than or equal to maxDH, use dune crest. If neither of the dune positions satisfy the conditions and an armoring feature intersects with the transect, use the armoring position. If none of the three are possible, __uBW__ and __uBH__ will be null. \n4. Copy the distance to shoreline and height above MHW (__Dist--__, __---zmhw__) to __uBW__ and __uBH__, respectively. \n\nNotes:\n- In some morphology datasets, missing elevation values at a point indicate that the point should not be used to measure beach width. In those cases, use the `skip_missing_z` argument to select whether or not to skip these points. ",
"_____no_output_____"
]
],
[
[
"# Load saved dataframe\ntrans_df = pd.read_pickle(os.path.join(scratch_dir, 'trans_df_beachmetrics.pkl'))",
"_____no_output_____"
],
[
"# Calculate distances from shore to dunes, etc.\ntrans_df = fwa.calc_BeachWidth_fill(extendedTrans, trans_df, maxDH, tID_fld, \n sitevals['MHW'], fill, skip_missing_z=True)",
"Fields uBW and uBH populated with beach width and beach height.\n"
]
],
[
[
"### Dist2Inlet\n\n\nDistance to nearest tidal inlet (__Dist2Inlet__) is computed as alongshore distance of each sampling transect from the nearest tidal inlet. This distance includes changes in the path of the shoreline instead of simply a Euclidean distance and reflects sediment transport pathways. It is measured using the oceanside shoreline between inlets (ShoreBetweenInlets). \n\nNote that the ShoreBetweenInlets feature class must be both 'dissolved' and 'singlepart' so that each feature represents one-and-only-one shoreline that runs the entire distance between two inlets or equivalent. If the shoreline is bounded on both sides by an inlet, measure the distance to both and assign the minimum distance of the two. If the shoreline meets only one inlet (meaning the study area ends before the island ends), use the distance to the only inlet. \n\nThe process uses the cut, disjoint, and length geometry methods and properties in ArcPy data access module. The function measure_Dist2Inlet() prints a warning when the difference in Dist2Inlet between two consecutive transects is greater than 300. ",
"_____no_output_____"
]
],
[
[
"# Calc Dist2Inlet in new dataframe \ndist_df = fwa.measure_Dist2Inlet(shoreline, extendedTrans, inletLines, tID_fld)\n\n# Join to transects\ntrans_df = fun.join_columns_id_check(trans_df, pd.DataFrame(dist_df.Dist2Inlet), tID_fld, fill=fill)\n\n# Save and view last 10 rows\ndist_df.to_pickle(os.path.join(scratch_dir, 'dist2inlet_df.pkl'))\ndist_df.tail(10)",
"Duration: 0:0:3.4 seconds\n"
]
],
[
[
"### Clip transects, get barrier widths\nCalculates __WidthLand__, __WidthFull__, and __WidthPart__, which measure different flavors of the cross-shore width of the barrier island. __WidthLand__ is the above-water distance between the back-barrier and seaward MHW shorelines. __WidthLand__ only includes regions of the barrier within the shoreline polygon (bndpoly_2sl) and does not extend into any of the sinuous or intervening back-barrier waterways and islands. __WidthFull__ is the total distance between the back-barrier and seaward MHW shorelines (including space occupied by waterways). __WidthPart__ is the width of only the most seaward portion of land within the shoreline. \n\nThese are calculated as follows: \n\n1. Clip the transect to the full island shoreline (Clip in the Analysis toolbox); \n2. For __WidthLand__, get the length of the multipart line segment from "SHAPE@LENGTH" feature class attribute. When the feature is multipart, this will include only the remaining portions of the transect; \n3. For __WidthPart__, convert the clipped transect from multipart to singlepart and get the length of the first line segment, which should be the most seaward; \n4. For __WidthFull__, calculate the distance between the first vertex and the last vertex of the clipped transect (Feature Class to NumPy Array with explode to points, pandas groupby, numpy hypot).",
"_____no_output_____"
]
],
[
[
"# Clip transects, get barrier widths\nwidths_df = fwa.calc_IslandWidths(extendedTrans, barrierBoundary, tID_fld=tID_fld)\n\n# # Save\nwidths_df.to_pickle(os.path.join(scratch_dir, 'widths_df.pkl'))\n\n# Join\ntrans_df = fun.join_columns_id_check(trans_df, widths_df, tID_fld, fill=fill)\n\n# Save\ntrans_df.to_pickle(os.path.join(scratch_dir, trans_name+'_null_prePts.pkl'))\ntrans_df.sample(5)",
"Clipping the transects to the barrier island boundaries ('clip2island')...\nGetting the width along each transect of the oceanside land (WidthPart)...\n...converting feature class to array...\n...converting array to dataframe...\nGetting the width along each transect of the entire barrier (WidthFull)...\nConverting feature class vertices to array with X and Y...\n...converting array to dataframe...\nGetting the width along each transect of above water portion of the barrier (WidthLand)...\n"
]
],
[
[
"## 5-m Points\nThe point dataset samples the land every 5 m along each shore-normal transect. \n\n### Split transects into points at 5-m intervals. \n\nThe point dataset is created from the tidied transects (tidyTrans, created during pre-processing) as follows: \n\n1. Clip the tidied transects (tidyTrans) to the shoreline polygon (bndpoly_2sl) , retaining only those portions of the transects that represent land.\n2. Produce a dataframe of point positions along each transect every 5 m starting from the ocean-side shoreline. This uses the positionAlongLine geometry method accessed with a Search Cursor and saves the outputs in a new dataframe. \n3. Create a point feature class from the dataframe. \n\nNote: Sometimes the system doesn't seem to register the new feature class (transPts_unsorted) for a while. I'm not sure how to work around that, other than just to wait. ",
"_____no_output_____"
]
],
[
[
"pts_df, pts_presort = fwa.TransectsToPointsDF(extTrans_tidy, barrierBoundary, fc_out=pts_presort)\nprint(\"OUTPUT: '{}' in scratch geodatabase.\".format(os.path.basename(pts_presort)))\n\n# Save\npts_df.to_pickle(os.path.join(scratch_dir, 'pts_presort.pkl'))",
"Clipping transects to within the shoreline bounds ('tidytrans_clipped')...\nGetting points every 5m along each transect and saving in new dataframe...\nConverting dataframe to feature class ('transPts_unsorted')...\n... converting dataframe to array... \n... converting array to feature class... \n\nDuration: 0:1:4.1 seconds\nOUTPUT: 'transPts_unsorted' in scratch geodatabase.\n"
]
],
[
[
"### Add Elevation and Slope to points\n\n__ptZ__ (later __ptZmhw__) and __ptSlp__ are the elevation and slope at the 5-m cell corresponding to the point. \n1. Create the slope and DEM rasters if they don't already exist. We use the 5-m DEM to generate a slope surface (Slope tool in 3D Analyst). \n2. Use Extract Multi Values to Points tool in Spatial Analyst. \n3. Convert the feature class back to a dataframe.",
"_____no_output_____"
]
],
[
[
"# Create slope raster from DEM\nif not arcpy.Exists(slopeGrid):\n arcpy.Slope_3d(elevGrid, slopeGrid, 'PERCENT_RISE')\n print(\"OUTPUT: slope file in designated home geodatabase.\")\n \n# Add elevation and slope values at points.\narcpy.sa.ExtractMultiValuesToPoints(pts_presort, [[elevGrid, 'ptZ'], [slopeGrid, 'ptSlp']])\nprint(\"OUTPUT: added slope and elevation to '{}' in designated scratch geodatabase.\".format(os.path.basename(pts_presort)))",
"OUTPUT: added slope and elevation to 'transPts_unsorted' in designated scratch geodatabase.\n"
],
[
"if 'SubType' in locals():\n # Add substrate type, geomorphic setting, veg type, veg density values at points.\n arcpy.sa.ExtractMultiValuesToPoints(pts_presort, [[SubType, 'SubType'], [VegType, 'VegType'], \n [VegDens, 'VegDens'], [GeoSet, 'GeoSet']])\n\n # Convert to dataframe\n pts_df = fwa.FCtoDF(pts_presort, xy=True, dffields=[tID_fld,'ptZ', 'ptSlp', 'SubType', \n 'VegType', 'VegDens', 'GeoSet'])\n # Recode fill values\n pts_df.replace({'GeoSet': {9999:np.nan}, 'SubType': {9999:np.nan}, 'VegType': {9999:np.nan},\n 'VegDens': {9999:np.nan}}, inplace=True)\nelse:\n print(\"Plover BN layers not specified (we only check for SubType), so we'll proceed without them. \")\n # Convert to dataframe\n pts_df = fwa.FCtoDF(pts_presort, xy=True, dffields=[tID_fld,'ptZ', 'ptSlp'])\n\n# Save and view sample\npts_df.to_pickle(os.path.join(scratch_dir, 'pts_extractedvalues_presort.pkl'))\npts_df.sample(5)",
"Converting feature class to array with X and Y...\n...converting array to dataframe...\n"
],
[
"# Print histogram of elevation extracted to points\nplots = pts_df.hist('ptZ')\n\n# Subplot Labels\nplots[0][0].set_xlabel(\"Elevation (m in NAVD88)\")\nplots[0][0].set_ylabel(\"Frequency\")\n\n# Display\nplt.show()\nplt.close()",
"_____no_output_____"
]
],
[
[
"### Calculate distances and sort points\n\n__SplitSort__ is a unique numeric identifier of the 5-m points at the study site, sorted by order along shoreline and by distance from oceanside. __SplitSort__ values are populated by sorting the points by __sort_ID__ and __Dist_Seg__ (see below). \n\n__Dist_Seg__ is the Euclidean distance between the point and the seaward shoreline (__SL_x__, __SL_y__). __Dist_MHWbay__ is the distance between the point and the bayside shoreline and is calculated by subtracting the __Dist_Seg__ value from the __WidthPart__ value of the transect. \n\n__DistSegDH__, __DistSegDL__, and __DistSegArm__ measure the distance of each 5-m point from the dune crest and dune toe position along a particular transect. They are calculated as the Euclidean distance between the 5-m point and the given feature. ",
"_____no_output_____"
]
],
[
[
"# Load saved dataframes\npts_df = pd.read_pickle(os.path.join(scratch_dir, 'pts_extractedvalues_presort.pkl'))\ntrans_df = pd.read_pickle(os.path.join(scratch_dir, trans_name+'_null_prePts.pkl'))",
"_____no_output_____"
],
[
"print(sorted_pt_flds)",
"['SplitSort', 'seg_x', 'seg_y', 'seg_lon', 'seg_lat', 'Dist_Seg', 'Dist_MHWbay', 'DistSegDH', 'DistSegDL', 'DistSegArm', 'ptZ', 'ptSlp', 'ptZmhw', 'GeoSet', 'SubType', 'VegDens', 'VegType', 'sort_ID', 'TransOrder', 'TransectId', 'DD_ID', 'Azimuth', 'LRR', 'SL_x', 'SL_y', 'Bslope', 'DL_x', 'DL_y', 'DL_z', 'DL_zmhw', 'DL_snapX', 'DL_snapY', 'DH_x', 'DH_y', 'DH_z', 'DH_zmhw', 'DH_snapX', 'DH_snapY', 'Arm_x', 'Arm_y', 'Arm_z', 'Arm_zmhw', 'DistDH', 'DistDL', 'DistArm', 'Dist2Inlet', 'WidthPart', 'WidthLand', 'WidthFull', 'uBW', 'uBH', 'ub_feat', 'mean_Zmhw', 'max_Zmhw', 'Construction', 'Development', 'Nourishment']\n"
],
[
"# Calculate DistSeg, Dist_MHWbay, DistSegDH, DistSegDL, DistSegArm, and sort points (SplitSort)\npts_df = fun.join_columns(pts_df, trans_df, tID_fld)\npts_df = fun.prep_points(pts_df, tID_fld, pID_fld, sitevals['MHW'], fill)\n\n# Aggregate ptZmhw to max and mean and join to transects\npts_df, zmhw = fun.aggregate_z(pts_df, sitevals['MHW'], tID_fld, 'ptZ', fill)\ntrans_df = fun.join_columns(trans_df, zmhw) \n\n# Join transect values to pts\npts_df = fun.join_columns(pts_df, trans_df, tID_fld)\n\n# pID_fld needs to be among the columns\nif not pID_fld in pts_df.columns:\n pts_df.reset_index(drop=False, inplace=True)\n\n# Match field names to those in sorted_pt_flds list\nfor fld in pts_df.columns:\n if fld not in sorted_pt_flds:\n for i, fldi in enumerate(sorted_pt_flds):\n if fldi.lower() == fld.lower():\n sorted_pt_flds[i] = fld \n print(fld)\n \n# Drop extra fields and sort columns\ntrans_df.drop(extra_fields, axis=1, inplace=True, errors='ignore')\nfor i, f in enumerate(sorted_pt_flds):\n for c in pts_df.columns:\n if f.lower() == c.lower():\n sorted_pt_flds[i] = c\npts_df = pts_df.reindex_axis(sorted_pt_flds, axis=1)\n\n# Save dataframes \ntrans_df.to_pickle(os.path.join(scratch_dir, trans_name+'_null.pkl'))\npts_df.to_pickle(os.path.join(scratch_dir, pts_name+'_null.pkl'))\n\n# View random rows from the points DF\npts_df.sample(5)",
"TransOrder\nTransectId\n"
],
[
"# Use pyproj to convert projected coordinates to geographic coordinates (lat, lon in NAD83)\nutm = pyproj.Proj(init='epsg:{}'.format(proj_code))\nnad = pyproj.Proj(init='epsg:4269') # NAD83\n\nin_y = pts_df['seg_y'].tolist()\nin_x = pts_df['seg_x'].tolist()\n\nlon, lat = pyproj.transform(utm, nad, in_x,in_y)\n\nlon_col = 'seg_lon'\nlat_col = 'seg_lat'\n\npts_df[lon_col] = lon\npts_df[lat_col] = lat",
"_____no_output_____"
]
],
[
[
"### Recode the values for CSV output and model running",
"_____no_output_____"
]
],
[
[
"# Recode\npts_df4csv = pts_df.replace({'SubType': {7777:'{1111, 2222}', 1000:'{1111, 3333}'}, \n 'VegType': {77:'{11, 22}', 88:'{22, 33}', 99:'{33, 44}'},\n 'VegDens': {666: '{111, 222}', 777: '{222, 333}', \n 888: '{333, 444}', 999: '{222, 333, 444}'}})\n\n# Fill NAs\npts_df4csv.fillna(fill, inplace=True) \n\n# Save and view sample\npts_df4csv.to_pickle(os.path.join(scratch_dir, pts_name+'_csv.pkl'))\npts_df4csv.sample(5)",
"_____no_output_____"
]
],
[
[
"## Quality checking\nLook at extracted profiles from around the island. Enter the transect ID within the available range when prompted. Evaluate the plots for consistency among variables. Repeat various times until you can be satisfied that the variables are consistent with each other and appear to represent reality. View areas with inconsistencies in a GIS.",
"_____no_output_____"
]
],
[
[
"desccols = ['DL_zmhw', 'DH_zmhw', 'Arm_zmhw', 'uBW', 'uBH', 'Dist2Inlet', \n 'WidthPart', 'WidthLand', 'WidthFull', 'mean_Zmhw', 'max_Zmhw']\n\n# Histograms\ntrans_df.hist(desccols, sharey=True, figsize=[15, 10], bins=20)\nplt.show()\nplt.close('all')",
"_____no_output_____"
],
[
"flds_dist = ['SplitSort', 'Dist_Seg', 'Dist_MHWbay', 'DistSegDH', 'DistSegDL', 'DistSegArm']\nflds_z = ['ptZmhw', 'ptZ', 'ptSlp']\npts_df.loc[:,flds_dist+flds_z].describe()\npts_df.hist(flds_dist, sharey=True, figsize=[15, 8], layout=(2,3))\npts_df.hist(flds_z, sharey=True, figsize=[15, 4], layout=(1,3))\n\nplt.show()\nplt.close('all')",
"_____no_output_____"
],
[
"# Prompt for transect identifier (sort_ID) and get all points from that transect.\ntrans_in = int(input('Transect ID (\"sort_ID\" {:d}-{:d}): '.format(int(pts_df[tID_fld].head(1)), int(pts_df[tID_fld].tail(1)))))\npts_set = pts_df[pts_df[tID_fld] == trans_in]\n\n# Plot\nfig = plt.figure(figsize=(13,10))\n\n# Plot the width of the island.\nax1 = fig.add_subplot(211)\ntry:\n fun.plot_island_profile(ax1, pts_set, sitevals['MHW'], sitevals['MTL'])\nexcept TypeError as err:\n print('TypeError: {}'.format(err))\n pass\n\n# Zoom in on the upper beach.\nax2 = fig.add_subplot(212)\ntry:\n fun.plot_beach_profile(ax2, pts_set, sitevals['MHW'], sitevals['MTL'], maxDH)\nexcept TypeError as err:\n print('TypeError: {}'.format(err))\n pass \n\n# Display\nplt.show()\nplt.close('all')",
"Transect ID (\"sort_ID\" 1-209): 123\n"
]
],
[
[
"### Report field values",
"_____no_output_____"
]
],
[
[
"# Load dataframe\npts_df4csv = pd.read_pickle(os.path.join(scratch_dir, pts_name+'_csv.pkl'))",
"_____no_output_____"
],
[
"xmlfile = os.path.join(scratch_dir, pts_name+'_eainfo.xml')\nfun.report_fc_values(pts_df4csv, field_defs, xmlfile)",
"Number of points in dataset: (23116, 57)\n\nSplitSort______________________________0 | 23115_______________ No fills_________No nulls\nseg_x_________________448084.80402533617 | 452803.0445057908___ Fills present____No nulls\nseg_y__________________4171861.443778258 | 4181033.861208152___ Fills present____No nulls\nseg_lon_______________-75.58887177619877 | -75.5359332027382___ Fills present____No nulls\nseg_lat_________________37.6925395570643 | inf_________________ No fills_________No nulls\nDist_Seg_____________________________0.0 | 1381.6446198404335__ Fills present____No nulls\nDist_MHWbay__________-1153.2924874305565 | 1325.7825880888165__ Fills present____No nulls\nDistSegDH____________-199.48027270549048 | 1348.930538204466___ Fills present____No nulls\nDistSegDL____________-184.51126384884847 | 1290.2886220563717__ Fills present____No nulls\nDistSegArm________________________-99999 | -99999______________ ONLY Fills_______No nulls\nptZ__________________-2.2101101875305176 | 5.461483001708984___ Fills present____No nulls\nptSlp_______________0.007053753826767206 | 29.598793029785156__ Fills present____No nulls\nptZmhw________________-2.550110101699829 | 5.121482849121094___ Fills present____No nulls\nGeoSet.............. -99999.0 | 1.0 | 2.0 | 4.0 | 3.0 | 5.0 | 6.0\nSubType............. -99999 | 4444.0 | {1111, 2222} | 3333.0\nVegDens............. -99999 | 111.0 | {111, 222} | {333, 444}\nVegType............. -99999 | 11.0 | {11, 22} | {22, 33} | {33, 44}\nsort_ID______________________________1.0 | 209.0_______________ No fills_________No nulls\nTransOrder________________________2466.0 | 2674.0______________ Fills present____No nulls\nTransectId________________________5715.0 | 6276.0______________ Fills present____No nulls\nDD_ID_____________________________190001 | 190209______________ No fills_________No nulls\nAzimuth_______________293.96254360333006 | 293.9625437640385___ No fills_________No nulls\nLRR________________________________-9.26 | -3.37_______________ Fills present____No nulls\nSL_x__________________448759.44129374344 | 452799.4161637714___ Fills present____No nulls\nSL_y___________________4171861.443778252 | 4180899.6429519113__ Fills present____No nulls\nBslope_________________________-0.098288 | -0.016536___________ Fills present____No nulls\nDL_x___________________448760.1696163323 | 452772.940858094____ Fills present____No nulls\nDL_y___________________4172052.000937877 | 4180908.8350786865__ Fills present____No nulls\nDL_z____________________________1.015217 | 3.169367____________ Fills present____No nulls\nDL_zmhw_________________________0.675217 | 2.829367____________ Fills present____No nulls\nDL_snapX_______________448750.6536462776 | 452773.89642075717__ Fills present____No nulls\nDL_snapY_______________4172030.590060259 | 4180910.985089139___ Fills present____No nulls\nDH_x___________________448744.9185539419 | 452760.3912519971___ Fills present____No nulls\nDH_y__________________4172057.6105364338 | 4180983.5851365346__ Fills present____No nulls\nDH_z____________________________0.895413 | 5.573827____________ Fills present____No nulls\nDH_zmhw_______________0.5554129999999999 | 5.233827____________ Fills present____No nulls\nDH_snapX_______________448735.8363173874 | 452761.3315641589___ Fills present____No nulls\nDH_snapY______________4172037.1755566844 | 4180985.5575111993__ Fills present____No nulls\nArm_x_____________________________-99999 | -99999______________ ONLY Fills_______No nulls\nArm_y_____________________________-99999 | -99999______________ ONLY Fills_______No nulls\nArm_z_____________________________-99999 | -99999______________ ONLY Fills_______No nulls\nArm_zmhw__________________________-99999 | -99999______________ ONLY Fills_______No nulls\nDistDH________________22.315820772806553 | 199.47872693005118__ Fills present____No nulls\nDistDL________________11.224554600143174 | 184.51115492249414__ Fills present____No nulls\nDistArm___________________________-99999 | -99999______________ ONLY Fills_______No nulls\nDist2Inlet____________21.712676921524036 | 5045.899971165767___ Fills present____No nulls\nWidthPart______________67.05241050625838 | 1325.7825880888165__ Fills present____No nulls\nWidthLand______________67.05241050625838 | 1325.7825880888165__ Fills present____No nulls\nWidthFull______________67.05241050625838 | 1386.817858855114___ Fills present____No nulls\nuBW___________________11.224554600143174 | 184.51115492249414__ Fills present____No nulls\nuBH_____________________________0.675217 | 2.829367____________ Fills present____No nulls\nub_feat............. -99999 | DL | DH\nmean_Zmhw____________-0.5136715173721313 | 1.2329341173171997__ Fills present____No nulls\nmax_Zmhw____________-0.22110992670059204 | 5.121482849121094___ Fills present____No nulls\nConstruction........ 111\nDevelopment......... 111\nNourishment......... 111\n"
]
],
[
[
"## Outputs\n\n### Transect-averaged\nOutput the transect-averaged metrics in the following formats:\n- transects, unpopulated except for ID values, as gdb feature class\n- transects, unpopulated except for ID values, as shapefile\n- populated transects with fill values as gdb feature class\n- populated transects with null values as gdb feature class\n- populated transects with fill values as shapefile\n- raster of beach width (__uBW__) by transect",
"_____no_output_____"
]
],
[
[
"# Load the dataframe\ntrans_df = pd.read_pickle(os.path.join(scratch_dir, trans_name+'_null.pkl'))",
"_____no_output_____"
],
[
"trans_df['Construction'] = 111\ntrans_df['Nourishment'] = 111\ntrans_df['Development'] = 111",
"_____no_output_____"
]
],
[
[
"#### Vector format",
"_____no_output_____"
]
],
[
[
"# Create transect file with only ID values and geometry to publish.\ntrans_flds = ['TRANSECTID', 'TRANSORDER', 'DD_ID']\nfor i, f in enumerate(trans_flds):\n for c in trans_df.columns:\n if f.lower() == c.lower():\n trans_flds[i] = c\n \ntrans_4pub = fwa.JoinDFtoFC(trans_df.loc[:,trans_flds], extendedTrans, tID_fld, out_fc=sitevals['code']+'_trans')\nout_shp = arcpy.FeatureClassToFeatureClass_conversion(trans_4pub, scratch_dir, sitevals['code']+'_trans.shp')\nprint(\"OUTPUT: {} in specified scratch_dir.\".format(os.path.basename(str(out_shp))))",
"Created met_trans from input dataframe and extTrans file.\nOUTPUT: met_trans.shp in specified scratch_dir.\n"
],
[
"trans_4pub",
"_____no_output_____"
],
[
"trans_4pubdf = fwa.FCtoDF(trans_4pub)\nxmlfile = os.path.join(scratch_dir, trans_4pub + '_eainfo.xml')\ntrans_df_extra_flds = fun.report_fc_values(trans_4pubdf, field_defs, xmlfile)",
"...converting feature class to array...\n...converting array to dataframe...\nNumber of points in dataset: (209, 7)\n\nShape............... nan\nsort_ID________________________________1 | 209_________________ No fills_________No nulls\nShape_Length___________4799.999998357434 | 4800.000003400497___ No fills_________No nulls\nTransectId________________________5715.0 | 6276.0______________ No fills_______Nulls present\nTransOrder________________________2466.0 | 2674.0______________ No fills_______Nulls present\nDD_ID_____________________________190001 | 190209______________ No fills_________No nulls\n\nWARNING: Field(s) ['OBJECTID_1'] in dataframe not included in field_defs.\n"
],
[
"# Create transect FC with fill values - Join values from trans_df to the transect FC as a new file.\ntrans_fc = fwa.JoinDFtoFC(trans_df, extendedTrans, tID_fld, out_fc=trans_name+'_fill')\n\n# Create transect FC with null values\nfwa.CopyFCandReplaceValues(trans_fc, fill, None, out_fc=trans_name+'_null', out_dir=home)\n\n# Save final transect SHP with fill values\nout_shp = arcpy.FeatureClassToFeatureClass_conversion(trans_fc, scratch_dir, trans_name+'_shp.shp')\nprint(\"OUTPUT: {} in specified scratch_dir.\".format(os.path.basename(str(out_shp))))",
"Created met14_trans_fill from input dataframe and extTrans file.\nOUTPUT: met14_trans_null\nOUTPUT: met14_trans_shp.shp in specified scratch_dir.\n"
]
],
[
[
"#### Raster - beach width\nIt may be necessary to close any Arc sessions you have open.",
"_____no_output_____"
]
],
[
[
"# Create a template raster corresponding to the transects. \nif not arcpy.Exists(rst_transID):\n print(\"{} was not found so we will create the base raster.\".format(os.path.basename(rst_transID)))\n outEucAll = arcpy.sa.EucAllocation(extTrans_tidy, maximum_distance=50, cell_size=cell_size, source_field=tID_fld)\n outEucAll.save(os.path.basename(rst_transID))\n\n# Create raster of uBW values by joining trans_df to the template raster.\nout_rst = fwa.JoinDFtoRaster(trans_df, os.path.basename(rst_transID), bw_rst, fill, tID_fld, 'uBW')",
"OUTPUT: met14_ubw. Field \"Value\" is ID and \"uBW\" is beachwidth.\n"
]
],
[
[
"### 5-m points\n\nOutput the point metrics in the following formats:\n- tabular, in CSV\n- populated points with fill values as gdb feature class\n- populated points with null values as gdb feature class\n- populated points with fill values as shapefile",
"_____no_output_____"
]
],
[
[
"# Load the saved dataframes\npts_df4csv = pd.read_pickle(os.path.join(scratch_dir, pts_name+'_csv.pkl'))\npts_df = pd.read_pickle(os.path.join(scratch_dir, pts_name+'_null.pkl'))",
"_____no_output_____"
],
[
"pts_df4csv['Construction'] = 111\npts_df4csv['Nourishment'] = 111\npts_df4csv['Development'] = 111\n\npts_df['Construction'] = 111\npts_df['Nourishment'] = 111\npts_df['Development'] = 111",
"_____no_output_____"
]
],
[
[
"#### Tabular format",
"_____no_output_____"
]
],
[
[
"# Save CSV in scratch_dir\ncsv_fname = os.path.join(scratch_dir, pts_name +'.csv')\npts_df4csv.to_csv(csv_fname, na_rep=fill, index=False)\n\nsz_mb = os.stat(csv_fname).st_size/(1024.0 * 1024.0)\nprint(\"OUTPUT: {} [{} MB] in specified scratch_dir. \".format(os.path.basename(csv_fname), sz_mb))",
"OUTPUT: met14_pts.csv [16.93202495574951 MB] in specified scratch_dir. \n"
]
],
[
[
"#### Vector format",
"_____no_output_____"
]
],
[
[
"# Convert pts_df to FC - automatically converts NaNs to fills (default fill is -99999)\npts_fc = fwa.DFtoFC_large(pts_df, out_fc=os.path.join(arcpy.env.workspace, pts_name+'_fill'), \n spatial_ref=utmSR, df_id=pID_fld, xy=[\"seg_x\", \"seg_y\"])\n\n# Save final FCs with null values\nfwa.CopyFCandReplaceValues(pts_fc, fill, None, out_fc=pts_name+'_null', out_dir=home)\n\n# Save final points as SHP with fill values\nout_pts_shp = arcpy.FeatureClassToFeatureClass_conversion(pts_fc, scratch_dir, pts_name+'_shp.shp')\nprint(\"OUTPUT: {} in specified scratch_dir.\".format(os.path.basename(str(out_pts_shp))))",
"Converting points DF to FC...\n... converting dataframe to array... \n... converting array to feature class... \n\nOUTPUT: met14_pts_fill\nDuration: 0:2:46.2 seconds\nOUTPUT: met14_pts_null\nOUTPUT: met14_pts_shp.shp in specified scratch_dir.\n"
]
],
[
[
"## Rerun create transects with values\n\n",
"_____no_output_____"
]
]
] | [
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"markdown"
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[
"code"
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[
"markdown"
]
] |
cb18e0a76d415a2057261dd2770c074e00d11da7 | 6,135 | ipynb | Jupyter Notebook | data_download_2018.ipynb | twdatascience/irs990 | cadddd0fe434c9f29ab328a0fc5f56efec953ac6 | [
"MIT"
] | null | null | null | data_download_2018.ipynb | twdatascience/irs990 | cadddd0fe434c9f29ab328a0fc5f56efec953ac6 | [
"MIT"
] | null | null | null | data_download_2018.ipynb | twdatascience/irs990 | cadddd0fe434c9f29ab328a0fc5f56efec953ac6 | [
"MIT"
] | null | null | null | 20.656566 | 98 | 0.49943 | [
[
[
"import numpy as np\nimport pandas as pd\nimport requests\nimport re\nimport json\nfrom os import walk\nfrom multiprocessing.pool import ThreadPool",
"_____no_output_____"
],
[
"URL = \"https://s3.amazonaws.com/irs-form-990/index_2018.json\"\n\nresponse = requests.get(URL)\nwith open(\"index_2018.json\", 'wb') as f:\n f.write(response.content)",
"_____no_output_____"
],
[
"with open(\"index_2018.json\") as f:\n data = json.load(f)\n data = data[list(data.keys())[0]]",
"_____no_output_____"
],
[
"df = pd.DataFrame.from_dict(data)\ndf.tail(5)",
"_____no_output_____"
],
[
"df.shape",
"_____no_output_____"
],
[
"def download_url(url):\n file_name_start = url.rfind('/') + 1\n file_name = url[file_name_start:]\n output_dir = \"/home/meso/git_repo/irs990/data/data_2018/\"\n file_name = output_dir + file_name\n \n r = requests.get(url, stream=True)\n if r.status_code == requests.codes.ok:\n with open(file_name, 'wb') as f:\n f.write(r.content)\n return url\n\nurls = []\nfor i in range(df.shape[0]): \n urls.append(data[i]['URL'])\n \nresults = ThreadPool(100).imap_unordered(download_url, urls)",
"_____no_output_____"
],
[
"f = []\nfor (dirpath, dirnames, filenames) in walk('/home/meso/git_repo/irs990/data/data_2018/'):\n f.extend(filenames)\n break",
"_____no_output_____"
],
[
"u = list(df['URL'])\nlen(u)",
"_____no_output_____"
],
[
"file_names_list = []\n\nfor i in range(len(u)):\n file_loc = u[i].rfind(\"/\") + 1\n file_name = u[i][file_loc:]\n file_names_list.append(file_name)",
"_____no_output_____"
],
[
"print(len(file_names_list), str(\":\"), len(f))",
"_____no_output_____"
],
[
"diff = list(set(file_names_list) - set(f))\nlen(diff)",
"_____no_output_____"
],
[
"file_loc = u[i].rfind(\"/\") + 1\nurl_pre = u[0][:file_loc]",
"_____no_output_____"
],
[
"urls = []\nfor i in range(len(diff)):\n url = url_pre + diff[i]\n urls.append(url)",
"_____no_output_____"
],
[
"for url in urls:\n download_url(url)",
"_____no_output_____"
],
[
"f = []\nfor (dirpath, dirnames, filenames) in walk('/home/meso/git_repo/irs990/data/data_2018/'):\n f.extend(filenames)\n break",
"_____no_output_____"
],
[
"len(f)",
"_____no_output_____"
],
[
"diff = list(set(file_names_list) - set(f))\nlen(diff)",
"_____no_output_____"
],
[
"def list_duplicates(seq):\n seen = set()\n seen_add = seen.add\n return [idx for idx, item in enumerate(seq) if item in seen or seen_add(item)]",
"_____no_output_____"
],
[
"print(list_duplicates(file_names_list))",
"_____no_output_____"
],
[
"dup_df = df.iloc[list_duplicates(file_names_list)]",
"_____no_output_____"
],
[
"dup_ein = pd.DataFrame(dup_df['EIN'])",
"_____no_output_____"
],
[
"dup_ein",
"_____no_output_____"
],
[
"duplicates = pd.DataFrame([])\n\nfor dup in range(dup_ein.shape[0]):\n ein = dup_ein.iloc[dup]['EIN']\n duplicates = duplicates.append(df[df['EIN'] == ein])",
"_____no_output_____"
],
[
"duplicates",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code",
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]
] |
cb18e0ac7e6bcbe76a44f4858e6ace9fd5d64f0e | 98,038 | ipynb | Jupyter Notebook | [02 - Modeling]/dos ver 5.2/router fetch/wat-r8-good.ipynb | chamikasudusinghe/nocml | d414da54e042d6f7505b81135882d6f1bd02f166 | [
"MIT"
] | null | null | null | [02 - Modeling]/dos ver 5.2/router fetch/wat-r8-good.ipynb | chamikasudusinghe/nocml | d414da54e042d6f7505b81135882d6f1bd02f166 | [
"MIT"
] | null | null | null | [02 - Modeling]/dos ver 5.2/router fetch/wat-r8-good.ipynb | chamikasudusinghe/nocml | d414da54e042d6f7505b81135882d6f1bd02f166 | [
"MIT"
] | null | null | null | 35.559666 | 118 | 0.221129 | [
[
[
"import pandas as pd",
"_____no_output_____"
],
[
"df = pd.read_csv('wat-good.csv')\ndf = df.loc[df['router'] == 8]\ndf = df.drop(columns=['router'])\ndf.to_csv('wat-r8-good.csv',index=False)",
"_____no_output_____"
],
[
"df = pd.read_csv('wat-r8-good.csv')\ndf",
"_____no_output_____"
],
[
"timearr = []\ninterval = 99\ncount = 0\nfor index, row in df.iterrows():\n if row[\"timestamp\"]<=interval:\n count+=1\n else:\n timearr.append([interval+1,count])\n count=1\n interval+=100\ntimearr.append([interval+1,count])",
"_____no_output_____"
],
[
"countarr = []\nincrearr = []\nmaxarr = []\nfor i in range(len(timearr)):\n for cnt in range(timearr[i][1],0,-1):\n countarr.append(cnt)\n maxarr.append(timearr[i][1])\n increment = timearr[i][1] - cnt + 1\n increarr.append(increment)\nprint(len(countarr))",
"20209\n"
],
[
"df",
"_____no_output_____"
],
[
"df = df.assign(packet_count_decr=countarr)\ndf = df.assign(packet_count_incr=increarr)\ndf = df.assign(max_packet_count=maxarr)\ndf[\"packet_count_index\"] = df[\"packet_count_decr\"]*df[\"packet_count_incr\"]\ndf[\"packet_max_index\"] = df[\"packet_count_index\"]*df[\"max_packet_count\"]\ndf[\"port_index\"] = df[\"outport\"]*df[\"inport\"]\ndf[\"cache_coherence_flit_index\"] = df[\"cache_coherence_type\"]*df[\"flit_id\"]\ndf[\"flit_index\"] = df[\"cache_coherence_flit_index\"]*df[\"flit_type\"]\ndf[\"traversal_index\"] = df[\"flit_index\"]*df[\"traversal_id\"]\ndf[\"cache_coherence_vnet_index\"] = df[\"cache_coherence_type\"]*df[\"vnet\"]\ndf[\"vnet_vc_index\"] = df[\"vnet\"]*df[\"vc\"]\ndf[\"vnet_vc_cc_index\"] = df[\"vnet\"]*df[\"cache_coherence_vnet_index\"]\n\ndf.head(50)",
"_____no_output_____"
],
[
"#df[\"packet_types\"] = df[\"packet_type\"]\n#df=pd.get_dummies(df, prefix=['outport', 'inport', 'packet_type'], columns=['outport', 'inport','packet_type'])",
"_____no_output_____"
],
[
"df.dtypes",
"_____no_output_____"
],
[
"#df['inport_1'] = 0\n#df['inport_3'] = 0\n#df['outport_3'] = 0",
"_____no_output_____"
],
[
"df['target'] = 1\ndf",
"_____no_output_____"
],
[
"df.to_csv('wat-r8-good.csv',index=False)",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code"
]
] |
cb1903dda4484c39310717f8b3612d3c75986c38 | 34,141 | ipynb | Jupyter Notebook | analysis/msSpeedComparison.ipynb | Theys96/GalaxySim | b208dd4562dc6628ceac0e2d40f0f7e9561a9438 | [
"BSD-3-Clause"
] | 3 | 2019-09-10T22:08:03.000Z | 2021-05-29T03:55:10.000Z | analysis/msSpeedComparison.ipynb | Theys96/GalaxySim | b208dd4562dc6628ceac0e2d40f0f7e9561a9438 | [
"BSD-3-Clause"
] | null | null | null | analysis/msSpeedComparison.ipynb | Theys96/GalaxySim | b208dd4562dc6628ceac0e2d40f0f7e9561a9438 | [
"BSD-3-Clause"
] | null | null | null | 277.569106 | 16,128 | 0.926393 | [
[
[
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt",
"_____no_output_____"
],
[
"barneshut = pd.read_csv('compTime_barneshut.csv', sep=',')\nms = pd.read_csv('compTime_ms.csv', sep=',')\n\nbarneshut_avg = barneshut.groupby('n').mean()\nms_avg = ms.groupby('n').mean()\n\nbarneshut_avg = np.column_stack((barneshut_avg.index.to_numpy(), barneshut_avg.to_numpy()))\nms_avg = np.column_stack((ms_avg.index.to_numpy(), ms_avg.to_numpy()))",
"_____no_output_____"
],
[
"fig = plt.figure(figsize=(7, 4))\nax1 = fig.add_subplot(111)\nax1.set_xlabel(r\"$N$\")\nax1.set_ylabel(\"ms\")\nax1.set_title(r\"Average computation time (1 iteration) for a varying number of bodies $N$\"+\"\\nMost-Significant-Bodies Method\")\nax1.scatter(ms_avg[:,0], ms_avg[:,1], c='r', marker=\"x\", label='Most-Significant-Bodies')\nfig.savefig('ms_time_comparison1.pdf')\nplt.show()",
"_____no_output_____"
],
[
"barneshut = pd.read_csv('compTime_barneshut.csv', sep=',')\nms = pd.read_csv('compTime_ms.csv', sep=',')\n\nfig = plt.figure(figsize=(7, 4))\nax1 = fig.add_subplot(111)\nax1.set_xlabel(r\"$N$\")\nax1.set_ylabel(\"ms\")\nax1.set_title(r\"Average computation time (1 iteration) for a varying number of bodies $N$\")\nax1.scatter(barneshut_avg[:,0], barneshut_avg[:,1], c='b', marker=\"x\", label=r'Barnes-Hut | $\\theta=0.5$')\nax1.scatter(ms_avg[:,0], ms_avg[:,1], c='r', marker=\"x\", label='Most-Significant-Bodies')\nax1.legend()\nfig.savefig('ms_time_comparison2.pdf')\nplt.show()",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
"code"
]
] |
cb190ceb0c3965ba82463fd37e826629d5d9039d | 164,243 | ipynb | Jupyter Notebook | Lab_1/Lab_1_code.ipynb | Szymon-Budziak/MOwNiT | 14955e4e22844be9f52b7723e898c56e3c16a2fe | [
"MIT"
] | null | null | null | Lab_1/Lab_1_code.ipynb | Szymon-Budziak/MOwNiT | 14955e4e22844be9f52b7723e898c56e3c16a2fe | [
"MIT"
] | null | null | null | Lab_1/Lab_1_code.ipynb | Szymon-Budziak/MOwNiT | 14955e4e22844be9f52b7723e898c56e3c16a2fe | [
"MIT"
] | null | null | null | 179.304585 | 50,900 | 0.907491 | [
[
[
"# MOwNiT – arytmetyka komputerowa",
"_____no_output_____"
],
[
"",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport matplotlib.pyplot as plt",
"_____no_output_____"
],
[
"x1 = 4\nn = 30",
"_____no_output_____"
],
[
"def visualize(points):\n plt.figure(figsize=(12,6))\n plt.axhline(y=3.14159, color='r', linestyle='--')\n plt.xlabel(\"Xi\")\n plt.ylabel(\"points\")\n plt.plot(points, marker=\"o\", markersize=10)\n plt.show()",
"_____no_output_____"
]
],
[
[
"## Informacje o używanych typach danych:",
"_____no_output_____"
]
],
[
[
"print(np.finfo(np.float32))",
"Machine parameters for float32\n---------------------------------------------------------------\nprecision = 6 resolution = 1.0000000e-06\nmachep = -23 eps = 1.1920929e-07\nnegep = -24 epsneg = 5.9604645e-08\nminexp = -126 tiny = 1.1754944e-38\nmaxexp = 128 max = 3.4028235e+38\nnexp = 8 min = -max\nsmallest_normal = 1.1754944e-38 smallest_subnormal = 1.4012985e-45\n---------------------------------------------------------------\n\n"
],
[
"print(np.finfo(np.double))",
"Machine parameters for float64\n---------------------------------------------------------------\nprecision = 15 resolution = 1.0000000000000001e-15\nmachep = -52 eps = 2.2204460492503131e-16\nnegep = -53 epsneg = 1.1102230246251565e-16\nminexp = -1022 tiny = 2.2250738585072014e-308\nmaxexp = 1024 max = 1.7976931348623157e+308\nnexp = 11 min = -max\nsmallest_normal = 2.2250738585072014e-308 smallest_subnormal = 4.9406564584124654e-324\n---------------------------------------------------------------\n\n"
],
[
"print(np.finfo(np.longdouble))",
"Machine parameters for float128\n---------------------------------------------------------------\nprecision = 18 resolution = 1e-18\nmachep = -63 eps = 1.084202172485504434e-19\nnegep = -64 epsneg = 5.42101086242752217e-20\nminexp = -16382 tiny = 3.3621031431120935063e-4932\nmaxexp = 16384 max = 1.189731495357231765e+4932\nnexp = 15 min = -max\nsmallest_normal = 3.3621031431120935063e-4932 smallest_subnormal = 4e-4951\n---------------------------------------------------------------\n\n"
]
],
[
[
"## Rozwiązanie dla liczb typu float:",
"_____no_output_____"
]
],
[
[
"def float_32_sequence_default():\n res = [np.float32(0) for _ in range(n)]\n res[0] = np.float32(x1)\n for k in range(n-1):\n calc_sqrt = np.sqrt(1 + (res[k]**2 / 2**(2*(k+2))))\n new_xk = 2**(2*(k+2)+1) * ((calc_sqrt - 1)/res[k])\n res[k+1] = np.float32(new_xk)\n return res",
"_____no_output_____"
],
[
"def float_32_sequence_reshaped():\n res = [np.float32(0) for _ in range(n)]\n res[0] = np.float32(x1)\n for k in range(n-1):\n calc_sqrt = np.sqrt(1 + (res[k]**2 / 2**(2*(k+2))))\n new_xk = 2*res[k]/(calc_sqrt+1)\n res[k+1] = np.float32(new_xk)\n return res",
"_____no_output_____"
]
],
[
[
"### Porównanie wyników oraz ich wizualizacja:",
"_____no_output_____"
],
[
"**Dla nieprzekształconego wzoru:**",
"_____no_output_____"
]
],
[
[
"f32_def_res = float_32_sequence_default()\nf32_def_res",
"<ipython-input-7-d14d9162c22d>:6: RuntimeWarning: invalid value encountered in double_scalars\n new_xk = 2**(2*(k+2)+1) * ((calc_sqrt - 1)/res[k])\n"
],
[
"f32_def_res[-1]",
"_____no_output_____"
],
[
"visualize(f32_def_res)",
"_____no_output_____"
]
],
[
[
"**Dla przekształconego wzoru:**",
"_____no_output_____"
]
],
[
[
"f32_resh_res = float_32_sequence_reshaped()\nf32_resh_res",
"_____no_output_____"
],
[
"f32_resh_res[-1]",
"_____no_output_____"
],
[
"visualize(f32_resh_res)",
"_____no_output_____"
]
],
[
[
"## Rozwiązanie dla liczb typu double:",
"_____no_output_____"
]
],
[
[
"def double_sequence_default():\n res = [np.double(0) for _ in range(n)]\n res[0] = np.double(x1)\n for k in range(n-1):\n calc_sqrt = np.sqrt(1 + (res[k]**2 / 2**(2*(k+2))))\n new_xk = 2**(2*(k+2)+1) * ((calc_sqrt - 1)/res[k])\n res[k+1] = np.double(new_xk)\n return res",
"_____no_output_____"
],
[
"def double_sequence_reshaped():\n res = [np.double(0) for _ in range(n)]\n res[0] = np.double(x1)\n for k in range(n-1):\n calc_sqrt = np.sqrt(1 + (res[k]**2 / 2**(2*(k+2))))\n new_xk = 2*res[k]/(calc_sqrt+1)\n res[k+1] = np.double(new_xk)\n return res",
"_____no_output_____"
]
],
[
[
"### Porównanie wyników:",
"_____no_output_____"
],
[
"**Dla nieprzekształconego wzoru:**",
"_____no_output_____"
]
],
[
[
"doub_def_res = double_sequence_default()\ndoub_def_res",
"<ipython-input-15-d7e301c33859>:6: RuntimeWarning: invalid value encountered in double_scalars\n new_xk = 2**(2*(k+2)+1) * ((calc_sqrt - 1)/res[k])\n"
],
[
"doub_def_res[-1]",
"_____no_output_____"
],
[
"visualize(doub_def_res)",
"_____no_output_____"
]
],
[
[
"**Dla przekształconego wzoru:**",
"_____no_output_____"
]
],
[
[
"doub_resh_res = double_sequence_reshaped()\ndoub_resh_res",
"_____no_output_____"
],
[
"doub_resh_res[-1]",
"_____no_output_____"
],
[
"visualize(doub_resh_res)",
"_____no_output_____"
]
],
[
[
"## Rozwiązanie dla liczb typu long double:",
"_____no_output_____"
]
],
[
[
"def longdouble_sequence_default():\n res = [np.longdouble(0) for _ in range(n)]\n res[0] = np.longdouble(x1)\n for k in range(n-1):\n calc_sqrt = np.sqrt(1 + (res[k]**2 / 2**(2*(k+2))))\n new_xk = 2**(2*(k+2)+1) * ((calc_sqrt - 1)/res[k])\n res[k+1] = np.longdouble(new_xk)\n return res",
"_____no_output_____"
],
[
"def longdouble_sequence_reshaped():\n res = [np.longdouble(0) for _ in range(n)]\n res[0] = np.longdouble(x1)\n for k in range(n-1):\n calc_sqrt = np.sqrt(1 + (res[k]**2 / 2**(2*(k+2))))\n new_xk = 2*res[k]/(calc_sqrt+1)\n res[k+1] = np.longdouble(new_xk)\n return res",
"_____no_output_____"
]
],
[
[
"### Porównanie wyników:",
"_____no_output_____"
],
[
"**Dla nieprzekształconego wzoru:**",
"_____no_output_____"
]
],
[
[
"l_doub_def_res = longdouble_sequence_default()\nl_doub_def_res",
"_____no_output_____"
],
[
"l_doub_def_res[-1]",
"_____no_output_____"
],
[
"visualize(l_doub_def_res)",
"_____no_output_____"
]
],
[
[
"**Dla przekształconego wzoru:**",
"_____no_output_____"
]
],
[
[
"l_doub_resh_res = longdouble_sequence_reshaped()\nl_doub_resh_res",
"_____no_output_____"
],
[
"l_doub_resh_res[-1]",
"_____no_output_____"
],
[
"visualize(l_doub_resh_res)",
"_____no_output_____"
]
]
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cb1912df067f2c70b0b44902df6b935df5123bad | 37,275 | ipynb | Jupyter Notebook | examples/Evolve_one_planet.ipynb | soumitrahazra/platypos | f4042f42a1727ff40e7c713656ef8760ba943a03 | [
"MIT"
] | 10 | 2020-05-11T16:12:04.000Z | 2022-02-27T00:47:19.000Z | examples/Evolve_one_planet.ipynb | lketzer/platypos | f4042f42a1727ff40e7c713656ef8760ba943a03 | [
"MIT"
] | null | null | null | examples/Evolve_one_planet.ipynb | lketzer/platypos | f4042f42a1727ff40e7c713656ef8760ba943a03 | [
"MIT"
] | 1 | 2021-11-29T23:39:59.000Z | 2021-11-29T23:39:59.000Z | 65.28021 | 21,144 | 0.748035 | [
[
[
"import sys\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom astropy import constants as const\n\n# remove this line if you installed platypos with pip\nsys.path.append('/work2/lketzer/work/gitlab/platypos_group/platypos/')\nimport platypos\nfrom platypos import Planet_LoFo14\nfrom platypos import Planet_Ot20\n# import the classes with fixed step size for completeness\nfrom platypos.planet_LoFo14_PAPER import Planet_LoFo14_PAPER\nfrom platypos.planet_Ot20_PAPER import Planet_Ot20_PAPER\nimport platypos.planet_models_LoFo14 as plmoLoFo14",
"_____no_output_____"
]
],
[
[
"# Create Planet object and stellar evolutionary track",
"_____no_output_____"
],
[
"## Example planet 1.1 - V1298Tau c with 5 Eearth mass core and measured radius (var. step)",
"_____no_output_____"
]
],
[
[
"# (David et al. 2019, Chandra observation)\nL_bol, mass_star, radius_star = 0.934, 1.101, 1.345 # solar units\nage_star = 23. # Myr\nLx_age = Lx_chandra = 1.3e30 # erg/s in energy band: (0.1-2.4 keV)\nLx_age_error = 1.4e29\n\n# use dictionary to store star-parameters\nstar_V1298Tau = {'star_id': 'V1298Tau', 'mass': mass_star, 'radius': radius_star, 'age': age_star, 'L_bol': L_bol, 'Lx_age': Lx_age}\n\nLx_1Gyr, Lx_5Gyr = 2.10*10**28, 1.65*10**27\ntrack_low = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": star_V1298Tau[\"age\"], \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \n \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 20., \"Lx_drop_factor\": 16.}\ntrack_med = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": star_V1298Tau[\"age\"], \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \n \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\ntrack_high = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": 240., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \n \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n\n# planet c\nplanet = {\"core_mass\": 5.0, \"radius\": 5.59, \"distance\": 0.0825, \"metallicity\": \"solarZ\"}\n\npl = Planet_LoFo14(star_V1298Tau, planet)\npl.__dict__",
"_____no_output_____"
]
],
[
[
"### Example planet 1.1.1 - V1298Tau c with 5 Eearth mass core and measured radius (fixed step)",
"_____no_output_____"
]
],
[
[
"pl = Planet_LoFo14_PAPER(star_V1298Tau, planet)",
"_____no_output_____"
]
],
[
[
"## Example planet 1.2 - V1298Tau c with mass estimate from Otegi et al. (2020) and measured radius (var step)",
"_____no_output_____"
]
],
[
[
"pl = Planet_Ot20(star_V1298Tau, planet)\npl.__dict__",
"_____no_output_____"
]
],
[
[
"### Example planet 1.2.1 - V1298Tau c with mass estimate from Otegi et al. (2020) and measured radius (fixed step)",
"_____no_output_____"
]
],
[
[
"pl = Planet_Ot20_PAPER(star_V1298Tau, planet)\npl.__dict__",
"_____no_output_____"
]
],
[
[
"## Example planet 2 - artificial planet with specified core mass and envelope mass fraction",
"_____no_output_____"
]
],
[
[
"Lx_1Gyr, Lx_5Gyr = 2.10*10**28, 1.65*10**27\n\ndict_star = {'star_id': 'star_age1.0_mass0.89',\n 'mass': 0.8879632311581124,\n 'radius': None,\n 'age': 1.0,\n 'L_bol': 1.9992811847525246e+33/const.L_sun.cgs.value,\n 'Lx_age': 1.298868513129789e+30}\n\ndict_pl = {'distance': 0.12248611607793611,\n 'metallicity': 'solarZ',\n'fenv': 3.7544067802231664,\n 'core_mass': 4.490153906104026}\n\ntrack = {\"t_start\": dict_star[\"age\"], \"t_sat\": 100., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \n \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n\npl = Planet_LoFo14(dict_star, dict_pl)\n#pl.__dict__",
"_____no_output_____"
]
],
[
[
"# Evolve & create outputs",
"_____no_output_____"
]
],
[
[
"%%time\n\nfolder_id = \"dummy\"\npath_save = os.getcwd() + \"/\" + folder_id +\"/\"\nif not os.path.exists(path_save):\n os.makedirs(path_save)\nelse:\n os.system(\"rm -r \" + path_save[:-1])\n os.makedirs(path_save)\n\nt_final = 5007.\npl.evolve_forward_and_create_full_output(t_final, 0.1, 0.1, \"yes\", \"yes\", track_high, path_save, folder_id)",
"CPU times: user 14.8 s, sys: 11.8 ms, total: 14.8 s\nWall time: 14.8 s\n"
]
],
[
[
"# Read in results and plot",
"_____no_output_____"
]
],
[
[
"df_pl = pl.read_results(path_save)\ndf_pl.head()",
"_____no_output_____"
],
[
"df_pl.tail()",
"_____no_output_____"
],
[
"# fig, ax = plt.subplots(figsize=(10,5))\n# ax.plot(df_pl[\"Time\"], df_pl[\"Lx\"])\n# ax.loglog()\n# plt.show()",
"_____no_output_____"
],
[
"fig, ax = plt.subplots(figsize=(10,5))\nage_arr = np.logspace(np.log10(pl.age), np.log10(t_final), 100)\n\nif (type(pl) == platypos.planet_LoFo14.Planet_LoFo14\n or type(pl) == platypos.planet_LoFo14_PAPER.Planet_LoFo14_PAPER):\n ax.plot(age_arr, plmoLoFo14.calculate_planet_radius(pl.core_mass, pl.fenv, age_arr, pl.flux, pl.metallicity), \\\n lw=2.5, label='thermal contraction only', color=\"blue\")\n ax.plot(df_pl[\"Time\"], df_pl[\"Radius\"], \n marker=\"None\", ls=\"--\", label='with photoevaporation', color=\"red\")\nelse:\n ax.plot(df_pl[\"Time\"], df_pl[\"Radius\"], marker=\"None\", ls=\"--\", label='with photoevaporation', color=\"red\")\n\nax.legend(fontsize=10)\nax.set_xlabel(\"Time [Myr]\", fontsize=16)\nax.set_ylabel(\"Radius [R$_\\oplus$]\", fontsize=16)\nax.set_xscale('log')\n#ax.set_ylim(5.15, 5.62)\nplt.show()",
"_____no_output_____"
]
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cb1915e53490f6487698c1d00e62368e380ad002 | 313 | ipynb | Jupyter Notebook | lab_2/.ipynb_checkpoints/Untitled-checkpoint.ipynb | MohamedBakrAli/Data-mining | b3b81503692374ceccfc8110be952450e33f7ca0 | [
"MIT"
] | null | null | null | lab_2/.ipynb_checkpoints/Untitled-checkpoint.ipynb | MohamedBakrAli/Data-mining | b3b81503692374ceccfc8110be952450e33f7ca0 | [
"MIT"
] | null | null | null | lab_2/.ipynb_checkpoints/Untitled-checkpoint.ipynb | MohamedBakrAli/Data-mining | b3b81503692374ceccfc8110be952450e33f7ca0 | [
"MIT"
] | null | null | null | 14.904762 | 45 | 0.517572 | [] | [] | [] |
cb1946d4811b597dbcaa6b5dab9b258970ad2428 | 135,128 | ipynb | Jupyter Notebook | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences | 3de61733433c3a214c4ddd116bc1c785f9e49674 | [
"MIT"
] | 1 | 2020-04-06T04:17:27.000Z | 2020-04-06T04:17:27.000Z | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | quantaosun/DeepLearningLifeSciences | 258066f904159a7c1c81aba16e74ae4e6b4263b5 | [
"MIT"
] | null | null | null | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | quantaosun/DeepLearningLifeSciences | 258066f904159a7c1c81aba16e74ae4e6b4263b5 | [
"MIT"
] | 1 | 2020-02-16T23:43:16.000Z | 2020-02-16T23:43:16.000Z | 188.200557 | 22,392 | 0.895603 | [
[
[
"### Prepare the Dataset for Building a Predictive Model\n\nAs a first step we will build a graph convolution model predict ERK2 activity. We will train the model to distinguish a set of ERK2 active compounds from a set of decoy compounds. The active and decoy compounds are derived from the DUD-E database. In order to generate the best model, we would like to decoys with property distributions similar to those of our active compounds. Let's say this was not the case and the inactive compounds had lower molecular weight than the active compounds. In this case our classifer may be trained to simply separate low molecular compounds from \nhigh molecular weight compounds. This classifer will have very limited utility in preactice. \n\nAs a first step, we will examine a few calculated properties of our active and decoy molecules. In order to build a reliable model, we need to ensure that the properties of the active molecules are similar to those of the decoy molecules. \n\nFirst lets import the libraries we will need. ",
"_____no_output_____"
]
],
[
[
"from rdkit import Chem\nfrom rdkit.Chem import Draw\nfrom rdkit.Chem.Draw import IPythonConsole\nimport pandas as pd\nfrom rdkit.Chem import PandasTools\nfrom rdkit.Chem import Descriptors\nfrom rdkit.Chem import rdmolops\nimport seaborn as sns",
"_____no_output_____"
]
],
[
[
"Now we can read a SMILES file into a Pandas dataframe and add an RDKit molecule to the dataframe.",
"_____no_output_____"
]
],
[
[
"active_df = pd.read_csv(\"mk01/actives_final.ism\",header=None,sep=\" \")\nactive_rows,active_cols = active_df.shape\nactive_df.columns = [\"SMILES\",\"ID\",\"ChEMBL_ID\"]\nactive_df[\"label\"] = [\"Active\"]*active_rows\nPandasTools.AddMoleculeColumnToFrame(active_df,\"SMILES\",\"Mol\")\n",
"_____no_output_____"
]
],
[
[
"Let's define a function to add caculated properties to a dataframe",
"_____no_output_____"
]
],
[
[
"def add_property_columns_to_df(df_in):\n df_in[\"mw\"] = [Descriptors.MolWt(mol) for mol in df_in.Mol]\n df_in[\"logP\"] = [Descriptors.MolLogP(mol) for mol in df_in.Mol]\n df_in[\"charge\"] = [rdmolops.GetFormalCharge(mol) for mol in df_in.Mol]",
"_____no_output_____"
]
],
[
[
"With this function in hand, we can calculate the molecular weight, LogP and formal charge of the molecules. Once we have these properties we can compare the distributions for the active and decoy sets. ",
"_____no_output_____"
]
],
[
[
"add_property_columns_to_df(active_df)",
"_____no_output_____"
]
],
[
[
"Let's look at the frist few rows of our dataframe to ensure that it makes sense.",
"_____no_output_____"
]
],
[
[
"active_df.head()",
"_____no_output_____"
]
],
[
[
"Now let's do the same thing with the decoy molecules",
"_____no_output_____"
]
],
[
[
"decoy_df = pd.read_csv(\"mk01/decoys_final.ism\",header=None,sep=\" \")\ndecoy_df.columns = [\"SMILES\",\"ID\"]\ndecoy_rows, decoy_cols = decoy_df.shape\ndecoy_df[\"label\"] = [\"Decoy\"]*decoy_rows\nPandasTools.AddMoleculeColumnToFrame(decoy_df,\"SMILES\",\"Mol\")\nadd_property_columns_to_df(decoy_df)",
"_____no_output_____"
],
[
"tmp_df = active_df.append(decoy_df)",
"_____no_output_____"
]
],
[
[
"With properties calculated for both the active and the decoy sets, we can compare the properties of the two compound sets. To do the comparison, we will use violin plots. A violin plot can be thought of as analogous to a boxplot. The violin plot provides a mirrored, horizontal view of a frequency distribution. Ideally, we would \nlike to see similar distributions for the active and decoy sets.",
"_____no_output_____"
]
],
[
[
"sns.violinplot(tmp_df[\"label\"],tmp_df[\"mw\"])",
"_____no_output_____"
]
],
[
[
"An examination of the distributions in the figures above show that the molecular weight distributions for the two sets\nare roughly equivalent. The decoy set has more low molecular weight molecules, but the center of the distribution, show as a box in the middle of each violin plot is in a similar location in both plots. ",
"_____no_output_____"
],
[
"We can use violin plots to perform a similar comparison of the LogP distributions. Again, we can see that the \ndistributions are similar with a few more decoys at the lower end of the distribution. ",
"_____no_output_____"
]
],
[
[
"sns.violinplot(tmp_df[\"label\"],tmp_df[\"logP\"])",
"_____no_output_____"
]
],
[
[
"Finally, we will do the same comparison with the formal charges of the molecules. ",
"_____no_output_____"
]
],
[
[
"sns.violinplot(tmp_df[\"label\"],tmp_df[\"charge\"])",
"_____no_output_____"
]
],
[
[
"In this case, we see a signficant difference. All of the active molecules are neutral, while some of the decoys \nare charged. Let see what fraction of the decoy molecules are charged. We can do this by creating a new dataframe\nwith just the charged molecules.",
"_____no_output_____"
]
],
[
[
"charged = decoy_df[decoy_df[\"charge\"] != 0]",
"_____no_output_____"
]
],
[
[
"A pandas dataframe has a property, shape, that returns the number of rows and columns in the dataframe. As such,\nelement[0] in the shape property will be the number of rows. Let's divide the number of rows in our dataframe of \ncharged molecules by the total number of rows in the decoy dataframe.",
"_____no_output_____"
]
],
[
[
"charged.shape[0]/decoy_df.shape[0]",
"_____no_output_____"
]
],
[
[
"The fact that 16% of the decoy compounds are charged, while none of the active compounds are is a concern. An examination of both sets indicate that charge states were assigned to the decoys, but not to the active molecules. In order to be consistent, we will use some code from the RDKit Cookbook to neutralize the molecules. First, we will import an RDKit function to neutralize charges.",
"_____no_output_____"
]
],
[
[
"from neutralize import NeutraliseCharges",
"_____no_output_____"
]
],
[
[
"Now we will create a new dataframe with the SMILES, ID, and label for the decoys. ",
"_____no_output_____"
]
],
[
[
"revised_decoy_df = decoy_df[[\"SMILES\",\"ID\",\"label\"]].copy()",
"_____no_output_____"
]
],
[
[
"With this new dataframe in hand, we can replace the SMILES with the SMILES for the neutral form of the molecule. The\nNeutraliseCharges function returns two values. The first is the SMILES for the neutral form of the molecule and the second is a boolean variable indicating whether the molecule was changed. In the code below, we only need the SMILES, so we will use the first element of the tuple returned by NeutraliseCharges.",
"_____no_output_____"
]
],
[
[
"revised_decoy_df[\"SMILES\"] = [NeutraliseCharges(x)[0] for x in revised_decoy_df[\"SMILES\"]]",
"_____no_output_____"
]
],
[
[
"Once we've replaced the SMILES, we can add a molecule column to our new dataframe and calculated properties again. ",
"_____no_output_____"
]
],
[
[
"PandasTools.AddMoleculeColumnToFrame(revised_decoy_df,\"SMILES\",\"Mol\")\nadd_property_columns_to_df(revised_decoy_df)",
"_____no_output_____"
]
],
[
[
"We can now append the dataframe with the active molecules to the one with the revised, neutral decoys and calculate\nanother box plot. ",
"_____no_output_____"
]
],
[
[
"new_tmp_df = active_df.append(revised_decoy_df)",
"_____no_output_____"
],
[
"sns.violinplot(new_tmp_df[\"label\"],new_tmp_df[\"charge\"])",
"_____no_output_____"
]
],
[
[
"An examination of the plot about show that there are very few charged molecules in the decoy set. We can use the same \ntechnique we used above to create a dataframe with only the charged molecules. We can then use this dataframe to determine the number of charged molecules remaining in the set. ",
"_____no_output_____"
]
],
[
[
"charged = revised_decoy_df[revised_decoy_df[\"charge\"] != 0]\ncharged.shape[0]/revised_decoy_df.shape[0]",
"_____no_output_____"
]
],
[
[
"We have now reduced the fraction of charged compounds from 16% to 0.3%. We can now be confident that our active and decoy sets are reasonbly well balanced. ",
"_____no_output_____"
],
[
"In order to use these datasets with DeepChem we need to write the molecules out as a csv file consisting of SMILES, Name, and an integer value indicating whether the compounds are active (labeled as 1) or inactive (labeled as 0).",
"_____no_output_____"
]
],
[
[
"active_df[\"is_active\"] = [1] * active_df.shape[0]\nrevised_decoy_df[\"is_active\"] = [0] * revised_decoy_df.shape[0]\ncombined_df = active_df.append(revised_decoy_df)[[\"SMILES\",\"ID\",\"is_active\"]]\ncombined_df.head()",
"_____no_output_____"
]
],
[
[
"Our final step in this section is to save our new combined_df as a csv file. The index=False option causes Pandas to not include the row number in the first column. ",
"_____no_output_____"
]
],
[
[
"combined_df.to_csv(\"dude_erk1_mk01.csv\")",
"_____no_output_____"
]
]
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cb1948ae121a20dd700ba9e693c436f7ee38092c | 70,044 | ipynb | Jupyter Notebook | training/notebooks/LBP features old.ipynb | MIPT-Oulu/3DHistoGrading | b779a154d0e5b104fc152c8952124768fb7b1dc6 | [
"MIT"
] | 1 | 2021-11-04T18:46:25.000Z | 2021-11-04T18:46:25.000Z | training/notebooks/LBP features old.ipynb | MIPT-Oulu/3DHistoGrading | b779a154d0e5b104fc152c8952124768fb7b1dc6 | [
"MIT"
] | 7 | 2018-08-14T07:35:53.000Z | 2018-09-07T12:17:10.000Z | training/notebooks/LBP features old.ipynb | MIPT-Oulu/3D-Histo-Grading | b779a154d0e5b104fc152c8952124768fb7b1dc6 | [
"MIT"
] | null | null | null | 176.433249 | 37,984 | 0.881175 | [
[
[
"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nimport xlsxwriter\nimport pandas as pd # Excel\nimport struct # Binary writing\n\nimport scipy.io as sio # Read .mat files\nimport h5py\n\nimport time\n\nfrom grading__old import *\n\nfrom ipywidgets import FloatProgress\nfrom IPython.display import display\n\nimport scipy.signal\nimport scipy.ndimage\n\nimport sklearn.metrics as skmet\nimport sklearn.decomposition as skdec\nimport sklearn.linear_model as sklin\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.model_selection import LeaveOneOut\nfrom sklearn.model_selection import KFold\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.preprocessing import normalize\nfrom sklearn import svm\nfrom sklearn import neighbors",
"C:\\Users\\sarytky\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n"
],
[
"def pipeline_lbp(impath, savepath, save, dtype='dat'):\n #Start time\n start_time = time.time()\n # Calculate MRELBP from dataset\n # Parameters\n dict = {'N':8, 'R':9,'r':3,'wc':5,'wr':(5,5)}\n mapping = getmapping(dict['N']) # mapping\n\n files = os.listdir(impath)\n files.sort()\n #print(files[32 * 2])\n #files.pop(32 * 2)\n #files.pop(32 * 2)\n #print(files)\n\n features = None # Reset feature array\n\n p = FloatProgress(min=0, max=len(files), description='Features:')\n display(p)\n for k in range(len(files)):\n #Load file\n if dtype == 'dat':\n p.value += 2\n if k > len(files) / 2 - 1:\n break\n file = os.path.join(impath,files[2 * k])\n try:\n Mz = loadbinary(file, np.float64)\n except:\n continue\n file = os.path.join(impath,files[2 * k + 1])\n try:\n sz = loadbinary(file, np.float64)\n except:\n continue\n else:\n file = os.path.join(impath,files[k])\n p.value += 1\n try:\n file = sio.loadmat(file)\n Mz = file['Mz']\n sz = file['sz']\n except NotImplementedError:\n file = h5py.File(file)\n Mz = file['Mz'][()]\n sz = file['sz'][()]\n \n #Combine mean and sd images\n image = Mz+sz\n #Grayscale normalization\n image = localstandard(image,23,5,5,1)\n # LBP\n Chist,Lhist,Shist,Rhist, lbpIL, lbpIS, lbpIR = MRELBP(image,dict['N'],dict['R'],dict['r'],dict['wc'],dict['wr'])\n f1 = Chist\n f2 = maplbp(Lhist,mapping)\n f3 = maplbp(Shist,mapping)\n f4 = maplbp(Rhist,mapping)\n #Concatenate features\n f = np.concatenate((f1.T,f2.T,f3.T,f4.T),axis=0)\n try:\n features = np.concatenate((features,f),axis=1)\n except ValueError:\n features = f\n # Save images\n if dtype == 'dat':\n cv2.imwrite(savepath + '\\\\' + files[2 * k][:-9] + '.png', lbpIS)\n else:\n cv2.imwrite(savepath + '\\\\' + files[k][:-9] + '.png', lbpIS)\n\n # Plot LBP images\n #plt.imshow(lbpIS); plt.show()\n #plt.imshow(lbpIL); plt.show()\n #plt.imshow(lbpIR); plt.show()\n\n # Save features\n writer = pd.ExcelWriter(save + r'\\LBP_features_python.xlsx')\n df1 = pd.DataFrame(features)\n df1.to_excel(writer, sheet_name='LBP_features')\n writer.save()\n \n t = time.time()-start_time\n print('Elapsed time: {0}s'.format(t))\n \n \ndef pipeline_load(featurepath, gpath, save, choice):\n #Start time\n start_time = time.time()\n # Load grades to array\n grades = pd.read_excel(gpath, 'Sheet1')\n grades = pd.DataFrame(grades).values\n fnames = grades[:,0].astype('str')\n g = list(grades[:,choice].astype('int'))\n #g.pop(32)\n g = np.array(g)\n print('Max grade: {0}, min grade: {1}'.format(max(g), min(g)))\n \n # Load features\n features = pd.read_excel(featurepath, 'LBP_features')\n features = pd.DataFrame(features).values.astype('int')\n print(features.shape)\n \n #PCA\n # PCA parameters: whitening, svd solver (auto/full)\n pca, score = ScikitPCA(features.T, 10, True, 'auto')\n #pca, score = PCA(features,10)\n print(score[0,:])\n print(score.shape)\n\n # Regression\n if min(g) > 0:\n g = g - min(g)\n pred1 = regress(score, g)\n pred2 = logreg(score, g>min(g))\n for p in range(len(pred1)):\n if pred1[p]<0:\n pred1[p] = 0\n if pred1[p] > max(g):\n pred1[p]=max(g)\n\n #Plotting PCA\n a = g\n b = np.round(pred1).astype('int')\n\n # ROC curve\n C1 = skmet.confusion_matrix(a,b)\n MSE1 = skmet.mean_squared_error(a,pred1)\n fpr, tpr, thresholds = skmet.roc_curve(a>0, np.round(pred1)>0, pos_label=1)\n AUC1 = skmet.auc(fpr,tpr)\n AUC2 = skmet.roc_auc_score(a>0,pred2)\n m, b = np.polyfit(a, pred1.flatten(), 1)\n R2 = skmet.r2_score(a,pred1.flatten())\n fig0 = plt.figure(figsize=(6,6))\n ax0 = fig0.add_subplot(111)\n ax0.plot(fpr,tpr)\n \n # Save prediction\n stats = np.zeros(len(g))\n stats[0] = MSE1\n stats[1] = AUC1\n stats[2] = AUC2\n tuples = list(zip(fnames, g, pred1[:,0], abs(g - pred1[:,0]), pred2, stats))\n writer = pd.ExcelWriter(save + r'\\prediction_python.xlsx')\n df1 = pd.DataFrame(tuples, columns=['Sample', 'Actual grade', 'Prediction', 'Difference', 'Logistic prediction', 'MSE, AUC1, AUC2'])\n df1.to_excel(writer, sheet_name='Prediction')\n writer.save()\n\n print('Confusion matrix')\n print(C1)\n print('Mean squared error, Area under curve 1 and 2')\n print(MSE1, AUC1, AUC2)#,MSE2,MSE3,MSE4)\n print('R2 score')\n print(R2)\n #print('Sample, grade, prediction')\n #for k in range(len(fnames)):\n # print(fnames[k],a[k],pred1[k])#,pred3[k])\n \n x = score[:,0]\n y = score[:,1]\n fig = plt.figure(figsize=(6,6))\n ax1 = fig.add_subplot(111)\n ax1.scatter(score[g<2,0],score[g<2,1],marker='o',color='b',label='Normal')\t\n ax1.scatter(score[g>1,0],score[g>1,1],marker='s',color='r',label='OA')\n for k in range(len(g)):\n txt = fnames[k][0:-4]+str(g[k])\n if g[k] >= 2:\n ax1.scatter(x[k],y[k],marker='s',color='r')\n else:\n ax1.scatter(x[k],y[k],marker='o',color='b')\n\n # Scatter plot actual vs prediction\n fig = plt.figure(figsize=(6,6))\n ax2 = fig.add_subplot(111)\n ax2.scatter(a,pred1.flatten())\n ax2.plot(a,m*a,'-',color='r')\n ax2.set_xlabel('Actual grade')\n ax2.set_ylabel('Predicted')\n for k in range(len(g)):\n txt = fnames[k]\n txt = txt+str(g[k])\n ax2.annotate(txt,xy=(a[k],pred1[k]),color='r')\n plt.show()",
"_____no_output_____"
]
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[
[
"### Load features",
"_____no_output_____"
]
],
[
[
"featurepath = r'Z:\\3DHistoData\\Grading\\LBP_features_surface.xlsx'\ngpath = r'Z:\\3DHistoData\\Grading\\PTAgreiditjanaytteet.xls'\nsave = r'Z:\\3DHistoData\\Grading'\n\ntotal = 1\nsurf = 2\ndeep = 5\ncc = 6\ndeepcell = 7\ndeepECM = 8\nccECM = 9\nccVasc = 10\n\nchoice = surf\npipeline_load(featurepath, gpath, save, choice)",
"Max grade: 3, min grade: 0\n(32, 36)\n[-0.61126449 1.55888136 0.81372917 0.47258567 -1.55170516 -1.5287743\n -0.89660894 2.45591342 -0.47300054 0.49480033]\n(36, 10)\nConfusion matrix\n[[ 5 3 0 0]\n [ 1 7 3 0]\n [ 0 1 10 2]\n [ 0 1 3 0]]\nMean squared error, Area under curve 1 and 2\n0.36308781086036107 0.7946428571428572 0.9464285714285714\nR2 score\n0.5953896793851865\n"
]
],
[
[
"### Calculate LBP features from .dat mean and std images",
"_____no_output_____"
]
],
[
[
"impath = r'Z:\\3DHistoData\\SurfaceImages\\Deep'\nimpath = r'V:\\Tuomas\\PTASurfaceImages'\ndtype = 'dat'\ndtype = 'mat'\nsavepath = r'Z:\\3DHistoData\\Grading\\LBP'\nsave = r'Z:\\3DHistoData\\Grading'\n\n\npipeline_lbp(impath, savepath, save, dtype)",
"_____no_output_____"
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cb19512bbccb42ed290e25f6ebf945f4b81b9162 | 14,991 | ipynb | Jupyter Notebook | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience | 3bdd7ae86a907db3339fdd7abc615ecaf5a683a6 | [
"MIT"
] | null | null | null | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience | 3bdd7ae86a907db3339fdd7abc615ecaf5a683a6 | [
"MIT"
] | null | null | null | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience | 3bdd7ae86a907db3339fdd7abc615ecaf5a683a6 | [
"MIT"
] | null | null | null | 54.315217 | 1,527 | 0.632179 | [
[
[
"# Desafio 5\n\nNeste desafio, vamos praticar sobre redução de dimensionalidade com PCA e seleção de variáveis com RFE. Utilizaremos o _data set_ [Fifa 2019](https://www.kaggle.com/karangadiya/fifa19), contendo originalmente 89 variáveis de mais de 18 mil jogadores do _game_ FIFA 2019.\n\n> Obs.: Por favor, não modifique o nome das funções de resposta.",
"_____no_output_____"
],
[
"## _Setup_ geral",
"_____no_output_____"
]
],
[
[
"from math import sqrt\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats as sct\nimport seaborn as sns\nimport statsmodels.api as sm\nimport statsmodels.stats as st\nfrom sklearn.decomposition import PCA\n\nfrom loguru import logger\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.feature_selection import RFE",
"_____no_output_____"
],
[
"# Algumas configurações para o matplotlib.\n\"\"\"\n %matplotlib inline\n\nfrom IPython.core.pylabtools import figsize\n\n\nfigsize(12, 8)\n\nsns.set()\n\"\"\"",
"_____no_output_____"
],
[
"fifa = pd.read_csv(\"fifa.csv\")",
"_____no_output_____"
],
[
"columns_to_drop = [\"Unnamed: 0\", \"ID\", \"Name\", \"Photo\", \"Nationality\", \"Flag\",\n \"Club\", \"Club Logo\", \"Value\", \"Wage\", \"Special\", \"Preferred Foot\",\n \"International Reputation\", \"Weak Foot\", \"Skill Moves\", \"Work Rate\",\n \"Body Type\", \"Real Face\", \"Position\", \"Jersey Number\", \"Joined\",\n \"Loaned From\", \"Contract Valid Until\", \"Height\", \"Weight\", \"LS\",\n \"ST\", \"RS\", \"LW\", \"LF\", \"CF\", \"RF\", \"RW\", \"LAM\", \"CAM\", \"RAM\", \"LM\",\n \"LCM\", \"CM\", \"RCM\", \"RM\", \"LWB\", \"LDM\", \"CDM\", \"RDM\", \"RWB\", \"LB\", \"LCB\",\n \"CB\", \"RCB\", \"RB\", \"Release Clause\"\n]\n\ntry:\n fifa.drop(columns_to_drop, axis=1, inplace=True)\nexcept KeyError:\n logger.warning(f\"Columns already dropped\")",
"_____no_output_____"
]
],
[
[
"## Inicia sua análise a partir daqui",
"_____no_output_____"
]
],
[
[
"# Sua análise começa aqui.\n\nfifa.dropna(inplace= True)\n",
"_____no_output_____"
]
],
[
[
"## Questão 1\n\nQual fração da variância consegue ser explicada pelo primeiro componente principal de `fifa`? Responda como um único float (entre 0 e 1) arredondado para três casas decimais.",
"_____no_output_____"
]
],
[
[
"def q1():\n # Retorne aqui o resultado da questão 1.\n pca = PCA(n_components = 1)\n project = pca.fit(fifa)\n varianciaExplicada = project.explained_variance_ratio_[0]\n return varianciaExplicada.round(3)\n pass",
"_____no_output_____"
]
],
[
[
"## Questão 2\n\nQuantos componentes principais precisamos para explicar 95% da variância total? Responda como un único escalar inteiro.",
"_____no_output_____"
]
],
[
[
"def q2():\n # Retorne aqui o resultado da questão 2.\n pca095 = PCA(n_components= 0.95)\n project = pca095.fit_transform(fifa)\n numeroComponentesPrincipais = project.shape[1]\n return numeroComponentesPrincipais\n pass",
"_____no_output_____"
]
],
[
[
"## Questão 3\n\nQual são as coordenadas (primeiro e segundo componentes principais) do ponto `x` abaixo? O vetor abaixo já está centralizado. Cuidado para __não__ centralizar o vetor novamente (por exemplo, invocando `PCA.transform()` nele). Responda como uma tupla de float arredondados para três casas decimais.",
"_____no_output_____"
]
],
[
[
"x = [0.87747123, -1.24990363, -1.3191255, -36.7341814,\n -35.55091139, -37.29814417, -28.68671182, -30.90902583,\n -42.37100061, -32.17082438, -28.86315326, -22.71193348,\n -38.36945867, -20.61407566, -22.72696734, -25.50360703,\n 2.16339005, -27.96657305, -33.46004736, -5.08943224,\n -30.21994603, 3.68803348, -36.10997302, -30.86899058,\n -22.69827634, -37.95847789, -22.40090313, -30.54859849,\n -26.64827358, -19.28162344, -34.69783578, -34.6614351,\n 48.38377664, 47.60840355, 45.76793876, 44.61110193,\n 49.28911284\n]",
"_____no_output_____"
],
[
"def q3():\n # Retorne aqui o resultado da questão 3.\n pca = PCA().fit(fifa)\n c1,c2 = pca.components_.dot(x)[0:2].round(3)\n return c1,c2\n pass",
"_____no_output_____"
]
],
[
[
"## Questão 4\n\nRealiza RFE com estimador de regressão linear para selecionar cinco variáveis, eliminando uma a uma. Quais são as variáveis selecionadas? Responda como uma lista de nomes de variáveis.",
"_____no_output_____"
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],
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[
"def q4():\n # Retorne aqui o resultado da questão 4.\n x = fifa.drop(columns=\"Overall\")\n y = fifa[\"Overall\"]\n\n rfe = RFE(estimator= LinearRegression(), n_features_to_select= 5)\n rfe.fit(x,y)\n\n indexFeatureSelect = rfe.get_support(indices=True)\n\n featureSelect = list(x.columns[indexFeatureSelect])\n\n return featureSelect\n pass",
"_____no_output_____"
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cb196c163853e275e7166992a1f8c503280a48db | 6,214 | ipynb | Jupyter Notebook | notebooks/exercises/7 - NumPy.ipynb | smasoka/python-introduction | 923d7e634e6db1016c783b999477cd86188d99fb | [
"MIT"
] | 10 | 2017-02-09T12:43:32.000Z | 2022-01-02T14:40:34.000Z | notebooks/exercises/7 - NumPy.ipynb | smasoka/python-introduction | 923d7e634e6db1016c783b999477cd86188d99fb | [
"MIT"
] | 1 | 2018-10-16T12:13:17.000Z | 2018-10-16T12:13:17.000Z | notebooks/exercises/7 - NumPy.ipynb | smasoka/python-introduction | 923d7e634e6db1016c783b999477cd86188d99fb | [
"MIT"
] | 12 | 2017-06-22T23:46:41.000Z | 2021-12-26T15:22:24.000Z | 23.014815 | 145 | 0.552945 | [
[
[
"# Exercises",
"_____no_output_____"
],
[
"## Simple array manipulation\n\nInvestigate the behavior of the statements below by looking\nat the values of the arrays a and b after assignments:\n```\na = np.arange(5)\nb = a\nb[2] = -1\nb = a[:]\nb[1] = -1\nb = a.copy()\nb[0] = -1\n```",
"_____no_output_____"
],
[
"Generate a 1D NumPy array containing numbers from -2 to 2\nin increments of 0.2. Use optional start and step arguments\nof **np.arange()** function.",
"_____no_output_____"
],
[
"Generate another 1D NumPy array containing 11 equally\nspaced values between 0.5 and 1.5. Extract every second\nelement of the array",
"_____no_output_____"
],
[
"Create a 4x4 array with arbitrary values.",
"_____no_output_____"
],
[
"Extract every element from the second row",
"_____no_output_____"
],
[
"Extract every element from the third column",
"_____no_output_____"
],
[
"Assign a value of 0.21 to upper left 2x2 subarray.",
"_____no_output_____"
],
[
"## Simple plotting\n\nPlot to the same graph **sin** and **cos** functions in the interval $[-\\pi/2, \\pi/2]$. Use $\\theta$ as x-label and insert also legends.",
"_____no_output_____"
],
[
"## Pie chart\n\nThe file \"../data/csc_usage.txt\" contains the usage of CSC servers by different disciplines. Plot a pie chart about the resource usage.",
"_____no_output_____"
],
[
"## Bonus exercises\n\n### Numerical derivative with finite differences\n\nDerivatives can be calculated numerically with the finite-difference method\nas: \n\n$$ f'(x_i) = \\frac{f(x_i + \\Delta x)- f(x_i - \\Delta x)}{2 \\Delta x} $$\n\nConstruct 1D Numpy array containing the values of xi in the interval $[0, \\pi/2]$ with spacing\n$\\Delta x = 0.1$. Evaluate numerically the derivative of **sin** in this\ninterval (excluding the end points) using the above formula. Try to avoid\n`for` loops. Compare the result to function **cos** in the same interval.",
"_____no_output_____"
],
[
"### Game of Life\n\nGame of life is a cellular automaton devised by John Conway\nin 70's: http://en.wikipedia.org/wiki/Conway's_Game_of_Life\n\nThe game consists of two dimensional orthogonal grid of\ncells. Cells are in two possible states, alive or dead. Each cell\ninteracts with its eight neighbours, and at each time step the\nfollowing transitions occur:\n* Any live cell with fewer than two live neighbours dies, as if\ncaused by underpopulation\n* Any live cell with more than three live neighbours dies, as if\nby overcrowding\n* Any live cell with two or three live neighbours lives on to\nthe next generation\n* Any dead cell with exactly three live neighbours becomes a\nlive cell\n\nThe initial pattern constitutes the seed of the system, and\nthe system is left to evolve according to rules. Deads and\nbirths happen simultaneously.\n\nImplement the Game of Life using Numpy, and visualize the\nevolution with Matplotlib's **imshow**. Try first 32x32\nsquare grid and cross-shaped initial pattern:\n\nTry also other grids and initial patterns (e.g. random\npattern). Try to avoid **for** loops.",
"_____no_output_____"
]
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cb1984bd87e6c7f655dad65a03b41ed866d80f74 | 29,124 | ipynb | Jupyter Notebook | c164s22ch3.ipynb | everestso/Fall21Spring22 | 0a1039f59f43086a96168211d7bdc7cae93cf3bd | [
"Apache-2.0"
] | null | null | null | c164s22ch3.ipynb | everestso/Fall21Spring22 | 0a1039f59f43086a96168211d7bdc7cae93cf3bd | [
"Apache-2.0"
] | null | null | null | c164s22ch3.ipynb | everestso/Fall21Spring22 | 0a1039f59f43086a96168211d7bdc7cae93cf3bd | [
"Apache-2.0"
] | null | null | null | 28.330739 | 270 | 0.454642 | [
[
[
"<a href=\"https://colab.research.google.com/github/everestso/Fall21Spring22/blob/main/c164s22ch3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# Tile Sliding Domain",
"_____no_output_____"
]
],
[
[
"import random\nimport heapq\n\nrandom.seed(13)",
"_____no_output_____"
],
[
"StateDimension=3\n# StateDimension=4\n#InitialState = [1,2,3,4,5,6,0,7,8]\nInitialState = \"123456078\"\n#GoalState=[1,2,3,4,5,6,7,8,0]\nGoalState = \"123456780\"\n# GoalState = \"123456789ABCDEF0\"\nActions = lambda s: ['u', 'd', 'l', 'r']\nOpposite=dict([('u','d'),('d','u'),('l','r'),('r','l'), (None, None)])",
"_____no_output_____"
],
[
"def Result(state, action):\n i = state.index('0')\n newState = list(state)\n row,col=i//StateDimension, i % StateDimension\n if ( (action=='u' and row==0) or\n (action=='d' and row==StateDimension-1) or\n (action=='l' and col==0) or\n (action=='r' and col==StateDimension-1)):\n return newState\n if action=='u':\n l,r = row*StateDimension+col, (row-1)*StateDimension+col\n elif action=='d':\n l,r = row*StateDimension+col, (row+1)*StateDimension+col\n elif action=='l':\n l,r = row*StateDimension+col, row*StateDimension+col-1\n elif action=='r' :\n l,r = row*StateDimension+col, row*StateDimension+col+1\n newState[l], newState[r] = newState[r], newState[l] \n return ''.join(newState)\n\ndef PrintState(s):\n for i in range(0,len(s),StateDimension):\n print(s[i:i+StateDimension])\n\ndef LegalMove(state, action):\n i = state.index('0')\n row,col=i//StateDimension, i % StateDimension\n if ( (action=='u' and row==0) or\n (action=='d' and row==StateDimension-1) or\n (action=='l' and col==0) or\n (action=='r' and col==StateDimension-1)):\n return False\n return True",
"_____no_output_____"
],
[
"def SingleTileManhattanDistance(tile, left, right):\n leftIndex = left.index(tile)\n rightIndex = right.index(tile)\n return (abs(leftIndex//StateDimension-rightIndex//StateDimension) +\n abs(leftIndex%StateDimension-rightIndex%StateDimension))\n \ndef ManhattanDistance(left, right):\n distances = [SingleTileManhattanDistance(tile, left, right) \n for tile in [str(c) for c in range(1, StateDimension**2)]]\n ### print (\"Distances= \", distances)\n return sum(distances)",
"_____no_output_____"
],
[
"def OutOfPlace(left, right):\n distances = [left[i]!=right[i] and right[i] != '0'\n for i in range(StateDimension**2)]\n return sum(distances)",
"_____no_output_____"
],
[
"PrintState(InitialState)",
"123\n456\n078\n"
],
[
"PrintState(GoalState)",
"123\n456\n780\n"
],
[
"print(\"ManhattanDistance= \", ManhattanDistance(InitialState, GoalState))\nprint(\"OutOfPlace= \", OutOfPlace(InitialState, GoalState))",
"ManhattanDistance= 2\nOutOfPlace= 2\n"
],
[
"PrintState(InitialState)\nprint()\nstate1 = Result(InitialState, 'u')\nPrintState(state1)\nprint()\nstate1 = Result(state1, 'r')\nPrintState(state1)",
"123\n456\n078\n\n123\n056\n478\n\n123\n506\n478\n"
]
],
[
[
"# Random Walk",
"_____no_output_____"
]
],
[
[
"def RandomWalk(state, steps):\n actionSequence = []\n actionLast = None\n for i in range(steps):\n action = None\n while action==None:\n action = random.choice(Actions(state))\n action = action if (LegalMove(state, action) \n and action!= Opposite[actionLast]) else None\n actionLast = action\n state = Result(state, action)\n actionSequence.append(action)\n return state, actionSequence",
"_____no_output_____"
],
[
"state1, sol = RandomWalk(InitialState, 50)\nPrintState(state1)\nprint (ManhattanDistance(state1, GoalState), sol)\n\nstate1, sol = RandomWalk(InitialState, 5)\nPrintState(InitialState)\nprint (sol)\nPrintState(state1)",
"840\n627\n135\n18 ['u', 'u', 'r', 'd', 'l', 'd', 'r', 'r', 'u', 'l', 'l', 'd', 'r', 'r', 'u', 'u', 'l', 'd', 'r', 'd', 'l', 'u', 'u', 'l', 'd', 'd', 'r', 'u', 'r', 'u', 'l', 'l', 'd', 'r', 'r', 'd', 'l', 'u', 'r', 'd', 'l', 'l', 'u', 'r', 'r', 'u', 'l', 'd', 'r', 'u']\n123\n456\n078\n['r', 'u', 'u', 'l', 'd']\n413\n026\n758\n"
],
[
"def ApplyMoves(actions, state):\n for action in actions:\n state = Result(state, action)\n return state",
"_____no_output_____"
],
[
"PrintState(InitialState)\nprint(['r','r'])\nPrintState(ApplyMoves(['r','r'],InitialState))",
"123\n456\n078\n['r', 'r']\n123\n456\n780\n"
],
[
"def ReverseMoves(actions):\n ret = [Opposite[a] for a in actions]\n ret.reverse()\n return ret",
"_____no_output_____"
],
[
"state1, sol = RandomWalk(GoalState, 5)\nPrintState(state1)\nprint (sol)\nprint(ReverseMoves(sol))\nPrintState (ApplyMoves(ReverseMoves(sol), state1))",
"203\n156\n478\n['l', 'l', 'u', 'u', 'r']\n['l', 'd', 'd', 'r', 'r']\n123\n456\n780\n"
]
],
[
[
"# Example 1",
"_____no_output_____"
]
],
[
[
"Problems = [RandomWalk(GoalState, 5) for _ in range(10)]\nfor i, s in Problems:\n print ('\"', i, '\" , \"', ''.join(map(str, ReverseMoves(s))), '\",', \n ManhattanDistance(i, GoalState), sep='')",
"\"123745806\" , \"lurrd\",5\n\"123746508\" , \"lurdr\",5\n\"412053786\" , \"urrdd\",5\n\"203156478\" , \"lddrr\",5\n\"413026758\" , \"urddr\",5\n\"413026758\" , \"urddr\",5\n\"203156478\" , \"lddrr\",5\n\"152430786\" , \"lurdd\",5\n\"152430786\" , \"lurdd\",5\n\"413026758\" , \"urddr\",5\n"
],
[
"NewState = ApplyMoves(\"dldrr\", \"103526478\")\nprint (NewState)\nPrintState(\"123456780\")\nprint()\nPrintState(\"103526478\")\nprint(OutOfPlace(\"103526478\", \"123456780\"))\nMD=[(1,0), (2, 1), (3, 0), (4, 1), (5, 1), (6, 0), (7, 1), (8, 1)]\nprint(ManhattanDistance(\"103526478\", \"123456780\"))",
"123456780\n123\n456\n780\n\n103\n526\n478\n5\n5\n"
],
[
"InitialState = \"412053786\"\nGoalState = \"123456780\"\nprint (\"ManhattanDistance=\", ManhattanDistance(InitialState, GoalState))\nprint (\"Out of Place= \", OutOfPlace(InitialState, GoalState))",
"ManhattanDistance= 5\nOut of Place= 5\n"
]
],
[
[
"# Example 2",
"_____no_output_____"
]
],
[
[
"Problems = [RandomWalk(GoalState, 100) for _ in range(20)]\nfor i, s in Problems:\n print ('\"', i, '\", ', ''.join(map(str, ReverseMoves(s))), '\",', \n OutOfPlace(i, GoalState), \" \", ManhattanDistance(i, GoalState), sep='')",
"\"847503612\", druldruullddrrulurddlulurddrullurrddlulurrdldlurdruullddrruullddrrulurddllurulddrruuldrullddrrulurdd\",8 20\n\"241703685\", ldrrululddruruldlurrddluurdluldrdlurdruullddrurdllurdruulldrulddruruldlurdruldlurdrdlluurrdllurrdldr\",7 12\n\"068475321\", rrdldlurrulddluruldrulddruuldrurdlldruruldlurrddlurulddrululdrurddlluurrddluldrulurddrullurrddluurdd\",7 18\n\"523418760\", llururdluldrruldldruldrurdlluurrddlluurddluurddruulddrulldrrullurrddlluurrdldruuldlurdrdlluurdruldrd\",4 8\n\"041567832\", druldrulddrruulddluurdrdlluurdrdllurrdluldrurdluulddrrulldrrullurddlurrullddrruldruulldrrdluuldrurdd\",8 16\n\"813267450\", lluurrddlurdluulddruldrruuldldruldrruulddrululddrrulurdllurrddlulurrddlurullddruldruruldldrrululdrdr\",7 12\n\"240683517\", ldrdlluurrdlulddruldruulddruurddluurdldruuldlurdldrulurdrdllurrdluurddlluurdrullddrrulldrruuldlurddr\",8 14\n\"740862153\", llddrurulddrululdrdruulddruldlurdlurdlurdlurrdlluurdruldrdluuldrulddrrulurdllurrddluurddlluurdruldrd\",8 14\n\"068347251\", rdrdlluurrddlluurrddluulddrruuldrdluuldrdluurrddllurdruldluurddruulldrrdllururddllururddllurdluurdrd\",8 20\n\"854601732\", urddlluurddruldruulldrulddruulddrulurrdlulddruurddlurdllururddlulurrdlldrrululdrdruullddrulurdlurrdd\",7 18\n\"745132860\", llururddlluurrddllururdlldruulddruruldldrruuldlurddlurrdlluurrddluldruldruldrrulurdlulddruuldrruldrd\",8 14\n\"586304712\", urdldluurdldrruuldrulldrdruullddrrululdrdluurrddllurruldrdlulurrddlluruldrrdluruldlurddrulurdlulddrr\",7 16\n\"342781065\", rulurrdlldrulurddruulldruldrrdlluurrddlurulldrurddlurdlluurdrullddruldrrululddruurdlurddlurdluulddrr\",8 14\n\"237401586\", lurrdllurrddlulurddluurddluurrddlluurdrullddrruuldrdlluurrdldluurdrullddrrullurddlururdldluurdruldrd\",6 12\n\"032641587\", drurdlldrrulldrurdlluurdrulldrdlurdluurdrdluurdluldrulddrulurrddluldrruulldrdluurdrullddrurdluurdldr\",7 12\n\"715604238\", dlurrullddruuldrurddlulurdlurddluurdldrruulldrdruuldldruldrrulurddluulddrrulldrurullddruurddlulurrdd\",8 16\n\"354172086\", uruldrdluurdrdllurrullddrruldruuldruldrdlluruldrurdlurdlldrrulurdldrulldruuldrdruulddrululddruulddrr\",7 12\n\"012743586\", rrddllururddllurrullddrruldruullddruurdluldrdruullddrruulldrurdldluurrdldruldruldluurrddlluurdrdlurd\",7 8\n\"471603825\", rdluurdlldruurddluuldrurdlurdluldrurddlulurddluurrdldruuldrulldrrullddruurddlulurrdlldrruullddrruldr\",8 14\n\"251604873\", dluurddrullurddlurrulddlurdruulldrdrulldrruuldldrruulldrrulldrrullddruruldrdluulddrrulldruulddruldrr\",8 12\n"
],
[
"InitialState = \"281607543\"\nGoalState = \"123456780\"\nprint (\"ManhattanDistance=\", ManhattanDistance(InitialState, GoalState))\nprint (\"Out of Place= \", OutOfPlace(InitialState, GoalState))",
"ManhattanDistance= 16\nOut of Place= 8\n"
],
[
"InitialState = \"076581324\"\nGoalState = \"123456780\"\nprint (\"ManhattanDistance=\", ManhattanDistance(InitialState, GoalState))\nprint (\"Out of Place= \", OutOfPlace(InitialState, GoalState))\nPrintState(InitialState)",
"ManhattanDistance= 18\nOut of Place= 8\n076\n581\n324\n"
],
[
"sol = \"drdlurrullddruruldlurrddlluurrddluldrruulddruullddrruullddrurdlulurrddluurdlulddrulurdldrurdluuldrdr\"\nprint (len(sol))\nprint(ApplyMoves(sol, InitialState))",
"100\n123456780\n"
]
],
[
[
"# Simple 15 Puzzle Test",
"_____no_output_____"
]
],
[
[
"GoalState = \"123456789ABCDEF0\"\ns15a = Result(GoalState, \"l\")\ns15b = Result(GoalState, \"u\")\n\nPrintState(GoalState)\nprint()\nPrintState(s15a)\nprint('')\nPrintState(s15b)",
"123\n456\n789\nABC\nDEF\n0\n\n['1', '2', '3']\n['4', '5', '6']\n['7', '8', '9']\n['A', 'B', 'C']\n['D', 'E', 'F']\n['0']\n\n123\n456\n789\nABC\n0EF\nD\n"
]
],
[
[
"# Discussion",
"_____no_output_____"
]
],
[
[
"StateDimension=3\ns1 = \"821357064\"\nsol1=\"ruurdllurrdlurddllurrdllurrd\"\nr = ApplyMoves(sol1, s1)\nPrintState(r)",
"123\n456\n780\n"
],
[
"StateDimension=4\ns1 = \"13275AE069C4DF8B\"\nsol1=\"dluullddrrruuldrddlurdlluulurrrddd\"\nr = ApplyMoves(sol1, s1)\nPrintState(r)",
"1234\n5678\n9ABC\nDEF0\n"
],
[
"StateDimension=4\ns=\"FAC42B061D89E537\"\nsol1=\"LLURDLDRDRURDLUULDDRUUULLDRDDRUULURDRDDLLLURURRDLDR\".lower()\nsol2=\"ddluuuldddrruluuldddruulddruulddruurruldlurrdlddluldrruuurdlddruuuldddruldlurrdllluruurdddllurrdlurdluldrruulurdldruuldddrruldlurrdlluurddlurulddrulurddr\"\nprint(len(sol1))\nprint(len(sol2))\n\nr = ApplyMoves(sol1, s)\nprint(\"------Solution 1-------\")\nPrintState(r)\nr = ApplyMoves(sol2, s)\nprint(\"------Solution 2-------\")\nPrintState(r)\n\n",
"51\n153\n------Solution 1-------\n1234\n5678\n9ABC\nDEF0\n------Solution 2-------\n1234\n5678\n9ABC\nDEF0\n"
]
],
[
[
"## Assignment Challenge Problems",
"_____no_output_____"
]
],
[
[
"StateDimension=4\ns=\"71A92CE03DB4658F\t\"\nsol1=\"LLLDDRURURDLDRULLULURRRDDLUULDDRUULDLDDRURULLDRRRD\".lower()\nprint(len(sol1))\n\nr = ApplyMoves(sol1, s)\nPrintState(r)",
"50\n1234\n5678\n9ABC\nDEF0\n\t\n"
],
[
"StateDimension=4\ns=\"02348697DF5A1EBC\"\nsol1=\"RDDRDLULDRUURDLULDDRRURULLULDDRRRULDRD\".lower()\nprint(len(sol1))\n\nr = ApplyMoves(sol1, s)\nPrintState(r)",
"38\n1234\n5678\n9ABC\nDEF0\n"
],
[
"StateDimension=4\ns=\"39A1D0EC7BF86452\"\nsol1=\"DLUURRRDDLLDRRULULDDRUULURDLLURDDDLUURDDRUURULLDDRRULDDR\".lower()\nprint(len(sol1))\n\nr = ApplyMoves(sol1, s)\nPrintState(r)",
"56\n1234\n5678\n9ABC\nDEF0\n"
],
[
"StateDimension=4\ns=\"EAB480FC19D56237\"\nsol1=\"LDRRUULLDDRDRURULLDRULDDLUURURDDLDRRUULDLUURDDDLUURDRD\".lower()\nprint(len(sol1))\n\nr = ApplyMoves(sol1, s)\nPrintState(r)",
"54\n1234\n5678\n9ABC\nDEF0\n"
],
[
"StateDimension=4\ns=\"7DB13C52F46E80A9\"\nsol1=\"RULLDRRRULLUULDRRURDDLLLURDDRRULULULDRURRDLLLURRDLDDLURDRR\".lower()\nprint(len(sol1))\n\nr = ApplyMoves(sol1, s)\nPrintState(r)\n",
"58\n1234\n5678\n9ABC\nDEF0\n"
]
]
] | [
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cb19bd0477340016474b85988eb9004cb771d038 | 1,279 | ipynb | Jupyter Notebook | edureka/9 - Model Selection and Boosting/Code 2.ipynb | Yazooliu/TempCode | e579b8b6eb4657267e9bc9589e47becbbe4ef72b | [
"MIT"
] | null | null | null | edureka/9 - Model Selection and Boosting/Code 2.ipynb | Yazooliu/TempCode | e579b8b6eb4657267e9bc9589e47becbbe4ef72b | [
"MIT"
] | null | null | null | edureka/9 - Model Selection and Boosting/Code 2.ipynb | Yazooliu/TempCode | e579b8b6eb4657267e9bc9589e47becbbe4ef72b | [
"MIT"
] | 1 | 2021-12-04T13:33:54.000Z | 2021-12-04T13:33:54.000Z | 23.685185 | 94 | 0.554339 | [
[
[
"import pandas as pd\nfrom sklearn import model_selection\nfrom sklearn.ensemble import AdaBoostClassifier\ndf = pd.read_csv('Diabetes.txt', sep=\",\", header=None)\ndf.columns = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\narray = df.values\nX = array[:,0:8]\nY = array[:,8]\nkfold = model_selection.KFold(n_splits=10, random_state=7)\nmodel = AdaBoostClassifier(n_estimators=30, random_state=7)\nresults = model_selection.cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())",
"0.76045796309\n"
]
]
] | [
"code"
] | [
[
"code"
]
] |
cb1a05b03590bf1748cdf405cd4e5fcd30f1546e | 120,528 | ipynb | Jupyter Notebook | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 | 9c567bf2c8b571021b120d879ba9edf7751b9f92 | [
"Apache-2.0"
] | 542 | 2019-11-10T12:09:31.000Z | 2022-03-28T11:39:07.000Z | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 | 9c567bf2c8b571021b120d879ba9edf7751b9f92 | [
"Apache-2.0"
] | 117 | 2019-11-12T09:39:24.000Z | 2022-03-12T00:20:41.000Z | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 | 9c567bf2c8b571021b120d879ba9edf7751b9f92 | [
"Apache-2.0"
] | 246 | 2019-11-09T21:53:24.000Z | 2022-03-29T00:57:07.000Z | 150.848561 | 49,916 | 0.876112 | [
[
[
"<a href=\"https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3)%20Understand%20transfer%20learning%20and%20the%20role%20of%20number%20of%20dataset%20classes%20in%20it%20-%20Keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# Goals\n\n### 1. Visualize deep learning network\n\n\n### 2. Understand how the final layer would change when number of classes in dataset changes",
"_____no_output_____"
],
[
"# What do you do with a deep learning model in transfer learning\n\n - These are the steps already done by contributors in pytorch, keras and mxnet\n - You take a deep learning architecture, such as resnet, densenet, or even custom network\n - Train the architecture on large datasets such as imagenet, coco, etc\n - The trained wieghts become your starting point for transfer learning\n \n \n - The final layer of this pretrained model has number of neurons = number of classes in the large dataset\n \n \n - In transfer learning\n - You take the network and load the pretrained weights on the network\n - Then remove the final layer that has the extra(or less) number of neurons\n - You add a new layer with number of neurons = number of classes in your custom dataset\n - Optionally you can add more layers in between this newly added final layer and the old network\n \n \n - Now you have two parts in your network\n - One that already existed, the pretrained one, the base network\n - The new sub-network or a single layer you added\n \n \n - The hyper-parameter we can see here: Freeze base network\n - Freezing base network makes the base network untrainable\n - The base network now acts as a feature extractor and only the next half is trained\n - If you do not freeze the base network the entire network is trained\n (You will take this part in next sessions)\n \n ",
"_____no_output_____"
],
[
"# Table of Contents\n\n\n## [Install](#0)\n\n\n## [Setup Default Params with Cats-Dogs dataset](#1)\n\n\n## [Visualize network](#2)\n\n\n## [Reset Default Params with new dataset - Logo classification](#3)\n\n\n## [Visualize the new network](#4)",
"_____no_output_____"
],
[
"<a id='0'></a>\n# Install Monk",
"_____no_output_____"
],
[
"## Using pip (Recommended)\n\n - colab (gpu) \n - All bakcends: `pip install -U monk-colab`\n \n\n - kaggle (gpu) \n - All backends: `pip install -U monk-kaggle`\n \n\n - cuda 10.2\t\n - All backends: `pip install -U monk-cuda102`\n - Gluon bakcned: `pip install -U monk-gluon-cuda102`\n\t - Pytorch backend: `pip install -U monk-pytorch-cuda102`\n - Keras backend: `pip install -U monk-keras-cuda102`\n \n\n - cuda 10.1\t\n - All backend: `pip install -U monk-cuda101`\n\t - Gluon bakcned: `pip install -U monk-gluon-cuda101`\n\t - Pytorch backend: `pip install -U monk-pytorch-cuda101`\n\t - Keras backend: `pip install -U monk-keras-cuda101`\n \n\n - cuda 10.0\t\n - All backend: `pip install -U monk-cuda100`\n\t - Gluon bakcned: `pip install -U monk-gluon-cuda100`\n\t - Pytorch backend: `pip install -U monk-pytorch-cuda100`\n\t - Keras backend: `pip install -U monk-keras-cuda100`\n \n\n - cuda 9.2\t\n - All backend: `pip install -U monk-cuda92`\n\t - Gluon bakcned: `pip install -U monk-gluon-cuda92`\n\t - Pytorch backend: `pip install -U monk-pytorch-cuda92`\n\t - Keras backend: `pip install -U monk-keras-cuda92`\n \n\n - cuda 9.0\t\n - All backend: `pip install -U monk-cuda90`\n\t - Gluon bakcned: `pip install -U monk-gluon-cuda90`\n\t - Pytorch backend: `pip install -U monk-pytorch-cuda90`\n\t - Keras backend: `pip install -U monk-keras-cuda90`\n \n\n - cpu \t\t\n - All backend: `pip install -U monk-cpu`\n\t - Gluon bakcned: `pip install -U monk-gluon-cpu`\n\t - Pytorch backend: `pip install -U monk-pytorch-cpu`\n\t - Keras backend: `pip install -U monk-keras-cpu`",
"_____no_output_____"
],
[
"## Install Monk Manually (Not recommended)\n \n### Step 1: Clone the library\n - git clone https://github.com/Tessellate-Imaging/monk_v1.git\n \n \n \n \n### Step 2: Install requirements \n - Linux\n - Cuda 9.0\n - `cd monk_v1/installation/Linux && pip install -r requirements_cu90.txt`\n - Cuda 9.2\n - `cd monk_v1/installation/Linux && pip install -r requirements_cu92.txt`\n - Cuda 10.0\n - `cd monk_v1/installation/Linux && pip install -r requirements_cu100.txt`\n - Cuda 10.1\n - `cd monk_v1/installation/Linux && pip install -r requirements_cu101.txt`\n - Cuda 10.2\n - `cd monk_v1/installation/Linux && pip install -r requirements_cu102.txt`\n - CPU (Non gpu system)\n - `cd monk_v1/installation/Linux && pip install -r requirements_cpu.txt`\n \n \n - Windows\n - Cuda 9.0 (Experimental support)\n - `cd monk_v1/installation/Windows && pip install -r requirements_cu90.txt`\n - Cuda 9.2 (Experimental support)\n - `cd monk_v1/installation/Windows && pip install -r requirements_cu92.txt`\n - Cuda 10.0 (Experimental support)\n - `cd monk_v1/installation/Windows && pip install -r requirements_cu100.txt`\n - Cuda 10.1 (Experimental support)\n - `cd monk_v1/installation/Windows && pip install -r requirements_cu101.txt`\n - Cuda 10.2 (Experimental support)\n - `cd monk_v1/installation/Windows && pip install -r requirements_cu102.txt`\n - CPU (Non gpu system)\n - `cd monk_v1/installation/Windows && pip install -r requirements_cpu.txt`\n \n \n - Mac\n - CPU (Non gpu system)\n - `cd monk_v1/installation/Mac && pip install -r requirements_cpu.txt`\n \n \n - Misc\n - Colab (GPU)\n - `cd monk_v1/installation/Misc && pip install -r requirements_colab.txt`\n - Kaggle (GPU)\n - `cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt`\n \n \n \n### Step 3: Add to system path (Required for every terminal or kernel run)\n - `import sys`\n - `sys.path.append(\"monk_v1/\");`",
"_____no_output_____"
],
[
"## Dataset - Sample \n - one having 2 classes\n - other having 16 classes",
"_____no_output_____"
]
],
[
[
"! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1jE-ckk0JbrdbJvIBaKMJWkTfbRDR2MaF' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1jE-ckk0JbrdbJvIBaKMJWkTfbRDR2MaF\" -O study_classes.zip && rm -rf /tmp/cookies.txt",
"_____no_output_____"
],
[
"! unzip -qq study_classes.zip",
"_____no_output_____"
]
],
[
[
"# Imports",
"_____no_output_____"
]
],
[
[
"#Using keras backend \n\n# When installed using pip\nfrom monk.keras_prototype import prototype\n\n\n# When installed manually (Uncomment the following)\n#import os\n#import sys\n#sys.path.append(\"monk_v1/\");\n#sys.path.append(\"monk_v1/monk/\");\n#from monk.keras_prototype import prototype",
"_____no_output_____"
]
],
[
[
"### Creating and managing experiments\n - Provide project name\n - Provide experiment name",
"_____no_output_____"
]
],
[
[
"gtf = prototype(verbose=1);\ngtf.Prototype(\"Project\", \"study-num-classes\");",
"Keras Version: 2.2.5\nTensorflow Version: 1.12.0\n\nExperiment Details\n Project: Project\n Experiment: study-num-classes\n Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.0_blocks/study_roadmap/change_post_num_layers/5_transfer_learning_params/1_number_of_classes_in_dataset/workspace/Project/study-num-classes/\n\n"
]
],
[
[
"### This creates files and directories as per the following structure\n \n \n workspace\n |\n |--------Project\n |\n |-----study-num-classes\n |\n |-----experiment-state.json\n |\n |-----output\n |\n |------logs (All training logs and graphs saved here)\n |\n |------models (all trained models saved here)",
"_____no_output_____"
],
[
"<a id='1'></a>\n# Setup Default Params with Cats-Dogs dataset",
"_____no_output_____"
]
],
[
[
"gtf.Default(dataset_path=\"study_classes/dogs_vs_cats\", \n model_name=\"resnet50\", \n num_epochs=5);",
"Dataset Details\n Train path: study_classes/dogs_vs_cats\n Val path: None\n CSV train path: None\n CSV val path: None\n\nDataset Params\n Input Size: 224\n Batch Size: 4\n Data Shuffle: True\n Processors: 4\n Train-val split: 0.7\n\nFound 36 images belonging to 2 classes.\nFound 14 images belonging to 2 classes.\nPre-Composed Train Transforms\n[{'RandomHorizontalFlip': {'p': 0.8}}, {'MeanSubtraction': {'mean': [0.485, 0.456, 0.406]}}]\n\nPre-Composed Val Transforms\n[{'RandomHorizontalFlip': {'p': 0.8}}, {'MeanSubtraction': {'mean': [0.485, 0.456, 0.406]}}]\n\nDataset Numbers\n Num train images: 36\n Num val images: 14\n Num classes: 2\n\nModel Params\n Model name: resnet50\n Use Gpu: True\n Gpu Memory Fraction: 0.6\n Use pretrained: True\n Freeze base network: True\n\nModel Details\n Loading pretrained model\n Model Loaded on device\n Model name: resnet50\n Num layers in model: 108\n Num trainable layers: 2\n\nOptimizer\n Name: sgd\n Learning rate: 0.0001\n Params: {'lr': 0.0001, 'momentum': 0.9, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n\n\n\nLearning rate scheduler\n Name: reduceonplateaulr\n Params: {'mode': 'min', 'factor': 0.1, 'patience': 1, 'verbose': True, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'epsilon': 1e-08}\n\nLoss\n Name: crossentropy\n Params: {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n\nTraining params\n Num Epochs: 5\n\nDisplay params\n Display progress: True\n Display progress realtime: True\n Save Training logs: True\n Save Intermediate models: True\n Intermediate model prefix: intermediate_model_\n\n"
]
],
[
[
"### From Data summary - Num classes: 2",
"_____no_output_____"
],
[
"<a id='2'></a>\n# Visualize network",
"_____no_output_____"
]
],
[
[
"gtf.Visualize_With_Netron(data_shape=(3, 224, 224), port=8081);",
"Using Netron To Visualize\nNot compatible on kaggle\nCompatible only for Jupyter Notebooks\nServing 'final.h5' at http://localhost:8081\n"
]
],
[
[
"## The final layer",
"_____no_output_____"
]
],
[
[
"from IPython.display import Image\nImage(filename='imgs/2_classes_base_keras.png')",
"_____no_output_____"
]
],
[
[
"<a id='3'></a>\n# Reset Default Params with new dataset - Logo classification",
"_____no_output_____"
]
],
[
[
"gtf = prototype(verbose=1);\ngtf.Prototype(\"Project\", \"study-num-classes\");",
"Keras Version: 2.2.5\nTensorflow Version: 1.12.0\n\nExperiment Details\n Project: Project\n Experiment: study-num-classes\n Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.0_blocks/study_roadmap/change_post_num_layers/5_transfer_learning_params/1_number_of_classes_in_dataset/workspace/Project/study-num-classes/\n\n"
],
[
"gtf.Default(dataset_path=\"study_classes/logos\", \n model_name=\"resnet50\", \n num_epochs=5);",
"Dataset Details\n Train path: study_classes/logos\n Val path: None\n CSV train path: None\n CSV val path: None\n\nDataset Params\n Input Size: 224\n Batch Size: 4\n Data Shuffle: True\n Processors: 4\n Train-val split: 0.7\n\nFound 82 images belonging to 16 classes.\nFound 28 images belonging to 16 classes.\nPre-Composed Train Transforms\n[{'RandomHorizontalFlip': {'p': 0.8}}, {'MeanSubtraction': {'mean': [0.485, 0.456, 0.406]}}]\n\nPre-Composed Val Transforms\n[{'RandomHorizontalFlip': {'p': 0.8}}, {'MeanSubtraction': {'mean': [0.485, 0.456, 0.406]}}]\n\nDataset Numbers\n Num train images: 82\n Num val images: 28\n Num classes: 16\n\nModel Params\n Model name: resnet50\n Use Gpu: True\n Gpu Memory Fraction: 0.6\n Use pretrained: True\n Freeze base network: True\n\nModel Details\n Loading pretrained model\n Model Loaded on device\n Model name: resnet50\n Num layers in model: 108\n Num trainable layers: 2\n\nOptimizer\n Name: sgd\n Learning rate: 0.0001\n Params: {'lr': 0.0001, 'momentum': 0.9, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\n\n\n\nLearning rate scheduler\n Name: reduceonplateaulr\n Params: {'mode': 'min', 'factor': 0.1, 'patience': 1, 'verbose': True, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'epsilon': 1e-08}\n\nLoss\n Name: crossentropy\n Params: {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\n\nTraining params\n Num Epochs: 5\n\nDisplay params\n Display progress: True\n Display progress realtime: True\n Save Training logs: True\n Save Intermediate models: True\n Intermediate model prefix: intermediate_model_\n\n"
]
],
[
[
"### From Data summary - Num classes: 16",
"_____no_output_____"
],
[
"<a id='4'></a>\n# Visualize network",
"_____no_output_____"
]
],
[
[
"gtf.Visualize_With_Netron(data_shape=(3, 224, 224), port=8082);",
"Using Netron To Visualize\nNot compatible on kaggle\nCompatible only for Jupyter Notebooks\nServing 'final.h5' at http://localhost:8082\n"
]
],
[
[
"## The final layer",
"_____no_output_____"
]
],
[
[
"from IPython.display import Image\nImage(filename='imgs/16_classes_base_keras.png')",
"_____no_output_____"
]
],
[
[
"# Goals Completed\n\n### 1. Visualize deep learning network\n\n### 2. Understand how the final layer would change when number of classes in dataset changes",
"_____no_output_____"
]
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cb1a065b30e06637fed4b38eb8b07ebbf407948a | 9,093 | ipynb | Jupyter Notebook | notebooks/Animations.ipynb | chickert/autonomous_vehicles | 3061968524f1c507f409ee9aeaf8625d1c1f725c | [
"MIT"
] | null | null | null | notebooks/Animations.ipynb | chickert/autonomous_vehicles | 3061968524f1c507f409ee9aeaf8625d1c1f725c | [
"MIT"
] | null | null | null | notebooks/Animations.ipynb | chickert/autonomous_vehicles | 3061968524f1c507f409ee9aeaf8625d1c1f725c | [
"MIT"
] | null | null | null | 27.807339 | 196 | 0.56615 | [
[
[
"# Automatically reload custom code modules when there are changes:\n%load_ext autoreload\n%autoreload 2\n",
"_____no_output_____"
],
[
"# Adjust relative path so that the notebook can find the code modules:\nimport sys\nsys.path.append('../code/')\n",
"_____no_output_____"
],
[
"import numpy as np\nimport pandas as pd\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\n%matplotlib notebook\n",
"_____no_output_____"
],
[
"# Import code modules:\nfrom structures import RingRoad\nfrom animations import Animation\n",
"_____no_output_____"
],
[
"# Hide warnings about safe distance violation (still in development):\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n",
"_____no_output_____"
]
],
[
[
"# Baseline:\n",
"_____no_output_____"
]
],
[
[
"# Define simulation:\nenv = RingRoad(\n num_vehicles = 22, # The vechicles at index 0 is an A.V.\n ring_length = 230.0, # The road is a cicle.\n starting_noise = 4.0, # Uniformly add noise to starting positions.\n temporal_res = 0.3, # Set the size of simulation steps (seconds).\n av_activate = 30, # Set when the PID controller is activated.\n seed = 286, # Set a random seed.\n)\n\n# Run the simulation for set number of time steps:\ntotal_time = 90 # In seconds.\ntotal_steps = int(np.ceil(total_time/env.dt))\nenv.run(steps=total_steps)\n\n# Build animation:\nanim = Animation(env, speedup=5.0, interval=5, mode='notebook')\nanim.animate_dashboard(draw_cars_to_scale=True, draw_safety_buffer=False, show_sigma=True)\n\n# # Show animation:\n# anim.show()\n",
"_____no_output_____"
],
[
"# # Save animation as GIF:\n# anim.save_gif(filepath=\"../outputs/baseline.gif\", overwrite=True)\n\n# # Stop animation:\n# anim.stop()\n",
"_____no_output_____"
]
],
[
[
"<a href='https://github.com/chickert/autonomous_vehicles/blob/main/outputs/baseline.gif'><img src='https://github.com/chickert/autonomous_vehicles/raw/main/outputs/baseline.gif' /></a>\n",
"_____no_output_____"
],
[
"# Extension 1\n",
"_____no_output_____"
]
],
[
[
"# Define simulation:\nnum_vehicles = 22\nnum_avs = 11\nenv = RingRoad(\n num_vehicles=num_vehicles, # The vechicles at index 0 is an A.V.\n ring_length=230.0, # The road is a cicle.\n starting_noise=4.0, # Uniformly add noise to starting positions.\n temporal_res=0.3, # Set the size of simulation steps (seconds).\n av_activate=30, # Set when the PID controller is activated.\n seed=286, # Set a random seed.\n num_avs=num_avs\n)\n\n# Run the simulation for set number of time steps:\ntotal_time = 90 # In seconds.\ntotal_steps = int(np.ceil(total_time/env.dt))\nenv.run(steps=total_steps)\n\n# Build animation:\nanim = Animation(env, speedup=5.0, interval=5, mode='notebook')\nanim.animate_dashboard(draw_cars_to_scale=True, draw_safety_buffer=False, show_sigma=True)\n\n# # Show animation:\n# anim.show()\n",
"_____no_output_____"
],
[
"# # Save animation as GIF:\n# anim.save_gif(filepath=\"../outputs/extension1.gif\", overwrite=True)\n\n# # Stop animation:\n# anim.stop()\n",
"_____no_output_____"
]
],
[
[
"<a href='https://github.com/chickert/autonomous_vehicles/blob/main/outputs/extension1.gif'><img src='https://github.com/chickert/autonomous_vehicles/raw/main/outputs/extension1.gif' /></a>\n",
"_____no_output_____"
],
[
"# Extension 2\n",
"_____no_output_____"
]
],
[
[
"# Define simulation:\na_sigma = 0.04\nb_sigma = 0.5\nenv = RingRoad(\n num_vehicles=22, # The vechicles at index 0 is an A.V.\n ring_length=230.0, # The road is a cicle.\n starting_noise=0., # Uniformly add noise to starting positions.\n temporal_res=0.3, # Set the size of simulation steps (seconds).\n av_activate=30, # Set when the PID controller is activated.\n seed=286, # Set a random seed.\n a_sigma=a_sigma,\n b_sigma=b_sigma,\n hv_heterogeneity=True,\n)\n\n# Run the simulation for set number of time steps:\ntotal_time = 90 # In seconds.\ntotal_steps = int(np.ceil(total_time/env.dt))\nenv.run(steps=total_steps)\n\n# Build animation:\nanim = Animation(env, speedup=5.0, interval=5, mode='notebook')\nanim.animate_dashboard(draw_cars_to_scale=True, draw_safety_buffer=False, show_sigma=True)\n\n# # Show animation:\n# anim.show()\n",
"_____no_output_____"
],
[
"# # Save animation as GIF:\n# anim.save_gif(filepath=\"../outputs/extension2.gif\", overwrite=True)\n\n# # Stop animation:\n# anim.stop()\n",
"_____no_output_____"
]
],
[
[
"<a href='https://github.com/chickert/autonomous_vehicles/blob/main/outputs/extension2.gif'><img src='https://github.com/chickert/autonomous_vehicles/raw/main/outputs/extension2.gif' /></a>\n",
"_____no_output_____"
],
[
"# Extension 3\n",
"_____no_output_____"
]
],
[
[
"# Define simulation:\nsigma_pct = 40\nenv = RingRoad(\n num_vehicles=22, # The vechicles at index 0 is an A.V.\n ring_length=230.0, # The road is a cicle.\n starting_noise=4.0, # Uniformly add noise to starting positions.\n temporal_res=0.3, # Set the size of simulation steps (seconds).\n av_activate=30, # Set when the PID controller is activated.\n seed=286, # Set a random seed.\n uncertain_avs=True,\n sigma_pct=sigma_pct\n)\n\n# Run the simulation for set number of time steps:\ntotal_time = 50 # In seconds.\ntotal_steps = int(np.ceil(total_time/env.dt))\nenv.run(steps=total_steps)\n\n# Build animation:\nanim = Animation(env, speedup=5.0, interval=5, mode='notebook')\nanim.animate_dashboard(draw_cars_to_scale=True, draw_safety_buffer=False, show_sigma=True)\n\n# # Show animation:\n# anim.show()\n",
"_____no_output_____"
],
[
"# # Save animation as GIF:\n# anim.save_gif(filepath=\"../outputs/extension3.gif\", overwrite=True)\n\n# # Stop animation:\n# anim.stop()\n",
"_____no_output_____"
]
],
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[
"<a href='https://github.com/chickert/autonomous_vehicles/blob/main/outputs/extension3.gif'><img src='https://github.com/chickert/autonomous_vehicles/raw/main/outputs/extension3.gif' /></a>\n",
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]
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cb1a0b6aa1b7eedce55d0ca25a5db5b92b7e640a | 5,230 | ipynb | Jupyter Notebook | Intro/nn_summary.ipynb | temuller/kaggle_competitions | 7ba42c0a82484d058f081a5d79f6e2134b602218 | [
"MIT"
] | null | null | null | Intro/nn_summary.ipynb | temuller/kaggle_competitions | 7ba42c0a82484d058f081a5d79f6e2134b602218 | [
"MIT"
] | null | null | null | Intro/nn_summary.ipynb | temuller/kaggle_competitions | 7ba42c0a82484d058f081a5d79f6e2134b602218 | [
"MIT"
] | null | null | null | 30.057471 | 238 | 0.58088 | [
[
[
"# **Neural Networks Summary**",
"_____no_output_____"
]
],
[
[
"from keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\nfrom keras.utils import to_categorical",
"_____no_output_____"
]
],
[
[
"## Regression",
"_____no_output_____"
]
],
[
[
"model = Sequential()\n\nn_cols = data.shape[1]\nmodel.add(Dense(5, activation='relu', input_shape=(n_cols, ))) # input shape has to be the same as number of columns\nmodel.add(Dense(5, activation='relu'))\nmodel.add(Dense(1)) # output layer\n\nmodel.compile(optimizer='adam', loss='mean_square_error') \n# adam is more effcicient than gradient descent.\n# it adapts the learning rate automatically\n\nmodel.fit(predictors, target)\npredictions = model.predict(test_data)",
"_____no_output_____"
]
],
[
[
"## Classification",
"_____no_output_____"
]
],
[
[
"model = Sequential()\n\nn_cols = data.shape[1]\ntarget = to_categorical(target)\n\nmodel.add(Dense(5, activation='relu', input_shape=(n_cols, ))) \nmodel.add(Dropout(0.2)) # dropout is a regularization technique to prevent overfitting. Normally ~0.2-0.4\nmodel.add(Dense(5, activation='relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(num_classes, activation='softmax')) \n# for classification the last layer has an activation function which ussually is softmax\n# in addition, the output dimension has to be the same as the calsses in the target\n\nmodel.compile(optimizer='adam', \n loss='categorical_crossentropy',\n metrics=['accuracy']) # to measure accuracy in classification\n\nmodel.fit(predictors, target, \n epochs=20, # number of iterations\n batch_size=50, # size of the splitted bachs (this only works with SGD?)\n validation_split=0.2, )\npredictions = model.predict(test_data)",
"_____no_output_____"
]
],
[
[
"## Convolutional Neural Networks (CNN) - supervised\n\nThis are mainly use for images as they reduce dimensionality. Check this [link](https://courses.edx.org/courses/course-v1:IBM+DL0101EN+3T2019/courseware/89227024130b43f684d95376901b65c8/052a444d45914712a597f0c58cbc4391/?child=first)",
"_____no_output_____"
]
],
[
[
"model = Sequential()\n\ninput_shape = (N, N, 3) # 3 for RGB images and 1 for gray scale images\n\nmodel.add(Conv2D(16, kernel_size=(2, 2), # size of the filter to use\n strides=(1, 1), # steps the filter is moved\n activation='relu', \n input_shape=input_shape)) \nmodel.add(MaxPool2D(pool_size(2, 2), strides=(1, 1))\nmodel.add(Conv2D(16, kernel_size=(2, 2), strides=(1, 1), activation='relu') \nmodel.add(MaxPool2D(pool_size(2, 2)) \nmodel.add(Flatten()) # so the data can proceed to the fully-connected layer\nmodel.add(Dense(100, activation='relu'))\nmodel.add(Dense(num_classes, activation='sotmax'))\n\nmodel.compile(optimizer='adam', \n loss='categorical_crossentropy',\n metrics=['accuracy']) # to measure accuracy in classification\n\nmodel.fit(predictors, target)\npredictions = model.predict(test_data)",
"_____no_output_____"
]
],
[
[
"## Recurrent Neural Networks (RNN) - supervised\n\nThis are networks with loops that take into account dependency of data like images in a movie.",
"_____no_output_____"
],
[
"## Autoencoders - unsupervised\n\nThese commpress and decompress functions learned from data. For this reason they are data-specific.\n\nThese are used in data de-noising and dimensionality reduction for data visualisation.",
"_____no_output_____"
]
]
] | [
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cb1a0bc882e5f2d014edab95c4ca2adc3f3102f2 | 28,125 | ipynb | Jupyter Notebook | Python Basic.ipynb | ramsvijay/basic_datatype_python | 2aa0db44a839ccc8d12fdd392e1a054dae4c1d30 | [
"MIT"
] | null | null | null | Python Basic.ipynb | ramsvijay/basic_datatype_python | 2aa0db44a839ccc8d12fdd392e1a054dae4c1d30 | [
"MIT"
] | null | null | null | Python Basic.ipynb | ramsvijay/basic_datatype_python | 2aa0db44a839ccc8d12fdd392e1a054dae4c1d30 | [
"MIT"
] | null | null | null | 16.304348 | 344 | 0.42912 | [
[
[
"# Welcome\n",
"_____no_output_____"
],
[
"a=10",
"_____no_output_____"
],
[
"b=10.3",
"_____no_output_____"
],
[
"type(a)",
"_____no_output_____"
],
[
"type(b)",
"_____no_output_____"
],
[
"a+b",
"_____no_output_____"
],
[
"a-b",
"_____no_output_____"
],
[
"c=50",
"_____no_output_____"
],
[
"a+b+c",
"_____no_output_____"
],
[
"d=3",
"_____no_output_____"
],
[
"c//d",
"_____no_output_____"
],
[
"c/d",
"_____no_output_____"
],
[
"c%d",
"_____no_output_____"
],
[
"a%d",
"_____no_output_____"
],
[
"2**5",
"_____no_output_____"
]
],
[
[
"# String Data type",
"_____no_output_____"
]
],
[
[
"'Hello'",
"_____no_output_____"
],
[
"\"Hello\"",
"_____no_output_____"
],
[
"'Hello' + ' world'",
"_____no_output_____"
],
[
"print('Hello World!')",
"Hello World!\n"
],
[
"a=\"kumaran\"",
"_____no_output_____"
],
[
"a[3]",
"_____no_output_____"
],
[
"a[1:]",
"_____no_output_____"
],
[
"a[0:]",
"_____no_output_____"
],
[
"a[:]",
"_____no_output_____"
],
[
"a[:-1]",
"_____no_output_____"
],
[
"a[:-10]",
"_____no_output_____"
],
[
"a[::-1]",
"_____no_output_____"
],
[
"a[::-10]",
"_____no_output_____"
],
[
"a[::2]",
"_____no_output_____"
],
[
"a[::3]",
"_____no_output_____"
],
[
"a=\"kumaran\"",
"_____no_output_____"
],
[
"a[::3]",
"_____no_output_____"
],
[
"a[2::1]",
"_____no_output_____"
],
[
"a.count",
"_____no_output_____"
],
[
"a.endswith",
"_____no_output_____"
],
[
"a.endswith('n')",
"_____no_output_____"
],
[
"a.endswith('m')",
"_____no_output_____"
],
[
"a.count('a')",
"_____no_output_____"
],
[
"a = \"kumran systems\"",
"_____no_output_____"
],
[
"a.title()",
"_____no_output_____"
],
[
"a.find('q')",
"_____no_output_____"
],
[
"a.find('a')",
"_____no_output_____"
],
[
"a=\"kumaran\"",
"_____no_output_____"
],
[
"a.find('a')",
"_____no_output_____"
],
[
"a.isnumeric()",
"_____no_output_____"
],
[
"a=\"ku1\"",
"_____no_output_____"
],
[
"a.isnumeric()",
"_____no_output_____"
],
[
"a=1",
"_____no_output_____"
],
[
"a.to_bytes()",
"_____no_output_____"
],
[
"a.bit_length()",
"_____no_output_____"
],
[
"a=12",
"_____no_output_____"
],
[
"a.bit_length()",
"_____no_output_____"
],
[
"a=\"kumaran\"",
"_____no_output_____"
],
[
"a.isspace()",
"_____no_output_____"
],
[
"a=\"kumar fgf\"",
"_____no_output_____"
],
[
"a.isspace()",
"_____no_output_____"
],
[
"a.islower()",
"_____no_output_____"
],
[
"a.isupper()",
"_____no_output_____"
],
[
"a =\"Kumr\"",
"_____no_output_____"
],
[
"a.isupper()",
"_____no_output_____"
],
[
"a.islower()",
"_____no_output_____"
],
[
"'Insert something from this string {}'.format('hello')",
"_____no_output_____"
],
[
"a= 'kiumran'",
"_____no_output_____"
],
[
"b=a",
"_____no_output_____"
],
[
"print(b)",
"kiumran\n"
]
],
[
[
"# List Object",
"_____no_output_____"
]
],
[
[
"a.isascii()",
"_____no_output_____"
],
[
"my_list = [1,2,\"ram\"]",
"_____no_output_____"
],
[
"my_list[2]",
"_____no_output_____"
],
[
"print(my_list)",
"[1, 2, 'ram']\n"
],
[
"my_list.index(1)",
"_____no_output_____"
],
[
"my_list.index('ram')",
"_____no_output_____"
],
[
"my_list.pop()",
"_____no_output_____"
],
[
"print(my_list)",
"[1, 2]\n"
],
[
"my_list.clear()",
"_____no_output_____"
],
[
"print(my_list)",
"[]\n"
],
[
"my_list.insert(10,'ram')",
"_____no_output_____"
],
[
"print(my_list)",
"['ram', 'ram', 'ram']\n"
],
[
"my_list *4",
"_____no_output_____"
],
[
"a = [[1,2,3],[4,5,6],[7,8,9]]",
"_____no_output_____"
],
[
"[row[1] for row in a]",
"_____no_output_____"
],
[
"my_dict = {'key':'ram','value':['a',1]}",
"_____no_output_____"
],
[
"print(my_dict['value'])",
"['a', 1]\n"
],
[
"print(my_dict.get('key'))",
"ram\n"
],
[
"my_dict.popitem()",
"_____no_output_____"
],
[
"print(my_dict)",
"{'key': 'ram', 'value': ['a', 1]}\n"
],
[
"my_dict.setdefault('key')",
"_____no_output_____"
],
[
"print(my_dict)",
"{'key': 'ram', 'value': ['a', 1]}\n"
],
[
"my_dict.update('key')",
"_____no_output_____"
]
],
[
[
"# Tuple",
"_____no_output_____"
]
],
[
[
"t=(1,2,3,4,{'key':'value'})",
"_____no_output_____"
],
[
"t.index({'key':'value'})",
"_____no_output_____"
]
],
[
[
"# Set",
"_____no_output_____"
]
],
[
[
"n={1.2,12,}",
"_____no_output_____"
],
[
"m={'rams'}",
"_____no_output_____"
]
],
[
[
"# m = n.copy",
"_____no_output_____"
]
],
[
[
"a=m",
"{1.2, 12, 1}\n"
],
[
"a.isdisjoint(12)",
"_____no_output_____"
],
[
"print(a)",
"{1.2, 12, 1}\n"
]
],
[
[
"# Boolean",
"_____no_output_____"
]
],
[
[
"True",
"_____no_output_____"
],
[
"not False",
"_____no_output_____"
]
]
] | [
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[
"code",
"code"
]
] |
cb1a0dc9fbcd942a117a1032fbddf9511ab24692 | 73,508 | ipynb | Jupyter Notebook | papers/4318-2020-Stonehouse/Examples/CreditRisk.ipynb | brandonmreese/sas-global-forum-2020 | 14f969eb98626fc4342b032c8fe0413adbcf21f8 | [
"Apache-2.0"
] | 30 | 2020-01-17T19:46:09.000Z | 2022-03-16T08:01:59.000Z | papers/4318-2020-Stonehouse/Examples/CreditRisk.ipynb | brandonmreese/sas-global-forum-2020 | 14f969eb98626fc4342b032c8fe0413adbcf21f8 | [
"Apache-2.0"
] | 6 | 2020-02-11T17:04:40.000Z | 2020-11-03T17:04:37.000Z | papers/4318-2020-Stonehouse/Examples/CreditRisk.ipynb | brandonmreese/sas-global-forum-2020 | 14f969eb98626fc4342b032c8fe0413adbcf21f8 | [
"Apache-2.0"
] | 70 | 2020-01-16T15:06:56.000Z | 2022-03-22T21:54:37.000Z | 98.801075 | 14,243 | 0.592793 | [
[
[
"import swat\n\nimport pandas as pd\nimport os\nfrom sys import platform\nimport riskpy\nfrom os.path import join as path",
"_____no_output_____"
],
[
"if \"CASHOST\" in os.environ:\n # Create a session to the CASHOST and CASPORT variables set in your environment\n conn = riskpy.SessionContext(session=swat.CAS(),\n caslib=\"CASUSER\")\nelse:\n # Otherwise set this to your host and port:\n host = \"riskpy.rqs-cloud.sashq-d.openstack.sas.com\"\n port = 5570\n conn = riskpy.SessionContext(session=swat.CAS(host, port), caslib=\"CASUSER\")",
"_____no_output_____"
],
[
"base_dir = '.'\n\n# Set output location\nif platform == \"win32\":\n # Windows...\n output_dir = 'u:\\\\temp'\nelse:\n # platform == \"linux\" or platform == \"linux2\" or platform == \"darwin\":\n output_dir = '/tmp'",
"_____no_output_____"
],
[
"mkt_data = riskpy.MarketData(\n current = pd.DataFrame(data={'uerate': 6.0}, index=[0]),\n risk_factors = ['uerate'])",
"_____no_output_____"
],
[
"my_scens = riskpy.Scenarios(\n name = \"my_scens\",\n market_data = mkt_data,\n data = path(\"datasources\",\"CreditRisk\",'uerate_scenario.xlsx'))",
"_____no_output_____"
],
[
"my_scens",
"_____no_output_____"
],
[
"cpty_df = pd.read_excel(path(\"datasources\",\"CreditRisk\",'uerate_cpty.xlsx'))\nloan_groups = riskpy.Counterparties(data=pd.read_excel(\n path(\"datasources\",\"CreditRisk\",'uerate_cpty.xlsx')))\nloan_groups.mapping = {\"cpty1\": \"score_uerate\"}",
"_____no_output_____"
],
[
"loan_groups",
"_____no_output_____"
],
[
"score_code_file=(path(\"methods\",\"CreditRisk\",'score_uerate.sas'))\nscoring_methods = riskpy.MethodLib(\n method_code=path(\"methods\",\"CreditRisk\",'score_uerate.sas'))",
"_____no_output_____"
],
[
"scoring_methods",
"_____no_output_____"
],
[
"my_scores = riskpy.Scores(counterparties=loan_groups,\n scenarios=my_scens,\n method_lib=scoring_methods)\nmy_scores.generate(session_context=conn, write_allscore=True)\n\nprint(my_scores.allscore.head())\nallscore_file = path(output_dir, 'simple_allscores.xlsx')\nmy_scores.allscore.to_excel(allscore_file)",
"NOTE: Executing action 'builtins.loadActionSet'.\nNOTE: Added action set 'riskRun'.\nNOTE: Action 'builtins.loadActionSet' used (Total process time):\nNOTE: real time 0.011323 seconds\nNOTE: cpu time 0.024998 seconds (220.77%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 705.72K (0.00%)\nNOTE: Executing action 'table.addTable'.\nNOTE: Action 'table.addTable' used (Total process time):\nNOTE: real time 0.034244 seconds\nNOTE: cpu time 0.063989 seconds (186.86%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 3.31M (0.00%)\nNOTE: bytes moved 1.69K\nNOTE: Added table 'Counterparties_DATA' to caslib 'CASUSER'\nNOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table MARKETDATA_CURRENT in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.097134 seconds\nNOTE: cpu time 0.208968 seconds (215.13%)\nNOTE: data movement time 0.002832 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 34.99M (0.00%)\nNOTE: bytes moved 0.18K\nNOTE: The table MARKETDATA_CURRENT has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.116156 seconds\nNOTE: cpu time 0.245963 seconds (211.75%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 34.99M (0.00%)\nNOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table MY_SCENS_DATA in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.160682 seconds\nNOTE: cpu time 0.331949 seconds (206.59%)\nNOTE: data movement time 0.002770 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 67.20M (0.01%)\nNOTE: bytes moved 0.83K\nNOTE: The table MY_SCENS_DATA has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.180372 seconds\nNOTE: cpu time 0.370945 seconds (205.66%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 67.20M (0.01%)\nNOTE: Executing action 'builtins.loadActionSet'.\nNOTE: Added action set 'riskMethods'.\nNOTE: Action 'builtins.loadActionSet' used (Total process time):\nNOTE: real time 0.010766 seconds\nNOTE: cpu time 0.022995 seconds (213.59%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 719.47K (0.00%)\nNOTE: Executing action 'riskMethods.add'.\nNOTE: Action 'riskMethods.add' used (Total process time):\nNOTE: real time 0.066686 seconds\nNOTE: cpu time 0.154978 seconds (232.40%)\nNOTE: data movement time 0.002790 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 5.41M (0.00%)\nNOTE: bytes moved 6.77K\nNOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table SCORES_ENV_IN in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.156734 seconds\nNOTE: cpu time 0.248962 seconds (158.84%)\nNOTE: data movement time 0.002668 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 67.23M (0.01%)\nNOTE: bytes moved 1.38K\nNOTE: The table SCORES_ENV_IN has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.175025 seconds\nNOTE: cpu time 0.281957 seconds (161.10%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 67.23M (0.01%)\nNOTE: Executing action 'riskRun.scoreCounterparties'.\nNOTE: Starting ScoreCounterparties action.\nNOTE: The table 'Scores_ENV_OUT' in caslib 'CASUSER(daston)' has 12 observations and 7 variables.\nNOTE: The table 'Scores_scen_states_out' in caslib 'CASUSER(daston)' has 7 observations and 5 variables.\nNOTE: The table 'Scores_scores' in caslib 'CASUSER(daston)' has 7 observations and 3 variables.\nNOTE: The table 'Scores_allscore' in caslib 'CASUSER(daston)' has 49 observations and 6 variables.\nNOTE: Action 'riskRun.scoreCounterparties' used (Total process time):\nNOTE: real time 0.711545 seconds\nNOTE: cpu time 1.336796 seconds (187.87%)\nNOTE: data movement time 0.008412 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 177.79M (0.02%)\nNOTE: bytes moved 14.15K\nSelected Rows from Table SCORES_ALLSCORE\n\n cptyid _horidx_ _rep_ AnalysisName _PD_ _LGD_\n0 uerate_sens_grp01 NaN NaN BASECASE 0.000013 0.05\n1 uerate_sens_grp01 1.0 1.0 adverse 0.000018 0.05\n2 uerate_sens_grp01 2.0 1.0 adverse 0.000025 0.05\n3 uerate_sens_grp01 3.0 1.0 adverse 0.000032 0.05\n4 uerate_sens_grp01 1.0 1.0 base 0.000014 0.05\n"
],
[
"my_scores",
"_____no_output_____"
],
[
"portfolio = riskpy.Portfolio(\n data=path(\"datasources\",\"CreditRisk\",'retail_portfolio.xlsx'),\n class_variables = [\"region\", \"cptyid\"])",
"_____no_output_____"
],
[
"eval_methods = riskpy.MethodLib(\n method_code=path(\"methods\",\"CreditRisk\",'credit_method2.sas'))",
"_____no_output_____"
],
[
"my_values = riskpy.Values(\n session_context=conn,\n portfolio=portfolio,\n output_variables=[\"Expected_Credit_Loss\"],\n scenarios=my_scens,\n scores=my_scores,\n method_lib=eval_methods,\n mapping = {\"Retail\": \"ecl_method\"})\nmy_values",
"_____no_output_____"
],
[
"my_values.evaluate(write_prices=True)\nallprice_df = my_values.fetch_prices(max_rows=100000)\nprint(my_values.allprice.head())\nallprice_file = path(output_dir, 'creditrisk_allprice.xlsx')\nallprice_df.to_excel(allprice_file)",
"NOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table PORTFOLIO in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.317023 seconds\nNOTE: cpu time 0.487925 seconds (153.91%)\nNOTE: data movement time 0.014851 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 69.69M (0.01%)\nNOTE: bytes moved 1.03M\nNOTE: The table PORTFOLIO has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.366828 seconds\nNOTE: cpu time 0.531920 seconds (145.01%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 69.69M (0.01%)\nNOTE: Executing action 'table.addTable'.\nNOTE: Action 'table.addTable' used (Total process time):\nNOTE: real time 0.040721 seconds\nNOTE: cpu time 0.052992 seconds (130.13%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 3.30M (0.00%)\nNOTE: bytes moved 1.69K\nNOTE: Added table 'Counterparties_DATA' to caslib 'CASUSER'\nNOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table MARKETDATA_CURRENT in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.092584 seconds\nNOTE: cpu time 0.158977 seconds (171.71%)\nNOTE: data movement time 0.002971 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 34.95M (0.00%)\nNOTE: bytes moved 0.18K\nNOTE: The table MARKETDATA_CURRENT has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.109841 seconds\nNOTE: cpu time 0.187972 seconds (171.13%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 34.95M (0.00%)\nNOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table MARKETDATA_CURRENT in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.091031 seconds\nNOTE: cpu time 0.156978 seconds (172.44%)\nNOTE: data movement time 0.002470 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 34.99M (0.00%)\nNOTE: bytes moved 0.18K\nNOTE: The table MARKETDATA_CURRENT has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.108635 seconds\nNOTE: cpu time 0.185971 seconds (171.19%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 34.99M (0.00%)\nNOTE: Executing action 'builtins.loadActionSet'.\nNOTE: Added action set 'riskMethods'.\nNOTE: Action 'builtins.loadActionSet' used (Total process time):\nNOTE: real time 0.007792 seconds\nNOTE: cpu time 0.010998 seconds (141.14%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 647.31K (0.00%)\nNOTE: Executing action 'riskMethods.add'.\nNOTE: Action 'riskMethods.add' used (Total process time):\nNOTE: real time 0.070773 seconds\nNOTE: cpu time 0.147977 seconds (209.09%)\nNOTE: data movement time 0.003322 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 10.78M (0.00%)\nNOTE: bytes moved 17.34K\nNOTE: Executing action 'table.loadTable'.\nNOTE: Cloud Analytic Services made the uploaded file available as table VALUES_ENV_IN in caslib CASUSER(daston).\nNOTE: Action 'table.loadTable' used (Total process time):\nNOTE: real time 0.156181 seconds\nNOTE: cpu time 0.245961 seconds (157.48%)\nNOTE: data movement time 0.002688 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 67.17M (0.01%)\nNOTE: bytes moved 2.82K\nNOTE: The table VALUES_ENV_IN has been created in caslib CASUSER(daston) from binary data uploaded to Cloud Analytic Services.\nNOTE: Action 'table.upload' used (Total process time):\nNOTE: real time 0.174533 seconds\nNOTE: cpu time 0.277957 seconds (159.26%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 67.17M (0.01%)\nNOTE: Executing action 'builtins.loadActionSet'.\nNOTE: Added action set 'riskRun'.\nNOTE: Action 'builtins.loadActionSet' used (Total process time):\nNOTE: real time 0.006723 seconds\nNOTE: cpu time 0.009999 seconds (148.73%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 647.31K (0.00%)\nNOTE: Executing action 'riskRun.evaluatePortfolio'.\nNOTE: The table 'Values_ENV_OUT' in caslib 'CASUSER(daston)' has 21 observations and 7 variables.\nNOTE: The table 'Values_scen_states_out' in caslib 'CASUSER(daston)' has 7 observations and 5 variables.\nNOTE: The table 'Values_values' in caslib 'CASUSER(daston)' has 10000 observations and 6 variables.\nNOTE: The table 'Values_allprice' in caslib 'CASUSER(daston)' has 70000 observations and 8 variables.\nNOTE: Action 'riskRun.evaluatePortfolio' used (Total process time):\nNOTE: real time 2.066422 seconds\nNOTE: cpu time 4.040386 seconds (195.53%)\nNOTE: data movement time 0.010564 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 205.05M (0.03%)\nNOTE: bytes moved 8.42M\nNOTE: Executing action 'table.fetch'.\nNOTE: Action 'table.fetch' used (Total process time):\nNOTE: real time 1.329983 seconds\nNOTE: cpu time 1.423784 seconds (107.05%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 7.61M (0.00%)\nNOTE: Retrieved table 'Values_allprice' from caslib 'CASUSER'\nSelected Rows from Table VALUES_ALLPRICE\n\n AnalysisName _horidx_ _rep_ _date_ FX_Rate instid \\\n0 BASECASE NaN NaN 2020-03-01 1.0 L6887 \n1 adverse 1.0 1.0 2020-02-01 1.0 L6887 \n2 adverse 2.0 1.0 2020-03-01 1.0 L6887 \n3 adverse 3.0 1.0 2020-04-01 1.0 L6887 \n4 base 1.0 1.0 2020-02-01 1.0 L6887 \n\n Expected_Credit_Loss Value \n0 3.492534 1.0 \n1 4.810488 1.0 \n2 6.848084 1.0 \n3 8.526695 1.0 \n4 3.731256 1.0 \n"
],
[
"results = riskpy.Results(\n session_context=conn,\n values=my_values,\n requests=[\"_TOP_\", [\"region\"]],\n out_type=\"values\"\n)\nresults_df = results.query().to_frame()\nprint(results_df.head())\nrollup_file = path(output_dir, 'creditrisk_rollup_by_region.xlsx')\nresults_df.to_excel(rollup_file)",
"NOTE: Executing action 'builtins.loadActionSet'.\nNOTE: Added action set 'riskresults'.\nNOTE: Action 'builtins.loadActionSet' used (Total process time):\nNOTE: real time 0.011122 seconds\nNOTE: cpu time 0.019000 seconds (170.83%)\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 647.84K (0.00%)\nNOTE: Executing action 'riskResults.query'.\nNOTE: Starting the query action.\nNOTE: The table 'Results_values' in caslib 'CASUSER(daston)' has 336 observations and 10 variables.\nNOTE: Action 'riskResults.query' used (Total process time):\nNOTE: real time 1.827813 seconds\nNOTE: cpu time 4.435326 seconds (242.66%)\nNOTE: data movement time 0.008973 seconds\nNOTE: total nodes 3 (48 cores)\nNOTE: total memory 756.70G\nNOTE: memory 178.90M (0.02%)\nNOTE: bytes moved 46.09K\nSelected Rows from Table RESULTS_VALUES\n\n region NInst ResultName _date_ _horizon_ _horidx_ _rep_ Value \\\n0 Region1 200.0 BASECASE 2020-03-01 NaN NaN NaN 200.0 \n1 Region1 200.0 adverse 2020-02-01 NaN 1.0 1.0 200.0 \n2 Region1 200.0 adverse 2020-03-01 NaN 2.0 1.0 200.0 \n3 Region1 200.0 adverse 2020-04-01 NaN 3.0 1.0 200.0 \n4 Region1 200.0 base 2020-02-01 NaN 1.0 1.0 200.0 \n\n Expected_Credit_Loss PL \n0 4.145759e+06 200.0 \n1 4.396209e+06 0.0 \n2 4.691725e+06 0.0 \n3 4.886033e+06 0.0 \n4 4.196179e+06 0.0 \n"
],
[
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],
[
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cb1a1088b71837aa191cf5c047815c3ff754f0db | 8,504 | ipynb | Jupyter Notebook | 11_openpyxl/report_01.ipynb | bryson0083/python_note | da44cf4015c5111953a6792788c19a00efbdc78b | [
"MIT"
] | null | null | null | 11_openpyxl/report_01.ipynb | bryson0083/python_note | da44cf4015c5111953a6792788c19a00efbdc78b | [
"MIT"
] | null | null | null | 11_openpyxl/report_01.ipynb | bryson0083/python_note | da44cf4015c5111953a6792788c19a00efbdc78b | [
"MIT"
] | null | null | null | 32.090566 | 1,181 | 0.502234 | [
[
[
"import pandas as pd\nfrom openpyxl import Workbook\nfrom openpyxl.styles import Border, Side, Font, Alignment\nfrom openpyxl.utils.dataframe import dataframe_to_rows",
"_____no_output_____"
]
],
[
[
"# 測試資料",
"_____no_output_____"
]
],
[
[
"# 表格資料title\ndata = [\n {\"route_id\": \"0001\", \"route_desc\": \"路線1\", \"num_of_people\": 100, \n \"origin_amt\": 1000, \"act_amt\": 600, \"subsidy_amt\": 400, \"avg_subsidy_amt_by_people\": 4},\n {\"route_id\": \"0002\", \"route_desc\": \"路線2\", \"num_of_people\": 100, \n \"origin_amt\": 1000, \"act_amt\": 600, \"subsidy_amt\": 400, \"avg_subsidy_amt_by_people\": 4},\n {\"route_id\": \"0003\", \"route_desc\": \"路線3\", \"num_of_people\": 100, \n \"origin_amt\": 1000, \"act_amt\": 600, \"subsidy_amt\": 400, \"avg_subsidy_amt_by_people\": 4},\n]\n\ndf = pd.DataFrame(data, columns=[\"route_id\", \"route_desc\", \"num_of_people\", \"origin_amt\", \n \"act_amt\", \"subsidy_amt\", \"avg_subsidy_amt_by_people\"])\ndf",
"_____no_output_____"
],
[
"df.columns=[\"路線\\n編號\", \"路線\\n名稱\", \"使用轉乘優惠\\n人數\", \"原始票收金額\", \"實際交易金額\", \n \"優惠補貼金額\", \"平均每人\\n優惠金額\"]",
"_____no_output_____"
]
],
[
[
"# EXCEL輸出格式產生",
"_____no_output_____"
]
],
[
[
"wb = Workbook()\nws = wb.active",
"_____no_output_____"
],
[
"# 設定print area\n# https://openpyxl.readthedocs.io/en/stable/print_settings.html\nws.print_options.horizontalCentered = True\n# ws.print_options.verticalCentered = True\nws.print_area = 'A1:G10'",
"_____no_output_____"
],
[
"# 設定輸出表格字體參數\ntable_title_ft = Font(name='標楷體', color='000000', size=14, bold=True)\ntable_ft = Font(name='標楷體', color='000000', size=14)\n\n# 表格格線設定\ntable_border = Border(left=Side(border_style='thin', color='000000'),\n right=Side(border_style='thin', color='000000'),\n top=Side(border_style='thin', color='000000'),\n bottom=Side(border_style='thin', color='000000'))",
"_____no_output_____"
],
[
"ws.insert_rows(1) # 在第一行插入一行\nws.merge_cells('A1:G1') # 欄位\nws[\"A1\"] = '表1 ○年○月○○客運公司○○○公車轉乘第一段票免費補貼金額申請表'\nws[\"A1\"].font = table_title_ft\nws[\"A1\"].alignment = Alignment(horizontal='center')",
"_____no_output_____"
],
[
"# DataFrame資料填入sheet row\nfor row in dataframe_to_rows(df, index=False, header=True):\n ws.append(row)",
"_____no_output_____"
],
[
"# Table加入格線\nrows = ws[\"A3:G6\"]\nfor row in rows:\n for cell in row:\n cell.border = table_border\n cell.font = table_ft",
"_____no_output_____"
],
[
"# 自動指定欄位寬度\n# https://stackoverflow.com/questions/13197574/openpyxl-adjust-column-width-size\n# from openpyxl.utils import get_column_letter\n\n# column_widths = []\n# for row in data:\n# for i, cell in enumerate(row):\n# if len(column_widths) > i:\n# if len(cell) > column_widths[i]:\n# column_widths[i] = len(cell)\n# else:\n# column_widths += [len(cell)]\n\n# for i, column_width in enumerate(column_widths):\n# ws.column_dimensions[get_column_letter(i+1)].width = column_width",
"_____no_output_____"
],
[
"# 直接指定欄位寬度\n# https://stackoverflow.com/questions/53906532/is-it-possible-to-change-the-column-width-using-openpyxl/53906585\nws.column_dimensions['A'].width = 10\nws.column_dimensions['B'].width = 10\nws.column_dimensions['C'].width = 23\nws.column_dimensions['D'].width = 23\nws.column_dimensions['E'].width = 23\nws.column_dimensions['F'].width = 23\nws.column_dimensions['G'].width = 23",
"_____no_output_____"
],
[
"# Table欄位title style設定\ntable_align_style = Alignment(wrapText=True, horizontal='center', vertical='center')\n\nfor rows in ws['A3':'G3']:\n for cell in rows:\n cell.alignment = table_align_style",
"_____no_output_____"
],
[
"# ws[\"A\":\"G\"].alignment = Alignment(wrapText=True, horizontal='center')\n# ws['A1'].alignment = Alignment(wrapText=True)\n# rows = sheet[\"A1:C3\"]\n# for row in rows:\n# for cell in row:\n# cell.border = border",
"_____no_output_____"
],
[
"wb.save(\"report_01_output.xlsx\")",
"_____no_output_____"
]
]
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cb1a15eaab956847faf1f9cd3a8613190ff318d3 | 12,998 | ipynb | Jupyter Notebook | JupyterNotebooks/Lessons/Lesson 2.ipynb | emilekhoury/CMPT-221L-621-21F | 3778809a41981cf75912ebf59796ec201e94054d | [
"MIT"
] | null | null | null | JupyterNotebooks/Lessons/Lesson 2.ipynb | emilekhoury/CMPT-221L-621-21F | 3778809a41981cf75912ebf59796ec201e94054d | [
"MIT"
] | null | null | null | JupyterNotebooks/Lessons/Lesson 2.ipynb | emilekhoury/CMPT-221L-621-21F | 3778809a41981cf75912ebf59796ec201e94054d | [
"MIT"
] | null | null | null | 53.053061 | 445 | 0.653716 | [
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cb1a1ff52e3c7ac6725ed229ffe4b38d6fdbceb8 | 149,431 | ipynb | Jupyter Notebook | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations | 2d1864b6e9cd26624c769ee1e970d69d19da7fbf | [
"CC-BY-4.0"
] | 5 | 2019-06-19T19:42:21.000Z | 2021-04-20T22:43:45.000Z | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BHTenseAndAspect | 2d1864b6e9cd26624c769ee1e970d69d19da7fbf | [
"CC-BY-4.0"
] | 2 | 2020-02-25T10:19:40.000Z | 2020-03-13T15:29:01.000Z | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations | 2d1864b6e9cd26624c769ee1e970d69d19da7fbf | [
"CC-BY-4.0"
] | null | null | null | 54.516965 | 502 | 0.436362 | [
[
[
"# Semantic Function Species (part 2)",
"_____no_output_____"
]
],
[
[
"from scripts.imports import *\n\nout = Exporter(\n paths['outdir'], \n 'semantics'\n)",
"_____no_output_____"
],
[
"from IPython.display import HTML, display",
"_____no_output_____"
],
[
"df.columns",
"_____no_output_____"
]
],
[
[
"# Miscellaneous Functions",
"_____no_output_____"
]
],
[
[
"df[df.funct_type == 'secondary'].function.value_counts()",
"_____no_output_____"
],
[
"funct2names = {\n 'purposive_ext':['purpext', 'Purposive Extent'],\n 'dist_posterior': ['distpost', 'Distance Posterior'],\n 'anterior_limitive': ['antlimit', 'Anterior Limitive'],\n 'dist_prospective': ['distprosp', 'Distance Prospective'],\n 'purposive': ['purp', 'Purposive'],\n 'anterior_dur_except': ['antdurex', 'Anterior Durative with \"Except\"'],\n 'posterior_dur_future': ['postdurfut', 'Posterior Durative Future'],\n}\n\n# automatically show examples\nfor funct in funct2names:\n exdf = df[df.function == funct].sort_values(by='notes').head(10)\n print(funct)\n display(\n ts.show(exdf, extra=['notes'], spread=-1)\n )\n print('-'*50)",
"purposive_ext\nshowing 10 of 10\n"
]
],
[
[
"# Pull Out Examples",
"_____no_output_____"
],
[
"## Purposive Extent",
"_____no_output_____"
]
],
[
[
"purpext_df = df[df.function == 'purposive_ext']\n\nout.number(\n purpext_df.shape[0],\n 'purpext_N'\n)",
"_____no_output_____"
],
[
"antlim_df = df[df.function == 'anterior_limitive']\n\nout.number(\n antlim_df.shape[0],\n 'antlim_N'\n)",
"_____no_output_____"
]
],
[
[
"# Difficult Cases",
"_____no_output_____"
],
[
"# Compound Time Adverbials",
"_____no_output_____"
]
],
[
[
"compound_ct = df[df.funct_type == 'compound'].function.value_counts()\n\nout.table(\n compound_ct,\n 'compound_funct_ct',\n caption='Sampled Compound Time Adverbial Frequencies',\n)",
"_____no_output_____"
],
[
"comp_clusters = {\n\n 'begin-to-end': [\n 'begin_to_end',\n 'habitual + begin_to_end',\n 'begin_to_end_habitual',\n 'simultaneous + begin_to_end',\n 'simultaneous + multi_begin_to_end',\n 'posterior_dur + begin_to_end + atelic_ext',\n 'begin_to_end + multi_antdur',\n ],\n\n 'coordinated location': [\n 'simultaneous_calendar',\n 'multi_simuls',\n 'simultaneous + anterior',\n 'simul_to_end',\n 'simultaneous + anterior_limitive?',\n 'simultaneous + anterior_dist',\n 'simultaneous + posterior',\n 'simultaneous + posteriors',\n 'simultaneous + dist_posterior',\n 'posterior + simultaneous',\n 'multi_antdur',\n 'anterior_dur + anterior',\n 'anterior + posterior',\n 'multi_posterior_dur',\n 'simultaneous + purposive_ext',\n ],\n\n 'coordinated extent': [\n 'multi_atelic_ext',\n ],\n \n 'location + extent': [\n 'simultaneous + atelic_ext',\n 'atelic_ext + simultaneous',\n 'anterior + atelic_ext',\n 'anterior + distance',\n 'atelic_ext + anterior + atelic_ext',\n 'anterior_dur + duration',\n 'dur_to_end',\n 'posterior + atelic_ext',\n 'dist_fut + atelic_ext',\n 'reg_recurr + atelic_ext',\n 'atelic_ext + habitual',\n ],\n \n 'distance sequential': [\n 'posterior + distance',\n ],\n \n}",
"_____no_output_____"
],
[
"attested = set(cl for name, functs in comp_clusters.items() for cl in functs)",
"_____no_output_____"
],
[
"set(compound_ct.index) - attested",
"_____no_output_____"
]
],
[
[
"## Auto Export Examples for Compounds",
"_____no_output_____"
]
],
[
[
"for cluster, labels in comp_clusters.items():\n display(HTML(f'<h2>{cluster.title()}</h2>'))\n for label in labels:\n print(label)\n ex_df = df[df.function == label]\n display(\n ts.show(\n ex_df\n )\n )\n display(HTML('<hr>'))",
"_____no_output_____"
]
],
[
[
"# Manually Extract Specific Cases",
"_____no_output_____"
],
[
"## Begin-to-end",
"_____no_output_____"
]
],
[
[
"b2edf = df[df.function.isin(comp_clusters['begin-to-end'])]\n\nout.number(\n b2edf.shape[0],\n 'begintoend_N'\n)",
"_____no_output_____"
]
],
[
[
"## Calendricals",
"_____no_output_____"
]
],
[
[
"caldf = df[df.function == 'simultaneous_calendar']\n\nout.number(\n caldf.shape[0],\n 'N_simul_calendar'\n)",
"_____no_output_____"
],
[
"caldf.times_utf8.value_counts()",
"_____no_output_____"
]
]
] | [
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cb1a2ac3f14b6201430b18d991f3f72334fc8c7d | 790,926 | ipynb | Jupyter Notebook | Part 8 - Deep Learning/Section 40 - Convolutional Neural Networks (CNN)/mine/CNN.ipynb | jithurjacob/MachineLearning_A-Z | 90db96c61260689f8aaedde31cce852fa5a6fef4 | [
"MIT"
] | null | null | null | Part 8 - Deep Learning/Section 40 - Convolutional Neural Networks (CNN)/mine/CNN.ipynb | jithurjacob/MachineLearning_A-Z | 90db96c61260689f8aaedde31cce852fa5a6fef4 | [
"MIT"
] | null | null | null | Part 8 - Deep Learning/Section 40 - Convolutional Neural Networks (CNN)/mine/CNN.ipynb | jithurjacob/MachineLearning_A-Z | 90db96c61260689f8aaedde31cce852fa5a6fef4 | [
"MIT"
] | 1 | 2021-06-27T09:23:58.000Z | 2021-06-27T09:23:58.000Z | 1,910.449275 | 782,815 | 0.500118 | [
[
[
"# building the CNN",
"_____no_output_____"
],
[
"from keras.models import Sequential",
"Using Theano backend.\nCan not use cuDNN on context None: cannot compile with cuDNN. We got this error:\nb'C:\\\\Users\\\\JITHUR~1\\\\AppData\\\\Local\\\\Temp\\\\try_flags_sas0njyl.c:4:10: fatal error: cudnn.h: No such file or directory\\r\\n #include <cudnn.h>\\r\\n ^~~~~~~~~\\r\\ncompilation terminated.\\r\\n'\nMapped name None to device cuda: GeForce 610M (0000:01:00.0)\n"
],
[
"from keras.layers import Convolution2D",
"_____no_output_____"
],
[
"from keras.layers import MaxPooling2D",
"_____no_output_____"
],
[
"from keras.layers import Flatten",
"_____no_output_____"
],
[
"from keras.layers import Dense",
"_____no_output_____"
],
[
"#initializing CNN",
"_____no_output_____"
],
[
"classifier = Sequential()",
"_____no_output_____"
],
[
"# add convolution layer",
"_____no_output_____"
],
[
"# for tf backend\nclassifier.add(Convolution2D(32,(3,3),input_shape=(64,64,3),activation='relu'))",
"WARNING (theano.gof.compilelock): Overriding existing lock by dead process '2872' (I am process '15208')\n"
],
[
"# add pooling layer",
"_____no_output_____"
],
[
"classifier.add(MaxPooling2D(pool_size=(2,2)))",
"_____no_output_____"
],
[
"# seond conv layer\nclassifier.add(Convolution2D(32,(3,3),activation='relu'))\nclassifier.add(MaxPooling2D(pool_size=(2,2)))",
"_____no_output_____"
],
[
"# flattening",
"_____no_output_____"
],
[
"classifier.add(Flatten())",
"_____no_output_____"
],
[
"# adding fully connected layer",
"_____no_output_____"
],
[
"classifier.add(Dense(output_dim=128,activation='relu'))",
"C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=128)`\n if __name__ == '__main__':\n"
],
[
"classifier.add(Dense(output_dim=1,activation='sigmoid'))",
"C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1)`\n if __name__ == '__main__':\n"
],
[
"# compiling the CNN",
"_____no_output_____"
],
[
"classifier.compile(optimizer = 'adam', loss='binary_crossentropy', metrics=['accuracy'])",
"_____no_output_____"
],
[
"# fitting the data",
"_____no_output_____"
],
[
"from keras.preprocessing.image import ImageDataGenerator",
"_____no_output_____"
],
[
"train_datagen = ImageDataGenerator(\n rescale=1./255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True)\n\ntest_datagen = ImageDataGenerator(rescale=1./255)",
"_____no_output_____"
],
[
"training_set = train_datagen.flow_from_directory(\n '../instructor/dataset/training_set',\n target_size=(64,64),\n batch_size=32,\n class_mode='binary')",
"Found 8000 images belonging to 2 classes.\n"
],
[
"test_set = test_datagen.flow_from_directory(\n '../instructor/dataset/test_set',\n target_size=(64,64),\n batch_size=32,\n class_mode='binary')",
"Found 2000 images belonging to 2 classes.\n"
],
[
"classifier.fit_generator(\n training_set,\n steps_per_epoch=8000,\n epochs=25,\n validation_data=test_set,\n validation_steps=2000)",
"Epoch 1/25\n5055/8000 [=================>............] - ETA: 1284s - loss: 0.4738 - acc: 0.7678 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"# COMP4096 Business Intelligence Group Project\n## COVID-19 Data Analysis and Prediction\n\n#### This part is written by Wong Tin Yau David (18207871).\n\n##### Datasets below are downloaded from https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv, which is provided by https://ourworldindata.org/. (An Open Source Project). With Filtering (only select data after 2021 because vaccines releases in this year and we would like to see the effectiveness)",
"_____no_output_____"
],
[
"### 1. Import Data 'owid-covid-data_2021.csv'",
"_____no_output_____"
]
],
[
[
"import pandas as pd\nimport numpy as np\npd.options.mode.chained_assignment = None # default='warn'\ndf = pd.read_csv('owid-covid-data_2021.csv')\ndf.head()",
"_____no_output_____"
],
[
"df.shape",
"_____no_output_____"
]
],
[
[
"### 2. Select the data only from G20 Countries only",
"_____no_output_____"
]
],
[
[
"g20df = df[(df[\"location\"]=='Australia')|(df[\"location\"]=='Canada')|(df[\"location\"]=='Saudi Arabia')|(df[\"location\"]=='United States')|(df[\"location\"]=='India')|(df[\"location\"]=='Russia')|(df[\"location\"]=='South Africa')|(df[\"location\"]=='Turkey')|(df[\"location\"]=='Argentina')|(df[\"location\"]=='Brazil')|(df[\"location\"]=='Mexico')|(df[\"location\"]=='France')|(df[\"location\"]=='Italy')|(df[\"location\"]=='Germany')|(df[\"location\"]=='United Kingdom')|(df[\"location\"]=='China')|(df[\"location\"]=='Indonesia')|(df[\"location\"]=='Japan')|(df[\"location\"]=='South Korea')]\ng20df.shape",
"_____no_output_____"
],
[
"droppable_features = []",
"_____no_output_____"
]
],
[
[
"### 3. Seach columns with mostly-missing values and drop columns with over 99% values",
"_____no_output_____"
]
],
[
[
"(g20df.isnull().sum()/g20df.shape[0]).sort_values(ascending=False)",
"_____no_output_____"
],
[
"droppable_features.append('weekly_icu_admissions_per_million')\ndroppable_features.append('weekly_icu_admissions')\ndroppable_features.append('weekly_hosp_admissions')\ndroppable_features.append('weekly_hosp_admissions_per_million')\n",
"_____no_output_____"
]
],
[
[
"### 4. Find Too Skewed Columns and Remove",
"_____no_output_____"
]
],
[
[
"pd.options.display.float_format = '{:,.4f}'.format\nsk_df = pd.DataFrame([{'column': c, 'uniq': g20df[c].nunique(), 'skewness': g20df[c].value_counts(normalize=True).values[0] * 100} for c in g20df.columns])\nsk_df = sk_df.sort_values('skewness', ascending=False)\nsk_df",
"_____no_output_____"
]
],
[
[
"### 5. Find columns that have more than 10% of missing values and filled with means ",
"_____no_output_____"
]
],
[
[
"null_counts = g20df.isnull().sum()\nnull_counts = null_counts / g20df.shape[0]\nnull_counts[null_counts > 0.1]",
"_____no_output_____"
],
[
"g20df.drop(droppable_features, axis=1, inplace=True)\n\ng20df.shape",
"_____no_output_____"
],
[
"g20df['icu_patients'].fillna((g20df['icu_patients'].mean()), inplace=True)\ng20df['icu_patients_per_million'].fillna((g20df['icu_patients_per_million'].mean()), inplace=True)\ng20df['hosp_patients'].fillna((g20df['hosp_patients'].mean()), inplace=True)\ng20df['hosp_patients_per_million'].fillna((g20df['hosp_patients_per_million'].mean()), inplace=True)\ng20df['new_tests'].fillna((g20df['new_tests'].mean()), inplace=True)\ng20df['total_tests'].fillna((g20df['total_tests'].mean()), inplace=True)\ng20df['total_tests_per_thousand'].fillna((g20df['total_tests_per_thousand'].mean()), inplace=True)\ng20df['new_tests_per_thousand'].fillna((g20df['new_tests_per_thousand'].mean()), inplace=True)\ng20df['new_tests_smoothed'].fillna((g20df['new_tests_smoothed'].mean()), inplace=True)\ng20df['new_tests_smoothed_per_thousand'].fillna((g20df['new_tests_smoothed_per_thousand'].mean()), inplace=True)\ng20df['total_vaccinations'].fillna((g20df['total_vaccinations'].mean()), inplace=True)",
"_____no_output_____"
]
],
[
[
"### 6. Find correlations between each attributes",
"_____no_output_____"
]
],
[
[
"cols = g20df.columns.tolist()",
"_____no_output_____"
],
[
"import seaborn as sns\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(10,10))\nco_cols = cols[:10]\nco_cols.append('new_vaccinations')\nsns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\nplt.title('Correlation between 1 ~ 10th columns')\nplt.show()",
"_____no_output_____"
],
[
"corr_remove = []\nco_cols = cols[10:20]\nco_cols.append('new_vaccinations')\nplt.figure(figsize=(10,10))\nsns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\nplt.title('Correlation between 11 ~ 20th columns')\nplt.show()",
"_____no_output_____"
],
[
"corr_remove = []\nco_cols = cols[20:30]\nco_cols.append('new_vaccinations')\nplt.figure(figsize=(10,10))\nsns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\nplt.title('Correlation between 21 ~ 30th columns')\nplt.show()",
"_____no_output_____"
],
[
"corr_remove = []\nco_cols = cols[30:40]\nco_cols.append('new_vaccinations')\nplt.figure(figsize=(10,10))\nsns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\nplt.title('Correlation between 31 ~ 40th columns')\nplt.show()",
"_____no_output_____"
],
[
"corr_remove = []\nco_cols = cols[40:50]\nco_cols.append('new_vaccinations')\nplt.figure(figsize=(10,10))\nsns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0.0)\nplt.title('Correlation between 41 ~ 50th columns')\nplt.show()",
"_____no_output_____"
],
[
"corr_remove = []\nco_cols = cols[50:]\nco_cols.append('new_vaccinations')\nplt.figure(figsize=(10,10))\nsns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0)\nplt.title('Correlation between from 51th to the last columns')\nplt.show()",
"_____no_output_____"
],
[
"corr = g20df.corr()\nhigh_corr = (corr >= 0.99).astype('uint8')\nplt.figure(figsize=(15,15))\nsns.heatmap(high_corr, cmap='RdBu_r', annot=True, center=0.0)\nplt.show()",
"_____no_output_____"
],
[
"fig, ax = plt.subplots()\nax.plot(organizedg20df['bymonth'],organizedg20df['total_new_cases_from_2021Jan'],color='green', linestyle=':', label='line 1')\nax.plot(organizedg20df['bymonth'],organizedg20df['total_vaccinations'], linestyle='--', label = 'line 2')\n\nax.legend(loc=1) #\n\nax.set_title('COVID-19 Cases versus Vaccinations', fontweight='bold',fontsize=18) \n\n\nax.set_xlabel('x label') # add xlabel\nax.set_ylabel('y label'); # add ylabel",
"_____no_output_____"
]
]
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cb1a4c987080c7d8d067248589ae755ca8ea2447 | 3,819 | ipynb | Jupyter Notebook | 0.16/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | drammock/mne-tools.github.io | 5d3a104d174255644d8d5335f58036e32695e85d | [
"BSD-3-Clause"
] | null | null | null | 0.16/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | drammock/mne-tools.github.io | 5d3a104d174255644d8d5335f58036e32695e85d | [
"BSD-3-Clause"
] | null | null | null | 0.16/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | drammock/mne-tools.github.io | 5d3a104d174255644d8d5335f58036e32695e85d | [
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[
[
"%matplotlib inline",
"_____no_output_____"
]
],
[
[
"\n# Analysis of evoked response using ICA and PCA reduction techniques\n\n\nThis example computes PCA and ICA of evoked or epochs data. Then the\nPCA / ICA components, a.k.a. spatial filters, are used to transform\nthe channel data to new sources / virtual channels. The output is\nvisualized on the average of all the epochs.\n\n",
"_____no_output_____"
]
],
[
[
"# Authors: Jean-Remi King <[email protected]>\n# Asish Panda <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.decoding import UnsupervisedSpatialFilter\n\nfrom sklearn.decomposition import PCA, FastICA\n\nprint(__doc__)\n\n# Preprocess data\ndata_path = sample.data_path()\n\n# Load and filter data, set up epochs\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\ntmin, tmax = -0.1, 0.3\nevent_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)\n\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.filter(1, 20, fir_design='firwin')\nevents = mne.read_events(event_fname)\n\npicks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n exclude='bads')\n\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,\n picks=picks, baseline=None, preload=True,\n verbose=False)\n\nX = epochs.get_data()",
"_____no_output_____"
]
],
[
[
"Transform data with PCA computed on the average ie evoked response\n\n",
"_____no_output_____"
]
],
[
[
"pca = UnsupervisedSpatialFilter(PCA(30), average=False)\npca_data = pca.fit_transform(X)\nev = mne.EvokedArray(np.mean(pca_data, axis=0),\n mne.create_info(30, epochs.info['sfreq'],\n ch_types='eeg'), tmin=tmin)\nev.plot(show=False, window_title=\"PCA\", time_unit='s')",
"_____no_output_____"
]
],
[
[
"Transform data with ICA computed on the raw epochs (no averaging)\n\n",
"_____no_output_____"
]
],
[
[
"ica = UnsupervisedSpatialFilter(FastICA(30), average=False)\nica_data = ica.fit_transform(X)\nev1 = mne.EvokedArray(np.mean(ica_data, axis=0),\n mne.create_info(30, epochs.info['sfreq'],\n ch_types='eeg'), tmin=tmin)\nev1.plot(show=False, window_title='ICA', time_unit='s')\n\nplt.show()",
"_____no_output_____"
]
]
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cb1a4deae4683b266869d77014ca64d89900f64b | 70,381 | ipynb | Jupyter Notebook | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas | 128091e7021158f39eb0ff97e0e63d76e778a52c | [
"CNRI-Python"
] | null | null | null | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas | 128091e7021158f39eb0ff97e0e63d76e778a52c | [
"CNRI-Python"
] | null | null | null | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas | 128091e7021158f39eb0ff97e0e63d76e778a52c | [
"CNRI-Python"
] | null | null | null | 65.409851 | 15,076 | 0.705759 | [
[
[
"# <font color=\"maroon\"><h4 align=\"center\">Pandas Group By</font>",
"_____no_output_____"
],
[
"**In this tutorial we are going to look at weather data from various cities and see how group by can be used to run some analytics.** ",
"_____no_output_____"
]
],
[
[
"import pandas as pd\ndf = pd.read_csv(\"weather_by_cities.csv\")\ndf",
"_____no_output_____"
]
],
[
[
"### For this dataset, get following answers,\n#### 1. What was the maximum temperature in each of these 3 cities?\n#### 2. What was the average windspeed in each of these 3 cities?\n",
"_____no_output_____"
]
],
[
[
"g = df.groupby(\"city\")\ng",
"_____no_output_____"
]
],
[
[
"**DataFrameGroupBy object looks something like below,**",
"_____no_output_____"
],
[
"",
"_____no_output_____"
]
],
[
[
"for city, data in g:\n print(\"city:\",city)\n print(\"\\n\")\n print(\"data:\",data) ",
"city: mumbai\n\n\ndata: day city temperature windspeed event\n4 1/1/2017 mumbai 90 5 Sunny\n5 1/2/2017 mumbai 85 12 Fog\n6 1/3/2017 mumbai 87 15 Fog\n7 1/4/2017 mumbai 92 5 Rain\ncity: new york\n\n\ndata: day city temperature windspeed event\n0 1/1/2017 new york 32 6 Rain\n1 1/2/2017 new york 36 7 Sunny\n2 1/3/2017 new york 28 12 Snow\n3 1/4/2017 new york 33 7 Sunny\ncity: paris\n\n\ndata: day city temperature windspeed event\n8 1/1/2017 paris 45 20 Sunny\n9 1/2/2017 paris 50 13 Cloudy\n10 1/3/2017 paris 54 8 Cloudy\n11 1/4/2017 paris 42 10 Cloudy\n"
]
],
[
[
"**This is similar to SQL,**\n\n**SELECT * from weather_data GROUP BY city**",
"_____no_output_____"
]
],
[
[
"g.get_group('mumbai')",
"_____no_output_____"
],
[
"g.max()",
"_____no_output_____"
],
[
"g.mean()",
"_____no_output_____"
]
],
[
[
"**This method of splitting your dataset in smaller groups and then applying an operation \n(such as min or max) to get aggregate result is called Split-Apply-Combine. It is illustrated in a diagram below**",
"_____no_output_____"
],
[
"",
"_____no_output_____"
]
],
[
[
"g.min()",
"_____no_output_____"
],
[
"g.describe()",
"_____no_output_____"
],
[
"g.size()",
"_____no_output_____"
],
[
"g.count()",
"_____no_output_____"
],
[
"%matplotlib inline\ng.plot()",
"_____no_output_____"
]
],
[
[
"<h4>Group data using custom function: Let's say you want to group your data using custom function. Here the requirement is to create three groups<h4>\n<ol>\n <li>Days when temperature was between 80 and 90</li>\n <li>Days when it was between 50 and 60</li>\n <li>Days when it was anything else</li>\n</ol>",
"_____no_output_____"
],
[
"For this you need to write custom grouping function and pass that to groupby",
"_____no_output_____"
]
],
[
[
"def grouper(df, idx, col):\n if 80 <= df[col].loc[idx] <= 90:\n return '80-90'\n elif 50 <= df[col].loc[idx] <= 60:\n return '50-60'\n else:\n return 'others'",
"_____no_output_____"
],
[
"g = df.groupby(lambda x: grouper(df, x, 'temperature'))\ng",
"_____no_output_____"
],
[
"for key, d in g:\n print(\"Group by Key: {}\\n\".format(key))\n print(d)",
"Group by Key: 50-60\n\n day city temperature windspeed event\n9 1/2/2017 paris 50 13 Cloudy\n10 1/3/2017 paris 54 8 Cloudy\nGroup by Key: 80-90\n\n day city temperature windspeed event\n4 1/1/2017 mumbai 90 5 Sunny\n5 1/2/2017 mumbai 85 12 Fog\n6 1/3/2017 mumbai 87 15 Fog\nGroup by Key: others\n\n day city temperature windspeed event\n0 1/1/2017 new york 32 6 Rain\n1 1/2/2017 new york 36 7 Sunny\n2 1/3/2017 new york 28 12 Snow\n3 1/4/2017 new york 33 7 Sunny\n7 1/4/2017 mumbai 92 5 Rain\n8 1/1/2017 paris 45 20 Sunny\n11 1/4/2017 paris 42 10 Cloudy\n"
]
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cb1a5088c3f5e104ad7c27d3c3a3a85a072d49fa | 20,249 | ipynb | Jupyter Notebook | Big-Data-Clusters/CU14/public/content/diagnose/tsg108-controller-failed-to-upgrade.ipynb | glienard/tigertoolbox | 766f28d23139a25c884f60ba50cbd39438e52aca | [
"MIT"
] | 1 | 2022-01-19T20:05:24.000Z | 2022-01-19T20:05:24.000Z | Big-Data-Clusters/CU14/public/content/diagnose/tsg108-controller-failed-to-upgrade.ipynb | glienard/tigertoolbox | 766f28d23139a25c884f60ba50cbd39438e52aca | [
"MIT"
] | null | null | null | Big-Data-Clusters/CU14/public/content/diagnose/tsg108-controller-failed-to-upgrade.ipynb | glienard/tigertoolbox | 766f28d23139a25c884f60ba50cbd39438e52aca | [
"MIT"
] | null | null | null | 20,249 | 20,249 | 0.609166 | [
[
[
"TSG108 - View the controller upgrade config map\n===============================================\n\nDescription\n-----------\n\nWhen running a Big Data Cluster upgrade using `azdata bdc upgrade`:\n\n`azdata bdc upgrade --name <namespace> --tag <tag>`\n\nIt may fail with:\n\n> Upgrading cluster to version 15.0.4003.10029\\_2\n>\n> NOTE: Cluster upgrade can take a significant amount of time depending\n> on configuration, network speed, and the number of nodes in the\n> cluster.\n>\n> Upgrading Control Plane. Control plane upgrade failed. Failed to\n> upgrade controller.\n\nSteps\n-----\n\nUse these steps to troubelshoot the problem.\n\n### Common functions\n\nDefine helper functions used in this notebook.",
"_____no_output_____"
]
],
[
[
"# Define `run` function for transient fault handling, suggestions on error, and scrolling updates on Windows\nimport sys\nimport os\nimport re\nimport platform\nimport shlex\nimport shutil\nimport datetime\n\nfrom subprocess import Popen, PIPE\nfrom IPython.display import Markdown\n\nretry_hints = {} # Output in stderr known to be transient, therefore automatically retry\nerror_hints = {} # Output in stderr where a known SOP/TSG exists which will be HINTed for further help\ninstall_hint = {} # The SOP to help install the executable if it cannot be found\n\ndef run(cmd, return_output=False, no_output=False, retry_count=0, base64_decode=False, return_as_json=False, regex_mask=None):\n \"\"\"Run shell command, stream stdout, print stderr and optionally return output\n\n NOTES:\n\n 1. Commands that need this kind of ' quoting on Windows e.g.:\n\n kubectl get nodes -o jsonpath={.items[?(@.metadata.annotations.pv-candidate=='data-pool')].metadata.name}\n\n Need to actually pass in as '\"':\n\n kubectl get nodes -o jsonpath={.items[?(@.metadata.annotations.pv-candidate=='\"'data-pool'\"')].metadata.name}\n\n The ' quote approach, although correct when pasting into Windows cmd, will hang at the line:\n \n `iter(p.stdout.readline, b'')`\n\n The shlex.split call does the right thing for each platform, just use the '\"' pattern for a '\n \"\"\"\n MAX_RETRIES = 5\n output = \"\"\n retry = False\n\n # When running `azdata sql query` on Windows, replace any \\n in \"\"\" strings, with \" \", otherwise we see:\n #\n # ('HY090', '[HY090] [Microsoft][ODBC Driver Manager] Invalid string or buffer length (0) (SQLExecDirectW)')\n #\n if platform.system() == \"Windows\" and cmd.startswith(\"azdata sql query\"):\n cmd = cmd.replace(\"\\n\", \" \")\n\n # shlex.split is required on bash and for Windows paths with spaces\n #\n cmd_actual = shlex.split(cmd)\n\n # Store this (i.e. kubectl, python etc.) to support binary context aware error_hints and retries\n #\n user_provided_exe_name = cmd_actual[0].lower()\n\n # When running python, use the python in the ADS sandbox ({sys.executable})\n #\n if cmd.startswith(\"python \"):\n cmd_actual[0] = cmd_actual[0].replace(\"python\", sys.executable)\n\n # On Mac, when ADS is not launched from terminal, LC_ALL may not be set, which causes pip installs to fail\n # with:\n #\n # UnicodeDecodeError: 'ascii' codec can't decode byte 0xc5 in position 4969: ordinal not in range(128)\n #\n # Setting it to a default value of \"en_US.UTF-8\" enables pip install to complete\n #\n if platform.system() == \"Darwin\" and \"LC_ALL\" not in os.environ:\n os.environ[\"LC_ALL\"] = \"en_US.UTF-8\"\n\n # When running `kubectl`, if AZDATA_OPENSHIFT is set, use `oc`\n #\n if cmd.startswith(\"kubectl \") and \"AZDATA_OPENSHIFT\" in os.environ:\n cmd_actual[0] = cmd_actual[0].replace(\"kubectl\", \"oc\")\n\n # To aid supportability, determine which binary file will actually be executed on the machine\n #\n which_binary = None\n\n # Special case for CURL on Windows. The version of CURL in Windows System32 does not work to\n # get JWT tokens, it returns \"(56) Failure when receiving data from the peer\". If another instance\n # of CURL exists on the machine use that one. (Unfortunately the curl.exe in System32 is almost\n # always the first curl.exe in the path, and it can't be uninstalled from System32, so here we\n # look for the 2nd installation of CURL in the path)\n if platform.system() == \"Windows\" and cmd.startswith(\"curl \"):\n path = os.getenv('PATH')\n for p in path.split(os.path.pathsep):\n p = os.path.join(p, \"curl.exe\")\n if os.path.exists(p) and os.access(p, os.X_OK):\n if p.lower().find(\"system32\") == -1:\n cmd_actual[0] = p\n which_binary = p\n break\n\n # Find the path based location (shutil.which) of the executable that will be run (and display it to aid supportability), this\n # seems to be required for .msi installs of azdata.cmd/az.cmd. (otherwise Popen returns FileNotFound) \n #\n # NOTE: Bash needs cmd to be the list of the space separated values hence shlex.split.\n #\n if which_binary == None:\n which_binary = shutil.which(cmd_actual[0])\n\n # Display an install HINT, so the user can click on a SOP to install the missing binary\n #\n if which_binary == None:\n print(f\"The path used to search for '{cmd_actual[0]}' was:\")\n print(sys.path)\n\n if user_provided_exe_name in install_hint and install_hint[user_provided_exe_name] is not None:\n display(Markdown(f'HINT: Use [{install_hint[user_provided_exe_name][0]}]({install_hint[user_provided_exe_name][1]}) to resolve this issue.'))\n\n raise FileNotFoundError(f\"Executable '{cmd_actual[0]}' not found in path (where/which)\")\n else: \n cmd_actual[0] = which_binary\n\n start_time = datetime.datetime.now().replace(microsecond=0)\n\n cmd_display = cmd\n if regex_mask is not None:\n regex = re.compile(regex_mask)\n cmd_display = re.sub(regex, '******', cmd)\n \n print(f\"START: {cmd_display} @ {start_time} ({datetime.datetime.utcnow().replace(microsecond=0)} UTC)\")\n print(f\" using: {which_binary} ({platform.system()} {platform.release()} on {platform.machine()})\")\n print(f\" cwd: {os.getcwd()}\")\n\n # Command-line tools such as CURL and AZDATA HDFS commands output\n # scrolling progress bars, which causes Jupyter to hang forever, to\n # workaround this, use no_output=True\n #\n\n # Work around a infinite hang when a notebook generates a non-zero return code, break out, and do not wait\n #\n wait = True \n\n try:\n if no_output:\n p = Popen(cmd_actual)\n else:\n p = Popen(cmd_actual, stdout=PIPE, stderr=PIPE, bufsize=1)\n with p.stdout:\n for line in iter(p.stdout.readline, b''):\n line = line.decode()\n if return_output:\n output = output + line\n else:\n if cmd.startswith(\"azdata notebook run\"): # Hyperlink the .ipynb file\n regex = re.compile(' \"(.*)\"\\: \"(.*)\"') \n match = regex.match(line)\n if match:\n if match.group(1).find(\"HTML\") != -1:\n display(Markdown(f' - \"{match.group(1)}\": \"{match.group(2)}\"'))\n else:\n display(Markdown(f' - \"{match.group(1)}\": \"[{match.group(2)}]({match.group(2)})\"'))\n\n wait = False\n break # otherwise infinite hang, have not worked out why yet.\n else:\n print(line, end='')\n\n if wait:\n p.wait()\n except FileNotFoundError as e:\n if install_hint is not None:\n display(Markdown(f'HINT: Use {install_hint} to resolve this issue.'))\n\n raise FileNotFoundError(f\"Executable '{cmd_actual[0]}' not found in path (where/which)\") from e\n\n exit_code_workaround = 0 # WORKAROUND: azdata hangs on exception from notebook on p.wait()\n\n if not no_output:\n for line in iter(p.stderr.readline, b''):\n try:\n line_decoded = line.decode()\n except UnicodeDecodeError:\n # NOTE: Sometimes we get characters back that cannot be decoded(), e.g.\n #\n # \\xa0\n #\n # For example see this in the response from `az group create`:\n #\n # ERROR: Get Token request returned http error: 400 and server \n # response: {\"error\":\"invalid_grant\",# \"error_description\":\"AADSTS700082: \n # The refresh token has expired due to inactivity.\\xa0The token was \n # issued on 2018-10-25T23:35:11.9832872Z\n #\n # which generates the exception:\n #\n # UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa0 in position 179: invalid start byte\n #\n print(\"WARNING: Unable to decode stderr line, printing raw bytes:\")\n print(line)\n line_decoded = \"\"\n pass\n else:\n\n # azdata emits a single empty line to stderr when doing an hdfs cp, don't\n # print this empty \"ERR:\" as it confuses.\n #\n if line_decoded == \"\":\n continue\n \n print(f\"STDERR: {line_decoded}\", end='')\n\n if line_decoded.startswith(\"An exception has occurred\") or line_decoded.startswith(\"ERROR: An error occurred while executing the following cell\"):\n exit_code_workaround = 1\n\n # inject HINTs to next TSG/SOP based on output in stderr\n #\n if user_provided_exe_name in error_hints:\n for error_hint in error_hints[user_provided_exe_name]:\n if line_decoded.find(error_hint[0]) != -1:\n display(Markdown(f'HINT: Use [{error_hint[1]}]({error_hint[2]}) to resolve this issue.'))\n\n # Verify if a transient error, if so automatically retry (recursive)\n #\n if user_provided_exe_name in retry_hints:\n for retry_hint in retry_hints[user_provided_exe_name]:\n if line_decoded.find(retry_hint) != -1:\n if retry_count < MAX_RETRIES:\n print(f\"RETRY: {retry_count} (due to: {retry_hint})\")\n retry_count = retry_count + 1\n output = run(cmd, return_output=return_output, retry_count=retry_count)\n\n if return_output:\n if base64_decode:\n import base64\n return base64.b64decode(output).decode('utf-8')\n else:\n return output\n\n elapsed = datetime.datetime.now().replace(microsecond=0) - start_time\n\n # WORKAROUND: We avoid infinite hang above in the `azdata notebook run` failure case, by inferring success (from stdout output), so\n # don't wait here, if success known above\n #\n if wait: \n if p.returncode != 0:\n raise SystemExit(f'Shell command:\\n\\n\\t{cmd_display} ({elapsed}s elapsed)\\n\\nreturned non-zero exit code: {str(p.returncode)}.\\n')\n else:\n if exit_code_workaround !=0 :\n raise SystemExit(f'Shell command:\\n\\n\\t{cmd_display} ({elapsed}s elapsed)\\n\\nreturned non-zero exit code: {str(exit_code_workaround)}.\\n')\n\n print(f'\\nSUCCESS: {elapsed}s elapsed.\\n')\n\n if return_output:\n if base64_decode:\n import base64\n return base64.b64decode(output).decode('utf-8')\n else:\n return output\n\n\n\n# Hints for tool retry (on transient fault), known errors and install guide\n#\nretry_hints = {'azdata': ['Endpoint sql-server-master does not exist', 'Endpoint livy does not exist', 'Failed to get state for cluster', 'Endpoint webhdfs does not exist', 'Adaptive Server is unavailable or does not exist', 'Error: Address already in use', 'Login timeout expired (0) (SQLDriverConnect)', 'SSPI Provider: No Kerberos credentials available', ], 'kubectl': ['A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond', ], 'python': [ ], }\nerror_hints = {'azdata': [['Please run \\'azdata login\\' to first authenticate', 'SOP028 - azdata login', '../common/sop028-azdata-login.ipynb'], ['The token is expired', 'SOP028 - azdata login', '../common/sop028-azdata-login.ipynb'], ['Reason: Unauthorized', 'SOP028 - azdata login', '../common/sop028-azdata-login.ipynb'], ['Max retries exceeded with url: /api/v1/bdc/endpoints', 'SOP028 - azdata login', '../common/sop028-azdata-login.ipynb'], ['Look at the controller logs for more details', 'TSG027 - Observe cluster deployment', '../diagnose/tsg027-observe-bdc-create.ipynb'], ['provided port is already allocated', 'TSG062 - Get tail of all previous container logs for pods in BDC namespace', '../log-files/tsg062-tail-bdc-previous-container-logs.ipynb'], ['Create cluster failed since the existing namespace', 'SOP061 - Delete a big data cluster', '../install/sop061-delete-bdc.ipynb'], ['Failed to complete kube config setup', 'TSG067 - Failed to complete kube config setup', '../repair/tsg067-failed-to-complete-kube-config-setup.ipynb'], ['Data source name not found and no default driver specified', 'SOP069 - Install ODBC for SQL Server', '../install/sop069-install-odbc-driver-for-sql-server.ipynb'], ['Can\\'t open lib \\'ODBC Driver 17 for SQL Server', 'SOP069 - Install ODBC for SQL Server', '../install/sop069-install-odbc-driver-for-sql-server.ipynb'], ['Control plane upgrade failed. Failed to upgrade controller.', 'TSG108 - View the controller upgrade config map', '../diagnose/tsg108-controller-failed-to-upgrade.ipynb'], ['NameError: name \\'azdata_login_secret_name\\' is not defined', 'SOP013 - Create secret for azdata login (inside cluster)', '../common/sop013-create-secret-for-azdata-login.ipynb'], ['ERROR: No credentials were supplied, or the credentials were unavailable or inaccessible.', 'TSG124 - \\'No credentials were supplied\\' error from azdata login', '../repair/tsg124-no-credentials-were-supplied.ipynb'], ['Please accept the license terms to use this product through', 'TSG126 - azdata fails with \\'accept the license terms to use this product\\'', '../repair/tsg126-accept-license-terms.ipynb'], ], 'kubectl': [['no such host', 'TSG010 - Get configuration contexts', '../monitor-k8s/tsg010-get-kubernetes-contexts.ipynb'], ['No connection could be made because the target machine actively refused it', 'TSG056 - Kubectl fails with No connection could be made because the target machine actively refused it', '../repair/tsg056-kubectl-no-connection-could-be-made.ipynb'], ], 'python': [['Library not loaded: /usr/local/opt/unixodbc', 'SOP012 - Install unixodbc for Mac', '../install/sop012-brew-install-odbc-for-sql-server.ipynb'], ['WARNING: You are using pip version', 'SOP040 - Upgrade pip in ADS Python sandbox', '../install/sop040-upgrade-pip.ipynb'], ], }\ninstall_hint = {'azdata': [ 'SOP063 - Install azdata CLI (using package manager)', '../install/sop063-packman-install-azdata.ipynb' ], 'kubectl': [ 'SOP036 - Install kubectl command line interface', '../install/sop036-install-kubectl.ipynb' ], }\n\n\nprint('Common functions defined successfully.')",
"_____no_output_____"
]
],
[
[
"### Get the Kubernetes namespace for the big data cluster\n\nGet the namespace of the Big Data Cluster use the kubectl command line\ninterface .\n\n**NOTE:**\n\nIf there is more than one Big Data Cluster in the target Kubernetes\ncluster, then either:\n\n- set \\[0\\] to the correct value for the big data cluster.\n- set the environment variable AZDATA\\_NAMESPACE, before starting\n Azure Data Studio.",
"_____no_output_____"
]
],
[
[
"# Place Kubernetes namespace name for BDC into 'namespace' variable\n\nif \"AZDATA_NAMESPACE\" in os.environ:\n namespace = os.environ[\"AZDATA_NAMESPACE\"]\nelse:\n try:\n namespace = run(f'kubectl get namespace --selector=MSSQL_CLUSTER -o jsonpath={{.items[0].metadata.name}}', return_output=True)\n except:\n from IPython.display import Markdown\n print(f\"ERROR: Unable to find a Kubernetes namespace with label 'MSSQL_CLUSTER'. SQL Server Big Data Cluster Kubernetes namespaces contain the label 'MSSQL_CLUSTER'.\")\n display(Markdown(f'HINT: Use [TSG081 - Get namespaces (Kubernetes)](../monitor-k8s/tsg081-get-kubernetes-namespaces.ipynb) to resolve this issue.'))\n display(Markdown(f'HINT: Use [TSG010 - Get configuration contexts](../monitor-k8s/tsg010-get-kubernetes-contexts.ipynb) to resolve this issue.'))\n display(Markdown(f'HINT: Use [SOP011 - Set kubernetes configuration context](../common/sop011-set-kubernetes-context.ipynb) to resolve this issue.'))\n raise\n\nprint(f'The SQL Server Big Data Cluster Kubernetes namespace is: {namespace}')",
"_____no_output_____"
]
],
[
[
"### View the upgrade configmap",
"_____no_output_____"
]
],
[
[
"run(f'kubectl get configmap -n {namespace} controller-upgrade-configmap -o yaml')",
"_____no_output_____"
],
[
"print(\"Notebook execution is complete.\")",
"_____no_output_____"
]
],
[
[
"Related\n-------\n\n- [TSG109 - Set upgrade timeouts](../repair/tsg109-upgrade-stalled.ipynb)\n",
"_____no_output_____"
]
]
] | [
"markdown",
"code",
"markdown",
"code",
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"markdown"
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[
"markdown"
],
[
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[
"markdown"
],
[
"code"
],
[
"markdown"
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[
"code",
"code"
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[
"markdown"
]
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cb1a513a3efac019ba1c47e3214adb84be6f58e3 | 5,726 | ipynb | Jupyter Notebook | examples/notebook/contrib/diet1_mip.ipynb | sreesubbash/or-tools | 701496e45d54fa9938afeedec43089314d93ec11 | [
"Apache-2.0"
] | 1 | 2021-03-30T21:10:27.000Z | 2021-03-30T21:10:27.000Z | examples/notebook/contrib/diet1_mip.ipynb | sreesubbash/or-tools | 701496e45d54fa9938afeedec43089314d93ec11 | [
"Apache-2.0"
] | null | null | null | examples/notebook/contrib/diet1_mip.ipynb | sreesubbash/or-tools | 701496e45d54fa9938afeedec43089314d93ec11 | [
"Apache-2.0"
] | null | null | null | 32.908046 | 247 | 0.538596 | [
[
[
"empty"
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[
"empty"
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cb1a60cbd05f4cd8cc14a7f0a6afb7561955774d | 62,364 | ipynb | Jupyter Notebook | Seminars/Seminar_3/Seminar.ipynb | JacksonGibsonESP/Sberbank-ML | 304e215ca019901f6f15b1fe9a3c3fad9b9e5053 | [
"MIT"
] | 3 | 2018-11-28T11:16:41.000Z | 2021-07-21T12:12:52.000Z | Seminars/Seminar_3/Seminar.ipynb | JacksonGibsonESP/Sberbank-ML | 304e215ca019901f6f15b1fe9a3c3fad9b9e5053 | [
"MIT"
] | null | null | null | Seminars/Seminar_3/Seminar.ipynb | JacksonGibsonESP/Sberbank-ML | 304e215ca019901f6f15b1fe9a3c3fad9b9e5053 | [
"MIT"
] | 9 | 2018-10-03T13:39:09.000Z | 2019-09-03T12:19:42.000Z | 102.910891 | 23,800 | 0.816641 | [
[
[
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt",
"_____no_output_____"
]
],
[
[
"### 1. Считаем данные",
"_____no_output_____"
]
],
[
[
"data_train = pd.read_csv(\"./Data/x_train.csv\", delimiter=';', header=None)\ntarget_train = pd.read_csv(\"./Data/y_train.csv\", delimiter=';', header=None)",
"_____no_output_____"
],
[
"data_train.head()",
"_____no_output_____"
],
[
"np.unique(target_train, return_counts=True)",
"_____no_output_____"
],
[
"y = np.array(target_train)",
"_____no_output_____"
]
],
[
[
"### 2. Посмотрим на RF на сырых данных",
"_____no_output_____"
]
],
[
[
"from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score",
"/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n from numpy.core.umath_tests import inner1d\n"
],
[
"def get_accuracies(X, Y):\n rf_clf = RandomForestClassifier(n_estimators=100)\n print(\"Accuracy: {0}\".format(cross_val_score(rf_clf, X, Y, scoring='accuracy', cv=5, n_jobs=-1)))",
"_____no_output_____"
],
[
"get_accuracies(data_train, y)",
"/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n"
]
],
[
[
"### 3. Feature processing",
"_____no_output_____"
]
],
[
[
"from sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2",
"_____no_output_____"
],
[
"X = data_train.copy()",
"_____no_output_____"
],
[
"def plot_importance(clf, X):\n importances = clf.feature_importances_\n std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)\n indices = np.argsort(importances)[::-1]\n\n plt.figure(figsize=(20, 8))\n plt.title(\"Feature importances\")\n plt.bar(range(X.shape[1]), importances[indices],\n color=\"r\", yerr=std[indices], align=\"center\")\n plt.xticks(range(X.shape[1]), X.columns[indices])\n plt.xlim([-1, X.shape[1]])\n plt.show()",
"_____no_output_____"
],
[
"rf_clf = RandomForestClassifier(n_estimators=100).fit(X, y)\nplot_importance(rf_clf, X)",
"/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n \"\"\"Entry point for launching an IPython kernel.\n"
],
[
"from sklearn.feature_selection import SelectFromModel",
"_____no_output_____"
],
[
"model = SelectFromModel(rf_clf, prefit=True)\nX_sfm = model.transform(X)\nX_sfm.shape",
"_____no_output_____"
],
[
"get_accuracies(X_sfm, y)",
"/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n \"\"\"Entry point for launching an IPython kernel.\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n"
],
[
"from sklearn.model_selection import StratifiedKFold\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.datasets import make_classification\n\n# Create the RFE object and compute a cross-validated score.\nrf_clf = RandomForestClassifier(n_estimators=100)\n# The \"accuracy\" scoring is proportional to the number of correct\n# classifications\nrfecv = RFECV(estimator=rf_clf, step=1, cv=StratifiedKFold(2),\n scoring='accuracy')\nrfecv.fit(X, y)\n\nprint(\"Optimal number of features : %d\" % rfecv.n_features_)\n\n# Plot number of features VS. cross-validation scores\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Cross validation score (nb of correct classifications)\")\nplt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\nplt.show()",
"/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n y = column_or_1d(y, warn=True)\n"
],
[
"X_rfecv = rfecv.transform(X)",
"_____no_output_____"
],
[
"get_accuracies(X_rfecv, y)",
"/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n estimator.fit(X_train, y_train, **fit_params)\n"
]
]
] | [
"code",
"markdown",
"code",
"markdown",
"code",
"markdown",
"code"
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cb1a61daa290e5475f4a3edb1519a8f7d5a8c6cb | 9,223 | ipynb | Jupyter Notebook | 4/z5.ipynb | iCarrrot/NLP | 34c99c8948a2b85b24523c1ac5938486385c335c | [
"MIT"
] | null | null | null | 4/z5.ipynb | iCarrrot/NLP | 34c99c8948a2b85b24523c1ac5938486385c335c | [
"MIT"
] | null | null | null | 4/z5.ipynb | iCarrrot/NLP | 34c99c8948a2b85b24523c1ac5938486385c335c | [
"MIT"
] | null | null | null | 31.477816 | 133 | 0.41841 | [
[
[
"import re\nimport numpy as np\nimport os\nos.sys.path.append('../1/')\nfrom z2 import loader\nfrom math import log\nimport sys\nimport heapq\nimport collections\nimport operator\n\n\nvowels = list('aeioóuyąę') + list('aeioóuyąę'.upper())\ncompacted_vovels = ['i' + x for x in vowels if x != 'i']\nword2tag = dict()\ntag2word = dict()\ndataPath = 'data/'\n\ndef stringNorm(sent, num=False):\n regex = re.compile(f'[,\\.!?:;\\'{\"0-9\" if not num else \"\"}\\*\\-“…\\(\\)„”—»«–––=\\[\\]’]')\n return regex.sub('',sent.lower())\n\ndef bigrams2unigrams(bigrams):\n return {w1: sum([float(bigrams[w1][w2]) for w2 in bigrams[w1]])/2 for w1 in bigrams}\n\ndef count_syllable(phrase, verose=False):\n res = 0\n for i, letter in enumerate(phrase):\n if letter in vowels:\n res += 1\n if verose:\n print(letter)\n if phrase[i:i+2] in compacted_vovels:\n res -= 1\n if verose:\n print(phrase[i:i+2])\n return res\n\n\nwith open(dataPath + \"supertags.txt\") as tags:\n for line in tags:\n word, tag = stringNorm(line, num=True).split()\n word2tag[word] = tag\n if tag in tag2word:\n tag2word[tag].append(word)\n else:\n tag2word[tag] = [word]\n \nbase = {} \nwith open(dataPath + \"superbazy.txt\") as file:\n for line in file:\n word, base_word = line.split()\n base[word] = base_word\n\n \nvectors = {}\nwith open(dataPath + \"poleval_base_vectors.txt\") as file:\n for line in file:\n vec = line.split()\n if 150< len(vec) < 250:\n x = np.array([float(x) for x in vec[1:]])\n vectors[vec[0]] = x / np.sqrt(x.T @ x)\n\n \n\nwith open(dataPath + 'rytmiczne_zdania_z_korpusu.txt') as f:\n sentences = [\n tuple(\n [\n [\n x for x in y.split()\n ] \n for y in line.split('RYM:')[1].rstrip(' .\\n').split('[*]')\n ]\n ) \n for line in f\n ] \n \n \n\n\nPMI = lambda w1, w2: log(\n float(bigrams[w1][w2] if w1 in bigrams and w2 in bigrams[w1] else 1) * uniSum / (unigrams[w1] * unigrams[w2]) \n + sys.float_info.min)\n \n ",
"_____no_output_____"
],
[
"\nfor i, x in enumerate(vectors):\n if i < 10:\n print(vectors[x].T@vectors[x].T)\n else:\n break",
"_____no_output_____"
],
[
"def get_rym(w):\n best = None\n for i in range(len(w)):\n if count_syllable(w[i:]) == 2:\n best = w[i:]\n return best\n\ndef sample_verset():\n index = np.random.choice(np.arange(len(sentences)))\n return sentences[index]\n\n\ndef get_accents(phrase):\n return [count_syllable(x) for x in phrase]\n",
"_____no_output_____"
],
[
"def sameTags(w):\n if w in word2tag:\n return tag2word[word2tag[w]]\n elif ('^' + w)[-3:] in word2tag:\n return tag2word[word2tag[('^' + w)[-3:]]]\n else:\n return []\n \ndef createAltWords(accent, verse, rime=None):\n return [list(\n set(\n filter(\n lambda x: count_syllable(x) == accent[i] ,\n sameTags(w)\n )\n ).intersection(\n {y for y in safeGrams}\n ))\n for i, w in enumerate(verse)\n ]\n\ndef createAltWords(accent, w, rime=None):\n return list(\n filter(\n lambda x: count_syllable(x) == accent and x != w and(rime is None or get_rym(x) == rime),\n sameTags(w)\n )\n ) \n \ndef get_rime_set(alts):\n return {get_rym(x) for x in alts}\n\n\ndef change_word(accent, word, rime=None):\n alts = createAltWords(accent, word, rime=rime)\n vec = vectors[base[word]]\n values = list(\n map(\n lambda x: vectors[base[x]].T @ vec if x in base and base[x] in vectors else 0,\n alts\n ))\n if len(alts) > 0:\n x = np.argmax(np.array(values))\n choosen = alts[x]\n return choosen, values[x]\n return None, 0\n\n\ndef find_common_rime(a1,w1,a2,w2):\n alts1 = createAltWords(a1, w1, rime=None)\n alts2 = createAltWords(a2, w2, rime=None)\n common_rimes = get_rime_set(alts1).intersection(get_rime_set(alts2) ) \n if len(common_rimes) == 0:\n return None\n \n vec1 = vectors[base[w1]]\n values1 = list(\n map(\n lambda x: vectors[base[x]].T @ vec1 if x in base and base[x] in vectors and get_rym(x) in common_rimes else 0,\n alts1\n ))\n \n x = np.argmax(np.array(values1))\n choosen1 = alts1[x]\n choosen2 = change_word(a2, w2, rime=get_rym(choosen1))[0]\n\n return choosen1, choosen2\n \ndef change_last(a1,w1,a2,w2):\n alt1 = change_word(a1, v1[-1], get_rym(v2[-1]))\n alt2 = change_word(a2, v2[-1], get_rym(v1[-1]))\n# print(alt1, alt2)\n if alt1[1] <= alt2[1] and alt2[1] > 0:\n return w1, alt2[0]\n elif alt2[1] < alt1[1] and alt1[1] > 0:\n return alt1[0], w2\n else:\n return find_common_rime(a1, v1[-1], a2, v2[-1])\n \ndef pretty_print(v1,v2):\n print(' '.join(v1)+'\\n'+' '.join(v2)+'\\n')",
"_____no_output_____"
],
[
"for _ in range(20):\n v1,v2 = sample_verset()\n a1 = [count_syllable(i) for i in v1] \n a2 = [count_syllable(i) for i in v2]\n pretty_print(v1,v2)\n for i, w1 in enumerate(v1):\n if np.random.rand(1) > 0.8:\n try:\n if i < len(v1) - 1:\n v1[i] = change_word(a1[i], w1)[0]\n else:\n v1[-1], v2[-1] = change_last(a1[i],w1,a2[-1],v2[-1])\n pretty_print(v1,v2)\n except:\n print('.')\n print('<->')\n for i, w2 in enumerate(v2):\n if np.random.rand(1) > 0.8:\n try:\n if i < len(v2) - 1:\n v2[i] = change_word(a2[i], w2)[0]\n else:\n v1[-1], v2[-1] = change_last(a1[-1],v1[-1],a2[i],w2)\n pretty_print(v1,v2)\n except:\n print('.')\n \n print('************')\n",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
"code",
"code"
]
] |
cb1a67254d77322f4940aeff9fe71e0664745609 | 10,218 | ipynb | Jupyter Notebook | Code/day09.ipynb | heibanke/learn_python_in_15days | 96658b1b5cd1e532ba57897237cc89f0861e76c2 | [
"MIT"
] | null | null | null | Code/day09.ipynb | heibanke/learn_python_in_15days | 96658b1b5cd1e532ba57897237cc89f0861e76c2 | [
"MIT"
] | null | null | null | Code/day09.ipynb | heibanke/learn_python_in_15days | 96658b1b5cd1e532ba57897237cc89f0861e76c2 | [
"MIT"
] | null | null | null | 23.762791 | 113 | 0.444412 | [
[
[
"# 15天入门Python3\n\nCopyRight by 黑板客 \n转载请联系heibanke_at_aliyun.com",
"_____no_output_____"
],
[
"**上节作业**\n\n八皇后\n",
"_____no_output_____"
]
],
[
[
"%load day08/eight_queen.py\n",
"_____no_output_____"
],
[
"a = gen_n_queen(5)\n",
"_____no_output_____"
],
[
"printsolution(next(a))",
"_____no_output_____"
]
],
[
[
"## day09：谈对象—高富帅和白富美\n\n1. <a href=\"#1\">面向对象编程</a>\n2. <a href=\"#2\">**封装**, 属性和方法</a>\n3. <a href=\"#3\">**继承**</a>\n4. <a href=\"#4\">**多态** 与 重载</a>\n5. <a href=\"#5\">作业</a>",
"_____no_output_____"
],
[
"## <a name=\"1\">面向对象编程</a>\n\n面向对象和面向过程的区别：\n\n1. 面向过程编程 (OPP) 定义各种数据，定义各种函数，然后操作数据和函数输出结果，解决一类问题。面向过程适合解决特定的简单问题。\n2. 面向对象编程 (OOP) 最重要的是把用到的数据和方法抽象成为类。使用不同的类进行建模。达到代码模块化。适合解决多变，复杂，不断更新的问题。\n\n三大特性：封装，继承，多态。",
"_____no_output_____"
]
],
[
[
"class A(object):\n nums_A = 0\n def __init__(self, data):\n # 初始化\n self.data = data\n A.nums_A += 1\n \n def methods(self):\n self.data -= 1\n\n \na = A(3)\nprint(a.data, A.nums_A)\n\nb = A(5)\nprint(b.data, A.nums_A)\n\na.methods()\nc = A(6)\n\nprint(a.data, A.nums_A)\n",
"_____no_output_____"
],
[
"%%html\n<style>\n.rendered_html td, .rendered_html th {text-align: left;}\n</style>",
"_____no_output_____"
]
],
[
[
"| A | \n| :- |\n|- data| \n|+ methods()| ",
"_____no_output_____"
],
[
"举个王者荣耀简化版的例子说明一下：\n\n你要开发一个游戏，游戏中每个玩家都是一个英雄。英雄有两种，比如亚瑟是近战英雄，后羿是远程英雄。\n每种英雄都有不同的属性。\n\n1. name\n2. defence\n3. attack\n4. life\n\n请模拟游戏里两个英雄互相PK的过程。看谁先game over。\n\n<img src=\"day09/yase_vs_houyi_small.png\" width=600></img>\n\n",
"_____no_output_____"
]
],
[
[
"# %load day09/pk_opp_01.py\n# 面向过程\ndef cal_damage(A, B):\n \"\"\"\n 计算A对B造成的伤害\n \"\"\"\n return A['attack'] * (1-B['defence']/(B['defence']+400))\n\n\ntank = {'name':'亚瑟1', 'defence':200, 'attack':80, 'life':600}\narcher = {'name':'后羿1', 'defence':100, 'attack':200, 'life':300}\n\n\ndef game_test(A, B):\n while A['life']>0 and B['life']>0:\n damage1 = cal_damage(A, B) #A对B造成伤害\n damage2 = cal_damage(B, A) #B对A造成伤害\n\n A['life'] -= damage2\n print('%s 对 %s 造成 %.2f 伤害，%s 还剩 %.2f 生命。'%(B['name'], A['name'], damage2, A['name'], A['life']))\n B['life'] -= damage1\n print('%s 对 %s 造成 %.2f 伤害，%s 还剩 %.2f 生命。'%(A['name'], B['name'], damage1, B['name'], B['life']))\n\n if A['life']<0:\n print('%s 被打败了'%(A['name']))\n if B['life']<0:\n print('%s 被打败了'%(B['name']))\n\ngame_test(tank, archer)",
"_____no_output_____"
]
],
[
[
"## <a name=\"2\">封装，属性和方法</a>\n",
"_____no_output_____"
],
[
"|Hero| \n| :- |\n|- name <br> - defence <br> - attack <br> - life|\n|+ \\__init\\__ (name, defence, attack, life) <br> + damage (enemy) <br> + alive ()| \n\n|Game| \n| :- |\n|- A <br> - B|\n|+ \\__init\\__ (A, B) <br> + start () <br> + end ()| ",
"_____no_output_____"
]
],
[
[
"# %load day09/pk_oop_01.py\nclass Hero(object):\n def __init__(self, name, defence, attack, life):\n self.name = name\n self.defence = defence\n self.attack = attack\n self.life = life\n \n def damage(self, enemy):\n d = self.attack * (1-enemy.defence/(enemy.defence+400))\n enemy.life -= d\n print('%s 对 %s 造成 %.2f 伤害，%s 还剩 %.2f 生命。'%(self.name, enemy.name, d, enemy.name, enemy.life))\n \n def alive(self):\n return self.life > 0\n \nclass Game(object):\n def __init__(self, A, B):\n self.A = A\n self.B = B\n \n def start(self):\n while self.A.alive() and self.B.alive():\n self.A.damage(self.B)\n self.B.damage(self.A)\n self.end()\n \n def end(self):\n if not self.B.alive():\n print('%s 被打败了'%(self.B.name))\n if not self.A.alive():\n print('%s 被打败了'%(self.A.name))\n \ndef game_test():\n A = Hero('亚瑟1', 200, 80, 600)\n B = Hero('后羿1', 100, 200, 300)\n\n\n game = Game(A, B)\n game.start()\n \ngame_test()",
"_____no_output_____"
]
],
[
[
"封装：对象的属性建议只能通过调用对象的方法来修改。只对对象的方法可见。",
"_____no_output_____"
],
[
"问题：当玩家很多时，生成亚瑟和后羿的属性只需要改变name，而其他属性是统一的。怎么更简化？",
"_____no_output_____"
],
[
"## <a name=\"3\">继承</a>\n\n子类继承父类的属性和方法。",
"_____no_output_____"
]
],
[
[
"# %load day09/pk_oop_02.py\nfrom day09.pk_oop_01 import Game\n\nclass Hero(object):\n nums = 0\n def __init__(self, name, defence, attack, life):\n self.name = name\n self.defence = defence\n self.attack = attack\n self.life = life\n Hero.nums += 1\n \n def damage(self, enemy):\n d = self.attack * (1-enemy.defence/(enemy.defence+400))\n enemy.life -= d\n print('%s 对 %s 造成 %.2f 伤害，%s 还剩 %.2f 生命。'%(self.name, enemy.name, d, enemy.name, enemy.life))\n \n def alive(self):\n return self.life > 0\n\nclass YaSe(Hero):\n nums = 0\n def __init__(self, name):\n Hero.__init__(self, name, 200, 80, 600)\n YaSe.nums += 1\n \nclass HouYi(Hero):\n nums = 0\n def __init__(self, name):\n Hero.__init__(self, name, 100, 200, 300)\n HouYi.nums += 1\n\n \ndef game_test():\n A = YaSe('亚瑟1')\n B = HouYi('后羿1')\n\n\n game = Game(A, B)\n game.start()\n \ngame_test()",
"_____no_output_____"
],
[
"class YaSe(Hero):\n nums = 0\n def __init__(self, name):\n Hero.__init__(self, name, 200, 80, 600)\n YaSe.nums += 1\ngame_test()",
"_____no_output_____"
],
[
"print(Hero.nums, YaSe.nums, HouYi.nums)",
"_____no_output_____"
]
],
[
[
"## <a name=\"4\">多态和重载\n \n多态，方法和子类动态绑定\n\n重载，多个同名的方法，参数不同。",
"_____no_output_____"
],
[
"让亚瑟的攻击带有吸血功能，每次将伤害的10%转化成自己的生命，看能否打败后羿。",
"_____no_output_____"
]
],
[
[
"class YaSe(Hero):\n def __init__(self, name):\n Hero.__init__(self, name, 200, 80, 2000)\n\n def damage(self, enemy, xixie = 0.1):\n d = self.attack * (1-enemy.defence/(enemy.defence+400))\n enemy.life -= d\n self.life += d * xixie\n print('%s 对 %s 造成 %.2f 伤害，%s 还剩 %.2f 生命。'%(self.name, enemy.name, d, enemy.name, enemy.life))\n\ngame_test()",
"_____no_output_____"
]
],
[
[
"<a name=\"5\">作业：（面向对象角度实现选做）</a>\n\n1. 如何让英雄可以使用道具，比如铠甲能够增加defence，增加生命。弓箭可以增加attack。\n2. 如果考虑装备有其他更多的作用，比如弓箭可以减少对方的一部分defence，铠甲受到伤害可以反弹一部分给对方，如何设计？\n3. 如果道具能让英雄有吸血功能，而且很多种英雄都有吸血功能，如何设计代码？\n4. 从面向过程和面向对象两个角度考虑实现上面的功能，对比不同。\n\n欢迎大家把做好的作业分享到讨论区。\n",
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cb1a6b2115ffe460c6f059c33bf2fd9f8ca7b4b3 | 96,708 | ipynb | Jupyter Notebook | Final.Project.Hassan/3- LBM- 2D (D2Q5) Heat conduction.ipynb | hasan2014/assignment-bank | 5afbd3f47a6d8407f4745377a1b0150f235add6b | [
"MIT"
] | 1 | 2015-06-09T23:16:15.000Z | 2015-06-09T23:16:15.000Z | Final.Project.Hassan/3- LBM- 2D (D2Q5) Heat conduction.ipynb | hasan2014/assignment-bank | 5afbd3f47a6d8407f4745377a1b0150f235add6b | [
"MIT"
] | null | null | null | Final.Project.Hassan/3- LBM- 2D (D2Q5) Heat conduction.ipynb | hasan2014/assignment-bank | 5afbd3f47a6d8407f4745377a1b0150f235add6b | [
"MIT"
] | null | null | null | 280.313043 | 30,889 | 0.905871 | [
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cb1a6c1464f31601699c483f43f92fbf3eebadc3 | 355,116 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC | 3650acfa040a2249ea8f1acebf0dc3bf6d21bf6f | [
"Apache-2.0"
] | null | null | null | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC | 3650acfa040a2249ea8f1acebf0dc3bf6d21bf6f | [
"Apache-2.0"
] | 32 | 2020-04-21T18:43:36.000Z | 2020-07-27T18:19:34.000Z | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC | 3650acfa040a2249ea8f1acebf0dc3bf6d21bf6f | [
"Apache-2.0"
] | null | null | null | 437.334975 | 47,424 | 0.964429 | [
[
[
"Starting with Chollet's advice -- program simple programs that can solve the first 10 tasks",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport json \nfrom PIL import Image, ImageDraw\nfrom IPython.display import Image as Im\nimport matplotlib.pyplot as plt\nimport collections",
"_____no_output_____"
],
[
"colorMap = {0:\"black\",1:\"blue\",2:\"red\", 3:\"green\",4:\"yellow\",5:\"grey\",6:\"magenta\",7:\"orange\",8:\"cyan\",9:\"brown\"}",
"_____no_output_____"
],
[
"# accuracy of predicted output to actual output (simply the sum of the differences)\ndef accuracy(inputGrid,outputGrid):\n inputGrid, outputGrid = returnArrays(inputGrid,outputGrid)\n acc = 0\n if inputGrid.shape != outputGrid.shape:\n return np.inf\n for i in range(inputGrid.shape[0]):\n for j in range(inputGrid.shape[1]):\n acc += np.abs(inputGrid[i][j]-outputGrid[i][j])\n return acc",
"_____no_output_____"
]
],
[
[
"### Functions for displaying grids",
"_____no_output_____"
]
],
[
[
"# displaying a single grid\ndef DisplayGrid(grid):\n grid = np.asarray(grid)\n nrows = len(grid[:,0])\n ncols = len(grid[0,:])\n height, width = nrows*50, ncols*50\n image = Image.new(size=(width,height),mode='RGB',color=(255,255,255))\n draw = ImageDraw.Draw(image)\n r = 0 \n for row in grid:\n c = 0\n for col in row:\n draw.rectangle(xy=[c*50,r*50,(c+1)*50,(r+1)*50], fill=colorMap[np.abs(grid[r][c])])\n c += 1\n r += 1\n for i in range(ncols):\n draw.line([(i+1)*50,0,(i+1)*50,height],fill=\"grey\")\n for i in range(nrows):\n draw.line([0,(i+1)*50,width,(i+1)*50],fill=\"grey\")\n display(image)\n",
"_____no_output_____"
],
[
"# displaying two grids side by side\ndef DisplayGrids(grid1,grid2):\n # Grid 1\n grid1 = np.asarray(grid1)\n nrows = len(grid1[:,0])\n ncols = len(grid1[0,:])\n height, width = nrows*50, ncols*50\n image1 = Image.new(size=(width,height),mode='RGB',color=(255,255,255))\n draw = ImageDraw.Draw(image1)\n r = 0 \n for row in grid1:\n c = 0\n for col in row:\n draw.rectangle(xy=[c*50,r*50,(c+1)*50,(r+1)*50], fill=colorMap[np.abs(grid1[r][c])])\n c += 1\n r += 1\n for i in range(ncols):\n draw.line([(i+1)*50,0,(i+1)*50,height],fill=\"grey\")\n for i in range(nrows):\n draw.line([0,(i+1)*50,width,(i+1)*50],fill=\"grey\")\n\n \n # Grid 2\n grid2 = np.asarray(grid2)\n nrows = len(grid2[:,0])\n ncols = len(grid2[0,:])\n height, width = nrows*50, ncols*50\n image2 = Image.new(size=(width,height),mode='RGB',color=(255,255,255))\n draw = ImageDraw.Draw(image2)\n r = 0 \n for row in grid2:\n c = 0\n for col in row:\n draw.rectangle(xy=[c*50,r*50,(c+1)*50,(r+1)*50], fill=colorMap[np.abs(grid2[r][c])])\n c += 1\n r += 1\n for i in range(ncols):\n draw.line([(i+1)*50,0,(i+1)*50,height],fill=\"grey\")\n for i in range(nrows):\n draw.line([0,(i+1)*50,width,(i+1)*50],fill=\"grey\")\n \n # Displaying\n fig, ax = plt.subplots(1,2,figsize=(50,50))\n ax[0].imshow(image1)\n ax[0].axis(\"off\")\n ax[1].imshow(image2)\n ax[1].axis(\"off\")",
"_____no_output_____"
]
],
[
[
"## Solving grid 1",
"_____no_output_____"
]
],
[
[
"filename = \"/Users/aysjajohnson/Desktop/ARC-master/data/training/007bbfb7.json\"\nwith open(filename, 'r') as f:\n grid = json.load(f)",
"_____no_output_____"
],
[
"inGrid = grid[\"train\"][0][\"input\"]\noutGrid = grid[\"train\"][0][\"output\"]",
"_____no_output_____"
],
[
"def returnArrays(inputGrid, outputGrid):\n return(np.asarray(inputGrid),np.asarray(outputGrid))",
"_____no_output_____"
],
[
"def inDim2outDim(inputGrid,outputGrid):\n inputGrid, outputGrid = returnArrays(inputGrid,outputGrid)\n if inputGrid.shape != outputGrid.shape:\n return(np.zeros((outputGrid.shape[0],outputGrid.shape[1])))\n else:\n return(inputGrid)",
"_____no_output_____"
],
[
"# the thing that actually solves this is mapping each square to a 3x3 area on the output and whenever there's a color,\n# copy and paste the input... so... very complicated. I'm just going to write a function that solves this and then\n# simplify\n\n# need to get these not to rely on outputGrid... you need to save that info somewhere though. For now I'm just going\n# to focus on ones where the size is the same\n\ndef copyPaste(inputGrid, outputGrid):\n inputGrid, outputGrid = returnArrays(inputGrid,outputGrid)\n inputHeight = inputGrid.shape[0]\n inputWidth = inputGrid.shape[1]\n outputHeight = outputGrid.shape[0]\n outputWidth = outputGrid.shape[1]\n outputBigger = (inputHeight*inputWidth)<(outputHeight*outputWidth)\n if outputBigger and (outputHeight%inputHeight == 0 or outputWidth%inputWidth == 0):\n output = np.zeros((outputHeight,outputWidth))\n for i in range(outputHeight)[::inputHeight]:\n for j in range(outputWidth)[::inputWidth]:\n output[i:i+inputHeight,j:j+inputWidth] = inputGrid\n return(output)\n else:\n return(inputGrid)",
"_____no_output_____"
],
[
"DisplayGrid(copyPaste(inGrid,outGrid))",
"_____no_output_____"
],
[
"def mask(inputGrid,outputGrid):\n inputGrid, outputGrid = returnArrays(inputGrid,outputGrid)\n inputHeight = inputGrid.shape[0]\n inputWidth = inputGrid.shape[1]\n outputHeight = outputGrid.shape[0]\n outputWidth = outputGrid.shape[1]\n outputBigger = (inputHeight*inputWidth)<(outputHeight*outputWidth)\n if outputBigger and outputHeight%inputHeight == 0 and outputWidth%inputWidth == 0:\n output = np.ones((outputHeight,outputWidth))\n mask = np.zeros((inputHeight,inputWidth))\n for i in range(outputHeight)[::3]:\n for j in range(outputWidth)[::3]:\n if inputGrid[int(i/inputHeight),int(j/inputWidth)] == 0:\n output[i:i+inputHeight,j:j+inputWidth] = mask\n return(output)\n else:\n return(inputGrid)",
"_____no_output_____"
],
[
"DisplayGrid(np.multiply(copyPaste(inGrid,outGrid),mask(inGrid,outGrid)))",
"_____no_output_____"
],
[
"for i in range(len(grid[\"train\"])):\n inGrid = grid[\"train\"][i][\"input\"]\n outGrid = grid[\"train\"][i][\"output\"]\n DisplayGrids(inGrid, np.multiply(copyPaste(inGrid,outGrid),mask(inGrid,outGrid)))",
"_____no_output_____"
],
[
"accuracy(grid[\"test\"][0][\"output\"],np.multiply(copyPaste(grid[\"test\"][0][\"input\"],grid[\"train\"][0][\"output\"]),mask(grid[\"test\"][0][\"input\"],grid[\"train\"][0][\"output\"])))",
"_____no_output_____"
],
[
"DisplayGrid(np.multiply(copyPaste(grid[\"test\"][0][\"input\"],grid[\"train\"][0][\"output\"]),mask(grid[\"test\"][0][\"input\"],grid[\"train\"][0][\"output\"])))",
"_____no_output_____"
]
],
[
[
"## Solving Grid 2",
"_____no_output_____"
]
],
[
[
"filename = \"/Users/aysjajohnson/Desktop/ARC-master/data/training/00d62c1b.json\"\nwith open(filename, 'r') as f:\n grid = json.load(f)",
"_____no_output_____"
],
[
"inGrid = grid[\"train\"][3][\"input\"]\noutGrid = grid[\"train\"][0][\"output\"]",
"_____no_output_____"
],
[
"DisplayGrids(inGrid,outGrid)",
"_____no_output_____"
],
[
"def bfs(grid, start, width, height, wall=3):\n goal = 10\n queue = collections.deque([[start]])\n seen = set([start])\n if grid[start[0],start[1]] == wall:\n return False\n while queue:\n path = queue.popleft()\n # print(\"current path\", path)\n x, y = path[-1]\n if grid[x][y] == goal:\n return False\n for x2, y2 in ((x+1,y), (x-1,y), (x,y+1), (x,y-1)):\n # print(\"considering\", (x2,y2))\n if 0 <= x2 < height and 0 <= y2 < width and grid[x2][y2] != wall and (x2, y2) not in seen:\n queue.append(path + [(x2, y2)])\n seen.add((x2, y2))\n # print(\"expanding queue\", queue)\n # print(\"no path\", path)\n return True",
"_____no_output_____"
],
[
"def fillBorders(grid, color=1):\n grid = np.asarray(grid)\n height = grid.shape[0]\n width = grid.shape[1]\n grid[:,0] = np.where(grid[:,0]==0,10,grid[:,0])\n grid[:,-1] = np.where(grid[:,-1]==0,10,grid[:,-1])\n grid[0,:] = np.where(grid[0,:]==0,10,grid[0,:])\n grid[-1,:] = np.where(grid[-1,:]==0,10,grid[-1,:])\n # bfs(grid,(2,3),width,height)\n for i in range(1,height-1):\n for j in range(1,width-1):\n # print(i,j)\n if bfs(grid,(i,j),width,height):\n grid[i][j] = color\n grid = np.where(grid==10,0,grid)\n return(grid)",
"_____no_output_____"
],
[
"DisplayGrids(inGrid,fillBorders(inGrid,4))",
"_____no_output_____"
],
[
"accuracy(grid[\"test\"][0][\"output\"],fillBorders(grid[\"test\"][0][\"input\"],4))",
"_____no_output_____"
],
[
"DisplayGrids(grid[\"test\"][0][\"output\"],fillBorders(grid[\"test\"][0][\"input\"],4))",
"_____no_output_____"
]
],
[
[
"## Solving Grid 3",
"_____no_output_____"
]
],
[
[
"filename = \"/Users/aysjajohnson/Desktop/ARC-master/data/training/017c7c7b.json\"\nwith open(filename, 'r') as f:\n grid = json.load(f)",
"_____no_output_____"
],
[
"inGrid = grid[\"train\"][0][\"input\"]\noutGrid = grid[\"train\"][0][\"output\"]",
"_____no_output_____"
],
[
"DisplayGrids(inGrid,outGrid)",
"_____no_output_____"
],
[
"# making copy and paste more general\ndef patternCompletion(inputGrid, outputGrid, color=1, pattern_length = 2):\n inputGrid, outputGrid = returnArrays(inputGrid,outputGrid)\n inputHeight = inputGrid.shape[0]\n inputWidth = inputGrid.shape[1]\n outputHeight = outputGrid.shape[0]\n outputWidth = outputGrid.shape[1]\n output = np.zeros((outputHeight,outputWidth))\n if inputHeight == outputHeight:\n ouputPattern = np.zeros((outputHeight-inputHeight,inputWidth))\n elif inputWidth == outputWidth:\n pattern = inputGrid[-pattern_length:,:]\n for i in range(inputHeight-pattern_length):\n if np.all(inputGrid[i:i+pattern_length,:] == np.asarray(pattern)):\n output[inputHeight:,:] = inputGrid[i+pattern_length:i+pattern_length + outputHeight-inputHeight,:]\n output[:inputHeight,:] = inputGrid\n return output\n else:\n return(inputGrid)",
"_____no_output_____"
],
[
"def changeColor(grid,color=1):\n grid = np.asarray(grid)\n return np.where(grid!=0, color,0)",
"_____no_output_____"
],
[
"DisplayGrid(changeColor(patternCompletion(inGrid,outGrid),2))",
"_____no_output_____"
],
[
"accuracy(grid[\"test\"][0][\"output\"],changeColor(patternCompletion(grid[\"test\"][0][\"input\"],grid[\"train\"][0][\"output\"]),2))",
"_____no_output_____"
],
[
"DisplayGrids(grid[\"test\"][0][\"output\"],changeColor(patternCompletion(grid[\"test\"][0][\"input\"],grid[\"train\"][0][\"output\"]),2))\n",
"_____no_output_____"
]
],
[
[
"## Solving Grid 4",
"_____no_output_____"
]
],
[
[
"filename = \"/Users/aysjajohnson/Desktop/ARC-master/data/training/025d127b.json\"\nwith open(filename, 'r') as f:\n grid = json.load(f)",
"_____no_output_____"
],
[
"inGrid = grid[\"train\"][0][\"input\"]\noutGrid = grid[\"train\"][0][\"output\"]",
"_____no_output_____"
],
[
"DisplayGrids(inGrid,outGrid)",
"_____no_output_____"
],
[
"def moveRight(grid):\n grid = np.asarray(grid)\n height = grid.shape[0]\n width = grid.shape[1]\n for i in range(height-1):\n for j in range(width-1):\n if grid[i][j] ",
"_____no_output_____"
]
]
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cb1a6d8aee876d078fbd70056d1d39d440f6a80f | 11,672 | ipynb | Jupyter Notebook | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy | d91a8dd3ef60febbae6fc90e3264295c9a0264a0 | [
"MIT"
] | null | null | null | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy | d91a8dd3ef60febbae6fc90e3264295c9a0264a0 | [
"MIT"
] | null | null | null | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy | d91a8dd3ef60febbae6fc90e3264295c9a0264a0 | [
"MIT"
] | null | null | null | 24.215768 | 285 | 0.557745 | [
[
[
"# Introdução ao Pandas - Viagens do Governo | Tratamento de dados",
"_____no_output_____"
],
[
"*Esse notebook usa o arquivo sobre [viagens de funcionários do governo](http://www.portaltransparencia.gov.br/viagens) disponibilizado no portal da transparência.*",
"_____no_output_____"
]
],
[
[
"import pandas as pd",
"_____no_output_____"
],
[
"df_viagem = pd.read_csv('viagens_2019.csv', encoding='latin-1', sep=';')",
"_____no_output_____"
]
],
[
[
"**Saber informações sobre uma coluna específica**",
"_____no_output_____"
]
],
[
[
"df_viagem['Valor passagens']",
"_____no_output_____"
]
],
[
[
"**Mudar as virgulas dos valores por pontos**",
"_____no_output_____"
]
],
[
[
"# o str é pra sinalizar que a troca do replace é uma string\ndf_viagem['Valor passagens'] = df_viagem['Valor passagens'].str.replace(',','.')",
"_____no_output_____"
]
],
[
[
"**Mudar o tipo de dado da coluna**",
"_____no_output_____"
]
],
[
[
"df_viagem['Valor passagens'] = df_viagem['Valor passagens'].astype(float)",
"_____no_output_____"
]
],
[
[
"**Contar quantas vezes aparece uma determinada coisa**",
"_____no_output_____"
]
],
[
[
"df_viagem['Destinos'].value_counts()",
"_____no_output_____"
]
],
[
[
"**Separar as cidades dos Estados ou Países**<br><br>\nO `split()` guarda o resultado em uma **lista**",
"_____no_output_____"
]
],
[
[
"# O parametro 'expand = True' indica que os elementos devem ser separados em diferentes colunas\ncol = df_viagem['Destinos'].str.split('/',1,expand = True)",
"_____no_output_____"
]
],
[
[
"**Incluir a coluna nova no dataframe**",
"_____no_output_____"
]
],
[
[
"df_viagem['cidade_destino'] = col[0]",
"_____no_output_____"
]
],
[
[
"**Separar conteudos diferentes que estão na mesma coluna**\n\nA coluna do dataframe possui valores nulos, estados e países misturados. Para resolver isso é preciso identificar **padrões**. No caso desse dataframe, o padrão é: <br>\n- **Estados** tem só **2 caracteres**\n- **Paises** estão escritos com nome completo, ou seja tem mais caracteres\n- Valores **nulos** estão escritos **None**\n\nAntes de tudo, vou armazenar o conteudo bagunçado em uma coluna que chamarei de 'Provisoria'",
"_____no_output_____"
]
],
[
[
"df_viagem['Provisoria'] = col[1]",
"_____no_output_____"
]
],
[
[
"**Criando um dataframe para cada conteudo separado**<br><br>\nTodos os dataframes vão ter uma coluna chamda 'Provisoria' com o conteudo separado por conteudo que foi filtrado a partir do dataframe original 'df_viagem'. Para isso filtrarei usando a função `len()`. Essa função apresenta a quantidade de conteúdo que for passado por parametro.",
"_____no_output_____"
],
[
"Criando um dataframe com o conteudo de estados ",
"_____no_output_____"
]
],
[
[
"#Estou colocando na coluna estado o conteudo da coluna provisória que tenha apenas dois caracteres\nestado = df_viagem[df_viagem['Provisoria'].str.len()==2]",
"_____no_output_____"
]
],
[
[
"Agora vou criar um dataframe com o conteudo de paises, incluindo nele todo conteudo que tenha informações maior que dois caracteres",
"_____no_output_____"
]
],
[
[
"pais = df_viagem[df_viagem['Provisoria'].str.len() > 2]",
"_____no_output_____"
]
],
[
[
"E por fim vou criar um dataframe com o conteudo None. A função `isnull()` identifica os valores nulos no dataframe. No caso do exemplo está identificando na coluna 'Provisoria' ",
"_____no_output_____"
]
],
[
[
"nulo = df_viagem[df_viagem['Provisoria'].isnull()]",
"_____no_output_____"
]
],
[
[
"**Renomeado as colunas dos dataframes**",
"_____no_output_____"
],
[
"Se na coluna 'Provisoria' do dataframe 'estado' só tem o conteudo relacionado aos estados, basta renomear a coluna 'Provisoria' para 'estado'.",
"_____no_output_____"
]
],
[
[
"#No dicionario é passado na chave o nome da coluna antiga e no valor o nome que quer renomear\n#O inplace serve para tornar a mudança definitiva\n\nestado.rename(columns = {'Provisoria' : 'estado'}, inplace = True)",
"_____no_output_____"
]
],
[
[
"Todas os estados são relacionados ao Brasil. Então vou criar dentro do dataframe 'estado' a coluna 'pais' e incluir o conteudo 'Brasil'",
"_____no_output_____"
]
],
[
[
"estado['país'] = 'Brasil'",
"_____no_output_____"
]
],
[
[
"No dataframe 'pais' vou renomear a coluna 'Provisoria' para 'país'",
"_____no_output_____"
]
],
[
[
"pais.rename(columns = {'Provisoria' : 'país'}, inplace = True)",
"_____no_output_____"
]
],
[
[
"Como os outros paises não tem a informação do estado (no caso desse dataframe), vou criar uma coluna 'estado' e incluir o conteudo 'Sem informação'",
"_____no_output_____"
]
],
[
[
"pais['estado'] = 'Sem informação'",
"_____no_output_____"
]
],
[
[
"No dataframe nulo, vou renomear a coluna 'Provisoria' para 'estado' ",
"_____no_output_____"
]
],
[
[
"nulo.rename(columns = {'Provisoria' : 'estado'}, inplace = True)",
"_____no_output_____"
]
],
[
[
"E inserir uma coluna 'país' com o conteudo 'Sem informação'",
"_____no_output_____"
]
],
[
[
"nulo['país'] = 'Sem informação'",
"_____no_output_____"
]
],
[
[
"Vou mudar o conteudo 'None' da coluna 'Estado' para 'Sem informação'",
"_____no_output_____"
]
],
[
[
"nulo['estado'] = 'Sem informação'",
"_____no_output_____"
]
],
[
[
"**Ordenando Colunas** <br><br>\nA ordem das colunas novas nos novos dataframes deveria ser 'cidade_destino', 'estado' e 'pais', mas essa ordem está diferente no dataframe 'pais'. Vou reordenar para todos ficarem iguais. ",
"_____no_output_____"
],
[
"1. Pegarei o nome de todas as colunas",
"_____no_output_____"
]
],
[
[
"pais.columns",
"_____no_output_____"
]
],
[
[
"2. Agora copio todas as colunas dentro dos colchetes duplos na ordem que eu considerar certa",
"_____no_output_____"
]
],
[
[
"pais = pais[['Identificador do processo de viagem', 'Situação',\n 'Código do órgão superior', 'Nome do órgão superior',\n 'Código órgão solicitante', 'Nome órgão solicitante', 'CPF viajante',\n 'Nome', 'Cargo', 'Período - Data de início', 'Período - Data de fim',\n 'Destinos', 'Motivo', 'Valor diárias', 'Valor passagens',\n 'Valor outros gastos', 'cidade_destino', 'estado', 'país']]",
"_____no_output_____"
]
],
[
[
"**Juntar os 3 dataframes em 1**",
"_____no_output_____"
]
],
[
[
"#ignore_true serve pra ignorar o indice de cada dataframe, se não vai ser impossivel junta-los\n\ndf_final = pd.concat([estado, pais, nulo], ignore_index = True)",
"_____no_output_____"
]
],
[
[
"**Removendo uma coluna**<br>\n\nAgora não faz mais sentido ter a coluna 'Destinos' afinal ela já foi tratada. Usarei o método `drop()` para fazer isso.",
"_____no_output_____"
]
],
[
[
"#O axis indica se eu quero fazer minha alteração em uma linha ou em uma coluna. 1 é coluna, 0 é linha.\n\ndf_final.drop('Destinos', inplace = True, axis = 1)",
"_____no_output_____"
]
],
[
[
"**Salvando um arquivo em csv**<br>\n\nÉ importante depois de tratado gerar um arquivo novo para não precisar rodar tudo e novo.",
"_____no_output_____"
]
],
[
[
"df_final.to_csv('viagens_tratado_2019.csv', encoding = 'latin-1', sep = ';')",
"_____no_output_____"
]
]
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cb1a78801bd5aa6bbce5e88f0c976c5b898c7fc9 | 5,624 | ipynb | Jupyter Notebook | assignment2/task2b.ipynb | Hallvardd/TDT4195-StarterCode | 2a0a6abfd07e139a380f1624632dea4fe6f5a7eb | [
"MIT"
] | null | null | null | assignment2/task2b.ipynb | Hallvardd/TDT4195-StarterCode | 2a0a6abfd07e139a380f1624632dea4fe6f5a7eb | [
"MIT"
] | null | null | null | assignment2/task2b.ipynb | Hallvardd/TDT4195-StarterCode | 2a0a6abfd07e139a380f1624632dea4fe6f5a7eb | [
"MIT"
] | null | null | null | 29.445026 | 101 | 0.479374 | [
[
[
"import torch\nimport matplotlib.pyplot as plt\nimport tqdm\nimport utils\nimport dataloaders\nimport numpy as np\nimport torchvision\nimport os\nfrom trainer import Trainer\ntorch.random.manual_seed(0)\nnp.random.seed(0)\ntorch.backends.cudnn.benchmark = False\ntorch.backends.cuda.deterministic = True",
"_____no_output_____"
]
],
[
[
"### Model Definition",
"_____no_output_____"
]
],
[
[
"class LeNet(torch.nn.Module):\n\n def __init__(self):\n super().__init__()\n \n ### START YOUR CODE HERE ### (You can change anything inside this block)\n num_input_nodes = 32*32\n num_hidden_nodes = 64\n num_classes = 10\n self.classifier = torch.nn.Sequential(\n torch.nn.Linear(num_input_nodes, num_hidden_nodes),\n torch.nn.ReLU(),\n torch.nn.Linear(num_hidden_nodes, num_classes)\n )\n ### END YOUR CODE HERE ### \n\n def forward(self, x):\n ### START YOUR CODE HERE ### (You can change anything inside this block) \n x = x.view(-1, 32*32) \n x = self.classifier(x)\n return x\n ### END YOUR CODE HERE ### \n",
"_____no_output_____"
]
],
[
[
"### Hyperparameters & Loss function",
"_____no_output_____"
]
],
[
[
"# Hyperparameters\nbatch_size = 64\nlearning_rate = 0.0192\nnum_epochs = 4\n\n\n# Use CrossEntropyLoss for multi-class classification\nloss_function = torch.nn.CrossEntropyLoss()",
"_____no_output_____"
]
],
[
[
"### Train model",
"_____no_output_____"
]
],
[
[
"\n\nimage_transform = torchvision.transforms.Compose([\n torchvision.transforms.Resize((32, 32)),\n torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize([0.5], [0.25])\n])\ndataloader_train, dataloader_val = dataloaders.load_dataset(batch_size, image_transform)\n\n# Model definition\nmodel = LeNet()\n# Transfer model to GPU memory (if possible)\nmodel = utils.to_cuda(model)\n\n# Define optimizer (Stochastic Gradient Descent)\noptimizer = torch.optim.SGD(model.parameters(),\n lr=learning_rate)\ntrainer = Trainer(\n model=model,\n dataloader_train=dataloader_train,\n dataloader_val=dataloader_val,\n batch_size=batch_size,\n loss_function=loss_function,\n optimizer=optimizer\n)\ntrain_loss_dict, val_loss_dict = trainer.train(num_epochs)",
"_____no_output_____"
]
],
[
[
"### Train Model",
"_____no_output_____"
]
],
[
[
"utils.plot_loss(train_loss_dict, label=\"Train Loss\")\nutils.plot_loss(val_loss_dict, label=\"Test Loss\")\n# Limit the y-axis of the plot (The range should not be increased!)\nplt.ylim([0, .4])\nplt.legend()\nplt.xlabel(\"Global Training Step\")\nplt.ylabel(\"Cross Entropy Loss\")\nos.makedirs(\"image_processed\", exist_ok=True)\nplt.savefig(os.path.join(\"image_processed\", \"task2.png\"))\n\nplt.show()\n\ntorch.save(model.state_dict(), \"saved_model.torch\")\n",
"_____no_output_____"
],
[
"\n# %%\nfinal_loss, final_acc = utils.compute_loss_and_accuracy(\n dataloader_val, model, loss_function)\nprint(f\"Final Validation loss: {final_loss}. Final Validation accuracy: {final_acc}\")\n\n# %%",
"_____no_output_____"
]
]
] | [
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cb1a7e170cc0ff00b946f003af8ba70bb444f263 | 656,003 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Regression Exploration-checkpoint.ipynb | elizabethvo/DataPy | ea583678475aa7f2d9c3fe880361b8a55afddc34 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Regression Exploration-checkpoint.ipynb | elizabethvo/DataPy | ea583678475aa7f2d9c3fe880361b8a55afddc34 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Regression Exploration-checkpoint.ipynb | elizabethvo/DataPy | ea583678475aa7f2d9c3fe880361b8a55afddc34 | [
"MIT"
] | null | null | null | 182.121877 | 92,592 | 0.879267 | [
[
[
"# HW7",
"_____no_output_____"
]
],
[
[
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl",
"_____no_output_____"
],
[
"%matplotlib inline",
"_____no_output_____"
]
],
[
[
"In order to ensure your plots are inline, make sure to run the matplotlib magic command. ",
"_____no_output_____"
],
[
"# Q1",
"_____no_output_____"
],
[
"You are provided with a csv file (shoes.csv) on canvas that contains 2 columns. \n\nThe first column is the height of the individuals being surveyed and the second column is their shoe size. We do not know anything else about the individuals.\n\nUsing the information in that file do the following:\n1. Create a scatter plot displaying shoe size on the y-axis and height on the x-axis.\n2. Compute the correlation between shoe size and height. \n3. Fit a linear regression line through this data.\n4. Use the linear regression line to predict what your shoe size would be. For this question just write down what your height is (in inches), then write down the predicted shoe size (based on the linear regression ). Finally write your own actual shoe size. How far off is the model’s prediction?",
"_____no_output_____"
]
],
[
[
"shoes = pd.read_csv('shoes.csv')",
"_____no_output_____"
],
[
"plt.scatter(shoes['HEIGHT (IN)'], shoes['SHOE SIZE (FT)'])\nplt.xlabel('Height (in inches)')\nplt.ylabel('Shoe Size (in feet)')\nplt.title('Shoe Size by Height')",
"_____no_output_____"
],
[
"#computing the correlation coefficient using the long method:",
"_____no_output_____"
],
[
"def standardize(anylist):\n '''convert any array of numbers to std units '''\n return (anylist - np.mean(anylist)) / np.std(anylist) ",
"_____no_output_____"
],
[
"standardize_x = standardize(shoes['HEIGHT (IN)'])\nstandardize_y = standardize(shoes['SHOE SIZE (FT)'])",
"_____no_output_____"
],
[
"#correlation coefficient r\nr = np.mean(standardize_x * standardize_y)\nr",
"_____no_output_____"
],
[
"#computing the correlation coefficient using np.corrcoef()\nnp.corrcoef(shoes['HEIGHT (IN)'], shoes['SHOE SIZE (FT)'])",
"_____no_output_____"
],
[
"#fitting a linear regression line\n\nplt.scatter(standardize_x, standardize_y) #graphs the scatter plot of data\nxvals = np.arange(-4, 3, 0.3) #setting the range of x values for regression line\nyvals = r * xvals #the regression y values (correlation coefficient * x values)\nplt.plot(xvals, yvals, color = 'g') #graphing the linear regression\nplt.title('Distribution of Shoe Sizes by Height')\nplt.xlabel('Height (in inches)')\nplt.ylabel('Shoe Size (in feet)')",
"_____no_output_____"
],
[
"#predicting shoe size",
"_____no_output_____"
],
[
"m, b = np.polyfit(shoes['HEIGHT (IN)'], shoes['SHOE SIZE (FT)'], 1)",
"_____no_output_____"
],
[
"m, b",
"_____no_output_____"
],
[
"# predicting using the linear regression: y = mx + b\n# My height: 63 inches\nmy_height = 63",
"_____no_output_____"
],
[
"my_shoe_size = (m * my_height) + b\nmy_shoe_size",
"_____no_output_____"
]
],
[
[
"- My actual shoe size is an 8. The model is off by about a size. ",
"_____no_output_____"
],
[
"# Q2",
"_____no_output_____"
],
[
"The department of transportation releases flight delay information annually. \n\nWe will use a small sample of this data to check if Chebychev's inequality holds.\n\nUse the airline_data.csv file. Plot the distribution of arrival delays. \n\nCompute the mean and standard deviation of the arrival delays. As per Chebychev's inequality, as least 88.88% of the data should be within 3 standard deviations of the mean. Is that true for this dataset? (support your answer with actual code)\n\nBased on this sample (and this sample alone), which airline would you avoid? Why?\n",
"_____no_output_____"
]
],
[
[
"delays = pd.read_excel('flightdelays.xlsx')\ndelays.shape",
"_____no_output_____"
],
[
"#cleaning data\ndelays = delays.dropna(subset=['ARRIVAL_DELAY'])\ndelays.shape",
"_____no_output_____"
],
[
"plt.style.use('fivethirtyeight')\nplt.hist(delays['ARRIVAL_DELAY'], normed = True, bins = 15, ec = 'k')\nplt.title('distribution of arrival delays', size = 'medium')\nplt.xlabel('Delay Amount')\nplt.ylabel('Frequency')",
"/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
],
[
[
"- As per Chebychev's inequality, as least 88.88% of the data should be within 3 standard deviations of the mean...",
"_____no_output_____"
]
],
[
[
"standardized_delays = (delays['ARRIVAL_DELAY'] - np.mean(delays['ARRIVAL_DELAY'])) / np.std(delays['ARRIVAL_DELAY'])",
"_____no_output_____"
],
[
"upper_bound = np.mean(delays['ARRIVAL_DELAY']) + 3 * np.std(delays['ARRIVAL_DELAY'])\nlower_bound = np.mean(delays['ARRIVAL_DELAY']) - 3 * np.std(delays['ARRIVAL_DELAY'])\nwithin_3_SDs = np.sum(np.logical_and(delays['ARRIVAL_DELAY'] < upper_bound, delays['ARRIVAL_DELAY'] > lower_bound))\nprint(within_3_SDs / len(delays) * 100)",
"97.87234042553192\n"
]
],
[
[
"- Yes, Chebychev's inequality is true for this dataset. ",
"_____no_output_____"
]
],
[
[
"# Determining which airline to avoid...",
"_____no_output_____"
],
[
"grouped_delays = delays.groupby(['AIRLINE'], as_index=False)\ngrouped_delays.agg({'ARRIVAL_DELAY' : 'mean'}).sort_values('ARRIVAL_DELAY', ascending = False)",
"_____no_output_____"
]
],
[
[
"- Based on this data alone, it would be a good idea to avoid Hawaii Airlines (HA) because it has the highest average of delay time. ",
"_____no_output_____"
],
[
"# Q3",
"_____no_output_____"
],
[
"The NFL players data has the heights and weights of some players in the NFL. It also tells you what position they play in.\n\n1. Plot a histogram of the heights. Does this seem normally distributed? What is the mean? What is the median? Are there any significant outliers?\n\n2. Plot a histogram of the weights. Does this seem normally distributed? What is the mean? What is the median? Are there any significant outliers?\n\n3. Does the distribution of weights depend on the position? If so, please convey that is a visual form.",
"_____no_output_____"
],
[
"#### 1: Heights",
"_____no_output_____"
]
],
[
[
"nfl = pd.read_csv('nfl_players.csv', encoding='latin-1')\nnfl.head()",
"_____no_output_____"
],
[
"#Plot a histogram of the heights.\nplt.hist(nfl['Height'], bins = np.arange(60, 85), ec = 'blue', color = 'lightskyblue')\nplt.title('distribution of heights', size = 'medium')\nplt.xlabel('Player Height')\nplt.ylabel('Frequency')",
"_____no_output_____"
]
],
[
[
"- The distribution looks vaguely but not quite normal. ",
"_____no_output_____"
]
],
[
[
"# Mean?\nnp.mean(nfl['Height'])",
"_____no_output_____"
]
],
[
[
"- The mean height of NFL players is 74.013",
"_____no_output_____"
]
],
[
[
"# Median?\nnp.median(nfl['Height'])",
"_____no_output_____"
]
],
[
[
"- The median height of NFL players is 74",
"_____no_output_____"
]
],
[
[
"# Outliers?\nnp.max(nfl['Height']), np.min(nfl['Height'])",
"_____no_output_____"
]
],
[
[
"- The minimum and maximum values are not significant enough to be outliers.",
"_____no_output_____"
],
[
"#### 2: Weights",
"_____no_output_____"
]
],
[
[
"#Plot a histogram of the weights.\nplt.hist(nfl['Weight'], bins = np.arange(150, 370, 10), ec = 'blue', color = 'lightskyblue')\nplt.title('distribution of weights', size = 'medium')\nplt.xlabel('player weight')\nplt.ylabel('frequency')",
"_____no_output_____"
]
],
[
[
"- The distribution of weights is not normal. ",
"_____no_output_____"
]
],
[
[
"# Mean?\nnp.mean(nfl['Weight'])",
"_____no_output_____"
],
[
"# Median?\nnp.median(nfl['Weight'])",
"_____no_output_____"
],
[
"# Outliers?\nnp.max(nfl['Weight']), np.min(nfl['Weight'])",
"_____no_output_____"
],
[
"nfl[['Weight']].sort_values('Weight', ascending=True)[:5]",
"_____no_output_____"
],
[
"nfl[['Weight']].sort_values('Weight', ascending=False)[:5]",
"_____no_output_____"
]
],
[
[
"- There seem to be no significantly large outliers because the data for both ends of the weight spectrum seem to be gradually increasing/decreasing.",
"_____no_output_____"
],
[
"#### 3. Does the distribution of weights depend on the position?",
"_____no_output_____"
]
],
[
[
"nfl.columns",
"_____no_output_____"
],
[
"weight_position = nfl[['Position', 'Weight']]\nweight_position.head()",
"_____no_output_____"
],
[
"# Histogramming the Distributions of Weights per Position",
"_____no_output_____"
],
[
"mpl.style.use('seaborn')\nnfl.hist(column = ['Weight'], by= ['Position'], figsize = (15, 40), \n layout = (12, 2), sharey = False, sharex = False,\n bins = np.arange(150, 364, 5))",
"_____no_output_____"
]
],
[
[
"- As we can see in the histograms above, the distribution of weights among players of different positions changes. Therefore, yes, the distribution of weights depends on the position.",
"_____no_output_____"
],
[
"# Q4",
"_____no_output_____"
],
[
"According to a fun statistics blog \n\n\"The consumption of ice cream (pints per person) and the number of murders in New York are positively correlated. That is, as the amount of ice cream sold per person increases, the number of murders increases.\"\n\nDoes this mean that ice cream causes murder? Why or why not? What could explain this positive association?",
"_____no_output_____"
],
[
"- No. Correlation does not imply cause; just because a mutual relationship can be observed between two variablies doesn't mean that one causes the other. One possible explanation for this positive association may be may be that there is some unknown variable that seperately influences ice cream sales and murders. In this case, it could be high temperatures; more ice cream sales as the temperature increases, and more murders on hotter days when the windows are left open.\n",
"_____no_output_____"
],
[
"# Q5",
"_____no_output_____"
],
[
"Use the music dataset to explore what goes into the making of a popular song.",
"_____no_output_____"
],
[
"Use the following stepwise approach to build a model that helps predict the popularity of a song.\n\n1. Understand the data. The data was taken from https://think.cs.vt.edu/corgis/csv/music/music.html. Take a look at the description of the data. If you do not understand what a particular column means, either remove it from the data or do some research to figure it out.\n2. Who are the top 10 artists in terms of artist hotness (basically recent popularity)? Which are the top 10 songs in terms of hotness?\n3. Create 5 different scatter plots with song hotness as the dependent variable and your choice of 5 columns as the independent variables. For each choice of independent variable, provide a one line explanation of why you think that column could influence song hotness.\n4. Create a linear regression model that helps predict song hotness. You can choose any or all of the 5 columns you picked to be part of the model.",
"_____no_output_____"
],
[
"#### a) Understanding the data, cleaning...",
"_____no_output_____"
]
],
[
[
"music = pd.read_csv('music.csv')\nmusic.shape, music.columns",
"_____no_output_____"
],
[
"music = music.rename(columns = {'terms' : 'genre'})",
"_____no_output_____"
],
[
"\nprint(music.shape)\nmusic.head(5)\n",
"(5648, 35)\n"
],
[
"#removing songs where song.hottnesss is 0 (outliers)\nmusic = music[music['song.hotttnesss'] != 0]",
"_____no_output_____"
]
],
[
[
"#### b. Who are the top 10 artists in terms of artist hotness?",
"_____no_output_____"
]
],
[
[
"sorted_hot_artists = music.sort_values(['artist.hotttnesss'], ascending=False)\ntop10 = sorted_hot_artists['artist.name'].unique()[:10]\n\nprint('These are the top 10 artists by hotness:')\nfor x in top10:\n print(x)",
"These are the top 10 artists by hotness:\nKanye West\nDaft Punk\nBlack Eyed Peas / Terry Dexter\nBlack Eyed Peas\nTaylor Swift\nColdplay\nRihanna\nEminem\nT.I.\nUsher\n"
]
],
[
[
"#### Which are the top 10 songs in terms of hotness?",
"_____no_output_____"
]
],
[
[
"song_hotness = music[['song.hotttnesss', 'artist.name', 'title']]\ntop_10_songs = song_hotness.sort_values('song.hotttnesss', ascending = False)[:10]\ntop_10_songs[['title', 'song.hotttnesss', 'artist.name']]",
"_____no_output_____"
]
],
[
[
"- These are the top 10 songs in terms of hotness.",
"_____no_output_____"
],
[
"### c. Create 5 different scatter plots",
"_____no_output_____"
],
[
"#### 1) Tempo's Effect on Song Hotness.",
"_____no_output_____"
]
],
[
[
"music = music.dropna(subset = ['familiarity', 'song.hotttnesss'])",
"_____no_output_____"
],
[
"r = np.corrcoef(music['familiarity'], music['song.hotttnesss'])\nr",
"_____no_output_____"
]
],
[
[
"- the correlation coefficient r, 0.5439, tells us that there is a significant relationship between familiarity and song hotness.",
"_____no_output_____"
]
],
[
[
"m, b = np.polyfit(music['familiarity'], music['song.hotttnesss'], 1)",
"_____no_output_____"
],
[
"plt.figure(figsize=(10,5))\n\nplt.scatter(music['familiarity'], music['song.hotttnesss'], color = 'dodgerblue')\n\n#regression line\nxvals = np.arange(0, 1.1, 0.1)\nyvals = m * xvals + b \nplt.plot(xvals, yvals, color = 'navy')\nplt.title('Song Hotness by Familiarity', size = 'medium')\nplt.xlabel('familiarity')\nplt.ylabel('hotness')",
"_____no_output_____"
]
],
[
[
"- The scatterplot shows that higher familiarity is significantly correlated with more popular songs; in general, the higher the familiarity, the hotter the song should be. This may be because people like to listen to songs over and over, familiar songs are catchy and easy to sing along to. ",
"_____no_output_____"
],
[
"#### 2) Duration's Effect on Song Hotness.\n",
"_____no_output_____"
]
],
[
[
"music = music.dropna(subset = ['duration', 'song.hotttnesss'])",
"_____no_output_____"
],
[
"#removing that one nasty outlier song that lasted over 2,050 ",
"_____no_output_____"
],
[
"cleaned_music = music[music['duration'] < np.max(music['duration'])]",
"_____no_output_____"
],
[
"r = np.corrcoef(cleaned_music['duration'], cleaned_music['song.hotttnesss'])\nr",
"_____no_output_____"
],
[
"plt.figure(figsize=(10,5))\n\nm, b = np.polyfit(cleaned_music['duration'], cleaned_music['song.hotttnesss'], 1)\nplt.scatter(cleaned_music['duration'], cleaned_music['song.hotttnesss'], color = 'royalblue')\n#regression line\nxvals = np.arange(0, 1760, 5)\nyvals = m * xvals + b \nplt.plot(xvals, yvals, color = 'k')\nplt.title('Song Hotness by Duration', size = 'medium')\nplt.xlabel('duration')\nplt.ylabel('hotness')",
"_____no_output_____"
]
],
[
[
"- The scatter plot shows that as song duration increases past 750, there are fewer songs with high hotness. This is probably because people have short attention spans and like music in the 3 - 4 minute range; 30 minute songs are too long to hit the popular charts.",
"_____no_output_____"
],
[
"#### 3) Key's Effect on Song Hotness.\n",
"_____no_output_____"
]
],
[
[
"music = music.dropna(subset = ['key', 'song.hotttnesss'])",
"_____no_output_____"
],
[
"r = np.corrcoef(music['key'], music['song.hotttnesss'])\nr",
"_____no_output_____"
],
[
"#removing that one nasty outlier key:",
"_____no_output_____"
],
[
"cleaned_music = music[music['key'] < np.max(music['key'])]",
"_____no_output_____"
],
[
"r = np.corrcoef(cleaned_music['key'], cleaned_music['song.hotttnesss'])\nr",
"_____no_output_____"
]
],
[
[
"- The correlation coefficient 0.001238 tells us that there is basically no linear relationship between key and song hottness.",
"_____no_output_____"
]
],
[
[
"plt.figure(figsize=(10,5))\n\nm, b = np.polyfit(cleaned_music['key'], cleaned_music['song.hotttnesss'], 1)\nplt.scatter(cleaned_music['key'], cleaned_music['song.hotttnesss'], color = 'blue')\n#regression line\nxvals = np.arange(0, 10, 5)\nyvals = m * xvals + b \nplt.title('Song Hotness by Key', size = 'medium')\nplt.xlabel('key')\nplt.ylabel('hotness')",
"_____no_output_____"
]
],
[
[
"- The scatter plot shows that there is generally an even distribution of hot songs by key. Therefore key has very little to no influence on the song's hotness.",
"_____no_output_____"
],
[
"#### 4) Loudness' Effect on Song Hotness.\n",
"_____no_output_____"
]
],
[
[
"music = music.dropna(subset = ['loudness', 'song.hotttnesss'])",
"_____no_output_____"
],
[
"r = np.corrcoef(music['loudness'], music['song.hotttnesss'])\nr",
"_____no_output_____"
]
],
[
[
"- the correlation coefficient r, 0.22587, tells us that there is a very slight linear relationship between song loudness and song hotness.",
"_____no_output_____"
]
],
[
[
"plt.figure(figsize=(10,5))\n\nm, b = np.polyfit(music['loudness'], music['song.hotttnesss'], 1)\nplt.scatter(music['loudness'], music['song.hotttnesss'], color = 'dodgerblue')\n#regression line\nxvals = np.arange(-45, 0, 1)\nyvals = m * xvals + b \nplt.plot(xvals, yvals, color = 'navy')\n\nplt.title('Song Hotness by Loudness', size = 'medium')\nplt.xlabel('loudness')\nplt.ylabel('hotness')",
"_____no_output_____"
]
],
[
[
"- The scatter plot shows that the number of hot songs is concentrated around louder songs; loudness has somewhat of an influence on the song's hotness. This might be because loud songs are popular among young adults for dancing.",
"_____no_output_____"
]
],
[
[
"music.columns",
"_____no_output_____"
]
],
[
[
"#### 5) Artist's hotness on song's hotness",
"_____no_output_____"
]
],
[
[
"music = music.dropna(subset = ['artist.hotttnesss', 'song.hotttnesss'])",
"_____no_output_____"
],
[
"r = np.corrcoef(music['artist.hotttnesss'], music['song.hotttnesss'])\nr",
"_____no_output_____"
]
],
[
[
"- The correlation coefficient r = 0.5223 shows that there is a relatively strong linear relationship between an artist's hotness and the song's hotness.",
"_____no_output_____"
]
],
[
[
"plt.figure(figsize=(10,5))\n\nm, b = np.polyfit(music['artist.hotttnesss'], music['song.hotttnesss'], 1)\nplt.scatter(music['artist.hotttnesss'], music['song.hotttnesss'], color = 'royalblue')\n#regression line\nxvals = np.arange(0, 1.2, .1)\nyvals = m * xvals + b \nplt.plot(xvals, yvals, color = 'navy')\n\nplt.title('Song Hotness by Artist Hotness', size = 'medium')\nplt.xlabel('artist hotness')\nplt.ylabel('song hotness')",
"_____no_output_____"
]
],
[
[
"- The scatterplot shows that there is a considerable relationship between an artist's hotness and their song's hotness. An explanation: the hotter an artist is, the larger their fanbase is. The larger the fanbase, the larger potential receptive audience for the song which means higher hotness.",
"_____no_output_____"
],
[
"#### d) Create a linear regression model that helps predict song hotness. You can choose any or all of the 5 columns you picked to be part of the model.",
"_____no_output_____"
]
],
[
[
"import statsmodels.api as sm",
"_____no_output_____"
],
[
"# create a df of the independent variables\nX = music[['artist.hotttnesss', 'familiarity', 'loudness']]\n# dependent variable. what are we predicting?\ny = music['song.hotttnesss']\n\n#we are fitting y = ax_1 + bx_2+ c and not just ax_1 + bx_2\nX = sm.add_constant(X) \n# OLS - ordinary least squares.\n# best possible hyperplane through the data\n# best = minimize sum of square distances\nest = sm.OLS(y, X).fit()\nest.summary()",
"_____no_output_____"
]
],
[
[
"#### - The model is:\n\n##### predicted song hotness = 0.1273 + 0.3121x + 0.3652y + 0.0032z \n\n.... where x is the song artist's hotness, y is the song's familiarity, and z is the song's loudness.",
"_____no_output_____"
],
[
"# Q6",
"_____no_output_____"
],
[
"Use the automobilie dataset\n\nPlot the distribution of the car mileage. Does this look like a normal distribution?\n\nPlot 3 different distributions based on the origin of the car. Which of the 3 is most normally distributed?\n\nUse the dataset to create a linear regression model that predicts the mileage of a car.\n\nThere are 3 values of the origin column - 1 means USA, 2 means Germany, and 3 means Japan.",
"_____no_output_____"
],
[
"After you have made your linear regression model, use it to predict the mpg of the following cars. Which one of these 3 cars does your model do best on? \n\nhttps://www.caranddriver.com/chevrolet/camaro/specs\n\nhttps://www.caranddriver.com/smart/fortwo/specs\n\nhttps://www.caranddriver.com/toyota/86/specs",
"_____no_output_____"
]
],
[
[
"automobiles = pd.read_csv('auto-mpg.csv')",
"_____no_output_____"
],
[
"automobiles = automobiles.dropna(subset= ['mpg', 'origin', 'weight', 'horsepower', \n 'model year', 'displacement', 'acceleration'])",
"_____no_output_____"
]
],
[
[
"#### Plot the distribution of the car mileage. Does this look like a normal distribution?\n",
"_____no_output_____"
]
],
[
[
"plt.hist(automobiles['mpg'], normed = True, bins = np.arange(5, 50), ec = 'k')\nplt.xlabel('Mileage', size = 'medium')\nplt.ylabel('Frequency', size = 'medium')\nplt.title('Mileage Distribution for All Cars')",
"/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
],
[
[
"- no, the distribution does not completely look like a normal distribution but it looks vaguely like one.",
"_____no_output_____"
],
[
"#### Plot 3 different distributions based on the origin of the car. Which of the 3 is most normally distributed?\n",
"_____no_output_____"
]
],
[
[
"np.unique(automobiles['origin'])",
"_____no_output_____"
],
[
"origin_1 = automobiles[automobiles['origin'] == 1]\norigin_2 = automobiles[automobiles['origin'] == 2]\norigin_3 = automobiles[automobiles['origin'] == 3]",
"_____no_output_____"
]
],
[
[
"#### mileage distribution for origin 1, USA\n",
"_____no_output_____"
]
],
[
[
"# mileage distribution for origin 1, USA\nplt.hist(standardize(origin_1['mpg']), normed = True, bins = 20, ec = 'k')\nplt.xlabel('Mileage for Origin 1 (USA)', size = 'medium')\nplt.ylabel('Frequency', size = 'medium')\nplt.title('Mileage Distribution for Origin 1 (USA) Cars')",
"/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
],
[
[
"- If you squint, this looks vaguely normally distributed.",
"_____no_output_____"
]
],
[
[
"# looking at the percent of data that lies between k standard deviations.\nk = 1\nupper_bound = np.mean(origin_1['mpg']) + k * np.std(origin_1['mpg'])\nlower_bound = np.mean(origin_1['mpg']) - k * np.std(origin_1['mpg'])\nwithin_3_SDs = np.sum(np.logical_and(origin_1['mpg'] < upper_bound, origin_1['mpg'] > lower_bound))\nprint(within_3_SDs / len(origin_1) * 100)",
"68.67469879518072\n"
]
],
[
[
"- We see that about 69% of the data lies between 1 standard dev. from the mean for origin 1 mileage.",
"_____no_output_____"
],
[
"#### mileage distribution for origin 2, Germany\n",
"_____no_output_____"
]
],
[
[
"# mileage distribution for origin 2, Germany\nplt.hist(standardize(origin_2['mpg']), normed = True, bins = 20, ec = 'k')\nplt.xlabel('Mileage for Origin 2 (GER) Cars', size = 'medium')\nplt.ylabel('Frequency', size = 'medium')\nplt.title('Mileage Distribution for Origin 2 (German) Cars')",
"/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
],
[
"# looking at the percent of data that lies between k standard deviations.\nk = 1\nupper_bound = np.mean(origin_2['mpg']) + k * np.std(origin_2['mpg'])\nlower_bound = np.mean(origin_2['mpg']) - k * np.std(origin_2['mpg'])\nwithin_3_SDs = np.sum(np.logical_and(origin_2['mpg'] < upper_bound, origin_2['mpg'] > lower_bound))\nprint(within_3_SDs / len(origin_2) * 100)",
"70.0\n"
]
],
[
[
"- We see that about 70% of the data lies between 1 standard dev. from the mean for origin 2 mileage.",
"_____no_output_____"
]
],
[
[
"# mileage distribution for origin 3, Japan\nplt.hist(standardize(origin_3['mpg']), normed = True, bins = 20, ec = 'k')\nplt.xlabel('Mileage for Origin 3 (JPN) Cars', size = 'medium')\nplt.ylabel('Frequency', size = 'medium')\nplt.title('Mileage Distribution for Origin 3 (Japanese) Cars')",
"/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
],
[
"# looking at the percent of data that lies between k standard deviations.\nk = 1\nupper_bound = np.mean(origin_3['mpg']) + k * np.std(origin_3['mpg'])\nlower_bound = np.mean(origin_3['mpg']) - k * np.std(origin_3['mpg'])\nwithin_3_SDs = np.sum(np.logical_and(origin_3['mpg'] < upper_bound, origin_3['mpg'] > lower_bound))\nprint(within_3_SDs / len(origin_3) * 100)",
"60.75949367088608\n"
]
],
[
[
"- We see that about 61% of the data lies between 1 standard dev. from the mean for origin 2 mileage.",
"_____no_output_____"
],
[
"- Of the 3 origins, the mileage distribution for origin 1 (USA) visually looks the most normally distributed but origin 2 (Germany) actually has more data (70%) for grouped within 1 SD of the mean than origin 1 (68%)",
"_____no_output_____"
],
[
"#### Use the dataset to create a linear regression model that predicts the mileage of a car.",
"_____no_output_____"
]
],
[
[
"# cleaning dataframe, removing rows with '?'\nvect_float = np.vectorize(float)\n\nautomobiles = automobiles[automobiles['horsepower'] != '?']\nautomobiles['horsepower'] = automobiles['horsepower'].apply(vect_float)",
"_____no_output_____"
],
[
"# create a df of the independent variables\nX = automobiles[['weight', 'displacement', 'horsepower']]\n# dependent variable. what are we predicting?\ny = automobiles['mpg']\n\n#we are fitting y = ax_1 + bx_2+ c and not just ax_1 + bx_2\nX = sm.add_constant(X) \n# OLS - ordinary least squares.\n# best possible hyperplane through the data\n# best = minimize sum of square distances\nest = sm.OLS(y, X).fit()\nest.summary()",
"_____no_output_____"
]
],
[
[
"#### - The model is:\n\n##### predicted mpg = 44.8559 - 0.0054x -0.0058y -0.0417z \n\n.... where x is the weight, y is the displacement, and z is the horsepower.",
"_____no_output_____"
],
[
"### Testing the model for the 3 different cars' mileages.",
"_____no_output_____"
],
[
"- we will compare the predicted mileages to the Fuel Economy Est-Combined MPG from the website.",
"_____no_output_____"
]
],
[
[
"#Car 1: 2018 Chevrolet Camaro \nactual_mileage = 25\nx = 3339 #weight\ny = 122 #displacement\nz = 275 #horsepower\npredicted_mpg = 44.8559 - 0.0054*x - 0.0058*y - 0.0417*z \nprint('The predicted mpg for the 2018 Chevrolet Camaro is: ' + str(predicted_mpg))\nprint('The difference between the predicted and actual milelage:')\nprint(str(actual_mileage - predicted_mpg))",
"The predicted mpg for the 2018 Chevrolet Camaro is: 14.650199999999998\nThe difference between the predicted and actual milelage:\n10.349800000000002\n"
],
[
"#Car 2: 2017 Smart Fortwo \nactual_mileage = 34\nx = 2050 #weight\ny = 55 #displacement\nz = 89 #horsepower\npredicted_mpg = 44.8559 - 0.0054*x - 0.0058*y - 0.0417*z \nprint('The predicted mpg for the 2017 Smart Fortwo is: ' + str(predicted_mpg))\nprint('The difference between the predicted and actual milelage:')\nprint(str(actual_mileage - predicted_mpg))",
"The predicted mpg for the 2017 Smart Fortwo is: 29.755599999999994\nThe difference between the predicted and actual milelage:\n4.244400000000006\n"
],
[
"#Car 3: 2018 Toyota 86\nactual_mileage = 24\nx = 2774 #weight\ny = 122 #displacement\nz = 205 #horsepower\npredicted_mpg = 44.8559 - 0.0054*x - 0.0058*y - 0.0417*z \nprint('The predicted mpg for the 2018 Toyota 86 is: ' + str(predicted_mpg))\nprint('The difference between the predicted and actual milelage:')\nprint(str(actual_mileage - predicted_mpg))",
"The predicted mpg for the 2018 Toyota 86 is: 20.620199999999997\nThe difference between the predicted and actual milelage:\n3.379800000000003\n"
]
],
[
[
"- The model is most accurate for the 2018 Toyota 86 because the difference between the actual and predicted mileages is the smallest.",
"_____no_output_____"
],
[
"# Q7 ",
"_____no_output_____"
],
[
"Using the bodyfat.csv file, create a regression model that predicts the bodyfat based on other factors in the dataset.\n\nRemember that you have to first identify correlations before you decide to put a variable into your regression model.",
"_____no_output_____"
]
],
[
[
"bodyfat = pd.read_excel('BodyFat.xls')",
"_____no_output_____"
],
[
"bodyfat.head()",
"_____no_output_____"
],
[
"def correlation(df, x, y):\n # r = avg(standardize(x) * standardize(y))\n x_std = standardize(df[x])\n y_std = standardize(df[y])\n return np.mean(x_std * y_std)",
"_____no_output_____"
],
[
"# The variables below have significant correlations with bodyfat\nprint(correlation(bodyfat, 'BODYFAT', 'WEIGHT'))\nprint(correlation(bodyfat, 'BODYFAT', 'DENSITY'))\nprint(correlation(bodyfat, 'BODYFAT', 'ADIPOSITY'))\nprint(correlation(bodyfat, 'BODYFAT', 'CHEST'))\nprint(correlation(bodyfat, 'BODYFAT', 'ABDOMEN'))\nprint(correlation(bodyfat, 'BODYFAT', 'HIP'))\nprint(correlation(bodyfat, 'BODYFAT', 'THIGH'))",
"0.6131561100313141\n-0.9880867267228655\n0.7279941849205852\n0.7028851557124898\n0.8137062216427914\n0.6256999272610364\n0.561284376117438\n"
]
],
[
[
"#### Making the linear regression model for bodyfat",
"_____no_output_____"
]
],
[
[
"# create a df of the independent variables\nX = bodyfat[['WEIGHT', 'DENSITY', 'ADIPOSITY', 'CHEST', 'ABDOMEN', 'HIP', 'THIGH']]\n# dependent variable. what are we predicting?\ny = bodyfat['BODYFAT']\n\nX = sm.add_constant(X) \n# OLS - ordinary least squares.\n# best possible hyperplane through the data\n# best = minimize sum of square distances\nest = sm.OLS(y, X).fit()\nest.summary()",
"_____no_output_____"
]
],
[
[
"#### - The model is:\n\n##### predicted bodyfat = 415.1138 - 0.0033x - 381.1288y - 0.0422z + 0.0338a + 0.0428b + 0.0214c - 0.0285d\n\n.... where x is the weight, y is the density, and z is the adiposity, a is the chest size, b is the abdomen size, c is the hip size, and d is the thigh size.",
"_____no_output_____"
]
]
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cb1a8d923afc02584e9fbf7343fab387f037fcd6 | 36,451 | ipynb | Jupyter Notebook | Deep_learning_udc/6_lstm_bigram.ipynb | ricsinaruto/Python-projects | 26924aaca973051181f0e7ab544e8dae5ffb4eb1 | [
"MIT"
] | 1 | 2017-05-01T10:07:02.000Z | 2017-05-01T10:07:02.000Z | Deep_learning_udc/6_lstm_bigram.ipynb | ricsinaruto/Python-projects | 26924aaca973051181f0e7ab544e8dae5ffb4eb1 | [
"MIT"
] | null | null | null | Deep_learning_udc/6_lstm_bigram.ipynb | ricsinaruto/Python-projects | 26924aaca973051181f0e7ab544e8dae5ffb4eb1 | [
"MIT"
] | 1 | 2018-08-28T16:14:00.000Z | 2018-08-28T16:14:00.000Z | 60.650582 | 1,490 | 0.574113 | [
[
[
"# These are all the modules we'll be using later. Make sure you can import them\n# before proceeding further.\nfrom __future__ import print_function\nimport os\nimport numpy as np\nimport random\nimport math\nimport string\nimport tensorflow as tf\nimport zipfile\nfrom six.moves import range\nfrom six.moves.urllib.request import urlretrieve\n\nurl = 'http://mattmahoney.net/dc/'\n\ndef maybe_download(filename, expected_bytes):\n \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n if not os.path.exists(filename):\n filename, _ = urlretrieve(url + filename, filename)\n statinfo = os.stat(filename)\n if statinfo.st_size == expected_bytes:\n print('Found and verified %s' % filename)\n else:\n print(statinfo.st_size)\n raise Exception(\n 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n return filename\n\nfilename = maybe_download('text8.zip', 31344016)\n\ndef read_data(filename):\n with zipfile.ZipFile(filename) as f:\n name = f.namelist()[0]\n data = tf.compat.as_str(f.read(name))\n return data\n \ntext = read_data(filename)\nprint('Data size %d' % len(text))\n\nvalid_size = 1000\nvalid_text = text[:valid_size]\ntrain_text = text[valid_size:]\ntrain_size = len(train_text)\nprint(train_size, train_text[:64])\nprint(valid_size, valid_text[:64])\n\nvocabulary_size = len(string.ascii_lowercase) + 1 # [a-z] + ' '\nfirst_letter = ord(string.ascii_lowercase[0])\n\ndef char2id(char):\n if char in string.ascii_lowercase:\n return ord(char) - first_letter + 1\n elif char == ' ':\n return 0\n else:\n print('Unexpected character: %s' % char)\n return 0\n \ndef id2char(dictid):\n if dictid > 0:\n return chr(dictid + first_letter - 1)\n else:\n return ' '\n\nprint(char2id('a'), char2id('z'), char2id(' '), char2id('ï'))\nprint(id2char(1), id2char(26), id2char(0))",
"Found and verified text8.zip\nData size 100000000\n99999000 ons anarchists advocate social relations based upon voluntary as\n1000 anarchism originated as a term of abuse first used against earl\nUnexpected character: ï\n1 26 0 0\na z \n"
],
[
"batch_size=64\nnum_unrollings=10\nembedding_size=27\n\nclass BatchGenerator(object):\n def __init__(self, text, batch_size, num_unrollings):\n self._text = text\n self._text_size = len(text)\n self._batch_size = batch_size\n self._num_unrollings = num_unrollings\n segment = self._text_size // batch_size\n self._cursor = [ offset * segment for offset in range(batch_size)]\n self._last_batch = self._next_batch()\n\n \n def _next_batch(self):\n \"\"\"Generate a single batch from the current cursor position in the data.\"\"\"\n batch = np.zeros(shape=(self._batch_size,2), dtype=np.int32)\n for b in range(self._batch_size):\n batch[b,0] = char2id(self._text[self._cursor[b]])\n batch[b,1] = char2id(self._text[self._cursor[b]+1])\n self._cursor[b] = (self._cursor[b] +1) % self._text_size\n return batch\n \n def next(self):\n \"\"\"Generate the next array of batches from the data. The array consists of\n the last batch of the previous array, followed by num_unrollings new ones.\n \"\"\"\n batches = [self._last_batch]\n for step in range(self._num_unrollings):\n batches.append(self._next_batch())\n self._last_batch = batches[-1]\n return batches\n\ndef characters(batches):\n \"\"\"Turn a 1-hot encoding or a probability distribution over the possible\n characters back into its (most likely) character representation.\"\"\"\n batches=list(map(list, zip(*batches)))\n s=list(\"\")\n for b in batches:\n ss=\"\"\n for c in b:\n ss+=id2char(int(c[0]))\n #ss+=id2char(int(c[1]))\n s.append(ss)\n return s\n\ndef characters2(probabilities):\n \"\"\"Turn a 1-hot encoding or a probability distribution over the possible\n characters back into its (most likely) character representation.\"\"\"\n s=[id2char(np.floor_divide(c,27)) for c in np.argmax(probabilities, 1)]\n s+=[id2char(c-27*np.floor_divide(c,27)) for c in np.argmax(probabilities, 1)]\n return s\n\ndef batches2string(batches):\n \"\"\"Convert a sequence of batches back into their (most likely) string\n representation.\"\"\"\n s = [''] * batches[0].shape[0]\n for b in batches:\n s = [''.join(x) for x in zip(s, characters(b))]\n return s\n\ntrain_batches = BatchGenerator(train_text, batch_size, num_unrollings)\nvalid_batches = BatchGenerator(valid_text, 1, 1)\n\nprint((np.array(train_batches.next())).shape)\nprint(characters(train_batches.next()))\nprint(characters(train_batches.next()))\nprint(characters(valid_batches.next()))\nprint(characters(valid_batches.next()))\n\n# ==========================\n# OTHER EVALUATION FUNCTIONS\n# ==========================\n\ndef logprob(predictions, labels):\n \"\"\"Log-probability of the true labels in a predicted batch.\"\"\"\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\n\ndef sample_distribution(distribution):\n \"\"\"Sample one element from a distribution assumed to be an array of normalized\n probabilities.\n \"\"\"\n r = random.uniform(0, 1)\n s = 0\n for i in range(len(distribution)):\n s += distribution[i]\n if s >= r:\n return i\n return len(distribution) - 1\n\ndef sample(prediction):\n \"\"\"Turn a (column) prediction into 1-hot encoded samples.\"\"\"\n p = np.zeros(shape=[1, vocabulary_size], dtype=np.float)\n p[0, sample_distribution(prediction[0])] = 1.0\n return p\n\ndef random_distribution():\n \"\"\"Generate a random column of probabilities.\"\"\"\n b = np.random.uniform(0.0, 26.0, size=[1, 1])\n return b[:,None]",
"(11, 64, 2)\n['ists advoca', 'ary governm', 'hes nationa', 'd monasteri', 'raca prince', 'chard baer ', 'rgical lang', 'for passeng', 'the nationa', 'took place ', 'ther well k', 'seven six s', 'ith a gloss', 'robably bee', 'to recogniz', 'ceived the ', 'icant than ', 'ritic of th', 'ight in sig', 's uncaused ', ' lost as in', 'cellular ic', 'e size of t', ' him a stic', 'drugs confu', ' take to co', ' the priest', 'im to name ', 'd barred at', 'standard fo', ' such as es', 'ze on the g', 'e of the or', 'd hiver one', 'y eight mar', 'the lead ch', 'es classica', 'ce the non ', 'al analysis', 'mormons bel', 't or at lea', ' disagreed ', 'ing system ', 'btypes base', 'anguages th', 'r commissio', 'ess one nin', 'nux suse li', ' the first ', 'zi concentr', ' society ne', 'elatively s', 'etworks sha', 'or hirohito', 'litical ini', 'n most of t', 'iskerdoo ri', 'ic overview', 'air compone', 'om acnm acc', ' centerline', 'e than any ', 'devotional ', 'de such dev']\n['ate social ', 'ments faile', 'al park pho', 'ies index s', 'ess of cast', ' h provided', 'guage among', 'gers in dec', 'al media an', ' during the', 'known manuf', 'seven a wid', 's covering ', 'en one of t', 'ze single a', ' first card', ' in jersey ', 'he poverty ', 'gns of huma', ' cause so a', 'n denatural', 'ce formatio', 'the input u', 'ck to pull ', 'usion inabi', 'omplete an ', 't of the mi', ' it fort de', 'ttempts by ', 'ormats for ', 'soteric chr', 'growing pop', 'riginal doc', 'e nine eigh', 'rch eight l', 'haracter li', 'al mechanic', ' gm compari', 's fundament', 'lieve the c', 'ast not par', ' upon by hi', ' example rl', 'ed on the w', 'he official', 'on at this ', 'ne three tw', 'inux enterp', ' daily coll', 'ration camp', 'ehru wished', 'stiff from ', 'arman s syd', 'o to begin ', 'itiatives t', 'these autho', 'icky ricard', 'w of mathem', 'ent of arm ', 'credited pr', 'e external ', ' other stat', ' buddhism e', 'vices possi']\n[' a']\n['an']\n"
],
[
"num_nodes = 64\nvocabulary_size=27*27\n\ngraph = tf.Graph()\nwith graph.as_default():\n \n # Parameters:\n # first parameter of each gate in 1 matrix:\n i_all = tf.Variable(tf.truncated_normal([embedding_size, 4*num_nodes], -0.1, 0.1))\n # second parameter of each gate in 1 matrix\n o_all = tf.Variable(tf.truncated_normal([num_nodes, 4*num_nodes], -0.1, 0.1))\n \n # Input gate: input, previous output, and bias.\n ib = tf.Variable(tf.zeros([1, num_nodes]))\n # Forget gate: input, previous output, and bias.\n fb = tf.Variable(tf.zeros([1, num_nodes]))\n # Memory cell: input, state and bias. \n cb = tf.Variable(tf.zeros([1, num_nodes]))\n # Output gate: input, previous output, and bias.\n ob = tf.Variable(tf.zeros([1, num_nodes]))\n # Variables saving state across unrollings.\n saved_output = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\n saved_state = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\n # Classifier weights and biases. \n \n \n #embeddings\n embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n w = tf.Variable(tf.truncated_normal([num_nodes, vocabulary_size],-0.1,0.1))\n b = tf.Variable(tf.zeros([vocabulary_size]))\n \n # Definition of the cell computation.\n def lstm_cell(i, o, state):\n \"\"\"Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf\n Note that in this formulation, we omit the various connections between the\n previous state and the gates.\"\"\"\n input_mat=tf.matmul(i,i_all)\n output_mat=tf.matmul(o,o_all)\n \n input_gate = tf.sigmoid(input_mat[:,0:num_nodes] + output_mat[:,0:num_nodes] + ib)\n forget_gate = tf.sigmoid(input_mat[:,num_nodes:2*num_nodes] + output_mat[:,num_nodes:2*num_nodes] + fb)\n update = input_mat[:,2*num_nodes:3*num_nodes] + output_mat[:,2*num_nodes:3*num_nodes] + cb\n state = forget_gate * state + input_gate * tf.tanh(update)\n output_gate = tf.sigmoid(input_mat[:,3*num_nodes:] + output_mat[:,3*num_nodes:] + ob)\n return output_gate * tf.tanh(state), state\n\n # Input data.\n train_data = list()\n for _ in range(num_unrollings+1):\n train_data.append(tf.placeholder(tf.int32, shape=[batch_size,2]))\n train_inputs = train_data[:num_unrollings]\n train_labels = train_data[1:] # labels are inputs shifted by one time step.\n\n # Unrolled LSTM loop.\n outputs = list()\n output = saved_output\n state = saved_state\n for i in train_inputs:\n i_concat=27*i[:,0]+i[:,1]\n embedded_i=tf.nn.embedding_lookup(embeddings,i_concat)\n output, state = lstm_cell(embedded_i, output, state)\n outputs.append(output)\n\n # State saving across unrollings.\n with tf.control_dependencies([saved_output.assign(output),\n saved_state.assign(state)]):\n # Classifier.\n outputs_concat=tf.concat(outputs,0)\n #try to compute similarity here as well\n logits=tf.nn.xw_plus_b(outputs_concat,w,b)\n print((logits).shape)\n \n #Compute one hot encodings\n label_batch=tf.concat(train_labels,0) \n label_batch=27*label_batch[:,0]+label_batch[:,1]\n print(label_batch.shape)\n sparse_labels = tf.reshape(label_batch, [-1, 1])\n derived_size = tf.shape(label_batch)[0]\n indices = tf.reshape(tf.range(0, derived_size, 1), [-1, 1])\n print(indices.shape,'indices.shape')\n concated = tf.concat([indices, sparse_labels],1)\n outshape = tf.stack([derived_size, vocabulary_size])\n labels = tf.sparse_to_dense(concated, outshape, 1.0, 0.0)\n\n loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))\n\n # Optimizer.\n global_step = tf.Variable(0)\n learning_rate = tf.train.exponential_decay(\n 10.0, global_step, 5000, 0.1, staircase=True)\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n gradients, v = zip(*optimizer.compute_gradients(loss))\n gradients, _ = tf.clip_by_global_norm(gradients, 1.25)\n optimizer = optimizer.apply_gradients(\n zip(gradients, v), global_step=global_step)\n\n # Predictions.\n train_prediction = tf.nn.softmax(logits)\n print(train_prediction.shape)\n \n # Sampling and validation eval: batch 1, no unrolling.\n norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n normalized_embeddings = embeddings / norm\n #valid_dataset=np.array([i for i in range(27)])\n #valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n \n sample_input = tf.placeholder(tf.int32, shape=[1])\n saved_sample_output = tf.Variable(tf.zeros([1, num_nodes]))\n saved_sample_state = tf.Variable(tf.zeros([1, num_nodes]))\n reset_sample_state = tf.group(\n saved_sample_output.assign(tf.zeros([1, num_nodes])),\n saved_sample_state.assign(tf.zeros([1, num_nodes])))\n embedded_sample=tf.nn.embedding_lookup(embeddings,sample_input)\n sample_output, sample_state = lstm_cell(embedded_sample, saved_sample_output, saved_sample_state)\n with tf.control_dependencies([saved_sample_output.assign(sample_output),\n saved_sample_state.assign(sample_state)]):\n \n sample_prediction = tf.nn.softmax(tf.nn.xw_plus_b(sample_output, w, b))\n #similarity = tf.matmul(sample_prediction, tf.transpose(normalized_embeddings))\n print(sample_prediction.shape)",
"(640, 729)\n(640,)\n(?, 1) indices.shape\n(640, 729)\n(1, 729)\n"
],
[
"num_steps = 50001\nsummary_frequency = 500\n\nwith tf.Session(graph=graph) as session:\n tf.global_variables_initializer().run()\n print('Initialized')\n mean_loss = 0\n for step in range(num_steps):\n batches = train_batches.next()\n feed_dict = dict()\n for i in range(num_unrollings + 1):\n feed_dict[train_data[i]] = batches[i]\n #print((feed_dict[train_data[i]]).shape)\n \n _, l, predictions, lr,train_lab = session.run([optimizer, loss, train_prediction, learning_rate,train_labels], feed_dict=feed_dict)\n mean_loss += l\n if step % summary_frequency == 0:\n if step > 0:\n mean_loss = mean_loss / summary_frequency\n # The mean loss is an estimate of the loss over the last few batches.\n print('Average loss at step %d: %f learning rate: %f' % (step, mean_loss, lr))\n mean_loss = 0\n \n #print(train_lab)\n #print(labels.shape[0])\n #print('Minibatch perplexity: ',np.exp(logprob(predictions, labels)))\n \n if step % (summary_frequency * 2) == 0:\n # Generate some samples.\n print('=' * 80)\n for _ in range(5):\n feed = np.zeros(shape=(1,), dtype=np.int32)\n feed[0,] =np.random.randint(0,729)\n sentence=id2char(np.floor_divide(feed[0],27))\n sentence+=id2char(feed[0]-27*np.floor_divide(feed[0],27)) \n reset_sample_state.run()\n for _ in range(50):\n prediction=sample_prediction.eval({sample_input:feed})\n k = sample(prediction)\n k=characters2(k)\n #print(k)\n feed = np.zeros(shape=(1,), dtype=np.int32)\n feed[0,] = 27*char2id(k[0])+char2id(k[1])\n sentence += k[0]\n #sentence+=k[1]\n #feed = np.zeros(shape=(1,), dtype=np.int32)\n #feed[0,] = np.argmax(prediction)\n #print(feed.shape)\n #sentence += id2char(feed[0,])\n print(sentence)\n print('=' * 80)\n # Measure validation set perplexity.\n reset_sample_state.run()\n valid_logprob = 0\n #for _ in range(valid_size):\n # b = valid_batches.next()\n #predictions = sample_prediction.eval({sample_input: b[0]})\n #valid_logprob = valid_logprob + logprob(predictions, b[1])\n #print('Validation set perplexity: %.2f' % float(np.exp(valid_logprob / valid_size)))",
"Initialized\nAverage loss at step 0: 6.593503 learning rate: 10.000000\n================================================================================\nggzpkfatftrjeyvinkihna bibnlpxjixlzsogxflmyenxtlvvlr\nykpxvcldktbegoudkmsqvwprgtnbosfsyjqxebeo iuqbivfjcgg\nndvwwgcjvwydolkjggycfbsrlzdlnuvaqccslbfegzrloivpexpv\nbtvgkf duivafnnuyzwlkmj lkochmlcpidvkdfxvtmmcfokmvg\njslwjbrovqwqbznaxyoyqpzshknsubmbffjvmz goygdikxkldqy\n================================================================================\nAverage loss at step 500: 2.399468 learning rate: 10.000000\nAverage loss at step 1000: 1.741558 learning rate: 10.000000\n================================================================================\nihqe nounged pricles to return their list of thet mu\nhxnt directx cervan d while beywire one nine nine fo\nmwctia sgent filtinuand most euported controse s sec\nsqibential comporks tirent stancest ting too reign r\nvlor the mide and by a surrom days america prish emp\n================================================================================\nAverage loss at step 1500: 1.658931 learning rate: 10.000000\nAverage loss at step 2000: 1.593923 learning rate: 10.000000\n================================================================================\nzrrench his most a game one nine attemption to he wi\npppone one one six five nine culture loss ofyiudency\nzucatios extraculating and exishines one tramen wher\noss to the smocup siltimes others went the haysey ar\nbngaclasations a planz que editcully partional imple\n================================================================================\nAverage loss at step 2500: 1.562919 learning rate: 10.000000\nAverage loss at step 3000: 1.525576 learning rate: 10.000000\n================================================================================\nxp nines that an essibl of musical decrops lrhester \nwzral comparows was camble states war controductly e\ndwwhones notems an appeared to matheses council of u\nudden he language war two veragure of the comic it c\npmman crainitable is a fir her inferuged incholer an\n================================================================================\nAverage loss at step 3500: 1.550643 learning rate: 10.000000\nAverage loss at step 4000: 1.535663 learning rate: 10.000000\n================================================================================\nqs planer of eart alged schoreina a bloers in the mo\nbhh slited for the king this the henry force the phi\nrlliday the ure the ruth mankeering of esc the folit\ngcanter physic detales his onice d ventions such a t\nbggraf and greatess don head the uzerstisivin begen \n================================================================================\nAverage loss at step 4500: 1.516052 learning rate: 10.000000\nAverage loss at step 5000: 1.527192 learning rate: 10.000000\n================================================================================\ncmmy infects ham air of haviline faiton six to these\nibble in pett of he this for resad and universian wa\nlddsaring to skruble and gann giaditional robert lib\nnuus because of rio chalon solved as the recordin ma\nmrr from dissore cooved on birth from mult value of \n================================================================================\nAverage loss at step 5500: 1.503751 learning rate: 10.000000\nAverage loss at step 6000: 1.515223 learning rate: 10.000000\n================================================================================\nsaaration these the noceed a does war discotability \nurrons american supposed and army runs oceaniforks a\nrffaid public govantial excecable outo wrolamations \nmg at country and when fssus track curry diefa lossi\nhnn armile ist of mecardian standantes tyler takeral\n================================================================================\nAverage loss at step 6500: 1.525350 learning rate: 10.000000\nAverage loss at step 7000: 1.516476 learning rate: 10.000000\n================================================================================\n zzero three the requirion n sevent this the holline\nq study floy political cirth waters from the intern\nabble of miklable point bthted countress is a venths\nbkvular for trafferes in mars convaligm and sim webu\nwaa in threse defeat four zero eight georginic can r\n================================================================================\nAverage loss at step 7500: 1.527310 learning rate: 10.000000\nAverage loss at step 8000: 1.499840 learning rate: 10.000000\n================================================================================\npeer gregid one in the worst of recorded the informa\nkyyop was he but would this styles acropistory a one\nuhhistering nine evolines an and bourn seen zero eit\nraator biogress and this and warfare p foliciture wh\ncdd a russion afroged it neat wavemberst persumen th\n================================================================================\nAverage loss at step 8500: 1.506366 learning rate: 10.000000\nAverage loss at step 9000: 1.502462 learning rate: 10.000000\n================================================================================\nqvscale writer occupations of listel common places w\nbqoeful d one for one article and normated by the so\njxr is government ramma not for elving shiftes offic\nlppe anther acquird a codity expecing two to hers fo\nfaaffrastered as localsm codo rather it has men used\n================================================================================\nAverage loss at step 9500: 1.491510 learning rate: 10.000000\nAverage loss at step 10000: 1.487080 learning rate: 1.000000\n================================================================================\ngaant descrimi prutal le and the voisptable whera th\njfr of the kophestria sologially in fifth but the mo\nreewhat in list d fifteen the air one nine th wel ca\negge and handle zero of they waltically the royck ne\nqkale embasuated companyted to the over primate huma\n================================================================================\nAverage loss at step 10500: 1.460810 learning rate: 1.000000\nAverage loss at step 11000: 1.447598 learning rate: 1.000000\n================================================================================\nsjjage and pagimes lenic many and circess and allunt\ngwwon only becomist undergated from fancily rail for\nmrr againg the proveries from these islamie more rec\nybboush old one nine nine eight five two two zero d \nnaabance twenties haciffaces in a plures yevilism wh\n================================================================================\nAverage loss at step 11500: 1.454794 learning rate: 1.000000\nAverage loss at step 12000: 1.420679 learning rate: 1.000000\n================================================================================\nr to weapoes by perfect si life that least of claud\nioons are their the prehourrap year of time area vol\nzkaer controtation of name sj localrly noble of the \n mmoved connections who wrised anyty two zero zero z\nskky befield the equating the compician life awards \n================================================================================\nAverage loss at step 12500: 1.443823 learning rate: 1.000000\nAverage loss at step 13000: 1.449472 learning rate: 1.000000\n================================================================================\notther measur biolation to celevatural the world war\njpsox seves male two zero hts to developloni hat sof\nftteldent process of video variaters be obtco in bui\nfaa has ganing release santer time brownk q launitin\newworch six four the unitagely which data very eleme\n================================================================================\nAverage loss at step 13500: 1.447044 learning rate: 1.000000\nAverage loss at step 14000: 1.435630 learning rate: 1.000000\n================================================================================\nwoould had inland declare hoton meaning what john s \npnnian amilitary increases two zero zero zero zero z\noee interposed and s next to himsterings in the robe\nycope in to two internantland this with a june embra\nmxm tewords withuare actoris the treated by all are \n================================================================================\n"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
"code"
]
] |
cb1a9dcf35279433ddc563898807732aba18fb1c | 160,196 | ipynb | Jupyter Notebook | Day_010_HW.ipynb | b15145456/6th-ML-Marathon | 726f4118e6787213552972dd210c93eccd407bed | [
"MIT"
] | null | null | null | Day_010_HW.ipynb | b15145456/6th-ML-Marathon | 726f4118e6787213552972dd210c93eccd407bed | [
"MIT"
] | null | null | null | Day_010_HW.ipynb | b15145456/6th-ML-Marathon | 726f4118e6787213552972dd210c93eccd407bed | [
"MIT"
] | null | null | null | 132.502895 | 45,594 | 0.788659 | [
[
[
"<a href=\"https://colab.research.google.com/github/b15145456/1st-ML-Marathon/blob/main/Day_010_HW.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# 作業 : (Kaggle)房價預測",
"_____no_output_____"
],
[
"# [作業目標]\n- 試著模仿範例寫法, 在房價預測中, 觀察去除離群值的影響",
"_____no_output_____"
],
[
"# [作業重點]\n- 觀察將極端值以上下限值取代, 對於分布與迴歸分數的影響 (In[5], Out[5])\n- 觀察將極端值資料直接刪除, 對於分布與迴歸分數的影響 (In[6], Out[6])",
"_____no_output_____"
]
],
[
[
"# 做完特徵工程前的所有準備 (與前範例相同)\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LinearRegression\n\n# data_path = 'data/'\ndf_train = pd.read_csv('house_train.csv.gz')\n\ntrain_Y = np.log1p(df_train['SalePrice'])\ndf = df_train.drop(['Id', 'SalePrice'] , axis=1)\ndf.head()",
"_____no_output_____"
],
[
"#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\nnum_features = []\nfor dtype, feature in zip(df.dtypes, df.columns):\n if dtype == 'float64' or dtype == 'int64':\n num_features.append(feature)\nprint(f'{len(num_features)} Numeric Features : {num_features}\\n')",
"36 Numeric Features : ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n\n"
],
[
"# 削減文字型欄位, 只剩數值型欄位\ndf = df[num_features]\ndf = df.fillna(-1)\nMMEncoder = MinMaxScaler()\ntrain_num = train_Y.shape[0]\ndf.head()",
"_____no_output_____"
]
],
[
[
"# 作業1\n* 試著限制 '1樓地板面積(平方英尺)' (1stFlrSF) 欄位的上下限, 看看能否再進一步提高分數?",
"_____no_output_____"
]
],
[
[
"# 顯示 1stFlrSF 與目標值的散佈圖\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.regplot(x = df['1stFlrSF'][:train_num], y=train_Y)\nplt.show()\n\n# 做線性迴歸, 觀察分數\ntrain_X = MMEncoder.fit_transform(df)\nestimator = LinearRegression()\ncross_val_score(estimator, train_X, train_Y, cv=5).mean()",
"_____no_output_____"
],
[
"# 將 1stFlrSF 限制在你覺得適合的範圍內, 調整離群值\n\"\"\"\nYour Code Here\n\"\"\"\n# df1 = df\n# df1.loc[df1['1stFlrSF'] > 2000,'1stFlrSF'] = 2000\n# df1.loc[df1['1stFlrSF'] <500,'1stFlrSF'] = 500\n\ndf['1stFlrSF'] = df['1stFlrSF'].clip(500, 2250)\nsns.regplot(x = df['1stFlrSF'], y=train_Y)\nplt.show()\n\n# 做線性迴歸, 觀察分數\ntrain_X = MMEncoder.fit_transform(df1)\nestimator = LinearRegression()\ncross_val_score(estimator, train_X, train_Y, cv=5).mean()",
"_____no_output_____"
]
],
[
[
"# 作業2\n* 續前題, 去除離群值有兩類方式 : 捨棄離群值(刪除離群的資料) 以及調整離群值, \n請試著用同樣的上下限, 改為 '捨棄離群值' 的方法, 看看結果會變好還是變差? 並試著解釋原因。",
"_____no_output_____"
]
],
[
[
"# 將 1stFlrSF 限制在你覺得適合的範圍內, 捨棄離群值\n\"\"\"\nYour Code Here\n\"\"\"\nkeep_indexs = (df['1stFlrSF']> 500) & (df['1stFlrSF']< 2250)\ndf = df[keep_indexs]\ntrain_Y = train_Y[keep_indexs]\nsns.regplot(x = df['1stFlrSF'], y=train_Y)\nplt.show()\n# df2 = df\n\n# del_id = df2.loc[df2['1stFlrSF']>2000].index\n# del_id.append(df2.loc[df2['1stFlrSF']<500].index)\n\n# df2.drop(del_id, inplace=True)\n# train_Y.drop(del_id, inplace=True)\n\n# 做線性迴歸, 觀察分數\ntrain_X = MMEncoder.fit_transform(df)\nestimator = LinearRegression()\ncross_val_score(estimator, train_X, train_Y, cv=5).mean()",
"_____no_output_____"
]
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"",
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cb1aa2600bd2bd415ab561ac2db00a8d6bca0afa | 3,927 | ipynb | Jupyter Notebook | DecisionTC.ipynb | yousifbigdata/Digit-ClassificationDT | 9cb90a3ecd3a8d3746fdb7fa7946511ad7daacd3 | [
"MIT"
] | null | null | null | DecisionTC.ipynb | yousifbigdata/Digit-ClassificationDT | 9cb90a3ecd3a8d3746fdb7fa7946511ad7daacd3 | [
"MIT"
] | null | null | null | DecisionTC.ipynb | yousifbigdata/Digit-ClassificationDT | 9cb90a3ecd3a8d3746fdb7fa7946511ad7daacd3 | [
"MIT"
] | null | null | null | 20.560209 | 174 | 0.498854 | [
[
[
"# Decision Tree Classification\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd",
"_____no_output_____"
],
[
"from sklearn.tree import DecisionTreeClassifier",
"_____no_output_____"
],
[
"# Importing the dataset\ndataset = pd.read_csv('train.csv').as_matrix()",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n \n"
],
[
"print(dataset)",
"[[1 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [1 0 0 ... 0 0 0]\n ...\n [7 0 0 ... 0 0 0]\n [6 0 0 ... 0 0 0]\n [9 0 0 ... 0 0 0]]\n"
],
[
"classifier = DecisionTreeClassifier()",
"_____no_output_____"
],
[
"X_train= dataset[0:21000,1:]",
"_____no_output_____"
],
[
"X_train_label=X_train[0:21000,0]",
"_____no_output_____"
],
[
"classifier.fit(X_train,X_train_label)",
"_____no_output_____"
],
[
"#Testing Data\nX_test=dataset[21000 : ,1 :]",
"_____no_output_____"
],
[
"actual_label = dataset[21000: , 0]",
"_____no_output_____"
],
[
"y_pred = classifier.predict(X_test)\ncount = 0 ;\nfor i in range(0,21000):\n count+= 1 if y_pred[i] == actual_label[i] else 0\nprint(\"Accurecy = \", (count/21000)* 1000)\n \n",
"Accurecy = 99.42857142857142\n"
]
]
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cb1aa2f98783700103fd715dad4c721556e64be6 | 347,666 | ipynb | Jupyter Notebook | doc/Notebooks/2020_contrasting_weather.ipynb | esowc/UNSEEN-open | f7f9921a78d7357116b3dd12ea46f5a323be145b | [
"MIT"
] | 7 | 2020-05-14T05:48:16.000Z | 2021-07-29T03:18:10.000Z | doc/Notebooks/2020_contrasting_weather.ipynb | esowc/UNSEEN-open | f7f9921a78d7357116b3dd12ea46f5a323be145b | [
"MIT"
] | null | null | null | doc/Notebooks/2020_contrasting_weather.ipynb | esowc/UNSEEN-open | f7f9921a78d7357116b3dd12ea46f5a323be145b | [
"MIT"
] | 4 | 2020-12-09T13:38:11.000Z | 2022-03-01T09:41:15.000Z | 118.576398 | 131,312 | 0.782817 | [
[
[
"## February and April 2020 precipitation anomalies\n\nIn this notebook, we will analyze precipitation anomalies of February and April 2020, which seemed to be very contrasting in weather. We use the EOBS dataset. ",
"_____no_output_____"
],
[
"### Import packages",
"_____no_output_____"
]
],
[
[
"##This is so variables get printed within jupyter\nfrom IPython.core.interactiveshell import InteractiveShell \nInteractiveShell.ast_node_interactivity = \"all\"",
"_____no_output_____"
],
[
"##import packages\nimport os\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cartopy\nimport cartopy.crs as ccrs\nimport matplotlib.ticker as mticker\n",
"_____no_output_____"
],
[
"os.chdir(os.path.abspath('../../')) # Change the working directory to UNSEEN-open\nos.getcwd() #print the working directory",
"_____no_output_____"
],
[
"### Set plot font size\nplt.rcParams['font.size'] = 10 ## change font size",
"_____no_output_____"
]
],
[
[
"### Load EOBS\n\nI downloaded EOBS (from 1950 - 2019) and the most recent EOBS data (2020) [here](https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php). Note, you have to register as E-OBS user.\n\nThe data has a daily timestep. I resample the data into monthly average mm/day. I chose not to use the total monthly precipitation because of leap days. ",
"_____no_output_____"
]
],
[
[
"EOBS = xr.open_dataset('../UK_example/EOBS/rr_ens_mean_0.25deg_reg_v20.0e.nc') ## open the data\nEOBS = EOBS.resample(time='1m').mean() ## Monthly averages\n# EOBS = EOBS.sel(time=EOBS['time.month'] == 2) ## Select only February\nEOBS",
"/soge-home/users/cenv0732/.conda/envs/UNSEEN-open/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n return np.nanmean(a, axis=axis, dtype=dtype)\n"
]
],
[
[
"Here I define the attributes, that xarray uses when plotting",
"_____no_output_____"
]
],
[
[
"EOBS['rr'].attrs = {'long_name': 'rainfall', ##Define the name\n 'units': 'mm/day', ## unit\n 'standard_name': 'thickness_of_rainfall_amount'} ## original name, not used\nEOBS['rr'].mean('time').plot() ## and show the 1950-2019 average February precipitation \n",
"_____no_output_____"
]
],
[
[
"The 2020 data file is separate and needs the same preprocessing:",
"_____no_output_____"
]
],
[
[
"EOBS2020 = xr.open_dataset('../UK_example/EOBS/rr_0.25deg_day_2020_grid_ensmean.nc.1') #open\nEOBS2020 = EOBS2020.resample(time='1m').mean() #Monthly mean\nEOBS2020['rr'].sel(time='2020-04').plot() #show map\nEOBS2020 ## display dataset",
"/soge-home/users/cenv0732/.conda/envs/UNSEEN-open/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n return np.nanmean(a, axis=axis, dtype=dtype)\n"
]
],
[
[
"### Plot the 2020 event\n\nI calculate the anomaly (deviation from the mean in mm/d) and divide this by the standard deviation to obtain the standardized anomalies. ",
"_____no_output_____"
]
],
[
[
"EOBS2020_anomaly = EOBS2020['rr'].groupby('time.month') - EOBS['rr'].groupby('time.month').mean('time')\nEOBS2020_anomaly\n\nEOBS2020_sd_anomaly = EOBS2020_anomaly.groupby('time.month') / EOBS['rr'].groupby('time.month').std('time')\n\nEOBS2020_sd_anomaly.attrs = {\n 'long_name': 'Monthly precipitation standardized anomaly',\n 'units': '-'\n}\n\nEOBS2020_sd_anomaly",
"_____no_output_____"
]
],
[
[
"I select February and April (tips on how to select this are appreciated)",
"_____no_output_____"
]
],
[
[
"EOBS2020_sd_anomaly\n# EOBS2020_sd_anomaly.sel(time = ['2020-02','2020-04']) ## Dont know how to select this by label?\nEOBS2020_sd_anomaly[[1,3],:,:] ## Dont know how to select this by label?\n",
"_____no_output_____"
]
],
[
[
"And plot using cartopy!",
"_____no_output_____"
]
],
[
[
"EOBS_plots = EOBS2020_sd_anomaly[[1, 3], :, :].plot(\n transform=ccrs.PlateCarree(),\n robust=True,\n extend = 'both',\n col='time',\n cmap=plt.cm.twilight_shifted_r,\n subplot_kws={'projection': ccrs.EuroPP()})\n\nfor ax in EOBS_plots.axes.flat:\n ax.add_feature(cartopy.feature.BORDERS, linestyle=':')\n ax.coastlines(resolution='50m')\n gl = ax.gridlines(crs=ccrs.PlateCarree(),\n draw_labels=False,\n linewidth=1,\n color='gray',\n alpha=0.5,\n linestyle='--')\n\n# plt.savefig('graphs/February_April_2020_precipAnomaly.png', dpi=300)",
"_____no_output_____"
]
]
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cb1ab64afe48e0ef4838990c309e2dddde02c397 | 637,984 | ipynb | Jupyter Notebook | docs/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb | digital-science/dimensions-api-lab | 16c69f9dec21c2eae86c3f58f0f6bd472516e94d | [
"MIT"
] | 57 | 2019-06-24T19:35:34.000Z | 2022-02-27T14:45:10.000Z | docs/.doctrees/nbsphinx/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb | digital-science/dimensions-api-lab | 16c69f9dec21c2eae86c3f58f0f6bd472516e94d | [
"MIT"
] | 6 | 2019-09-04T19:14:40.000Z | 2021-12-09T15:54:41.000Z | docs/.doctrees/nbsphinx/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb | digital-science/dimensions-api-lab | 16c69f9dec21c2eae86c3f58f0f6bd472516e94d | [
"MIT"
] | 16 | 2019-08-13T04:24:01.000Z | 2022-03-04T07:49:11.000Z | 80.614607 | 61,510 | 0.724108 | [
[
[
"# Collaboration Patterns By Year (International, Domestic, Internal)\n\nUsing the count capability of the API, Dimensions allows you to quickly identify international, domestic, and inernal Collaboration\n\nThis notebook shows how to quickly identify international, domestic, and internal collaboration using the [Organizations data source](https://docs.dimensions.ai/dsl/datasource-organizations.html) and the [Publications data source](https://docs.dimensions.ai/dsl/datasource-publications.html) available via the [Dimensions Analytics API](https://docs.dimensions.ai/dsl/). \n",
"_____no_output_____"
],
[
"## Prerequisites\n\nPlease install the latest versions of these libraries to run this notebook. ",
"_____no_output_____"
]
],
[
[
"!pip install dimcli plotly -U --quiet \n\n#\n# load libraries\nimport dimcli\nfrom dimcli.utils import *\n\nimport json, sys, time\nimport pandas as pd\nimport plotly.express as px # plotly>=4.8.1\nif not 'google.colab' in sys.modules:\n # make js dependecies local / needed by html exports\n from plotly.offline import init_notebook_mode\n init_notebook_mode(connected=True)\n\nprint(\"==\\nLogging in..\")\n# https://digital-science.github.io/dimcli/getting-started.html#authentication\nENDPOINT = \"https://app.dimensions.ai\"\nif 'google.colab' in sys.modules:\n import getpass\n KEY = getpass.getpass(prompt='API Key: ') \n dimcli.login(key=KEY, endpoint=ENDPOINT)\nelse:\n KEY = \"\"\n dimcli.login(key=KEY, endpoint=ENDPOINT)\ndsl = dimcli.Dsl()",
"_____no_output_____"
]
],
[
[
"## 1. Lookup the University that you are interested in",
"_____no_output_____"
]
],
[
[
"dsl.query(\"\"\"\nsearch organizations for \"melbourne\" return organizations\n\"\"\").as_dataframe()",
"Returned Organizations: 15 (total = 15)\n\u001b[2mTime: 1.95s\u001b[0m\n"
],
[
"institution = \"grid.1008.9\"",
"_____no_output_____"
]
],
[
[
"## 2. Publications output by year",
"_____no_output_____"
]
],
[
[
"allpubs = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\nallpubs.columns = ['year', 'pubs']\npx.bar(allpubs, x=\"year\", y=\"pubs\")",
"Returned Year: 11\n\u001b[2mTime: 5.44s\u001b[0m\n"
]
],
[
[
"## 3. International publications",
"_____no_output_____"
]
],
[
[
"international = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and count(research_org_countries) > 1\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\ninternational.columns = ['year', 'international_count']\npx.bar(international, x=\"year\", y=\"international_count\")",
"Returned Year: 11\n\u001b[2mTime: 0.54s\u001b[0m\n"
]
],
[
[
"## 4. Domestic",
"_____no_output_____"
]
],
[
[
"domestic = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and count(research_org_countries) = 1\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\ndomestic.columns = ['year', 'domestic_count']\npx.bar(domestic, x=\"year\", y=\"domestic_count\")",
"Returned Year: 11\n\u001b[2mTime: 0.66s\u001b[0m\n"
]
],
[
[
"## 5. Internal",
"_____no_output_____"
]
],
[
[
"internal = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and count(research_orgs) = 1\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\ninternal.columns = ['year', 'internal_count']\npx.bar(internal, x=\"year\", y=\"internal_count\")",
"Returned Year: 11\n\u001b[2mTime: 0.59s\u001b[0m\n"
]
],
[
[
"## 6. Joining up All metrics together ",
"_____no_output_____"
]
],
[
[
"jdf = allpubs.set_index('year'). \\\n join(international.set_index('year')). \\\n join(domestic.set_index('year')). \\\n join(internal.set_index('year')) \n\njdf",
"_____no_output_____"
],
[
"px.bar(jdf, title=\"University of Melbourne: publications collaboration\")",
"_____no_output_____"
]
],
[
[
"## 7. How does this compare to Australia?",
"_____no_output_____"
]
],
[
[
"auallpubs = dsl.query(\"\"\"\n \n search publications \n where research_org_countries.name= \"Australia\"\n and type=\"article\"\n and year > 2010\n return year\n \n \"\"\").as_dataframe()\n\nauallpubs.columns = ['year', 'all_count']\n\nauintpubs = dsl.query(\"\"\"\n \n search publications \n where research_org_countries.name= \"Australia\"\n and type=\"article\"\n and year > 2010\n and count(research_org_countries) > 1\n return year\n \n \"\"\").as_dataframe()\n\nauintpubs.columns = ['year', 'all_int_count']\n\n\naudompubs = dsl.query(\"\"\"\n \n search publications \n where research_org_countries.name= \"Australia\"\n and type=\"article\"\n and year > 2010\n and count(research_org_countries) = 1\n return year\n \n \"\"\").as_dataframe()\n\naudompubs.columns = ['year', 'all_dom_count']\n\nauinternalpubs = dsl.query(\"\"\"\n \n search publications \n where \n research_org_countries.name= \"Australia\"\n and count(research_orgs) = 1\n and type=\"article\"\n and year > 2010\n return year\n \n \"\"\").as_dataframe()\n\nauinternalpubs.columns = ['year', 'all_internal_count']\n\naudf = auallpubs.set_index('year'). \\\n join(auintpubs.set_index('year')). \\\n join(audompubs.set_index('year')). \\\n join(auinternalpubs.set_index('year')). \\\n sort_values(by=['year'])\n\npx.bar(audf, title=\"Australia: publications collaboration\")",
"Returned Year: 11\n\u001b[2mTime: 0.76s\u001b[0m\nReturned Year: 11\n\u001b[2mTime: 0.74s\u001b[0m\nReturned Year: 11\n\u001b[2mTime: 0.70s\u001b[0m\nReturned Year: 11\n\u001b[2mTime: 0.74s\u001b[0m\n"
]
],
[
[
"## 8. How does this compare to a different Institution (University of Toronto)?",
"_____no_output_____"
]
],
[
[
"institution = \"grid.17063.33\"\n\nallpubs = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\nallpubs.columns = ['year', 'pubs']\n\n\n\ninternational = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and count(research_org_countries) > 1\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\ninternational.columns = ['year', 'international_count']\n\n\ndomestic = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and count(research_org_countries) = 1\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\ndomestic.columns = ['year', 'domestic_count']\n\ninternal = dsl.query(f\"\"\"\n \n search publications \n where research_orgs.id = \"{institution}\"\n and type=\"article\"\n and count(research_orgs) = 1\n and year > 2010\n return year\n \n \n \"\"\").as_dataframe()\n\ninternal.columns = ['year', 'internal_count']\n\n\njdf = allpubs.set_index('year'). \\\n join(international.set_index('year')). \\\n join(domestic.set_index('year')). \\\n join(internal.set_index('year')) \n\npx.bar(jdf, title=\"Univ. of Toronto: publications collaboration\")\n",
"Returned Year: 11\n\u001b[2mTime: 0.64s\u001b[0m\nReturned Year: 11\n\u001b[2mTime: 0.56s\u001b[0m\nReturned Year: 11\n\u001b[2mTime: 0.59s\u001b[0m\nReturned Year: 11\n\u001b[2mTime: 0.59s\u001b[0m\n"
]
],
[
[
"---\n## Want to learn more?\n\nCheck out the [Dimensions API Lab](https://api-lab.dimensions.ai/) website, which contains many tutorials and reusable Jupyter notebooks for scholarly data analytics. ",
"_____no_output_____"
]
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cb1ac55adf8cc2fc6b9fb9a2f31bb708880cca99 | 938,563 | ipynb | Jupyter Notebook | EarthquakePrediction/010-feature_engineering.ipynb | AidanCooper/Kaggle | 01dff47392806778b1c78124b279f7dfb9709cc0 | [
"MIT"
] | null | null | null | EarthquakePrediction/010-feature_engineering.ipynb | AidanCooper/Kaggle | 01dff47392806778b1c78124b279f7dfb9709cc0 | [
"MIT"
] | 1 | 2020-03-31T11:54:20.000Z | 2020-03-31T11:54:20.000Z | EarthquakePrediction/010-feature_engineering.ipynb | AidanCooper/Kaggle | 01dff47392806778b1c78124b279f7dfb9709cc0 | [
"MIT"
] | 1 | 2022-02-21T07:34:18.000Z | 2022-02-21T07:34:18.000Z | 844.031475 | 818,152 | 0.940685 | [
[
[
"# LANL Earthquake Prediction\n\n<a href=\"https://www.kaggle.com/c/LANL-Earthquake-Prediction/overview\">Link to competition on Kaggle</a>\n\nThis notebook is a reimplementation of <a href=\"https://www.kaggle.com/tunguz/andrews-features-only\">Andrews Feature Only</a>, with some modifications.",
"_____no_output_____"
],
[
"## Feature Engineering\n\nThe large training dataset needs to be prepared to closely match the test data, which comprise independent series of 150,000 acoustic measurements. Thus, the training data shall be sliced into separate series of 150,000 measurements, and then summary features will be computed across the training and test series to serve as inputs for modelling.",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom tqdm import tqdm_notebook\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import MinMaxScaler\nfrom scipy.signal import hilbert\nfrom scipy.signal import hann\nfrom scipy.signal import convolve\nfrom scipy import stats\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")",
"_____no_output_____"
]
],
[
[
"### Load Data",
"_____no_output_____"
]
],
[
[
"%%time\ntrain = pd.read_csv('data/raw/train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32})\nprint(train.shape)\ntrain.head()",
"(629145480, 2)\nWall time: 2min 55s\n"
],
[
"train.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 629145480 entries, 0 to 629145479\nData columns (total 2 columns):\nacoustic_data int16\ntime_to_failure float32\ndtypes: float32(1), int16(1)\nmemory usage: 3.5 GB\n"
]
],
[
[
"The dataset is large, occupying 3.5 GB of memory and comprising over 600 million rows. The data can be visualised by sampling a small portion of it.",
"_____no_output_____"
]
],
[
[
"train_acoustic_data_small = train['acoustic_data'].values[::50]\ntrain_time_to_failure_small = train['time_to_failure'].values[::50]\n\nfig, ax1 = plt.subplots(figsize=(16, 8))\nplt.title(\"acoustic_data vs time_to_failure (2% of data sampled)\")\nplt.plot(train_acoustic_data_small, color='b')\nax1.set_ylabel('acoustic_data', color='b')\nplt.legend(['acoustic_data'])\nax2 = ax1.twinx()\nplt.plot(train_time_to_failure_small, color='r')\nax2.set_ylabel('time_to_failure', color='r')\nplt.legend(['time_to_failure'], loc=(0.875, 0.9))\nplt.grid(False)\n\nplt.savefig('reports/figures/acoustic_data_vs_time_to_failure.png')\n\ndel train_acoustic_data_small\ndel train_time_to_failure_small",
"_____no_output_____"
]
],
[
[
"### Create Segments\n\nSlice the training data into series of 150,000 measurements.",
"_____no_output_____"
]
],
[
[
"rows = 150000\nsegments = int(np.floor(train.shape[0] / rows))\n\nX_tr = pd.DataFrame(index=range(segments), dtype=np.float64)\ny_tr = pd.DataFrame(index=range(segments), dtype=np.float64, columns=['time_to_failure'])",
"_____no_output_____"
]
],
[
[
"### Generate Features\n\nCompute features for the training and test segments.",
"_____no_output_____"
]
],
[
[
" def add_trend_feature(arr, abs_values=False):\n idx = np.array(range(len(arr)))\n if abs_values:\n arr = np.abs(arr)\n lr = LinearRegression()\n lr.fit(idx.reshape(-1, 1), arr)\n return lr.coef_[0]\n\ndef classic_sta_lta(x, length_sta, length_lta):\n # Short Term Average and Long Term Average\n sta = np.cumsum(x ** 2)\n\n # Convert to float\n sta = np.require(sta, dtype=np.float)\n\n # Copy for LTA\n lta = sta.copy()\n\n # Compute the STA and the LTA\n sta[length_sta:] = sta[length_sta:] - sta[:-length_sta]\n sta /= length_sta\n lta[length_lta:] = lta[length_lta:] - lta[:-length_lta]\n lta /= length_lta\n\n # Pad zeros\n sta[:length_lta - 1] = 0\n\n # Avoid division by zero by setting zero values to tiny float\n dtiny = np.finfo(0.0).tiny\n idx = lta < dtiny\n lta[idx] = dtiny\n\n return sta / lta",
"_____no_output_____"
],
[
"for segment in tqdm_notebook(range(segments)):\n seg = train.iloc[segment*rows:segment*rows+rows]\n x = pd.Series(seg['acoustic_data'].values)\n y = seg['time_to_failure'].values[-1]\n \n y_tr.loc[segment, 'time_to_failure'] = y\n X_tr.loc[segment, 'mean'] = x.mean()\n X_tr.loc[segment, 'std'] = x.std()\n X_tr.loc[segment, 'max'] = x.max()\n X_tr.loc[segment, 'min'] = x.min()\n \n \n X_tr.loc[segment, 'mean_change_abs'] = np.mean(np.diff(x))\n X_tr.loc[segment, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(x) / x[:-1]))[0])\n X_tr.loc[segment, 'abs_max'] = np.abs(x).max()\n X_tr.loc[segment, 'abs_min'] = np.abs(x).min()\n \n X_tr.loc[segment, 'std_first_50000'] = x[:50000].std()\n X_tr.loc[segment, 'std_last_50000'] = x[-50000:].std()\n X_tr.loc[segment, 'std_first_10000'] = x[:10000].std()\n X_tr.loc[segment, 'std_last_10000'] = x[-10000:].std()\n \n X_tr.loc[segment, 'avg_first_50000'] = x[:50000].mean()\n X_tr.loc[segment, 'avg_last_50000'] = x[-50000:].mean()\n X_tr.loc[segment, 'avg_first_10000'] = x[:10000].mean()\n X_tr.loc[segment, 'avg_last_10000'] = x[-10000:].mean()\n \n X_tr.loc[segment, 'min_first_50000'] = x[:50000].min()\n X_tr.loc[segment, 'min_last_50000'] = x[-50000:].min()\n X_tr.loc[segment, 'min_first_10000'] = x[:10000].min()\n X_tr.loc[segment, 'min_last_10000'] = x[-10000:].min()\n \n X_tr.loc[segment, 'max_first_50000'] = x[:50000].max()\n X_tr.loc[segment, 'max_last_50000'] = x[-50000:].max()\n X_tr.loc[segment, 'max_first_10000'] = x[:10000].max()\n X_tr.loc[segment, 'max_last_10000'] = x[-10000:].max()\n \n X_tr.loc[segment, 'max_to_min'] = x.max() / np.abs(x.min())\n X_tr.loc[segment, 'max_to_min_diff'] = x.max() - np.abs(x.min())\n X_tr.loc[segment, 'count_big'] = len(x[np.abs(x) > 500])\n X_tr.loc[segment, 'sum'] = x.sum()\n \n X_tr.loc[segment, 'mean_change_rate_first_50000'] = np.mean(np.nonzero((np.diff(x[:50000]) / x[:50000][:-1]))[0])\n X_tr.loc[segment, 'mean_change_rate_last_50000'] = np.mean(np.nonzero((np.diff(x[-50000:]) / x[-50000:][:-1]))[0])\n X_tr.loc[segment, 'mean_change_rate_first_10000'] = np.mean(np.nonzero((np.diff(x[:10000]) / x[:10000][:-1]))[0])\n X_tr.loc[segment, 'mean_change_rate_last_10000'] = np.mean(np.nonzero((np.diff(x[-10000:]) / x[-10000:][:-1]))[0])\n \n X_tr.loc[segment, 'q95'] = np.quantile(x, 0.95)\n X_tr.loc[segment, 'q99'] = np.quantile(x, 0.99)\n X_tr.loc[segment, 'q05'] = np.quantile(x, 0.05)\n X_tr.loc[segment, 'q01'] = np.quantile(x, 0.01)\n \n X_tr.loc[segment, 'abs_q95'] = np.quantile(np.abs(x), 0.95)\n X_tr.loc[segment, 'abs_q99'] = np.quantile(np.abs(x), 0.99)\n X_tr.loc[segment, 'abs_q05'] = np.quantile(np.abs(x), 0.05)\n X_tr.loc[segment, 'abs_q01'] = np.quantile(np.abs(x), 0.01)\n \n X_tr.loc[segment, 'trend'] = add_trend_feature(x)\n X_tr.loc[segment, 'abs_trend'] = add_trend_feature(x, abs_values=True)\n X_tr.loc[segment, 'abs_mean'] = np.abs(x).mean()\n X_tr.loc[segment, 'abs_std'] = np.abs(x).std()\n \n X_tr.loc[segment, 'mad'] = x.mad()\n X_tr.loc[segment, 'kurt'] = x.kurtosis()\n X_tr.loc[segment, 'skew'] = x.skew()\n X_tr.loc[segment, 'med'] = x.median()\n \n X_tr.loc[segment, 'Hilbert_mean'] = np.abs(hilbert(x)).mean()\n X_tr.loc[segment, 'Hann_window_mean'] = (convolve(x, hann(150), mode='same') / sum(hann(150))).mean()\n X_tr.loc[segment, 'classic_sta_lta1_mean'] = classic_sta_lta(x, 500, 10000).mean()\n X_tr.loc[segment, 'classic_sta_lta2_mean'] = classic_sta_lta(x, 5000, 100000).mean()\n X_tr.loc[segment, 'classic_sta_lta3_mean'] = classic_sta_lta(x, 3333, 6666).mean()\n X_tr.loc[segment, 'classic_sta_lta4_mean'] = classic_sta_lta(x, 10000, 25000).mean()\n X_tr.loc[segment, 'Moving_average_700_mean'] = x.rolling(window=700).mean().mean(skipna=True)\n X_tr.loc[segment, 'Moving_average_1500_mean'] = x.rolling(window=1500).mean().mean(skipna=True)\n X_tr.loc[segment, 'Moving_average_3000_mean'] = x.rolling(window=3000).mean().mean(skipna=True)\n X_tr.loc[segment, 'Moving_average_6000_mean'] = x.rolling(window=6000).mean().mean(skipna=True)\n ewma = pd.Series.ewm\n X_tr.loc[segment, 'exp_Moving_average_300_mean'] = (ewma(x, span=300).mean()).mean(skipna=True)\n X_tr.loc[segment, 'exp_Moving_average_3000_mean'] = ewma(x, span=3000).mean().mean(skipna=True)\n X_tr.loc[segment, 'exp_Moving_average_30000_mean'] = ewma(x, span=6000).mean().mean(skipna=True)\n no_of_std = 2\n X_tr.loc[segment, 'MA_700MA_std_mean'] = x.rolling(window=700).std().mean()\n X_tr.loc[segment,'MA_700MA_BB_high_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] + no_of_std * X_tr.loc[segment, 'MA_700MA_std_mean']).mean()\n X_tr.loc[segment,'MA_700MA_BB_low_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] - no_of_std * X_tr.loc[segment, 'MA_700MA_std_mean']).mean()\n X_tr.loc[segment, 'MA_400MA_std_mean'] = x.rolling(window=400).std().mean()\n X_tr.loc[segment,'MA_400MA_BB_high_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] + no_of_std * X_tr.loc[segment, 'MA_400MA_std_mean']).mean()\n X_tr.loc[segment,'MA_400MA_BB_low_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] - no_of_std * X_tr.loc[segment, 'MA_400MA_std_mean']).mean()\n X_tr.loc[segment, 'MA_1000MA_std_mean'] = x.rolling(window=1000).std().mean()\n \n X_tr.loc[segment, 'iqr'] = np.subtract(*np.percentile(x, [75, 25]))\n X_tr.loc[segment, 'q999'] = np.quantile(x,0.999)\n X_tr.loc[segment, 'q001'] = np.quantile(x,0.001)\n X_tr.loc[segment, 'ave10'] = stats.trim_mean(x, 0.1)\n \n for windows in [10, 100, 1000]:\n x_roll_std = x.rolling(windows).std().dropna().values\n x_roll_mean = x.rolling(windows).mean().dropna().values\n \n X_tr.loc[segment, 'ave_roll_std_' + str(windows)] = x_roll_std.mean()\n X_tr.loc[segment, 'std_roll_std_' + str(windows)] = x_roll_std.std()\n X_tr.loc[segment, 'max_roll_std_' + str(windows)] = x_roll_std.max()\n X_tr.loc[segment, 'min_roll_std_' + str(windows)] = x_roll_std.min()\n X_tr.loc[segment, 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01)\n X_tr.loc[segment, 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05)\n X_tr.loc[segment, 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95)\n X_tr.loc[segment, 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99)\n X_tr.loc[segment, 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.diff(x_roll_std))\n X_tr.loc[segment, 'av_change_rate_roll_std_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0])\n X_tr.loc[segment, 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max()\n \n X_tr.loc[segment, 'ave_roll_mean_' + str(windows)] = x_roll_mean.mean()\n X_tr.loc[segment, 'std_roll_mean_' + str(windows)] = x_roll_mean.std()\n X_tr.loc[segment, 'max_roll_mean_' + str(windows)] = x_roll_mean.max()\n X_tr.loc[segment, 'min_roll_mean_' + str(windows)] = x_roll_mean.min()\n X_tr.loc[segment, 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01)\n X_tr.loc[segment, 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05)\n X_tr.loc[segment, 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95)\n X_tr.loc[segment, 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99)\n X_tr.loc[segment, 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.diff(x_roll_mean))\n X_tr.loc[segment, 'av_change_rate_roll_mean_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0])\n X_tr.loc[segment, 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max()",
"_____no_output_____"
],
[
"print(\"Number of training segments: {}\".format(X_tr.shape[0]))\nprint(\"Number of features: {}\".format(X_tr.shape[1]))\nX_tr.head()",
"Number of training segments: 4194\nNumber of features: 138\n"
],
[
"plt.figure(figsize=(44, 24))\ncols = list(np.abs(X_tr.corrwith(y_tr['time_to_failure'])).sort_values(ascending=False).head(24).index)\nmms = MinMaxScaler()\nX_tr_scaled = pd.DataFrame(columns=cols, data=mms.fit_transform(X_tr[cols]))\ny_tr_scaled = mms.fit_transform(y_tr)\nfor i, col in enumerate(cols):\n plt.subplot(6, 4, i + 1)\n plt.plot(X_tr_scaled[col], color='b')\n plt.title(col)\n ax1.set_ylabel(col, color='b')\n\n ax2 = ax1.twinx()\n plt.plot(y_tr_scaled, color='r')\n ax2.set_ylabel('time_to_failure', color='r')\n plt.legend([col, 'time_to_failure'], loc=(0.875, 0.9))\n plt.grid(False)\n \nplt.savefig('reports/figures/feature_correlations.png')",
"_____no_output_____"
],
[
"submission = pd.read_csv('data/sample_submission.csv', index_col='seg_id')\nX_test = pd.DataFrame(columns=X_tr.columns, dtype=np.float64, index=submission.index)\n\nfor i, seg_id in enumerate(tqdm_notebook(X_test.index)):\n seg = pd.read_csv('data/test/' + seg_id + '.csv')\n \n x = pd.Series(seg['acoustic_data'].values)\n X_test.loc[seg_id, 'mean'] = x.mean()\n X_test.loc[seg_id, 'std'] = x.std()\n X_test.loc[seg_id, 'max'] = x.max()\n X_test.loc[seg_id, 'min'] = x.min()\n \n X_test.loc[seg_id, 'mean_change_abs'] = np.mean(np.diff(x))\n X_test.loc[seg_id, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(x) / x[:-1]))[0])\n X_test.loc[seg_id, 'abs_max'] = np.abs(x).max()\n X_test.loc[seg_id, 'abs_min'] = np.abs(x).min()\n \n X_test.loc[seg_id, 'std_first_50000'] = x[:50000].std()\n X_test.loc[seg_id, 'std_last_50000'] = x[-50000:].std()\n X_test.loc[seg_id, 'std_first_10000'] = x[:10000].std()\n X_test.loc[seg_id, 'std_last_10000'] = x[-10000:].std()\n \n X_test.loc[seg_id, 'avg_first_50000'] = x[:50000].mean()\n X_test.loc[seg_id, 'avg_last_50000'] = x[-50000:].mean()\n X_test.loc[seg_id, 'avg_first_10000'] = x[:10000].mean()\n X_test.loc[seg_id, 'avg_last_10000'] = x[-10000:].mean()\n \n X_test.loc[seg_id, 'min_first_50000'] = x[:50000].min()\n X_test.loc[seg_id, 'min_last_50000'] = x[-50000:].min()\n X_test.loc[seg_id, 'min_first_10000'] = x[:10000].min()\n X_test.loc[seg_id, 'min_last_10000'] = x[-10000:].min()\n \n X_test.loc[seg_id, 'max_first_50000'] = x[:50000].max()\n X_test.loc[seg_id, 'max_last_50000'] = x[-50000:].max()\n X_test.loc[seg_id, 'max_first_10000'] = x[:10000].max()\n X_test.loc[seg_id, 'max_last_10000'] = x[-10000:].max()\n \n X_test.loc[seg_id, 'max_to_min'] = x.max() / np.abs(x.min())\n X_test.loc[seg_id, 'max_to_min_diff'] = x.max() - np.abs(x.min())\n X_test.loc[seg_id, 'count_big'] = len(x[np.abs(x) > 500])\n X_test.loc[seg_id, 'sum'] = x.sum()\n \n X_test.loc[seg_id, 'mean_change_rate_first_50000'] = np.mean(np.nonzero((np.diff(x[:50000]) / x[:50000][:-1]))[0])\n X_test.loc[seg_id, 'mean_change_rate_last_50000'] = np.mean(np.nonzero((np.diff(x[-50000:]) / x[-50000:][:-1]))[0])\n X_test.loc[seg_id, 'mean_change_rate_first_10000'] = np.mean(np.nonzero((np.diff(x[:10000]) / x[:10000][:-1]))[0])\n X_test.loc[seg_id, 'mean_change_rate_last_10000'] = np.mean(np.nonzero((np.diff(x[-10000:]) / x[-10000:][:-1]))[0])\n \n X_test.loc[seg_id, 'q95'] = np.quantile(x,0.95)\n X_test.loc[seg_id, 'q99'] = np.quantile(x,0.99)\n X_test.loc[seg_id, 'q05'] = np.quantile(x,0.05)\n X_test.loc[seg_id, 'q01'] = np.quantile(x,0.01)\n \n X_test.loc[seg_id, 'abs_q95'] = np.quantile(np.abs(x), 0.95)\n X_test.loc[seg_id, 'abs_q99'] = np.quantile(np.abs(x), 0.99)\n X_test.loc[seg_id, 'abs_q05'] = np.quantile(np.abs(x), 0.05)\n X_test.loc[seg_id, 'abs_q01'] = np.quantile(np.abs(x), 0.01)\n \n X_test.loc[seg_id, 'trend'] = add_trend_feature(x)\n X_test.loc[seg_id, 'abs_trend'] = add_trend_feature(x, abs_values=True)\n X_test.loc[seg_id, 'abs_mean'] = np.abs(x).mean()\n X_test.loc[seg_id, 'abs_std'] = np.abs(x).std()\n \n X_test.loc[seg_id, 'mad'] = x.mad()\n X_test.loc[seg_id, 'kurt'] = x.kurtosis()\n X_test.loc[seg_id, 'skew'] = x.skew()\n X_test.loc[seg_id, 'med'] = x.median()\n \n X_test.loc[seg_id, 'Hilbert_mean'] = np.abs(hilbert(x)).mean()\n X_test.loc[seg_id, 'Hann_window_mean'] = (convolve(x, hann(150), mode='same') / sum(hann(150))).mean()\n X_test.loc[seg_id, 'classic_sta_lta1_mean'] = classic_sta_lta(x, 500, 10000).mean()\n X_test.loc[seg_id, 'classic_sta_lta2_mean'] = classic_sta_lta(x, 5000, 100000).mean()\n X_test.loc[seg_id, 'classic_sta_lta3_mean'] = classic_sta_lta(x, 3333, 6666).mean()\n X_test.loc[seg_id, 'classic_sta_lta4_mean'] = classic_sta_lta(x, 10000, 25000).mean()\n X_test.loc[seg_id, 'Moving_average_700_mean'] = x.rolling(window=700).mean().mean(skipna=True)\n X_test.loc[seg_id, 'Moving_average_1500_mean'] = x.rolling(window=1500).mean().mean(skipna=True)\n X_test.loc[seg_id, 'Moving_average_3000_mean'] = x.rolling(window=3000).mean().mean(skipna=True)\n X_test.loc[seg_id, 'Moving_average_6000_mean'] = x.rolling(window=6000).mean().mean(skipna=True)\n ewma = pd.Series.ewm\n X_test.loc[seg_id, 'exp_Moving_average_300_mean'] = (ewma(x, span=300).mean()).mean(skipna=True)\n X_test.loc[seg_id, 'exp_Moving_average_3000_mean'] = ewma(x, span=3000).mean().mean(skipna=True)\n X_test.loc[seg_id, 'exp_Moving_average_30000_mean'] = ewma(x, span=6000).mean().mean(skipna=True)\n no_of_std = 2\n X_test.loc[seg_id, 'MA_700MA_std_mean'] = x.rolling(window=700).std().mean()\n X_test.loc[seg_id,'MA_700MA_BB_high_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X_test.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X_test.loc[seg_id,'MA_700MA_BB_low_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X_test.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X_test.loc[seg_id, 'MA_400MA_std_mean'] = x.rolling(window=400).std().mean()\n X_test.loc[seg_id,'MA_400MA_BB_high_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X_test.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X_test.loc[seg_id,'MA_400MA_BB_low_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X_test.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X_test.loc[seg_id, 'MA_1000MA_std_mean'] = x.rolling(window=1000).std().mean()\n \n X_test.loc[seg_id, 'iqr'] = np.subtract(*np.percentile(x, [75, 25]))\n X_test.loc[seg_id, 'q999'] = np.quantile(x,0.999)\n X_test.loc[seg_id, 'q001'] = np.quantile(x,0.001)\n X_test.loc[seg_id, 'ave10'] = stats.trim_mean(x, 0.1)\n \n for windows in [10, 100, 1000]:\n x_roll_std = x.rolling(windows).std().dropna().values\n x_roll_mean = x.rolling(windows).mean().dropna().values\n \n X_test.loc[seg_id, 'ave_roll_std_' + str(windows)] = x_roll_std.mean()\n X_test.loc[seg_id, 'std_roll_std_' + str(windows)] = x_roll_std.std()\n X_test.loc[seg_id, 'max_roll_std_' + str(windows)] = x_roll_std.max()\n X_test.loc[seg_id, 'min_roll_std_' + str(windows)] = x_roll_std.min()\n X_test.loc[seg_id, 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01)\n X_test.loc[seg_id, 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05)\n X_test.loc[seg_id, 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95)\n X_test.loc[seg_id, 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99)\n X_test.loc[seg_id, 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.diff(x_roll_std))\n X_test.loc[seg_id, 'av_change_rate_roll_std_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0])\n X_test.loc[seg_id, 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max()\n \n X_test.loc[seg_id, 'ave_roll_mean_' + str(windows)] = x_roll_mean.mean()\n X_test.loc[seg_id, 'std_roll_mean_' + str(windows)] = x_roll_mean.std()\n X_test.loc[seg_id, 'max_roll_mean_' + str(windows)] = x_roll_mean.max()\n X_test.loc[seg_id, 'min_roll_mean_' + str(windows)] = x_roll_mean.min()\n X_test.loc[seg_id, 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01)\n X_test.loc[seg_id, 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05)\n X_test.loc[seg_id, 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95)\n X_test.loc[seg_id, 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99)\n X_test.loc[seg_id, 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.diff(x_roll_mean))\n X_test.loc[seg_id, 'av_change_rate_roll_mean_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0])\n X_test.loc[seg_id, 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max()",
"_____no_output_____"
],
[
"X_test.head()",
"_____no_output_____"
]
],
[
[
"## Save Prepared Datasets",
"_____no_output_____"
]
],
[
[
"X_tr.to_csv('data/processed/010_train_features.csv', index=False)\ny_tr.to_csv('data/processed/010_train_target.csv', index=False)\nX_test.to_csv('data/processed/010_test_features.csv', index=True)",
"_____no_output_____"
]
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cb1ac92456c5547b9ab9a213ba6c992f176f503f | 25,685 | ipynb | Jupyter Notebook | analysis/gfp/gfp_conditioning_with_edit_distances.ipynb | johli/genesis | 5424c1888d4330e505ad87412e7f1cc5dd828888 | [
"MIT"
] | 12 | 2020-02-02T14:29:15.000Z | 2021-09-12T08:05:43.000Z | analysis/gfp/gfp_conditioning_with_edit_distances.ipynb | johli/genesis | 5424c1888d4330e505ad87412e7f1cc5dd828888 | [
"MIT"
] | 1 | 2022-01-04T08:04:00.000Z | 2022-01-10T08:49:04.000Z | analysis/gfp/gfp_conditioning_with_edit_distances.ipynb | johli/genesis | 5424c1888d4330e505ad87412e7f1cc5dd828888 | [
"MIT"
] | 3 | 2020-03-10T22:24:05.000Z | 2021-05-05T13:23:01.000Z | 39.515385 | 186 | 0.504224 | [
[
[
"import warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport sys\nimport itertools\nfrom keras.layers import Input, Dense, Reshape, Flatten\nfrom keras import layers, initializers\nfrom keras.models import Model, load_model\nimport keras.backend as K\nimport numpy as np\nfrom seqtools import SequenceTools as ST\nfrom gfp_gp import SequenceGP\nfrom util import AA, AA_IDX\nfrom util import build_vae\nfrom sklearn.model_selection import train_test_split, ShuffleSplit\nfrom keras.callbacks import EarlyStopping\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom gan import WGAN\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\nimport scipy.stats\nfrom scipy.stats import norm\nfrom scipy.optimize import minimize\nfrom keras.utils.generic_utils import get_custom_objects\nfrom util import one_hot_encode_aa, partition_data, get_balaji_predictions, get_samples\nfrom util import convert_idx_array_to_aas, build_pred_vae_model, get_experimental_X_y\nfrom util import get_gfp_X_y_aa\nfrom losses import neg_log_likelihood\nimport json\nplt.rcParams['figure.dpi'] = 300\nclass color:\n PURPLE = '\\033[95m'\n CYAN = '\\033[96m'\n DARKCYAN = '\\033[36m'\n BLUE = '\\033[94m'\n GREEN = '\\033[92m'\n YELLOW = '\\033[93m'\n RED = '\\033[91m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n END = '\\033[0m'\n\nimport tensorflow as tf\nfrom keras.backend.tensorflow_backend import set_session\n\ndef contain_tf_gpu_mem_usage() :\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n set_session(sess)\n\ncontain_tf_gpu_mem_usage()\n",
"Using TensorFlow backend.\n"
],
[
"#Load GFP training dataset\n\nit = 0\n\nTRAIN_SIZE = 5000\ntrain_size_str = \"%ik\" % (TRAIN_SIZE/1000)\nnum_models = [1, 5, 20][it]\nRANDOM_STATE = it + 1\n\nX_train, y_train, gt_train = get_experimental_X_y(random_state=RANDOM_STATE, train_size=TRAIN_SIZE)\n",
"_____no_output_____"
],
[
"#Print the 50th, 80th, 95th and 100th percentile of oracle scores\n\nprint(np.percentile(y_train, 50))\nprint(np.percentile(y_train, 80))\nprint(np.percentile(y_train, 95))\nprint(np.percentile(y_train, 100))\n",
"3.12042461331375\n3.142383237048391\n3.1558655520498666\n3.1798934456199195\n"
],
[
"def build_model(M):\n x = Input(shape=(M, 20,))\n y = Flatten()(x)\n y = Dense(50, activation='elu')(y)\n y = Dense(2)(y)\n model = Model(inputs=x, outputs=y)\n return model\n\ndef evaluate_ground_truth(X_aa, ground_truth, save_file=None):\n y_gt = ground_truth.predict(X_aa, print_every=100000)[:, 0]\n if save_file is not None:\n np.save(save_file, y_gt)\n \ndef train_and_save_oracles(X_train, y_train, n=10, suffix='', batch_size=100):\n for i in range(n):\n model = build_model(X_train.shape[1])\n model.compile(optimizer='adam',\n loss=neg_log_likelihood,\n )\n early_stop = EarlyStopping(monitor='val_loss', \n min_delta=0, \n patience=5, \n verbose=1)\n\n model.fit(X_train, y_train, \n epochs=100, \n batch_size=batch_size, \n validation_split=0.1, \n callbacks=[early_stop],\n verbose=2)\n model.save(\"models/oracle_%i%s.h5\" % (i, suffix))",
"_____no_output_____"
],
[
"import editdistance\n\ndef compute_edit_distance(seqs, opt_len=None) :\n shuffle_index = np.arange(len(seqs))\n shuffle_index = shuffle_index[::-1]\n \n seqs_shuffled = [seqs[shuffle_index[i]] for i in range(len(seqs))]\n edit_distances = np.ravel([float(editdistance.eval(seq_1, seq_2)) for seq_1, seq_2 in zip(seqs, seqs_shuffled)])\n if opt_len is not None :\n edit_distances /= opt_len\n \n return edit_distances\n\ndef weighted_ml_opt(X_train, oracles, ground_truth, vae_0, weights_type='dbas',\n LD=20, iters=20, samples=500, homoscedastic=False, homo_y_var=0.1,\n quantile=0.95, verbose=False, alpha=1, train_gt_evals=None,\n cutoff=1e-6, it_epochs=10, enc1_units=50):\n \n assert weights_type in ['cbas', 'dbas','rwr', 'cem-pi', 'fbvae']\n L = X_train.shape[1]\n vae = build_vae(latent_dim=LD,\n n_tokens=20, seq_length=L,\n enc1_units=enc1_units)\n\n traj = np.zeros((iters, 7))\n oracle_samples = np.zeros((iters, samples))\n gt_samples = np.zeros((iters, samples))\n edit_distance_samples = np.zeros((iters, samples))\n oracle_max_seq = None\n oracle_max = -np.inf\n gt_of_oracle_max = -np.inf\n y_star = -np.inf\n \n \n # FOR REVIEW:\n all_seqs = pd.DataFrame(0, index=range(int((iters-1)*samples)), columns=['seq', 'val'])\n l_ = 0\n \n for t in range(iters):\n ### Take Samples ###\n zt = np.random.randn(samples, LD)\n if t > 0:\n Xt_p = vae.decoder_.predict(zt)\n Xt = get_samples(Xt_p)\n else:\n Xt = X_train\n \n ### Evaluate ground truth and oracle ###\n yt, yt_var = get_balaji_predictions(oracles, Xt)\n if homoscedastic:\n yt_var = np.ones_like(yt) * homo_y_var\n Xt_aa = np.argmax(Xt, axis=-1)\n if t == 0 and train_gt_evals is not None:\n yt_gt = train_gt_evals\n else:\n yt_gt = ground_truth.predict(Xt_aa, print_every=1000000)[:, 0]\n \n ### Calculate weights for different schemes ###\n if t > 0:\n if weights_type == 'cbas': \n log_pxt = np.sum(np.log(Xt_p) * Xt, axis=(1, 2))\n X0_p = vae_0.decoder_.predict(zt)\n log_px0 = np.sum(np.log(X0_p) * Xt, axis=(1, 2))\n w1 = np.exp(log_px0-log_pxt)\n y_star_1 = np.percentile(yt, quantile*100)\n if y_star_1 > y_star:\n y_star = y_star_1\n w2= scipy.stats.norm.sf(y_star, loc=yt, scale=np.sqrt(yt_var))\n weights = w1*w2 \n elif weights_type == 'cem-pi':\n pi = scipy.stats.norm.sf(max_train_gt, loc=yt, scale=np.sqrt(yt_var))\n pi_thresh = np.percentile(pi, quantile*100)\n weights = (pi > pi_thresh).astype(int)\n elif weights_type == 'dbas':\n y_star_1 = np.percentile(yt, quantile*100)\n if y_star_1 > y_star:\n y_star = y_star_1\n weights = scipy.stats.norm.sf(y_star, loc=yt, scale=np.sqrt(yt_var))\n elif weights_type == 'rwr':\n weights = np.exp(alpha*yt)\n weights /= np.sum(weights)\n else:\n weights = np.ones(yt.shape[0])\n max_train_gt = np.max(yt_gt)\n \n yt_max_idx = np.argmax(yt)\n yt_max = yt[yt_max_idx]\n if yt_max > oracle_max:\n oracle_max = yt_max\n try:\n oracle_max_seq = convert_idx_array_to_aas(Xt_aa[yt_max_idx-1:yt_max_idx])[0]\n except IndexError:\n print(Xt_aa[yt_max_idx-1:yt_max_idx])\n gt_of_oracle_max = yt_gt[yt_max_idx]\n \n ### Record and print results ##\n if t == 0:\n rand_idx = np.random.randint(0, len(yt), samples)\n oracle_samples[t, :] = yt[rand_idx]\n gt_samples[t, :] = yt_gt[rand_idx]\n edit_distance_samples[t, :] = compute_edit_distance(convert_idx_array_to_aas(Xt_aa[rand_idx, ...]))\n if t > 0:\n oracle_samples[t, :] = yt\n gt_samples[t, :] = yt_gt\n edit_distance_samples[t, :] = compute_edit_distance(convert_idx_array_to_aas(Xt_aa))\n \n traj[t, 0] = np.max(yt_gt)\n traj[t, 1] = np.mean(yt_gt)\n traj[t, 2] = np.std(yt_gt)\n traj[t, 3] = np.max(yt)\n traj[t, 4] = np.mean(yt)\n traj[t, 5] = np.std(yt)\n traj[t, 6] = np.mean(yt_var)\n \n if verbose:\n print(weights_type.upper(), t, traj[t, 0], color.BOLD + str(traj[t, 1]) + color.END, \n traj[t, 2], traj[t, 3], color.BOLD + str(traj[t, 4]) + color.END, traj[t, 5], traj[t, 6], np.median(edit_distance_samples[t, :]))\n \n ### Train model ###\n if t == 0:\n vae.encoder_.set_weights(vae_0.encoder_.get_weights())\n vae.decoder_.set_weights(vae_0.decoder_.get_weights())\n vae.vae_.set_weights(vae_0.vae_.get_weights())\n else:\n cutoff_idx = np.where(weights < cutoff)\n Xt = np.delete(Xt, cutoff_idx, axis=0)\n yt = np.delete(yt, cutoff_idx, axis=0)\n weights = np.delete(weights, cutoff_idx, axis=0)\n vae.fit([Xt], [Xt, np.zeros(Xt.shape[0])],\n epochs=it_epochs,\n batch_size=10,\n shuffle=False,\n sample_weight=[weights, weights],\n verbose=0)\n \n max_dict = {'oracle_max' : oracle_max, \n 'oracle_max_seq': oracle_max_seq, \n 'gt_of_oracle_max': gt_of_oracle_max}\n return traj, oracle_samples, gt_samples, edit_distance_samples, max_dict",
"_____no_output_____"
],
[
"def fb_opt(X_train, oracles, ground_truth, vae_0, weights_type='fbvae',\n LD=20, iters=20, samples=500, \n quantile=0.8, verbose=False, train_gt_evals=None,\n it_epochs=10, enc1_units=50):\n \n assert weights_type in ['fbvae']\n L = X_train.shape[1]\n vae = build_vae(latent_dim=LD,\n n_tokens=20, seq_length=L,\n enc1_units=enc1_units)\n\n traj = np.zeros((iters, 7))\n oracle_samples = np.zeros((iters, samples))\n gt_samples = np.zeros((iters, samples))\n edit_distance_samples = np.zeros((iters, samples))\n oracle_max_seq = None\n oracle_max = -np.inf\n gt_of_oracle_max = -np.inf\n y_star = - np.inf\n for t in range(iters):\n ### Take Samples and evaluate ground truth and oracle ##\n zt = np.random.randn(samples, LD)\n if t > 0:\n Xt_sample_p = vae.decoder_.predict(zt)\n Xt_sample = get_samples(Xt_sample_p)\n yt_sample, _ = get_balaji_predictions(oracles, Xt_sample)\n Xt_aa_sample = np.argmax(Xt_sample, axis=-1)\n yt_gt_sample = ground_truth.predict(Xt_aa_sample, print_every=1000000)[:, 0]\n else:\n Xt = X_train\n yt, _ = get_balaji_predictions(oracles, Xt)\n Xt_aa = np.argmax(Xt, axis=-1)\n fb_thresh = np.percentile(yt, quantile*100)\n if train_gt_evals is not None:\n yt_gt = train_gt_evals\n else:\n yt_gt = ground_truth.predict(Xt_aa, print_every=1000000)[:, 0]\n \n ### Calculate threshold ###\n if t > 0:\n threshold_idx = np.where(yt_sample >= fb_thresh)[0]\n n_top = len(threshold_idx)\n sample_arrs = [Xt_sample, yt_sample, yt_gt_sample, Xt_aa_sample]\n full_arrs = [Xt, yt, yt_gt, Xt_aa]\n \n for l in range(len(full_arrs)):\n sample_arr = sample_arrs[l]\n full_arr = full_arrs[l]\n sample_top = sample_arr[threshold_idx]\n full_arr = np.concatenate([sample_top, full_arr])\n full_arr = np.delete(full_arr, range(full_arr.shape[0]-n_top, full_arr.shape[0]), axis=0)\n full_arrs[l] = full_arr\n Xt, yt, yt_gt, Xt_aa = full_arrs\n yt_max_idx = np.argmax(yt)\n yt_max = yt[yt_max_idx]\n if yt_max > oracle_max:\n oracle_max = yt_max\n try:\n oracle_max_seq = convert_idx_array_to_aas(Xt_aa[yt_max_idx-1:yt_max_idx])[0]\n except IndexError:\n print(Xt_aa[yt_max_idx-1:yt_max_idx])\n gt_of_oracle_max = yt_gt[yt_max_idx]\n \n ### Record and print results ##\n\n rand_idx = np.random.randint(0, len(yt), samples)\n oracle_samples[t, :] = yt[rand_idx]\n gt_samples[t, :] = yt_gt[rand_idx]\n edit_distance_samples[t, :] = compute_edit_distance(convert_idx_array_to_aas(Xt_aa[rand_idx, ...]))\n\n traj[t, 0] = np.max(yt_gt)\n traj[t, 1] = np.mean(yt_gt)\n traj[t, 2] = np.std(yt_gt)\n traj[t, 3] = np.max(yt)\n traj[t, 4] = np.mean(yt)\n traj[t, 5] = np.std(yt)\n if t > 0:\n traj[t, 6] = n_top\n else:\n traj[t, 6] = 0\n \n if verbose:\n print(weights_type.upper(), t, traj[t, 0], color.BOLD + str(traj[t, 1]) + color.END, \n traj[t, 2], traj[t, 3], color.BOLD + str(traj[t, 4]) + color.END, traj[t, 5], traj[t, 6], np.median(edit_distance_samples[t, :]))\n \n ### Train model ###\n if t == 0:\n vae.encoder_.set_weights(vae_0.encoder_.get_weights())\n vae.decoder_.set_weights(vae_0.decoder_.get_weights())\n vae.vae_.set_weights(vae_0.vae_.get_weights())\n else:\n \n vae.fit([Xt], [Xt, np.zeros(Xt.shape[0])],\n epochs=1,\n batch_size=10,\n shuffle=False,\n verbose=0)\n \n max_dict = {'oracle_max' : oracle_max, \n 'oracle_max_seq': oracle_max_seq, \n 'gt_of_oracle_max': gt_of_oracle_max}\n return traj, oracle_samples, gt_samples, edit_distance_samples, max_dict\n",
"_____no_output_____"
],
[
"def train_experimental_oracles():\n TRAIN_SIZE = 5000\n train_size_str = \"%ik\" % (TRAIN_SIZE/1000)\n i = 1\n num_models = [1, 5, 20]\n for i in range(len(num_models)):\n RANDOM_STATE = i+1\n nm = num_models[i]\n X_train, y_train, _ = get_experimental_X_y(random_state=RANDOM_STATE, train_size=TRAIN_SIZE)\n suffix = '_%s_%i_%i' % (train_size_str, nm, RANDOM_STATE)\n train_and_save_oracles(X_train, y_train, batch_size=10, n=nm, suffix=suffix)",
"_____no_output_____"
],
[
"def train_experimental_vaes(i_list=[0, 2]):\n TRAIN_SIZE = 5000\n train_size_str = \"%ik\" % (TRAIN_SIZE/1000)\n suffix = '_%s' % train_size_str\n for i in i_list:\n RANDOM_STATE = i + 1\n X_train, _, _ = get_experimental_X_y(random_state=RANDOM_STATE, train_size=TRAIN_SIZE)\n vae_0 = build_vae(latent_dim=20,\n n_tokens=20, \n seq_length=X_train.shape[1],\n enc1_units=50)\n vae_0.fit([X_train], [X_train, np.zeros(X_train.shape[0])],\n epochs=100,\n batch_size=10,\n verbose=2)\n vae_0.encoder_.save_weights(\"models/vae_0_encoder_weights%s_%i.h5\"% (suffix, RANDOM_STATE))\n vae_0.decoder_.save_weights(\"models/vae_0_decoder_weights%s_%i.h5\"% (suffix, RANDOM_STATE))\n vae_0.vae_.save_weights(\"models/vae_0_vae_weights%s_%i.h5\"% (suffix, RANDOM_STATE))",
"_____no_output_____"
],
[
"def run_experimental_weighted_ml(it, repeat_start=0, repeats=3):\n \n assert it in [0, 1, 2]\n \n TRAIN_SIZE = 5000\n train_size_str = \"%ik\" % (TRAIN_SIZE/1000)\n num_models = [1, 5, 20][it]\n RANDOM_STATE = it + 1\n \n X_train, y_train, gt_train = get_experimental_X_y(random_state=RANDOM_STATE, train_size=TRAIN_SIZE)\n \n vae_suffix = '_%s_%i' % (train_size_str, RANDOM_STATE)\n oracle_suffix = '_%s_%i_%i' % (train_size_str, num_models, RANDOM_STATE)\n \n vae_0 = build_vae(latent_dim=20,\n n_tokens=20, \n seq_length=X_train.shape[1],\n enc1_units=50)\n\n vae_0.encoder_.load_weights(\"models/vae_0_encoder_weights%s.h5\" % vae_suffix)\n vae_0.decoder_.load_weights(\"models/vae_0_decoder_weights%s.h5\"% vae_suffix)\n vae_0.vae_.load_weights(\"models/vae_0_vae_weights%s.h5\"% vae_suffix)\n \n ground_truth = SequenceGP(load=True, load_prefix=\"data/gfp_gp\")\n \n loss = neg_log_likelihood\n get_custom_objects().update({\"neg_log_likelihood\": loss})\n \n oracles = [build_model(X_train.shape[1]) for i in range(num_models)]\n for i in range(num_models) :\n oracles[i].load_weights(\"models/oracle_%i%s.h5\" % (i, oracle_suffix))\n \n test_kwargs = [\n {'weights_type':'cbas', 'quantile': 1},\n {'weights_type':'rwr', 'alpha': 20},\n {'weights_type':'dbas', 'quantile': 0.95},\n {'weights_type':'cem-pi', 'quantile': 0.8},\n {'weights_type': 'fbvae', 'quantile': 0.8}\n ]\n \n base_kwargs = {\n 'homoscedastic': False,\n 'homo_y_var': 0.01,\n 'train_gt_evals':gt_train,\n 'samples':100,\n 'cutoff':1e-6,\n 'it_epochs':10,\n 'verbose':True,\n 'LD': 20,\n 'enc1_units':50,\n 'iters': 50\n }\n \n if num_models==1:\n base_kwargs['homoscedastic'] = True\n base_kwargs['homo_y_var'] = np.mean((get_balaji_predictions(oracles, X_train)[0] - y_train)**2)\n \n for k in range(repeat_start, repeats):\n for j in range(len(test_kwargs)):\n test_name = test_kwargs[j]['weights_type']\n suffix = \"_%s_%i_%i_w_edit_distances\" % (train_size_str, RANDOM_STATE, k)\n if test_name == 'fbvae':\n if base_kwargs['iters'] > 100:\n suffix += '_long'\n \n print(suffix)\n kwargs = {}\n kwargs.update(test_kwargs[j])\n kwargs.update(base_kwargs)\n [kwargs.pop(k) for k in ['homoscedastic', 'homo_y_var', 'cutoff', 'it_epochs']]\n test_traj, test_oracle_samples, test_gt_samples, test_edit_distance_samples, test_max = fb_opt(np.copy(X_train), oracles, ground_truth, vae_0, **kwargs)\n else:\n if base_kwargs['iters'] > 100:\n suffix += '_long'\n kwargs = {}\n kwargs.update(test_kwargs[j])\n kwargs.update(base_kwargs)\n test_traj, test_oracle_samples, test_gt_samples, test_edit_distance_samples, test_max = weighted_ml_opt(np.copy(X_train), oracles, ground_truth, vae_0, **kwargs)\n np.save('results/%s_traj%s.npy' %(test_name, suffix), test_traj)\n np.save('results/%s_oracle_samples%s.npy' % (test_name, suffix), test_oracle_samples)\n np.save('results/%s_gt_samples%s.npy'%(test_name, suffix), test_gt_samples )\n np.save('results/%s_edit_distance_samples%s.npy'%(test_name, suffix), test_edit_distance_samples )\n\n with open('results/%s_max%s.json'% (test_name, suffix), 'w') as outfile:\n json.dump(test_max, outfile)\n ",
"_____no_output_____"
],
[
"train_experimental_oracles()\ntrain_experimental_vaes()",
"_____no_output_____"
],
[
"run_experimental_weighted_ml(0, repeat_start=0, repeats=1)\n",
"_____no_output_____"
],
[
"run_experimental_weighted_ml(1, repeat_start=0, repeats=1)\n",
"_____no_output_____"
],
[
"run_experimental_weighted_ml(2, repeat_start=0, repeats=1)\n",
"_____no_output_____"
],
[
"run_experimental_weighted_ml(0, repeat_start=1, repeats=3)\n",
"_____no_output_____"
],
[
"run_experimental_weighted_ml(1, repeat_start=1, repeats=3)\n",
"_____no_output_____"
],
[
"run_experimental_weighted_ml(2, repeat_start=1, repeats=3)\n",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
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"code",
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]
] |
cb1af0b8ddd2750e8005d7bcdb942db50650d5ce | 10,059 | ipynb | Jupyter Notebook | Nelder_Mead_Simplex_Optimizer.ipynb | MarcelMG/NelderMeadSimplex_Python | ebd4151e16def194bdbd386fd6d4de73ac4b3741 | [
"MIT"
] | null | null | null | Nelder_Mead_Simplex_Optimizer.ipynb | MarcelMG/NelderMeadSimplex_Python | ebd4151e16def194bdbd386fd6d4de73ac4b3741 | [
"MIT"
] | 1 | 2021-03-25T04:17:29.000Z | 2021-03-25T04:18:23.000Z | Nelder_Mead_Simplex_Optimizer.ipynb | MarcelMG/NelderMeadSimplex_Python | ebd4151e16def194bdbd386fd6d4de73ac4b3741 | [
"MIT"
] | null | null | null | 43.925764 | 133 | 0.534844 | [
[
[
"import numpy as np",
"_____no_output_____"
],
[
"class NelderMeadSimplexOptimizer:\n reflection_coeff = 1.0\n expansion_coeff = 2.0\n contraction_coeff = 0.5\n shrinking_coeff = 0.5\n \n # <objective_function>: objective function, should match the specified dimension\n # <dimension>: dimension of parameter vector (integer)\n # <initial values>: list of <dimension+1> np.arrays of length <dimension> each\n # <stop_thresh>: float value stopping criterion, absolute of objective function value of best vs. worst vertex\n # <max_iter>: stopping criterion, maximum number of iterations\n def __init__(self, objective_function, dimension, initial_values, stop_thresh=1e-4, max_iter=500):\n self.obj_func = objective_function\n self.dimension = dimension\n self.vertices_and_values = []\n self.stop_thresh = stop_thresh\n self.max_iter = max_iter\n for vertex_iterator in initial_values:\n # create list of tuples (objective function value, vertex)\n self.vertices_and_values.append( (self.obj_func(vertex_iterator), vertex_iterator) )\n \n @staticmethod\n def create_random_vertices(dimension, center, scale):\n vertex_list = []\n rng = np.random.default_rng()\n for i in range(dimension+1):\n vertex_list.append( center + float(scale) * rng.random((dimension,)) )\n return vertex_list\n \n def calculate_centroid(self):\n # calculate center of all vertices except the worst\n self.centroid = np.zeros(self.dimension)\n for i in range(len(self.vertices_and_values)-1):\n self.centroid += self.vertices_and_values[i][1]\n self.centroid /= float( len(self.vertices_and_values) - 1 )\n \n def sort_vertices(self):\n # sort obj. func. values and their vertices by the obj. func. value\n self.vertices_and_values = sorted(self.vertices_and_values, key=lambda tup: tup[0])\n # store 2 best and 2 worst values and vertices separately\n self.best = self.vertices_and_values[0]\n self.second_best = self.vertices_and_values[1]\n self.second_worst = self.vertices_and_values[-2]\n self.worst = self.vertices_and_values[-1]\n \n def reflect(self):\n new_vertex = self.centroid * ( 1.0 + self.reflection_coeff ) - self.reflection_coeff * self.worst[1]\n new_obj_func_value = self.obj_func(new_vertex)\n return (new_obj_func_value, new_vertex)\n \n def expand(self):\n new_vertex = self.centroid * ( 1.0 + self.expansion_coeff ) - self.expansion_coeff * self.worst[1]\n new_obj_func_value = self.obj_func(new_vertex)\n return (new_obj_func_value, new_vertex)\n \n def contract(self, _vertex):\n new_vertex = self.centroid * ( 1.0 - self.contraction_coeff ) + self.contraction_coeff * _vertex\n new_obj_func_value = self.obj_func(new_vertex)\n return (new_obj_func_value, new_vertex) \n \n def shrink(self):\n # iterate over all vertices except the best (first) one\n for i in range(1, len(self.vertices_and_values)):\n # shrink\n new_vertex = self.best[1] * (1.0 - self.shrinking_coeff) + self.shrinking_coeff * self.vertices_and_values[i][1]\n new_obj_func_value = self.obj_func(new_vertex)\n # replace vertices and new objective function values\n self.vertices_and_values[i] = (new_obj_func_value, new_vertex)\n \n def find_minimum(self, verbose=False):\n num_iterations = 0\n while(True):\n num_iterations += 1\n self.sort_vertices()\n self.calculate_centroid()\n # do reflection\n (reflection_value, reflection_vertex) = self.reflect()\n if( (reflection_value < self.second_worst[0]) and (reflection_value <= self.best[0]) ):\n # accept reflection, replace worst vertex by reflection\n self.vertices_and_values[-1] = (reflection_value, reflection_vertex)\n if(verbose):\n print(\"reflection\")\n elif( reflection_value < self.best[0] ):\n # do expansion\n (expansion_value, expansion_vertex) = self.expand()\n if( expansion_value <= reflection_value):\n # accept expansion, replace worst vertex by expansion\n self.vertices_and_values[-1] = (expansion_value, expansion_vertex)\n if(verbose):\n print(\"expansion\")\n else:\n # accept reflection, replace worst vertex by reflection\n self.vertices_and_values[-1] = (reflection_value, reflection_vertex)\n elif( (reflection_value < self.worst[0]) and (reflection_value >= self.second_worst[0]) ):\n # do outside contraction towards reflection\n (outside_contraction_value, outside_contraction_vertex) = self.contract(reflection_vertex)\n if( outside_contraction_value <= reflection_value ):\n # accept outside contraction, replace worst vertex by outside contraction\n self.vertices_and_values[-1] = (outside_contraction_value, outside_contraction_vertex)\n if(verbose):\n print(\"outside contraction\")\n else:\n # shrink\n self.shrink()\n if(verbose):\n print(\"shrink\")\n else: # reflection_value >= self.worst[0]\n # do inside contraction towards worst\n (inside_contraction_value, inside_contraction_vertex) = self.contract(self.worst[1])\n if( inside_contraction_value <= self.worst[0]):\n # accept inside contraction, replace worst vertex by inside contraction\n self.vertices_and_values[-1] = (inside_contraction_value, inside_contraction_vertex)\n if(verbose):\n print(\"inside contraction\")\n else:\n # shrink\n self.shrink()\n if(verbose):\n print(\"shrink\")\n if(verbose):\n print(\"minimum:\", self.best[0], \"vertex:\", self.best[1]) \n distance = abs(self.worst[0] - self.best[0])\n if(verbose):\n print(\"dist:\", distance)\n if( distance < self.stop_thresh ):\n break\n \n self.sort_vertices()\n return (self.best)",
"_____no_output_____"
],
[
"# use Rosenbrock a.k.a. Banana-function as our test objective function\ndef banana(x):\n return pow(1-x[0], 2) + 100.0 * pow( x[1] - pow(x[0], 2), 2)",
"_____no_output_____"
],
[
"# create random initial vertices centered around (1, 1) with a maximum deviation of 2\n# i.e. the initial points will lie somewhere inside (-1..3, -1..3)\ninit_vertices = NelderMeadSimplexOptimizer.create_random_vertices(2, np.array([1,1]), 2.0)\nprint(\"initial simplex:\")\nfor vertex in init_vertices:\n print(vertex)\n# create a NMS-optimizer instance with our objective function, initial vertices and a dimension of 2\noptimizer = NelderMeadSimplexOptimizer(banana, 2, init_vertices)\n# run optimizer\n(min_value, min_vertex) = optimizer.find_minimum()\nprint(\"\\nfound minimum\", min_value, \"at\", min_vertex)",
"initial simplex:\n[2.09453953 1.33740285]\n[1.266325 1.68667349]\n[1.18139491 2.89288835]\n\nfound minimum 0.0005891722445137289 at [1.02214691 1.04379089]\n"
]
]
] | [
"code"
] | [
[
"code",
"code",
"code",
"code"
]
] |
cb1af8516a53a86ac04b644588fd9e745bee4eba | 138,203 | ipynb | Jupyter Notebook | jupyter/DenseNet.ipynb | KabirSingh114/DeepFake_Face_Detection | 0cf1ce3e69a7e4a18adff889dab4f6db029a0f25 | [
"MIT"
] | null | null | null | jupyter/DenseNet.ipynb | KabirSingh114/DeepFake_Face_Detection | 0cf1ce3e69a7e4a18adff889dab4f6db029a0f25 | [
"MIT"
] | null | null | null | jupyter/DenseNet.ipynb | KabirSingh114/DeepFake_Face_Detection | 0cf1ce3e69a7e4a18adff889dab4f6db029a0f25 | [
"MIT"
] | null | null | null | 138,203 | 138,203 | 0.723342 | [
[
[
"from google.colab import drive\ndrive.mount('/content/gdrive')",
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n\nEnter your authorization code:\n··········\nMounted at /content/gdrive\n"
],
[
"import tarfile\ntfile = tarfile.open(\"/content/gdrive/My Drive/Deep Learning Groupwork/Project/Data.tar\")\ntfile.extractall()",
"_____no_output_____"
],
[
"training_dir = '/content/Data/Train'\nval_dir = '/content/Data/Validation'\nfinetunedir = '/content/Data/FineTune'\ntestdir = '/content/Data/Test'",
"_____no_output_____"
],
[
"import os\nfrom os import listdir\nfrom os.path import isfile, join\nmypath = '/content/Data/Test/Fake'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\n\nmypath = '/content/Data/Test/Real'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\nmypath = '/content/Data/Train/Fake'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\n\nmypath = '/content/Data/Train/Real'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\n\nmypath = '/content/Data/Validation/Real'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\n\nmypath = '/content/Data/Validation/Fake'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\n\nmypath = '/content/Data/FineTune/Real'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")\n\nmypath = '/content/Data/FineTune/Fake'\nprint(mypath)\nfor f in listdir(mypath):\n #print(f[0])\n if f[0] == '.':\n try:\n os.remove(join(mypath, f))\n except: \n print(\"file not deleted\")",
"/content/Data/Test/Fake\n/content/Data/Test/Real\n/content/Data/Train/Fake\n/content/Data/Train/Real\n/content/Data/Validation/Real\n/content/Data/Validation/Fake\n/content/Data/FineTune/Real\n/content/Data/FineTune/Fake\n"
],
[
"import os\nos.chdir('/content/gdrive/My Drive/Deep Learning Groupwork/Project/Code - Kabir/Code/FDFtNet')\nprint(os.getcwd())",
"/content/gdrive/.shortcut-targets-by-id/1jYF3kVIQJkVJsrkkSHd7eO3SMxnC2fRY/Deep Learning Groupwork/Project/Code - Kabir/Code/FDFtNet\n"
],
[
"!python3 pretrain.py -network='denseNet' -train_dir='/content/Data/Train' -val_dir='/content/Data/Validation' -batch_size=128 -reduce_patience=100 -step=200 -epochs=100",
"Using TensorFlow backend.\n2020-04-27 14:48:33.741938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 14:48:39.311227: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n2020-04-27 14:48:39.374624: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:39.375589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \npciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\ncoreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n2020-04-27 14:48:39.375634: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 14:48:39.672116: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 14:48:39.851470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-04-27 14:48:39.874952: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-04-27 14:48:40.148936: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-04-27 14:48:40.211705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-04-27 14:48:40.726262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-04-27 14:48:40.726459: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:40.727463: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:40.728472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-04-27 14:48:40.752739: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2300000000 Hz\n2020-04-27 14:48:40.753017: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x224abc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n2020-04-27 14:48:40.753055: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n2020-04-27 14:48:40.937026: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:40.938159: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x224ad80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n2020-04-27 14:48:40.938193: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n2020-04-27 14:48:40.939451: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:40.940366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \npciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\ncoreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n2020-04-27 14:48:40.940428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 14:48:40.940474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 14:48:40.940510: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-04-27 14:48:40.940546: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-04-27 14:48:40.940579: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-04-27 14:48:40.940615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-04-27 14:48:40.940650: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-04-27 14:48:40.940760: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:40.941702: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:40.942715: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-04-27 14:48:40.946698: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 14:48:47.365430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n2020-04-27 14:48:47.365508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 \n2020-04-27 14:48:47.365538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N \n2020-04-27 14:48:47.371079: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:47.372133: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 14:48:47.373085: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n2020-04-27 14:48:47.373154: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\nModel: \"densenet121\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 64, 64, 3) 0 \n__________________________________________________________________________________________________\nzero_padding2d_1 (ZeroPadding2D (None, 70, 70, 3) 0 input_1[0][0] \n__________________________________________________________________________________________________\nconv1/conv (Conv2D) (None, 32, 32, 64) 9408 zero_padding2d_1[0][0] \n__________________________________________________________________________________________________\nconv1/bn (BatchNormalization) (None, 32, 32, 64) 256 conv1/conv[0][0] \n__________________________________________________________________________________________________\nconv1/relu (Activation) (None, 32, 32, 64) 0 conv1/bn[0][0] \n__________________________________________________________________________________________________\nzero_padding2d_2 (ZeroPadding2D (None, 34, 34, 64) 0 conv1/relu[0][0] \n__________________________________________________________________________________________________\npool1 (MaxPooling2D) (None, 16, 16, 64) 0 zero_padding2d_2[0][0] \n__________________________________________________________________________________________________\nconv2_block1_0_bn (BatchNormali (None, 16, 16, 64) 256 pool1[0][0] \n__________________________________________________________________________________________________\nconv2_block1_0_relu (Activation (None, 16, 16, 64) 0 conv2_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block1_1_conv (Conv2D) (None, 16, 16, 128) 8192 conv2_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block1_1_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block1_1_relu (Activation (None, 16, 16, 128) 0 conv2_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block1_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv2_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block1_concat (Concatenat (None, 16, 16, 96) 0 pool1[0][0] \n conv2_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block2_0_bn (BatchNormali (None, 16, 16, 96) 384 conv2_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block2_0_relu (Activation (None, 16, 16, 96) 0 conv2_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block2_1_conv (Conv2D) (None, 16, 16, 128) 12288 conv2_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block2_1_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block2_1_relu (Activation (None, 16, 16, 128) 0 conv2_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block2_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv2_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block2_concat (Concatenat (None, 16, 16, 128) 0 conv2_block1_concat[0][0] \n conv2_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block3_0_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block3_0_relu (Activation (None, 16, 16, 128) 0 conv2_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block3_1_conv (Conv2D) (None, 16, 16, 128) 16384 conv2_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block3_1_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block3_1_relu (Activation (None, 16, 16, 128) 0 conv2_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block3_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv2_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block3_concat (Concatenat (None, 16, 16, 160) 0 conv2_block2_concat[0][0] \n conv2_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block4_0_bn (BatchNormali (None, 16, 16, 160) 640 conv2_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block4_0_relu (Activation (None, 16, 16, 160) 0 conv2_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block4_1_conv (Conv2D) (None, 16, 16, 128) 20480 conv2_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block4_1_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block4_1_relu (Activation (None, 16, 16, 128) 0 conv2_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block4_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv2_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block4_concat (Concatenat (None, 16, 16, 192) 0 conv2_block3_concat[0][0] \n conv2_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block5_0_bn (BatchNormali (None, 16, 16, 192) 768 conv2_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block5_0_relu (Activation (None, 16, 16, 192) 0 conv2_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block5_1_conv (Conv2D) (None, 16, 16, 128) 24576 conv2_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block5_1_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block5_1_relu (Activation (None, 16, 16, 128) 0 conv2_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block5_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv2_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block5_concat (Concatenat (None, 16, 16, 224) 0 conv2_block4_concat[0][0] \n conv2_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block6_0_bn (BatchNormali (None, 16, 16, 224) 896 conv2_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv2_block6_0_relu (Activation (None, 16, 16, 224) 0 conv2_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block6_1_conv (Conv2D) (None, 16, 16, 128) 28672 conv2_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block6_1_bn (BatchNormali (None, 16, 16, 128) 512 conv2_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv2_block6_1_relu (Activation (None, 16, 16, 128) 0 conv2_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv2_block6_2_conv (Conv2D) (None, 16, 16, 32) 36864 conv2_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv2_block6_concat (Concatenat (None, 16, 16, 256) 0 conv2_block5_concat[0][0] \n conv2_block6_2_conv[0][0] \n__________________________________________________________________________________________________\npool2_bn (BatchNormalization) (None, 16, 16, 256) 1024 conv2_block6_concat[0][0] \n__________________________________________________________________________________________________\npool2_relu (Activation) (None, 16, 16, 256) 0 pool2_bn[0][0] \n__________________________________________________________________________________________________\npool2_conv (Conv2D) (None, 16, 16, 128) 32768 pool2_relu[0][0] \n__________________________________________________________________________________________________\npool2_pool (AveragePooling2D) (None, 8, 8, 128) 0 pool2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block1_0_bn (BatchNormali (None, 8, 8, 128) 512 pool2_pool[0][0] \n__________________________________________________________________________________________________\nconv3_block1_0_relu (Activation (None, 8, 8, 128) 0 conv3_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block1_1_conv (Conv2D) (None, 8, 8, 128) 16384 conv3_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block1_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block1_1_relu (Activation (None, 8, 8, 128) 0 conv3_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block1_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block1_concat (Concatenat (None, 8, 8, 160) 0 pool2_pool[0][0] \n conv3_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block2_0_bn (BatchNormali (None, 8, 8, 160) 640 conv3_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block2_0_relu (Activation (None, 8, 8, 160) 0 conv3_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block2_1_conv (Conv2D) (None, 8, 8, 128) 20480 conv3_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block2_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block2_1_relu (Activation (None, 8, 8, 128) 0 conv3_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block2_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block2_concat (Concatenat (None, 8, 8, 192) 0 conv3_block1_concat[0][0] \n conv3_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block3_0_bn (BatchNormali (None, 8, 8, 192) 768 conv3_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block3_0_relu (Activation (None, 8, 8, 192) 0 conv3_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block3_1_conv (Conv2D) (None, 8, 8, 128) 24576 conv3_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block3_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block3_1_relu (Activation (None, 8, 8, 128) 0 conv3_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block3_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block3_concat (Concatenat (None, 8, 8, 224) 0 conv3_block2_concat[0][0] \n conv3_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block4_0_bn (BatchNormali (None, 8, 8, 224) 896 conv3_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block4_0_relu (Activation (None, 8, 8, 224) 0 conv3_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block4_1_conv (Conv2D) (None, 8, 8, 128) 28672 conv3_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block4_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block4_1_relu (Activation (None, 8, 8, 128) 0 conv3_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block4_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block4_concat (Concatenat (None, 8, 8, 256) 0 conv3_block3_concat[0][0] \n conv3_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block5_0_bn (BatchNormali (None, 8, 8, 256) 1024 conv3_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block5_0_relu (Activation (None, 8, 8, 256) 0 conv3_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block5_1_conv (Conv2D) (None, 8, 8, 128) 32768 conv3_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block5_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block5_1_relu (Activation (None, 8, 8, 128) 0 conv3_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block5_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block5_concat (Concatenat (None, 8, 8, 288) 0 conv3_block4_concat[0][0] \n conv3_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block6_0_bn (BatchNormali (None, 8, 8, 288) 1152 conv3_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block6_0_relu (Activation (None, 8, 8, 288) 0 conv3_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block6_1_conv (Conv2D) (None, 8, 8, 128) 36864 conv3_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block6_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block6_1_relu (Activation (None, 8, 8, 128) 0 conv3_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block6_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block6_concat (Concatenat (None, 8, 8, 320) 0 conv3_block5_concat[0][0] \n conv3_block6_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block7_0_bn (BatchNormali (None, 8, 8, 320) 1280 conv3_block6_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block7_0_relu (Activation (None, 8, 8, 320) 0 conv3_block7_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block7_1_conv (Conv2D) (None, 8, 8, 128) 40960 conv3_block7_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block7_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block7_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block7_1_relu (Activation (None, 8, 8, 128) 0 conv3_block7_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block7_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block7_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block7_concat (Concatenat (None, 8, 8, 352) 0 conv3_block6_concat[0][0] \n conv3_block7_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block8_0_bn (BatchNormali (None, 8, 8, 352) 1408 conv3_block7_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block8_0_relu (Activation (None, 8, 8, 352) 0 conv3_block8_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block8_1_conv (Conv2D) (None, 8, 8, 128) 45056 conv3_block8_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block8_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block8_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block8_1_relu (Activation (None, 8, 8, 128) 0 conv3_block8_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block8_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block8_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block8_concat (Concatenat (None, 8, 8, 384) 0 conv3_block7_concat[0][0] \n conv3_block8_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block9_0_bn (BatchNormali (None, 8, 8, 384) 1536 conv3_block8_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block9_0_relu (Activation (None, 8, 8, 384) 0 conv3_block9_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block9_1_conv (Conv2D) (None, 8, 8, 128) 49152 conv3_block9_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block9_1_bn (BatchNormali (None, 8, 8, 128) 512 conv3_block9_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block9_1_relu (Activation (None, 8, 8, 128) 0 conv3_block9_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block9_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block9_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block9_concat (Concatenat (None, 8, 8, 416) 0 conv3_block8_concat[0][0] \n conv3_block9_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block10_0_bn (BatchNormal (None, 8, 8, 416) 1664 conv3_block9_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block10_0_relu (Activatio (None, 8, 8, 416) 0 conv3_block10_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block10_1_conv (Conv2D) (None, 8, 8, 128) 53248 conv3_block10_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block10_1_bn (BatchNormal (None, 8, 8, 128) 512 conv3_block10_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block10_1_relu (Activatio (None, 8, 8, 128) 0 conv3_block10_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block10_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block10_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block10_concat (Concatena (None, 8, 8, 448) 0 conv3_block9_concat[0][0] \n conv3_block10_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block11_0_bn (BatchNormal (None, 8, 8, 448) 1792 conv3_block10_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block11_0_relu (Activatio (None, 8, 8, 448) 0 conv3_block11_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block11_1_conv (Conv2D) (None, 8, 8, 128) 57344 conv3_block11_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block11_1_bn (BatchNormal (None, 8, 8, 128) 512 conv3_block11_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block11_1_relu (Activatio (None, 8, 8, 128) 0 conv3_block11_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block11_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block11_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block11_concat (Concatena (None, 8, 8, 480) 0 conv3_block10_concat[0][0] \n conv3_block11_2_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block12_0_bn (BatchNormal (None, 8, 8, 480) 1920 conv3_block11_concat[0][0] \n__________________________________________________________________________________________________\nconv3_block12_0_relu (Activatio (None, 8, 8, 480) 0 conv3_block12_0_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block12_1_conv (Conv2D) (None, 8, 8, 128) 61440 conv3_block12_0_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block12_1_bn (BatchNormal (None, 8, 8, 128) 512 conv3_block12_1_conv[0][0] \n__________________________________________________________________________________________________\nconv3_block12_1_relu (Activatio (None, 8, 8, 128) 0 conv3_block12_1_bn[0][0] \n__________________________________________________________________________________________________\nconv3_block12_2_conv (Conv2D) (None, 8, 8, 32) 36864 conv3_block12_1_relu[0][0] \n__________________________________________________________________________________________________\nconv3_block12_concat (Concatena (None, 8, 8, 512) 0 conv3_block11_concat[0][0] \n conv3_block12_2_conv[0][0] \n__________________________________________________________________________________________________\npool3_bn (BatchNormalization) (None, 8, 8, 512) 2048 conv3_block12_concat[0][0] \n__________________________________________________________________________________________________\npool3_relu (Activation) (None, 8, 8, 512) 0 pool3_bn[0][0] \n__________________________________________________________________________________________________\npool3_conv (Conv2D) (None, 8, 8, 256) 131072 pool3_relu[0][0] \n__________________________________________________________________________________________________\npool3_pool (AveragePooling2D) (None, 4, 4, 256) 0 pool3_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block1_0_bn (BatchNormali (None, 4, 4, 256) 1024 pool3_pool[0][0] \n__________________________________________________________________________________________________\nconv4_block1_0_relu (Activation (None, 4, 4, 256) 0 conv4_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block1_1_conv (Conv2D) (None, 4, 4, 128) 32768 conv4_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block1_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block1_1_relu (Activation (None, 4, 4, 128) 0 conv4_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block1_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block1_concat (Concatenat (None, 4, 4, 288) 0 pool3_pool[0][0] \n conv4_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block2_0_bn (BatchNormali (None, 4, 4, 288) 1152 conv4_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block2_0_relu (Activation (None, 4, 4, 288) 0 conv4_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block2_1_conv (Conv2D) (None, 4, 4, 128) 36864 conv4_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block2_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block2_1_relu (Activation (None, 4, 4, 128) 0 conv4_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block2_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block2_concat (Concatenat (None, 4, 4, 320) 0 conv4_block1_concat[0][0] \n conv4_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block3_0_bn (BatchNormali (None, 4, 4, 320) 1280 conv4_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block3_0_relu (Activation (None, 4, 4, 320) 0 conv4_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block3_1_conv (Conv2D) (None, 4, 4, 128) 40960 conv4_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block3_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block3_1_relu (Activation (None, 4, 4, 128) 0 conv4_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block3_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block3_concat (Concatenat (None, 4, 4, 352) 0 conv4_block2_concat[0][0] \n conv4_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block4_0_bn (BatchNormali (None, 4, 4, 352) 1408 conv4_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block4_0_relu (Activation (None, 4, 4, 352) 0 conv4_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block4_1_conv (Conv2D) (None, 4, 4, 128) 45056 conv4_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block4_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block4_1_relu (Activation (None, 4, 4, 128) 0 conv4_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block4_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block4_concat (Concatenat (None, 4, 4, 384) 0 conv4_block3_concat[0][0] \n conv4_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block5_0_bn (BatchNormali (None, 4, 4, 384) 1536 conv4_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block5_0_relu (Activation (None, 4, 4, 384) 0 conv4_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block5_1_conv (Conv2D) (None, 4, 4, 128) 49152 conv4_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block5_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block5_1_relu (Activation (None, 4, 4, 128) 0 conv4_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block5_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block5_concat (Concatenat (None, 4, 4, 416) 0 conv4_block4_concat[0][0] \n conv4_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block6_0_bn (BatchNormali (None, 4, 4, 416) 1664 conv4_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block6_0_relu (Activation (None, 4, 4, 416) 0 conv4_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block6_1_conv (Conv2D) (None, 4, 4, 128) 53248 conv4_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block6_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block6_1_relu (Activation (None, 4, 4, 128) 0 conv4_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block6_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block6_concat (Concatenat (None, 4, 4, 448) 0 conv4_block5_concat[0][0] \n conv4_block6_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block7_0_bn (BatchNormali (None, 4, 4, 448) 1792 conv4_block6_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block7_0_relu (Activation (None, 4, 4, 448) 0 conv4_block7_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block7_1_conv (Conv2D) (None, 4, 4, 128) 57344 conv4_block7_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block7_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block7_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block7_1_relu (Activation (None, 4, 4, 128) 0 conv4_block7_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block7_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block7_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block7_concat (Concatenat (None, 4, 4, 480) 0 conv4_block6_concat[0][0] \n conv4_block7_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block8_0_bn (BatchNormali (None, 4, 4, 480) 1920 conv4_block7_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block8_0_relu (Activation (None, 4, 4, 480) 0 conv4_block8_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block8_1_conv (Conv2D) (None, 4, 4, 128) 61440 conv4_block8_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block8_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block8_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block8_1_relu (Activation (None, 4, 4, 128) 0 conv4_block8_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block8_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block8_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block8_concat (Concatenat (None, 4, 4, 512) 0 conv4_block7_concat[0][0] \n conv4_block8_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block9_0_bn (BatchNormali (None, 4, 4, 512) 2048 conv4_block8_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block9_0_relu (Activation (None, 4, 4, 512) 0 conv4_block9_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block9_1_conv (Conv2D) (None, 4, 4, 128) 65536 conv4_block9_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block9_1_bn (BatchNormali (None, 4, 4, 128) 512 conv4_block9_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block9_1_relu (Activation (None, 4, 4, 128) 0 conv4_block9_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block9_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block9_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block9_concat (Concatenat (None, 4, 4, 544) 0 conv4_block8_concat[0][0] \n conv4_block9_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block10_0_bn (BatchNormal (None, 4, 4, 544) 2176 conv4_block9_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block10_0_relu (Activatio (None, 4, 4, 544) 0 conv4_block10_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block10_1_conv (Conv2D) (None, 4, 4, 128) 69632 conv4_block10_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block10_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block10_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block10_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block10_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block10_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block10_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block10_concat (Concatena (None, 4, 4, 576) 0 conv4_block9_concat[0][0] \n conv4_block10_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block11_0_bn (BatchNormal (None, 4, 4, 576) 2304 conv4_block10_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block11_0_relu (Activatio (None, 4, 4, 576) 0 conv4_block11_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block11_1_conv (Conv2D) (None, 4, 4, 128) 73728 conv4_block11_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block11_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block11_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block11_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block11_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block11_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block11_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block11_concat (Concatena (None, 4, 4, 608) 0 conv4_block10_concat[0][0] \n conv4_block11_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block12_0_bn (BatchNormal (None, 4, 4, 608) 2432 conv4_block11_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block12_0_relu (Activatio (None, 4, 4, 608) 0 conv4_block12_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block12_1_conv (Conv2D) (None, 4, 4, 128) 77824 conv4_block12_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block12_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block12_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block12_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block12_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block12_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block12_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block12_concat (Concatena (None, 4, 4, 640) 0 conv4_block11_concat[0][0] \n conv4_block12_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block13_0_bn (BatchNormal (None, 4, 4, 640) 2560 conv4_block12_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block13_0_relu (Activatio (None, 4, 4, 640) 0 conv4_block13_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block13_1_conv (Conv2D) (None, 4, 4, 128) 81920 conv4_block13_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block13_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block13_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block13_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block13_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block13_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block13_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block13_concat (Concatena (None, 4, 4, 672) 0 conv4_block12_concat[0][0] \n conv4_block13_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block14_0_bn (BatchNormal (None, 4, 4, 672) 2688 conv4_block13_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block14_0_relu (Activatio (None, 4, 4, 672) 0 conv4_block14_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block14_1_conv (Conv2D) (None, 4, 4, 128) 86016 conv4_block14_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block14_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block14_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block14_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block14_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block14_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block14_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block14_concat (Concatena (None, 4, 4, 704) 0 conv4_block13_concat[0][0] \n conv4_block14_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block15_0_bn (BatchNormal (None, 4, 4, 704) 2816 conv4_block14_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block15_0_relu (Activatio (None, 4, 4, 704) 0 conv4_block15_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block15_1_conv (Conv2D) (None, 4, 4, 128) 90112 conv4_block15_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block15_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block15_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block15_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block15_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block15_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block15_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block15_concat (Concatena (None, 4, 4, 736) 0 conv4_block14_concat[0][0] \n conv4_block15_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block16_0_bn (BatchNormal (None, 4, 4, 736) 2944 conv4_block15_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block16_0_relu (Activatio (None, 4, 4, 736) 0 conv4_block16_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block16_1_conv (Conv2D) (None, 4, 4, 128) 94208 conv4_block16_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block16_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block16_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block16_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block16_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block16_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block16_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block16_concat (Concatena (None, 4, 4, 768) 0 conv4_block15_concat[0][0] \n conv4_block16_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block17_0_bn (BatchNormal (None, 4, 4, 768) 3072 conv4_block16_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block17_0_relu (Activatio (None, 4, 4, 768) 0 conv4_block17_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block17_1_conv (Conv2D) (None, 4, 4, 128) 98304 conv4_block17_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block17_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block17_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block17_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block17_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block17_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block17_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block17_concat (Concatena (None, 4, 4, 800) 0 conv4_block16_concat[0][0] \n conv4_block17_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block18_0_bn (BatchNormal (None, 4, 4, 800) 3200 conv4_block17_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block18_0_relu (Activatio (None, 4, 4, 800) 0 conv4_block18_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block18_1_conv (Conv2D) (None, 4, 4, 128) 102400 conv4_block18_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block18_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block18_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block18_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block18_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block18_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block18_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block18_concat (Concatena (None, 4, 4, 832) 0 conv4_block17_concat[0][0] \n conv4_block18_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block19_0_bn (BatchNormal (None, 4, 4, 832) 3328 conv4_block18_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block19_0_relu (Activatio (None, 4, 4, 832) 0 conv4_block19_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block19_1_conv (Conv2D) (None, 4, 4, 128) 106496 conv4_block19_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block19_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block19_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block19_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block19_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block19_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block19_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block19_concat (Concatena (None, 4, 4, 864) 0 conv4_block18_concat[0][0] \n conv4_block19_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block20_0_bn (BatchNormal (None, 4, 4, 864) 3456 conv4_block19_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block20_0_relu (Activatio (None, 4, 4, 864) 0 conv4_block20_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block20_1_conv (Conv2D) (None, 4, 4, 128) 110592 conv4_block20_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block20_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block20_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block20_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block20_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block20_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block20_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block20_concat (Concatena (None, 4, 4, 896) 0 conv4_block19_concat[0][0] \n conv4_block20_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block21_0_bn (BatchNormal (None, 4, 4, 896) 3584 conv4_block20_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block21_0_relu (Activatio (None, 4, 4, 896) 0 conv4_block21_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block21_1_conv (Conv2D) (None, 4, 4, 128) 114688 conv4_block21_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block21_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block21_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block21_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block21_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block21_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block21_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block21_concat (Concatena (None, 4, 4, 928) 0 conv4_block20_concat[0][0] \n conv4_block21_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block22_0_bn (BatchNormal (None, 4, 4, 928) 3712 conv4_block21_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block22_0_relu (Activatio (None, 4, 4, 928) 0 conv4_block22_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block22_1_conv (Conv2D) (None, 4, 4, 128) 118784 conv4_block22_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block22_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block22_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block22_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block22_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block22_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block22_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block22_concat (Concatena (None, 4, 4, 960) 0 conv4_block21_concat[0][0] \n conv4_block22_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block23_0_bn (BatchNormal (None, 4, 4, 960) 3840 conv4_block22_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block23_0_relu (Activatio (None, 4, 4, 960) 0 conv4_block23_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block23_1_conv (Conv2D) (None, 4, 4, 128) 122880 conv4_block23_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block23_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block23_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block23_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block23_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block23_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block23_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block23_concat (Concatena (None, 4, 4, 992) 0 conv4_block22_concat[0][0] \n conv4_block23_2_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block24_0_bn (BatchNormal (None, 4, 4, 992) 3968 conv4_block23_concat[0][0] \n__________________________________________________________________________________________________\nconv4_block24_0_relu (Activatio (None, 4, 4, 992) 0 conv4_block24_0_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block24_1_conv (Conv2D) (None, 4, 4, 128) 126976 conv4_block24_0_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block24_1_bn (BatchNormal (None, 4, 4, 128) 512 conv4_block24_1_conv[0][0] \n__________________________________________________________________________________________________\nconv4_block24_1_relu (Activatio (None, 4, 4, 128) 0 conv4_block24_1_bn[0][0] \n__________________________________________________________________________________________________\nconv4_block24_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv4_block24_1_relu[0][0] \n__________________________________________________________________________________________________\nconv4_block24_concat (Concatena (None, 4, 4, 1024) 0 conv4_block23_concat[0][0] \n conv4_block24_2_conv[0][0] \n__________________________________________________________________________________________________\npool4_bn (BatchNormalization) (None, 4, 4, 1024) 4096 conv4_block24_concat[0][0] \n__________________________________________________________________________________________________\npool4_relu (Activation) (None, 4, 4, 1024) 0 pool4_bn[0][0] \n__________________________________________________________________________________________________\npool4_conv (Conv2D) (None, 4, 4, 512) 524288 pool4_relu[0][0] \n__________________________________________________________________________________________________\npool4_pool (AveragePooling2D) (None, 2, 2, 512) 0 pool4_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block1_0_bn (BatchNormali (None, 2, 2, 512) 2048 pool4_pool[0][0] \n__________________________________________________________________________________________________\nconv5_block1_0_relu (Activation (None, 2, 2, 512) 0 conv5_block1_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block1_1_conv (Conv2D) (None, 2, 2, 128) 65536 conv5_block1_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block1_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block1_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block1_1_relu (Activation (None, 2, 2, 128) 0 conv5_block1_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block1_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block1_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block1_concat (Concatenat (None, 2, 2, 544) 0 pool4_pool[0][0] \n conv5_block1_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block2_0_bn (BatchNormali (None, 2, 2, 544) 2176 conv5_block1_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block2_0_relu (Activation (None, 2, 2, 544) 0 conv5_block2_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block2_1_conv (Conv2D) (None, 2, 2, 128) 69632 conv5_block2_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block2_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block2_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block2_1_relu (Activation (None, 2, 2, 128) 0 conv5_block2_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block2_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block2_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block2_concat (Concatenat (None, 2, 2, 576) 0 conv5_block1_concat[0][0] \n conv5_block2_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block3_0_bn (BatchNormali (None, 2, 2, 576) 2304 conv5_block2_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block3_0_relu (Activation (None, 2, 2, 576) 0 conv5_block3_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block3_1_conv (Conv2D) (None, 2, 2, 128) 73728 conv5_block3_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block3_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block3_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block3_1_relu (Activation (None, 2, 2, 128) 0 conv5_block3_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block3_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block3_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block3_concat (Concatenat (None, 2, 2, 608) 0 conv5_block2_concat[0][0] \n conv5_block3_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block4_0_bn (BatchNormali (None, 2, 2, 608) 2432 conv5_block3_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block4_0_relu (Activation (None, 2, 2, 608) 0 conv5_block4_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block4_1_conv (Conv2D) (None, 2, 2, 128) 77824 conv5_block4_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block4_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block4_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block4_1_relu (Activation (None, 2, 2, 128) 0 conv5_block4_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block4_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block4_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block4_concat (Concatenat (None, 2, 2, 640) 0 conv5_block3_concat[0][0] \n conv5_block4_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block5_0_bn (BatchNormali (None, 2, 2, 640) 2560 conv5_block4_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block5_0_relu (Activation (None, 2, 2, 640) 0 conv5_block5_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block5_1_conv (Conv2D) (None, 2, 2, 128) 81920 conv5_block5_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block5_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block5_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block5_1_relu (Activation (None, 2, 2, 128) 0 conv5_block5_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block5_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block5_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block5_concat (Concatenat (None, 2, 2, 672) 0 conv5_block4_concat[0][0] \n conv5_block5_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block6_0_bn (BatchNormali (None, 2, 2, 672) 2688 conv5_block5_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block6_0_relu (Activation (None, 2, 2, 672) 0 conv5_block6_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block6_1_conv (Conv2D) (None, 2, 2, 128) 86016 conv5_block6_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block6_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block6_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block6_1_relu (Activation (None, 2, 2, 128) 0 conv5_block6_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block6_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block6_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block6_concat (Concatenat (None, 2, 2, 704) 0 conv5_block5_concat[0][0] \n conv5_block6_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block7_0_bn (BatchNormali (None, 2, 2, 704) 2816 conv5_block6_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block7_0_relu (Activation (None, 2, 2, 704) 0 conv5_block7_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block7_1_conv (Conv2D) (None, 2, 2, 128) 90112 conv5_block7_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block7_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block7_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block7_1_relu (Activation (None, 2, 2, 128) 0 conv5_block7_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block7_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block7_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block7_concat (Concatenat (None, 2, 2, 736) 0 conv5_block6_concat[0][0] \n conv5_block7_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block8_0_bn (BatchNormali (None, 2, 2, 736) 2944 conv5_block7_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block8_0_relu (Activation (None, 2, 2, 736) 0 conv5_block8_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block8_1_conv (Conv2D) (None, 2, 2, 128) 94208 conv5_block8_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block8_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block8_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block8_1_relu (Activation (None, 2, 2, 128) 0 conv5_block8_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block8_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block8_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block8_concat (Concatenat (None, 2, 2, 768) 0 conv5_block7_concat[0][0] \n conv5_block8_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block9_0_bn (BatchNormali (None, 2, 2, 768) 3072 conv5_block8_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block9_0_relu (Activation (None, 2, 2, 768) 0 conv5_block9_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block9_1_conv (Conv2D) (None, 2, 2, 128) 98304 conv5_block9_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block9_1_bn (BatchNormali (None, 2, 2, 128) 512 conv5_block9_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block9_1_relu (Activation (None, 2, 2, 128) 0 conv5_block9_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block9_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block9_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block9_concat (Concatenat (None, 2, 2, 800) 0 conv5_block8_concat[0][0] \n conv5_block9_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block10_0_bn (BatchNormal (None, 2, 2, 800) 3200 conv5_block9_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block10_0_relu (Activatio (None, 2, 2, 800) 0 conv5_block10_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block10_1_conv (Conv2D) (None, 2, 2, 128) 102400 conv5_block10_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block10_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block10_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block10_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block10_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block10_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block10_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block10_concat (Concatena (None, 2, 2, 832) 0 conv5_block9_concat[0][0] \n conv5_block10_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block11_0_bn (BatchNormal (None, 2, 2, 832) 3328 conv5_block10_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block11_0_relu (Activatio (None, 2, 2, 832) 0 conv5_block11_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block11_1_conv (Conv2D) (None, 2, 2, 128) 106496 conv5_block11_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block11_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block11_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block11_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block11_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block11_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block11_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block11_concat (Concatena (None, 2, 2, 864) 0 conv5_block10_concat[0][0] \n conv5_block11_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block12_0_bn (BatchNormal (None, 2, 2, 864) 3456 conv5_block11_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block12_0_relu (Activatio (None, 2, 2, 864) 0 conv5_block12_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block12_1_conv (Conv2D) (None, 2, 2, 128) 110592 conv5_block12_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block12_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block12_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block12_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block12_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block12_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block12_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block12_concat (Concatena (None, 2, 2, 896) 0 conv5_block11_concat[0][0] \n conv5_block12_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block13_0_bn (BatchNormal (None, 2, 2, 896) 3584 conv5_block12_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block13_0_relu (Activatio (None, 2, 2, 896) 0 conv5_block13_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block13_1_conv (Conv2D) (None, 2, 2, 128) 114688 conv5_block13_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block13_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block13_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block13_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block13_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block13_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block13_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block13_concat (Concatena (None, 2, 2, 928) 0 conv5_block12_concat[0][0] \n conv5_block13_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block14_0_bn (BatchNormal (None, 2, 2, 928) 3712 conv5_block13_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block14_0_relu (Activatio (None, 2, 2, 928) 0 conv5_block14_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block14_1_conv (Conv2D) (None, 2, 2, 128) 118784 conv5_block14_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block14_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block14_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block14_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block14_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block14_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block14_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block14_concat (Concatena (None, 2, 2, 960) 0 conv5_block13_concat[0][0] \n conv5_block14_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block15_0_bn (BatchNormal (None, 2, 2, 960) 3840 conv5_block14_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block15_0_relu (Activatio (None, 2, 2, 960) 0 conv5_block15_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block15_1_conv (Conv2D) (None, 2, 2, 128) 122880 conv5_block15_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block15_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block15_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block15_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block15_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block15_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block15_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block15_concat (Concatena (None, 2, 2, 992) 0 conv5_block14_concat[0][0] \n conv5_block15_2_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block16_0_bn (BatchNormal (None, 2, 2, 992) 3968 conv5_block15_concat[0][0] \n__________________________________________________________________________________________________\nconv5_block16_0_relu (Activatio (None, 2, 2, 992) 0 conv5_block16_0_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block16_1_conv (Conv2D) (None, 2, 2, 128) 126976 conv5_block16_0_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block16_1_bn (BatchNormal (None, 2, 2, 128) 512 conv5_block16_1_conv[0][0] \n__________________________________________________________________________________________________\nconv5_block16_1_relu (Activatio (None, 2, 2, 128) 0 conv5_block16_1_bn[0][0] \n__________________________________________________________________________________________________\nconv5_block16_2_conv (Conv2D) (None, 2, 2, 32) 36864 conv5_block16_1_relu[0][0] \n__________________________________________________________________________________________________\nconv5_block16_concat (Concatena (None, 2, 2, 1024) 0 conv5_block15_concat[0][0] \n conv5_block16_2_conv[0][0] \n__________________________________________________________________________________________________\nbn (BatchNormalization) (None, 2, 2, 1024) 4096 conv5_block16_concat[0][0] \n__________________________________________________________________________________________________\nrelu (Activation) (None, 2, 2, 1024) 0 bn[0][0] \n__________________________________________________________________________________________________\navg_pool (GlobalAveragePooling2 (None, 1024) 0 relu[0][0] \n__________________________________________________________________________________________________\nfc1000 (Dense) (None, 2) 2050 avg_pool[0][0] \n==================================================================================================\nTotal params: 7,039,554\nTrainable params: 6,955,906\nNon-trainable params: 83,648\n__________________________________________________________________________________________________\n364\nFound 66329 images belonging to 2 classes.\nFound 20700 images belonging to 2 classes.\nEpoch 1/100\n2020-04-27 14:50:24.033037: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 14:50:25.623883: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n200/200 [==============================] - 123s 616ms/step - loss: 0.3008 - accuracy: 0.8693 - val_loss: 0.1130 - val_accuracy: 0.5673\nEpoch 2/100\n200/200 [==============================] - 72s 360ms/step - loss: 0.1505 - accuracy: 0.9398 - val_loss: 0.0087 - val_accuracy: 0.6635\nEpoch 3/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.1169 - accuracy: 0.9544 - val_loss: 0.0650 - val_accuracy: 0.9459\nEpoch 4/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.1003 - accuracy: 0.9606 - val_loss: 0.1035 - val_accuracy: 0.9072\nEpoch 5/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0870 - accuracy: 0.9668 - val_loss: 0.0406 - val_accuracy: 0.8699\nEpoch 6/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0749 - accuracy: 0.9700 - val_loss: 0.0109 - val_accuracy: 0.8185\nEpoch 7/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0686 - accuracy: 0.9748 - val_loss: 0.1167 - val_accuracy: 0.9579\nEpoch 8/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0603 - accuracy: 0.9773 - val_loss: 0.1271 - val_accuracy: 0.9027\nEpoch 9/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0549 - accuracy: 0.9792 - val_loss: 0.0976 - val_accuracy: 0.9682\nEpoch 10/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0548 - accuracy: 0.9785 - val_loss: 0.3307 - val_accuracy: 0.9430\nEpoch 11/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0492 - accuracy: 0.9819 - val_loss: 0.0163 - val_accuracy: 0.9490\nEpoch 12/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0426 - accuracy: 0.9831 - val_loss: 0.0038 - val_accuracy: 0.9443\nEpoch 13/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0376 - accuracy: 0.9862 - val_loss: 0.0908 - val_accuracy: 0.9688\nEpoch 14/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0324 - accuracy: 0.9877 - val_loss: 0.0142 - val_accuracy: 0.9412\nEpoch 15/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0395 - accuracy: 0.9852 - val_loss: 0.0045 - val_accuracy: 0.8204\nEpoch 16/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0362 - accuracy: 0.9861 - val_loss: 0.1994 - val_accuracy: 0.9662\nEpoch 17/100\n200/200 [==============================] - 71s 353ms/step - loss: 0.0286 - accuracy: 0.9891 - val_loss: 0.0316 - val_accuracy: 0.9544\nEpoch 18/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0294 - accuracy: 0.9887 - val_loss: 0.1043 - val_accuracy: 0.9709\nEpoch 19/100\n200/200 [==============================] - 70s 352ms/step - loss: 0.0305 - accuracy: 0.9894 - val_loss: 0.0639 - val_accuracy: 0.9563\nEpoch 20/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0265 - accuracy: 0.9900 - val_loss: 0.1852 - val_accuracy: 0.9707\nEpoch 21/100\n200/200 [==============================] - 71s 353ms/step - loss: 0.0246 - accuracy: 0.9905 - val_loss: 0.0197 - val_accuracy: 0.9150\nEpoch 22/100\n200/200 [==============================] - 71s 354ms/step - loss: 0.0228 - accuracy: 0.9920 - val_loss: 0.0234 - val_accuracy: 0.9650\nEpoch 23/100\n200/200 [==============================] - 70s 352ms/step - loss: 0.0214 - accuracy: 0.9924 - val_loss: 0.4246 - val_accuracy: 0.9611\nEpoch 24/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0170 - accuracy: 0.9938 - val_loss: 0.0757 - val_accuracy: 0.9730\nEpoch 25/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0185 - accuracy: 0.9930 - val_loss: 0.1515 - val_accuracy: 0.9579\nEpoch 26/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0209 - accuracy: 0.9917 - val_loss: 0.1780 - val_accuracy: 0.9713\nEpoch 27/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0188 - accuracy: 0.9932 - val_loss: 0.0637 - val_accuracy: 0.9804\nEpoch 28/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0194 - accuracy: 0.9930 - val_loss: 0.1603 - val_accuracy: 0.9684\nEpoch 29/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0164 - accuracy: 0.9943 - val_loss: 0.0130 - val_accuracy: 0.9689\nEpoch 30/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0171 - accuracy: 0.9937 - val_loss: 0.0946 - val_accuracy: 0.9751\nEpoch 31/100\n200/200 [==============================] - 72s 358ms/step - loss: 0.0154 - accuracy: 0.9948 - val_loss: 0.0347 - val_accuracy: 0.9640\nEpoch 32/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0134 - accuracy: 0.9952 - val_loss: 0.0844 - val_accuracy: 0.9792\nEpoch 33/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0147 - accuracy: 0.9945 - val_loss: 0.1057 - val_accuracy: 0.9750\nEpoch 34/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0147 - accuracy: 0.9947 - val_loss: 0.0265 - val_accuracy: 0.8813\nEpoch 35/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0140 - accuracy: 0.9949 - val_loss: 0.0904 - val_accuracy: 0.9610\nEpoch 36/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0161 - accuracy: 0.9942 - val_loss: 7.0816e-04 - val_accuracy: 0.9277\nEpoch 37/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0137 - accuracy: 0.9953 - val_loss: 0.0100 - val_accuracy: 0.9330\nEpoch 38/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0117 - accuracy: 0.9955 - val_loss: 0.2024 - val_accuracy: 0.9761\nEpoch 39/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0140 - accuracy: 0.9949 - val_loss: 0.0241 - val_accuracy: 0.9663\nEpoch 40/100\n200/200 [==============================] - 72s 358ms/step - loss: 0.0094 - accuracy: 0.9965 - val_loss: 0.0617 - val_accuracy: 0.9787\nEpoch 41/100\n200/200 [==============================] - 72s 358ms/step - loss: 0.0123 - accuracy: 0.9957 - val_loss: 0.0147 - val_accuracy: 0.9782\nEpoch 42/100\n200/200 [==============================] - 71s 357ms/step - loss: 0.0069 - accuracy: 0.9971 - val_loss: 0.1859 - val_accuracy: 0.9743\nEpoch 43/100\n200/200 [==============================] - 72s 358ms/step - loss: 0.0132 - accuracy: 0.9953 - val_loss: 0.0178 - val_accuracy: 0.9668\nEpoch 44/100\n200/200 [==============================] - 72s 358ms/step - loss: 0.0092 - accuracy: 0.9962 - val_loss: 0.0704 - val_accuracy: 0.9798\nEpoch 45/100\n200/200 [==============================] - 71s 355ms/step - loss: 0.0086 - accuracy: 0.9967 - val_loss: 0.1219 - val_accuracy: 0.9779\nEpoch 46/100\n200/200 [==============================] - 71s 356ms/step - loss: 0.0096 - accuracy: 0.9964 - val_loss: 4.5746e-04 - val_accuracy: 0.9782\nEpoch 47/100\n200/200 [==============================] - 71s 353ms/step - loss: 0.0107 - accuracy: 0.9961 - val_loss: 0.1585 - val_accuracy: 0.9769\n"
],
[
"!python3 '/content/gdrive/My Drive/Deep Learning Groupwork/Project/Code - Kabir/Code/FDFtNet/network/DenseNet.py'",
"2020-04-27 12:21:31.437180: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\nUsing TensorFlow backend.\n"
],
[
"",
"_____no_output_____"
],
[
"!python3 test.py -test_dir='/content/Data/Test' -model='/content/gdrive/My Drive/Deep Learning Groupwork/Project/Code - Kabir/Code/FDFtNet/models/weights-densenet-million'",
"2020-04-27 16:00:08.344765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\nUsing TensorFlow backend.\nFound 16565 images belonging to 2 classes.\n2020-04-27 16:00:12.409852: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n2020-04-27 16:00:12.427738: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.428624: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \npciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\ncoreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n2020-04-27 16:00:12.428668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 16:00:12.431017: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 16:00:12.445052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-04-27 16:00:12.445523: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-04-27 16:00:12.448136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-04-27 16:00:12.449334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-04-27 16:00:12.454486: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-04-27 16:00:12.454637: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.455627: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.456508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-04-27 16:00:12.463541: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2300000000 Hz\n2020-04-27 16:00:12.463776: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e95100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n2020-04-27 16:00:12.463842: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n2020-04-27 16:00:12.560289: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.561573: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e94d80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n2020-04-27 16:00:12.561612: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n2020-04-27 16:00:12.561989: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.562963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \npciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\ncoreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n2020-04-27 16:00:12.563039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 16:00:12.563105: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 16:00:12.563150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-04-27 16:00:12.563190: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-04-27 16:00:12.563234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-04-27 16:00:12.563273: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-04-27 16:00:12.563312: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-04-27 16:00:12.563467: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.564447: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:12.565354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-04-27 16:00:12.565448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 16:00:13.142155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n2020-04-27 16:00:13.142218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 \n2020-04-27 16:00:13.142237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N \n2020-04-27 16:00:13.142595: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:13.143592: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:00:13.144424: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n2020-04-27 16:00:13.144483: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n2020-04-27 16:01:37.065092: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 16:01:37.349524: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n130/130 [==============================] - 9s 67ms/step\n[[1.000 0.000]\n [1.000 0.000]\n [1.000 0.000]\n ...\n [0.000 1.000]\n [0.000 1.000]\n [0.000 1.000]]\n100% 130/130 [00:21<00:00, 6.01it/s]\n[0 0 0 ... 1 1 1]\n[0 0 0 ... 1 1 1]\n precision recall f1-score support\n\n 0 0.96 0.99 0.98 7986\n 1 1.00 0.96 0.98 8579\n\n accuracy 0.98 16565\n macro avg 0.98 0.98 0.98 16565\nweighted avg 0.98 0.98 0.98 16565\n\n[[7945 41]\n [ 352 8227]]\nAUROC: 0.998648\n0.02910561041818762\ntest_acc: 0.9762752792031392\n"
],
[
"!python3 fdft.py -model='/content/gdrive/My Drive/Deep Learning Groupwork/Project/Code - Kabir/Code/FDFtNet/models/weights-densenet-million' -ft_dir='/content/Data/FineTune' -val_dir='/content/Data/Validation' -network='denseNet' -test_dir='/content/Data/Test'",
"2020-04-27 16:06:04.888606: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\nUsing TensorFlow backend.\nFound 1932 images belonging to 2 classes.\nFound 20700 images belonging to 2 classes.\n2020-04-27 16:06:08.320746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n2020-04-27 16:06:08.335493: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.336450: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \npciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\ncoreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n2020-04-27 16:06:08.336500: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 16:06:08.338226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 16:06:08.340058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-04-27 16:06:08.340396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-04-27 16:06:08.342564: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-04-27 16:06:08.343835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-04-27 16:06:08.347941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-04-27 16:06:08.348104: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.349097: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.350000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-04-27 16:06:08.356486: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2300000000 Hz\n2020-04-27 16:06:08.356735: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2124d80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n2020-04-27 16:06:08.356774: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n2020-04-27 16:06:08.450403: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.451881: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2124bc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n2020-04-27 16:06:08.451932: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n2020-04-27 16:06:08.452229: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.453181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \npciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\ncoreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n2020-04-27 16:06:08.453248: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 16:06:08.453311: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 16:06:08.453371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n2020-04-27 16:06:08.453433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n2020-04-27 16:06:08.453473: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n2020-04-27 16:06:08.453517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n2020-04-27 16:06:08.453580: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-04-27 16:06:08.453730: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.454674: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:08.455520: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n2020-04-27 16:06:08.455587: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n2020-04-27 16:06:09.033298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n2020-04-27 16:06:09.033361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 \n2020-04-27 16:06:09.033387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N \n2020-04-27 16:06:09.033672: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:09.034711: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2020-04-27 16:06:09.035641: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n2020-04-27 16:06:09.035714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n<tensorflow.python.keras.engine.training.Model object at 0x7f01f2a77080>\n(1, 1, 32, 4)\n(1, 1, 64, 8)\n(1, 1, 96, 12)\nModel: \"model_3\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 64, 64, 3) 0 \n__________________________________________________________________________________________________\ndensenet121 (Model) (None, 2, 2, 1024) 7037504 input_1[0][0] \n__________________________________________________________________________________________________\nmodel_1 (Model) (None, 576) 1914368 densenet121[1][0] \n__________________________________________________________________________________________________\nmodel_2 (Model) (None, 576) 85710 input_1[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 576) 0 model_1[1][0] \n model_2[1][0] \n__________________________________________________________________________________________________\ndense_1 (Dense) (None, 2) 1154 add_4[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 2) 0 dense_1[0][0] \n==================================================================================================\nTotal params: 9,038,736\nTrainable params: 8,942,160\nNon-trainable params: 96,576\n__________________________________________________________________________________________________\nEpoch 1/100\n2020-04-27 16:08:32.414581: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n2020-04-27 16:08:32.834138: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n200/200 [==============================] - 172s 862ms/step - loss: 0.2671 - acc: 0.9016 - val_loss: 0.0972 - val_acc: 0.9267\nEpoch 2/100\n200/200 [==============================] - 105s 527ms/step - loss: 0.1519 - acc: 0.9540 - val_loss: 0.1757 - val_acc: 0.8853\nEpoch 3/100\n200/200 [==============================] - 104s 522ms/step - loss: 0.1108 - acc: 0.9697 - val_loss: 0.2934 - val_acc: 0.9326\nEpoch 4/100\n200/200 [==============================] - 106s 529ms/step - loss: 0.0914 - acc: 0.9771 - val_loss: 0.6383 - val_acc: 0.8884\nEpoch 5/100\n200/200 [==============================] - 105s 524ms/step - loss: 0.0705 - acc: 0.9835 - val_loss: 0.5156 - val_acc: 0.8952\nEpoch 6/100\n200/200 [==============================] - 106s 530ms/step - loss: 0.1001 - acc: 0.9742 - val_loss: 0.3009 - val_acc: 0.9093\nEpoch 7/100\n200/200 [==============================] - 105s 527ms/step - loss: 0.0895 - acc: 0.9767 - val_loss: 0.9120 - val_acc: 0.9159\nEpoch 8/100\n200/200 [==============================] - 107s 535ms/step - loss: 0.0614 - acc: 0.9854 - val_loss: 0.4669 - val_acc: 0.9076\nEpoch 9/100\n200/200 [==============================] - 106s 528ms/step - loss: 0.0512 - acc: 0.9885 - val_loss: 0.4546 - val_acc: 0.9300\nEpoch 10/100\n200/200 [==============================] - 107s 533ms/step - loss: 0.0389 - acc: 0.9916 - val_loss: 0.8170 - val_acc: 0.8911\nEpoch 11/100\n200/200 [==============================] - 106s 529ms/step - loss: 0.0474 - acc: 0.9886 - val_loss: 0.6939 - val_acc: 0.9275\nEpoch 12/100\n200/200 [==============================] - 107s 537ms/step - loss: 0.0333 - acc: 0.9929 - val_loss: 0.2092 - val_acc: 0.9292\nEpoch 13/100\n200/200 [==============================] - 106s 529ms/step - loss: 0.0478 - acc: 0.9873 - val_loss: 0.4670 - val_acc: 0.9299\nFound 16565 images belonging to 2 classes.\n130/130 [==============================] - 16s 123ms/step\n[[1.000 0.000]\n [1.000 0.000]\n [1.000 0.000]\n ...\n [0.000 1.000]\n [0.000 1.000]\n [0.000 1.000]]\n100% 130/130 [00:23<00:00, 5.50it/s]\n[0 0 0 ... 1 1 1]\n[0 0 0 ... 1 1 1]\n precision recall f1-score support\n\n 0 0.91 0.96 0.94 7986\n 1 0.96 0.92 0.94 8579\n\n accuracy 0.94 16565\n macro avg 0.94 0.94 0.94 16565\nweighted avg 0.94 0.94 0.94 16565\n\n[[7666 320]\n [ 724 7855]]\nAUROC: 0.984421\n0.13246818285069875\ntest_acc: 0.9369755508602475\n"
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cb1afaf9e13333dae5f2559d84bc62ef769f4104 | 13,638 | ipynb | Jupyter Notebook | Datasets/Terrain/srtm_landforms.ipynb | dmendelo/earthengine-py-notebooks | 515567fa2702b436daf449fff02f5c690003cf94 | [
"MIT"
] | 2 | 2020-02-05T02:36:18.000Z | 2021-03-23T11:02:39.000Z | Datasets/Terrain/srtm_landforms.ipynb | Fernigithub/earthengine-py-notebooks | 32689dc5da4a86e46ea30d8b22241866c1f7cf61 | [
"MIT"
] | null | null | null | Datasets/Terrain/srtm_landforms.ipynb | Fernigithub/earthengine-py-notebooks | 32689dc5da4a86e46ea30d8b22241866c1f7cf61 | [
"MIT"
] | 3 | 2021-01-06T17:33:08.000Z | 2022-02-18T02:14:18.000Z | 78.83237 | 8,248 | 0.834433 | [
[
[
"<table class=\"ee-notebook-buttons\" align=\"left\">\n <td><a target=\"_blank\" href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Terrain/srtm_landforms.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n <td><a target=\"_blank\" href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Datasets/Terrain/srtm_landforms.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n <td><a target=\"_blank\" href=\"https://mybinder.org/v2/gh/giswqs/earthengine-py-notebooks/master?filepath=Datasets/Terrain/srtm_landforms.ipynb\"><img width=58px src=\"https://mybinder.org/static/images/logo_social.png\" />Run in binder</a></td>\n <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Datasets/Terrain/srtm_landforms.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n</table>",
"_____no_output_____"
],
[
"## Install Earth Engine API\nInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The **geehydro** Python package builds on the [folium](https://github.com/python-visualization/folium) package and implements several methods for displaying Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, `Map.centerObject()`, and `Map.setOptions()`.\nThe magic command `%%capture` can be used to hide output from a specific cell. Uncomment these lines if you are running this notebook for the first time.",
"_____no_output_____"
]
],
[
[
"# %%capture\n# !pip install earthengine-api\n# !pip install geehydro",
"_____no_output_____"
]
],
[
[
"Import libraries",
"_____no_output_____"
]
],
[
[
"import ee\nimport folium\nimport geehydro",
"_____no_output_____"
]
],
[
[
"Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. Uncomment the line `ee.Authenticate()` \nif you are running this notebook for the first time or if you are getting an authentication error. ",
"_____no_output_____"
]
],
[
[
"# ee.Authenticate()\nee.Initialize()",
"_____no_output_____"
]
],
[
[
"## Create an interactive map \nThis step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. \nThe optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `ESRI`.",
"_____no_output_____"
]
],
[
[
"Map = folium.Map(location=[40, -100], zoom_start=4)\nMap.setOptions('HYBRID')",
"_____no_output_____"
]
],
[
[
"## Add Earth Engine Python script ",
"_____no_output_____"
]
],
[
[
"dataset = ee.Image('CSP/ERGo/1_0/Global/SRTM_landforms')\nlandforms = dataset.select('constant')\nlandformsVis = {\n 'min': 11.0,\n 'max': 42.0,\n 'palette': [\n '141414', '383838', '808080', 'EBEB8F', 'F7D311', 'AA0000', 'D89382',\n 'DDC9C9', 'DCCDCE', '1C6330', '68AA63', 'B5C98E', 'E1F0E5', 'a975ba',\n '6f198c'\n ],\n}\nMap.setCenter(-105.58, 40.5498, 11)\nMap.addLayer(landforms, landformsVis, 'Landforms')\n",
"_____no_output_____"
]
],
[
[
"## Display Earth Engine data layers ",
"_____no_output_____"
]
],
[
[
"Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True)\nMap",
"_____no_output_____"
]
]
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cb1afeadcaef832a88ea720b08c2622888ed21e5 | 39,069 | ipynb | Jupyter Notebook | 2020 Осенний семестр/Практическое задание 2/Петров_Задание_2.ipynb | mosalov/Notebook_For_AI_Main | a693d29bf0bdcf824cb4f1eca86ff54b67ba7428 | [
"MIT"
] | 6 | 2021-09-20T10:28:18.000Z | 2022-03-14T18:39:17.000Z | 2020 Осенний семестр/Практическое задание 2/Петров_Задание_2.ipynb | mosalov/Notebook_For_AI_Main | a693d29bf0bdcf824cb4f1eca86ff54b67ba7428 | [
"MIT"
] | 122 | 2020-09-07T11:57:57.000Z | 2022-03-22T06:47:03.000Z | 2020 Осенний семестр/Практическое задание 2/Петров_Задание_2.ipynb | mosalov/Notebook_For_AI_Main | a693d29bf0bdcf824cb4f1eca86ff54b67ba7428 | [
"MIT"
] | 97 | 2020-09-07T11:32:19.000Z | 2022-03-31T10:27:38.000Z | 39,069 | 39,069 | 0.952213 | [
[
[
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sklearn as sk",
"_____no_output_____"
],
[
"# Генерируем уникальный seed\nmy_code = \"Петров\"\nseed_limit = 2 ** 32\nmy_seed = int.from_bytes(my_code.encode(), \"little\") % seed_limit\n\nnp.random.seed(my_seed)",
"_____no_output_____"
],
[
"# Формируем случайную нормально распределенную выборку sample1 и её отсортированную версию sample2\nsample1 = np.random.normal(0, 1, 10000)\nsample2 = np.sort(sample1)\n\nplt.hist(sample1, bins=100)\nplt.show()\nplt.hist(sample2, bins=100)\nplt.show()",
"_____no_output_____"
],
[
"percent_60 = int(len(sample1) * 0.6)\n# Создайте подвыборку s_1, состоящую из первых 60% элементов sample1\ns_1 = sample1[0:percent_60]\n#Создайте подвыборку s_rnd_1, выбрав в нее 60% случайных элементов из sample1\ns_rnd_1 = np.random.choice(sample1, percent_60, replace=False)\n# Выведите и сравните гистограммы для s_1 и s_rnd_1:\n# Опишите и объясните результатp\nplt.hist(s_1, bins=100)\nplt.show()\nplt.hist(s_rnd_1, bins=100)\nplt.show()",
"_____no_output_____"
],
[
"# Создайте подвыборку s_2, состоящую из первых 60% элементов sample2\ns_2 = sample2[0:percent_60]\n# Создайте подвыборку s_rnd_2, выбрав в нее 60% случайных элементов из sample2\ns_rnd_2 = np.random.choice(sample2, percent_60, replace=False)\n# Выведите и сравните гистограммы для s_2 и s_rnd_2:\n# Опишите и объясните результат\nplt.hist(s_2, bins=100)\nplt.show()\nplt.hist(s_rnd_2, bins=100)\nplt.show()",
"_____no_output_____"
],
[
"# Почему результаты для sample1 и sample2 различаются?",
"_____no_output_____"
]
]
] | [
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cb1b1c9b0e784842ad59b51a2a617c8b7e28f1f2 | 3,168 | ipynb | Jupyter Notebook | ch2-linked-list/2-4.ipynb | jellycsc/cracking-the-coding-interview-practice | 396c8da5559cc39fdd4a7f86d3113727eb683aeb | [
"MIT"
] | 1 | 2019-06-01T01:11:48.000Z | 2019-06-01T01:11:48.000Z | ch2-linked-list/2-4.ipynb | jellycsc/cracking-the-coding-interview-practice | 396c8da5559cc39fdd4a7f86d3113727eb683aeb | [
"MIT"
] | null | null | null | ch2-linked-list/2-4.ipynb | jellycsc/cracking-the-coding-interview-practice | 396c8da5559cc39fdd4a7f86d3113727eb683aeb | [
"MIT"
] | null | null | null | 21.261745 | 78 | 0.479798 | [
[
[
"import setup\nfrom ctci_utils import LinkedListNode",
"_____no_output_____"
]
],
[
[
"## Example Input",
"_____no_output_____"
]
],
[
[
"ll = LinkedListNode(1).add_to_front(LinkedListNode(2)).add_to_front(\n LinkedListNode(10)).add_to_front(LinkedListNode(5)).add_to_front(\n LinkedListNode(8)).add_to_front(LinkedListNode(5)).add_to_front(\n LinkedListNode(3))\nll.print_forward()",
"3 -> 5 -> 8 -> 5 -> 10 -> 2 -> 1 -> None\n"
],
[
"ll2 = LinkedListNode(1).add_to_front(LinkedListNode(2)).add_to_front(\n LinkedListNode(1)).add_to_front(LinkedListNode(3)).add_to_front(\n LinkedListNode(8)).add_to_front(LinkedListNode(5)).add_to_front(\n LinkedListNode(7))\nll2.print_forward()",
"7 -> 5 -> 8 -> 3 -> 1 -> 2 -> 1 -> None\n"
]
],
[
[
"## Example Solution (Two pointers)",
"_____no_output_____"
]
],
[
[
"def partition(node, num):\n back = node\n while back.next is not None:\n if back.val < num:\n back = back.next\n else:\n if back.next.val >= num:\n back = back.next\n else:\n tmp = back.next\n back.next = tmp.next\n node = node.add_to_front(tmp)\n return node",
"_____no_output_____"
],
[
"result = partition(ll, 5)\nresult.print_forward()",
"3 -> 5 -> 8 -> 5 -> 10 -> None\n"
],
[
"result2 = partition(ll2, 5)\nresult2.print_forward()",
"1 -> 2 -> 1 -> 3 -> 7 -> 5 -> 8 -> None\n"
]
]
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cb1b1caff3a67442bba563aadad38fbf0e85952c | 127,892 | ipynb | Jupyter Notebook | TestingBokeh/.ipynb_checkpoints/TryingBasicBokeh-checkpoint.ipynb | yjzhang/uncurl_app | e432f85f017839df6f082a127e4ec8dd08074ce0 | [
"MIT"
] | 7 | 2020-04-17T07:34:38.000Z | 2021-12-25T23:04:13.000Z | TestingBokeh/.ipynb_checkpoints/TryingBasicBokeh-checkpoint.ipynb | yjzhang/uncurl_app | e432f85f017839df6f082a127e4ec8dd08074ce0 | [
"MIT"
] | 1 | 2021-07-30T23:05:31.000Z | 2021-07-30T23:05:31.000Z | TestingBokeh/.ipynb_checkpoints/TryingBasicBokeh-checkpoint.ipynb | yjzhang/uncurl_app | e432f85f017839df6f082a127e4ec8dd08074ce0 | [
"MIT"
] | null | null | null | 80.739899 | 11,653 | 0.564938 | [
[
[
"from bokeh.io import output_notebook, show, reset_output\nimport numpy as np\noutput_notebook()",
"_____no_output_____"
],
[
"from IPython.display import IFrame\nIFrame('https://demo.bokehplots.com/apps/sliders', width=900, height=500)",
"_____no_output_____"
]
],
[
[
"### Basic scatterplot",
"_____no_output_____"
]
],
[
[
"from bokeh.io import output_notebook, show\nfrom bokeh.plotting import figure",
"_____no_output_____"
],
[
"# create a new plot with default tools, using figure\np = figure(plot_width=400, plot_height=400)\n\n# add a circle renderer with a size, color, and alpha\np.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=15, line_color=\"navy\", fill_color=\"orange\", fill_alpha=0.5)\n\nshow(p) # show the results",
"_____no_output_____"
]
],
[
[
"### Interactive visualization using sliders",
"_____no_output_____"
]
],
[
[
"from bokeh.layouts import row, column\nfrom bokeh.models import CustomJS, ColumnDataSource, Slider\nimport matplotlib.pyplot as plt",
"_____no_output_____"
],
[
"x = [x*0.005 for x in range(0, 201)]\n\noutput_notebook()\n\nsource = ColumnDataSource(data=dict(x=x, y=x))\n\nplot = figure(plot_width=400, plot_height=400)\nplot.scatter('x', 'y', source=source, line_width=3, line_alpha=0.6)\n\nslider = Slider(start=0.1, end=6, value=1, step=.1, title=\"power\")\n\nupdate_curve = CustomJS(args=dict(source=source, slider=slider), code=\"\"\"\n var data = source.get('data');\n var f = slider.value;\n x = data['x']\n y = data['y']\n for (i = 0; i < x.length; i++) {\n y[i] = Math.pow(x[i], f)\n }\n source.change.emit();\n\"\"\")\nslider.js_on_change('value', update_curve)\n\n\nshow(row(slider, plot))",
"_____no_output_____"
],
[
"#scatterplot using sliders\nx = [x*0.005 for x in range(0, 21)]\n\noutput_notebook()\n\nsource = ColumnDataSource(data=dict(x=x, y=x))\n\nplot = figure(plot_width=400, plot_height=400)\nplot.scatter('x', 'y', source=source, line_width=3, line_alpha=0.6)\n\nslider = Slider(start=0.1, end=6, value=1, step=.1, title=\"power\")\n\nupdate_curve = CustomJS(args=dict(source=source, slider=slider), code=\"\"\"\n var data = source.get('data');\n var f = slider.value;\n x = data['x']\n y = data['y']\n for (i = 0; i < x.length; i++) {\n y[i] = Math.pow(x[i], f)\n }\n source.change.emit();\n\"\"\")\nslider.js_on_change('value', update_curve)\n\nprint source.data['y']\n\nshow(row(slider, plot))",
"_____no_output_____"
],
[
"#Making equivalent of diffusion \nArr = np.random.rand(2,100)\n\nsource = ColumnDataSource(data=dict(x=Arr[0,], y=Arr[1,]))\nplot = figure(plot_width=400, plot_height=400)\nplot.scatter('x', 'y', source=source, line_width=3, line_alpha=0.6)\n\nslider = Slider(start=1, end=8, value=1, step=1, title=\"Diffusion_steps\")\nslider2 = Slider(start=1, end=8, value=1, step=1, title=\"Anti_Diffusion_steps\")\n\nupdate_curve = CustomJS(args=dict(source=source, slider=slider), code=\"\"\"\n var data = source.get('data');\n var f = slider.value;\n x = data['x']\n y = data['y']\n for (i = 0; i < x.length; i++) {\n x[i] = Math.pow(x[i], f)\n y[i] = Math.pow(y[i], f)\n }\n source.change.emit();\n\"\"\")\n\nupdate_curve2 = CustomJS(args=dict(source=source, slider=slider2), code=\"\"\"\n var data = source.get('data');\n var f = slider.value;\n x = data['x']\n y = data['y']\n for (i = 0; i < x.length; i++) {\n x[i] = Math.pow(x[i], 1/f)\n y[i] = Math.pow(y[i], 1/f)\n }\n source.change.emit();\n\"\"\")\n\nslider.js_on_change('value', update_curve)\nslider2.js_on_change('value', update_curve2)\n\nshow(row(column(slider,slider2), plot))\n",
"_____no_output_____"
],
[
"from bokeh.models import TapTool, CustomJS, ColumnDataSource\n\ncallback = CustomJS(code=\"alert('hello world')\")\ntap = TapTool(callback=callback)\n\np = figure(plot_width=600, plot_height=300, tools=[tap])\n\np.circle(x=[1, 2, 3, 4, 5], y=[2, 5, 8, 2, 7], size=20)\n\nshow(p)",
"_____no_output_____"
],
[
"from bokeh.models import ColumnDataSource, OpenURL, TapTool\nfrom bokeh.plotting import figure, output_file, show\n\noutput_file(\"openurl.html\")\n\np = figure(plot_width=400, plot_height=400,\n tools=\"tap\", title=\"Click the Dots\")\n\nsource = ColumnDataSource(data=dict(\n x=[1, 2, 3, 4, 5],\n y=[2, 5, 8, 2, 7],\n color=[\"navy\", \"orange\", \"olive\", \"firebrick\", \"gold\"]\n ))\n\np.circle('x', 'y', color='color', size=20, source=source)\n\nurl = \"http://www.colors.commutercreative.com/@color/\"\ntaptool = p.select(type=TapTool)\ntaptool.callback = OpenURL(url=url)\n\nshow(p)",
"_____no_output_____"
],
[
"from bokeh.models import ColumnDataSource, TapTool, DataRange1d, Plot, LinearAxis, Grid, HoverTool\nfrom bokeh.plotting import figure, output_file, show\nfrom bokeh.models.glyphs import HBar\n\n\np = figure(plot_width=400, plot_height=400,\n tools=\"tap\", title=\"Click the Dots\")\n\nsource = ColumnDataSource(data=dict(\n x=[1, 2, 3, 4, 5],\n y=[2, 5, 8, 2, 7],\n color=[\"navy\", \"orange\", \"olive\", \"firebrick\", \"gold\"]\n ))\n\np.circle('x', 'y', color='color', size=20, source=source)\n\nsource2 = ColumnDataSource(data=dict(\n x=[1,2],\n y=[1,2]))\n\n\ncallback = CustomJS(args=dict(source2=source2), code=\"\"\"\n var data = source2.get('data');\n var geom = cb_data['geometries'];\n \n data['x'] = [geom[0].x+1,geom[0].x-1]\n data['y'] = [geom[0].y+1,geom[0].y-1] \n \n source2.trigger('change'); \n\"\"\")\n\n\ndef callback2(source2 = source2):\n data = source2.get('data')\n geom = cb_obj.get('geometries')\n \n data['x'] = [geom['x']+1,geom['x']-1]\n data['y'] = [geom['y']+1,geom['y']-1] \n \n source2.trigger('change')\n\ntaptool = p.select(type=TapTool)\ntaptool.callback = CustomJS.from_py_func(callback2); \n\nxdr = DataRange1d()\nydr = DataRange1d()\n\n\n\n\np2 = figure(plot_width=400, plot_height=400)\n\np2.vbar(x=source2.data['x'], width=0.5, bottom=0,\n top=source2.data['y'], color=\"firebrick\")\n\n\n#glyph = HBar(source2.data['x'], source2.data['y'], left=0, height=0.5, fill_color=\"#b3de69\")\n\n#p2.add_glyph(source2, glyph)\n\n\n\n#p2.add_glyph(source, glyph)\n\n\n\n\nshow(row(p,p2))\nupdate()\n",
"_____no_output_____"
],
[
"source = ColumnDataSource(data=dict(\n x=[1, 2, 3, 4, 5],\n y=[2, 5, 8, 2, 7],\n color=[\"navy\", \"orange\", \"olive\", \"firebrick\", \"gold\"]\n ))",
"_____no_output_____"
],
[
"source2.data['x']",
"_____no_output_____"
]
]
] | [
"code",
"markdown",
"code",
"markdown",
"code"
] | [
[
"code",
"code"
],
[
"markdown"
],
[
"code",
"code"
],
[
"markdown"
],
[
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code",
"code"
]
] |
cb1b4838ec460f71a59a93b477e97679eaba593e | 139,971 | ipynb | Jupyter Notebook | C3_W3_Lab_1_Distributed_Training.ipynb | jimfhahn/Machine-Learning-Tutorials | a3d00212bc44bbcc8592fa5d843ec3756f782a2d | [
"CC0-1.0"
] | null | null | null | C3_W3_Lab_1_Distributed_Training.ipynb | jimfhahn/Machine-Learning-Tutorials | a3d00212bc44bbcc8592fa5d843ec3756f782a2d | [
"CC0-1.0"
] | null | null | null | C3_W3_Lab_1_Distributed_Training.ipynb | jimfhahn/Machine-Learning-Tutorials | a3d00212bc44bbcc8592fa5d843ec3756f782a2d | [
"CC0-1.0"
] | null | null | null | 141.099798 | 17,494 | 0.477835 | [
[
[
"<a href=\"https://colab.research.google.com/github/jimfhahn/Machine-Learning-Tutorials/blob/master/C3_W3_Lab_1_Distributed_Training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# Ungraded lab: Distributed Strategies with TF and Keras\n------------------------\n",
"_____no_output_____"
],
[
"\nWelcome, during this ungraded lab you are going to perform a distributed training strategy using TensorFlow and Keras, specifically the [`tf.distribute.MultiWorkerMirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy). \n\nWith the help of this strategy, a Keras model that was designed to run on single-worker can seamlessly work on multiple workers with minimal code change. In particular you will:\n\n\n1. Perform training with a single worker.\n2. Understand the requirements for a multi-worker setup (`tf_config` variable) and using context managers for implementing distributed strategies.\n3. Use magic commands to simulate different machines.\n4. Perform a multi-worker training strategy.\n\nThis notebook is based on the official [Multi-worker training with Keras](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras) notebook, which covers some additional topics in case you want a deeper dive into this topic.\n\n[Distributed Training with TensorFlow](https://www.tensorflow.org/guide/distributed_training) guide is also available for an overview of the distribution strategies TensorFlow supports for those interested in a deeper understanding of `tf.distribute.Strategy` APIs.\n\nLet's get started!",
"_____no_output_____"
],
[
"## Setup\n\nFirst, some necessary imports.",
"_____no_output_____"
]
],
[
[
"import os\nimport sys\nimport json\nimport time",
"_____no_output_____"
]
],
[
[
"Before importing TensorFlow, make a few changes to the environment.\n\n- Disable all GPUs. This prevents errors caused by the workers all trying to use the same GPU. **For a real application each worker would be on a different machine.**\n\n\n- Add the current directory to python's path so modules in this directory can be imported.",
"_____no_output_____"
]
],
[
[
"# Disable GPUs\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n\n# Add current directory to path\nif '.' not in sys.path:\n sys.path.insert(0, '.')",
"_____no_output_____"
]
],
[
[
"The previous step is important since this notebook relies on writting files using the magic command `%%writefile` and then importing them as modules.\n\nNow that the environment configuration is ready, import TensorFlow.\n",
"_____no_output_____"
]
],
[
[
"import tensorflow as tf\n\n# Ignore warnings\ntf.get_logger().setLevel('ERROR')",
"_____no_output_____"
]
],
[
[
"### Dataset and model definition",
"_____no_output_____"
],
[
"Next create an `mnist.py` file with a simple model and dataset setup. This python file will be used by the worker-processes in this tutorial.\n\nThe name of this file derives from the dataset you will be using which is called [mnist](https://keras.io/api/datasets/mnist/) and consists of 60,000 28x28 grayscale images of the first 10 digits.",
"_____no_output_____"
]
],
[
[
"%%writefile mnist.py\n\n# import os\nimport tensorflow as tf\nimport numpy as np\n\ndef mnist_dataset(batch_size):\n # Load the data\n (x_train, y_train), _ = tf.keras.datasets.mnist.load_data()\n # Normalize pixel values for x_train and cast to float32\n x_train = x_train / np.float32(255)\n # Cast y_train to int64\n y_train = y_train.astype(np.int64)\n # Define repeated and shuffled dataset\n train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(60000).repeat().batch(batch_size)\n return train_dataset\n\n\ndef build_and_compile_cnn_model():\n # Define simple CNN model using Keras Sequential\n model = tf.keras.Sequential([\n tf.keras.layers.InputLayer(input_shape=(28, 28)),\n tf.keras.layers.Reshape(target_shape=(28, 28, 1)),\n tf.keras.layers.Conv2D(32, 3, activation='relu'),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation='relu'),\n tf.keras.layers.Dense(10)\n ])\n\n # Compile model\n model.compile(\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n metrics=['accuracy'])\n \n return model",
"Writing mnist.py\n"
]
],
[
[
"Check that the file was succesfully created:",
"_____no_output_____"
]
],
[
[
"!ls *.py",
"mnist.py\n"
]
],
[
[
"Import the mnist module you just created and try training the model for a small number of epochs to observe the results of a single worker to make sure everything works correctly.",
"_____no_output_____"
]
],
[
[
"# Import your mnist model\nimport mnist\n\n# Set batch size\nbatch_size = 64\n\n# Load the dataset\nsingle_worker_dataset = mnist.mnist_dataset(batch_size)\n\n# Load compiled CNN model\nsingle_worker_model = mnist.build_and_compile_cnn_model()\n\n# As training progresses, the loss should drop and the accuracy should increase.\nsingle_worker_model.fit(single_worker_dataset, epochs=3, steps_per_epoch=70)",
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11493376/11490434 [==============================] - 0s 0us/step\n11501568/11490434 [==============================] - 0s 0us/step\nEpoch 1/3\n70/70 [==============================] - 4s 47ms/step - loss: 2.2809 - accuracy: 0.1750\nEpoch 2/3\n70/70 [==============================] - 3s 48ms/step - loss: 2.2095 - accuracy: 0.3922\nEpoch 3/3\n70/70 [==============================] - 3s 47ms/step - loss: 2.1291 - accuracy: 0.5094\n"
]
],
[
[
"Everything is working as expected! \n\nNow you will see how multiple workers can be used as a distributed strategy.",
"_____no_output_____"
],
[
"## Multi-worker Configuration\n\nNow let's enter the world of multi-worker training. In TensorFlow, the `TF_CONFIG` environment variable is required for training on multiple machines, each of which possibly has a different role. `TF_CONFIG` is a JSON string used to specify the cluster configuration on each worker that is part of the cluster.\n\nThere are two components of `TF_CONFIG`: `cluster` and `task`. \n\nLet's dive into how they are used:\n\n`cluster`:\n- **It is the same for all workers** and provides information about the training cluster, which is a dict consisting of different types of jobs such as `worker`.\n\n- In multi-worker training with `MultiWorkerMirroredStrategy`, there is usually one `worker` that takes on a little more responsibility like saving checkpoint and writing summary file for TensorBoard in addition to what a regular `worker` does. \n-Such a worker is referred to as the `chief` worker, and it is customary that the `worker` with `index` 0 is appointed as the chief `worker` (in fact this is how `tf.distribute.Strategy` is implemented).\n\n`task`:\n- Provides information of the current task and is different on each worker. It specifies the `type` and `index` of that worker. \n\nHere is an example configuration:",
"_____no_output_____"
]
],
[
[
"tf_config = {\n 'cluster': {\n 'worker': ['localhost:12345', 'localhost:23456']\n },\n 'task': {'type': 'worker', 'index': 0}\n}",
"_____no_output_____"
]
],
[
[
"Here is the same `TF_CONFIG` serialized as a JSON string:",
"_____no_output_____"
]
],
[
[
"json.dumps(tf_config)",
"_____no_output_____"
]
],
[
[
"### Explaining the TF_CONFIG example\n\nIn this example you set a `TF_CONFIG` with 2 workers on `localhost`. In practice, users would create multiple workers on external IP addresses/ports, and set `TF_CONFIG` on each worker appropriately.\n\nSince you set the task `type` to `\"worker\"` and the task `index` to `0`, **this machine is the first worker and will be appointed as the chief worker**. \n\nNote that other machines will need to have the `TF_CONFIG` environment variable set as well, and it should have the same `cluster` dict, but different task `type` or task `index` depending on what the roles of those machines are. For instance, for the second worker you would set `tf_config['task']['index']=1`.\n",
"_____no_output_____"
],
[
"### Quick Note on Environment variables and subprocesses in notebooks\n\nAbove, `tf_config` is just a local variable in python. To actually use it to configure training, this dictionary needs to be serialized as JSON, and placed in the `TF_CONFIG` environment variable.\n\nIn the next section, you'll spawn new subprocesses for each worker using the `%%bash` magic command. Subprocesses inherit environment variables from their parent, so they can access `TF_CONFIG`. \n\nYou would never really launch your jobs this way (as subprocesses of an interactive Python runtime), but it's how you will do it for the purposes of this tutorial.",
"_____no_output_____"
],
[
"## Choose the right strategy\n\nIn TensorFlow there are two main forms of distributed training:\n\n* Synchronous training, where the steps of training are synced across the workers and replicas, and\n* Asynchronous training, where the training steps are not strictly synced.\n\n`MultiWorkerMirroredStrategy`, which is the recommended strategy for synchronous multi-worker training is the one you will be using.\n\nTo train the model, use an instance of `tf.distribute.MultiWorkerMirroredStrategy`.\n\n",
"_____no_output_____"
]
],
[
[
"strategy = tf.distribute.MultiWorkerMirroredStrategy()",
"_____no_output_____"
]
],
[
[
"`MultiWorkerMirroredStrategy` creates copies of all variables in the model's layers on each device across all workers. It uses `CollectiveOps`, a TensorFlow op for collective communication, to aggregate gradients and keep the variables in sync. The [official TF distributed training guide](https://www.tensorflow.org/guide/distributed_training) has more details about this.\n",
"_____no_output_____"
],
[
"### Implement Distributed Training via Context Managers\n\nTo distribute the training to multiple-workers all you need to do is to enclose the model building and `model.compile()` call inside `strategy.scope()`. \n\nThe distribution strategy's scope dictates how and where the variables are created, and in the case of `MultiWorkerMirroredStrategy`, the variables created are `MirroredVariable`s, and they are replicated on each of the workers.\n",
"_____no_output_____"
]
],
[
[
"# Implementing distributed strategy via a context manager\nwith strategy.scope():\n multi_worker_model = mnist.build_and_compile_cnn_model()",
"_____no_output_____"
]
],
[
[
"Note: `TF_CONFIG` is parsed and TensorFlow's GRPC servers are started at the time `MultiWorkerMirroredStrategy()` is called, so the `TF_CONFIG` environment variable must be set before a `tf.distribute.Strategy` instance is created. \n\n**Since `TF_CONFIG` is not set yet the above strategy is effectively single-worker training**.\n\n",
"_____no_output_____"
],
[
"## Train the model\n\n### Create training script\n\nTo actually run with `MultiWorkerMirroredStrategy` you'll need to run worker processes and pass a `TF_CONFIG` to them.\n\nLike the `mnist.py` file written earlier, here is the `main.py` that each of the workers will run:",
"_____no_output_____"
]
],
[
[
"%%writefile main.py\n\nimport os\nimport json\n\nimport tensorflow as tf\nimport mnist # Your module\n\n# Define batch size\nper_worker_batch_size = 64\n\n# Get TF_CONFIG from the env variables and save it as JSON\ntf_config = json.loads(os.environ['TF_CONFIG'])\n\n# Infer number of workers from tf_config\nnum_workers = len(tf_config['cluster']['worker'])\n\n# Define strategy\nstrategy = tf.distribute.MultiWorkerMirroredStrategy()\n\n# Define global batch size\nglobal_batch_size = per_worker_batch_size * num_workers\n\n# Load dataset\nmulti_worker_dataset = mnist.mnist_dataset(global_batch_size)\n\n# Create and compile model following the distributed strategy\nwith strategy.scope():\n multi_worker_model = mnist.build_and_compile_cnn_model()\n\n# Train the model\nmulti_worker_model.fit(multi_worker_dataset, epochs=3, steps_per_epoch=70)",
"Writing main.py\n"
]
],
[
[
"In the code snippet above note that the `global_batch_size`, which gets passed to `Dataset.batch`, is set to `per_worker_batch_size * num_workers`. This ensures that each worker processes batches of `per_worker_batch_size` examples regardless of the number of workers.",
"_____no_output_____"
],
[
"The current directory should now contain both Python files:",
"_____no_output_____"
]
],
[
[
"!ls *.py",
"main.py mnist.py\n"
]
],
[
[
"### Set TF_CONFIG environment variable\n\nNow json-serialize the `TF_CONFIG` and add it to the environment variables:",
"_____no_output_____"
]
],
[
[
"# Set TF_CONFIG env variable\nos.environ['TF_CONFIG'] = json.dumps(tf_config)",
"_____no_output_____"
]
],
[
[
"And terminate all background processes:",
"_____no_output_____"
]
],
[
[
"# first kill any previous runs\n%killbgscripts",
"All background processes were killed.\n"
]
],
[
[
"### Launch the first worker\n\nNow, you can launch a worker process that will run the `main.py` and use the `TF_CONFIG`:",
"_____no_output_____"
]
],
[
[
"%%bash --bg\npython main.py &> job_0.log",
"Starting job # 0 in a separate thread.\n"
]
],
[
[
"There are a few things to note about the above command:\n\n1. It uses the `%%bash` which is a [notebook \"magic\"](https://ipython.readthedocs.io/en/stable/interactive/magics.html) to run some bash commands.\n2. It uses the `--bg` flag to run the `bash` process in the background, because this worker will not terminate. It waits for all the workers before it starts.\n\nThe backgrounded worker process won't print output to this notebook, so the `&>` redirects its output to a file, so you can see what happened.\n\nSo, wait a few seconds for the process to start up:",
"_____no_output_____"
]
],
[
[
"# Wait for logs to be written to the file\ntime.sleep(10)",
"_____no_output_____"
]
],
[
[
"Now look what's been output to the worker's logfile so far using the `cat` command:",
"_____no_output_____"
]
],
[
[
"%%bash\ncat job_0.log",
"2021-09-11 18:07:51.001016: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n2021-09-11 18:07:51.001099: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (c7276df34cc0): /proc/driver/nvidia/version does not exist\n2021-09-11 18:07:51.024213: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:272] Initialize GrpcChannelCache for job worker -> {0 -> localhost:12345, 1 -> localhost:23456}\n2021-09-11 18:07:51.024494: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:427] Started server with target: grpc://localhost:12345\n"
]
],
[
[
"The last line of the log file should say: `Started server with target: grpc://localhost:12345`. The first worker is now ready, and is waiting for all the other worker(s) to be ready to proceed.",
"_____no_output_____"
],
[
"### Launch the second worker\n\nNow update the `tf_config` for the second worker's process to pick up:",
"_____no_output_____"
]
],
[
[
"tf_config['task']['index'] = 1\nos.environ['TF_CONFIG'] = json.dumps(tf_config)",
"_____no_output_____"
]
],
[
[
"Now launch the second worker. This will start the training since all the workers are active (so there's no need to background this process):",
"_____no_output_____"
]
],
[
[
"%%bash\npython main.py",
"Epoch 1/3\n\r 1/70 [..............................] - ETA: 1:31 - loss: 2.3172 - accuracy: 0.0547\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/70 [..............................] - ETA: 10s - loss: 2.3061 - accuracy: 0.0742 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/70 [>.............................] - ETA: 10s - loss: 2.3088 - accuracy: 0.0781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/70 [>.............................] - ETA: 10s - loss: 2.3085 - accuracy: 0.0820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/70 [=>............................] - ETA: 10s - loss: 2.3049 - accuracy: 0.0812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/70 [=>............................] - ETA: 10s - loss: 2.3040 - accuracy: 0.0807\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/70 [==>...........................] - ETA: 9s - loss: 2.3031 - accuracy: 0.0804 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/70 [==>...........................] - ETA: 9s - loss: 2.3025 - accuracy: 0.0840\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/70 [==>...........................] - ETA: 9s - loss: 2.3011 - accuracy: 0.0842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/70 [===>..........................] - ETA: 9s - loss: 2.2994 - accuracy: 0.0875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/70 [===>..........................] - ETA: 9s - loss: 2.2996 - accuracy: 0.0881\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/70 [====>.........................] - ETA: 9s - loss: 2.2988 - accuracy: 0.0898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/70 [====>.........................] - ETA: 9s - loss: 2.2969 - accuracy: 0.0962\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/70 [=====>........................] - ETA: 8s - loss: 2.2953 - accuracy: 0.0999\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/70 [=====>........................] - ETA: 8s - loss: 2.2945 - accuracy: 0.1021\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/70 [=====>........................] - ETA: 8s - loss: 2.2933 - accuracy: 0.1055\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/70 [======>.......................] - ETA: 8s - loss: 2.2928 - accuracy: 0.1085\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/70 [======>.......................] - ETA: 8s - loss: 2.2926 - accuracy: 0.1081\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/70 [=======>......................] - ETA: 8s - loss: 2.2917 - accuracy: 0.1098\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/70 [=======>......................] - ETA: 7s - loss: 2.2909 - accuracy: 0.1129\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/70 [========>.....................] - ETA: 7s - loss: 2.2906 - accuracy: 0.1153\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/70 [========>.....................] - ETA: 7s - loss: 2.2900 - accuracy: 0.1168\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/70 [========>.....................] - ETA: 7s - loss: 2.2893 - accuracy: 0.1182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r24/70 [=========>....................] - ETA: 7s - loss: 2.2886 - accuracy: 0.1191\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/70 [=========>....................] - ETA: 7s - loss: 2.2878 - accuracy: 0.1206\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/70 [==========>...................] - ETA: 6s - loss: 2.2872 - accuracy: 0.1223\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r27/70 [==========>...................] - ETA: 6s - loss: 2.2868 - accuracy: 0.1262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r28/70 [===========>..................] - ETA: 6s - loss: 2.2861 - accuracy: 0.1256\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r29/70 [===========>..................] - ETA: 6s - loss: 2.2853 - accuracy: 0.1274\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r30/70 [===========>..................] - ETA: 6s - loss: 2.2848 - accuracy: 0.1281\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31/70 [============>.................] - ETA: 6s - loss: 2.2840 - accuracy: 0.1300\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/70 [============>.................] - ETA: 6s - loss: 2.2833 - accuracy: 0.1333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r33/70 [=============>................] - ETA: 5s - loss: 2.2825 - accuracy: 0.1352\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r34/70 [=============>................] - ETA: 5s - loss: 2.2817 - accuracy: 0.1379\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r35/70 [==============>...............] - ETA: 5s - loss: 2.2807 - accuracy: 0.1404\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r36/70 [==============>...............] - ETA: 5s - loss: 2.2800 - accuracy: 0.1432\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/70 [==============>...............] - ETA: 5s - loss: 2.2793 - accuracy: 0.1457\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38/70 [===============>..............] - ETA: 5s - loss: 2.2783 - accuracy: 0.1488\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r39/70 [===============>..............] - ETA: 4s - loss: 2.2777 - accuracy: 0.1512\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r40/70 [================>.............] - ETA: 4s - loss: 2.2765 - accuracy: 0.1543\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r41/70 [================>.............] - ETA: 4s - loss: 2.2758 - accuracy: 0.1582\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r42/70 [=================>............] - ETA: 4s - loss: 2.2751 - accuracy: 0.1607\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r43/70 [=================>............] - ETA: 4s - loss: 2.2744 - accuracy: 0.1646\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r44/70 [=================>............] - ETA: 4s - loss: 2.2736 - accuracy: 0.1665\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/70 [==================>...........] - ETA: 3s - loss: 2.2728 - accuracy: 0.1694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r46/70 [==================>...........] - ETA: 3s - loss: 2.2724 - accuracy: 0.1698\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r47/70 [===================>..........] - ETA: 3s - loss: 2.2715 - accuracy: 0.1727\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/70 [===================>..........] - ETA: 3s - loss: 2.2707 - accuracy: 0.1755\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r49/70 [====================>.........] - ETA: 3s - loss: 2.2700 - accuracy: 0.1781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/70 [====================>.........] - ETA: 3s - loss: 2.2693 - accuracy: 0.1808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r51/70 [====================>.........] - ETA: 3s - loss: 2.2684 - accuracy: 0.1850\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r52/70 [=====================>........] - ETA: 2s - loss: 2.2677 - accuracy: 0.1875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/70 [=====================>........] - ETA: 2s - loss: 2.2672 - accuracy: 0.1890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r54/70 [======================>.......] - ETA: 2s - loss: 2.2668 - accuracy: 0.1907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r55/70 [======================>.......] - ETA: 2s - loss: 2.2661 - accuracy: 0.1922\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r56/70 [=======================>......] - ETA: 2s - loss: 2.2655 - accuracy: 0.1941\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r57/70 [=======================>......] - ETA: 2s - loss: 2.2650 - accuracy: 0.1956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58/70 [=======================>......] - ETA: 1s - loss: 2.2643 - accuracy: 0.1984\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r59/70 [========================>.....] - ETA: 1s - loss: 2.2638 - accuracy: 0.2007\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r60/70 [========================>.....] - ETA: 1s - loss: 2.2631 - accuracy: 0.2029\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/70 [=========================>....] - ETA: 1s - loss: 2.2626 - accuracy: 0.2052\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r62/70 [=========================>....] - ETA: 1s - loss: 2.2620 - accuracy: 0.2077\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r63/70 [==========================>...] - ETA: 1s - loss: 2.2612 - accuracy: 0.2103\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r64/70 [==========================>...] - ETA: 0s - loss: 2.2607 - accuracy: 0.2133\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r65/70 [==========================>...] - ETA: 0s - loss: 2.2601 - accuracy: 0.2149\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r66/70 [===========================>..] - ETA: 0s - loss: 2.2593 - accuracy: 0.2182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r67/70 [===========================>..] - ETA: 0s - loss: 2.2587 - accuracy: 0.2200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r68/70 [============================>.] - ETA: 0s - loss: 2.2579 - accuracy: 0.2235\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r69/70 [============================>.] - ETA: 0s - loss: 2.2573 - accuracy: 0.2262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - ETA: 0s - loss: 2.2566 - accuracy: 0.2291\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - 12s 158ms/step - loss: 2.2566 - accuracy: 0.2291\nEpoch 2/3\n\r 1/70 [..............................] - ETA: 10s - loss: 2.2029 - accuracy: 0.4531\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/70 [..............................] - ETA: 10s - loss: 2.2066 - accuracy: 0.4062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/70 [>.............................] - ETA: 10s - loss: 2.2092 - accuracy: 0.4089\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/70 [>.............................] - ETA: 10s - loss: 2.2102 - accuracy: 0.3945\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/70 [=>............................] - ETA: 10s - loss: 2.2064 - accuracy: 0.4016\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/70 [=>............................] - ETA: 10s - loss: 2.2035 - accuracy: 0.4102\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/70 [==>...........................] - ETA: 10s - loss: 2.2026 - accuracy: 0.4107\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/70 [==>...........................] - ETA: 9s - loss: 2.2008 - accuracy: 0.4180 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/70 [==>...........................] - ETA: 9s - loss: 2.2028 - accuracy: 0.4167\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/70 [===>..........................] - ETA: 9s - loss: 2.2016 - accuracy: 0.4156\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/70 [===>..........................] - ETA: 9s - loss: 2.2018 - accuracy: 0.4162\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/70 [====>.........................] - ETA: 9s - loss: 2.2003 - accuracy: 0.4245\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/70 [====>.........................] - ETA: 9s - loss: 2.1990 - accuracy: 0.4297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/70 [=====>........................] - ETA: 8s - loss: 2.1991 - accuracy: 0.4297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/70 [=====>........................] - ETA: 8s - loss: 2.1984 - accuracy: 0.4297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/70 [=====>........................] - ETA: 8s - loss: 2.1986 - accuracy: 0.4272\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/70 [======>.......................] - ETA: 8s - loss: 2.1983 - accuracy: 0.4228\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/70 [======>.......................] - ETA: 8s - loss: 2.1984 - accuracy: 0.4201\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/70 [=======>......................] - ETA: 8s - loss: 2.1975 - accuracy: 0.4227\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/70 [=======>......................] - ETA: 7s - loss: 2.1969 - accuracy: 0.4266\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/70 [========>.....................] - ETA: 7s - loss: 2.1962 - accuracy: 0.4278\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/70 [========>.....................] - ETA: 7s - loss: 2.1953 - accuracy: 0.4311\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/70 [========>.....................] - ETA: 7s - loss: 2.1941 - accuracy: 0.4361\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r24/70 [=========>....................] - ETA: 7s - loss: 2.1937 - accuracy: 0.4372\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/70 [=========>....................] - ETA: 7s - loss: 2.1932 - accuracy: 0.4391\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/70 [==========>...................] - ETA: 6s - loss: 2.1917 - accuracy: 0.4447\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r27/70 [==========>...................] - ETA: 6s - loss: 2.1911 - accuracy: 0.4442\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r28/70 [===========>..................] - ETA: 6s - loss: 2.1909 - accuracy: 0.4434\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r29/70 [===========>..................] - ETA: 6s - loss: 2.1902 - accuracy: 0.4456\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r30/70 [===========>..................] - ETA: 6s - loss: 2.1899 - accuracy: 0.4445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31/70 [============>.................] - ETA: 6s - loss: 2.1887 - accuracy: 0.4473\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/70 [============>.................] - ETA: 6s - loss: 2.1882 - accuracy: 0.4482\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r33/70 [=============>................] - ETA: 5s - loss: 2.1874 - accuracy: 0.4493\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r34/70 [=============>................] - ETA: 5s - loss: 2.1864 - accuracy: 0.4520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r35/70 [==============>...............] - ETA: 5s - loss: 2.1860 - accuracy: 0.4520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r36/70 [==============>...............] - ETA: 5s - loss: 2.1851 - accuracy: 0.4540\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/70 [==============>...............] - ETA: 5s - loss: 2.1841 - accuracy: 0.4554\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38/70 [===============>..............] - ETA: 5s - loss: 2.1831 - accuracy: 0.4568\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r39/70 [===============>..............] - ETA: 4s - loss: 2.1827 - accuracy: 0.4573\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r40/70 [================>.............] - ETA: 4s - loss: 2.1823 - accuracy: 0.4576\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r41/70 [================>.............] - ETA: 4s - loss: 2.1814 - accuracy: 0.4594\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r42/70 [=================>............] - ETA: 4s - loss: 2.1806 - accuracy: 0.4602\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r43/70 [=================>............] - ETA: 4s - loss: 2.1799 - accuracy: 0.4613\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r44/70 [=================>............] - ETA: 4s - loss: 2.1790 - accuracy: 0.4631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/70 [==================>...........] - ETA: 3s - loss: 2.1784 - accuracy: 0.4635\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r46/70 [==================>...........] - ETA: 3s - loss: 2.1779 - accuracy: 0.4625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r47/70 [===================>..........] - ETA: 3s - loss: 2.1773 - accuracy: 0.4626\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/70 [===================>..........] - ETA: 3s - loss: 2.1765 - accuracy: 0.4637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r49/70 [====================>.........] - ETA: 3s - loss: 2.1758 - accuracy: 0.4646\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/70 [====================>.........] - ETA: 3s - loss: 2.1747 - accuracy: 0.4663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r51/70 [====================>.........] - ETA: 3s - loss: 2.1739 - accuracy: 0.4674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r52/70 [=====================>........] - ETA: 2s - loss: 2.1733 - accuracy: 0.4674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/70 [=====================>........] - ETA: 2s - loss: 2.1726 - accuracy: 0.4685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r54/70 [======================>.......] - ETA: 2s - loss: 2.1718 - accuracy: 0.4708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r55/70 [======================>.......] - ETA: 2s - loss: 2.1712 - accuracy: 0.4716\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r56/70 [=======================>......] - ETA: 2s - loss: 2.1706 - accuracy: 0.4734\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r57/70 [=======================>......] - ETA: 2s - loss: 2.1700 - accuracy: 0.4729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58/70 [=======================>......] - ETA: 1s - loss: 2.1694 - accuracy: 0.4743\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r59/70 [========================>.....] - ETA: 1s - loss: 2.1686 - accuracy: 0.4762\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r60/70 [========================>.....] - ETA: 1s - loss: 2.1677 - accuracy: 0.4777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/70 [=========================>....] - ETA: 1s - loss: 2.1674 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r62/70 [=========================>....] - ETA: 1s - loss: 2.1665 - accuracy: 0.4792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r63/70 [==========================>...] - ETA: 1s - loss: 2.1657 - accuracy: 0.4808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r64/70 [==========================>...] - ETA: 0s - loss: 2.1651 - accuracy: 0.4812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r65/70 [==========================>...] - ETA: 0s - loss: 2.1644 - accuracy: 0.4822\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r66/70 [===========================>..] - ETA: 0s - loss: 2.1638 - accuracy: 0.4824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r67/70 [===========================>..] - ETA: 0s - loss: 2.1633 - accuracy: 0.4830\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r68/70 [============================>.] - ETA: 0s - loss: 2.1625 - accuracy: 0.4845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r69/70 [============================>.] - ETA: 0s - loss: 2.1616 - accuracy: 0.4862\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - ETA: 0s - loss: 2.1609 - accuracy: 0.4868\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - 11s 158ms/step - loss: 2.1609 - accuracy: 0.4868\nEpoch 3/3\n\r 1/70 [..............................] - ETA: 10s - loss: 2.1134 - accuracy: 0.5469\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/70 [..............................] - ETA: 11s - loss: 2.0980 - accuracy: 0.5742\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/70 [>.............................] - ETA: 10s - loss: 2.0936 - accuracy: 0.5964\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/70 [>.............................] - ETA: 10s - loss: 2.0894 - accuracy: 0.6016\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/70 [=>............................] - ETA: 10s - loss: 2.0895 - accuracy: 0.6047\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/70 [=>............................] - ETA: 10s - loss: 2.0917 - accuracy: 0.5977\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/70 [==>...........................] - ETA: 10s - loss: 2.0945 - accuracy: 0.5859\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/70 [==>...........................] - ETA: 10s - loss: 2.0951 - accuracy: 0.5869\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/70 [==>...........................] - ETA: 9s - loss: 2.0941 - accuracy: 0.5842 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/70 [===>..........................] - ETA: 9s - loss: 2.0947 - accuracy: 0.5867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/70 [===>..........................] - ETA: 9s - loss: 2.0941 - accuracy: 0.5874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/70 [====>.........................] - ETA: 9s - loss: 2.0937 - accuracy: 0.5892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/70 [====>.........................] - ETA: 9s - loss: 2.0937 - accuracy: 0.5865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/70 [=====>........................] - ETA: 9s - loss: 2.0932 - accuracy: 0.5854\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/70 [=====>........................] - ETA: 8s - loss: 2.0924 - accuracy: 0.5849\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/70 [=====>........................] - ETA: 8s - loss: 2.0921 - accuracy: 0.5806\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/70 [======>.......................] - ETA: 8s - loss: 2.0908 - accuracy: 0.5800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/70 [======>.......................] - ETA: 8s - loss: 2.0893 - accuracy: 0.5842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/70 [=======>......................] - ETA: 8s - loss: 2.0883 - accuracy: 0.5872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/70 [=======>......................] - ETA: 8s - loss: 2.0879 - accuracy: 0.5879\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/70 [========>.....................] - ETA: 7s - loss: 2.0872 - accuracy: 0.5856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/70 [========>.....................] - ETA: 7s - loss: 2.0865 - accuracy: 0.5842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/70 [========>.....................] - ETA: 7s - loss: 2.0860 - accuracy: 0.5856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r24/70 [=========>....................] - ETA: 7s - loss: 2.0842 - accuracy: 0.5876\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/70 [=========>....................] - ETA: 7s - loss: 2.0838 - accuracy: 0.5872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/70 [==========>...................] - ETA: 6s - loss: 2.0832 - accuracy: 0.5865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r27/70 [==========>...................] - ETA: 6s - loss: 2.0821 - accuracy: 0.5880\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r28/70 [===========>..................] - ETA: 6s - loss: 2.0827 - accuracy: 0.5882\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r29/70 [===========>..................] - ETA: 6s - loss: 2.0819 - accuracy: 0.5897\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r30/70 [===========>..................] - ETA: 6s - loss: 2.0816 - accuracy: 0.5898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31/70 [============>.................] - ETA: 6s - loss: 2.0805 - accuracy: 0.5910\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/70 [============>.................] - ETA: 6s - loss: 2.0803 - accuracy: 0.5881\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r33/70 [=============>................] - ETA: 5s - loss: 2.0798 - accuracy: 0.5874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r34/70 [=============>................] - ETA: 5s - loss: 2.0795 - accuracy: 0.5866\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r35/70 [==============>...............] - ETA: 5s - loss: 2.0791 - accuracy: 0.5857\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r36/70 [==============>...............] - ETA: 5s - loss: 2.0782 - accuracy: 0.5870\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/70 [==============>...............] - ETA: 5s - loss: 2.0782 - accuracy: 0.5855\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38/70 [===============>..............] - ETA: 5s - loss: 2.0778 - accuracy: 0.5845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r39/70 [===============>..............] - ETA: 4s - loss: 2.0766 - accuracy: 0.5871\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r40/70 [================>.............] - ETA: 4s - loss: 2.0763 - accuracy: 0.5865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r41/70 [================>.............] - ETA: 4s - loss: 2.0758 - accuracy: 0.5867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r42/70 [=================>............] - ETA: 4s - loss: 2.0749 - accuracy: 0.5887\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r43/70 [=================>............] - ETA: 4s - loss: 2.0736 - accuracy: 0.5907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r44/70 [=================>............] - ETA: 4s - loss: 2.0724 - accuracy: 0.5920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/70 [==================>...........] - ETA: 3s - loss: 2.0714 - accuracy: 0.5922\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r46/70 [==================>...........] - ETA: 3s - loss: 2.0703 - accuracy: 0.5944\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r47/70 [===================>..........] - ETA: 3s - loss: 2.0696 - accuracy: 0.5946\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/70 [===================>..........] - ETA: 3s - loss: 2.0687 - accuracy: 0.5949\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r49/70 [====================>.........] - ETA: 3s - loss: 2.0677 - accuracy: 0.5965\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/70 [====================>.........] - ETA: 3s - loss: 2.0664 - accuracy: 0.5973\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r51/70 [====================>.........] - ETA: 2s - loss: 2.0660 - accuracy: 0.5956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r52/70 [=====================>........] - ETA: 2s - loss: 2.0648 - accuracy: 0.5975\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/70 [=====================>........] - ETA: 2s - loss: 2.0642 - accuracy: 0.5976\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r54/70 [======================>.......] - ETA: 2s - loss: 2.0637 - accuracy: 0.5974\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r55/70 [======================>.......] - ETA: 2s - loss: 2.0629 - accuracy: 0.5980\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r56/70 [=======================>......] - ETA: 2s - loss: 2.0619 - accuracy: 0.6004\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r57/70 [=======================>......] - ETA: 2s - loss: 2.0608 - accuracy: 0.6018\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58/70 [=======================>......] - ETA: 1s - loss: 2.0599 - accuracy: 0.6036\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r59/70 [========================>.....] - ETA: 1s - loss: 2.0593 - accuracy: 0.6043\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r60/70 [========================>.....] - ETA: 1s - loss: 2.0591 - accuracy: 0.6048\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/70 [=========================>....] - ETA: 1s - loss: 2.0578 - accuracy: 0.6063\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r62/70 [=========================>....] - ETA: 1s - loss: 2.0572 - accuracy: 0.6061\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r63/70 [==========================>...] - ETA: 1s - loss: 2.0565 - accuracy: 0.6064\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r64/70 [==========================>...] - ETA: 0s - loss: 2.0556 - accuracy: 0.6062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r65/70 [==========================>...] - ETA: 0s - loss: 2.0546 - accuracy: 0.6067\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r66/70 [===========================>..] - ETA: 0s - loss: 2.0539 - accuracy: 0.6071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r67/70 [===========================>..] - ETA: 0s - loss: 2.0531 - accuracy: 0.6070\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r68/70 [============================>.] - ETA: 0s - loss: 2.0521 - accuracy: 0.6079\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r69/70 [============================>.] - ETA: 0s - loss: 2.0517 - accuracy: 0.6073\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - ETA: 0s - loss: 2.0508 - accuracy: 0.6080\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - 11s 157ms/step - loss: 2.0508 - accuracy: 0.6080\n"
]
],
[
[
"Now if you recheck the logs written by the first worker you'll see that it participated in training that model:",
"_____no_output_____"
]
],
[
[
"%%bash\ncat job_0.log",
"2021-09-11 18:07:51.001016: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n2021-09-11 18:07:51.001099: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (c7276df34cc0): /proc/driver/nvidia/version does not exist\n2021-09-11 18:07:51.024213: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:272] Initialize GrpcChannelCache for job worker -> {0 -> localhost:12345, 1 -> localhost:23456}\n2021-09-11 18:07:51.024494: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:427] Started server with target: grpc://localhost:12345\n2021-09-11 18:08:11.477812: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:695] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: \"TensorSliceDataset/_2\"\nop: \"TensorSliceDataset\"\ninput: \"Placeholder/_0\"\ninput: \"Placeholder/_1\"\nattr {\n key: \"Toutput_types\"\n value {\n list {\n type: DT_FLOAT\n type: DT_INT64\n }\n }\n}\nattr {\n key: \"output_shapes\"\n value {\n list {\n shape {\n dim {\n size: 28\n }\n dim {\n size: 28\n }\n }\n shape {\n }\n }\n }\n}\n\n2021-09-11 18:08:11.770268: W tensorflow/core/framework/dataset.cc:679] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n2021-09-11 18:08:11.772519: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\nEpoch 1/3\n\r 1/70 [..............................] - ETA: 1:33 - loss: 2.3172 - accuracy: 0.0547\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/70 [..............................] - ETA: 11s - loss: 2.3061 - accuracy: 0.0742 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/70 [>.............................] - ETA: 10s - loss: 2.3088 - accuracy: 0.0781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/70 [>.............................] - ETA: 10s - loss: 2.3085 - accuracy: 0.0820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/70 [=>............................] - ETA: 10s - loss: 2.3049 - accuracy: 0.0812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/70 [=>............................] - ETA: 10s - loss: 2.3040 - accuracy: 0.0807\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/70 [==>...........................] - ETA: 10s - loss: 2.3031 - accuracy: 0.0804\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/70 [==>...........................] - ETA: 9s - loss: 2.3025 - accuracy: 0.0840 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/70 [==>...........................] - ETA: 9s - loss: 2.3011 - accuracy: 0.0842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/70 [===>..........................] - ETA: 9s - loss: 2.2994 - accuracy: 0.0875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/70 [===>..........................] - ETA: 9s - loss: 2.2996 - accuracy: 0.0881\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/70 [====>.........................] - ETA: 9s - loss: 2.2988 - accuracy: 0.0898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/70 [====>.........................] - ETA: 9s - loss: 2.2969 - accuracy: 0.0962\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/70 [=====>........................] - ETA: 8s - loss: 2.2953 - accuracy: 0.0999\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/70 [=====>........................] - ETA: 8s - loss: 2.2945 - accuracy: 0.1021\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/70 [=====>........................] - ETA: 8s - loss: 2.2933 - accuracy: 0.1055\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/70 [======>.......................] - ETA: 8s - loss: 2.2928 - accuracy: 0.1085\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/70 [======>.......................] - ETA: 8s - loss: 2.2926 - accuracy: 0.1081\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/70 [=======>......................] - ETA: 8s - loss: 2.2917 - accuracy: 0.1098\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/70 [=======>......................] - ETA: 7s - loss: 2.2909 - accuracy: 0.1129\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/70 [========>.....................] - ETA: 7s - loss: 2.2906 - accuracy: 0.1153\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/70 [========>.....................] - ETA: 7s - loss: 2.2900 - accuracy: 0.1168\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/70 [========>.....................] - ETA: 7s - loss: 2.2893 - accuracy: 0.1182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r24/70 [=========>....................] - ETA: 7s - loss: 2.2886 - accuracy: 0.1191\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/70 [=========>....................] - ETA: 7s - loss: 2.2878 - accuracy: 0.1206\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/70 [==========>...................] - ETA: 6s - loss: 2.2872 - accuracy: 0.1223\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r27/70 [==========>...................] - ETA: 6s - loss: 2.2868 - accuracy: 0.1262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r28/70 [===========>..................] - ETA: 6s - loss: 2.2861 - accuracy: 0.1256\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r29/70 [===========>..................] - ETA: 6s - loss: 2.2853 - accuracy: 0.1274\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r30/70 [===========>..................] - ETA: 6s - loss: 2.2848 - accuracy: 0.1281\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31/70 [============>.................] - ETA: 6s - loss: 2.2840 - accuracy: 0.1300\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/70 [============>.................] - ETA: 6s - loss: 2.2833 - accuracy: 0.1333\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r33/70 [=============>................] - ETA: 5s - loss: 2.2825 - accuracy: 0.1352\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r34/70 [=============>................] - ETA: 5s - loss: 2.2817 - accuracy: 0.1379\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r35/70 [==============>...............] - ETA: 5s - loss: 2.2807 - accuracy: 0.1404\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r36/70 [==============>...............] - ETA: 5s - loss: 2.2800 - accuracy: 0.1432\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/70 [==============>...............] - ETA: 5s - loss: 2.2793 - accuracy: 0.1457\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38/70 [===============>..............] - ETA: 5s - loss: 2.2783 - accuracy: 0.1488\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r39/70 [===============>..............] - ETA: 4s - loss: 2.2777 - accuracy: 0.1512\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r40/70 [================>.............] - ETA: 4s - loss: 2.2765 - accuracy: 0.1543\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r41/70 [================>.............] - ETA: 4s - loss: 2.2758 - accuracy: 0.1582\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r42/70 [=================>............] - ETA: 4s - loss: 2.2751 - accuracy: 0.1607\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r43/70 [=================>............] - ETA: 4s - loss: 2.2744 - accuracy: 0.1646\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r44/70 [=================>............] - ETA: 4s - loss: 2.2736 - accuracy: 0.1665\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/70 [==================>...........] - ETA: 3s - loss: 2.2728 - accuracy: 0.1694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r46/70 [==================>...........] - ETA: 3s - loss: 2.2724 - accuracy: 0.1698\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r47/70 [===================>..........] - ETA: 3s - loss: 2.2715 - accuracy: 0.1727\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/70 [===================>..........] - ETA: 3s - loss: 2.2707 - accuracy: 0.1755\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r49/70 [====================>.........] - ETA: 3s - loss: 2.2700 - accuracy: 0.1781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/70 [====================>.........] - ETA: 3s - loss: 2.2693 - accuracy: 0.1808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r51/70 [====================>.........] - ETA: 3s - loss: 2.2684 - accuracy: 0.1850\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r52/70 [=====================>........] - ETA: 2s - loss: 2.2677 - accuracy: 0.1875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/70 [=====================>........] - ETA: 2s - loss: 2.2672 - accuracy: 0.1890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r54/70 [======================>.......] - ETA: 2s - loss: 2.2668 - accuracy: 0.1907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r55/70 [======================>.......] - ETA: 2s - loss: 2.2661 - accuracy: 0.1922\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r56/70 [=======================>......] - ETA: 2s - loss: 2.2655 - accuracy: 0.1941\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r57/70 [=======================>......] - ETA: 2s - loss: 2.2650 - accuracy: 0.1956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58/70 [=======================>......] - ETA: 1s - loss: 2.2643 - accuracy: 0.1984\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r59/70 [========================>.....] - ETA: 1s - loss: 2.2638 - accuracy: 0.2007\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r60/70 [========================>.....] - ETA: 1s - loss: 2.2631 - accuracy: 0.2029\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/70 [=========================>....] - ETA: 1s - loss: 2.2626 - accuracy: 0.2052\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r62/70 [=========================>....] - ETA: 1s - loss: 2.2620 - accuracy: 0.2077\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r63/70 [==========================>...] - ETA: 1s - loss: 2.2612 - accuracy: 0.2103\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r64/70 [==========================>...] - ETA: 0s - loss: 2.2607 - accuracy: 0.2133\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r65/70 [==========================>...] - ETA: 0s - loss: 2.2601 - accuracy: 0.2149\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r66/70 [===========================>..] - ETA: 0s - loss: 2.2593 - accuracy: 0.2182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r67/70 [===========================>..] - ETA: 0s - loss: 2.2587 - accuracy: 0.2200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r68/70 [============================>.] - ETA: 0s - loss: 2.2579 - accuracy: 0.2235\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r69/70 [============================>.] - ETA: 0s - loss: 2.2573 - accuracy: 0.2262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - ETA: 0s - loss: 2.2566 - accuracy: 0.2291\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - 12s 158ms/step - loss: 2.2566 - accuracy: 0.2291\nEpoch 2/3\n\r 1/70 [..............................] - ETA: 10s - loss: 2.2029 - accuracy: 0.4531\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/70 [..............................] - ETA: 10s - loss: 2.2066 - accuracy: 0.4062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/70 [>.............................] - ETA: 10s - loss: 2.2092 - accuracy: 0.4089\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/70 [>.............................] - ETA: 10s - loss: 2.2102 - accuracy: 0.3945\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/70 [=>............................] - ETA: 10s - loss: 2.2064 - accuracy: 0.4016\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/70 [=>............................] - ETA: 10s - loss: 2.2035 - accuracy: 0.4102\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/70 [==>...........................] - ETA: 10s - loss: 2.2026 - accuracy: 0.4107\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/70 [==>...........................] - ETA: 9s - loss: 2.2008 - accuracy: 0.4180 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/70 [==>...........................] - ETA: 9s - loss: 2.2028 - accuracy: 0.4167\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/70 [===>..........................] - ETA: 9s - loss: 2.2016 - accuracy: 0.4156\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/70 [===>..........................] - ETA: 9s - loss: 2.2018 - accuracy: 0.4162\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/70 [====>.........................] - ETA: 9s - loss: 2.2003 - accuracy: 0.4245\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/70 [====>.........................] - ETA: 9s - loss: 2.1990 - accuracy: 0.4297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/70 [=====>........................] - ETA: 8s - loss: 2.1991 - accuracy: 0.4297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/70 [=====>........................] - ETA: 8s - loss: 2.1984 - accuracy: 0.4297\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/70 [=====>........................] - ETA: 8s - loss: 2.1986 - accuracy: 0.4272\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/70 [======>.......................] - ETA: 8s - loss: 2.1983 - accuracy: 0.4228\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/70 [======>.......................] - ETA: 8s - loss: 2.1984 - accuracy: 0.4201\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/70 [=======>......................] - ETA: 8s - loss: 2.1975 - accuracy: 0.4227\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/70 [=======>......................] - ETA: 7s - loss: 2.1969 - accuracy: 0.4266\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/70 [========>.....................] - ETA: 7s - loss: 2.1962 - accuracy: 0.4278\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/70 [========>.....................] - ETA: 7s - loss: 2.1953 - accuracy: 0.4311\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/70 [========>.....................] - ETA: 7s - loss: 2.1941 - accuracy: 0.4361\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r24/70 [=========>....................] - ETA: 7s - loss: 2.1937 - accuracy: 0.4372\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/70 [=========>....................] - ETA: 7s - loss: 2.1932 - accuracy: 0.4391\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/70 [==========>...................] - ETA: 6s - loss: 2.1917 - accuracy: 0.4447\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r27/70 [==========>...................] - ETA: 6s - loss: 2.1911 - accuracy: 0.4442\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r28/70 [===========>..................] - ETA: 6s - loss: 2.1909 - accuracy: 0.4434\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r29/70 [===========>..................] - ETA: 6s - loss: 2.1902 - accuracy: 0.4456\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r30/70 [===========>..................] - ETA: 6s - loss: 2.1899 - accuracy: 0.4445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31/70 [============>.................] - ETA: 6s - loss: 2.1887 - accuracy: 0.4473\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/70 [============>.................] - ETA: 6s - loss: 2.1882 - accuracy: 0.4482\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r33/70 [=============>................] - ETA: 5s - loss: 2.1874 - accuracy: 0.4493\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r34/70 [=============>................] - ETA: 5s - loss: 2.1864 - accuracy: 0.4520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r35/70 [==============>...............] - ETA: 5s - loss: 2.1860 - accuracy: 0.4520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r36/70 [==============>...............] - ETA: 5s - loss: 2.1851 - accuracy: 0.4540\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/70 [==============>...............] - ETA: 5s - loss: 2.1841 - accuracy: 0.4554\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38/70 [===============>..............] - ETA: 5s - loss: 2.1831 - accuracy: 0.4568\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r39/70 [===============>..............] - ETA: 4s - loss: 2.1827 - accuracy: 0.4573\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r40/70 [================>.............] - ETA: 4s - loss: 2.1823 - accuracy: 0.4576\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r41/70 [================>.............] - ETA: 4s - loss: 2.1814 - accuracy: 0.4594\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r42/70 [=================>............] - ETA: 4s - loss: 2.1806 - accuracy: 0.4602\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r43/70 [=================>............] - ETA: 4s - loss: 2.1799 - accuracy: 0.4613\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r44/70 [=================>............] - ETA: 4s - loss: 2.1790 - accuracy: 0.4631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/70 [==================>...........] - ETA: 3s - loss: 2.1784 - accuracy: 0.4635\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r46/70 [==================>...........] - ETA: 3s - loss: 2.1779 - accuracy: 0.4625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r47/70 [===================>..........] - ETA: 3s - loss: 2.1773 - accuracy: 0.4626\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/70 [===================>..........] - ETA: 3s - loss: 2.1765 - accuracy: 0.4637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r49/70 [====================>.........] - ETA: 3s - loss: 2.1758 - accuracy: 0.4646\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/70 [====================>.........] - ETA: 3s - loss: 2.1747 - accuracy: 0.4663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r51/70 [====================>.........] - ETA: 3s - loss: 2.1739 - accuracy: 0.4674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r52/70 [=====================>........] - ETA: 2s - loss: 2.1733 - accuracy: 0.4674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/70 [=====================>........] - ETA: 2s - loss: 2.1726 - accuracy: 0.4685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r54/70 [======================>.......] - ETA: 2s - loss: 2.1718 - accuracy: 0.4708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r55/70 [======================>.......] - ETA: 2s - loss: 2.1712 - accuracy: 0.4716\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r56/70 [=======================>......] - ETA: 2s - loss: 2.1706 - accuracy: 0.4734\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r57/70 [=======================>......] - ETA: 2s - loss: 2.1700 - accuracy: 0.4729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58/70 [=======================>......] - ETA: 1s - loss: 2.1694 - accuracy: 0.4743\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r59/70 [========================>.....] - ETA: 1s - loss: 2.1686 - accuracy: 0.4762\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r60/70 [========================>.....] - ETA: 1s - loss: 2.1677 - accuracy: 0.4777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/70 [=========================>....] - ETA: 1s - loss: 2.1674 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r62/70 [=========================>....] - ETA: 1s - loss: 2.1665 - accuracy: 0.4792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r63/70 [==========================>...] - ETA: 1s - loss: 2.1657 - accuracy: 0.4808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r64/70 [==========================>...] - ETA: 0s - loss: 2.1651 - accuracy: 0.4812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r65/70 [==========================>...] - ETA: 0s - loss: 2.1644 - accuracy: 0.4822\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r66/70 [===========================>..] - ETA: 0s - loss: 2.1638 - accuracy: 0.4824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r67/70 [===========================>..] - ETA: 0s - loss: 2.1633 - accuracy: 0.4830\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r68/70 [============================>.] - ETA: 0s - loss: 2.1625 - accuracy: 0.4845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r69/70 [============================>.] - ETA: 0s - loss: 2.1616 - accuracy: 0.4862\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - ETA: 0s - loss: 2.1609 - accuracy: 0.4868\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r70/70 [==============================] - 11s 158ms/step - loss: 2.1609 - accuracy: 0.4868\nEpoch 3/3\n\r 1/70 [..............................] - ETA: 10s - loss: 2.1134 - accuracy: 0.5469\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 2/70 [..............................] - ETA: 9s - loss: 2.0980 - accuracy: 0.5742 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 3/70 [>.............................] - ETA: 10s - loss: 2.0936 - accuracy: 0.5964\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 4/70 [>.............................] - ETA: 10s - loss: 2.0894 - accuracy: 0.6016\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/70 [=>............................] - ETA: 10s - loss: 2.0895 - accuracy: 0.6047\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 6/70 [=>............................] - ETA: 10s - loss: 2.0917 - accuracy: 0.5977\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 7/70 [==>...........................] - ETA: 10s - loss: 2.0945 - accuracy: 0.5859\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 8/70 [==>...........................] - ETA: 9s - loss: 2.0951 - accuracy: 0.5869 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 9/70 [==>...........................] - ETA: 9s - loss: 2.0941 - accuracy: 0.5842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/70 [===>..........................] - ETA: 9s - loss: 2.0947 - accuracy: 0.5867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r11/70 [===>..........................] - ETA: 9s - loss: 2.0941 - accuracy: 0.5874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r12/70 [====>.........................] - ETA: 9s - loss: 2.0937 - accuracy: 0.5892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/70 [====>.........................] - ETA: 9s - loss: 2.0937 - accuracy: 0.5865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r14/70 [=====>........................] - ETA: 8s - loss: 2.0932 - accuracy: 0.5854\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r15/70 [=====>........................] - ETA: 8s - loss: 2.0924 - accuracy: 0.5849\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r16/70 [=====>........................] - ETA: 8s - loss: 2.0921 - accuracy: 0.5806\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r17/70 [======>.......................] - ETA: 8s - loss: 2.0908 - accuracy: 0.5800\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r18/70 [======>.......................] - ETA: 8s - loss: 2.0893 - accuracy: 0.5842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/70 [=======>......................] - ETA: 8s - loss: 2.0883 - accuracy: 0.5872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r20/70 [=======>......................] - ETA: 7s - loss: 2.0879 - accuracy: 0.5879\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r21/70 [========>.....................] - ETA: 7s - loss: 2.0872 - accuracy: 0.5856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r22/70 [========>.....................] - ETA: 7s - loss: 2.0865 - accuracy: 0.5842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r23/70 [========>.....................] - ETA: 7s - loss: 2.0860 - accuracy: 0.5856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r24/70 [=========>....................] - ETA: 7s - loss: 2.0842 - accuracy: 0.5876\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/70 [=========>....................] - ETA: 7s - loss: 2.0838 - accuracy: 0.5872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/70 [==========>...................] - ETA: 6s - loss: 2.0832 - accuracy: 0.5865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r27/70 [==========>...................] - ETA: 6s - loss: 2.0821 - accuracy: 0.5880\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r28/70 [===========>..................] - ETA: 6s - loss: 2.0827 - accuracy: 0.5882\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r29/70 [===========>..................] - ETA: 6s - loss: 2.0819 - accuracy: 0.5897\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r30/70 [===========>..................] - ETA: 6s - loss: 2.0816 - accuracy: 0.5898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31/70 [============>.................] - ETA: 6s - loss: 2.0805 - accuracy: 0.5910\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/70 [============>.................] - ETA: 6s - loss: 2.0803 - accuracy: 0.5881\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r33/70 [=============>................] - ETA: 5s - loss: 2.0798 - accuracy: 0.5874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r34/70 [=============>................] - ETA: 5s - loss: 2.0795 - accuracy: 0.5866\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r35/70 [==============>...............] - ETA: 5s - loss: 2.0791 - accuracy: 0.5857\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r36/70 [==============>...............] - ETA: 5s - loss: 2.0782 - accuracy: 0.5870\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/70 [==============>...............] - ETA: 5s - loss: 2.0782 - accuracy: 0.5855\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r38/70 [===============>..............] - ETA: 5s - loss: 2.0778 - accuracy: 0.5845\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r39/70 [===============>..............] - ETA: 4s - loss: 2.0766 - accuracy: 0.5871\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r40/70 [================>.............] - ETA: 4s - loss: 2.0763 - accuracy: 0.5865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r41/70 [================>.............] - ETA: 4s - loss: 2.0758 - accuracy: 0.5867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r42/70 [=================>............] - ETA: 4s - loss: 2.0749 - accuracy: 0.5887\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r43/70 [=================>............] - ETA: 4s - loss: 2.0736 - accuracy: 0.5907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r44/70 [=================>............] - ETA: 4s - loss: 2.0724 - accuracy: 0.5920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/70 [==================>...........] - ETA: 3s - loss: 2.0714 - accuracy: 0.5922\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r46/70 [==================>...........] - ETA: 3s - loss: 2.0703 - accuracy: 0.5944\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r47/70 [===================>..........] - ETA: 3s - loss: 2.0696 - accuracy: 0.5946\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/70 [===================>..........] - ETA: 3s - loss: 2.0687 - accuracy: 0.5949\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r49/70 [====================>.........] - ETA: 3s - loss: 2.0677 - accuracy: 0.5965\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/70 [====================>.........] - ETA: 3s - loss: 2.0664 - accuracy: 0.5973\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r51/70 [====================>.........] - ETA: 2s - loss: 2.0660 - accuracy: 0.5956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r52/70 [=====================>........] - ETA: 2s - loss: 2.0648 - accuracy: 0.5975\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/70 [=====================>........] - ETA: 2s - loss: 2.0642 - accuracy: 0.5976\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r54/70 [======================>.......] - ETA: 2s - loss: 2.0637 - accuracy: 0.5974\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r55/70 [======================>.......] - ETA: 2s - loss: 2.0629 - accuracy: 0.5980\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r56/70 [=======================>......] - ETA: 2s - loss: 2.0619 - accuracy: 0.6004\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r57/70 [=======================>......] - ETA: 2s - loss: 2.0608 - accuracy: 0.6018\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r58/70 [=======================>......] - ETA: 1s - loss: 2.0599 - accuracy: 0.6036\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r59/70 [========================>.....] - ETA: 1s - loss: 2.0593 - accuracy: 0.6043\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r60/70 [========================>.....] - ETA: 1s - loss: 2.0591 - accuracy: 0.6048\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/70 [=========================>....] - ETA: 1s - loss: 2.0578 - accuracy: 0.6063\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r62/70 [=========================>....] - ETA: 1s - loss: 2.0572 - accuracy: 0.6061\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r63/70 [==========================>...] - ETA: 1s - loss: 2.0565 - accuracy: 0.6064\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r64/70 [==========================>...] - ETA: 0s - loss: 2.0556 - accuracy: 0.6062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r65/70 [==========================>...] - ETA: 0s - loss: 2.0546 - accuracy: 0.6067\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r66/70 [===========================>..] - ETA: 0s - loss: 2.0539 - accuracy: 0.6071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r67/70 [===========================>..] - ETA: 0s - loss: 2.0531 - accuracy: 0.6070\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r68/70 [============================>.] - ETA: 0s - loss: 2.0521 - accuracy: 0.6079\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r69/70 [============================>.] - ETA: 0s - loss: 2.0517 - 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"Unsurprisingly this ran _slower_ than the the test run at the beginning of this tutorial. **Running multiple workers on a single machine only adds overhead**. The goal here was not to improve the training time, but only to give an example of multi-worker training.",
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"-----------------------------\n**Congratulations on finishing this ungraded lab!** Now you should have a clearer understanding of how to implement distributed strategies with Tensorflow and Keras. \n\nAlthough this tutorial didn't show the true power of a distributed strategy since this will require multiple machines operating under the same network, you now know how this process looks like at a high level. \n\nIn practice and especially with very big models, distributed strategies are commonly used as they provide a way of better managing resources to perform time-consuming tasks, such as training in a fraction of the time that it will take without the strategy.\n\n**Keep it up!**",
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cb1b4b3b516940f05a26391718b90a5263e1ec09 | 775,308 | ipynb | Jupyter Notebook | ABPSoton10k/Data_Analysis.ipynb | RobFirth/zoidberg | accc1a7f73e296169a2c52aae4af07e7453b1122 | [
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],
[
"def convert_to_ap_Time(df, key):\n print(key)\n df[key] = pd.to_datetime(df[key])\n df[key] = Time([t1.astype(str) for t1 in df[key].values], format=\"isot\")\n return df\n\ndef convert_times_to_datetime(df):\n columns = [\"Gun Time\", \"Chip Time\", \"TOD\", \"Beat the Bridge\", \"Beat the Bridge.1\"]\n \n for key in columns:\n df = convert_to_ap_Time(df, key)\n df = convert_Time_to_seconds(df, key)\n return df\n\ndef convert_Time_to_seconds(df, key):\n t0 = Time(\"2017-05-04T00:00:00.000\", format=\"isot\")\n df[\"sub\" + key] = df[key] - t0\n df[\"sub\" + key] = [t.sec for t in df[\"sub\" + key].values]\n return df\n\ndef find_astronomers(df):\n astronomers = (\"Robert FIRTH\", \"Stephen BROWETT\", \"Mathew SMITH\", \"Sadie JONES\")\n astro_df = df[df[\"Name\"].isin((astronomers))]\n return astro_df\n\ndef plot_hist_with_astronomers(df, astro_df, key):\n rob_time = astro_df[key][158]/60.\n mat_time = astro_df[key][737]/60.\n steve_time = astro_df[key][1302]/60.\n sadie_time = astro_df[key][576]/60.\n\n mean_time = df[key].mean()/60\n median_time = df[key].median()/60\n\n plt.hist(df[key]/60., bins = 100)\n\n plt.plot([rob_time, rob_time], [0, 70], lw = 2, label = \"Rob\")\n plt.plot([mat_time, mat_time], [0, 70], lw = 2, label = \"Mat\")\n plt.plot([steve_time, steve_time], [0, 70], lw = 2, label = \"Steve\")\n plt.plot([sadie_time, sadie_time], [0, 70], lw = 2, label = \"Sadie\")\n\n plt.plot([mean_time, mean_time], [0, 70], lw = 2, color = \"Black\", ls = \":\", label = \"Mean\")\n plt.plot([median_time, median_time], [0, 70], lw = 2, color = \"Black\", ls = \"--\", label = \"Median\")\n plt.xlabel(key.replace(\"sub\", \"\") + \" Minutes\")\n\n plt.legend()",
"_____no_output_____"
],
[
"results_path = \"/Users/berto/Code/zoidberg/ABPSoton10k/data/Results10k.csv\"\n\ndf = pd.read_csv(results_path)\n# df = df.drop(df.index[len(df)-10:])\ndf = df.drop(df.loc[df[\"Gun Time\"] == \"DNF\"].index)\ndf = df.drop(df.loc[df[\"Gun Time\"] == \"QRY\"].index)\ndf = df.drop(df.loc[df[\"Beat the Bridge\"] == \"99:99:99\"].index)",
"_____no_output_____"
],
[
"df.columns",
"_____no_output_____"
],
[
"df = convert_times_to_datetime(df)",
"Gun Time\nChip Time\nTOD\nBeat the Bridge\nBeat the Bridge.1\n"
],
[
"astro_df = find_astronomers(df)",
"_____no_output_____"
],
[
"astro_df",
"_____no_output_____"
],
[
"# key = \"subGun Time\"\nkey = \"subChip Time\"\n\nrob_time = astro_df[key][158]/60.\nmat_time = astro_df[key][737]/60.\nsteve_time = astro_df[key][1302]/60.\nsadie_time = astro_df[key][576]/60.\n\nmean_time = df[key].mean()/60\nmedian_time = df[key].median()/60\n\nplt.hist(df[key]/60., bins = 100)\n\nplt.plot([rob_time, rob_time], [0, 70], lw = 2, label = \"Rob\")\nplt.plot([mat_time, mat_time], [0, 70], lw = 2, label = \"Mat\")\nplt.plot([steve_time, steve_time], [0, 70], lw = 2, label = \"Steve\")\nplt.plot([sadie_time, sadie_time], [0, 70], lw = 2, label = \"Sadie\")\n\n\nplt.plot([mean_time, mean_time], [0, 70], lw = 2, color = \"Black\", ls = \":\", label = \"Mean\")\nplt.plot([median_time, median_time], [0, 70], lw = 2, color = \"Black\", ls = \"--\", label = \"Median\")\nplt.xlabel(key.replace(\"sub\", \"\") + \" Minutes\")\nplt.legend()",
"_____no_output_____"
],
[
"plot_hist_with_astronomers(df=df, astro_df=astro_df, key=\"subBeat the Bridge\")",
"_____no_output_____"
]
],
[
[
"## Chip Time vs Bridge Time",
"_____no_output_____"
]
],
[
[
"keyx = \"subChip Time\"\nkeyy = \"subBeat the Bridge\"\n\ncorr_co = np.corrcoef(df[keyx]/60., df[keyy]/60.)\n\nplt.scatter(df[keyx]/60., df[keyy]/60.)\n\nplt.xlabel(keyx.replace(\"sub\", \"\") + \" Minutes\")\nplt.ylabel(keyy.replace(\"sub\", \"\") + \" Minutes\")",
"_____no_output_____"
],
[
"print(corr_co[1,0])",
"0.981240820274\n"
]
],
[
[
"## Time vs Bib Number",
"_____no_output_____"
]
],
[
[
"keyx = \"subChip Time\"\nkeyy = \"Bib No\"\n\ncorr_co = np.corrcoef(df[keyx]/60., df[keyy])\n\nplt.scatter(df[keyx]/60., df[keyy])\n\nplt.xlabel(keyx.replace(\"sub\", \"\") + \" Minutes\")\nplt.ylabel(keyy.replace(\"sub\", \"\"))",
"_____no_output_____"
],
[
"print(corr_co[1,0])",
"0.0808167937219\n"
],
[
"# plt.scatter(df[\"Pos\"], df[\"subChip Time\"])\n# plt.scatter(df[\"subChip Time\"], df[\"subBeat the Bridge\"])\nplt.scatter(df[\"Pos\"], df[\"G/Pos\"])",
"_____no_output_____"
],
[
"# print(df.groupby(\"Gender\"))\nplt.scatter((df[\"subGun Time\"] - df[\"subChip Time\"])/60., df[\"subGun Time\"]/60.)",
"_____no_output_____"
],
[
"# plt.scatter(df[\"subChip Time\"]/60., df[\"Bib No\"])",
"_____no_output_____"
],
[
"# df.",
"_____no_output_____"
],
[
"# df.columns",
"_____no_output_____"
],
[
"# fig = plt.figure(figsize=[8, 4])\n# fig.subplots_adjust(left = 0.09, bottom = 0.13, top = 0.99,\n# right = 0.99, hspace=0, wspace = 0)\n\n# ax1 = fig.add_subplot(111)\n\n# ax1.scatter(df[df[\"Club\"] == \"NaN\"][\"subChip Time\"]/60., df[df[\"Club\"] == \"NaN\"][\"subBeat the Bridge\"]/60., color = \"Orange\")\n# ax1.scatter(df[df[\"Club\"] != \"NaN\"][\"subChip Time\"]/60., df[df[\"Club\"] != \"NaN\"][\"subBeat the Bridge\"]/60., color = \"Blue\")",
"_____no_output_____"
],
[
"clubs = df[\"Club\"].unique()",
"_____no_output_____"
],
[
"clubs = [clubs[i] for i in np.arange(len(clubs)) if i != 1]",
"_____no_output_____"
],
[
"keyx = \"subChip Time\"\nkeyy = \"subBeat the Bridge\"\n\ncorr_co = np.corrcoef(df[keyx][df[\"Club\"].isin(clubs)]/60., df[keyy][df[\"Club\"].isin(clubs)]/60.)\n\nplt.scatter(df[keyx][df[\"Club\"].isin(clubs)]/60., df[keyy][df[\"Club\"].isin(clubs)]/60., label = \"clubbed\")\n# plt.scatter(df[keyx][df[\"Club\"].isin(np.invert(clubs))]/60., df[keyy][df[\"Club\"].isin(np.invert(clubs))]/60.)\nkeyx = \"subChip Time\"\nkeyy = \"subBeat the Bridge\"\n\ncorr_co = np.corrcoef(df[keyx]/60., df[keyy]/60.)\n\nplt.scatter(df[keyx]/60., df[keyy]/60., label = \"unclubbed\", zorder = -9)\n\nplt.xlabel(keyx.replace(\"sub\", \"\") + \" Minutes\")\nplt.ylabel(keyy.replace(\"sub\", \"\") + \" Minutes\")\n\nplt.legend()",
"_____no_output_____"
],
[
"plt.hist(df[keyx][df[\"Club\"].isin(clubs)]/60,label = \"clubbed\", normed = True, alpha = 0.7)\nplt.hist(df[keyx]/60,label = \"unclubbed\", zorder = -99, normed= True, alpha = 0.7)\n",
"_____no_output_____"
],
[
"plt.scatter((df[\"subGun Time\"][df[\"Club\"].isin(clubs)] - df[\"subChip Time\"][df[\"Club\"].isin(clubs)])/60., df[\"subGun Time\"][df[\"Club\"].isin(clubs)]/60.)\nplt.scatter((df[\"subGun Time\"] - df[\"subChip Time\"])/60., df[\"subGun Time\"]/60., zorder = -99)",
"_____no_output_____"
],
[
"print(df[keyx].mean()/60.)\nprint(df[keyx][df[\"Club\"].isin(clubs)].mean()/60.)",
"62.066337053110196\n60.520030413625314\n"
],
[
"df[[\"Club\", \"Name\", \"subChip Time\"]][df[\"Club\"].isin(clubs)]",
"_____no_output_____"
],
[
"# convert_to_ap_Time(df)\nt0 = Time(\"2017-04-26T00:00:00.000\", format=\"isot\")",
"_____no_output_____"
],
[
"t1 = df[\"Gun Time\"].values[0]",
"_____no_output_____"
],
[
"t1",
"_____no_output_____"
],
[
"t1 - t0",
"_____no_output_____"
],
[
"col = df[\"Gun Time\"] - t0",
"_____no_output_____"
],
[
"x = col[0]",
"_____no_output_____"
],
[
"x.",
"_____no_output_____"
],
[
"col.sec",
"_____no_output_____"
]
]
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cb1b4b72d3d4216be5cc65cd68b063f4d74efd7d | 108,423 | ipynb | Jupyter Notebook | notebooks/05_neighbors.ipynb | AlanGanem/scikit-density | 87b3a9793f91d2c3a2845019c0dd93848d998f9d | [
"Apache-2.0"
] | null | null | null | notebooks/05_neighbors.ipynb | AlanGanem/scikit-density | 87b3a9793f91d2c3a2845019c0dd93848d998f9d | [
"Apache-2.0"
] | null | null | null | notebooks/05_neighbors.ipynb | AlanGanem/scikit-density | 87b3a9793f91d2c3a2845019c0dd93848d998f9d | [
"Apache-2.0"
] | null | null | null | 110.748723 | 38,856 | 0.768979 | [
[
[
"#default_exp neighbors",
"_____no_output_____"
],
[
"#hide\nfrom nbdev.showdoc import *",
"_____no_output_____"
],
[
"#hide\n%load_ext autoreload\n%autoreload 2\n\nimport sys\nsys.path.append('..')",
"_____no_output_____"
]
],
[
[
"- weighted NN based on (possibly batch) grad descent of feature weights\n- find optimizer engine\n- find fast KNN for query time\n- Define metric specific sampling function (based on distance)) (possibly optimize func hyperparams during training, like $\\alpha$ for $P_{sample} = Dist^{-\\alpha}$ and others)\n- Define cost function (possibly a product of entropy/variance divided by the KL div from percentiles dist and flat dirichlet (hypercube))\n- Study cvxpy\n- study metric learn\n",
"_____no_output_____"
]
],
[
[
"import os\nfrom functools import partial\nimport numpy as np\n\nfrom scipy import sparse\nfrom scipy.optimize import minimize\n\nfrom skdensity.utils import cos_sim_query, sample_from_dist_array, sparse_mul_row, make_bimodal_regression,make_distplot\n\nfrom skdensity.metrics import kde_entropy, quantile, bimodal_variance, marginal_variance",
"_____no_output_____"
]
],
[
[
"## Training data",
"_____no_output_____"
]
],
[
[
"X_train, y_train, X_test, y_test = make_bimodal_regression(10000, random_state = 42, bimodal_inbalance = 5)",
"_____no_output_____"
]
],
[
[
"# Weighted KNN density estimator cvxpy",
"_____no_output_____"
]
],
[
[
"from time import time\ndef train_func(x):\n # draw batch from x\n #X_train, y_train, \n n_samples = 40\n batch_size = X_train.shape[0]//5\n tic = time()\n n_neighbors = max(2, int(x[-1]))\n weights = x[:-1]\n idx = np.random.choice([*range(X_train.shape[0])], size = batch_size, replace = False)\n X_batch,y_batch = X_train[idx], y_train[idx]\n # transform search space and query vector through weights\n X_batch = sparse_mul_row(X_batch, weights)\n X_ = sparse_mul_row(X_train, weights)\n # make query of idx and wieghts\n idx, sim = cos_sim_query(X_batch, X_, n_neighbors = n_neighbors)\n # draw samples from y\n sampled_idxs = sample_from_dist_array(arr = idx, size = n_samples, weights = sim)[:,:,-1] \n samples = np.take(y_train, indices = sampled_idxs, axis = 0)\n # calculate variance\n loss = bimodal_var(samples).mean()\n #loss = -kde_entropy(quantile(y_batch,samples))[0]\n toc = time()\n print(f'iteration took {round(toc-tic,2)}s | loss: {loss}') \n return loss",
"_____no_output_____"
],
[
"x0 = np.concatenate([np.ones(X_train.shape[1]), 8*np.ones(1)])\nf = train_func",
"_____no_output_____"
],
[
"params = minimize(fun = f, x0 = x0,method = 'CG',options = {'maxiter':1000},)",
"iteration took 0.6s | loss: 9921.617022167675\niteration took 0.56s | loss: 9613.360734629226\niteration took 0.59s | loss: 9745.231325271689\niteration took 0.56s | loss: 9876.43035496605\niteration took 0.59s | loss: 9058.224739814375\niteration took 0.54s | loss: 9184.728424656654\niteration took 0.57s | loss: 9907.755288934912\niteration took 0.57s | loss: 9440.936946589085\niteration took 0.55s | loss: 9394.337932207507\niteration took 0.54s | loss: 8335.31292503648\niteration took 0.57s | loss: 9276.387525419424\niteration took 0.59s | loss: 9753.651839272803\niteration took 0.56s | loss: 9772.860516843968\niteration took 0.58s | loss: 9952.971544487384\niteration took 0.58s | loss: 9556.869822576982\niteration took 0.61s | loss: 9624.651710543767\niteration took 0.6s | loss: 9110.515350661068\niteration took 0.58s | loss: 9578.007501907416\niteration took 0.6s | loss: 10389.985280641575\niteration took 0.6s | loss: 9699.046453260462\niteration took 0.58s | loss: 10564.476141254583\niteration took 0.57s | loss: 8486.794341852945\niteration took 0.58s | loss: 9243.419694767414\niteration took 0.6s | loss: 9698.337071894171\niteration took 0.58s | loss: 9728.472185911367\niteration took 0.6s | loss: 10095.243635178305\niteration took 0.57s | loss: 9664.149326780214\niteration took 0.58s | loss: 9930.690839087265\niteration took 0.58s | loss: 9944.743327686721\niteration took 0.58s | loss: 9091.56812749366\niteration took 0.58s | loss: 9880.739578208164\niteration took 0.58s | loss: 10337.764966031775\niteration took 0.56s | loss: 9561.340271599094\niteration took 0.55s | loss: 9545.888310909286\niteration took 0.6s | loss: 9190.089659314268\niteration took 0.58s | loss: 9477.46358222284\niteration took 0.55s | loss: 10513.63692011676\niteration took 0.59s | loss: 9884.704174753972\niteration took 0.6s | loss: 9396.238504358766\niteration took 0.58s | loss: 9121.236765549666\niteration took 0.59s | loss: 9525.253394714131\niteration took 0.58s | loss: 9831.36863993385\niteration took 0.58s | loss: 9822.655450409144\niteration took 0.59s | loss: 9837.59792004359\niteration took 0.6s | loss: 10174.52459509138\niteration took 0.58s | loss: 9951.31535172509\niteration took 0.61s | loss: 9093.606379911254\niteration took 0.61s | loss: 9870.718527173682\niteration took 0.57s | loss: 10066.278259630115\niteration took 0.59s | loss: 9742.366803578032\niteration took 0.61s | loss: 10024.979682078165\niteration took 0.58s | loss: 9304.135853628031\niteration took 0.56s | loss: 9629.115785797503\niteration took 0.6s | loss: 9996.326348065882\niteration took 0.58s | loss: 10370.730370182871\niteration took 0.57s | loss: 9212.796615435258\niteration took 0.56s | loss: 10097.251752045675\niteration took 0.59s | loss: 9127.456934044412\niteration took 0.57s | loss: 11087.025501779555\niteration took 0.55s | loss: 9748.687158800223\niteration took 0.57s | loss: 9981.013016845363\niteration took 0.56s | loss: 10143.41688283169\niteration took 0.58s | loss: 10463.044992944759\niteration took 0.59s | loss: 9505.203080803132\niteration took 0.59s | loss: 9631.162294855543\niteration took 0.56s | loss: 10017.532648590319\niteration took 0.55s | loss: 9124.297012344285\niteration took 0.59s | loss: 9155.777305430178\niteration took 0.56s | loss: 9001.56093404148\niteration took 0.59s | loss: 9893.90821702721\niteration took 0.6s | loss: 9552.319588684242\niteration took 0.58s | loss: 10517.252078093961\niteration took 0.56s | loss: 9212.761792755355\niteration took 0.58s | loss: 9771.903789564369\niteration took 0.65s | loss: 9299.93834761159\niteration took 0.64s | loss: 8888.77639020373\niteration took 0.64s | loss: 9792.516760749295\niteration took 0.64s | loss: 9732.537252564618\niteration took 0.64s | loss: 10091.995025952403\niteration took 0.63s | loss: 9611.600288584625\niteration took 0.67s | loss: 9645.0106805502\niteration took 0.65s | loss: 9541.355274589758\niteration took 0.67s | loss: 9885.57044556156\niteration took 0.67s | loss: 9977.943686386277\niteration took 0.72s | loss: 9420.905108492025\niteration took 0.7s | loss: 10018.276757622292\niteration took 0.7s | loss: 9174.59749963553\niteration took 0.76s | loss: 9998.252880975917\niteration took 0.71s | loss: 10098.41202167828\niteration took 0.65s | loss: 10087.372089516108\niteration took 0.55s | loss: 10059.1607289127\niteration took 0.56s | loss: 9639.641351942433\niteration took 0.58s | loss: 9190.439633485606\niteration took 0.59s | loss: 9033.577570700305\niteration took 0.59s | loss: 9391.394167824401\niteration took 0.56s | loss: 10532.950175245182\niteration took 0.58s | loss: 9331.884150637281\niteration took 0.58s | loss: 9753.857088499106\niteration took 0.62s | loss: 8966.944255958411\niteration took 0.59s | loss: 9093.868894853142\niteration took 0.55s | loss: 9304.709215706447\niteration took 0.6s | loss: 10229.885927912668\niteration took 0.59s | loss: 10229.005598547277\niteration took 0.58s | loss: 9727.84610937025\niteration took 0.6s | loss: 10076.914156821367\niteration took 0.6s | loss: 9058.360480413448\niteration took 0.62s | loss: 10296.943563070969\niteration took 0.58s | loss: 9699.164000273102\niteration took 0.59s | loss: 10091.774650361484\niteration took 0.6s | loss: 10460.861718143038\niteration took 0.58s | loss: 9472.57827520299\niteration took 0.56s | loss: 10747.554871763476\niteration took 0.58s | loss: 9263.381024612838\niteration took 0.81s | loss: 9413.256439052007\niteration took 0.58s | loss: 9649.880258185613\niteration took 0.57s | loss: 8942.24016944419\niteration took 0.6s | loss: 9569.66032462329\niteration took 0.57s | loss: 9409.141730073809\niteration took 0.56s | loss: 10785.588300686324\niteration took 0.59s | loss: 9523.142955572921\niteration took 0.58s | loss: 10239.75437533824\niteration took 0.6s | loss: 9141.810242999945\niteration took 0.58s | loss: 9414.09938261766\niteration took 0.57s | loss: 8795.436175852808\niteration took 0.55s | loss: 9818.156199093537\niteration took 0.56s | loss: 8854.714359181975\niteration took 0.58s | loss: 9136.537311475366\niteration took 0.59s | loss: 9478.388906833607\niteration took 0.58s | loss: 9556.474194386798\niteration took 0.6s | loss: 9213.831980730965\niteration took 0.6s | loss: 10142.984394397427\niteration took 0.54s | loss: 10535.03126600808\niteration took 0.56s | loss: 10086.4277706449\niteration took 0.59s | loss: 9395.9559087428\niteration took 0.56s | loss: 8898.571865462736\niteration took 0.6s | loss: 9937.111173797586\niteration took 0.56s | loss: 9030.407595181572\niteration took 0.57s | loss: 9732.826946962465\niteration took 0.58s | loss: 10039.381233187112\niteration took 0.6s | loss: 9207.986457584895\niteration took 0.61s | loss: 9418.683157294272\niteration took 0.6s | loss: 9877.231787491699\niteration took 0.61s | loss: 9909.560247344523\niteration took 0.56s | loss: 10009.110551514354\niteration took 0.58s | loss: 9068.364596591033\niteration took 0.56s | loss: 9141.1117533693\niteration took 0.55s | loss: 9556.348761108973\niteration took 0.61s | loss: 10505.557429630651\niteration took 0.54s | loss: 9729.014193972791\niteration took 0.58s | loss: 9135.573091829678\niteration took 0.59s | loss: 9034.735465514274\niteration took 0.59s | loss: 9443.441909252424\niteration took 0.56s | loss: 9684.838152646236\niteration took 0.58s | loss: 8864.425377261074\niteration took 0.59s | loss: 9200.015324331902\niteration took 0.57s | loss: 8995.6320574224\niteration took 0.57s | loss: 9752.647128936276\niteration took 0.58s | loss: 9192.89742282365\niteration took 0.6s | loss: 10325.82856232189\niteration took 0.6s | loss: 9179.183393839981\niteration took 0.57s | loss: 10111.466514931608\niteration took 0.58s | loss: 9383.432021501498\niteration took 0.59s | loss: 9849.311576751632\niteration took 0.59s | loss: 10020.264306415655\niteration took 0.57s | loss: 8753.356291742004\niteration took 0.59s | loss: 9060.309552983603\niteration took 0.61s | loss: 9957.835706970705\niteration took 0.6s | loss: 9969.402119593837\niteration took 0.6s | loss: 9308.572705364668\niteration took 0.56s | loss: 8670.116580684988\niteration took 0.58s | loss: 10428.90306515182\niteration took 0.6s | loss: 9290.378134566381\niteration took 0.58s | loss: 9597.816762571143\niteration took 0.58s | loss: 10194.454425813154\niteration took 0.59s | loss: 9061.212251453484\n"
],
[
"params",
"_____no_output_____"
]
],
[
[
"# Weighted KNN DensityEstimator PyTorch",
"_____no_output_____"
]
],
[
[
"import pytorch_lightning as pl\n\nfrom torch.utils.data import TensorDataset, DataLoader\nimport torch\nfrom torch import nn\n\nfrom torch.autograd import Variable as V\n\ndef update_tensor(tensor, new_values): \n try: \n tensor = tensor.data.fill_(1)*torch.Tensor(new_values)\n except: print(tensor.shape, new_values.shape); raise\n return tensor",
"_____no_output_____"
],
[
"class WeightedKNNTorch(pl.LightningModule):\n @property\n def weighted_query_space(self,):\n return sparse_mul_row(self.raw_query_space,self.weights.clone().detach().numpy()).astype('double')\n \n def __init__(self, X, y, n_neighbors, n_samples, layers = [], batch_size = 256):\n super().__init__()\n self.weights = torch.ones(X.shape[1], requires_grad = True)\n self.weights = nn.Parameter(self.weights, requires_grad=True)\n self.raw_query_space = sparse.csr_matrix(X)#X should be a sparse matrix\n self.y_ = y\n self.n_neighbors = n_neighbors\n self.n_samples = n_samples\n self.samples_tensor = torch.zeros(batch_size, n_samples, y.shape[-1], requires_grad = True)\n \n def _cos_sim_query(self,query_vector,query_space, n_neighbors):\n idx,sim = cos_sim_query(query_vector,query_space,n_neighbors)\n #drops the closest match which is the similarity of the row with itself\n #maybe n_neighbors > 1 deals with it\n #idx, sim = idx[:,1:], sim[:,1:]\n return idx,sim\n \n def _sample_values(self, idx, sim, n_samples):\n sampled_idxs = sample_from_dist_array(arr = idx, size = n_samples, weights = sim)[:,:,-1] \n samples = np.take(self.y_, indices = sampled_idxs, axis = 0)\n return update_tensor(self.samples_tensor, samples)\n \n def forward(self, x):\n # in lightning, forward defines the prediction/inference actions\n x = sparse_mul_row(\n x.numpy(),\n self.weights.clone().detach().numpy()\n ).astype('double')\n idx, sim = self._cos_sim_query(x,self.weighted_query_space, self.n_neighbors)\n samples = self._sample_values(idx,sim,self.n_samples)\n update_tensor(self.samples_tensor, samples)\n return self.samples_tensor\n\n def training_step(self, batch, batch_idx):\n if batch_idx == 0:\n self.loss_tensor = torch.ones(1).data.fill_(1000)\n self.loss_tensor.requires_grad = True\n # training_step defined the train loop. It is independent of forward\n X, y = batch\n samples = self.forward(X) \n loss_tensor = self.loss_tensor\n #minimize uncertainty\n if batch_idx%2 == 0:\n loss = np.array([bimodal_variance(samples.clone().detach()).mean()])\n update_tensor(loss_tensor,loss)\n #loss = -kde_entropy(quantile(y.numpy(),samples)) \n else:\n #maximize entropy\n loss = np.array([bimodal_variance(samples.clone().detach()).mean()])\n update_tensor(loss_tensor,loss)\n #loss = -kde_entropy(quantile(y.numpy(),samples)) \n self.log('train_loss', loss_tensor) \n \n return loss_tensor\n\n def configure_optimizers(self): \n optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n return optimizer\n ",
"_____no_output_____"
],
[
"\nmodel = WeightedKNNTorch(X_train,y_train,50,200)",
"_____no_output_____"
],
[
"tensor_x = torch.Tensor(X_train) # transform to torch tensor\ntensor_y = torch.Tensor(y_train)\n\nmy_dataset = TensorDataset(tensor_x,tensor_y) # create your datset\nmy_dataloader = DataLoader(my_dataset, batch_size = 256) # create your dataloader",
"_____no_output_____"
],
[
"trainer = pl.Trainer(max_epochs=10)\n\ntrainer.fit(model, my_dataloader)",
"GPU available: False, used: False\nTPU available: None, using: 0 TPU cores\n\n | Name | Type | Params\n------------------------------\n------------------------------\n15 Trainable params\n0 Non-trainable params\n15 Total params\n"
],
[
"i += 45\nsample = model.forward(torch.Tensor(X_test[i]))\ntrue_value = y_test[i]",
"_____no_output_____"
],
[
"import seaborn as sns\nimport matplotlib.pyplot as plt\nmake_distplot(sample,true_value ,y_test)",
"_____no_output_____"
],
[
"class LitAutoEncoder(pl.LightningModule):\n def training_step(self, batch, batch_idx, optimizer_idx):\n # access your optimizers with use_pl_optimizer=False. Default is True\n (opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)\n\n loss_a = ...\n self.manual_backward(loss_a, opt_a)\n opt_a.step()\n opt_a.zero_grad()\n\n loss_b = ...\n self.manual_backward(loss_b, opt_b, retain_graph=True)\n self.manual_backward(loss_b, opt_b)\n opt_b.step()\n opt_b.zero_grad()",
"_____no_output_____"
]
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cb1b5af51b048b7d4e9d56cf8794437546b753dc | 996,348 | ipynb | Jupyter Notebook | PCA/PCA.ipynb | nachiket273/ML_Algo_Implemented | 74ae47fdf620545fdf8c934c5997784faadaebb7 | [
"MIT"
] | 7 | 2020-08-03T13:43:53.000Z | 2022-02-18T20:38:51.000Z | PCA/PCA.ipynb | nachiket273/ML_Algo_Implemented | 74ae47fdf620545fdf8c934c5997784faadaebb7 | [
"MIT"
] | null | null | null | PCA/PCA.ipynb | nachiket273/ML_Algo_Implemented | 74ae47fdf620545fdf8c934c5997784faadaebb7 | [
"MIT"
] | 2 | 2020-09-06T21:54:16.000Z | 2022-01-22T19:59:33.000Z | 1,332.016043 | 169,296 | 0.958163 | [
[
[
"%reload_ext autoreload\n%autoreload 2\n%matplotlib inline",
"_____no_output_____"
],
[
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.datasets import load_digits, load_iris\nfrom sklearn.model_selection import train_test_split\nfrom pca import pca as MyPCA",
"_____no_output_____"
]
],
[
[
"# Load Digit Dataset",
"_____no_output_____"
]
],
[
[
"digits = load_digits()",
"_____no_output_____"
],
[
"def draw_digits(X, y):\n fig = plt.figure(1, figsize=(8, 8))\n plt.scatter(X[:, 0], X[:, 1],\n c=y, edgecolor='none', alpha=0.5,\n cmap=plt.cm.get_cmap('Spectral', 10))\n plt.xlabel('component 1')\n plt.ylabel('component 2')\n plt.colorbar()\n plt.show();",
"_____no_output_____"
]
],
[
[
"# sklearn PCA",
"_____no_output_____"
]
],
[
[
"pca = PCA(n_components=2, random_state=17).fit(digits.data)",
"_____no_output_____"
],
[
"data_pca = pca.transform(digits.data)",
"_____no_output_____"
],
[
"pca.explained_variance_ratio_, pca.explained_variance_, pca.singular_values_, pca.components_",
"_____no_output_____"
],
[
"data_pca",
"_____no_output_____"
],
[
"draw_digits(data_pca, digits.target)",
"_____no_output_____"
]
],
[
[
"# Our Implementation",
"_____no_output_____"
]
],
[
[
"pca1 = MyPCA(n_components=2, solver='svd')",
"_____no_output_____"
],
[
"pca1.fit(digits.data)",
"_____no_output_____"
],
[
"data_pca1 = pca1.transform(digits.data)",
"_____no_output_____"
],
[
"pca1.explained_variance_ratio_, pca1.explained_variance_, pca1.singular_values_, pca1.components_",
"_____no_output_____"
],
[
"data_pca1",
"_____no_output_____"
],
[
"draw_digits(data_pca1, digits.target)",
"_____no_output_____"
]
],
[
[
"### eig solver",
"_____no_output_____"
]
],
[
[
"pca_eig = MyPCA(n_components=2, solver='eig')",
"_____no_output_____"
],
[
"pca_eig.fit(digits.data)",
"_____no_output_____"
],
[
"data_eig = pca_eig.transform(digits.data)",
"_____no_output_____"
],
[
"pca_eig.explained_variance_ratio_, pca_eig.explained_variance_, pca_eig.singular_values_, pca_eig.components_",
"_____no_output_____"
],
[
"data_eig",
"_____no_output_____"
],
[
"draw_digits(data_eig, digits.target)",
"_____no_output_____"
]
],
[
[
"# Iris Dataset",
"_____no_output_____"
],
[
"Let's try to plot 3 components after PCA.<br>\nhttps://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py",
"_____no_output_____"
]
],
[
[
"from mpl_toolkits.mplot3d import Axes3D",
"_____no_output_____"
],
[
"def plot_components(X, y):\n fig = plt.figure(1, figsize=(12, 8))\n plt.clf()\n ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n \n for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:\n ax.text3D(X[y == label, 0].mean(),\n X[y == label, 1].mean() + 1.5,\n X[y == label, 2].mean(), name,\n horizontalalignment='center',\n bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))\n\n # Reorder the labels to have colors matching the cluster results\n y = np.choose(y, [1, 2, 0]).astype(np.float)\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral,\n edgecolor='k')\n\n ax.w_xaxis.set_ticklabels([])\n ax.w_yaxis.set_ticklabels([])\n ax.w_zaxis.set_ticklabels([])\n\n plt.show()",
"_____no_output_____"
],
[
"iris = load_iris()",
"_____no_output_____"
],
[
"X, y = iris.data, iris.target",
"_____no_output_____"
]
],
[
[
"# sklearn",
"_____no_output_____"
]
],
[
[
"pca_3d = PCA(n_components=3, random_state=17).fit(X)",
"_____no_output_____"
],
[
"X_3d = pca_3d.transform(X)",
"_____no_output_____"
],
[
"plot_components(X_3d, y)",
"_____no_output_____"
]
],
[
[
"# Our's: Solver:svd",
"_____no_output_____"
]
],
[
[
"pca_3d_svd = MyPCA(n_components=3)",
"_____no_output_____"
],
[
"pca_3d_svd.fit(X)",
"_____no_output_____"
],
[
"X_3d_svd = pca_3d_svd.transform(X)",
"_____no_output_____"
],
[
"plot_components(X_3d_svd, y)",
"_____no_output_____"
]
],
[
[
"# Our's: Solver:eig fit_transform",
"_____no_output_____"
]
],
[
[
"pca_3d_eig = MyPCA(n_components=3, solver='eig')",
"_____no_output_____"
],
[
"X_3d_eig = pca_3d_eig.fit_transform(X)",
"_____no_output_____"
],
[
"plot_components(X_3d_eig, y)",
"_____no_output_____"
]
]
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cb1b69c8968ca2717ea2a5b3f4c92e2f813b610b | 17,884 | ipynb | Jupyter Notebook | Hough Transform Circles.ipynb | Mgosi/Hough-Transform | c10ec07d8cef55321558341cc42a0272c84078f9 | [
"MIT"
] | null | null | null | Hough Transform Circles.ipynb | Mgosi/Hough-Transform | c10ec07d8cef55321558341cc42a0272c84078f9 | [
"MIT"
] | null | null | null | Hough Transform Circles.ipynb | Mgosi/Hough-Transform | c10ec07d8cef55321558341cc42a0272c84078f9 | [
"MIT"
] | null | null | null | 23.286458 | 318 | 0.401532 | [
[
[
"import cv2\nimport numpy as np\nimg = cv2.imread(\"hough.jpg\", 0)\nprint(type(img))\nimg = np.asarray(img)\n\n#Fetching the rows and columns\nrows = len(img)\ncols = len(img[0])",
"<class 'numpy.ndarray'>\n"
]
],
[
[
"# Sobel Operator",
"_____no_output_____"
]
],
[
[
"#initializing Sobel Operator\ngx_sobel = [[-1,-2,-1],\n [0,0,0],\n [1,2,1]]\n\ngy_sobel = [[-1,0,1],\n [-2,0,2],\n [-1,0,1]]\n\nx_sobel = list(map(list,zip(*gx_sobel)))\ny_sobel = list(map(list,zip(*gy_sobel)))\n",
"_____no_output_____"
]
],
[
[
"# Applying Gradient",
"_____no_output_____"
]
],
[
[
"#Used to pad the image\nimg_x = [[0]*902 for i in range(602)]\nimg_y= [[0]*902 for i in range(602)]\n\n#Finding the Gradient to distinguish the images clearly. Especially the edges of the images\n#Along X Axis\n\nfor i in range(1,img.shape[0]-1):\n for j in range(1,img.shape[1]-1):\n img_x[i][j] = img[i+1][j] - img[i-1][j] #Subtract the neighbouring pixels on the x-axis\n\n#Along Y Axis \nfor i in range(1,img.shape[0]-1):\n for j in range(1,img.shape[1]-1):\n img_y[i][j] = img[i][j+1] - img[i][j-1] #Subtract the neighbouring pixels on the y-axis\n\nimg_x = np.asarray(img_x)\nimg_y = np.asarray(img_y)\n\ncv2.imwrite('T3_Images/Gradient_X.png',(img_x))\ncv2.imwrite('T3_Images/Gradient_Y.png',(img_y))\n",
"C:\\Anaconda3.6\\lib\\site-packages\\ipykernel_launcher.py:10: RuntimeWarning: overflow encountered in ubyte_scalars\n # Remove the CWD from sys.path while we load stuff.\nC:\\Anaconda3.6\\lib\\site-packages\\ipykernel_launcher.py:15: RuntimeWarning: overflow encountered in ubyte_scalars\n from ipykernel import kernelapp as app\n"
]
],
[
[
"# Convolution",
"_____no_output_____"
]
],
[
[
"#Convolution\nimport math\nsobelgx = [[0]*img.shape[1] for i in range(img.shape[0])] #Creating empty 2D Lists\nsobelgy = [[0]*img.shape[1] for i in range(img.shape[0])]\nsobel = [[0]*img.shape[1] for i in range(img.shape[0])]\nsobel_add = [[0]*img.shape[1] for i in range(img.shape[0])]\n\n#Multiplying the sobel operator with the corresponding image pixel for that patch of the image to find the covoluted pixel value for (x,y)\nfor x in range(1, rows-1):\n for y in range (1, cols-1):\n gx = x_sobel[0][0] * img[x-1][y-1] + x_sobel[0][1] * img[x][y-1] + x_sobel[0][2] * img[x+1][y-1] + (x_sobel[1][0] * img[x-1][y]) + (x_sobel[1][1] * img[x][y]) + (x_sobel[1][2] * img[x+1][y]) + (x_sobel[2][0] * img[x-1][y+1]) + (x_sobel[2][1] * img[x][y+1]) + (x_sobel[2][2] * img[x+1][y+1])\n sobelgy[x-1][y-1] = gx\n gy = (y_sobel[0][0] * img[x-1][y-1]) + (y_sobel[0][1] * img[x][y-1]) + (y_sobel[0][2] * img[x+1][y-1]) + (y_sobel[1][0] * img[x-1][y]) + (y_sobel[1][1] * img[x][y]) + (y_sobel[1][2] * img[x+1][y]) + (y_sobel[2][0] * img[x-1][y+1]) + (y_sobel[2][1] * img[x][y+1]) + (y_sobel[2][2] * img[x+1][y+1])\n sobelgx[x-1][y-1] = gy\n \n sobel[x-1][y-1] = math.sqrt((gx*gx + gy*gy)) #Combining the outputs of the Sobel-X and Sobel-Y by Root of Squared Sum.\n sobel_add[x-1][y-1] = (gx + gy)/2 #Combining the outputs of the Sobel-x and Sobel-Y by adding the values\n\ncv2.imwrite('T3_Images/Sobel_X.png',np.asarray(sobelgx)) \ncv2.imwrite('T3_Images/Sobel_Y.png',np.asarray(sobelgy))\ncv2.imwrite('T3_Images/Sobel.png',np.asarray(sobel))\ncv2.imwrite('T3_Images/Sobel_add.png',np.asarray(sobel_add))\n",
"_____no_output_____"
]
],
[
[
"# Normalization",
"_____no_output_____"
]
],
[
[
"theta = []\nfor i in range(0,360):\n theta.append(i)\ntheta = np.asarray(theta)",
"_____no_output_____"
],
[
"img = cv2.imread(\"T3_Images/Sobel_Y.png\", 0)\nprint (img)\nR = 22\nthres_img = np.zeros((img.shape[0], img.shape[1]))\nfor i in range(img.shape[0]):\n for j in range(img.shape[1]):\n if img[i][j] > 100:\n thres_img[i][j] = 255\nthres_img = np.asarray(thres_img)\ncv2.imwrite(\"T3_Images/T_sobel.jpg\", thres_img)\nthres_img.shape, img.shape",
"[[ 1 2 0 ... 0 0 0]\n [ 1 2 0 ... 11 0 0]\n [ 2 0 0 ... 0 0 0]\n ...\n [ 0 0 0 ... 8 0 0]\n [ 0 0 0 ... 0 0 0]\n [ 0 0 0 ... 0 0 0]]\n"
],
[
"def circumference_point(x,y, theta, R):\n points = []\n for t in theta:\n a = int((y - R*np.cos(math.radians(t))))\n b = int((x - R*np.sin(math.radians(t))))\n points.append([a,b])\n return points",
"_____no_output_____"
],
[
"def accumulator_matrix(thres_img,theta, R):\n \n acc = np.zeros((2*thres_img.shape[0], 2*thres_img.shape[1]))\n for x in range(thres_img.shape[0]):\n for y in range(thres_img.shape[1]):\n if thres_img[x][y] == 255:\n circum_points = circumference_point(x,y,theta,R)\n #print (circum_points)\n for point in circum_points:\n acc[point[0]][point[1]] += 1\n cv2.imwrite(\"Bonus_accumulator.jpg\",acc)\n return acc",
"_____no_output_____"
],
[
"import math\nacc = accumulator_matrix(thres_img, theta, R)",
"_____no_output_____"
],
[
"cv2.imwrite(\"T3_Images/Bonus_acc.jpg\",acc)",
"_____no_output_____"
],
[
"# sobelgx = cv2.imread(\"red_img.jpg\", 0)\n# cv2.imwrite(\"gray_red_img.jpg\",sobelgx)",
"_____no_output_____"
],
[
"def max_indices(arr, k):\n '''\n Returns the indices of the k first largest elements of arr\n (in descending order in values)\n '''\n assert k <= arr.size, 'k should be smaller or equal to the array size'\n arr_ = arr.astype(float) # make a copy of arr\n max_idxs = []\n for _ in range(k):\n max_element = np.max(arr_)\n if np.isinf(max_element):\n break\n else:\n idx = np.where(arr_ == max_element)\n max_idxs.append(idx)\n arr_[idx] = -np.inf\n return convert(np.asarray(max_idxs))",
"_____no_output_____"
],
[
"def convert(k):\n edge_points = []\n for i in range(k.shape[0]):\n n = k[i][0].shape[0]\n if n>1:\n for j in range(n):\n edge_points.append([k[i][0][j], k[i][1][j]])\n else:\n edge_points.append([k[i][0][0], k[i][1][0]])\n return edge_points",
"_____no_output_____"
],
[
"\ne = []\nedge_points = max_indices(acc, 60)\n#edge_points\nfor i in range(len(edge_points)):\n if (i in range(50,56)):\n print (i)\n else:\n print (i)\n e.append(edge_points[i])\nprint (len(e), len(edge_points))\nedge_points = e\nlen(edge_points)\ne\n",
"0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n100\n101\n102\n103\n104\n105\n106\n107\n108\n109\n110\n111\n112\n113\n114\n115\n116\n117\n118\n119\n120\n121\n122\n123\n124\n125\n126\n127\n128\n129\n130\n131\n132\n133\n134\n135\n136\n137\n138\n139\n140\n141\n142\n143\n144\n145\n146\n147\n148\n149\n150\n151\n152\n153\n154\n155\n156\n157\n158\n159\n160\n161\n156 162\n"
],
[
"o_img = cv2.imread(\"hough.jpg\")\nprint (edge_points[0][0], edge_points[0][1])\nfor i in range(len(edge_points)):\n cv2.circle(o_img, (edge_points[i][0],edge_points[i][1]), R, (0,255,0))\ncv2.imwrite(\"T3_Images/circle.jpg\",o_img)",
"519 85\n"
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cb1b6cd2639eb5f554cf19c2485fc42737af9ac4 | 13,907 | ipynb | Jupyter Notebook | analysis/Hannah/milestone1.ipynb | data301-2020-winter1/course-project-group_6022 | 892e9f42c757e53094ea07185136393aa4353d5d | [
"MIT"
] | null | null | null | analysis/Hannah/milestone1.ipynb | data301-2020-winter1/course-project-group_6022 | 892e9f42c757e53094ea07185136393aa4353d5d | [
"MIT"
] | 1 | 2020-11-26T22:09:39.000Z | 2020-11-26T22:09:39.000Z | analysis/Hannah/milestone1.ipynb | data301-2020-winter1/course-project-group_6022 | 892e9f42c757e53094ea07185136393aa4353d5d | [
"MIT"
] | null | null | null | 37.384409 | 122 | 0.325951 | [
[
[
"import pandas as pd\n\ndf = pd.read_csv(\"/Users/hannahwilloner/data_301/course-project-group_6022/data/raw/COVID19 cases Toronto.csv\")\ndf",
"_____no_output_____"
]
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] | [
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[
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cb1b7195fb98eac6b381963274799542d9d8230b | 7,818 | ipynb | Jupyter Notebook | Analysis/Notebooks/Spiral Dataset/2_Measure_Curvature.ipynb | fiji/Kappa | 71378eeecf4d7dd0d9d0f1477b8fc4b6ba383f3b | [
"MIT"
] | null | null | null | Analysis/Notebooks/Spiral Dataset/2_Measure_Curvature.ipynb | fiji/Kappa | 71378eeecf4d7dd0d9d0f1477b8fc4b6ba383f3b | [
"MIT"
] | 8 | 2020-08-16T16:32:08.000Z | 2021-08-10T09:43:51.000Z | Analysis/Notebooks/Spiral Dataset/2_Measure_Curvature.ipynb | fiji/Kappa | 71378eeecf4d7dd0d9d0f1477b8fc4b6ba383f3b | [
"MIT"
] | 1 | 2022-03-18T02:06:26.000Z | 2022-03-18T02:06:26.000Z | 34.139738 | 182 | 0.562164 | [
[
[
"**Important**: This notebook is different from the other as it directly calls **ImageJ Kappa plugin** using the [`scyjava` ImageJ brige](https://github.com/scijava/scyjava).\n\nSince Kappa uses ImageJ1 features, you might not be able to run this notebook on an headless machine (need to be tested).",
"_____no_output_____"
]
],
[
[
"from pathlib import Path\nimport pandas as pd\nimport numpy as np\nfrom tqdm.auto import tqdm\n\nimport sys; sys.path.append(\"../../\")\nimport pykappa\n\n# Init ImageJ with Fiji plugins\n# It can take a while if Java artifacts are not yet cached.\nimport imagej\njava_deps = []\njava_deps.append('org.scijava:Kappa:1.7.1')\nij = imagej.init(\"+\".join(java_deps), headless=False)\n\nimport jnius\n\n# Load Java classes\nKappaFrame = jnius.autoclass('sc.fiji.kappa.gui.KappaFrame')\nCurvesExporter = jnius.autoclass('sc.fiji.kappa.gui.CurvesExporter')\n\n# Load ImageJ services\ndsio = ij.context.getService(jnius.autoclass('io.scif.services.DatasetIOService'))\ndsio = jnius.cast('io.scif.services.DatasetIOService', dsio)\n\n# Set data path\ndata_dir = Path(\"/home/hadim/.data/Postdoc/Kappa/spiral_curve_SDM/\")\n\n# Pixel size used when fixed\nfixed_pixel_size = 0.16\n\n# Used to select pixels around the initialization curves\nbase_radius_um = 1.6\n\nenable_control_points_adjustment = True\n\n# \"Point Distance Minimization\" or \"Squared Distance Minimization\"\nif '_SDM' in data_dir.name:\n fitting_algorithm = \"Squared Distance Minimization\"\nelse:\n fitting_algorithm = \"Point Distance Minimization\"\nfitting_algorithm",
"_____no_output_____"
],
[
"experiment_names = ['variable_snr', 'variable_initial_position', 'variable_pixel_size', 'variable_psf_size']\nexperiment_names = ['variable_psf_size']\n\nfor experiment_name in tqdm(experiment_names, total=len(experiment_names)):\n \n experiment_path = data_dir / experiment_name\n fnames = sorted(list(experiment_path.glob(\"*.tif\")))\n n = len(fnames)\n\n for fname in tqdm(fnames, total=n, leave=False):\n \n tqdm.write(str(fname))\n \n kappa_path = fname.with_suffix(\".kapp\")\n assert kappa_path.exists(), f'{kappa_path} does not exist.'\n\n curvatures_path = fname.with_suffix(\".csv\")\n \n if not curvatures_path.is_file():\n\n frame = KappaFrame(ij.context)\n frame.getKappaMenubar().openImageFile(str(fname))\n frame.resetCurves()\n frame.getKappaMenubar().loadCurveFile(str(kappa_path))\n frame.getCurves().setAllSelected()\n\n # Compute threshold according to the image\n dataset = dsio.open(str(fname))\n mean = ij.op().stats().mean(dataset).getRealDouble() \n std = ij.op().stats().stdDev(dataset).getRealDouble()\n threshold = int(mean + std * 2)\n\n # Used fixed pixel size or the one in the filename\n if fname.stem.startswith('pixel_size'):\n pixel_size = float(fname.stem.split(\"_\")[-2])\n if experiment_name == 'variable_psf_size':\n pixel_size = 0.01\n else:\n pixel_size = fixed_pixel_size\n \n base_radius = int(np.round(base_radius_um / pixel_size))\n\n # Set curve fitting parameters\n frame.setEnableCtrlPtAdjustment(enable_control_points_adjustment)\n frame.setFittingAlgorithm(fitting_algorithm)\n frame.getInfoPanel().thresholdRadiusSpinner.setValue(ij.py.to_java(base_radius))\n frame.getInfoPanel().thresholdSlider.setValue(threshold)\n frame.getInfoPanel().updateConversionField(str(pixel_size))\n \n # Fit the curves\n frame.fitCurves()\n \n # Save fitted curves\n frame.getKappaMenubar().saveCurveFile(str(fname.with_suffix(\".FITTED.kapp\")))\n\n # Export results\n exporter = CurvesExporter(frame)\n exporter.exportToFile(str(curvatures_path), False)\n\n # Remove duplicate rows during CSV export.\n df = pd.read_csv(curvatures_path)\n df = df.drop_duplicates()\n df.to_csv(curvatures_path)",
"_____no_output_____"
],
[
"0.13**2",
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cb1b751bdfef88d9077a2a4d853ef94e91388f2e | 2,498 | ipynb | Jupyter Notebook | examples/basic/pca.ipynb | jtpils/vtkplotter | fbed73d6ed3f049dca3421c64e2e82ac1eef69a8 | [
"MIT"
] | 7 | 2020-11-21T14:23:27.000Z | 2022-03-03T02:01:35.000Z | examples/basic/pca.ipynb | jtpils/vtkplotter | fbed73d6ed3f049dca3421c64e2e82ac1eef69a8 | [
"MIT"
] | null | null | null | examples/basic/pca.ipynb | jtpils/vtkplotter | fbed73d6ed3f049dca3421c64e2e82ac1eef69a8 | [
"MIT"
] | 2 | 2021-11-06T10:40:31.000Z | 2022-03-03T02:01:34.000Z | 25.489796 | 120 | 0.532426 | [
[
[
"\"\"\"\nDraw the PCA (Principal Component Analysis) ellipsoid that contains\n50% of a cloud of Points, then check if points are inside the surface.\nExtra info is stored in actor.info['sphericity'], 'va', 'vb', 'vc'.\n\"\"\"\nfrom vtkplotter import *\nimport numpy as np\n\nvp = Plotter()\n\npts = np.random.randn(500, 3) # random gaussian point cloud\n\nact = pcaEllipsoid(pts, pvalue=0.5, pcaAxes=1) # act is a group of [ellipse, 3 axes]\nvp += act\n\nipts = act.getActor(0).insidePoints(pts) # get the ellipse and select points inside mesh\nopts = act.getActor(0).insidePoints(pts, invert=True)\nvp += Points(ipts, c=\"g\")\nvp += Points(opts, c=\"r\")\n\nprintc(\"inside points #\", len(ipts), c='g')\nprintc(\"outside points #\", len(opts), c='r')\nprintc(\"sphericity :\", act.info[\"sphericity\"])\nvp.show()",
"\u001b[1m\u001b[32minside points # 251\u001b[0m\n\u001b[1m\u001b[31moutside points # 249\u001b[0m\n\u001b[1msphericity : 0.004073146307998164\u001b[0m\n"
],
[
"closePlotter()",
"_____no_output_____"
]
]
] | [
"code"
] | [
[
"code",
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] |
cb1b7cbd613db3abe1c8eb2a832783d5565c34d1 | 36,033 | ipynb | Jupyter Notebook | SIR_Pertussis_Model_(simplified_6x6_ODE).ipynb | felipeescallon/Pertussis-Model | 2e7f78753c7eb90a017fc45173f77bef614c7fea | [
"CC0-1.0"
] | 1 | 2021-04-14T03:06:23.000Z | 2021-04-14T03:06:23.000Z | SIR_Pertussis_Model_(simplified_6x6_ODE).ipynb | felipeescallon/Pertussis-Model | 2e7f78753c7eb90a017fc45173f77bef614c7fea | [
"CC0-1.0"
] | null | null | null | SIR_Pertussis_Model_(simplified_6x6_ODE).ipynb | felipeescallon/Pertussis-Model | 2e7f78753c7eb90a017fc45173f77bef614c7fea | [
"CC0-1.0"
] | null | null | null | 251.979021 | 31,222 | 0.901729 | [
[
[
"<a href=\"https://colab.research.google.com/github/felipeescallon/Pertussis-Model/blob/main/SIR_Pertussis_Model_(simplified_6x6_ODE).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
],
[
"# Pertussis Model \n\nBased on the paper: https://pubmed.ncbi.nlm.nih.gov/11561978/\n\n*By: Andrés Felipe Escallón Portilla*\n\n**March 2021**",
"_____no_output_____"
],
[
"SIR Pertussis Model (simplified 6x6 ODE):",
"_____no_output_____"
]
],
[
[
"import numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\n# Total population, N.\n#N = 15490000 #1996 (Netherlands)\n\n#B=80000 #new borns \nB=100 #%\n\n# Initial number of susceptible, infected and recovered individuals\nS10, V0, I10, S20, I20, R0 = B, 0, 0 , 0, 0, 0\n\n# Model parameters:\n\nv_mean, lambda_mean, sigmaV, sigmaI, rho1, rho2 = 0.85, 0.003, 0.1, 0.05, 25, 25\n\n# A grid of time points (in years)\nL=75\na = np.linspace(0, L, L)\n\n# SIR Pertussis Model: 6x6 Ordinary Differential Equations (ODE)\ndef deriv(y, a, v_mean, lambda_mean, sigmaV, sigmaI, rho1, rho2): #a=age\n\n S1, V, I1, S2, I2, R = y\n dS1da = -v_mean * S1 - lambda_mean * S1\n dVda = v_mean * S1 - sigmaV * V\n dI1da = lambda_mean * S1 - rho1 * I1\n dS2da = sigmaV * V + sigmaI * R - lambda_mean * S2\n dI2da = lambda_mean * S2 - rho1 * I2\n dRda = rho1 * I1 + rho2 * I2 - sigmaI * R\n\n return dS1da, dVda, dI1da, dS2da, dI2da, dRda\n\n# Initial conditions vector\ny0 = S10, V0, I10, S20, I20, R0 \n# Integrate the SIR equations over the time grid, a.\nret = odeint(deriv, y0, a, args=(v_mean, lambda_mean, sigmaV, sigmaI, rho1, rho2))\nS1, V, I1, S2, I2, R = ret.T\n\n# Plotting data on six separate curves for S1(a), V(a), I1(a), S2(a), I2(a), and R(a)\nfig = plt.figure(facecolor='w')\nax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)\nax.plot(a, S1, 'b', alpha=0.5, lw=2, label='Susceptible1')\nax.plot(a, V, 'r', alpha=0.5, lw=2, label='Vaccinated')\nax.plot(a, I1, 'g', alpha=0.5, lw=2, label='Infected1')\nax.plot(a, S2, 'm', alpha=0.5, lw=2, label='Susceptible2')\nax.plot(a, I2, 'y', alpha=0.5, lw=2, label='Infected2')\nax.plot(a, R, 'k', alpha=0.5, lw=2, label='Recovered with immunity')\nax.set_xlabel('Age [years]')\nax.set_ylabel('Number [%]') #B=100\nax.yaxis.set_tick_params(length=0)\nax.xaxis.set_tick_params(length=0)\nax.grid(b=True, which='major', c='w', lw=2, ls='-')\nlegend = ax.legend()\nlegend.get_frame().set_alpha(0.5)\nfor spine in ('top', 'right', 'bottom', 'left'):\n ax.spines[spine].set_visible(False)\nplt.show()",
"_____no_output_____"
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cb1b86819a0e5dd4d50aa16595193715359cfa42 | 1,983 | ipynb | Jupyter Notebook | matplotlib/gallery_jupyter/user_interfaces/embedding_in_gtk3_panzoom_sgskip.ipynb | kingreatwill/penter | 2d027fd2ae639ac45149659a410042fe76b9dab0 | [
"MIT"
] | 13 | 2020-01-04T07:37:38.000Z | 2021-08-31T05:19:58.000Z | matplotlib/gallery_jupyter/user_interfaces/embedding_in_gtk3_panzoom_sgskip.ipynb | kingreatwill/penter | 2d027fd2ae639ac45149659a410042fe76b9dab0 | [
"MIT"
] | 3 | 2020-06-05T22:42:53.000Z | 2020-08-24T07:18:54.000Z | matplotlib/gallery_jupyter/user_interfaces/embedding_in_gtk3_panzoom_sgskip.ipynb | kingreatwill/penter | 2d027fd2ae639ac45149659a410042fe76b9dab0 | [
"MIT"
] | 9 | 2020-10-19T04:53:06.000Z | 2021-08-31T05:20:01.000Z | 36.722222 | 889 | 0.592537 | [
[
[
"%matplotlib inline",
"_____no_output_____"
]
],
[
[
"\n# Embedding in GTK3 with a navigation toolbar\n\n\nDemonstrate NavigationToolbar with GTK3 accessed via pygobject.\n",
"_____no_output_____"
]
],
[
[
"import gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\n\nfrom matplotlib.backends.backend_gtk3 import (\n NavigationToolbar2GTK3 as NavigationToolbar)\nfrom matplotlib.backends.backend_gtk3agg import (\n FigureCanvasGTK3Agg as FigureCanvas)\nfrom matplotlib.figure import Figure\nimport numpy as np\n\nwin = Gtk.Window()\nwin.connect(\"delete-event\", Gtk.main_quit)\nwin.set_default_size(400, 300)\nwin.set_title(\"Embedding in GTK\")\n\nf = Figure(figsize=(5, 4), dpi=100)\na = f.add_subplot(1, 1, 1)\nt = np.arange(0.0, 3.0, 0.01)\ns = np.sin(2*np.pi*t)\na.plot(t, s)\n\nvbox = Gtk.VBox()\nwin.add(vbox)\n\n# Add canvas to vbox\ncanvas = FigureCanvas(f) # a Gtk.DrawingArea\nvbox.pack_start(canvas, True, True, 0)\n\n# Create toolbar\ntoolbar = NavigationToolbar(canvas, win)\nvbox.pack_start(toolbar, False, False, 0)\n\nwin.show_all()\nGtk.main()",
"_____no_output_____"
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] | [
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cb1b9258200e929228f48bfd49da9a8b8bc7e32f | 142,976 | ipynb | Jupyter Notebook | classification/learnai_classification2/Exercises/Ex_EDA.start.ipynb | lsantiago002/ai4i-learn | d903d8f1a99a6af714aa66a6389f374dd4d05a42 | [
"MIT"
] | null | null | null | classification/learnai_classification2/Exercises/Ex_EDA.start.ipynb | lsantiago002/ai4i-learn | d903d8f1a99a6af714aa66a6389f374dd4d05a42 | [
"MIT"
] | null | null | null | classification/learnai_classification2/Exercises/Ex_EDA.start.ipynb | lsantiago002/ai4i-learn | d903d8f1a99a6af714aa66a6389f374dd4d05a42 | [
"MIT"
] | null | null | null | 175.862239 | 57,968 | 0.678415 | [
[
[
"# Classification 2\n\n## Exercise 1: Exploratory Data Analysis\n\n### Overview\n\nThe objective of this course is to build models to predict customer churn for a fictitious telco company. Before we start creating models, let's begin by having a closer look at our data and doing some basic data wrangling.\n\nGo through this notebook and modify the code accordingly (i.e. #TASK) based on the text and/or the comments.\n\n### Data\nDownload data from here:\nhttps://public.dhe.ibm.com/software/data/sw-library/cognos/mobile/C11/data/Telco_customer_churn.xlsx\n\nDescription of data (for a newer version)\nhttps://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113\n\n### Importing Libraries",
"_____no_output_____"
]
],
[
[
"import numpy as np\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# TASK: Import visualization libraries, matplotlib and seaborn using standard aliases plt and sns respectively\n\nimport warnings\n\n# silence all warnings\nwarnings.filterwarnings(\"ignore\")\n\n# plotting settings\nplt.style.use(['seaborn-paper'])\nplt.rcParams['font.family'] = 'helvetica'",
"_____no_output_____"
]
],
[
[
"### Reading in the Data",
"_____no_output_____"
]
],
[
[
"# TASK: Read in the Excel file. Use the parameter na_values=\" \" to convert any empty cells to a NA value.\n# You may also need to use parameter engine='openpyxl') in newer versions of pandas if you encounter an XLRD error.\ndata = pd.read_excel('../../data/raw/Telco_customer_churn.xlsx', na_values='NA',engine='openpyxl') # TASK: Use pandas to read in an Excel file. \n\ndata.head()",
"_____no_output_____"
],
[
"data.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 7043 entries, 0 to 7042\nData columns (total 33 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 CustomerID 7043 non-null object \n 1 Count 7043 non-null int64 \n 2 Country 7043 non-null object \n 3 State 7043 non-null object \n 4 City 7043 non-null object \n 5 Zip Code 7043 non-null int64 \n 6 Lat Long 7043 non-null object \n 7 Latitude 7043 non-null float64\n 8 Longitude 7043 non-null float64\n 9 Gender 7043 non-null object \n 10 Senior Citizen 7043 non-null object \n 11 Partner 7043 non-null object \n 12 Dependents 7043 non-null object \n 13 Tenure Months 7043 non-null int64 \n 14 Phone Service 7043 non-null object \n 15 Multiple Lines 7043 non-null object \n 16 Internet Service 7043 non-null object \n 17 Online Security 7043 non-null object \n 18 Online Backup 7043 non-null object \n 19 Device Protection 7043 non-null object \n 20 Tech Support 7043 non-null object \n 21 Streaming TV 7043 non-null object \n 22 Streaming Movies 7043 non-null object \n 23 Contract 7043 non-null object \n 24 Paperless Billing 7043 non-null object \n 25 Payment Method 7043 non-null object \n 26 Monthly Charges 7043 non-null float64\n 27 Total Charges 7043 non-null object \n 28 Churn Label 7043 non-null object \n 29 Churn Value 7043 non-null int64 \n 30 Churn Score 7043 non-null int64 \n 31 CLTV 7043 non-null int64 \n 32 Churn Reason 1869 non-null object \ndtypes: float64(3), int64(6), object(24)\nmemory usage: 1.8+ MB\n"
],
[
"# Define columns to keep and filter the original dataset\ncols_to_keep = ['CustomerID', 'Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure Months', 'Phone Service', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Payment Method', 'Monthly Charges', 'Total Charges', 'Churn Label']\ndata = data[cols_to_keep]",
"_____no_output_____"
],
[
"# TASK: Rename the multi-worded columns to remove the space \n# HINT: You can either manually remove the spaces in the column name list or use a loop to remove the space\ndata.columns = [('').join(col.split(' ')) for col in data.columns]\ndata.columns",
"_____no_output_____"
]
],
[
[
"### Basic Information",
"_____no_output_____"
]
],
[
[
"# TASK: Display the number of rows and columns for the dataset\nprint(\"Rows & Columns: {}\".format(data.shape))",
"Rows & Columns: (7032, 21)\n"
],
[
"data.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 7043 entries, 0 to 7042\nData columns (total 21 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 CustomerID 7043 non-null object \n 1 Gender 7043 non-null object \n 2 SeniorCitizen 7043 non-null object \n 3 Partner 7043 non-null object \n 4 Dependents 7043 non-null object \n 5 TenureMonths 7043 non-null int64 \n 6 PhoneService 7043 non-null object \n 7 MultipleLines 7043 non-null object \n 8 InternetService 7043 non-null object \n 9 OnlineSecurity 7043 non-null object \n 10 OnlineBackup 7043 non-null object \n 11 DeviceProtection 7043 non-null object \n 12 TechSupport 7043 non-null object \n 13 StreamingTV 7043 non-null object \n 14 StreamingMovies 7043 non-null object \n 15 Contract 7043 non-null object \n 16 PaperlessBilling 7043 non-null object \n 17 PaymentMethod 7043 non-null object \n 18 MonthlyCharges 7043 non-null float64\n 19 TotalCharges 7043 non-null object \n 20 ChurnLabel 7043 non-null object \ndtypes: float64(1), int64(1), object(19)\nmemory usage: 1.1+ MB\n"
],
[
"# TASK: Display the datatypes for the columns in the dataframe i.e. use the dtypes variable\n# How many columns are numerical and how many are non-numerical\nobj_cols = data.select_dtypes(include=\"object\").columns\nprint(\"Number of non-numerical columns: {}\".format(len(obj_cols)))\n\nnum_cols = data.select_dtypes(exclude=\"object\").columns\nprint(\"Number of numerical columns: {}\".format(len(num_cols)))\n\n# check that cat + num = total\nassert(len(obj_cols) + len(num_cols) == len(data.columns))\n\ndata.dtypes\n",
"Number of non-numerical columns: 24\nNumber of numerical columns: 9\n"
],
[
"# TASK: use count() on the dataframe to count the number of entries for each of the column. Are there any columns with missing values?\nprint(data.count())\n\n# to check for missing % \nprint(\"\\n\")\nprint(data.isnull().mean()) # TotalCharges has null values, but relatively small 0.001562\n # ChurnReason has ~73% mising values\n",
"CustomerID 7043\nGender 7043\nSeniorCitizen 7043\nPartner 7043\nDependents 7043\nTenureMonths 7043\nPhoneService 7043\nMultipleLines 7043\nInternetService 7043\nOnlineSecurity 7043\nOnlineBackup 7043\nDeviceProtection 7043\nTechSupport 7043\nStreamingTV 7043\nStreamingMovies 7043\nContract 7043\nPaperlessBilling 7043\nPaymentMethod 7043\nMonthlyCharges 7043\nTotalCharges 7043\nChurnLabel 7043\ndtype: int64\n\n\nCustomerID 0.0\nGender 0.0\nSeniorCitizen 0.0\nPartner 0.0\nDependents 0.0\nTenureMonths 0.0\nPhoneService 0.0\nMultipleLines 0.0\nInternetService 0.0\nOnlineSecurity 0.0\nOnlineBackup 0.0\nDeviceProtection 0.0\nTechSupport 0.0\nStreamingTV 0.0\nStreamingMovies 0.0\nContract 0.0\nPaperlessBilling 0.0\nPaymentMethod 0.0\nMonthlyCharges 0.0\nTotalCharges 0.0\nChurnLabel 0.0\ndtype: float64\n"
],
[
"# TASK: Use nunique() on the dataframe to count the number of unique values for each of the columns\nprint(data.nunique())",
"CustomerID 7043\nGender 2\nSeniorCitizen 2\nPartner 2\nDependents 2\nTenureMonths 73\nPhoneService 2\nMultipleLines 3\nInternetService 3\nOnlineSecurity 3\nOnlineBackup 3\nDeviceProtection 3\nTechSupport 3\nStreamingTV 3\nStreamingMovies 3\nContract 3\nPaperlessBilling 2\nPaymentMethod 4\nMonthlyCharges 1585\nTotalCharges 6531\nChurnLabel 2\ndtype: int64\n"
],
[
"data.iloc[20,2:]\n",
"_____no_output_____"
],
[
"data.head(10)",
"_____no_output_____"
]
],
[
[
"TASK: Display first few values of the dataframe\nBased on this and the previous display, how would you describe the columns with a small number (less than 10) of unique values?\n\nBased from above, we can say that all the customers are in the same country and state. Most of the columns are categorical like <code>Gender</code> with *Male* or *Female* values, <code>SeniorCitizen</code> with *Yes* or *No* values, <code>ChurnLabel</code> with Yes or No with equivalent <code>ChrunValue</code> of 1 or 0 respectively, etc. It can be observed that there are columns with many different unique values such as the <code>CustomerID</code> which makes sense as each customer should have its own unique id. <code>ZipCode</code>, <code>Latitude</code> and <code>Longitude</code> which have the same number of possible values make sense as the state has the limited number of zip codes.\n\nWe have also observed that there are continuous values in the columns: <code>TotalCharges</code> and <code>MothlyCharges</code> and discrete values in the column <code>TenureMonths</code> which indicates the number of months that the customer use the Telco service.",
"_____no_output_____"
]
],
[
[
"# TASK: Let's analyze the values for the categorical features (columns with less than 10 unique values)\nfor id, row in data.nunique().iteritems(): # this counts the number of unique values for each feature and returns the result as a dictionary\n if(row < 10):\n # TASK: Print out the unique values for the feature\n print(\"{}\\t{}\".format(id, row))",
"Gender\t2\nSeniorCitizen\t2\nPartner\t2\nDependents\t2\nPhoneService\t2\nMultipleLines\t3\nInternetService\t3\nOnlineSecurity\t3\nOnlineBackup\t3\nDeviceProtection\t3\nTechSupport\t3\nStreamingTV\t3\nStreamingMovies\t3\nContract\t3\nPaperlessBilling\t2\nPaymentMethod\t4\nChurnLabel\t2\n"
],
[
"# For columns with 3 or 4 unique values, display them to see if they make sense\nfor col in ['MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', \"PaymentMethod\"]:\n print(\"{} : {}\".format(col, np.unique(data[col].values)))",
"MultipleLines : ['No' 'No phone service' 'Yes']\nInternetService : ['DSL' 'Fiber optic' 'No']\nOnlineSecurity : ['No' 'No internet service' 'Yes']\nOnlineBackup : ['No' 'No internet service' 'Yes']\nDeviceProtection : ['No' 'No internet service' 'Yes']\nTechSupport : ['No' 'No internet service' 'Yes']\nStreamingTV : ['No' 'No internet service' 'Yes']\nStreamingMovies : ['No' 'No internet service' 'Yes']\nContract : ['Month-to-month' 'One year' 'Two year']\nPaymentMethod : ['Bank transfer (automatic)' 'Credit card (automatic)' 'Electronic check'\n 'Mailed check']\n"
]
],
[
[
"**Observations**\n\n- The value 'No phone service' found in MultipleLines is already captured by the PhoneService feature ('No' value)\n- The value 'No internet service' found in the several features is already captured by InternetService feature ('No' value)\n- Values that are longer or more complex may need to be simplified.\n\nConclusion: These values can be considered duplicated information as they are found in the PhoneService and InternetService features. There are several options to consider here:\n\n- Retain all features and values as is\n- Convert the 'No Internet Service'/'No phone service' to 'No' in the features as PhoneService and InternetService features has already captured this information\n- Remove the PhoneService feature as MultipleLines feature has this information. To remove the InternetService feature, we would have to 'fold in' the values in the other features e.g. the values for OnlineSecurity could be changed to ['DSL_No','DSL_Yes','FiberOptic_No','FiberOptic_Yes','No internet service']\n\nFor this course, we will be using the second option (without justification). You are encouraged to test the others options during modelling to see if there are any impact.",
"_____no_output_____"
],
[
"### Data Wrangling\n\nBased on the discoveries made above, we will be modifying our data before continuing the exploration.",
"_____no_output_____"
]
],
[
[
"# Replace 'No phone service'\ndata['MultipleLines'] = data['MultipleLines'].replace({'No phone service':'No'})",
"_____no_output_____"
],
[
"# TASK: Replace 'No internet service'\nfor col in ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']:\n # similar to the operation for 'No phone service' above\n data[col] = data[col].replace({'No internet service':'No'})",
"_____no_output_____"
],
[
"# Simplify the values made up of phrases\ndata['PaymentMethod'] = data['PaymentMethod'].replace({\n 'Bank transfer (automatic)':'transfer',\n 'Credit card (automatic)':'creditcard',\n 'Electronic check':'echeck',\n 'Mailed check':'mcheck'\n})\n\ndata['InternetService'] = data['InternetService'].replace({\n 'Fiber optic':'FiberOptic'\n})\n\ndata['Contract'] = data['Contract'].replace({\n 'Month-to-month':'M2M',\n 'One year':'OneYear',\n 'Two year':'TwoYear'\n})",
"_____no_output_____"
],
[
"# Remove the rows with empty TotalCharges value\ndata = data[data[\"TotalCharges\"].notnull()]",
"_____no_output_____"
],
[
"data.shape",
"_____no_output_____"
],
[
"data.info()\n# we can see that the TotalCharges column is of object data type, we must change it into float ",
"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 7043 entries, 0 to 7042\nData columns (total 21 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 CustomerID 7043 non-null object \n 1 Gender 7043 non-null object \n 2 SeniorCitizen 7043 non-null object \n 3 Partner 7043 non-null object \n 4 Dependents 7043 non-null object \n 5 TenureMonths 7043 non-null int64 \n 6 PhoneService 7043 non-null object \n 7 MultipleLines 7043 non-null object \n 8 InternetService 7043 non-null object \n 9 OnlineSecurity 7043 non-null object \n 10 OnlineBackup 7043 non-null object \n 11 DeviceProtection 7043 non-null object \n 12 TechSupport 7043 non-null object \n 13 StreamingTV 7043 non-null object \n 14 StreamingMovies 7043 non-null object \n 15 Contract 7043 non-null object \n 16 PaperlessBilling 7043 non-null object \n 17 PaymentMethod 7043 non-null object \n 18 MonthlyCharges 7043 non-null float64\n 19 TotalCharges 7043 non-null object \n 20 ChurnLabel 7043 non-null object \ndtypes: float64(1), int64(1), object(19)\nmemory usage: 1.2+ MB\n"
],
[
"# After data wrangling, repeat prints\nprint(\"Rows & Columns: {}\".format(data.shape))\nprint(\"################################################\")\n# Number of unique values for each of the columns\nprint(data.nunique())\nprint(\"################################################\")\n# Check the data types\nprint(data.dtypes)\nprint(\"################################################\")\n# Display first few values\nprint(data.head())",
"Rows & Columns: (7043, 21)\n################################################\nCustomerID 7043\nGender 2\nSeniorCitizen 2\nPartner 2\nDependents 2\nTenureMonths 73\nPhoneService 2\nMultipleLines 2\nInternetService 3\nOnlineSecurity 2\nOnlineBackup 2\nDeviceProtection 2\nTechSupport 2\nStreamingTV 2\nStreamingMovies 2\nContract 3\nPaperlessBilling 2\nPaymentMethod 4\nMonthlyCharges 1585\nTotalCharges 6531\nChurnLabel 2\ndtype: int64\n################################################\nCustomerID object\nGender object\nSeniorCitizen object\nPartner object\nDependents object\nTenureMonths int64\nPhoneService object\nMultipleLines object\nInternetService object\nOnlineSecurity object\nOnlineBackup object\nDeviceProtection object\nTechSupport object\nStreamingTV object\nStreamingMovies object\nContract object\nPaperlessBilling object\nPaymentMethod object\nMonthlyCharges float64\nTotalCharges object\nChurnLabel object\ndtype: object\n################################################\n CustomerID Gender SeniorCitizen Partner Dependents TenureMonths \\\n0 3668-QPYBK Male No No No 2 \n1 9237-HQITU Female No No Yes 2 \n2 9305-CDSKC Female No No Yes 8 \n3 7892-POOKP Female No Yes Yes 28 \n4 0280-XJGEX Male No No Yes 49 \n\n PhoneService MultipleLines InternetService OnlineSecurity ... \\\n0 Yes No DSL Yes ... \n1 Yes No FiberOptic No ... \n2 Yes Yes FiberOptic No ... \n3 Yes Yes FiberOptic No ... \n4 Yes Yes FiberOptic No ... \n\n DeviceProtection TechSupport StreamingTV StreamingMovies Contract \\\n0 No No No No M2M \n1 No No No No M2M \n2 Yes No Yes Yes M2M \n3 Yes Yes Yes Yes M2M \n4 Yes No Yes Yes M2M \n\n PaperlessBilling PaymentMethod MonthlyCharges TotalCharges ChurnLabel \n0 Yes mcheck 53.85 108.15 Yes \n1 Yes echeck 70.70 151.65 Yes \n2 Yes echeck 99.65 820.5 Yes \n3 Yes echeck 104.80 3046.05 Yes \n4 Yes transfer 103.70 5036.3 Yes \n\n[5 rows x 21 columns]\n"
],
[
"# Randomly display 1 row from the dataframe\nprint(data.sample(n=1).iloc[0])",
"CustomerID 1169-WCVAK\nGender Male\nSeniorCitizen No\nPartner Yes\nDependents No\nTenureMonths 19\nPhoneService Yes\nMultipleLines Yes\nInternetService FiberOptic\nOnlineSecurity Yes\nOnlineBackup Yes\nDeviceProtection No\nTechSupport Yes\nStreamingTV No\nStreamingMovies No\nContract M2M\nPaperlessBilling Yes\nPaymentMethod creditcard\nMonthlyCharges 88.8\nTotalCharges 1672.35\nChurnLabel No\nName: 6137, dtype: object\n"
],
[
"# TASK: Save the data as a CSV fiile\ndata.to_csv(\"../../data/interim/0telco_churn.csv\", index=False)",
"_____no_output_____"
]
],
[
[
"### Additional Exploration\n\n**TASK:** This is the open-ended section of the exercise. Use any exploration techniques that you know to further explore and understand your data. We expect a number of visualizations that can show the relationships between features as well as between features and the outcome variable 'ChurnLabel'. Some of the questions in the quiz may require you to perform additional analyses.",
"_____no_output_____"
],
[
"\nFrom the initial exploration above, the following questions have been answered:\n* What is the shape of your data? Number of rows and columns.\n* How many of the columns are numerical and how many are categorical?\n* What is the name of the column to be predicted?\n* For the numerical columns, how many missing values are there for each column?\n* For the categorical columns, how many missing values are there for each column?\n\nHowever, we will still need to do further exploration using visualizations to better understand the data using the following questions as the goal:\n* For the numerical columns, what does the distributions look like?\n* How are the various attributes correlated to the outcome variable?\n* What visualizations can you use to highlight outliers in the data?\n",
"_____no_output_____"
],
[
"**Recapping on the columns**\n\nBefore we begin to decide on the type of visualisations to use to aim our understanding, let's recap on the columns we have and the data type of it.",
"_____no_output_____"
]
],
[
[
"print(\"Categorical columns: {}\\n\".format(obj_cols))\nprint(\"Numerical columns: {}\\n\".format(num_cols))\nprint(\"Total columns: {} categorical, {} numerical.\".format(len(obj_cols), len(num_cols)))",
"Categorical columns: Index(['CustomerID', 'Gender', 'SeniorCitizen', 'Partner', 'Dependents',\n 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',\n 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',\n 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',\n 'TotalCharges', 'ChurnLabel'],\n dtype='object')\n\nNumerical columns: Index(['TenureMonths', 'MonthlyCharges'], dtype='object')\n\nTotal columns: 19 categorical, 2 numerical.\n"
]
],
[
[
"**Utilities Functions**\n\nLet's define some helper functions that will help us iterate through all the columns with same datatype.",
"_____no_output_____"
]
],
[
[
"# Example: Look at Churn vs MonthCharges\nplt.clf()\nfor label in ['Yes','No']:\n subset = data[data.ChurnLabel==label]\n \n # Draw the density plot\n sns.distplot(subset['MonthlyCharges'], hist = False, kde = True,\n kde_kws = {'linewidth': 3, 'shade':True},\n label = label)\n \n# Plot formatting\nplt.legend(prop={'size': 16}, title = 'ChurnLabel')\nplt.title('Density Plot with ChurnLabel')\nplt.xlabel('') # Monthly Charges\nplt.ylabel('Density')\nplt.show()",
"findfont: Font family ['helvetica'] not found. Falling back to DejaVu Sans.\nfindfont: Font family ['helvetica'] not found. Falling back to DejaVu Sans.\nfindfont: Font family ['helvetica'] not found. Falling back to DejaVu Sans.\nfindfont: Font family ['helvetica'] not found. Falling back to DejaVu Sans.\nfindfont: Font family ['helvetica'] not found. Falling back to DejaVu Sans.\n"
],
[
"# Additional Exploration",
"_____no_output_____"
]
]
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[
[
"import util\n\nimport jax\nimport jax.numpy as np\n\nimport pandas as pd\n\nimport matplotlib.pyplot as plt",
"\nBad key \"nbagg.transparent\" on line 426 in\n/usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle.\nYou probably need to get an updated matplotlibrc file from\nhttps://github.com/matplotlib/matplotlib/blob/v3.2.1/matplotlibrc.template\nor from the matplotlib source distribution\n\nBad key \"animation.mencoder_path\" on line 509 in\n/usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle.\nYou probably need to get an updated matplotlibrc file from\nhttps://github.com/matplotlib/matplotlib/blob/v3.2.1/matplotlibrc.template\nor from the matplotlib source distribution\n\nBad key \"animation.mencoder_args\" on line 512 in\n/usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle.\nYou probably need to get an updated matplotlibrc file from\nhttps://github.com/matplotlib/matplotlib/blob/v3.2.1/matplotlibrc.template\nor from the matplotlib source distribution\nIn /usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \nThe text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\nIn /usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \nThe savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\nIn /usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \nThe pgf.debug rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\nIn /usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \nThe verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\nIn /usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \nThe verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n"
],
[
"import numpy as base_np\nfrom epiweeks import Week, Year\nstart = '2020-03-15'\nforecast_start = '2020-04-19'\n\nnum_weeks = 8\ndata = util.load_state_data()\nplaces = sorted(list(data.keys()))\n#places = ['AK', 'AL']\n\nallQuantiles = [0.01,0.025]+list(np.arange(0.05,0.95+0.05,0.05)) + [0.975,0.99]\n\nforecast_date = pd.to_datetime('2020-04-19')\ncurrentEpiWeek = Week.fromdate(forecast_date) - 1\n\n\nforecast = {'quantile':[], 'value':[], 'type':[], 'location':[], 'target':[]}\n\nprint (currentEpiWeek)\nfor place in places:\n prior_samples, mcmc_samples, post_pred_samples = util.load_samples(place, path='out')\n forecast_samples = post_pred_samples['z_future']\n t = pd.date_range(start=forecast_start, periods=forecast_samples.shape[1], freq='D')\n weekly_df = pd.DataFrame(index=t, data=np.transpose(forecast_samples)).resample(\"1w\",label='right').last()\n weekly_df[weekly_df<0.] = 0.\n for time, samples in weekly_df.iterrows():\n for q in allQuantiles:\n deathPrediction = base_np.percentile(samples,q*100)\n forecast[\"quantile\"].append(\"{:.3f}\".format(q))\n forecast[\"value\"].append(deathPrediction)\n forecast[\"type\"].append(\"quantile\")\n forecast[\"location\"].append(place)\n horizon_date = Week.fromdate(time)\n week_ahead = horizon_date.week - currentEpiWeek.week\n forecast[\"target\"].append(\"{:d} wk ahead cum death\".format(week_ahead))\n currentEpiWeek_datetime = currentEpiWeek.startdate()\n forecast[\"forecast_date\"] = \"{:4d}-{:02d}-{:02d}\".format(currentEpiWeek_datetime.year,currentEpiWeek_datetime.month,currentEpiWeek_datetime.day)\n if q==0.50:\n forecast[\"quantile\"].append(\"NA\")\n forecast[\"value\"].append(deathPrediction)\n forecast[\"type\"].append(\"point\")\n forecast[\"location\"].append(place)\n forecast[\"target\"].append(\"{:d} wk ahead cum death\".format(week_ahead))\n forecast[\"forecast_date\"] = \"{:4d}-{:02d}-{:02d}\".format(currentEpiWeek_datetime.year,currentEpiWeek_datetime.month,currentEpiWeek_datetime.day)\n #base_np.quantile(hosp,axis=1,q=allQuantiles)",
"202016\n"
],
[
"\nforecast = pd.DataFrame(forecast)\n",
"_____no_output_____"
],
[
"forecast.loc[forecast.type==\"point\"]\n",
"_____no_output_____"
],
[
"\nfips_codes = pd.read_csv('/Users/gcgibson/covid19-forecast-hub/template/state_fips_codes.csv')\n",
"_____no_output_____"
],
[
"df_truth = forecast.merge(fips_codes, left_on='location', right_on='state', how='left')\ndf_truth[\"state_code\"] = df_truth[\"state_code\"].astype(int)\ndf_truth = df_truth[[\"quantile\", \"value\", \"type\", \"state_code\",\"target\",\"forecast_date\"]]\n\ndf_truth = df_truth.rename(columns={\"state_code\": \"location\"})\n\n",
"_____no_output_____"
],
[
"import datetime\n\ndf_truth['location'] = df_truth['location'].apply(lambda x: '{0:0>2}'.format(x))\n#df_truth['forecast_date'] = datetime.datetime(2020, 4, 19)\ndf_truth.to_csv(f'out/sub.csv', float_format=\"%.0f\")\ndf_truth",
"_____no_output_____"
]
]
] | [
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cb1b9cc239ac4e358391d4115a035f1074ca2336 | 24,953 | ipynb | Jupyter Notebook | scripts/3-construccion-conceptos-estadisticos-analiticos/1-estimadores/estimadores.ipynb | OscarPalominoC/CursoFundamentosDeEstadisticaYAnalisisDeDatosConPython | a65d389674732178356b6027846e3fc682345921 | [
"MIT"
] | 1 | 2020-09-01T15:54:28.000Z | 2020-09-01T15:54:28.000Z | scripts/3-construccion-conceptos-estadisticos-analiticos/1-estimadores/estimadores.ipynb | OscarPalominoC/CursoFundamentosDeEstadisticaYAnalisisDeDatosConPython | a65d389674732178356b6027846e3fc682345921 | [
"MIT"
] | null | null | null | scripts/3-construccion-conceptos-estadisticos-analiticos/1-estimadores/estimadores.ipynb | OscarPalominoC/CursoFundamentosDeEstadisticaYAnalisisDeDatosConPython | a65d389674732178356b6027846e3fc682345921 | [
"MIT"
] | null | null | null | 106.636752 | 10,260 | 0.880696 | [
[
[
"import sklearn\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom scipy import stats\nimport seaborn as sns\n\n%matplotlib inline",
"_____no_output_____"
],
[
"from scipy.stats import norm\n\nx1 = 3\nmu1 = 4\nmu2 = 7\nsigma = 1\n# pdf = Probability Density Function\np_muestra = norm.pdf(x1, mu2, sigma)\n\np_muestra",
"_____no_output_____"
],
[
"from scipy.stats import norm\n\nx1 = 3\nx2 = 10\n\nmu1 = 4\nmu2 = 7\nsigma = 1\n# pdf = Probability Density Function\np_muestra = norm.pdf(x1, mu1, sigma) * norm.pdf(x2, mu1, sigma)\n\np_muestra",
"_____no_output_____"
],
[
"muestra_10 = norm.rvs(5, sigma, size= 10)\nmuestra_10",
"_____no_output_____"
],
[
"data1 = norm.rvs(mu1, sigma, size= 100000)\ndata2 = norm.rvs(mu2, sigma, size= 100000)",
"_____no_output_____"
],
[
"ax = sns.distplot(data1, bins = 50, color = 'blue', kde = False)\nax.set(xlabel = 'Distribución normal mu1', ylabel = 'Frecuencia')\n\nax = sns.distplot(data2, bins = 50, color = 'red', kde = False)\nax.set(xlabel = 'Distribución normal mu1', ylabel = 'Frecuencia')",
"_____no_output_____"
],
[
"muestra_10\ny = list([])\n\nfor i in range(10):\n y.append(3000)",
"_____no_output_____"
],
[
"ax = sns.distplot(data1, bins = 50, color = 'blue', kde = False)\nax.set(xlabel = 'Distribución normal mu1', ylabel = 'Frecuencia')\n\nax = sns.distplot(data2, bins = 50, color = 'red', kde = False)\nax.set(xlabel = 'Distribución normal mu1', ylabel = 'Frecuencia')\n\nax.scatter(muestra_10, y, c = 'k')",
"_____no_output_____"
]
],
[
[
"Bajo una muestra podemos tener la probabilidad de ocurrencia basado en mu1 o mu2, con una diferencia grande.\nPodemos ver cómo la muestra puede pertenecer con mayor o menor probabilidad a alguna de las 2 hipotesis sobre el parámetro poblacional mu1.",
"_____no_output_____"
]
]
] | [
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cb1ba33b9b0036542a83e00a6d312bead196cec1 | 59,808 | ipynb | Jupyter Notebook | animals/arhtropods/.ipynb_checkpoints/terrestrial arthropods-checkpoint.ipynb | yinonbaron/biomass_distribution | 783a8d2f59754bde9b0ea802512b131abbe7d8a0 | [
"MIT"
] | 1 | 2021-05-17T13:55:48.000Z | 2021-05-17T13:55:48.000Z | animals/arhtropods/.ipynb_checkpoints/terrestrial arthropods-checkpoint.ipynb | yinonbaron/biomass_distribution | 783a8d2f59754bde9b0ea802512b131abbe7d8a0 | [
"MIT"
] | null | null | null | animals/arhtropods/.ipynb_checkpoints/terrestrial arthropods-checkpoint.ipynb | yinonbaron/biomass_distribution | 783a8d2f59754bde9b0ea802512b131abbe7d8a0 | [
"MIT"
] | 2 | 2018-01-10T08:53:35.000Z | 2021-05-17T13:55:50.000Z | 37.263551 | 764 | 0.452899 | [
[
[
"# Estimating the biomass of terrestrial arthropods\nTo estimate the biomass of terrestrial arthropods, we rely on two parallel methods - a method based on average biomass densities of arthropods extrapolated to the global ice-free land surface, and a method based on estimates of the average carbon content of a characteristic arthropod and the total number of terrestrial arthropods.\n\n## Average biomass densities method\nWe collected values from the literature on the biomass densities of arthropods per unit area. We assume, based on [Stork et al.](http://dx.doi.org/10.1007/978-94-009-1685-2_1), most of the biomass is located in the soil, litter or in the canopy of trees. We thus estimate a mean biomass density of arhtropods in soil, litter and in canopies, sum those biomass densities and apply them across the entire ice-free land surface.\n\n### Litter arthropod biomass\nWe complied a list of values from several different habitats. Most of the measurements are from forests and savannas. For some of the older studies, we did not have access to the original data, but to a summary of the data made by two main studies: [Gist & Crossley](http://dx.doi.org/10.2307/2424109) and [Brockie & Moeed](http://dx.doi.org/10.1007/BF00377108). Here is a sample of the data from Gist & Grossley:",
"_____no_output_____"
]
],
[
[
"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import gmean\nimport sys\nsys.path.insert(0, '../../statistics_helper/')\nfrom CI_helper import *\npd.options.display.float_format = '{:,.1f}'.format\n# Load global stocks data\ngc_data = pd.read_excel('terrestrial_arthropods_data.xlsx','Gist & Crossley',skiprows=1)\ngc_data.head()",
"_____no_output_____"
]
],
[
[
"Here is a sample from Brockie & Moeed:",
"_____no_output_____"
]
],
[
[
"bm_data = pd.read_excel('terrestrial_arthropods_data.xlsx','Brockie & Moeed',skiprows=1)\nbm_data.head()",
"_____no_output_____"
]
],
[
[
"We calculate the sum of biomass of all the groups of arthropods in each study to provide an estimate for the total biomass density of arthropods in litter:",
"_____no_output_____"
]
],
[
[
"gc_study = gc_data.groupby('Study').sum()\nbm_study = bm_data.groupby('Study').sum()\n\nprint('The estimate from Brockie & Moeed:')\nbm_study",
"The estimate from Brockie & Moeed:\n"
],
[
"print('The estimate from Gist & Crossley:')\ngc_study",
"The estimate from Gist & Crossley:\n"
]
],
[
[
"In cases where data is coflicting between the two studies, we calculate the mean. We merge the data from the papers to generate a list of estimates on the total biomass density of arhtropods",
"_____no_output_____"
]
],
[
[
"# Concat the data from the two studies\nconc = pd.concat([gc_study,bm_study])\nconc_mean = conc.groupby(conc.index).mean()\nconc_mean",
"_____no_output_____"
]
],
[
[
"We calculate from the dry weight and wet weight estimates the biomass density in g C $m^{-2}$ by assuming 70% water content and 50% carbon in dry mass:",
"_____no_output_____"
]
],
[
[
"# Fill places with no dry weight estimate with 30% of the wet weight estimate \nconc_mean['Dry weight [g m^-2]'].fillna(conc_mean['Wet weight [g m^-2]']*0.3,inplace=True)\n\n# Calculate carbon biomass as 50% of dry weight\nconc_mean['Biomass density [g C m^-2]'] = conc_mean['Dry weight [g m^-2]']/2\nconc_mean['Biomass density [g C m^-2]']",
"_____no_output_____"
]
],
[
[
"We calculate the geometric mean of the estimates from the different studies as our best estimate of the biomass density of litter arthropods.",
"_____no_output_____"
]
],
[
[
"litter_biomass_density = gmean(conc_mean.iloc[0:5,3])\nprint('Our best estimate for the biomass density of arthropods in litter is ≈%.0f g C m^-2' %litter_biomass_density)",
"Our best estimate for the biomass density of arthropods in litter is ≈1 g C m^-2\n"
]
],
[
[
"### Soil arthropod biomass\nAs our source for estimating the biomass of soil arthropods, we use these data collected from the literature, which are detailed below:",
"_____no_output_____"
]
],
[
[
"# Load additional data\nsoil_data = pd.read_excel('terrestrial_arthropods_data.xlsx','Soil',index_col='Reference')\nsoil_data",
"_____no_output_____"
]
],
[
[
"We calculate the geometric mean of the estimate for the biomass density of arthropods in soils:",
"_____no_output_____"
]
],
[
[
"# Calculate the geometric mean of the estimates of the biomass density of soil arthropods\nsoil_biomass_density = gmean(soil_data['Biomass density [g C m^-2]'])\n\nprint('Our best estimate for the biomass density of arthropods in soils is ≈%.0f g C m^-2' %soil_biomass_density)",
"Our best estimate for the biomass density of arthropods in soils is ≈1 g C m^-2\n"
]
],
[
[
"If we sum the biomass density of soil and litter arthropods, we arrive at an estimate of ≈2 g C m^-2, which is inline with the data from Kitazawa et al. of 1-2 g C m^-2.",
"_____no_output_____"
],
[
"### Canopy arthropod biomass\nData on the biomass density of canopy arthropods is much less abundant. We extracted from the literature the following values:",
"_____no_output_____"
]
],
[
[
"# Load the data on the biomass density of canopy arthropods\ncanopy_data = pd.read_excel('terrestrial_arthropods_data.xlsx', 'Canopy',index_col='Reference')\ncanopy_data",
"_____no_output_____"
]
],
[
[
"We calculate the geometric mean of the estimates for the biomass density of arthropods in canopies:",
"_____no_output_____"
]
],
[
[
"# Calculate the geometric mean of the estimates of biomass densitiy of canopy arthropods\ncanopy_biomass_density = gmean(canopy_data['Biomass density [g C m^-2]'])\nprint('Our best estimate for the biomass density of arthropods in canopies is ≈%.1f g C m^-2' %canopy_biomass_density)",
"Our best estimate for the biomass density of arthropods in canopies is ≈0.7 g C m^-2\n"
]
],
[
[
"To generate our best estimate for the biomass of arthropods using estimates of biomass densities, we sum the estimates for the biomass density of arthropods in soils and in canopies, and apply this density over the entire ice-free land surface of $1.3×10^{14} \\: m^2$:",
"_____no_output_____"
]
],
[
[
"# Sum the biomass densities of arthropods in soils and in canopies\ntotal_denisty = litter_biomass_density+soil_biomass_density+canopy_biomass_density\n\n# Apply the average biomass density across the entire ice-free land surface\nmethod1_estimate = total_denisty*1.3e14\n\nprint('Our best estimate for the biomass of terrestrial arthropods using average biomass densities is ≈%.1f Gt C' %(method1_estimate/1e15))",
"Our best estimate for the biomass of terrestrial arthropods using average biomass densities is ≈0.4 Gt C\n"
]
],
[
[
"## Average carbon content method\nIn this method, in order to estimate the total biomass of arthropods, we calculate the carbon content of a characteristic arthropod, and multiply this carbon content by an estimate for the total number of arthropods.\nWe rely both on data from Gist & Crossley which detail the total number of arthropods per unit area as well as the total biomass of arthropods per unit area for serveal studies. Form this data we can calculate the characteristic carbon content of a single arthropod assuming 50% carbon in dry mass:",
"_____no_output_____"
]
],
[
[
"pd.options.display.float_format = '{:,.1e}'.format\n\n# Calculate the carbon content of a single arthropod by dividing the dry weight by 2 (assuming 50% carbon in\n# dry weight) and dividing the result by the total number of individuals\ngc_study['Carbon content [g C per individual]'] = gc_study['Dry weight [g m^-2]']/2/gc_study['Density of individuals [N m^-2]']\n\ngc_study",
"_____no_output_____"
]
],
[
[
"We combine the data from these studies with data from additional sources detailed below:",
"_____no_output_____"
]
],
[
[
"# Load additional data sources\nother_carbon_content_data = pd.read_excel('terrestrial_arthropods_data.xlsx', 'Carbon content',index_col='Reference')\n\nother_carbon_content_data",
"_____no_output_____"
]
],
[
[
"We calculate the geometric mean of the estimates from the difference studies and use it as our best estimate for the carbon content of a characteristic arthropod:",
"_____no_output_____"
]
],
[
[
"# Calculate the geometric mean of the estimates from the different studies on the average carbon content of a single arthropod.\naverage_carbon_content = gmean(pd.concat([other_carbon_content_data,gc_study])['Carbon content [g C per individual]'])\nprint('Our best estimate for the carbon content of a characteristic arthropod is %.1e g C' % average_carbon_content)",
"Our best estimate for the carbon content of a characteristic arthropod is 1.2e-04 g C\n"
]
],
[
[
"To estimate the total biomass of arthropods using the characteristic carbon content method, we multiply our best estiamte of the carbon content of a single arthropod by an estimate of the total number of arthropods made by [Williams](http://dx.doi.org/10.1086/282115). Williams estiamted a total of $~10^{18}$ individual insects in soils. We assume this estimate of the total number of insects is close to the total number of arthropods (noting that in this estimate Williams also included collembola which back in 1960 were considered insects, and are usually very numerous because of their small size). To estimate the total biomass of arthropods, we multiply the carbon content of a single arthropod by the the estimate for the total number of arthropods:",
"_____no_output_____"
]
],
[
[
"# Total number of insects estimated by Williams\ntot_num_arthropods = 1e18\n\n# Calculate the total biomass of arthropods\nmethod2_estimate = average_carbon_content*tot_num_arthropods\nprint('Our best estimate for the biomass of terrestrial arthropods using average biomass densities is ≈%.1f Gt C' %(method2_estimate/1e15))",
"Our best estimate for the biomass of terrestrial arthropods using average biomass densities is ≈0.1 Gt C\n"
]
],
[
[
"Our best estimate for the biomass of arthropods is the geometric mean of the estimates from the two methods:",
"_____no_output_____"
]
],
[
[
"# Calculate the geometric mean of the estimates using the two methods\nbest_estimate = gmean([method1_estimate,method2_estimate])\nprint('Our best estimate for the biomass of terrestrial arthropods is ≈%.1f Gt C' %(best_estimate/1e15)) ",
"Our best estimate for the biomass of terrestrial arthropods is ≈0.2 Gt C\n"
]
],
[
[
"# Uncertainty analysis\nTo assess the uncertainty associated with the estimate of the biomass of terrestrial arthropods, we compile a collection of the different sources of uncertainty, and combine them to project the total uncertainty. We survey the interstudy uncertainty for estimates within each method, the total uncertainty of each method and the uncertainty of the geometric mean of the values from the two methods.\n\n## Average biomass densities method\nWe calculate the 95% confidence interval for the geometric mean of the biomass densities reported for soil and canopy arthropods:",
"_____no_output_____"
]
],
[
[
"litter_CI = geo_CI_calc(conc_mean['Biomass density [g C m^-2]'])\nsoil_CI = geo_CI_calc(soil_data['Biomass density [g C m^-2]'])\ncanopy_CI = geo_CI_calc(canopy_data['Biomass density [g C m^-2]'])\nprint('The 95 percent confidence interval for the average biomass density of soil arthropods is ≈%.1f-fold' %litter_CI)\nprint('The 95 percent confidence interval for the average biomass density of soil arthropods is ≈%.1f-fold' %soil_CI)\nprint('The 95 percent confidence interval for the average biomass density of canopy arthropods is ≈%.1f-fold' %canopy_CI)",
"The 95 percent confidence interval for the average biomass density of soil arthropods is ≈2.0-fold\nThe 95 percent confidence interval for the average biomass density of soil arthropods is ≈1.8-fold\nThe 95 percent confidence interval for the average biomass density of canopy arthropods is ≈1.9-fold\n"
]
],
[
[
"To estimate the uncertainty of the global biomass estimate using the average biomass density method, we propagate the uncertainties of the soil and canopy biomass density:",
"_____no_output_____"
]
],
[
[
"method1_CI = CI_sum_prop(estimates=np.array([litter_biomass_density,soil_biomass_density,canopy_biomass_density]),mul_CIs=np.array([litter_CI,soil_CI,canopy_CI]))\nprint('The 95 percent confidence interval biomass of arthropods using the biomass densities method is ≈%.1f-fold' %method1_CI)",
"The 95 percent confidence interval biomass of arthropods using the biomass densities method is ≈1.5-fold\n"
]
],
[
[
"## Average carbon content method\nAs a measure of the uncertainty of the estimate of the total biomass of arthropods using the average carbon content method, we calculate the 95% confidence interval of the geometric mean of the estimates from different studies of the carbon content of a single arthropod:",
"_____no_output_____"
]
],
[
[
"carbon_content_CI = geo_CI_calc(pd.concat([other_carbon_content_data,gc_study])['Carbon content [g C per individual]'])\nprint('The 95 percent confidence interval of the carbon content of a single arthropod is ≈%.1f-fold' %carbon_content_CI)",
"The 95 percent confidence interval of the carbon content of a single arthropod is ≈4.1-fold\n"
]
],
[
[
"We combine this uncertainty of the average carbon content of a single arthropod with the uncertainty reported in Williams on the total number of insects of about one order of magnitude. This provides us with a measure of the uncertainty of the estimate of the biomass of arthropods using the average carbon content method.",
"_____no_output_____"
]
],
[
[
"# The uncertainty of the total number of insects from Williams\ntot_num_arthropods_CI = 10\n\n# Combine the uncertainties of the average carbon content of a single arthropod and the uncertainty of \n# the total number of arthropods\nmethod2_CI = CI_prod_prop(np.array([carbon_content_CI,tot_num_arthropods_CI]))\nprint('The 95 percent confidence interval biomass of arthropods using the average carbon content method is ≈%.1f-fold' %method2_CI)",
"The 95 percent confidence interval biomass of arthropods using the average carbon content method is ≈14.9-fold\n"
]
],
[
[
"## Inter-method uncertainty\nWe calculate the 95% conficence interval of the geometric mean of the estimates of the biomass of arthropods using the average biomass density or the average carbon content:",
"_____no_output_____"
]
],
[
[
"inter_CI = geo_CI_calc(np.array([method1_estimate,method2_estimate]))\nprint('The inter-method uncertainty of the geometric mean of the estimates of the biomass of arthropods is ≈%.1f' % inter_CI)",
"The inter-method uncertainty of the geometric mean of the estimates of the biomass of arthropods is ≈3.0\n"
]
],
[
[
"As our best projection for the uncertainty associated with the estimate of the biomass of terrestrial arthropods, we take the highest uncertainty among the collection of uncertainties we generate, which is the ≈15-fold uncertainty of the average carbon content method. ",
"_____no_output_____"
]
],
[
[
"mul_CI = np.max([inter_CI,method1_CI,method2_CI])\nprint('Our best projection for the uncertainty associated with the estimate of the biomass of terrestrial arthropods is ≈%.1f-fold' %mul_CI)",
"Our best projection for the uncertainty associated with the estimate of the biomass of terrestrial arthropods is ≈14.9-fold\n"
]
],
[
[
"## The biomass of termites\nAs we state in the Supplementary Information, there are some groups of terrestrial arthropods for which better estimates are available. An example is the biomass of termites. We use the data in [Sanderson](http://dx.doi.org/10.1029/96GB01893) to estimate the global biomass of termites:",
"_____no_output_____"
]
],
[
[
"# Load termite data\ntermite_data = pd.read_excel('terrestrial_arthropods_data.xlsx', 'Sanderson', skiprows=1, index_col=0)\n\n# Multiply biomass density by biome area and sum over biomes\ntermite_biomass = (termite_data['Area [m^2]']* termite_data['Biomass density [g wet weight m^-2]']).sum()\n\n# Calculate carbon mass assuming carbon is 15% of wet weight\ntermite_biomass *= 0.15\n\nprint('The estimate of the total biomass of termites based on Sanderson is ≈%.2f Gt C' %(termite_biomass/1e15))",
"The estimate of the total biomass of termites based on Sanderson is ≈0.07 Gt C\n"
]
]
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cb1baa5aa3bf4ad92b10b4bf33749fa3bfb9743b | 8,497 | ipynb | Jupyter Notebook | 3_Pandas/08_Exercise2.ipynb | tkaya94/UdemyDataScience | 83fe006bace0e91a273006546df3f3ee408b7797 | [
"MIT"
] | 1 | 2022-01-05T12:52:20.000Z | 2022-01-05T12:52:20.000Z | 3_Pandas/08_Exercise2.ipynb | tkaya94/UdemyDataScience | 83fe006bace0e91a273006546df3f3ee408b7797 | [
"MIT"
] | null | null | null | 3_Pandas/08_Exercise2.ipynb | tkaya94/UdemyDataScience | 83fe006bace0e91a273006546df3f3ee408b7797 | [
"MIT"
] | 5 | 2021-05-21T20:28:45.000Z | 2022-01-10T10:41:42.000Z | 49.690058 | 2,051 | 0.375191 | [
[
[
"# Pandas Exercise",
"_____no_output_____"
]
],
[
[
"import matplotlib.pyplot as plt\nimport numpy as np\nnp.random.seed(0)\nimport pandas as pd",
"_____no_output_____"
],
[
"def df_info(df: pd.DataFrame) -> None:\n return df.head(n=20).style",
"_____no_output_____"
]
],
[
[
"## Cars Auction Dataset\n\n| Feature | Type | Description |\n|--------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Price | Integer | The sale price of the vehicle in the ad |\n| Years | Integer | The vehicle registration year |\n| Brand | String | The brand of car |\n| Model | String | model of the vehicle |\n| Color | String | Color of the vehicle |\n| State/City | String | The location in which the car is being available for purchase |\n| Mileage | Float | miles traveled by vehicle |\n| Title Status | String | This feature included binary classification, which are clean title vehicles and salvage insurance |\n| Condition | String | Time |",
"_____no_output_____"
]
],
[
[
"df = pd.read_csv(\"../data/USA_cars_datasets.csv\")\n\nprint(df.columns)",
"Index(['price', 'brand', 'model', 'year', 'title_status', 'mileage', 'color',\n 'state', 'country', 'condition'],\n dtype='object')\n"
],
[
"df.head()",
"_____no_output_____"
]
],
[
[
"## Exercise 1\n\n- Get the counts for the us states",
"_____no_output_____"
],
[
"## Exercise 2\n\n- Get all cars from the state of new mexico",
"_____no_output_____"
],
[
"## Exercise 3\n\n- Compute the mean mileage of all cars from new york",
"_____no_output_____"
],
[
"## Exercise 4\n\n- Remove all entries where the year is below 2019",
"_____no_output_____"
],
[
"## Exercise 5\n\n- Replace all color values by the first character of the color name\nE.g.: 'blue' => 'b'",
"_____no_output_____"
]
]
] | [
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cb1bad8b703c1ab2f1ec29226d2d036ed547c3a4 | 124,373 | ipynb | Jupyter Notebook | Logistic-Regression-Titanic-example.ipynb | arjavanb/Data_practices | e2319eb26e4c06076584a6c27cfe8fda354e6751 | [
"Apache-2.0"
] | null | null | null | Logistic-Regression-Titanic-example.ipynb | arjavanb/Data_practices | e2319eb26e4c06076584a6c27cfe8fda354e6751 | [
"Apache-2.0"
] | null | null | null | Logistic-Regression-Titanic-example.ipynb | arjavanb/Data_practices | e2319eb26e4c06076584a6c27cfe8fda354e6751 | [
"Apache-2.0"
] | null | null | null | 106.483733 | 21,416 | 0.82731 | [
[
[
"import pandas as pd\nimport numpy as np",
"_____no_output_____"
],
[
"import seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline",
"_____no_output_____"
],
[
"train = pd.read_csv('titanic_train.csv')\ntest = pd.read_csv('titanic_test.csv')",
"_____no_output_____"
],
[
"train.head(5)",
"_____no_output_____"
],
[
"train.info()",
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n"
],
[
"sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis', cbar=False)",
"_____no_output_____"
],
[
"sns.heatmap(test.isnull(), yticklabels=False, cmap='viridis', cbar=False)",
"_____no_output_____"
],
[
"sns.countplot(x='Survived', data=train, hue='Sex')",
"_____no_output_____"
],
[
"test.columns",
"_____no_output_____"
],
[
"sns.distplot(train[train['Survived'] == 1]['Age'].dropna(), hist=False, bins=40, color='r')\nsns.distplot(train[train['Survived'] == 0]['Age'].dropna(), hist=False, bins=40)\n",
"_____no_output_____"
],
[
"sns.distplot(train[train['Survived'] == 1]['Fare'], hist=False, bins=40, color='r')\nsns.distplot(train[train['Survived'] == 0]['Fare'], hist=False, bins=40)",
"_____no_output_____"
],
[
"plt.figure(figsize=(10, 4))\nsns.set_style('white', rc={'axes.grid': True})\nsns.boxplot(x='Pclass', y='Age', data=train, hue='Sex')",
"_____no_output_____"
]
],
[
[
"you can use sns.axes_style() to return all the styling of the axes and then you can override them with set_style()",
"_____no_output_____"
]
],
[
[
"# we can read the age mean age value for each category but if you want to be very precise!\nfor pclass in [1, 2, 3]:\n for sex in ['male', 'female']:\n print(pclass, sex, round(train[(train['Pclass']==pclass) & (train['Sex']==sex)]['Age'].mean(), 2))",
"1 male 41.28\n1 female 34.61\n2 male 30.74\n2 female 28.72\n3 male 26.51\n3 female 21.75\n"
]
],
[
[
"in order to define our impute function, the best way is to create a nice structured object to avoid many 'if' estatements",
"_____no_output_____"
]
],
[
[
"age_class = {1:{'male': 41.28, 'female': 34.61}, 2: {'male': 30.74, 'female': 28.72}, 3: {'male': 25.51, 'female': 21.7}}",
"_____no_output_____"
],
[
"def impute_age(cols):\n Age, Pclass, Sex = tuple(cols)\n \n if pd.isnull(Age):\n return age_class[Pclass][Sex]\n# if pclass==1 and sex=='male': return 41.28\n# elif pclass==1 and sex=='female': return 34.61\n# elif pclass==2 and sex=='male': return 30.74\n# elif pclass==2 and sex=='female': return 28.72\n# elif pclass==3 and sex=='male': return 26.51\n# else:\n# return 21.75\n else:\n return Age\ntrain['Age'] = train[['Age', 'Pclass', 'Sex']].apply(impute_age, axis=1)",
"_____no_output_____"
],
[
"sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')",
"_____no_output_____"
],
[
"train.drop('Cabin', axis=1, inplace=True)",
"_____no_output_____"
],
[
"train.dropna(inplace=True)",
"_____no_output_____"
],
[
"train = pd.get_dummies(train, columns=['Sex', 'Embarked'], drop_first=True)",
"_____no_output_____"
],
[
"train.head()",
"_____no_output_____"
],
[
"train.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)",
"_____no_output_____"
],
[
"train.head()",
"_____no_output_____"
]
],
[
[
"we will not use test dataset because it is basically for kaggle competition. But, let's take a look at it",
"_____no_output_____"
]
],
[
[
"sns.heatmap(test.isnull(), yticklabels=False, cbar=False, cmap='viridis')",
"_____no_output_____"
],
[
"test['Age'] = test[['Age', 'Pclass', 'Sex']].apply(impute_age, axis=1)",
"_____no_output_____"
],
[
"test.drop('Cabin', axis=1, inplace=True)",
"_____no_output_____"
],
[
"test.dropna(inplace=True)",
"_____no_output_____"
]
],
[
[
"We need to convert categorical columns to numerical values using dummy variables",
"_____no_output_____"
]
],
[
[
"test = pd.get_dummies(test, columns=['Sex', 'Embarked'], drop_first=True)",
"_____no_output_____"
],
[
"test.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)",
"_____no_output_____"
],
[
"X = train.drop('Survived', axis=1)\ny = train['Survived']",
"_____no_output_____"
],
[
"from sklearn.model_selection import train_test_split",
"_____no_output_____"
],
[
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)",
"_____no_output_____"
],
[
"from sklearn.linear_model import LogisticRegression",
"_____no_output_____"
],
[
"logreg = LogisticRegression()",
"_____no_output_____"
],
[
"logreg.fit(X_train, y_train)",
"_____no_output_____"
],
[
"predictions = logreg.predict(X_test)",
"_____no_output_____"
],
[
"from sklearn.metrics import confusion_matrix, classification_report",
"_____no_output_____"
],
[
"con_mat = np.array([['TN', 'FP'],\n ['FN', 'TP']])\n\npd.DataFrame(data=con_mat, index=['Actual 0', 'Actual 1'], columns=['Predicted 0', 'Predicted 1'])",
"_____no_output_____"
]
],
[
[
"if you go horizontally, you can calculate recall, and vertically yeilds the precision value. avg/Total is the accuracy",
"_____no_output_____"
]
],
[
[
"confusion_matrix(y_test, predictions) ",
"_____no_output_____"
],
[
"print(classification_report(y_test, predictions))",
" precision recall f1-score support\n\n 0 0.82 0.86 0.84 167\n 1 0.75 0.69 0.72 100\n\navg / total 0.80 0.80 0.80 267\n\n"
]
]
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cb1bb44a7ea15027f4c7e6a82ee82d1ad48b228a | 70,397 | ipynb | Jupyter Notebook | notebooks/4_training.ipynb | fahmiduldul/ds-sparkify | d99bde6399270d8c42a72b2ebbc66ba6ed074062 | [
"MIT"
] | null | null | null | notebooks/4_training.ipynb | fahmiduldul/ds-sparkify | d99bde6399270d8c42a72b2ebbc66ba6ed074062 | [
"MIT"
] | null | null | null | notebooks/4_training.ipynb | fahmiduldul/ds-sparkify | d99bde6399270d8c42a72b2ebbc66ba6ed074062 | [
"MIT"
] | null | null | null | 63.997273 | 18,566 | 0.691606 | [
[
[
"## 4. Training",
"_____no_output_____"
],
[
"In this part of notebook we will try to train the model using feature extracted dataset and do model evaluation to see how well it predicts churn",
"_____no_output_____"
],
[
"### Setup Prerequisite",
"_____no_output_____"
]
],
[
[
"!pip install pyspark",
"Requirement already satisfied: pyspark in /usr/local/lib/python3.7/dist-packages (3.1.1)\nRequirement already satisfied: py4j==0.10.9 in /usr/local/lib/python3.7/dist-packages (from pyspark) (0.10.9)\n"
],
[
"from google.colab import drive\ndrive.mount('/content/drive')",
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
],
[
[
"### Import Needed Library, Initialize Spark and Load Dataframe",
"_____no_output_____"
]
],
[
[
"from pyspark.sql import SparkSession\nimport pyspark.sql.functions as F\nimport pyspark.sql.types as T\n\nfrom pyspark.ml.classification import GBTClassifier, RandomForestClassifier\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.evaluation import BinaryClassificationEvaluator\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.tuning import TrainValidationSplit, ParamGridBuilder\n\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns",
"_____no_output_____"
],
[
"spark = SparkSession.builder.appName('sparkify-train').getOrCreate()",
"_____no_output_____"
],
[
"# load data and change is_churn column into label column\n\nisOnColab = True # CHANGE THIS VARIABLE IF RUNNING ON DATAPROC\n\npath = '/content/drive/MyDrive/datasets/dsnd-sparkify/ml_df.parquet' if isOnColab else 'gs://udacity-dsnd/ml_df.parquet'\ndf = spark.read.parquet(path)\ndf = df.withColumn('label', F.when(F.col(\"is_churn\"), 1).otherwise(0))\ndf.show(5)",
"+-------+-------------------+--------+----------+-------------+------------------+----------+-----------+---------+------------------+----------+-----+------+-----+-----+-------+-------+------------+-------------+----------+------------+-----+\n| userId| up_ts|is_churn|song_count|subs_duration| song_rate|n_playlist|thumbs_down|thumbs_up| avg_sess_len|sess_count| ipad|iphone|linux|macos|windows|n_error|n_friend_add|n_cancel_page|n_unq_song|n_unq_artist|label|\n+-------+-------------------+--------+----------+-------------+------------------+----------+-----------+---------+------------------+----------+-----+------+-----+-----+-------+-------+------------+-------------+----------+------------+-----+\n|1071843|2018-11-08 13:16:59| false| 1190| 22| 54.09090909090909| 39| 6| 51|27020.272727272728| 11|false| false|false| true| false| 1| 32| 0| 1104| 902| 0|\n|1120784|2018-10-11 14:16:28| false| 1502| 50| 30.04| 45| 17| 122|14162.807692307691| 26|false| false|false|false| true| 2| 24| 0| 1357| 1042| 0|\n|1128522|2018-11-13 17:46:22| true| 203| 2| 101.5| 9| 6| 12|17188.666666666668| 3|false| false|false| true| false| 0| 4| 0| 198| 190| 1|\n|1130061|2018-10-11 20:04:50| false| 96| 50| 1.92| 4| 4| 3| 11930.5| 2|false| false|false| true| false| 0| 2| 1| 96| 92| 0|\n|1135039|2018-10-04 11:31:06| false| 643| 57|11.280701754385966| 29| 4| 49|22156.285714285714| 7|false| false|false|false| true| 2| 12| 0| 611| 513| 0|\n+-------+-------------------+--------+----------+-------------+------------------+----------+-----------+---------+------------------+----------+-----+------+-----+-----+-------+-------+------------+-------------+----------+------------+-----+\nonly showing top 5 rows\n\n"
]
],
[
[
"### Vectorize Feature and Do Train Test Split",
"_____no_output_____"
]
],
[
[
"# features columns\nfeature_cols = df.columns[3:-1]\n\nassembler = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")\ndf = assembler.transform(df)\n\n# take only features and label column\ndf = df.select([\"features\", \"label\"])",
"_____no_output_____"
],
[
"train_df, test_df = df.randomSplit([0.9,0.1], seed=42)",
"_____no_output_____"
],
[
"print(\"train\")\ntrain_df.groupby(\"label\").count().show()\n\nprint(\"test\")\ntest_df.groupby(\"label\").count().show()",
"train\n+-----+-----+\n|label|count|\n+-----+-----+\n| 1| 4470|\n| 0| 9148|\n+-----+-----+\n\ntest\n+-----+-----+\n|label|count|\n+-----+-----+\n| 1| 480|\n| 0| 1037|\n+-----+-----+\n\n"
]
],
[
[
"We have around 1:2 ratio between 1 and 0 label on both dataset. It shows that we have balanced train and test data split.",
"_____no_output_____"
],
[
"### Build Grid Search, Train Model and Predict Test Data",
"_____no_output_____"
],
[
"In this case we will use gradient boosted tree (GBT) algorithm to predic churn. I use GBT because it has good accuracy but still give us reasonable explainability. For hyperparameter tuning, I tune the tree depth and the number of trees. Then we choose the best model using pyspark built-in BinaryClassificationEvaluator with areaUnderROC metric,",
"_____no_output_____"
]
],
[
[
"train_df.printSchema()",
"root\n |-- features: vector (nullable = true)\n |-- label: integer (nullable = false)\n\n"
],
[
"# ML algorithm\ngbt = GBTClassifier() # GBT algorithm\nrf = RandomForestClassifier() # RF algorithm\n\n# Grid search parameter\ngbt_grid = ParamGridBuilder() \\\n .addGrid(gbt.maxDepth, [5, 6, 7]) \\\n .addGrid(gbt.maxIter, [15, 20, 25]) \\\n .build()\n\nrf_grid = ParamGridBuilder() \\\n .addGrid(rf.maxDepth, [5, 6, 7]) \\\n .addGrid(rf.numTrees, [15, 20, 25]) \\\n .build()\n\n# train validation split to search through all grid\ngbt_tvs = TrainValidationSplit(estimator=gbt,\n estimatorParamMaps=gbt_grid,\n evaluator=BinaryClassificationEvaluator(),\n trainRatio=0.75,\n seed=42)\n\nrf_tvs = TrainValidationSplit(estimator=rf,\n estimatorParamMaps=rf_grid,\n evaluator=BinaryClassificationEvaluator(),\n trainRatio=0.75,\n seed=42)",
"_____no_output_____"
],
[
"# train model\ngbt_model = gbt_tvs.fit(train_df)\nrf_model = rf_tvs.fit(train_df)",
"_____no_output_____"
],
[
"gbt_model.bestModel",
"_____no_output_____"
],
[
"rf_model.bestModel",
"_____no_output_____"
]
],
[
[
"From the result above, the best model is using 25 trees and 7 depth",
"_____no_output_____"
]
],
[
[
"gbt_model.bestModel.featureImportances",
"_____no_output_____"
]
],
[
[
"As seen above, the most important feature in gradient boost model are number 1 (subscription duration) with value 0.2645, number 2 (songs heard per day of subscription) with value 0.1197, number 7 (sessions count) with value 0.087 and number 15 (cancel confirmation page visit) with value 0.109",
"_____no_output_____"
],
[
"### Evaluate model",
"_____no_output_____"
],
[
"Before evaluate the prediction I create function to ease evaluation later on",
"_____no_output_____"
]
],
[
[
"def evaluate(df, label_col='label', pred_col='prediction'):\n '''\n INPUT:\n df - spark dataframe\n label_col - name of label column\n pred_col - name of prediction column\n\n OUTPUT:\n res - pandas dataframe of metrics\n '''\n\n temp_df = df.select([label_col, pred_col]).toPandas()\n \n return pd.DataFrame.from_dict({\n \"accuracy\" : [accuracy_score(temp_df[label_col], temp_df[pred_col])],\n \"precision\" : [precision_score(temp_df[label_col], temp_df[pred_col])],\n \"recall\" : [recall_score(temp_df[label_col], temp_df[pred_col])],\n \"f1\" : [f1_score(temp_df[label_col], temp_df[pred_col])],\n \"roc_auc\" : [roc_auc_score(temp_df[label_col], temp_df[pred_col])],\n })",
"_____no_output_____"
],
[
"# predict on test dataframe\npreds_gbt_df = gbt_model.transform(test_df)\npreds_rf_df = rf_model.transform(test_df)",
"_____no_output_____"
],
[
"# metrics result\nevaluate(preds_gbt_df)",
"_____no_output_____"
],
[
"evaluate(preds_rf_df)",
"_____no_output_____"
],
[
"# confusion matrix\nc_mat_gbt = confusion_matrix(preds_gbt_df.select(\"label\").toPandas(),\n preds_gbt_df.select(\"prediction\").toPandas())\nc_mat_rf = confusion_matrix(preds_rf_df.select(\"label\").toPandas(),\n preds_rf_df.select(\"prediction\").toPandas())\n\n\nfig, axes = plt.subplots(1,2, figsize=(15,5))\n\nsns.heatmap(c_mat_gbt, annot=True, fmt=\"d\", cmap=\"YlGnBu\", ax=axes[0])\nsns.heatmap(c_mat_rf, annot=True, fmt=\"d\", cmap=\"YlGnBu\", ax=axes[1])\n\naxes[0].title.set_text(\"GBT Model\")\naxes[1].title.set_text(\"RF Model\")",
"_____no_output_____"
]
],
[
[
"With metrics shown above, We got good f1 and roc auc score. But as you see in the confusion matrix, accuracy value is mostly driven by high true negative value. But We are more concerned about true positive value so we can prevent user from churning. If We want to see how good our model predict churn we can calculate with recall score (312 / (312 + 168)) and get the value of 65%.",
"_____no_output_____"
],
[
"### Train Model with Weighted Dataset",
"_____no_output_____"
],
[
"Since we are more concerned on increase true positive value, I will use weighted dataset to retrain the model. If the label is negative, I give 0.7 value and positive 1 value.",
"_____no_output_____"
]
],
[
[
"# add weight column\ntrain_df_w = train_df.withColumn(\"weight\", F.when(F.col(\"label\") == 1, 1).otherwise(0.7))\ntrain_df_w.show(5)",
"+--------------------+-----+------+\n| features|label|weight|\n+--------------------+-----+------+\n|(18,[0,1,2,3,4,6,...| 0| 0.7|\n|(18,[0,1,2,3,4,6,...| 0| 0.7|\n|(18,[0,1,2,3,4,6,...| 0| 0.7|\n|(18,[0,1,2,3,4,6,...| 0| 0.7|\n|(18,[0,1,2,3,4,6,...| 0| 0.7|\n+--------------------+-----+------+\nonly showing top 5 rows\n\n"
]
],
[
[
"I use the same model, grid and validation method as before but We need to specify weight column name so they can use it in training process.",
"_____no_output_____"
]
],
[
[
"# gradient boosted tree algorithm\ngbt_w = GBTClassifier().setWeightCol(\"weight\")\nrf_w = RandomForestClassifier(weightCol=\"weight\")\n\n# train validation split to search through all grid\ngbt_tvs_w = TrainValidationSplit(estimator=gbt_w,\n estimatorParamMaps=gbt_grid,\n evaluator=BinaryClassificationEvaluator().setWeightCol(\"weight\"),\n trainRatio=0.75,\n seed=42)\n\nrf_tvs_w = TrainValidationSplit(estimator=rf_w,\n estimatorParamMaps=rf_grid,\n evaluator=BinaryClassificationEvaluator().setWeightCol(\"weight\"),\n trainRatio=0.75,\n seed=42)",
"_____no_output_____"
],
[
"# train model with weighted dataset\ngbt_model_w = gbt_tvs_w.fit(train_df_w)\nrf_model_w = rf_tvs_w.fit(train_df_w)",
"_____no_output_____"
],
[
"gbtw_model.bestModel",
"_____no_output_____"
]
],
[
[
"After training the model is using 20 trees and 7 tree depth",
"_____no_output_____"
]
],
[
[
"gbtw_model.bestModel.featureImportances",
"_____no_output_____"
]
],
[
[
"As seen above, the most influencing features are subscription duration, songs played per day, session count and number of cancellation confirmation page visited. The same as model before but with different value",
"_____no_output_____"
],
[
"### Evaluate Weighted Model",
"_____no_output_____"
]
],
[
[
"# predict using weighted model\npreds_gbt_w_df = gbt_model_w.transform(test_df)\npreds_rf_w_df = rf_model_w.transform(test_df)",
"_____no_output_____"
],
[
"evaluate(preds_gbt_w_df)",
"_____no_output_____"
],
[
"evaluate(preds_rf_w_df)",
"_____no_output_____"
],
[
"# confusion matrix\nc_mat_gbt_w = confusion_matrix(preds_gbt_w_df.select(\"label\").toPandas(),\n preds_gbt_w_df.select(\"prediction\").toPandas())\nc_mat_rf_w = confusion_matrix(preds_rf_w_df.select(\"label\").toPandas(),\n preds_rf_w_df.select(\"prediction\").toPandas())\n\n\nfig, axes = plt.subplots(1,2, figsize=(15,5))\n\nsns.heatmap(c_mat_gbt_w, annot=True, fmt=\"d\", cmap=\"YlGnBu\", ax=axes[0])\nsns.heatmap(c_mat_rf_w, annot=True, fmt=\"d\", cmap=\"YlGnBu\", ax=axes[1])\n\naxes[0].title.set_text(\"GBT Weighted Model\")\naxes[1].title.set_text(\"RF Weighted Model\")",
"_____no_output_____"
]
],
[
[
"As seen above all the metrics produce better result, especially recall score. But on the down side the number of true negative reduced.",
"_____no_output_____"
]
]
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cb1bb64b2536f46fbb32fae9bf15ccbe99353d2d | 57,255 | ipynb | Jupyter Notebook | PyCitySchools/PyCitySchools_Solution.ipynb | esnaaf1/Trends-in-standard-Tests | 66d078e57db190c6b3fba908d70941b6e3973c3a | [
"ADSL"
] | null | null | null | PyCitySchools/PyCitySchools_Solution.ipynb | esnaaf1/Trends-in-standard-Tests | 66d078e57db190c6b3fba908d70941b6e3973c3a | [
"ADSL"
] | null | null | null | PyCitySchools/PyCitySchools_Solution.ipynb | esnaaf1/Trends-in-standard-Tests | 66d078e57db190c6b3fba908d70941b6e3973c3a | [
"ADSL"
] | null | null | null | 37.495088 | 202 | 0.437289 | [
[
[
"# PyCitySChools Solution\n* Submitted by: Farshad Esnaashari\n * Data Analytics Bootcap\n * M-W session",
"_____no_output_____"
]
],
[
[
"# Dependencies and Setup\nimport pandas as pd\n\n# File to Load (Remember to Change These)\nschool_data_to_load = \"Resources/schools_complete.csv\"\nstudent_data_to_load = \"Resources/students_complete.csv\"\n\n# Read School and Student Data File and store into Pandas Data Frames\nschool_data = pd.read_csv(school_data_to_load)\nstudent_data = pd.read_csv(student_data_to_load)\n\n# Combine the data into a single dataset\nschool_data_complete = pd.merge(student_data, school_data, how=\"left\", on=[\"school_name\", \"school_name\"])",
"_____no_output_____"
]
],
[
[
"## District Summary\n\n* Calculate the total number of schools\n\n* Calculate the total number of students\n\n* Calculate the total budget\n\n* Calculate the average math score \n\n* Calculate the average reading score\n\n* Calculate the overall passing rate (overall average score), i.e. (avg. math score + avg. reading score)/2\n\n* Calculate the percentage of students with a passing math score (70 or greater)\n\n* Calculate the percentage of students with a passing reading score (70 or greater)\n\n* Create a dataframe to hold the above results\n\n* Optional: give the displayed data cleaner formatting",
"_____no_output_____"
]
],
[
[
"#Calculate total student in the school_data\ntotal_schools=school_data[\"school_name\"].count()\n\n#Calculate total number of students (use size column) in the school_data\ntotal_students= school_data[\"size\"].sum()\n\n#calculate total budget (use budget in the school_data)\ntotal_budget = school_data[\"budget\"].sum()\n\n#calculate average math score in the student_data\naverage_math_score =student_data[\"math_score\"].mean()\n\n#Calcilate avergage readning score in the student_data\naverage_reading_score=student_data[\"reading_score\"].mean()\n\n# Use the loc function to create a subset a dataframe with students passing math\nstudents_passing_math = student_data.loc[student_data[\"math_score\"] >= 70]\n\n# Calculate percent passing math\npct_passing_math = 100*students_passing_math[\"student_name\"].count()/total_students\n\n#Use the loc function to create a dataframe with students passing reading\nstudents_passing_reading = student_data.loc[student_data[\"reading_score\"] >= 70]\n\n#calculte percent passing reading\npct_passing_reading = 100* students_passing_reading[\"student_name\"].count()/total_students\n\n# put the results in a data frame called district_summary\ndistrict_summary=pd.DataFrame({\"Total Schools\":[total_schools],\n \"Total Students\": [total_students],\n \"Total Budget\": [total_budget], \n \"Average Math Score\": [average_math_score], \n \"Average Reading Score\": [average_reading_score],\n \"% Passing Math\": [pct_passing_math],\n \"% Passing Reading\": [pct_passing_reading],\n \"% Overall Passing Rate\": [(average_math_score+average_reading_score)/2]})\n\n# Use Map to format the total students and total buddget columns\ndistrict_summary[\"Total Students\"]=district_summary[\"Total Students\"].map(\"{:,}\".format)\ndistrict_summary[\"Total Budget\"]=district_summary[\"Total Budget\"].map(\"${:,.2f}\".format)\n\n#display the dataframe\ndistrict_summary.head()",
"_____no_output_____"
]
],
[
[
"## School Summary",
"_____no_output_____"
],
[
"* Create an overview table that summarizes key metrics about each school, including:\n * School Name\n * School Type\n * Total Students\n * Total School Budget\n * Per Student Budget\n * Average Math Score\n * Average Reading Score\n * % Passing Math\n * % Passing Reading\n * Overall Passing Rate (Average of the above two)\n \n* Create a dataframe to hold the above results",
"_____no_output_____"
]
],
[
[
"# (Note: I used the new groupby aggregation function with relabeling that is already available \n# in my current PythonData installation. \n\n# To create the school summary, I create dataframes in 4 steps:\n\n#step 1: create school_df_1 for average math and reading scores\nschool_df_1 = school_data_complete.groupby('school_name').agg(\n total_students=('student_name', 'count'),\n avg_math_score =('math_score','mean'),\n avg_reading_score = ('reading_score','mean'))\n\n#Step 2.a: create a dataframe to hold students with passig math scores (>=70)\npassing_math_df= school_data_complete.loc[school_data_complete['math_score'] >=70]\n\n#Step 2.b: # group passing math dataframe by school_name and count the rows\nschool_df_2 = passing_math_df.groupby('school_name').agg(\n total_passing_math = ('student_name','count'))\n\n\n#step 3.a: create a dataframe to hold students with passing reading scores (>=70)\npassing_reading_df= school_data_complete.loc[school_data_complete['reading_score'] >=70]\n\n#step 3.b: group passing reading dataframe by school_name and count the rows\nschool_df_3 = passing_reading_df.groupby('school_name').agg(\n total_passing_reading = ('student_name','count'))\n\n# Step 4: create a dataframe from the school_data with subset of school_name, type, budget, and size\nschool_df_4 = school_data.loc[:,['school_name','type','budget', 'size']]\n\n# merge the previous 4 dataframes (school_df_1 to school_Df_4) into a school_summary dataframe\nschool_summary = pd.merge(school_df_1, school_df_2, on='school_name')\nschool_summary = pd.merge(school_summary, school_df_3, on='school_name')\nschool_summary = pd.merge(school_summary, school_df_4, on='school_name')\n\n\n# Calculate percent passing math into a new column\nschool_summary[\"pct_passing_math\"]= 100*school_summary[\"total_passing_math\"]/school_summary[\"total_students\"]\n\n# Calciate percent passing reading into a new column\nschool_summary['pct_passing_reading'] = 100*school_summary[\"total_passing_reading\"]/school_summary[\"total_students\"]\n\n# create a new column for percent overall passing rate\nschool_summary['overall_passing_rate'] = (school_summary['pct_passing_math']+ \\\n school_summary['pct_passing_reading'])/2\n\n# create a new column for per student budget\nschool_summary['per_student_budget'] = school_summary['budget']/school_summary['size']\n\n# Reorganize the columns using a list\n\nschool_summary = school_summary[['school_name','type', 'total_students',\n 'budget', 'per_student_budget',\n 'avg_math_score', 'avg_reading_score',\n 'pct_passing_math', 'pct_passing_reading',\n 'overall_passing_rate']]\n\n# Rename the columns\nschool_summary = school_summary.rename(columns={\"type\":\"School Type\",\n \"total_students\": \"Total Students\",\n \"budget\": \"Total School Budget\",\n \"per_student_budget\": \"Per Student Budget\",\n \"avg_math_score\": \"Average Math Score\",\n \"avg_reading_score\": \"Average Reading Score\",\n \"pct_passing_math\": \"% Passing Math\",\n \"pct_passing_reading\": \"% Passing Reading\",\n \"overall_passing_rate\": \"% Overall Passing Rate\"})\n\n#Format columns for Total School Budget, Per Student Budget using the map function\nschool_summary[\"Total School Budget\"] = school_summary[\"Total School Budget\"].map(\"${:,.2f}\".format)\nschool_summary[\"Per Student Budget\"] = school_summary[\"Per Student Budget\"].map(\"${:,.2f}\".format)\n# school_summary = school_summary.set_index(\"school_name\")",
"_____no_output_____"
]
],
[
[
"## Top Performing Schools (By Passing Rate)",
"_____no_output_____"
],
[
"* Sort and display the top five schools in overall passing rate",
"_____no_output_____"
]
],
[
[
"#sort the school_summary dataframe on overall_passing_rate in descening order\ntop_5_schools = school_summary.sort_values(\"% Overall Passing Rate\", ascending=False)\ntop_5_schools.set_index(\"school_name\",inplace=True)\ntop_5_schools.head()",
"_____no_output_____"
]
],
[
[
"## Bottom Performing Schools (By Passing Rate)",
"_____no_output_____"
],
[
"* Sort and display the five worst-performing schools",
"_____no_output_____"
]
],
[
[
"# sort the school_summary dataframe on overall_passing_rate in ascending order\nbottom_5_schools = school_summary.sort_values(\"% Overall Passing Rate\")\nbottom_5_schools.set_index(\"school_name\", inplace=True)\nbottom_5_schools.head()",
"_____no_output_____"
]
],
[
[
"## Math Scores by Grade",
"_____no_output_____"
],
[
"* Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school.\n\n * Create a pandas series for each grade. Hint: use a conditional statement.\n \n * Group each series by school\n \n * Combine the series into a dataframe\n \n * Optional: give the displayed data cleaner formatting",
"_____no_output_____"
]
],
[
[
"# Use the \"loc\" function with conditional filtering to build 4 dataframes for grades 9 to 12 avergae scores\n\nnine_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"9th\"].rename(columns={\"math_score\": \"9th\"})\nten_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"10th\"].rename(columns={\"math_score\": \"10th\"})\neleven_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"11th\"].rename(columns={\"math_score\": \"11th\"})\ntwelve_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"12th\"].rename(columns={\"math_score\": \"12th\"})\n\n\n# calculate the averge math scores for each frame grouped by school_name\navg_9_grades = nine_grade.groupby('school_name').agg({'9th':'mean'})\n\navg_10_grades = ten_grade.groupby('school_name').agg({'10th':'mean'})\n\navg_11_grades = eleven_grade.groupby('school_name').agg({'11th':'mean'})\n\navg_12_grades = twelve_grade.groupby('school_name').agg({'12th': 'mean'})\n\n#merge the 4 dataframes into math_scores_by_grade\nmath_scores_by_grade = pd.merge(avg_9_grades, avg_10_grades, on=\"school_name\")\nmath_scores_by_grade = pd.merge(math_scores_by_grade, avg_11_grades, on=\"school_name\")\nmath_scores_by_grade = pd.merge(math_scores_by_grade, avg_12_grades, on=\"school_name\")\n\nmath_scores_by_grade\n",
"_____no_output_____"
]
],
[
[
"## Reading Score by Grade ",
"_____no_output_____"
],
[
"* Perform the same operations as above for reading scores",
"_____no_output_____"
]
],
[
[
"# build 4 dataframes for average scores in grades 9 to 12\nnine_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"9th\"].rename(columns={\"reading_score\": \"9th\"})\nten_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"10th\"].rename(columns={\"reading_score\": \"10th\"})\neleven_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"11th\"].rename(columns={\"reading_score\": \"11th\"})\ntwelve_grade = school_data_complete.loc[school_data_complete[\"grade\"] ==\"12th\"].rename(columns={\"reading_score\": \"12th\"})\n\n# group by school_name and calculate the reading score average for each grade\navg_9_grades = nine_grade.groupby('school_name').agg({'9th':'mean'})\n\navg_10_grades = ten_grade.groupby('school_name').agg({'10th':'mean'})\n \n\navg_11_grades = eleven_grade.groupby('school_name').agg({'11th':'mean'})\n \n\navg_12_grades = twelve_grade.groupby('school_name').agg({'12th':'mean'})\n\n# merge the 4 dataframes into the read_scores_by_grade\nreading_scores_by_grade = pd.merge(avg_9_grades, avg_10_grades, on=\"school_name\")\nreading_scores_by_grade = pd.merge(reading_scores_by_grade, avg_11_grades, on=\"school_name\")\nreading_scores_by_grade = pd.merge(reading_scores_by_grade, avg_12_grades, on=\"school_name\")\n\nreading_scores_by_grade",
"_____no_output_____"
]
],
[
[
"## Scores by School Spending",
"_____no_output_____"
],
[
"* Create a table that breaks down school performances based on average Spending Ranges (Per Student). Use 4 reasonable bins to group school spending. Include in the table each of the following:\n * Average Math Score\n * Average Reading Score\n * % Passing Math\n * % Passing Reading\n * Overall Passing Rate (Average of the above two)",
"_____no_output_____"
]
],
[
[
"# Set bins and group names for ranges of per student spending \nspending_bins = [0, 585, 615, 645, 675]\ngroup_names = [\"<$585\", \"$585-615\", \"$615-645\", \"$645-675\"]",
"_____no_output_____"
],
[
"# create dataframe with subset of columns from the school summary using the loc function\nsummary_by_spending=school_summary.loc[:,[\"Per Student Budget\", \"Average Math Score\", \n \"Average Reading Score\", \"% Passing Math\", \n \"% Passing Reading\", \"% Overall Passing Rate\" ]]\n\n#cast Per Student Budget(in $s) into a float\nsummary_by_spending[\"Per Student Budget\"]= summary_by_spending[\"Per Student Budget\"].str.slice(start=1).astype(float)\n\n\n# bin the spending per student\nsummary_by_spending[\"Spending Ranges (Per Student)\"]= pd.cut(summary_by_spending[\"Per Student Budget\"], \n spending_bins, labels=group_names)\n\n# group data by the bins\nsummary_by_spending = summary_by_spending.groupby(\"Spending Ranges (Per Student)\").agg(\n {'Average Math Score': 'mean', \n 'Average Reading Score': 'mean',\n '% Passing Math':'mean',\n '% Passing Reading': 'mean',\n '% Overall Passing Rate': 'mean'})\n\n\nsummary_by_spending\n",
"_____no_output_____"
]
],
[
[
"## Scores by School Size",
"_____no_output_____"
],
[
"* Perform the same operations as above, based on school size.",
"_____no_output_____"
]
],
[
[
"# create lists for size ranges and group_names\nsize_bins = [0, 1000, 2000, 5000]\ngroup_names = [\"Small (<1000)\", \"Medium (1000-2000)\", \"Large (2000-5000)\"]",
"_____no_output_____"
],
[
"# create a subset dataframe from the school summary\nsummary_by_size=school_summary.loc[:,[\"Total Students\", \"Average Math Score\", \n \"Average Reading Score\", \"% Passing Math\", \n \"% Passing Reading\", \"% Overall Passing Rate\" ]]\n\n# bin the school student sizes in the dataframe\nsummary_by_size[\"School Size\"]= pd.cut(summary_by_size[\"Total Students\"], \n size_bins, labels=group_names)\n# group by the school size\nsummary_by_size = summary_by_size.groupby(\"School Size\").agg(\n {'Average Math Score': 'mean', \n 'Average Reading Score': 'mean',\n '% Passing Math': 'mean',\n '% Passing Reading': 'mean',\n '% Overall Passing Rate': 'mean'})\nsummary_by_size",
"_____no_output_____"
]
],
[
[
"## Scores by School Type",
"_____no_output_____"
],
[
"* Perform the same operations as above, based on school type.",
"_____no_output_____"
]
],
[
[
"# create a dataframe with subset of columns from the school summary\nsummary_by_type=school_summary.loc[:,[\"School Type\", \"Average Math Score\", \n \"Average Reading Score\", \"% Passing Math\", \n \"% Passing Reading\", \"% Overall Passing Rate\" ]]\n\n# group by school type and do the statistices using the agg function \nsummary_by_type = summary_by_type.groupby(\"School Type\").agg(\n {'Average Math Score':'mean', \n 'Average Reading Score': 'mean',\n '% Passing Math': 'mean',\n '% Passing Reading': 'mean',\n '% Overall Passing Rate': 'mean'})\nsummary_by_type",
"_____no_output_____"
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cb1bb9310a948be5ac1c20af7c70a25f9baf9c15 | 7,296 | ipynb | Jupyter Notebook | lab9/lab9.ipynb | mprzewie/MOwNiT_2 | d2a492d210d3acfa1246de82c4a9b818956b4271 | [
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] | null | null | null | lab9/lab9.ipynb | mprzewie/MOwNiT_2 | d2a492d210d3acfa1246de82c4a9b818956b4271 | [
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"MIT"
] | 2 | 2019-04-10T09:35:51.000Z | 2019-05-29T10:19:18.000Z | 27.741445 | 103 | 0.484923 | [
[
[
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom ipywidgets import interact, fixed\nimport ipywidgets as widgets",
"_____no_output_____"
]
],
[
[
"Zasymuluj wahadlo matematyczne rozwiazując numerycznie rownanie różniczkowe je opisujace\n(rownież dla dużych wychyleń). \n\n$$\n\\frac{d^2x}{dt^2} + \\frac{g}{l} sin(x) = 0\n$$",
"_____no_output_____"
]
],
[
[
"g = 9.81\nl = 1",
"_____no_output_____"
],
[
"def pendulum(initial_theta, time, initial_omega=0, time_step=0.01, g=g, l=l):\n theta = initial_theta\n omega = initial_omega\n t = 0\n while(t < time):\n a = -(g / l) * np.sin(theta)\n omega += a * time_step\n theta += omega * time_step + a * (time_step ** 2) /2\n t += time_step\n return theta",
"_____no_output_____"
],
[
"omega = 1 / np.sqrt(l /g) # = 2*Pi / T (where T = 2*Pi sqrt(l/g))\nstep = 0.001\nT = np.arange(0,30,0.1)\nTheta = np.array([pendulum(np.pi / 16, t, time_step=step) for t in T])\nactual_theta = np.cos(omega * T)\nplt.plot(T, Theta)\nplt.plot(T, actual_theta)\nplt.show()",
"_____no_output_____"
],
[
"def demonstrate_pendulum(initial_theta, time, initial_omega=0, time_step_magn=-2, g=9.8, l=1):\n time_step = 10**time_step_magn\n theta = pendulum(initial_theta, time, initial_omega, time_step, g)\n x = np.sin(theta)\n y = - np.sqrt(l**2 - x**2)\n line_x = np.linspace(x,0)\n line_y = np.linspace(y,0)\n plt.scatter(x,y)\n plt.plot(line_x, line_y, color='black')\n plt.scatter([0],[0])\n plt.axis((-1, 1, -2, 0.5))\n \n plt.show()\n ",
"_____no_output_____"
],
[
"interact(demonstrate_pendulum, \n time=widgets.FloatSlider(min=0, max=1000, value=0, step=0.01), \n initial_theta=widgets.FloatSlider(min=0, max=2*np.pi, value=np.pi/4, step=0.01),\n time_step_magn=widgets.IntSlider(min=-5, max=5, value=-2)\n )",
"_____no_output_____"
]
],
[
[
"Zasymuluj układ grawitacyjny : gwiazda i przylatujace cialo niebieskie z pewna (zadawana\nprzez uzytkownika) prędkością poczatkową.",
"_____no_output_____"
],
[
"$$\ng = G\\frac{M}{r^2}\n$$",
"_____no_output_____"
]
],
[
[
"@np.vectorize\ndef g(r, G=1, M=1, r_max=1):\n return (G * M) / r**2 if r > r_max else ((G * M) / r_max**2) * (r / r_max)\n\n",
"_____no_output_____"
],
[
"def gravity(time, initial_pos, M=0, initial_v=0, time_step=0.1, r_max=1):\n t = 0\n x = initial_pos\n v = initial_v\n r = np.abs(x)\n a = - g(r, M=M, r_max=r_max) * (x / r)\n X = [x]\n while(t < time):\n r = np.abs(x)\n a = - g(r, M=M, r_max=r_max) * (x / r)\n v += a * time_step\n x += v * time_step + a * ((time_step ** 2) / 2)\n t += time_step\n X.append(x)\n return np.array(X), v, a",
"_____no_output_____"
],
[
"def demonstrate_gravity(time, in_x, in_y, in_vx, in_vy, time_step_magn, M, r_max):\n pos = in_x + in_y * 1j\n v = in_vx + in_vy * 1j\n time_step = 10**time_step_magn\n pos, v, a = gravity(time, pos, initial_v=v, M=M, r_max=r_max)\n\n X = np.real(pos)\n Y = np.imag(pos)\n x = X[-1]\n y = Y[-1]\n line_x = np.linspace(x,0)\n line_y = np.linspace(y,0)\n v_x = np.real(v)\n v_y = np.imag(v)\n line_v_x = np.linspace(x, x + v_x)\n line_v_y = np.linspace(y, y + v_y)\n a_x = np.real(a)\n a_y = np.imag(a)\n line_a_x = np.linspace(x, x + a_x)\n line_a_y = np.linspace(y, y + a_y)\n \n print(x, y)\n print(v) \n print(a) \n ax=plt.gca()\n ax.add_patch(plt.Circle((0,0), r_max, color='r', fill=False)) \n plt.scatter([0],[0], color='red')\n\n plt.scatter(x,y, color='blue')\n plt.plot(X, Y, color='blue')\n plt.plot(line_v_x, line_v_y, color='green')\n plt.scatter([x + v_x], [y + v_y], color='green', marker='^')\n plt.plot(line_a_x, line_a_y, color='purple')\n plt.scatter([x + a_x], [y + a_y], color='purple', marker='^')\n \n plt.axis((-5, 5, -5, 5))\n \n plt.show()\n ",
"_____no_output_____"
],
[
"interact(demonstrate_gravity, \n time=widgets.FloatSlider(min=0, max=1000, value=0, step=0.1), \n time_step_magn=widgets.IntSlider(min=-5, max=5, value=-3),\n in_x=widgets.FloatSlider(min=-5, max=5, value=2, step=0.1), \n in_y=widgets.FloatSlider(min=-5, max=5, value=2, step=0.1), \n in_vx=widgets.FloatSlider(min=-5, max=5, value=-0.5, step=0.1), \n in_vy=widgets.FloatSlider(min=-5, max=5, value=0.5, step=0.1),\n M=widgets.FloatSlider(min=0, max=10, value=1, step=0.1),\n r_max=widgets.FloatSlider(min=0, max=2, value=1, step=0.1)\n )",
"_____no_output_____"
],
[
"plt.Circle((0,0), 1, color='r') \nplt.show()\n",
"_____no_output_____"
]
]
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cb1bbe0664f71c7868c6307d1e0210d2877070bc | 1,537 | ipynb | Jupyter Notebook | openpmd_viewer/notebook_starter/Template_notebook.ipynb | AlexanderSinn/openPMD-viewer | 85bb7d18f57289d490d37a0b3cf710981d11ff6a | [
"BSD-3-Clause-LBNL"
] | 51 | 2015-10-08T21:07:28.000Z | 2022-01-31T06:16:32.000Z | openpmd_viewer/notebook_starter/Template_notebook.ipynb | AlexanderSinn/openPMD-viewer | 85bb7d18f57289d490d37a0b3cf710981d11ff6a | [
"BSD-3-Clause-LBNL"
] | 239 | 2015-10-09T18:11:00.000Z | 2022-03-31T22:45:14.000Z | openpmd_viewer/notebook_starter/Template_notebook.ipynb | AlexanderSinn/openPMD-viewer | 85bb7d18f57289d490d37a0b3cf710981d11ff6a | [
"BSD-3-Clause-LBNL"
] | 40 | 2015-10-08T17:11:36.000Z | 2022-03-30T21:21:09.000Z | 21.054795 | 123 | 0.564736 | [
[
[
"**Instructions:** replace the string in the second cell, and execute each cell (to execute a cell, hit `Shift+Enter`)",
"_____no_output_____"
]
],
[
[
"import numpy as np\n%matplotlib notebook\n# or `%matplotlib inline` for non-interactive plots\n# or `%matplotlib widget` when using JupyterLab (github.com/matplotlib/jupyter-matplotlib)\nimport matplotlib.pyplot as plt\nfrom openpmd_viewer import OpenPMDTimeSeries",
"_____no_output_____"
],
[
"# Replace the string below, to point to your data\nts = OpenPMDTimeSeries('./diags/hdf5/')",
"_____no_output_____"
],
[
"# Interactive GUI\nts.slider()",
"_____no_output_____"
]
]
] | [
"markdown",
"code"
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[
"markdown"
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"code",
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cb1bc43a83c4a83cfa2f8ad6811efaca832caf2f | 18,361 | ipynb | Jupyter Notebook | 7.16.ipynb | hzh029/hzh1 | 6420653e7669b02151e906530e0dad1e0c227606 | [
"Apache-2.0"
] | null | null | null | 7.16.ipynb | hzh029/hzh1 | 6420653e7669b02151e906530e0dad1e0c227606 | [
"Apache-2.0"
] | null | null | null | 7.16.ipynb | hzh029/hzh1 | 6420653e7669b02151e906530e0dad1e0c227606 | [
"Apache-2.0"
] | null | null | null | 17.240376 | 74 | 0.436469 | [
[
[
"# 基本程序设计\n- 一切代码输入，请使用英文输入法",
"_____no_output_____"
]
],
[
[
"print('joker is bad man')",
"joker is bad man\n"
]
],
[
[
"## 编写一个简单的程序\n- 圆公式面积： area = radius \\* radius \\* 3.1415",
"_____no_output_____"
],
[
"### 在Python里面不需要定义数据的类型",
"_____no_output_____"
]
],
[
[
"radius = 100 # 定义变量\narea = radius * radius * 3.14 # 普通代码，* 代表乘法\nprint(area) # 最后打印出结果",
"31400.0\n"
]
],
[
[
"## 控制台的读取与输入\n- input 输入进去的是字符串\n- eval",
"_____no_output_____"
],
[
"- 在jupyter用shift + tab 键可以跳出解释文档",
"_____no_output_____"
]
],
[
[
"variable = input('请输入一个数字')\nprint(variable) ",
"请输入一个数字1000\n1000\n"
]
],
[
[
"## 变量命名的规范\n- 由字母、数字、下划线构成\n- 不能以数字开头 \\*\n- 标识符不能是关键词(实际上是可以强制改变的，但是对于代码规范而言是极其不适合)\n- 可以是任意长度\n- 驼峰式命名",
"_____no_output_____"
]
],
[
[
"print(12)",
"12\n"
],
[
"import os\ndef go(num):\n os.system('echo hahah')\nprint = go\nprint(12)",
"_____no_output_____"
]
],
[
[
"## 变量、赋值语句和赋值表达式\n- 变量: 通俗理解为可以变化的量\n- x = 2 \\* x + 1 在数学中是一个方程，而在语言中它是一个表达式\n- test = test + 1 \\* 变量在赋值之前必须有值",
"_____no_output_____"
]
],
[
[
"x = 100\nx = 2 * x + 1 # 赋值语句，在赋值之前，一定要有值\nprint(x)",
"201\n"
],
[
"a = eval(input('数字'))\nprint(type(a))\nprint(a * 3)",
"数字100\n<class 'int'>\n300\n"
]
],
[
[
"## 同时赋值\nvar1, var2,var3... = exp1,exp2,exp3...",
"_____no_output_____"
]
],
[
[
"Joekr, Mistt,hahah,lalal = 'lalal',120,120.33333,True\nprint(Joekr,Mistt,hahah,lalal)",
"lalal 120 120.33333 True\n"
]
],
[
[
"## 定义常量\n- 常量：表示一种定值标识符，适合于多次使用的场景。比如PI\n- 注意：在其他低级语言中如果定义了常量，那么，该常量是不可以被改变的，但是在Python中一切皆对象，常量也是可以被改变的",
"_____no_output_____"
]
],
[
[
"chart = 100.1\nchart = 'hahahah'\nchart = True\nprint(chart)",
"True\n"
],
[
"import math\nprint(math.pi)",
"3.141592653589793\n"
]
],
[
[
"## 数值数据类型和运算符\n- 在Python中有两种数值类型（int 和 float）适用于加减乘除、模、幂次\n<img src = \"../Photo/01.jpg\"></img>",
"_____no_output_____"
],
[
"## 运算符 /、//、**",
"_____no_output_____"
]
],
[
[
"number1 = 100\nnumber2 = 500\nprint(number1 + number2)",
"600\n"
],
[
"number3 = 100.0\nnumber4 = 500.0\nprint(number3 + number4)",
"600.0\n"
],
[
"number3 = 100.0\nnumber4 = 500.0\nprint(number3 - number4)",
"-400.0\n"
],
[
"number3 = 100.0\nnumber4 = 500.0\nprint(number3 * number4)",
"50000.0\n"
],
[
"number3 = 100.0\nnumber4 = 500.0\nprint(number3 / number4)",
"0.2\n"
],
[
"number3 = 100.0\nnumber4 = 500.0\nprint(number3 // number4)",
"0.0\n"
],
[
"number3 = 100.0\nnumber4 = 2\nprint(number3 ** number4)",
"10000.0\n"
]
],
[
[
"## 运算符 %",
"_____no_output_____"
]
],
[
[
"number3 = 100.0\nnumber4 = 500.0\nprint(number3 % number4)",
"100.0\n"
]
],
[
[
"## EP：\n- 25/4 多少，如果要将其转变为整数该怎么改写\n- 输入一个数字判断是奇数还是偶数\n- 进阶: 输入一个秒数，写一个程序将其转换成分和秒：例如500秒等于8分20秒\n- 进阶: 如果今天是星期六，那么10天以后是星期几？ 提示：每个星期的第0天是星期天",
"_____no_output_____"
]
],
[
[
"res = 25//4\nprint(res)",
"6\n"
],
[
"input_number = input('input number')\ninput_number_int = eval(input_number)\nif input_number_int == int:\n if input_number_int % 2 == 0:\n print('偶数')\n else:\n print('奇数')\nelse:\n if input_number_int % 2.0 == 0.0:\n print('偶数')\n else:\n print('奇数')",
"input number20.0\n偶数\n"
],
[
"time = eval(input('input'))\nfen = time // 60\nmiao = time % 60\nprint(fen,'分',miao,'秒')\nprint('%d分%d秒'%(fen,miao))",
"input500\n8 分 20 秒\n8分20秒\n"
],
[
"time1 = 6\ntime2 = eval(input('输入'))\nresult = (time1 + time2) % 7\nprint(result)",
"输入10\n2\n"
]
],
[
[
"## 科学计数法\n- 1.234e+2\n- 1.234e-2",
"_____no_output_____"
]
],
[
[
"num1=1.234e+2\nnum2 = 1.234e-2\nprint(num1,num2)",
"123.4 0.01234\n"
]
],
[
[
"## 计算表达式和运算优先级\n<img src = \"../Photo/02.png\"></img>\n<img src = \"../Photo/03.png\"></img>",
"_____no_output_____"
]
],
[
[
"x = eval(input('x'))\ny = eval(input('y'))\na = eval(input('a'))\nb = eval(input('b'))\nc = eval(input('c'))\npart_1 = (3 + 4 * x) / 5\npart_2 = (10 * (y-5)* (a+b+c))/ x\npart_3 = 9*(4/x + (9+x)/y)\nprint(part_1 - part_2 + part_3)",
"_____no_output_____"
]
],
[
[
"## 增强型赋值运算\n<img src = \"../Photo/04.png\"></img>",
"_____no_output_____"
]
],
[
[
"a = 1\na += 100 # a = a + 100\nprint(a)",
"101\n"
]
],
[
[
"## 类型转换\n- float -> int\n- 四舍五入 round",
"_____no_output_____"
]
],
[
[
"int(25 / 4) # 转换成整型",
"_____no_output_____"
],
[
"str(25 / 4) # 转换成字符串",
"_____no_output_____"
],
[
"float(25//5) # 转换成浮点",
"_____no_output_____"
],
[
"round(25/4,1) # 四舍五入",
"_____no_output_____"
]
],
[
[
"## EP:\n- 如果一个年营业税为0.06%，那么对于197.55e+2的年收入，需要交税为多少？(结果保留2为小数)\n- 必须使用科学计数法",
"_____no_output_____"
]
],
[
[
"water_floawer = 153\nbai = 153 //100\nshi = 153 //10 % 10\nge = 153 % 10\nif water_floawer == bai ** 3 + shi **3 + ge **3:\n print('是水仙花')\nelse:\n print('NO')",
"是水仙花\n"
],
[
"round(197.55e+2 * 0.06e-2,2)",
"_____no_output_____"
]
],
[
[
"# Project\n- 用Python写一个贷款计算器程序：输入的是月供(monthlyPayment) 输出的是总还款数(totalpayment)\n",
"_____no_output_____"
]
],
[
[
"贷款数 = eval(input('请输入贷款数'))\n月利率 = eval(input('月利率'))\n年限 = eval(input('年限'))\n月供= ( (贷款数 * 月利率) / (1-(1/(1+月利率)**(年限*12))))\n总还款数 = 月供 * 年限 * 12\nprint(总还款数)",
"请输入贷款数100\n月利率100\n年限100\n12000000.0\n"
],
[
"import time\nprint(time.time())",
"1531727407.061894\n"
]
],
[
[
"# Homework\n- 1\n<img src=\"../Photo/06.png\"></img>",
"_____no_output_____"
]
],
[
[
"celsius=eval(input(\"请输入摄氏温度\"))\nfahrenheit=(9/5)*celsius+32\nprint(fahrenheit)",
"请输入摄氏温度43\n109.4\n"
]
],
[
[
"- 2\n<img src=\"../Photo/07.png\"></img>",
"_____no_output_____"
]
],
[
[
"pi=3.14\nradius=eval(input(\"请输入半径\"))\nlength=eval(input(\"请输入高\"))\narea=radius*radius*pi\nvolume=area*length\nprint(area)\nprint(volume)",
"请输入半径5.5\n请输入高12\n94.985\n1139.82\n"
]
],
[
[
"- 3\n<img src=\"../Photo/08.png\"></img>",
"_____no_output_____"
]
],
[
[
"yingchi=eval(input(\"请输入英尺数\"))\nmichi=yingchi*0.305\nprint(michi)",
"请输入英尺数16.5\n5.0325\n"
]
],
[
[
"- 4\n<img src=\"../Photo/10.png\"></img>",
"_____no_output_____"
]
],
[
[
"water_amount=eval(input(\"请输入水量\"))\ntemperature_ini=eval(input(\"请输入初始温度\"))\ntemperature_fin=eval(input(\"请输入最终温度\"))\nQ=water_amount*(temperature_fin-temperature_ini)*4184\nprint(Q)",
"请输入水量55.5\n请输入初始温度3.5\n请输入最终温度10.5\n1625484.0\n"
]
],
[
[
"- 5\n<img src=\"../Photo/11.png\"></img>",
"_____no_output_____"
]
],
[
[
"差额=eval(input(\"请输入差额\"))\n年利率=eval(input(\"请输入年利率\"))\n利息=差额*(年利率/1200)\nprint(利息)",
"请输入差额1000\n请输入年利率3.5\n2.916666666666667\n"
]
],
[
[
"- 6\n<img src=\"../Photo/12.png\"></img>",
"_____no_output_____"
]
],
[
[
"v0=eval(input(\"v0\"))\nv1=eval(input(\"v1\")) \nt=eval(input(\"t\"))\na=(v1-v0)/t\nprint(a)",
"v05.5\nv150.9\nt4.5\n10.088888888888889\n"
]
],
[
[
"- 7 进阶\n<img src=\"../Photo/13.png\"></img>",
"_____no_output_____"
]
],
[
[
"月存额=eval(input(\"月存额\"))\n第一个月=月存额*(1+0.05/12)\nprint(\"第一个月后，账户里的数目变为：\",第一个月)\n第二个月=(月存额+第一个月)*(1+0.05/12)\nprint(\"第二个月后，账户里的数目变为：\",第二个月)",
"月存额100\n第一个月后，账户里的数目变为： 100.41666666666667\n第二个月后，账户里的数目变为： 201.25173611111111\n"
]
],
[
[
"- 8 进阶\n<img src=\"../Photo/14.png\"></img>",
"_____no_output_____"
]
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"markdown"
],
[
"code"
],
[
"markdown"
]
] |
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