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[ [ [ "### Cell Painting morphological (CP) and L1000 gene expression (GE) profiles for the following datasets:\n \n- **CDRP**-BBBC047-Bray-CP-GE (Cell line: U2OS) : \n * $\\bf{CP}$ There are 30,430 unique compounds for CP dataset, median number of replicates --> 4\n * $\\bf{GE}$ There are 21,782 unique compounds for GE dataset, median number of replicates --> 3\n * 20,131 compounds are present in both datasets.\n\n- **CDRP-bio**-BBBC036-Bray-CP-GE (Cell line: U2OS) : \n * $\\bf{CP}$ There are 2,242 unique compounds for CP dataset, median number of replicates --> 8\n * $\\bf{GE}$ There are 1,917 unique compounds for GE dataset, median number of replicates --> 2\n * 1916 compounds are present in both datasets.\n\n \n- **LUAD**-BBBC041-Caicedo-CP-GE (Cell line: A549) : \n * $\\bf{CP}$ There are 593 unique alleles for CP dataset, median number of replicates --> 8\n * $\\bf{GE}$ There are 529 unique alleles for GE dataset, median number of replicates --> 8\n * 525 alleles are present in both datasets.\n \n \n- **TA-ORF**-BBBC037-Rohban-CP-GE (Cell line: U2OS) :\n * $\\bf{CP}$ There are 323 unique alleles for CP dataset, median number of replicates --> 5\n * $\\bf{GE}$ There are 327 unique alleles for GE dataset, median number of replicates --> 2\n * 150 alleles are present in both datasets.\n \n- **LINCS**-Pilot1-CP-GE (Cell line: U2OS) :\n * $\\bf{CP}$ There are 1570 unique compounds across 7 doses for CP dataset, median number of replicates --> 5\n * $\\bf{GE}$ There are 1402 unique compounds for GE dataset, median number of replicates --> 3\n * $N_{p/d}$: 6984 compounds are present in both datasets.\n--------------------------------------------\n #### Link to the processed profiles:\n \n https://cellpainting-datasets.s3.us-east-1.amazonaws.com/Rosetta-GE-CP", "_____no_output_____" ] ], [ [ "%matplotlib notebook\n%load_ext autoreload\n%autoreload 2\nimport numpy as np\nimport scipy.spatial\nimport pandas as pd\nimport sklearn.decomposition\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nfrom cmapPy.pandasGEXpress.parse import parse\nfrom utils.replicateCorrs import replicateCorrs\nfrom utils.saveAsNewSheetToExistingFile import saveAsNewSheetToExistingFile,saveDF_to_CSV_GZ_no_timestamp\nfrom importlib import reload\nfrom utils.normalize_funcs import standardize_per_catX\n# sns.set_style(\"whitegrid\")\n# np.__version__\npd.__version__", "_____no_output_____" ] ], [ [ "### Input / ouput files:", "_____no_output_____" ], [ "- **CDRPBIO**-BBBC047-Bray-CP-GE (Cell line: U2OS) : \n * $\\bf{CP}$ \n * Input:\n * Output:\n \n * $\\bf{GE}$ \n * Input: .mat files that are generated using https://github.com/broadinstitute/2014_wawer_pnas\n * Output:\n \n- **LUAD**-BBBC041-Caicedo-CP-GE (Cell line: A549) : \n * $\\bf{CP}$ \n * Input:\n * Output:\n \n * $\\bf{GE}$ \n * Input:\n * Output:\n \n- **TA-ORF**-BBBC037-Rohban-CP-GE (Cell line: U2OS) :\n * $\\bf{CP}$ \n * Input:\n * Output:\n \n * $\\bf{GE}$ \n * Input: https://data.broadinstitute.org/icmap/custom/TA/brew/pc/TA.OE005_U2OS_72H/\n * Output:\n \n### Reformat Cell-Painting Data Sets\n- CDRP and TA-ORF are in /storage/data/marziehhaghighi/Rosetta/raw-profiles/\n- Luad is already processed by Juan, source of the files is at /storage/luad/profiles_cp\n in case you want to reformat", "_____no_output_____" ] ], [ [ "fileName='RepCorrDF'\n### dirs on gpu cluster\n# rawProf_dir='/storage/data/marziehhaghighi/Rosetta/raw-profiles/'\n# procProf_dir='/home/marziehhaghighi/workspace_rosetta/workspace/'\n\n### dirs on ec2\nrawProf_dir='/home/ubuntu/bucket/projects/2018_04_20_Rosetta/workspace/raw-profiles/'\n# procProf_dir='./'\nprocProf_dir='/home/ubuntu/bucket/projects/2018_04_20_Rosetta/workspace/'\n# s3://imaging-platform/projects/2018_04_20_Rosetta/workspace/preprocessed_data\n# aws s3 sync preprocessed_data s3://cellpainting-datasets/Rosetta-GE-CP/preprocessed_data --profile jumpcpuser\n\nfilename='../../results/RepCor/'+fileName+'.xlsx'\n", "_____no_output_____" ], [ "# ls ../../\n# https://cellpainting-datasets.s3.us-east-1.amazonaws.com/", "_____no_output_____" ] ], [ [ "# CDRP-BBBC047-Bray", "_____no_output_____" ], [ "### GE - L1000 - CDRP", "_____no_output_____" ] ], [ [ "os.listdir(rawProf_dir+'/l1000_CDRP/')", "_____no_output_____" ], [ "cdrp_dataDir=rawProf_dir+'/l1000_CDRP/'\ncpd_info = pd.read_csv(cdrp_dataDir+\"/compounds.txt\", sep=\"\\t\", dtype=str)\ncpd_info.columns", "_____no_output_____" ], [ "from scipy.io import loadmat\nx = loadmat(cdrp_dataDir+'cdrp.all.prof.mat')\n\nk1=x['metaWell']['pert_id'][0][0]\nk2=x['metaGen']['AFFX_PROBE_ID'][0][0]\nk3=x['metaWell']['pert_dose'][0][0]\nk4=x['metaWell']['det_plate'][0][0]\n# pert_dose\n# x['metaWell']['pert_id'][0][0][0][0][0]\npertID = []\nprobID=[]\nfor r in range(len(k1)):\n v = k1[r][0][0]\n pertID.append(v)\n# probID.append(k2[r][0][0])\n\nfor r in range(len(k2)):\n probID.append(k2[r][0][0])\n \npert_dose=[]\ndet_plate=[]\nfor r in range(len(k3)):\n pert_dose.append(k3[r][0])\n det_plate.append(k4[r][0][0]) \n \ndataArray=x['pclfc'];\ncdrp_l1k_rep = pd.DataFrame(data=dataArray,columns=probID)\ncdrp_l1k_rep['pert_id']=pertID\ncdrp_l1k_rep['pert_dose']=pert_dose\ncdrp_l1k_rep['det_plate']=det_plate\ncdrp_l1k_rep['BROAD_CPD_ID']=cdrp_l1k_rep['pert_id'].str[:13]\ncdrp_l1k_rep2=pd.merge(cdrp_l1k_rep, cpd_info, how='left',on=['BROAD_CPD_ID'])\nl1k_features_cdrp=cdrp_l1k_rep2.columns[cdrp_l1k_rep2.columns.str.contains(\"_at\")]\ncdrp_l1k_rep2['pert_id_dose']=cdrp_l1k_rep2['BROAD_CPD_ID']+'_'+cdrp_l1k_rep2['pert_dose'].round(2).astype(str)\ncdrp_l1k_rep2['pert_sample_dose']=cdrp_l1k_rep2['pert_id']+'_'+cdrp_l1k_rep2['pert_dose'].round(2).astype(str)\n\n# cdrp_l1k_df.head()\nprint(cpd_info.shape,cdrp_l1k_rep.shape,cdrp_l1k_rep2.shape)\n\ncdrp_l1k_rep2['pert_id_dose']=cdrp_l1k_rep2['pert_id_dose'].replace('DMSO_-666.0', 'DMSO')\ncdrp_l1k_rep2['pert_sample_dose']=cdrp_l1k_rep2['pert_sample_dose'].replace('DMSO_-666.0', 'DMSO')\n\nsaveDF_to_CSV_GZ_no_timestamp(cdrp_l1k_rep2,procProf_dir+'preprocessed_data/CDRP-BBBC047-Bray/L1000/replicate_level_l1k.csv.gz');\n# cdrp_l1k_rep2.head()", "(32324, 4) (68120, 981) (68120, 986)\n" ], [ "# cpd_info", "_____no_output_____" ] ], [ [ "### CP - CDRP", "_____no_output_____" ] ], [ [ "profileType=['_augmented','_normalized']\n\nbioactiveFlag=\"\";# either \"-bioactive\" or \"\"\n\nplates=os.listdir(rawProf_dir+'/CDRP'+bioactiveFlag+'/')\nfor pt in profileType[1:2]:\n repLevelCDRP0=[]\n for p in plates:\n# repLevelCDRP0.append(pd.read_csv(rawProf_dir+'/CDRP/'+p+'/'+p+pt+'.csv'))\n repLevelCDRP0.append(pd.read_csv(rawProf_dir+'/CDRP'+bioactiveFlag+'/'+p+'/'+p+pt+'.csv')) #if bioactive\n repLevelCDRP = pd.concat(repLevelCDRP0)\n metaCDRP1=pd.read_csv(rawProf_dir+'/CP_CDRP/metadata/metadata_CDRP.csv')\n # metaCDRP1=metaCDRP1.rename(columns={\"PlateName\":\"Metadata_Plate_Map_Name\",'Well':'Metadata_Well'})\n # metaCDRP1['Metadata_Well']=metaCDRP1['Metadata_Well'].str.lower()\n repLevelCDRP2=pd.merge(repLevelCDRP, metaCDRP1, how='left',on=['Metadata_broad_sample'])\n# repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_broad_sample']+'_'+repLevelCDRP2['Metadata_mmoles_per_liter'].round(0).astype(int).astype(str)\n# repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_pert_id']+'_'+(repLevelCDRP2['Metadata_mmoles_per_liter']*2).round(0).astype(int).astype(str)\n repLevelCDRP2[\"Metadata_mmoles_per_liter2\"]=(repLevelCDRP2[\"Metadata_mmoles_per_liter\"]*2).round(2)\n repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_broad_sample']+'_'+repLevelCDRP2['Metadata_mmoles_per_liter2'].astype(str)\n\n repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_Sample_Dose'].replace('DMSO_0.0', 'DMSO')\n repLevelCDRP2['Metadata_pert_id']=repLevelCDRP2['Metadata_pert_id'].replace(np.nan, 'DMSO')\n \n# repLevelCDRP2.to_csv(procProf_dir+'preprocessed_data/CDRPBIO-BBBC036-Bray/CellPainting/replicate_level_cp'+pt+'.csv.gz',index=False,compression='gzip')\n\n# ,\n if bioactiveFlag:\n dataFolderName='CDRPBIO-BBBC036-Bray'\n saveDF_to_CSV_GZ_no_timestamp(repLevelCDRP2,procProf_dir+'preprocessed_data/'+dataFolderName+\\\n '/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n else:\n# sgfsgf\n dataFolderName='CDRP-BBBC047-Bray'\n saveDF_to_CSV_GZ_no_timestamp(repLevelCDRP2,procProf_dir+'preprocessed_data/'+dataFolderName+\\\n '/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n\n print(metaCDRP1.shape,repLevelCDRP.shape,repLevelCDRP2.shape)", "_____no_output_____" ], [ "dataFolderName='CDRP-BBBC047-Bray'\ncp_feats=repLevelCDRP.columns[repLevelCDRP.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")].tolist()\nfeatures_to_remove =find_correlation(repLevelCDRP2[cp_feats], threshold=0.9, remove_negative=False)\nrepLevelCDRP2_var_sel=repLevelCDRP2.drop(columns=features_to_remove)\nsaveDF_to_CSV_GZ_no_timestamp(repLevelCDRP2_var_sel,procProf_dir+'preprocessed_data/'+dataFolderName+\\\n '/CellPainting/replicate_level_cp'+'_normalized_variable_selected'+'.csv.gz')", "/home/ubuntu/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:3167: RuntimeWarning: compression has no effect when passing file-like object as input.\n formatter.save()\n" ], [ "# features_to_remove\n# features_to_remove\n# features_to_remove", "_____no_output_____" ], [ "repLevelCDRP2['Nuclei_Texture_Variance_RNA_3_0']", "_____no_output_____" ], [ "# repLevelCDRP2.shape\n# cp_scaled.columns[cp_scaled.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")].tolist()", "_____no_output_____" ] ], [ [ "# CDRP-bio-BBBC036-Bray", "_____no_output_____" ], [ "### GE - L1000 - CDRPBIO", "_____no_output_____" ] ], [ [ "bioactiveFlag=\"-bioactive\";# either \"-bioactive\" or \"\"\nplates=os.listdir(rawProf_dir+'/CDRP'+bioactiveFlag+'/')", "_____no_output_____" ], [ "# plates", "_____no_output_____" ], [ "cdrp_l1k_rep2_bioactive=cdrp_l1k_rep2[cdrp_l1k_rep2[\"pert_sample_dose\"].isin(repLevelCDRP2.Metadata_Sample_Dose.unique().tolist())]\n", "_____no_output_____" ], [ "cdrp_l1k_rep.det_plate", "_____no_output_____" ] ], [ [ "### CP - CDRPBIO", "_____no_output_____" ] ], [ [ "profileType=['_augmented','_normalized','_normalized_variable_selected']\n\nbioactiveFlag=\"-bioactive\";# either \"-bioactive\" or \"\"\n\nplates=os.listdir(rawProf_dir+'/CDRP'+bioactiveFlag+'/')\nfor pt in profileType:\n repLevelCDRP0=[]\n for p in plates:\n# repLevelCDRP0.append(pd.read_csv(rawProf_dir+'/CDRP/'+p+'/'+p+pt+'.csv'))\n repLevelCDRP0.append(pd.read_csv(rawProf_dir+'/CDRP'+bioactiveFlag+'/'+p+'/'+p+pt+'.csv')) #if bioactive\n repLevelCDRP = pd.concat(repLevelCDRP0)\n metaCDRP1=pd.read_csv(rawProf_dir+'/CP_CDRP/metadata/metadata_CDRP.csv')\n # metaCDRP1=metaCDRP1.rename(columns={\"PlateName\":\"Metadata_Plate_Map_Name\",'Well':'Metadata_Well'})\n # metaCDRP1['Metadata_Well']=metaCDRP1['Metadata_Well'].str.lower()\n repLevelCDRP2=pd.merge(repLevelCDRP, metaCDRP1, how='left',on=['Metadata_broad_sample'])\n# repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_broad_sample']+'_'+repLevelCDRP2['Metadata_mmoles_per_liter'].round(0).astype(int).astype(str)\n# repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_pert_id']+'_'+(repLevelCDRP2['Metadata_mmoles_per_liter']*2).round(0).astype(int).astype(str)\n repLevelCDRP2[\"Metadata_mmoles_per_liter2\"]=(repLevelCDRP2[\"Metadata_mmoles_per_liter\"]*2).round(2)\n repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_broad_sample']+'_'+repLevelCDRP2['Metadata_mmoles_per_liter2'].astype(str)\n\n repLevelCDRP2['Metadata_Sample_Dose']=repLevelCDRP2['Metadata_Sample_Dose'].replace('DMSO_0.0', 'DMSO')\n repLevelCDRP2['Metadata_pert_id']=repLevelCDRP2['Metadata_pert_id'].replace(np.nan, 'DMSO')\n \n# repLevelCDRP2.to_csv(procProf_dir+'preprocessed_data/CDRPBIO-BBBC036-Bray/CellPainting/replicate_level_cp'+pt+'.csv.gz',index=False,compression='gzip')\n\n# ,\n if bioactiveFlag:\n dataFolderName='CDRPBIO-BBBC036-Bray'\n saveDF_to_CSV_GZ_no_timestamp(repLevelCDRP2,procProf_dir+'preprocessed_data/'+dataFolderName+\\\n '/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n else:\n dataFolderName='CDRP-BBBC047-Bray'\n saveDF_to_CSV_GZ_no_timestamp(repLevelCDRP2,procProf_dir+'preprocessed_data/'+dataFolderName+\\\n '/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n\n print(metaCDRP1.shape,repLevelCDRP.shape,repLevelCDRP2.shape)", "_____no_output_____" ] ], [ [ "# LUAD-BBBC041-Caicedo", "_____no_output_____" ], [ "### GE - L1000 - LUAD", "_____no_output_____" ] ], [ [ "os.listdir(rawProf_dir+'/l1000_LUAD/input/')", "_____no_output_____" ], [ "os.listdir(rawProf_dir+'/l1000_LUAD/output/')", "_____no_output_____" ], [ "luad_dataDir=rawProf_dir+'/l1000_LUAD/'\nluad_info1 = pd.read_csv(luad_dataDir+\"/input/TA.OE014_A549_96H.map\", sep=\"\\t\", dtype=str)\nluad_info2 = pd.read_csv(luad_dataDir+\"/input/TA.OE015_A549_96H.map\", sep=\"\\t\", dtype=str)\nluad_info=pd.concat([luad_info1, luad_info2], ignore_index=True)\nluad_info.head()", "_____no_output_____" ], [ "luad_l1k_df = parse(luad_dataDir+\"/output/high_rep_A549_8reps_141230_ZSPCINF_n4232x978.gctx\").data_df.T.reset_index()\nluad_l1k_df=luad_l1k_df.rename(columns={\"cid\":\"id\"})\n# cdrp_l1k_df['XX']=cdrp_l1k_df['cid'].str[0]\n# cdrp_l1k_df['BROAD_CPD_ID']=cdrp_l1k_df['cid'].str[2:15]\nluad_l1k_df2=pd.merge(luad_l1k_df, luad_info, how='inner',on=['id'])\nluad_l1k_df2=luad_l1k_df2.rename(columns={\"x_mutation_status\":\"allele\"})\n\nl1k_features=luad_l1k_df2.columns[luad_l1k_df2.columns.str.contains(\"_at\")]\nluad_l1k_df2['allele']=luad_l1k_df2['allele'].replace('UnTrt', 'DMSO')\nprint(luad_info.shape,luad_l1k_df.shape,luad_l1k_df2.shape)\nsaveDF_to_CSV_GZ_no_timestamp(luad_l1k_df2,procProf_dir+'/preprocessed_data/LUAD-BBBC041-Caicedo/L1000/replicate_level_l1k.csv.gz')", "(5945, 54) (4232, 979) (4232, 1032)\n" ], [ "luad_l1k_df_scaled = standardize_per_catX(luad_l1k_df2,'det_plate',l1k_features.tolist());\nx_l1k_luad=replicateCorrs(luad_l1k_df_scaled.reset_index(drop=True),'allele',l1k_features,1)\n# x_l1k_luad=replicateCorrs(luad_l1k_df2[luad_l1k_df2['allele']!='DMSO'].reset_index(drop=True),'allele',l1k_features,1)\n# saveAsNewSheetToExistingFile(filename,x_l1k_luad[2],'l1k-luad')", "here3\n" ] ], [ [ "### CP - LUAD", "_____no_output_____" ] ], [ [ "profileType=['_augmented','_normalized','_normalized_variable_selected']\nplates=os.listdir('/storage/luad/profiles_cp/LUAD-BBBC043-Caicedo/')\nfor pt in profileType[1:2]:\n repLevelLuad0=[]\n for p in plates:\n repLevelLuad0.append(pd.read_csv('/storage/luad/profiles_cp/LUAD-BBBC043-Caicedo/'+p+'/'+p+pt+'.csv'))\n repLevelLuad = pd.concat(repLevelLuad0)\n metaLuad1=pd.read_csv(rawProf_dir+'/CP_LUAD/metadata/combined_platemaps_AHB_20150506_ssedits.csv')\n metaLuad1=metaLuad1.rename(columns={\"PlateName\":\"Metadata_Plate_Map_Name\",'Well':'Metadata_Well'})\n metaLuad1['Metadata_Well']=metaLuad1['Metadata_Well'].str.lower()\n # metaLuad2=pd.read_csv('~/workspace_rosetta/workspace/raw_profiles/CP_LUAD/metadata/barcode_platemap.csv')\n # Y[Y['Metadata_Well']=='g05']['Nuclei_Texture_Variance_Mito_5_0']\n repLevelLuad2=pd.merge(repLevelLuad, metaLuad1, how='inner',on=['Metadata_Plate_Map_Name','Metadata_Well'])\n repLevelLuad2['x_mutation_status']=repLevelLuad2['x_mutation_status'].replace(np.nan, 'DMSO')\n cp_features=repLevelLuad2.columns[repLevelLuad2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\n# repLevelLuad2.to_csv(procProf_dir+'preprocessed_data/LUAD-BBBC041-Caicedo/CellPainting/replicate_level_cp'+pt+'.csv.gz',index=False,compression='gzip')\n saveDF_to_CSV_GZ_no_timestamp(repLevelLuad2,procProf_dir+'preprocessed_data/LUAD-BBBC041-Caicedo/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n print(metaLuad1.shape,repLevelLuad.shape,repLevelLuad2.shape) ", "_____no_output_____" ], [ "pt=['_normalized']\n# Read save data\nrepLevelLuad2=pd.read_csv('./preprocessed_data/LUAD-BBBC041-Caicedo/CellPainting/replicate_level_cp'+pt[0]+'.csv.gz')\n\n# repLevelTA.head()\ncp_features=repLevelLuad2.columns[repLevelLuad2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\ncols2remove0=[i for i in cp_features if ((repLevelLuad2[i].isnull()).sum(axis=0)/repLevelLuad2.shape[0])>0.05]\nprint(cols2remove0)\nrepLevelLuad2=repLevelLuad2.drop(cols2remove0, axis=1);\ncp_features=repLevelLuad2.columns[repLevelLuad2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\nrepLevelLuad2 = repLevelLuad2.interpolate()\nrepLevelLuad2 = standardize_per_catX(repLevelLuad2,'Metadata_Plate',cp_features.tolist());\ndf1=repLevelLuad2[~repLevelLuad2['x_mutation_status'].isnull()].reset_index(drop=True)\nx_cp_luad=replicateCorrs(df1,'x_mutation_status',cp_features,1)\nsaveAsNewSheetToExistingFile(filename,x_cp_luad[2],'cp-luad')", "_____no_output_____" ] ], [ [ "# TA-ORF-BBBC037-Rohban", "_____no_output_____" ], [ "### GE - L1000 ", "_____no_output_____" ] ], [ [ "taorf_datadir=rawProf_dir+'/l1000_TA_ORF/'\ngene_info = pd.read_csv(taorf_datadir+\"TA.OE005_U2OS_72H.map.txt\", sep=\"\\t\", dtype=str)\n# gene_info.columns\n# TA.OE005_U2OS_72H_INF_n729x22268.gctx\n# TA.OE005_U2OS_72H_QNORM_n729x978.gctx\n# TA.OE005_U2OS_72H_ZSPCINF_n729x22268.gctx\n# TA.OE005_U2OS_72H_ZSPCQNORM_n729x978.gctx\ntaorf_l1k0 = parse(taorf_datadir+\"TA.OE005_U2OS_72H_ZSPCQNORM_n729x978.gctx\")\n# taorf_l1k0 = parse(taorf_datadir+\"TA.OE005_U2OS_72H_QNORM_n729x978.gctx\")\ntaorf_l1k_df0=taorf_l1k0.data_df\ntaorf_l1k_df=taorf_l1k_df0.T.reset_index()\nl1k_features=taorf_l1k_df.columns[taorf_l1k_df.columns.str.contains(\"_at\")]\ntaorf_l1k_df=taorf_l1k_df.rename(columns={\"cid\":\"id\"})\ntaorf_l1k_df2=pd.merge(taorf_l1k_df, gene_info, how='inner',on=['id'])\n# print(taorf_l1k_df.shape,gene_info.shape,taorf_l1k_df2.shape)\ntaorf_l1k_df2.head()\n# x_genesymbol_mutation\ntaorf_l1k_df2['pert_id']=taorf_l1k_df2['pert_id'].replace('CMAP-000', 'DMSO')\n# compression_opts = dict(method='zip',archive_name='out.csv') \n# taorf_l1k_df2.to_csv(procProf_dir+'preprocessed_data/TA-ORF-BBBC037-Rohban/L1000/replicate_level_l1k.csv.gz',index=False,compression=compression_opts)\nsaveDF_to_CSV_GZ_no_timestamp(taorf_l1k_df2,procProf_dir+'preprocessed_data/TA-ORF-BBBC037-Rohban/L1000/replicate_level_l1k.csv.gz')\nprint(gene_info.shape,taorf_l1k_df.shape,taorf_l1k_df2.shape)\n# gene_info.head()", "/home/ubuntu/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:3167: RuntimeWarning: compression has no effect when passing file-like object as input.\n formatter.save()\n" ], [ "taorf_l1k_df2.groupby(['x_genesymbol_mutation']).size().describe()", "_____no_output_____" ], [ "taorf_l1k_df2.groupby(['pert_id']).size().describe()", "_____no_output_____" ] ], [ [ "#### Check Replicate Correlation", "_____no_output_____" ] ], [ [ "# df1=taorf_l1k_df2[taorf_l1k_df2['pert_id']!='CMAP-000']\n\ndf1_scaled = standardize_per_catX(taorf_l1k_df2,'det_plate',l1k_features.tolist());\ndf1_scaled2=df1_scaled[df1_scaled['pert_id']!='DMSO']\nx=replicateCorrs(df1_scaled2,'pert_id',l1k_features,1)", "here3\n" ] ], [ [ "### CP - TAORF", "_____no_output_____" ] ], [ [ "profileType=['_augmented','_normalized','_normalized_variable_selected']\nplates=os.listdir(rawProf_dir+'TA-ORF-BBBC037-Rohban/')\nfor pt in profileType[0:1]:\n repLevelTA0=[]\n for p in plates:\n repLevelTA0.append(pd.read_csv(rawProf_dir+'TA-ORF-BBBC037-Rohban/'+p+'/'+p+pt+'.csv'))\n repLevelTA = pd.concat(repLevelTA0)\n metaTA1=pd.read_csv(rawProf_dir+'/CP_TA_ORF/metadata/metadata_TA.csv')\n metaTA2=pd.read_csv(rawProf_dir+'/CP_TA_ORF/metadata/metadata_TA_2.csv')\n# metaTA2=metaTA2.rename(columns={\"Metadata_broad_sample\":\"Metadata_broad_sample_2\",'Metadata_Treatment':'Gene Allele Name'})\n metaTA=pd.merge(metaTA2, metaTA1, how='left',on=['Metadata_broad_sample'])\n# metaTA2=metaTA2.rename(columns={\"Metadata_Treatment\":\"Metadata_pert_name\"})\n# repLevelTA2=pd.merge(repLevelTA, metaTA2, how='left',on=['Metadata_pert_name'])\n repLevelTA2=pd.merge(repLevelTA, metaTA, how='left',on=['Metadata_broad_sample'])\n\n# repLevelTA2=repLevelTA2.rename(columns={\"Gene Allele Name\":\"Allele\"})\n repLevelTA2['Metadata_broad_sample']=repLevelTA2['Metadata_broad_sample'].replace(np.nan, 'DMSO')\n saveDF_to_CSV_GZ_no_timestamp(repLevelTA2,procProf_dir+'/preprocessed_data/TA-ORF-BBBC037-Rohban/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n print(metaTA.shape,repLevelTA.shape,repLevelTA2.shape)\n", "(323, 4) (1920, 1801) (1920, 1804)\n" ], [ "# repLevelTA.head()\ncp_features=repLevelTA2.columns[repLevelTA2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\ncols2remove0=[i for i in cp_features if ((repLevelTA2[i].isnull()).sum(axis=0)/repLevelTA2.shape[0])>0.05]\nprint(cols2remove0)\nrepLevelTA2=repLevelTA2.drop(cols2remove0, axis=1);\n# cp_features=list(set(cp_features)-set(cols2remove0))\n# repLevelTA2=repLevelTA2.replace('nan', np.nan)\nrepLevelTA2 = repLevelTA2.interpolate()\ncp_features=repLevelTA2.columns[repLevelTA2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\nrepLevelTA2 = standardize_per_catX(repLevelTA2,'Metadata_Plate',cp_features.tolist());\ndf1=repLevelTA2[~repLevelTA2['Metadata_broad_sample'].isnull()].reset_index(drop=True)\nx_taorf_cp=replicateCorrs(df1,'Metadata_broad_sample',cp_features,1)\n# saveAsNewSheetToExistingFile(filename,x_taorf_cp[2],'cp-taorf')", "[]\n" ], [ "# plates", "_____no_output_____" ] ], [ [ "# LINCS-Pilot1", "_____no_output_____" ], [ "### GE - L1000 - LINCS", "_____no_output_____" ] ], [ [ "os.listdir(rawProf_dir+'/l1000_LINCS/2016_04_01_a549_48hr_batch1_L1000/')", "_____no_output_____" ], [ "os.listdir(rawProf_dir+'/l1000_LINCS/metadata/')", "_____no_output_____" ], [ "data_meta_match_ls=[['level_3','level_3_q2norm_n27837x978.gctx','col_meta_level_3_REP.A_A549_only_n27837.txt'],\n ['level_4W','level_4W_zspc_n27837x978.gctx','col_meta_level_3_REP.A_A549_only_n27837.txt'],\n ['level_4','level_4_zspc_n27837x978.gctx','col_meta_level_3_REP.A_A549_only_n27837.txt'],\n ['level_5_modz','level_5_modz_n9482x978.gctx','col_meta_level_5_REP.A_A549_only_n9482.txt'],\n ['level_5_rank','level_5_rank_n9482x978.gctx','col_meta_level_5_REP.A_A549_only_n9482.txt']]", "_____no_output_____" ], [ "lincs_dataDir=rawProf_dir+'/l1000_LINCS/'\nlincs_pert_info = pd.read_csv(lincs_dataDir+\"/metadata/REP.A_A549_pert_info.txt\", sep=\"\\t\", dtype=str)\nlincs_meta_level3 = pd.read_csv(lincs_dataDir+\"/metadata/col_meta_level_3_REP.A_A549_only_n27837.txt\", sep=\"\\t\", dtype=str)\n# lincs_info1 = pd.read_csv(lincs_dataDir+\"/metadata/REP.A_A549_pert_info.txt\", sep=\"\\t\", dtype=str)\nprint(lincs_meta_level3.shape)\nlincs_meta_level3.head()\n# lincs_info2 = pd.read_csv(lincs_dataDir+\"/input/TA.OE015_A549_96H.map\", sep=\"\\t\", dtype=str)\n# lincs_info=pd.concat([lincs_info1, lincs_info2], ignore_index=True)\n# lincs_info.head()", "(27837, 45)\n" ], [ "# lincs_meta_level3.groupby('distil_id').size()\nlincs_meta_level3['distil_id'].unique().shape", "_____no_output_____" ], [ "# lincs_meta_level3.columns.tolist()\n# lincs_meta_level3.pert_id", "_____no_output_____" ], [ "ls /home/ubuntu/workspace_rosetta/workspace/software/2018_04_20_Rosetta/preprocessed_data/LINCS-Pilot1/CellPainting", "\u001b[0m\u001b[01;34mCellPainting\u001b[0m/ \u001b[01;34mL1000\u001b[0m/\r\n" ], [ "# procProf_dir+'preprocessed_data/LINCS-Pilot1/'\nprocProf_dir", "_____no_output_____" ], [ "for el in data_meta_match_ls:\n lincs_l1k_df=parse(lincs_dataDir+\"/2016_04_01_a549_48hr_batch1_L1000/\"+el[1]).data_df.T.reset_index()\n lincs_meta0 = pd.read_csv(lincs_dataDir+\"/metadata/\"+el[2], sep=\"\\t\", dtype=str)\n lincs_meta=pd.merge(lincs_meta0, lincs_pert_info, how='left',on=['pert_id'])\n lincs_meta=lincs_meta.rename(columns={\"distil_id\":\"cid\"})\n lincs_l1k_df2=pd.merge(lincs_l1k_df, lincs_meta, how='inner',on=['cid'])\n lincs_l1k_df2['pert_id_dose']=lincs_l1k_df2['pert_id']+'_'+lincs_l1k_df2['nearest_dose'].astype(str)\n lincs_l1k_df2['pert_id_dose']=lincs_l1k_df2['pert_id_dose'].replace('DMSO_-666', 'DMSO')\n# lincs_l1k_df2.to_csv(procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+el[0]+'.csv.gz',index=False,compression='gzip')\n saveDF_to_CSV_GZ_no_timestamp(lincs_l1k_df2,procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+el[0]+'.csv.gz')", "_____no_output_____" ], [ "# lincs_l1k_df2", "_____no_output_____" ], [ "lincs_l1k_rep['pert_id_dose'].unique()", "_____no_output_____" ], [ "lincs_l1k_rep = pd.read_csv(procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+data_meta_match_ls[1][0]+'.csv.gz')\n# l1k_features=lincs_l1k_rep.columns[lincs_l1k_rep.columns.str.contains(\"_at\")]\n# x=replicateCorrs(lincs_l1k_rep[lincs_l1k_rep['pert_iname_x']!='DMSO'].reset_index(drop=True),'pert_id',l1k_features,1)\n# # saveAsNewSheetToExistingFile(filename,x[2],'l1k-lincs')\n# # lincs_l1k_rep.head()", "_____no_output_____" ], [ "lincs_l1k_rep.pert_id.unique().shape", "_____no_output_____" ], [ "lincs_l1k_rep = pd.read_csv(procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+data_meta_match_ls[2][0]+'.csv.gz')\nlincs_l1k_rep.columns[lincs_l1k_rep.columns.str.contains('dose')]", "_____no_output_____" ], [ "lincs_l1k_rep[['pert_dose', 'pert_dose_unit', 'pert_idose', 'nearest_dose']]", "_____no_output_____" ], [ "lincs_l1k_rep['nearest_dose'].unique()", "_____no_output_____" ], [ "# lincs_l1k_rep.rna_plate.unique()", "_____no_output_____" ], [ "lincs_l1k_rep = pd.read_csv(procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+data_meta_match_ls[2][0]+'.csv.gz')\nl1k_features=lincs_l1k_rep.columns[lincs_l1k_rep.columns.str.contains(\"_at\")]\nlincs_l1k_rep = standardize_per_catX(lincs_l1k_rep,'det_plate',l1k_features.tolist());\nx=replicateCorrs(lincs_l1k_rep[lincs_l1k_rep['pert_iname_x']!='DMSO'].reset_index(drop=True),'pert_id',l1k_features,1)", "_____no_output_____" ], [ "lincs_l1k_rep = pd.read_csv(procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+data_meta_match_ls[2][0]+'.csv.gz')\nl1k_features=lincs_l1k_rep.columns[lincs_l1k_rep.columns.str.contains(\"_at\")]\nlincs_l1k_rep = standardize_per_catX(lincs_l1k_rep,'det_plate',l1k_features.tolist());\nx_l1k_lincs=replicateCorrs(lincs_l1k_rep[lincs_l1k_rep['pert_iname_x']!='DMSO'].reset_index(drop=True),'pert_id_dose',l1k_features,1)\nsaveAsNewSheetToExistingFile(filename,x_l1k_lincs[2],'l1k-lincs')", "/home/ubuntu/workspace_rosetta/workspace/software/2018_04_20_Rosetta/utils/replicateCorrs.py:44: RuntimeWarning: Mean of empty slice\n repCorrDf.loc[u,'RepCor']=np.nanmean(repCorr)\n/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1113: RuntimeWarning: Mean of empty slice\n return np.nanmean(a, axis, out=out, keepdims=keepdims)\n" ], [ "lincs_l1k_rep = pd.read_csv(procProf_dir+'preprocessed_data/LINCS-Pilot1/L1000/'+data_meta_match_ls[2][0]+'.csv.gz')\nl1k_features=lincs_l1k_rep.columns[lincs_l1k_rep.columns.str.contains(\"_at\")]\nlincs_l1k_rep = standardize_per_catX(lincs_l1k_rep,'det_plate',l1k_features.tolist());\nx_l1k_lincs=replicateCorrs(lincs_l1k_rep[lincs_l1k_rep['pert_iname_x']!='DMSO'].reset_index(drop=True),'pert_id_dose',l1k_features,1)\nsaveAsNewSheetToExistingFile(filename,x_l1k_lincs[2],'l1k-lincs')", "_____no_output_____" ], [ "saveAsNewSheetToExistingFile(filename,x[2],'l1k-lincs')", "_____no_output_____" ] ], [ [ "raw data\n", "_____no_output_____" ] ], [ [ "# set(repLevelLuad2)-set(Y1.columns)", "_____no_output_____" ], [ "# Y1[['Allele', 'Category', 'Clone ID', 'Gene Symbol']].head()", "_____no_output_____" ], [ "# repLevelLuad2[repLevelLuad2['PublicID']=='BRDN0000553807'][['Col','InsertLength','NCBIGeneID','Name','OtherDescriptions','PublicID','Row','Symbol','Transcript','Vector','pert_type','x_mutation_status']].head()", "_____no_output_____" ] ], [ [ "#### Check Replicate Correlation", "_____no_output_____" ], [ "### CP - LINCS", "_____no_output_____" ] ], [ [ "# Ran the following on:\n# https://ec2-54-242-99-61.compute-1.amazonaws.com:5006/notebooks/workspace_nucleolar/2020_07_20_Nucleolar_Calico/1-NucleolarSizeMetrics.ipynb\n# Metadata\ndef recode_dose(x, doses, return_level=False):\n closest_index = np.argmin([np.abs(dose - x) for dose in doses])\n if np.isnan(x):\n return 0\n if return_level:\n return closest_index + 1\n else:\n return doses[closest_index]\n \nprimary_dose_mapping = [0.04, 0.12, 0.37, 1.11, 3.33, 10, 20]\n\n\nmetadata=pd.read_csv(\"/home/ubuntu/bucket/projects/2018_04_20_Rosetta/workspace/raw-profiles/CP_LINCS/metadata/matadata_lincs_2.csv\")\nmetadata['Metadata_mmoles_per_liter']=metadata.mmoles_per_liter.values.round(2)\nmetadata=metadata.rename(columns={\"Assay_Plate_Barcode\": \"Metadata_Plate\",'broad_sample':'Metadata_broad_sample','well_position':'Metadata_Well'})\n\nlincs_submod_root_dir=\"/home/ubuntu/datasetsbucket/lincs-cell-painting/\"\n\nprofileType=['_augmented','_normalized','_normalized_dmso',\\\n '_normalized_feature_select','_normalized_feature_select_dmso']\n# profileType=['_normalized']\n# plates=metadata.Assay_Plate_Barcode.unique().tolist()\nplates=metadata.Metadata_Plate.unique().tolist()\nfor pt in profileType[4:5]:\n repLevelLINCS0=[]\n \n for p in plates:\n profile_add=lincs_submod_root_dir+\"/profiles/2016_04_01_a549_48hr_batch1/\"+p+\"/\"+p+pt+\".csv.gz\"\n if os.path.exists(profile_add):\n repLevelLINCS0.append(pd.read_csv(profile_add))\n \n repLevelLINCS = pd.concat(repLevelLINCS0)\n meta_lincs1=metadata.rename(columns={\"broad_sample\": \"Metadata_broad_sample\"})\n # metaCDRP1=metaCDRP1.rename(columns={\"PlateName\":\"Metadata_Plate_Map_Name\",'Well':'Metadata_Well'})\n # metaCDRP1['Metadata_Well']=metaCDRP1['Metadata_Well'].str.lower()\n \n repLevelLINCS2=pd.merge(repLevelLINCS,meta_lincs1,how='left', on=[\"Metadata_broad_sample\",\"Metadata_Well\",\"Metadata_Plate\",'Metadata_mmoles_per_liter'])\n \n\n repLevelLINCS2 = repLevelLINCS2.assign(Metadata_dose_recode=(repLevelLINCS2.Metadata_mmoles_per_liter.apply(\n lambda x: recode_dose(x, primary_dose_mapping, return_level=False))))\n repLevelLINCS2['Metadata_pert_id_dose']=repLevelLINCS2['Metadata_pert_id']+'_'+repLevelLINCS2['Metadata_dose_recode'].astype(str)\n# repLevelLINCS2['Metadata_Sample_Dose']=repLevelLINCS2['Metadata_broad_sample']+'_'+repLevelLINCS2['Metadata_dose_recode'].astype(str)\n repLevelLINCS2['Metadata_pert_id_dose']=repLevelLINCS2['Metadata_pert_id_dose'].replace(np.nan, 'DMSO')\n# saveDF_to_CSV_GZ_no_timestamp(repLevelLINCS2,procProf_dir+'/preprocessed_data/LINCS-Pilot1/CellPainting/replicate_level_cp'+pt+'.csv.gz')\n print(meta_lincs1.shape,repLevelLINCS.shape,repLevelLINCS2.shape)", "(53760, 17) (52223, 1243) (52223, 1257)\n" ], [ "# (8120, 15) (52223, 1810) (688699, 1825)\n# repLevelLINCS", "_____no_output_____" ], [ "# pd.merge(repLevelLINCS,meta_lincs1,how='left', on=[\"Metadata_broad_sample\"]).shape\nrepLevelLINCS.shape,meta_lincs1.shape", "_____no_output_____" ], [ "(8120, 15) (52223, 1238) (52223, 1253)", "_____no_output_____" ], [ "csv_l1k_lincs=pd.read_csv('./preprocessed_data/LINCS-Pilot1/L1000/replicate_level_l1k'+'.csv.gz')\ncsv_pddf=pd.read_csv('./preprocessed_data/LINCS-Pilot1/CellPainting/replicate_level_cp'+pt[0]+'.csv.gz')", "_____no_output_____" ], [ "csv_l1k_lincs.head()", "_____no_output_____" ], [ "csv_l1k_lincs.pert_id_dose.unique()", "_____no_output_____" ], [ "csv_pddf.Metadata_pert_id_dose.unique()", "_____no_output_____" ] ], [ [ "#### Read saved data", "_____no_output_____" ] ], [ [ "repLevelLINCS2.groupby(['Metadata_pert_id']).size()", "_____no_output_____" ], [ "repLevelLINCS2.groupby(['Metadata_pert_id_dose']).size().describe()", "_____no_output_____" ], [ "repLevelLINCS2.Metadata_Plate.unique().shape", "_____no_output_____" ], [ "repLevelLINCS2['Metadata_pert_id_dose'].unique().shape", "_____no_output_____" ], [ "# csv_pddf['Metadata_mmoles_per_liter'].round(0).unique()\n# np.sort(csv_pddf['Metadata_mmoles_per_liter'].unique())", "_____no_output_____" ], [ "csv_pddf.groupby(['Metadata_dose_recode']).size()#.median()", "_____no_output_____" ], [ "# repLevelLincs2=csv_pddf.copy()\nimport gc\ncp_features=repLevelLincs2.columns[repLevelLincs2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\ncols2remove0=[i for i in cp_features if ((repLevelLincs2[i].isnull()).sum(axis=0)/repLevelLincs2.shape[0])>0.05]\nprint(cols2remove0)\nrepLevelLincs3=repLevelLincs2.drop(cols2remove0, axis=1);\nprint('here0')\n# cp_features=list(set(cp_features)-set(cols2remove0))\n# repLevelTA2=repLevelTA2.replace('nan', np.nan)\ndel repLevelLincs2\ngc.collect()\nprint('here0')\ncp_features=repLevelLincs3.columns[repLevelLincs3.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\nrepLevelLincs3[cp_features] = repLevelLincs3[cp_features].interpolate()\nprint('here1')\nrepLevelLincs3 = standardize_per_catX(repLevelLincs3,'Metadata_Plate',cp_features.tolist());\nprint('here1')\n\n# df0=repLevelCDRP3[repLevelCDRP3['Metadata_broad_sample']!='DMSO'].reset_index(drop=True)\n# repSizeDF=repLevelLincs3.groupby(['Metadata_broad_sample']).size().reset_index()\nrepSizeDF=repLevelLincs3.groupby(['Metadata_pert_id_dose']).size().reset_index()\n\nhighRepComp=repSizeDF[repSizeDF[0]>1].Metadata_pert_id_dose.tolist()\nhighRepComp.remove('DMSO')\n# df0=repLevelLincs3[(repLevelLincs3['Metadata_broad_sample'].isin(highRepComp)) &\\\n# (repLevelLincs3['Metadata_dose_recode']==1.11)]\ndf0=repLevelLincs3[(repLevelLincs3['Metadata_pert_id_dose'].isin(highRepComp))]\nx_lincs_cp=replicateCorrs(df0,'Metadata_pert_id_dose',cp_features,1)\n# saveAsNewSheetToExistingFile(filename,x_lincs_cp[2],'cp-lincs')", "['Cells_RadialDistribution_FracAtD_DNA_1of4', 'Cells_RadialDistribution_FracAtD_DNA_2of4', 'Cells_RadialDistribution_FracAtD_DNA_3of4', 'Cells_RadialDistribution_FracAtD_DNA_4of4', 'Cells_RadialDistribution_MeanFrac_DNA_1of4', 'Cells_RadialDistribution_MeanFrac_DNA_2of4', 'Cells_RadialDistribution_MeanFrac_DNA_3of4', 'Cells_RadialDistribution_MeanFrac_DNA_4of4', 'Cells_RadialDistribution_RadialCV_DNA_1of4', 'Cells_RadialDistribution_RadialCV_DNA_2of4', 'Cells_RadialDistribution_RadialCV_DNA_3of4', 'Cells_RadialDistribution_RadialCV_DNA_4of4', 'Cytoplasm_RadialDistribution_FracAtD_DNA_1of4', 'Cytoplasm_RadialDistribution_FracAtD_DNA_2of4', 'Cytoplasm_RadialDistribution_FracAtD_DNA_3of4', 'Cytoplasm_RadialDistribution_FracAtD_DNA_4of4', 'Cytoplasm_RadialDistribution_MeanFrac_DNA_1of4', 'Cytoplasm_RadialDistribution_MeanFrac_DNA_2of4', 'Cytoplasm_RadialDistribution_MeanFrac_DNA_3of4', 'Cytoplasm_RadialDistribution_MeanFrac_DNA_4of4', 'Cytoplasm_RadialDistribution_RadialCV_DNA_1of4', 'Cytoplasm_RadialDistribution_RadialCV_DNA_2of4', 'Cytoplasm_RadialDistribution_RadialCV_DNA_3of4', 'Cytoplasm_RadialDistribution_RadialCV_DNA_4of4', 'Nuclei_RadialDistribution_FracAtD_DNA_1of4', 'Nuclei_RadialDistribution_FracAtD_DNA_2of4', 'Nuclei_RadialDistribution_FracAtD_DNA_3of4', 'Nuclei_RadialDistribution_FracAtD_DNA_4of4', 'Nuclei_RadialDistribution_MeanFrac_DNA_1of4', 'Nuclei_RadialDistribution_MeanFrac_DNA_2of4', 'Nuclei_RadialDistribution_MeanFrac_DNA_3of4', 'Nuclei_RadialDistribution_MeanFrac_DNA_4of4', 'Nuclei_RadialDistribution_RadialCV_DNA_1of4', 'Nuclei_RadialDistribution_RadialCV_DNA_2of4', 'Nuclei_RadialDistribution_RadialCV_DNA_3of4', 'Nuclei_RadialDistribution_RadialCV_DNA_4of4']\nhere0\nhere0\nhere1\nhere1\nhere2\n" ], [ "repSizeDF", "_____no_output_____" ], [ "# repLevelLincs2=csv_pddf.copy()\n\n# cp_features=repLevelLincs2.columns[repLevelLincs2.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\n# cols2remove0=[i for i in cp_features if ((repLevelLincs2[i].isnull()).sum(axis=0)/repLevelLincs2.shape[0])>0.05]\n# print(cols2remove0)\n# repLevelLincs3=repLevelLincs2.drop(cols2remove0, axis=1);\n# # cp_features=list(set(cp_features)-set(cols2remove0))\n# # repLevelTA2=repLevelTA2.replace('nan', np.nan)\n# repLevelLincs3 = repLevelLincs3.interpolate()\n\n# repLevelLincs3 = standardize_per_catX(repLevelLincs3,'Metadata_Plate',cp_features.tolist());\n\n# cp_features=repLevelLincs3.columns[repLevelLincs3.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\n# # df0=repLevelCDRP3[repLevelCDRP3['Metadata_broad_sample']!='DMSO'].reset_index(drop=True)\n# # repSizeDF=repLevelLincs3.groupby(['Metadata_broad_sample']).size().reset_index()\nrepSizeDF=repLevelLincs3.groupby(['Metadata_pert_id']).size().reset_index()\n\nhighRepComp=repSizeDF[repSizeDF[0]>1].Metadata_pert_id.tolist()\n# highRepComp.remove('DMSO')\n# df0=repLevelLincs3[(repLevelLincs3['Metadata_broad_sample'].isin(highRepComp)) &\\\n# (repLevelLincs3['Metadata_dose_recode']==1.11)]\ndf0=repLevelLincs3[(repLevelLincs3['Metadata_pert_id'].isin(highRepComp))]\nx_lincs_cp=replicateCorrs(df0,'Metadata_pert_id',cp_features,1)\n# saveAsNewSheetToExistingFile(filename,x_lincs_cp[2],'cp-lincs')", "here2\n" ], [ "# x=replicateCorrs(df0,'Metadata_broad_sample',cp_features,1)\n# highRepComp[-1]\n", "_____no_output_____" ], [ "saveAsNewSheetToExistingFile(filename,x[2],'cp-lincs')", "_____no_output_____" ], [ "# repLevelLincs3.Metadata_Plate\nrepLevelLincs3.head()", "_____no_output_____" ], [ "# csv_pddf[(csv_pddf['Metadata_dose_recode']==0.04) & (csv_pddf['Metadata_pert_id']==\"BRD-A00147595\")][['Metadata_Plate','Metadata_Well']].drop_duplicates()", "_____no_output_____" ], [ "# csv_pddf[(csv_pddf['Metadata_dose_recode']==0.04) & (csv_pddf['Metadata_pert_id']==\"BRD-A00147595\") &\n# (csv_pddf['Metadata_Plate']=='SQ00015196') & (csv_pddf['Metadata_Well']==\"B12\")][csv_pddf.columns[1820:]].drop_duplicates()", "_____no_output_____" ], [ "# def standardize_per_catX(df,column_name):\ncolumn_name='Metadata_Plate'\nrepLevelLincs_scaled_perPlate=repLevelLincs3.copy()\nrepLevelLincs_scaled_perPlate[cp_features.tolist()]=repLevelLincs3[cp_features.tolist()+[column_name]].groupby(column_name).transform(lambda x: (x - x.mean()) / x.std()).values", "_____no_output_____" ], [ "# def standardize_per_catX(df,column_name):\n# # column_name='Metadata_Plate'\n# cp_features=df.columns[df.columns.str.contains(\"Cells_|Cytoplasm_|Nuclei_\")]\n# df_scaled_perPlate=df.copy()\n# df_scaled_perPlate[cp_features.tolist()]=\\\n# df[cp_features.tolist()+[column_name]].groupby(column_name)\\\n# .transform(lambda x: (x - x.mean()) / x.std()).values\n# return df_scaled_perPlate", "_____no_output_____" ], [ "df0=repLevelLincs_scaled_perPlate[(repLevelLincs_scaled_perPlate['Metadata_Sample_Dose'].isin(highRepComp))]\nx=replicateCorrs(df0,'Metadata_broad_sample',cp_features,1)", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import torch\n\nx = torch.ones(5) # input tensor\ny = torch.zeros(3) # expected output\nw = torch.randn(5, 3, requires_grad=True)\nb = torch.randn(3, requires_grad=True)\nz = torch.matmul(x, w)+b\nloss = torch.nn.functional.binary_cross_entropy_with_logits(z, y)", "_____no_output_____" ], [ "print('Gradient function for z =', z.grad_fn)\nprint('Gradient function for loss =', loss.grad_fn)", "Gradient function for z = <AddBackward0 object at 0x7ff7fd77e940>\nGradient function for loss = <BinaryCrossEntropyWithLogitsBackward object at 0x7ff7fd77efa0>\n" ], [ "loss.backward()\nprint(w.grad)\nprint(b.grad)", "tensor([[0.0159, 0.2858, 0.0136],\n [0.0159, 0.2858, 0.0136],\n [0.0159, 0.2858, 0.0136],\n [0.0159, 0.2858, 0.0136],\n [0.0159, 0.2858, 0.0136]])\ntensor([0.0159, 0.2858, 0.0136])\n" ], [ "z = torch.matmul(x, w)+b\nprint(z.requires_grad)\n\nwith torch.no_grad():\n z = torch.matmul(x, w)+b\nprint(z.requires_grad)", "True\nFalse\n" ], [ "z = torch.matmul(x, w)+b\nz_det = z.detach()\nprint(z_det.requires_grad)", "False\n" ], [ "inp = torch.eye(5, requires_grad=True)\nout = (inp+1).pow(2)\nout.backward(torch.ones_like(inp), retain_graph=True)\nprint(\"First call\\n\", inp.grad)\nout.backward(torch.ones_like(inp), retain_graph=True)\nprint(\"\\nSecond call\\n\", inp.grad)\ninp.grad.zero_()\nout.backward(torch.ones_like(inp), retain_graph=True)\nprint(\"\\nCall after zeroing gradients\\n\", inp.grad)", "First call\n tensor([[4., 2., 2., 2., 2.],\n [2., 4., 2., 2., 2.],\n [2., 2., 4., 2., 2.],\n [2., 2., 2., 4., 2.],\n [2., 2., 2., 2., 4.]])\n\nSecond call\n tensor([[8., 4., 4., 4., 4.],\n [4., 8., 4., 4., 4.],\n [4., 4., 8., 4., 4.],\n [4., 4., 4., 8., 4.],\n [4., 4., 4., 4., 8.]])\n\nCall after zeroing gradients\n tensor([[4., 2., 2., 2., 2.],\n [2., 4., 2., 2., 2.],\n [2., 2., 4., 2., 2.],\n [2., 2., 2., 4., 2.],\n [2., 2., 2., 2., 4.]])\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Introduction\nVisualization of statistics that support the claims of Black Lives Matter movement, data from 2015 and 2016.\n\nData source: https://www.theguardian.com/us-news/ng-interactive/2015/jun/01/about-the-counted\n\nIdea from BuzzFeed article: https://www.buzzfeednews.com/article/peteraldhous/race-and-police-shootings", "_____no_output_____" ], [ "### Imports\nLibraries and data", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\n\nfrom bokeh.io import output_notebook, show, export_png\nfrom bokeh.plotting import figure, output_file\nfrom bokeh.models import HoverTool, ColumnDataSource,NumeralTickFormatter\nfrom bokeh.palettes import Spectral4, PuBu4\nfrom bokeh.transform import dodge\nfrom bokeh.layouts import gridplot", "_____no_output_____" ], [ "selectcolumns=['raceethnicity','armed']\ndf1 = pd.read_csv('the-counted-2015.csv',usecols=selectcolumns)\ndf1.head()", "_____no_output_____" ], [ "df2 = pd.read_csv('the-counted-2016.csv',usecols=selectcolumns)\ndf2.head()", "_____no_output_____" ], [ "df=pd.concat([df1,df2])\ndf.shape # df contains \"The Counted\" data from both 2015 and 2016", "_____no_output_____" ] ], [ [ "Source for ethnicities percentage in 2015: https://www.statista.com/statistics/270272/percentage-of-us-population-by-ethnicities/\n\nSource for population total: https://en.wikipedia.org/wiki/Demography_of_the_United_States#Vital_statistics_from_1935", "_____no_output_____" ] ], [ [ "ethndic={\"White\": 61.72,\n \"Latino\": 17.66,\n \"Black\": 12.38,\n \"Others\": (5.28+2.05+0.73+0.17)\n }\n#print(type(ethndic))\nprint(ethndic)\npopulation=(321442000 + 323100000)/2 # average between 2015 and 2016 data\n# estimates by ethnicity\nethnestim={\"White\": round((population*ethndic[\"White\"]/100)),\n \"Latino\": round((population*ethndic[\"Latino\"]/100)),\n \"Black\": round((population*ethndic[\"Black\"]/100)),\n \"Others\": round((population*ethndic[\"Others\"]/100))\n }\nprint(ethnestim)", "{'White': 61.72, 'Latino': 17.66, 'Black': 12.38, 'Others': 8.23}\n{'White': 198905661, 'Latino': 56913059, 'Black': 39897150, 'Others': 26522903}\n" ] ], [ [ "# Analysis", "_____no_output_____" ] ], [ [ "df.groupby(by='raceethnicity').describe()", "_____no_output_____" ] ], [ [ "Check if there are any missing values:", "_____no_output_____" ] ], [ [ "df.isna().sum()", "_____no_output_____" ], [ "df = df[(df.raceethnicity != 'Arab-American') & (df.raceethnicity != 'Unknown')]\n# no data available about the percentage of this ethnicity over population, so it is discarded\ndf.replace(to_replace=['Asian/Pacific Islander','Native American','Other'],value='Others',inplace=True)\n# those categories all fall under Others in the population percentages found online\ndf.replace(to_replace=['Hispanic/Latino'],value='Latino',inplace=True)\n# this value is renamed for consistency with population ethnicity data", "_____no_output_____" ], [ "df.groupby(by='raceethnicity').describe()", "_____no_output_____" ], [ "def givepercent (dtf,ethnicity):\n # Function to compute percentages by ethnicity\n return round(((dtf.raceethnicity == ethnicity).sum()/(dtf.shape[0])*100),2)", "_____no_output_____" ], [ "killed={\"White\":(df.raceethnicity == 'White').sum(),\n \"Latino\": (df.raceethnicity == 'Latino').sum(),\n \"Black\": (df.raceethnicity == 'Black').sum(),\n \"Others\": (df.raceethnicity == 'Others').sum()\n }\nprint(killed)\nkilledperc={\"White\": givepercent(df,'White'), \n \"Latino\": givepercent(df,'Latino'),\n \"Black\": givepercent(df,'Black'),\n \"Others\": givepercent(df,'Others')\n }\nprint(killedperc)", "{'White': 1158, 'Latino': 378, 'Black': 573, 'Others': 83}\n{'White': 52.83, 'Latino': 17.24, 'Black': 26.14, 'Others': 3.79}\n" ], [ "df.groupby(by='armed').describe()", "_____no_output_____" ] ], [ [ "The analysis is limited to the value *No*, but could consider *Disputed* and *Non-lethal firearm*, which constitute other 108 data points.", "_____no_output_____" ] ], [ [ "dfunarmed = df[(df.armed == 'No')]\ndfunarmed.groupby(by='raceethnicity').describe()", "_____no_output_____" ], [ "unarmed={\"White\":(dfunarmed.raceethnicity == 'White').sum(),\n \"Latino\": (dfunarmed.raceethnicity == 'Latino').sum(),\n \"Black\": (dfunarmed.raceethnicity == 'Black').sum(),\n \"Others\": (dfunarmed.raceethnicity == 'Others').sum()\n }\nprint(unarmed)\nunarmedperc={\"White\":givepercent(dfunarmed,'White'),\n \"Latino\": givepercent(dfunarmed,'Latino'),\n \"Black\": givepercent(dfunarmed,'Black'),\n \"Others\": givepercent(dfunarmed,'Others')\n }\nprint(unarmedperc)", "{'White': 201, 'Latino': 67, 'Black': 121, 'Others': 11}\n{'White': 50.25, 'Latino': 16.75, 'Black': 30.25, 'Others': 2.75}\n" ], [ "def percent1ethn (portion,population,decimals):\n # Function to compute the percentage of the portion killed over a given population\n return round((portion/population*100),decimals)", "_____no_output_____" ], [ "killed1ethn={\"White\": percent1ethn(killed['White'],ethnestim['White'],6), \n \"Latino\": percent1ethn(killed['Latino'],ethnestim['Latino'],6), \n \"Black\": percent1ethn(killed['Black'],ethnestim['Black'],6), \n \"Others\": percent1ethn(killed['Others'],ethnestim['Others'],6)\n }\nprint(killed1ethn)\nunarmedoverkilled={\"White\": percent1ethn(unarmed['White'],killed['White'],2), \n \"Latino\": percent1ethn(unarmed['Latino'],killed['Latino'],2), \n \"Black\": percent1ethn(unarmed['Black'],killed['Black'],2), \n \"Others\": percent1ethn(unarmed['Others'],killed['Others'],2)\n }\nprint(unarmedoverkilled)", "{'White': 0.000582, 'Latino': 0.000664, 'Black': 0.001436, 'Others': 0.000313}\n{'White': 17.36, 'Latino': 17.72, 'Black': 21.12, 'Others': 13.25}\n" ] ], [ [ "**Hypothesis testing**", "_____no_output_____" ] ], [ [ "whitesample=ethnestim['White']\nblacksample=ethnestim['Black']\nwkilled=killed['White']\nbkilled=killed['Black']\npw=wkilled/whitesample\npb=bkilled/blacksample\n\n#happened by chance?\n#Hnull pw-pb = 0 (no difference between white and black)\n#Halt pw-pb != 0 (the two proportions are different)\n\n#Significance level = 5%\n\n# Test statistic: Z-statistic\ndifference=pb-pw\nprint(difference)\nstandarderror=np.sqrt(((pw*(1-pw))/whitesample)+((pb*(1-pb))/blacksample))\nzstat=(difference)/standarderror\nprint(zstat)\n\n# Z-score for significance level\nzscore=1.96\nif zstat > zscore:\n print(\"The null hypothesis is rejected.\")\nelse:\n print(\"The null hypothesis is not rejected.\")", "8.540072690469898e-06\n13.688442014042046\nThe null hypothesis is rejected.\n" ], [ "whitesample=killed['White']\nblacksample=killed['Black']\nwunarmed=unarmed['White']\nbunarmed=unarmed['Black']\npw=wunarmed/whitesample\npb=bunarmed/blacksample\n\n#happened by chance?\n#Hnull pw-pb = 0 (no difference between white and black)\n#Halt pw-pb != 0 (the two proportions are different)\n\n#Significance level = 5%\n\n# Test statistic: Z-statistic\ndifference=pb-pw\nprint(difference)\nstandarderror=np.sqrt(((pw*(1-pw))/whitesample)+((pb*(1-pb))/blacksample))\nzstat=(difference)/standarderror\nprint(zstat)\n\n# Z-score for significance level\nzscore=1.96\nif zstat > zscore:\n print(\"The null hypothesis is rejected.\")\nelse:\n print(\"The null hypothesis is not rejected.\")", "0.037594154934035034\n1.8463487953468565\nThe null hypothesis is not rejected.\n" ], [ "ethnicities = list(ethndic.keys())\npopulethn = list(ethndic.values())\nkilled = list(killedperc.values())\nunarmed = list(unarmedperc.values())\n\ndata1 = {'ethnicities' : ethnicities,\n 'populethn' : populethn,\n 'killed' : killed,\n 'unarmed' : unarmed}\n\nsource = ColumnDataSource(data=data1)", "_____no_output_____" ] ], [ [ "# Results", "_____no_output_____" ] ], [ [ "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save,box_select\"\npalette=Spectral4\ntitlefontsize='16pt'\n\ncplot = figure(title=\"The Counted (data from 2015 and 2016)\", tools=TOOLS,\n x_range=ethnicities, y_range=(0, 75))#, sizing_mode='scale_both')\n\ncplot.vbar(x=dodge('ethnicities', 0.25, range=cplot.x_range),top='populethn', source=source,\n width=0.4,line_width=0 ,line_color=None, legend='Ethnicity % over population',\n color=str(Spectral4[0]), name='populethn')\n\ncplot.vbar(x=dodge('ethnicities', -0.25, range=cplot.x_range), top='killed', source=source,\n width=0.4, line_width=0 ,line_color=None, legend=\"Killed % over total killed\",\n color=str(Spectral4[2]), name=\"killed\")\n\ncplot.vbar(x=dodge('ethnicities', 0.0, range=cplot.x_range), top='unarmed', source=source,\n width=0.4, line_width=0 ,line_color=None, legend=\"Unarmed % over total unarmed\",\n color=str(Spectral4[1]), name=\"unarmed\")\n\ncplot.add_tools(HoverTool(names=[\"unarmed\"],\n tooltips=[\n ( 'Population', '@populethn{(00.00)}%' ),\n ( 'Killed', '@killed{(00.00)}%' ),\n ( 'Unarmed', '@unarmed{(00.00)}%' )], # Fields beginning with @ display values from ColumnDataSource. \n mode='vline'))\n\n#cplot.x_range.range_padding = 0.1\ncplot.xgrid.grid_line_color = None\n\ncplot.legend.location = \"top_right\"\ncplot.xaxis.axis_label = \"Ethnicity\"\ncplot.xaxis.axis_label_text_font_size='18pt'\n\ncplot.xaxis.minor_tick_line_color = None\ncplot.title.text_font_size=titlefontsize\ncplot.legend.label_text_font_size='16pt'\ncplot.xaxis.major_label_text_font_size='16pt'\ncplot.yaxis.major_label_text_font_size='16pt'", "_____no_output_____" ], [ "perckillethn = list(killed1ethn.values())\n\ndata2 = {'ethnicities' : ethnicities,\n 'perckillethn' : perckillethn}\n\nsource = ColumnDataSource(data=dict(data2, color=PuBu4))", "_____no_output_____" ], [ "plot2 = figure(title=\"Killed % over population with same ethnicity\",\n tools=TOOLS, x_range=ethnicities, y_range=(0, max(perckillethn)*1.2))#, sizing_mode='scale_both')\n\nplot2.vbar(x=dodge('ethnicities', 0.0, range=cplot.x_range), top='perckillethn', source=source,\n width=0.4, line_width=0 ,line_color=None, legend=\"\",\n color='color', name=\"perckillethn\")\n\nplot2.add_tools(HoverTool(names=[\"perckillethn\"],\n tooltips=[\n ( 'Killed', '@perckillethn{(0.00000)}%' )],\n #( 'Unarmed', '@unarmed{(00.00)}%' )], # Fields beginning with @ display values from ColumnDataSource. \n mode='vline'))\n\n#plot2.x_range.range_padding = 0.1\nplot2.xgrid.grid_line_color = None\n\nplot2.xaxis.axis_label = \"Ethnicity\"\nplot2.xaxis.axis_label_text_font_size='18pt'\n\nplot2.xaxis.minor_tick_line_color = None\nplot2.title.text_font_size=titlefontsize\nplot2.xaxis.major_label_text_font_size='16pt'\nplot2.yaxis.major_label_text_font_size='16pt'\nplot2.yaxis[0].formatter = NumeralTickFormatter(format=\"0.0000\")", "_____no_output_____" ], [ "percunarmethn = list(unarmedoverkilled.values())\n\ndata3 = {'ethnicities' : ethnicities,\n 'percunarmethn' : percunarmethn}\n\nsource = ColumnDataSource(data=dict(data3, color=PuBu4))", "_____no_output_____" ], [ "plot3 = figure(title=\"Unarmed % over killed with same ethnicity\",\n tools=TOOLS, x_range=ethnicities, y_range=(0, max(percunarmethn)*1.2))#, sizing_mode='scale_both')\n\nplot3.vbar(x=dodge('ethnicities', 0.0, range=cplot.x_range), top='percunarmethn', source=source,\n width=0.4, line_width=0 ,line_color=None, legend=\"\",\n color='color', name=\"percunarmethn\")\n\nplot3.add_tools(HoverTool(names=[\"percunarmethn\"],\n tooltips=[\n ( 'Unarmed', '@percunarmethn{(00.00)}%' )],\n #( 'Unarmed', '@unarmed{(00.00)}%' )], # Fields beginning with @ display values from ColumnDataSource. \n mode='vline'))\n\n#plot3.x_range.range_padding = 0.1\nplot3.xgrid.grid_line_color = None\n\nplot3.xaxis.axis_label = \"Ethnicity\"\nplot3.xaxis.axis_label_text_font_size='18pt'\n\nplot3.xaxis.minor_tick_line_color = None\nplot3.title.text_font_size=titlefontsize\nplot3.xaxis.major_label_text_font_size='16pt'\nplot3.yaxis.major_label_text_font_size='16pt'", "_____no_output_____" ], [ "output_file(\"thecounted.html\", title=\"The Counted Visualization\")\n\noutput_notebook()\ngplot=gridplot([cplot, plot2, plot3], sizing_mode='stretch_both', ncols=3)#, plot_width=800, plot_height=600)\nshow(gplot) # open a browser\nexport_png(gplot, filename=\"bokeh_thecounted.png\")", "_____no_output_____" ] ], [ [ "Hover on the bar charts to read the percentage values.", "_____no_output_____" ], [ "# Conclusions\nThe plot shows that if the people shot by police were proportional to the population distribution, the orange and green bar charts should have been almost the same height as the blue ones. Although this is true for Latino ethnicity, it is not for the Black one: this is the second most represented among killed and among those killed who were unarmed.", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# ML 101\n\n## RDP and line simplification\n\nRDP is a line simplifaction algorithm that can be used to reduce the number of points.", "_____no_output_____" ] ], [ [ "!pip install git+git://github.com/mariolpantunes/knee@main#egg=knee", "_____no_output_____" ], [ "%matplotlib inline\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.figsize'] = [16, 9]\nimport numpy as np\nimport knee.linear_fit as lf\nimport knee.rdp as rdp\n\n# generate some data\nx = np.arange(0, 100, 0.1)\ny = np.sin(x)\n\nprint(len(x))\n\nplt.plot(x,y)\nplt.show()", "_____no_output_____" ], [ "# generate the points\npoints = np.array([x,y]).T\nreduced, removed = rdp.rdp(points, 0.01, cost=lf.Linear_Metrics.rpd)\nreduced_points = points[reduced]\n\nprint(f'Lenght = {len(reduced_points)}')\n\nplt.plot(x,y)\nx = reduced_points[:,0]\ny = reduced_points[:,1]\nplt.plot(x,y, 'ro')\nplt.show()", "_____no_output_____" ] ], [ [ "## Knee detection\n\nElbow/knee detection is a method to select te ideal cut-off point in a performance curve", "_____no_output_____" ] ], [ [ "# generate some data\nx = np.arange(0.1, 10, 0.1)\ny = 1/(x**2)\n\nplt.plot(x,y)\nplt.show()", "_____no_output_____" ], [ "import knee.lmethod as lmethod\n\n# generate the points\npoints = np.array([x,y]).T\n\nidx = lmethod.knee(points)\n\nplt.plot(x,y)\nplt.plot(x[idx], y[idx], 'ro')\nplt.show()", "_____no_output_____" ], [ "import knee.dfdt as dfdt\n\n# generate the points\npoints = np.array([x,y]).T\n\nidx = dfdt.knee(points)\n\nprint(idx)\n\nplt.plot(x,y)\nplt.plot(x[idx], y[idx], 'ro')\nplt.show()", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import json\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom scipy.stats import norm\n\ndf = pd.read_csv('user_info.csv')\n\nfrom selenium import webdriver\noptions = webdriver.ChromeOptions()\noptions.add_argument('--ignore-certificate-errors')\noptions.add_argument('--incognito')\noptions.add_argument('--headless')\ndriver = webdriver.Chrome(\"../assets/chromedriver\", options=options)", "_____no_output_____" ], [ "class Player:\n def __init__(self, name, level, rating, prestige, games_won, qps, medals, hero):\n self.name = name\n self.level = level\n self.rating = rating\n self.prestige = prestige\n self.qps = qps\n self.medals = medals\n self.hero = hero\n self.kd_ratio = [i/(1+sum([qps.elims,qps.deaths])) for i in [qps.elims,qps.deaths]]\n self.games_won = games_won\n\nclass Stats:\n def __init__(self, elims=0, dmg_done=0, deaths=0, solo_kills=0):\n self.elims = elims\n self.dmg_done = dmg_done\n self.deaths = deaths\n self.solo_kills = solo_kills\n \nclass Medals:\n def __init__(self, bronze=0, silver=0, gold=0):\n self.bronze = bronze\n self.silver = silver\n self.gold = gold\n \nhero_list = ['ana','ashe','baptiste','bastion','brigitte','dVa','doomfist',\n 'genji','hanzo','junkrat','lucio','mccree','mei','mercy','moira',\n 'orisa','pharah','reaper','reinhardt','roadhog','soldier76','sombra',\n 'symmetra','torbjorn','tracer','widowmaker','winston','wreckingBall',\n 'zarya','zenyatta','sigma']\n\ndef create_player(js):\n heroes = {}\n if 'quickPlayStats' not in js:\n for hero in hero_list:\n heroes.update({hero: Stats(0,0,0,0)})\n return Player(js['name'], js['level'],js['rating'],js['prestige'], 0, Stats(), Medals(), heroes)\n if 'careerStats' not in js['quickPlayStats']:\n for hero in hero_list:\n heroes.update({hero: Stats(0,0,0,0)})\n return Player(js['name'], js['level'],js['rating'],js['prestige'], 0, Stats(), Medals(), heroes)\n if js.get('quickPlayStats',{}).get('careerStats',{}) == None or 'allHeroes' not in js.get('quickPlayStats',{}).get('careerStats',{}):\n for hero in hero_list:\n heroes.update({hero: Stats(0,0,0,0)})\n return Player(js['name'], js['level'],js['rating'],js['prestige'], 0, Stats(), Medals(), heroes)\n \n elims = 0\n damageDone = 0\n deaths = 0\n soloKills = 0\n\n if js['quickPlayStats']['careerStats']['allHeroes']['combat'] != None:\n\n if 'eliminations' in js['quickPlayStats']['careerStats']['allHeroes']['combat']:\n elims = js['quickPlayStats']['careerStats']['allHeroes']['combat']['eliminations']\n\n if 'damageDone' in js['quickPlayStats']['careerStats']['allHeroes']['combat']:\n damageDone = js['quickPlayStats']['careerStats']['allHeroes']['combat']['damageDone']\n\n if 'deaths' in js['quickPlayStats']['careerStats']['allHeroes']['combat']:\n deaths = js['quickPlayStats']['careerStats']['allHeroes']['combat']['deaths']\n\n if 'soloKills' in js['quickPlayStats']['careerStats']['allHeroes']['combat']:\n soloKills = js['quickPlayStats']['careerStats']['allHeroes']['combat']['soloKills']\n \n qps = Stats(elims,damageDone,deaths,soloKills)\n\n medals = Medals(js['quickPlayStats']['awards'].get('medalsBronze'),\n js['quickPlayStats']['awards'].get('medalsSilver'),\n js['quickPlayStats']['awards'].get('medalsGold'))\n \n for hero in hero_list:\n print(hero)\n if hero in js['quickPlayStats']['careerStats']:\n elims = 0\n damageDone = 0\n deaths = 0\n soloKills = 0\n \n if js['quickPlayStats']['careerStats'][hero]['combat'] != None:\n \n if 'eliminations' in js['quickPlayStats']['careerStats'][hero]['combat']:\n elims = js['quickPlayStats']['careerStats'][hero]['combat']['eliminations']\n\n if 'damageDone' in js['quickPlayStats']['careerStats'][hero]['combat']:\n damageDone = js['quickPlayStats']['careerStats'][hero]['combat']['damageDone']\n\n if 'deaths' in js['quickPlayStats']['careerStats'][hero]['combat']:\n deaths = js['quickPlayStats']['careerStats'][hero]['combat']['deaths']\n\n if 'soloKills' in js['quickPlayStats']['careerStats'][hero]['combat']:\n soloKills = js['quickPlayStats']['careerStats'][hero]['combat']['soloKills']\n \n heroes.update({hero: Stats(elims,damageDone,deaths,soloKills)})\n else:\n heroes.update({hero: Stats(0,0,0,0)})\n \n return Player(js['name'], js['level'],js['rating'],js['prestige'], js['quickPlayStats']['games']['won'], qps, medals, heroes)\n\ndef df_object(p):\n item = [p.name,p.level,p.rating,p.prestige,p.games_won,p.qps.elims,p.qps.dmg_done,\n p.qps.deaths,p.qps.solo_kills,p.medals.bronze,p.medals.silver,p.medals.gold]\n \n for hero in hero_list:\n item.extend([p.hero[hero].elims,p.hero[hero].dmg_done,p.hero[hero].deaths,p.hero[hero].solo_kills])\n \n return item", "_____no_output_____" ], [ "usernames = pd.read_csv('../assets/data/usernames_scraped_fixed.csv')\nusernames.head()\nlen(usernames['users'])", "_____no_output_____" ], [ "##dataframe setup\ncolumns = ['username','level','rating','prestige','games_won','qps_elims','qps_dmg_done',\n 'qps_deaths','qps_solo_kills','medals_bronze','medals_silver','medals_gold']\n\nfor hero in hero_list:\n hero_data = [f'{hero}_elims',f'{hero}_dmg_done',f'{hero}_deaths',f'{hero}_solo_kills']\n columns.extend(hero_data)\n\ndata = pd.DataFrame(columns=columns)\n\namount = 0\nfor user in usernames['users'].values:\n url = f\"https://ow-api.com/v1/stats/pc/us/{user}/complete\"\n print(url)\n response = requests.get(url)\n j = json.loads(response.text)\n u = create_player(j)\n data.loc[len(data), :] = df_object(u)\n amount += 1\n \n percent = np.round((amount/len(usernames['users'])),decimals=2)\n clear_output()\n progress = widgets.IntProgress(\n value=amount,\n min=0,\n max=len(usernames['users'].values),\n step=1,\n description=f'{percent}%',\n bar_style='info', # 'success', 'info', 'warning', 'danger' or ''\n orientation='horizontal'\n )\n display(progress)", "_____no_output_____" ], [ "data.head()\ndata.tail()\n\ndf = pd.read_csv('user_info.csv')\nprint(df.shape)\ndf = df.append(data)\ndf.shape, data.shape\n\ndata.to_csv('user_info.csv',index=False)", "(8, 136)\n" ], [ "# def s(username):\n# global search\n# search = username\n \n# interactive(s, username='')", "_____no_output_____" ], [ "# usernames = pd.read_csv('usernames_scraped_fixed.csv')\n# usernames.head()\n\n# df = pd.read_csv('usernames_scraped.csv')", "_____no_output_____" ], [ "# username_scraped = []\n\n# def str2bool(v):\n# return v.lower() in (\"True\", \"true\")\n\n# for name in df['users']:\n# driver.get(f\"https://playoverwatch.com/en-us/search?q={name}\")\n# time.sleep(2)\n# page_source = driver.page_source\n \n# soup = BeautifulSoup(page_source)\n# players = soup.find_all('a', class_=\"player-badge\")\n \n# for element in players:\n# locked = str2bool(element.find(\"div\", {\"data-visibility-private\": True})['data-visibility-private'])\n# if(locked == False):\n# username_scraped.append(element.find(class_='player-badge-name').text.replace('#', '-'))\n \n# print(len(username_scraped))", "_____no_output_____" ], [ "# print(len(username_scraped))\n\n# df1 = pd.read_csv('usernames_scraped_fixed.csv')\n\n# df2 = pd.DataFrame(username_scraped,columns=['users'])\n\n# df1 = df1.append(df2)\n\n# df1.to_csv('usernames_scraped_fixed.csv',index=False)\n\n# df1.shape", "_____no_output_____" ], [ "# usernames['users'].values", "_____no_output_____" ], [ "# def on_change(b):\n# global player\n# player = name=dropbox.value\n# print('player')\n\n# dropbox = widgets.Select(\n# options=usernames['users'].values,\n# value=usernames['users'].values[0],\n# description='User:',\n# disabled=False\n# )\n# dropbox.observe(on_change, names='value')\n \n# display(dropbox)", "_____no_output_____" ], [ "# player", "_____no_output_____" ], [ "# soup = BeautifulSoup(page_source)\n\n# players = soup.find_all('a', class_=\"player-badge\")\n \n# def f(name):\n# return name\n\n# def on_button_clicked(b):\n# global player\n# player = name=b.description\n\n# displays = []\n# for element in players:\n# locked = str2bool(element.find(\"div\", {\"data-visibility-private\": True})['data-visibility-private'])\n# if(locked == True):\n# tooltip = 'Sorry, player has their profile set to private...'\n# icon = 'lock'\n# else:\n# tooltip = \"Click to view this player\"\n# icon = 'unlock'\n# button = widgets.Button(\n# description=element.find(class_='player-badge-name').text.capitalize().replace('#', '-'),\n# disabled=locked,\n# button_style='', # 'success', 'info', 'warning', 'danger' or ''\n# icon=icon,\n# tooltip=tooltip\n# )\n# out = widgets.Output()\n \n# button.on_click(on_button_clicked)\n# display(button,out)", "_____no_output_____" ], [ "# url = f\"https://ow-api.com/v1/stats/pc/us/{player}/complete\"\n# print(url)\n# response = requests.get(url)\n\n# print(response)", "_____no_output_____" ], [ "# j = json.loads(response.text)\n# if(j['private'] == True):\n# print(\"Sorry can't load this profile. it's private\")\n# else:\n# print(j['name'])", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## Download MNIST", "_____no_output_____" ] ], [ [ "# For Google Colaboratory\nimport sys, os\nif 'google.colab' in sys.modules:\n from google.colab import drive\n drive.mount('/content/gdrive')\n file_name = 'generate_mnist.ipynb'\n import subprocess\n path_to_file = subprocess.check_output('find . -type f -name ' + str(file_name), shell=True).decode(\"utf-8\")\n print(path_to_file)\n path_to_file = path_to_file.replace(file_name,\"\").replace('\\n',\"\")\n os.chdir(path_to_file)\n !pwd", "_____no_output_____" ], [ "import torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "trainset = torchvision.datasets.MNIST(root='./temp', train=True,\n download=True, transform=transforms.ToTensor())\ntestset = torchvision.datasets.MNIST(root='./temp', train=False,\n download=True, transform=transforms.ToTensor())", "_____no_output_____" ], [ "idx=4\npic, label =trainset[idx]\npic=pic.squeeze()\nplt.imshow(pic.numpy(), cmap='gray')\nplt.show()\nprint(label)", "_____no_output_____" ], [ "train_data=torch.Tensor(60000,28,28)\ntrain_label=torch.LongTensor(60000)\nfor idx , example in enumerate(trainset):\n train_data[idx]=example[0].squeeze()\n train_label[idx]=example[1]\ntorch.save(train_data,'train_data.pt')\ntorch.save(train_label,'train_label.pt')", "_____no_output_____" ], [ "test_data=torch.Tensor(10000,28,28)\ntest_label=torch.LongTensor(10000)\nfor idx , example in enumerate(testset):\n test_data[idx]=example[0].squeeze()\n test_label[idx]=example[1]\ntorch.save(test_data,'test_data.pt')\ntorch.save(test_label,'test_label.pt')", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# 1. Write a Python Program to Find LCM?", "_____no_output_____" ] ], [ [ "# Given some numbers, LCM of these numbers is the smallest positive integer that is divisible by all the numbers numbers\ndef LCM(ls): \n lar = max(ls) \n flag=1\n while(flag==1): \n for i in ls:\n if(lar%i!=0): \n flag=1\n break \n else:\n flag=0\n continue\n lcm=lar\n lar += 1 \n return lcm \nn=int(input(\"Enter the range of your input \"))\nls=[]\nfor i in range(n):\n a=int(input(\"Enter the number \"))\n ls.append(a)\nprint(\"The LCM of entered numbers is\", LCM(ls))", "Enter the range of your input 3\nEnter the number 4\nEnter the number 6\nEnter the number 8\nThe LCM of entered numbers is 24\n" ] ], [ [ "# 2. Write a Python Program to Find HCF?", "_____no_output_____" ] ], [ [ "def HCF(ls):\n lar=0\n for i in range(2,min(ls)+1) :\n count=0\n for j in ls:\n if j%i==0:\n count=count+1\n if count==len(ls):\n lar=i\n return lar\n if count!=len(ls):\n return 1\nn=int(input(\"Enter the range of your input \"))\nls=[]\nfor i in range(n):\n a=int(input(\"Enter the number \"))\n ls.append(a)\nprint(\"The LCM of entered numbers is\", HCF(ls))", "Enter the range of your input 3\nEnter the number 4\nEnter the number 6\nEnter the number 8\nThe LCM of entered numbers is 2\n" ] ], [ [ "# 3. Write a Python Program to Convert Decimal to Binary, Octal and Hexadecimal?", "_____no_output_____" ] ], [ [ "a=int(input(\"Enter the Decimal value: \"))\ndef ToBinary(x):\n binary=\" \"\n while(x!=0 and x>0):\n binary=binary+str(x%2)\n x=int(x/2)\n return(binary[::-1])\ndef ToOctal(x):\n if(x<8):\n print(x)\n else:\n octal=\" \"\n while(x!=0 and x>0):\n octal=octal+str(x%8)\n x=int(x/8)\n return(octal[::-1])\ndef ToHexa(x):\n if(x<10):\n print(x)\n else:\n ls={10:\"A\", 11:\"B\", 12:\"C\", 13:\"D\", 14:\"E\", 15:\"F\"}\n Hexa=\" \"\n while(x!=0 and x>0):\n if(x%16) in ls.keys():\n Hexa=Hexa+ls[x%16]\n else:\n Hexa=Hexa+str(x%16)\n x=int(x/16)\n return(Hexa[::-1])\nprint(\"Binary of\",a,\"is\", ToBinary(a))\nprint(\"Octal of\",a,\"is\", ToOctal(a))\nprint(\"Hexadecimal of\",a,\"is\", ToHexa(a))", "Enter the Decimal value: 15\nBinary of 15 is 1111 \nOctal of 15 is 17 \nHexadecimal of 15 is F \n" ] ], [ [ "# 4. Write a Python Program To Find ASCII value of a character?", "_____no_output_____" ] ], [ [ "a=input(\"Enter the character to find ASCII: \")\nprint(\"ASCII of \",a ,\"is\", ord(a))", "Enter the character to find ASCII: g\nASCII of g is 103\n" ] ], [ [ "# 5.Write a Python Program to Make a Simple Calculator with 4 basic mathematical operations?", "_____no_output_____" ] ], [ [ "class Calculator:\n def Add(self,x,y):\n return x+y\n def Subtract(self,x,y):\n return x-y\n def Multiply(self,x,y):\n return x*y\n def Divide(self,x,y):\n try:\n return x/y\n except Exception as es:\n print(\"An error has occured\",es)\nCalc = Calculator()\nprint(\"Choose your Calculator Operation: \")\nprint(\"1 : Addition\")\nprint(\"2 : Subtraction\")\nprint(\"3 : Multipilication\")\nprint(\"4 : Division\")\na=int(input(\"Enter Your Selection: \"))\nx=int(input(\"Enter Your 1st number: \"))\ny=int(input(\"Enter Your 2nd number: \"))\nif(a==1):\n print(\"You chose Addition: \")\n print(\"The result of your operation is\", Calc.Add(x,y))\nelif(a==2):\n print(\"You chose Subtraction: \")\n print(\"The result of your operation is\", Calc.Subtract(x,y))\nelif(a==3):\n print(\"You chose Multipilication: \")\n print(\"The result of your operation is\", Calc.Multiply(x,y))\nelif(a==4):\n print(\"You chose Division: \")\n print(\"The result of your operation is\", Calc.Divide(x,y))\nelse:\n print(\"You chose a wrong operation\")", "Choose your Calculator Operation: \n1 : Addition\n2 : Subtraction\n3 : Multipilication\n4 : Division\nEnter Your Selection: 1\nEnter Your 1st number: 5\nEnter Your 2nd number: 5\nYou chose addition: \nThe result of your opperation is 10\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Download the ULB Fraud Dataset\n\nAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n\nTERMS OF USE:\nThis data is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/. It is provided through the Google Cloud Public Datasets Program and is provided \"AS IS\" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.", "_____no_output_____" ] ], [ [ "import pandas as pd\n\nfrom google.cloud import bigquery", "_____no_output_____" ], [ "client = bigquery.Client()\n\nsql = \"\"\" \nSELECT * FROM `bigquery-public-data.ml_datasets.ulb_fraud_detection`\n\"\"\"\n\ndf = client.query(sql).to_dataframe()", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "df.to_csv('../dataset/creditcard.csv', index=False)", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "\n# Data Wrangling, Analysis and Visualization of @WeLoveDogs twitter data.\n", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nimport tweepy as ty\nimport requests\nimport json\nimport io\nimport time", "_____no_output_____" ] ], [ [ "## Gathering", "_____no_output_____" ] ], [ [ "df = pd.read_csv('twitter-archive-enhanced.csv')", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "image_response = requests.get(r'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv')", "_____no_output_____" ], [ "image_df = pd.read_csv(io.StringIO(image_response.content.decode('utf-8')), sep='\\t')", "_____no_output_____" ], [ "image_df.head()", "_____no_output_____" ], [ "image_df.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2075 entries, 0 to 2074\nData columns (total 12 columns):\ntweet_id 2075 non-null int64\njpg_url 2075 non-null object\nimg_num 2075 non-null int64\np1 2075 non-null object\np1_conf 2075 non-null float64\np1_dog 2075 non-null bool\np2 2075 non-null object\np2_conf 2075 non-null float64\np2_dog 2075 non-null bool\np3 2075 non-null object\np3_conf 2075 non-null float64\np3_dog 2075 non-null bool\ndtypes: bool(3), float64(3), int64(2), object(4)\nmemory usage: 152.1+ KB\n" ], [ "CONSUMER_KEY = '<My Consumer Key>'\nCONSUMER_SECRET = '<My Consumer Secret>'\nACCESS_TOKEN = '<My Access Token>'\nACCESS_TOKEN_SECRET = '<My Access Token Secret>'", "_____no_output_____" ], [ "auth = ty.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)", "_____no_output_____" ], [ "auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)", "_____no_output_____" ], [ "api = ty.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)", "_____no_output_____" ], [ "tweet_count = 0\nfor id in df['tweet_id']:\n with open('tweet_json.txt', 'a') as file:\n try:\n start = time.time()\n tweet = api.get_status(id, tweet_mode='extended')\n st = json.dumps(tweet._json)\n file.writelines(st + '\\n')\n tweet_count += 1\n if tweet_count % 20 == 0 or tweet_count == len(df): \n end = time.time()\n print('Tweet id: {0}\\tDownload Time: {1} sec\\tTweets Downloaded: {2}.'.format(id, (end-start), tweet_count))\n except Exception as e:\n print('Exception occured for tweet {0} : {1}'.format(id, str(e)))", "Exception occured for tweet 888202515573088257 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 888078434458587136\tDownload Time: 0.5414106845855713 sec\tTweets Downloaded: 20.\nTweet id: 884562892145688576\tDownload Time: 0.5888540744781494 sec\tTweets Downloaded: 40.\nTweet id: 880465832366813184\tDownload Time: 0.9378821849822998 sec\tTweets Downloaded: 60.\nTweet id: 877316821321428993\tDownload Time: 1.639167308807373 sec\tTweets Downloaded: 80.\nException occured for tweet 873697596434513921 : [{'code': 144, 'message': 'No status found with that ID.'}]\nException occured for tweet 872668790621863937 : [{'code': 34, 'message': 'Sorry, that page does not exist.'}]\nTweet id: 872620804844003328\tDownload Time: 0.8575863838195801 sec\tTweets Downloaded: 100.\nException occured for tweet 869988702071779329 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 868880397819494401\tDownload Time: 0.9696576595306396 sec\tTweets Downloaded: 120.\nException occured for tweet 866816280283807744 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 863907417377173506\tDownload Time: 0.547405481338501 sec\tTweets Downloaded: 140.\nException occured for tweet 861769973181624320 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 860177593139703809\tDownload Time: 3.960103750228882 sec\tTweets Downloaded: 160.\nTweet id: 856330835276025856\tDownload Time: 0.5950930118560791 sec\tTweets Downloaded: 180.\nTweet id: 852912242202992640\tDownload Time: 0.5863792896270752 sec\tTweets Downloaded: 200.\nTweet id: 849051919805034497\tDownload Time: 1.6753406524658203 sec\tTweets Downloaded: 220.\nTweet id: 845812042753855489\tDownload Time: 0.5766551494598389 sec\tTweets Downloaded: 240.\nException occured for tweet 845459076796616705 : [{'code': 144, 'message': 'No status found with that ID.'}]\nException occured for tweet 842892208864923648 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 841680585030541313\tDownload Time: 0.8575961589813232 sec\tTweets Downloaded: 260.\nTweet id: 838561493054533637\tDownload Time: 0.5514862537384033 sec\tTweets Downloaded: 280.\nException occured for tweet 837012587749474308 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 835574547218894849\tDownload Time: 0.5296859741210938 sec\tTweets Downloaded: 300.\nTweet id: 833722901757046785\tDownload Time: 0.8748948574066162 sec\tTweets Downloaded: 320.\nTweet id: 831670449226514432\tDownload Time: 0.8104021549224854 sec\tTweets Downloaded: 340.\nTweet id: 828708714936930305\tDownload Time: 0.5546495914459229 sec\tTweets Downloaded: 360.\nException occured for tweet 827228250799742977 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 826476773533745153\tDownload Time: 0.5224771499633789 sec\tTweets Downloaded: 380.\nTweet id: 823333489516937216\tDownload Time: 0.7836413383483887 sec\tTweets Downloaded: 400.\nTweet id: 821107785811234820\tDownload Time: 0.5480344295501709 sec\tTweets Downloaded: 420.\nTweet id: 819004803107983360\tDownload Time: 0.6690359115600586 sec\tTweets Downloaded: 440.\nTweet id: 816829038950027264\tDownload Time: 0.7895915508270264 sec\tTweets Downloaded: 460.\nTweet id: 813910438903693312\tDownload Time: 0.5418002605438232 sec\tTweets Downloaded: 480.\nTweet id: 812466873996607488\tDownload Time: 0.560112476348877 sec\tTweets Downloaded: 500.\nTweet id: 808344865868283904\tDownload Time: 0.5334210395812988 sec\tTweets Downloaded: 520.\nTweet id: 805207613751304193\tDownload Time: 0.5202441215515137 sec\tTweets Downloaded: 540.\nException occured for tweet 802247111496568832 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 801854953262350336\tDownload Time: 0.5162663459777832 sec\tTweets Downloaded: 560.\nTweet id: 799297110730567681\tDownload Time: 0.5272245407104492 sec\tTweets Downloaded: 580.\nTweet id: 797236660651966464\tDownload Time: 0.8399364948272705 sec\tTweets Downloaded: 600.\nTweet id: 794332329137291264\tDownload Time: 0.5941627025604248 sec\tTweets Downloaded: 620.\nTweet id: 792883833364439040\tDownload Time: 0.9444077014923096 sec\tTweets Downloaded: 640.\nTweet id: 789986466051088384\tDownload Time: 0.5694825649261475 sec\tTweets Downloaded: 660.\nTweet id: 787397959788929025\tDownload Time: 0.5749082565307617 sec\tTweets Downloaded: 680.\nTweet id: 784826020293709826\tDownload Time: 0.5000665187835693 sec\tTweets Downloaded: 700.\nTweet id: 781661882474196992\tDownload Time: 0.5563163757324219 sec\tTweets Downloaded: 720.\nTweet id: 779123168116150273\tDownload Time: 0.5381345748901367 sec\tTweets Downloaded: 740.\nTweet id: 776819012571455488\tDownload Time: 0.548180341720581 sec\tTweets Downloaded: 760.\nException occured for tweet 775096608509886464 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 773704687002451968\tDownload Time: 0.5356895923614502 sec\tTweets Downloaded: 780.\nTweet id: 771171053431250945\tDownload Time: 0.8415000438690186 sec\tTweets Downloaded: 800.\nException occured for tweet 771004394259247104 : [{'code': 179, 'message': 'Sorry, you are not authorized to see this status.'}]\nException occured for tweet 770743923962707968 : [{'code': 144, 'message': 'No status found with that ID.'}]\nTweet id: 768554158521745409\tDownload Time: 0.5458414554595947 sec\tTweets Downloaded: 820.\nTweet id: 765371061932261376\tDownload Time: 0.56394362449646 sec\tTweets Downloaded: 840.\nTweet id: 761334018830917632\tDownload Time: 0.5361735820770264 sec\tTweets Downloaded: 860.\nTweet id: 759446261539934208\tDownload Time: 0.5952544212341309 sec\tTweets Downloaded: 880.\n" ], [ "print('There are {} records for which tweets does not exist in Twitter database.'.format(len(df) - tweet_count))", "There are 15 records for which tweets does not exist in Twitter database.\n" ], [ "tweet_ids = []\nfavorite_count = []\nretweet_count = []", "_____no_output_____" ], [ "with open('tweet_json.txt', 'r') as file:\n for line in file.readlines():\n data = json.loads(line)\n tweet_ids.append(data['id'])\n favorite_count.append(data['favorite_count'])\n retweet_count.append(data['retweet_count'])", "_____no_output_____" ], [ "favorite_retweet_df = pd.DataFrame(data={'tweet_id': tweet_ids, 'favorite_count': favorite_count, \n 'retweet_count': retweet_count})", "_____no_output_____" ], [ "favorite_retweet_df.head()", "_____no_output_____" ], [ "favorite_retweet_df.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2341 entries, 0 to 2340\nData columns (total 3 columns):\ntweet_id 2341 non-null int64\nfavorite_count 2341 non-null int64\nretweet_count 2341 non-null int64\ndtypes: int64(3)\nmemory usage: 54.9 KB\n" ] ], [ [ "## Assessing\n\n### Quality Issues\n\n - The WeRateDogs Twitter archive contains some retweets also. We only need to consider original tweets for this project.\n - `tweet_id` column having integer datatype in all the dataframes. Conversion to string required. Since, We are not going to do any mathematical operations with it.\n - `rating_denominator` value should be 10 since we are giving rating out of 10.\n - For some tweets, The values of `rating_numerator` column are very high, possibly outliers.\n - `name` column has **None** string and *a*, *an*, *the* as values.\n - Extract dog **stages** from the tweet text (if present) for null values.\n - `timestamp` column is given as **string**. Convert it to date.\n - Since we are not using retweets, `in_reply_to_status_id`, `in_reply_to_user_id`, `source`, `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp` columns are not required.\n - `image_df` contains tweets that do not belong to a dog. \n - `source` column in main dataframe is of no use in the analysis as it only tell us about the source of tweet.\n \n### Tidiness Issues\n\n - A dog can have one stage at a time, still we are having 4 columns to store one piece of information.\n - We are having 3 predicted breeds of dogs in the image prediction file. But only required the one with higher probability, given that it is a breed of dog.\n - All 3 dataframes contains same tweet_id column, which we can use to merge them and use as one dataframe for our analysis.", "_____no_output_____" ] ], [ [ "sum(df.duplicated())", "_____no_output_____" ], [ "# name and stages having wrong values for no. of records\ndf.tail()", "_____no_output_____" ], [ "# tweet id is int64, retweet related columns are not required\n# name and dog stages showing full data but most of them are None.\n# timestamp having string datatype\ndf.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2356 entries, 0 to 2355\nData columns (total 17 columns):\ntweet_id 2356 non-null int64\nin_reply_to_status_id 78 non-null float64\nin_reply_to_user_id 78 non-null float64\ntimestamp 2356 non-null object\nsource 2356 non-null object\ntext 2356 non-null object\nretweeted_status_id 181 non-null float64\nretweeted_status_user_id 181 non-null float64\nretweeted_status_timestamp 181 non-null object\nexpanded_urls 2297 non-null object\nrating_numerator 2356 non-null int64\nrating_denominator 2356 non-null int64\nname 2356 non-null object\ndoggo 2356 non-null object\nfloofer 2356 non-null object\npupper 2356 non-null object\npuppo 2356 non-null object\ndtypes: float64(4), int64(3), object(10)\nmemory usage: 313.0+ KB\n" ], [ "# rating_numerator is having min value of 0 and max of 1776 (outliers)\ndf.describe()", "_____no_output_____" ], [ "# denominator value should be 10\nsum(df['rating_denominator'] != 10)", "_____no_output_____" ], [ "df.rating_numerator.value_counts()", "_____no_output_____" ] ], [ [ "## Cleaning", "_____no_output_____" ] ], [ [ "# Making a copy of the data so that original data will be unchanged\ndf_clean = df.copy()\nimage_df_clean = image_df.copy()\nfavorite_retweet_df_clean = favorite_retweet_df.copy()", "_____no_output_____" ] ], [ [ "## Quality Issues\n\n### Define\n\n - Replacing all the **None** strings and *a*, *an* and *the* to **np.nan** in `name` column using pandas **_replace()_** function. \n \n### Code", "_____no_output_____" ] ], [ [ "df_clean['name'] = df_clean['name'].replace({'None': np.nan, 'a': np.nan, 'an': np.nan, 'the': np.nan})", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2356 entries, 0 to 2355\nData columns (total 17 columns):\ntweet_id 2356 non-null int64\nin_reply_to_status_id 78 non-null float64\nin_reply_to_user_id 78 non-null float64\ntimestamp 2356 non-null object\nsource 2356 non-null object\ntext 2356 non-null object\nretweeted_status_id 181 non-null float64\nretweeted_status_user_id 181 non-null float64\nretweeted_status_timestamp 181 non-null object\nexpanded_urls 2297 non-null object\nrating_numerator 2356 non-null int64\nrating_denominator 2356 non-null int64\nname 1541 non-null object\ndoggo 2356 non-null object\nfloofer 2356 non-null object\npupper 2356 non-null object\npuppo 2356 non-null object\ndtypes: float64(4), int64(3), object(10)\nmemory usage: 313.0+ KB\n" ] ], [ [ "### Define\n\n- `tweet_id` column having integer datatype in all the dataframes. Converting it to string using pandas **_astype()_** function.\n- `timestamp` column is given as string. Converting it to date using pandas **to_datetime()** function.\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean['tweet_id'] = df_clean['tweet_id'].astype(str)\nimage_df_clean['tweet_id'] = image_df_clean['tweet_id'].astype(str)\nfavorite_retweet_df_clean['tweet_id'] = favorite_retweet_df_clean['tweet_id'].astype(str)", "_____no_output_____" ], [ "df_clean.timestamp = pd.to_datetime(df_clean.timestamp)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.dtypes", "_____no_output_____" ], [ "image_df_clean.dtypes", "_____no_output_____" ], [ "favorite_retweet_df_clean.dtypes", "_____no_output_____" ] ], [ [ "### Define\n\n- Converting `rating_denominator` column value to 10 value when it is not, using pandas boolean indexing. Since we are giving rating out of 10.\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean.loc[df_clean.rating_denominator != 10, 'rating_denominator'] = 10", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "sum(df_clean.rating_denominator != 10)", "_____no_output_____" ] ], [ [ "### Define\n\n- Removing retweets since we are only concerned about original tweets. Since, there is null present in `in_reply_to_status_id`, `retweeted_status_id` columns for retweets. Droping these records using the pandas drop() function.\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean.drop(index=df_clean[df_clean.retweeted_status_id.notnull()].index, inplace=True)", "_____no_output_____" ], [ "df_clean.drop(index=df_clean[df_clean.in_reply_to_status_id.notnull()].index, inplace=True)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "sum(df_clean.retweeted_status_id.notnull())", "_____no_output_____" ], [ "sum(df_clean.in_reply_to_status_id.notnull())", "_____no_output_____" ] ], [ [ "### Define\n\n- Removing `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp` columns since they are related to retweet info. Hence, not required.\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean.drop(columns=['in_reply_to_status_id', 'in_reply_to_user_id', 'retweeted_status_id', \n 'retweeted_status_user_id', 'retweeted_status_timestamp'], inplace=True)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2097 entries, 0 to 2355\nData columns (total 12 columns):\ntweet_id 2097 non-null object\ntimestamp 2097 non-null datetime64[ns]\nsource 2097 non-null object\ntext 2097 non-null object\nexpanded_urls 2094 non-null object\nrating_numerator 2097 non-null int64\nrating_denominator 2097 non-null int64\nname 1425 non-null object\ndoggo 2097 non-null object\nfloofer 2097 non-null object\npupper 2097 non-null object\npuppo 2097 non-null object\ndtypes: datetime64[ns](1), int64(2), object(9)\nmemory usage: 213.0+ KB\n" ] ], [ [ "### Define\n\n- Removing `source` column from df_clean using pandas **_drop_** function.\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean.drop(columns=['source'], inplace=True)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2097 entries, 0 to 2355\nData columns (total 11 columns):\ntweet_id 2097 non-null object\ntimestamp 2097 non-null datetime64[ns]\ntext 2097 non-null object\nexpanded_urls 2094 non-null object\nrating_numerator 2097 non-null int64\nrating_denominator 2097 non-null int64\nname 1425 non-null object\ndoggo 2097 non-null object\nfloofer 2097 non-null object\npupper 2097 non-null object\npuppo 2097 non-null object\ndtypes: datetime64[ns](1), int64(2), object(8)\nmemory usage: 196.6+ KB\n" ] ], [ [ "## Tidiness Issues\n\n### Define\n\n- Replacing 3 predictions to one dog breed with higher probability, given that it is a breed of dog, with the use of pandas **_apply()_** function.\n\n### Code", "_____no_output_____" ] ], [ [ "non_dog_ind = image_df_clean.query('not p1_dog and not p2_dog and not p3_dog').index", "_____no_output_____" ], [ "image_df_clean.drop(index=non_dog_ind, inplace=True)", "_____no_output_____" ], [ "def get_priority_dog(dog):\n return dog['p1'] if dog['p1_dog'] else dog['p2'] if dog['p2_dog'] else dog['p3']", "_____no_output_____" ], [ "image_df_clean['dog_breed'] = image_df_clean.apply(get_priority_dog, axis=1)", "_____no_output_____" ], [ "image_df_clean.drop(columns=['p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog', 'img_num'], \n inplace=True)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "image_df_clean.head()", "_____no_output_____" ] ], [ [ "### Define\n\n- There are 4 column present for specifing dog breed, which can be done using one column only. Creating a column name `dog_stage` and adding present dog breed in it.\n\n### Code", "_____no_output_____" ] ], [ [ "def get_dog_stage(dog):\n if dog['doggo'] != 'None':\n return dog['doggo']\n elif dog['floofer'] != 'None':\n return dog['floofer']\n elif dog['pupper'] != 'None':\n return dog['pupper']\n else:\n return dog['puppo'] # if last entry is also nan, we have to return nan anyway", "_____no_output_____" ], [ "df_clean['dog_stage'] = df_clean.apply(get_dog_stage, axis=1)", "_____no_output_____" ], [ "df_clean.drop(columns=['doggo', 'floofer', 'pupper', 'puppo'], inplace=True)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2097 entries, 0 to 2355\nData columns (total 8 columns):\ntweet_id 2097 non-null object\ntimestamp 2097 non-null datetime64[ns]\ntext 2097 non-null object\nexpanded_urls 2094 non-null object\nrating_numerator 2097 non-null int64\nrating_denominator 2097 non-null int64\nname 1425 non-null object\ndog_stage 2097 non-null object\ndtypes: datetime64[ns](1), int64(2), object(5)\nmemory usage: 147.4+ KB\n" ] ], [ [ "### Define\n\n- All 3 dataframes contains same `tweet_id` column, which we can use ot merge them and use as one dataframe for our analysis.\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean = pd.merge(df_clean, image_df_clean, on='tweet_id')", "_____no_output_____" ], [ "df_clean = pd.merge(df_clean, favorite_retweet_df_clean, on='tweet_id')", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.head()", "_____no_output_____" ] ], [ [ "## Quality Issues\n\n### Define\n\n- Try to extract dog dstage from tweet text using regular expressions and series **_str.extract()_** function.\n\n### Code", "_____no_output_____" ] ], [ [ "stages = df_clean[df_clean.dog_stage == 'None'].text.str.extract(r'(doggo|pupper|floof|puppo|pup)', expand=True)", "_____no_output_____" ], [ "len(df_clean[df_clean.dog_stage == 'None'])", "_____no_output_____" ], [ "df_clean.loc[stages.index, 'dog_stage'] = stages[0]", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "len(df_clean[df_clean.dog_stage.isnull()])", "_____no_output_____" ] ], [ [ "### Define\n\n- Removing outliers from `rating_numerator`\n\n### Code", "_____no_output_____" ] ], [ [ "df_clean.boxplot(column=['rating_numerator'], figsize=(20,8), vert=False)", "_____no_output_____" ] ], [ [ "- As clear from the boxplot, `rating_numerator` has a number of outliers which can affect our analysis. So, removing all the rating points above 15 abd below 7 to reduce the effect of outliers.", "_____no_output_____" ] ], [ [ "df_clean.drop(index=df_clean.query('rating_numerator > 15 or rating_numerator < 7').index, inplace=True)", "_____no_output_____" ] ], [ [ "### Test", "_____no_output_____" ] ], [ [ "df_clean.boxplot(column=['rating_numerator'], figsize=(20,8), vert=False)", "_____no_output_____" ] ], [ [ "- As we can see now, there are no outliers present in the datat anymore.", "_____no_output_____" ], [ "## Storing Data in a CSV file", "_____no_output_____" ] ], [ [ "df_clean.to_csv('twitter_archive_master.csv', index=False)\nprint('Save Done !')", "Save Done !\n" ] ], [ [ "## Data Analysis and Visualization", "_____no_output_____" ] ], [ [ "ax = df_clean.plot.scatter('rating_numerator', 'favorite_count', figsize=(10, 10), title='Rating VS. Favorites')\nax.set_xlabel('Ratings')\nax.set_ylabel('Favorites')", "_____no_output_____" ] ], [ [ "## Insight 1:\n\n- Number of favorite count is increasing with the rating. i.e. Dogs getting more rating in the tweets are likely to receive more likes (favorites). ", "_____no_output_____" ] ], [ [ "df_clean.dog_breed.value_counts()", "_____no_output_____" ] ], [ [ "## Insight 2\n\n- Most of the pictures present in @WeLoveDogs twitter account are of Golden Retriever, followed by Labrador Retriever, Pembroke and Chihuahua .", "_____no_output_____" ] ], [ [ "df_clean.loc[df_clean.favorite_count.idxmax()][['dog_breed', 'favorite_count']]", "_____no_output_____" ], [ "df_clean.loc[df_clean.favorite_count.idxmin()][['dog_breed', 'favorite_count']]", "_____no_output_____" ], [ "df_clean.loc[df_clean.retweet_count.idxmax()][['dog_breed', 'retweet_count']]", "_____no_output_____" ], [ "df_clean.loc[df_clean.retweet_count.idxmin()][['dog_breed', 'retweet_count']]", "_____no_output_____" ] ], [ [ "## Insight 3\n\n- The dog with highest number of favorites (likes) is an Labrador retriever while the one with lowest number of favorites is an english setter.\n- The same dogs who got highest and lowest favorites count also received highest and lowest retweet count respectively.\n\n**So, if a tweet got more likes(favorites), it got better chances to have more retweets than the one who got low number of likes(favorites) and vice versa.**", "_____no_output_____" ] ] ]

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[ [ [ "import keras\nimport keras.backend as K\nfrom keras.datasets import mnist\nfrom keras.models import Sequential, Model, load_model\nfrom keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda\nfrom keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute, TimeDistributed, Bidirectional\nfrom keras.layers import Concatenate, Reshape, Conv2DTranspose, Embedding, Multiply, Activation\n\nfrom functools import partial\n\nfrom collections import defaultdict\n\nimport os\nimport pickle\nimport numpy as np\n\nimport scipy.sparse as sp\nimport scipy.io as spio\n\nimport isolearn.io as isoio\nimport isolearn.keras as isol\n\nimport matplotlib.pyplot as plt\n\nfrom sequence_logo_helper import dna_letter_at, plot_dna_logo\n\nfrom sklearn import preprocessing\nimport pandas as pd\n\nimport tensorflow as tf\n\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" ], [ "\n#optimus 5-prime functions \ndef test_data(df, model, test_seq, obs_col, output_col='pred'):\n '''Predict mean ribosome load using model and test set UTRs'''\n \n # Scale the test set mean ribosome load\n scaler = preprocessing.StandardScaler()\n scaler.fit(df[obs_col].reshape(-1,1))\n \n # Make predictions\n predictions = model.predict(test_seq).reshape(-1)\n \n # Inverse scaled predicted mean ribosome load and return in a column labeled 'pred'\n df.loc[:,output_col] = scaler.inverse_transform(predictions)\n return df\n\n\ndef one_hot_encode(df, col='utr', seq_len=50):\n # Dictionary returning one-hot encoding of nucleotides. \n nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n \n # Creat empty matrix.\n vectors=np.empty([len(df),seq_len,4])\n \n # Iterate through UTRs and one-hot encode\n for i,seq in enumerate(df[col].str[:seq_len]): \n seq = seq.lower()\n a = np.array([nuc_d[x] for x in seq])\n vectors[i] = a\n return vectors\n\n\ndef r2(x,y):\n slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n return r_value**2\n\n\n#Train data\ndf = pd.read_csv(\"../../../seqprop/examples/optimus5/GSM3130435_egfp_unmod_1.csv\")\n\ndf.sort_values('total_reads', inplace=True, ascending=False)\ndf.reset_index(inplace=True, drop=True)\ndf = df.iloc[:280000]\n# The training set has 260k UTRs and the test set has 20k UTRs.\n#e_test = df.iloc[:20000].copy().reset_index(drop = True)\ne_train = df.iloc[20000:].copy().reset_index(drop = True)\ne_train.loc[:,'scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'rl'].values.reshape(-1,1))\n\nseq_e_train = one_hot_encode(e_train,seq_len=50)\nx_train = seq_e_train\nx_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1], x_train.shape[2]))\ny_train = np.array(e_train['scaled_rl'].values)\ny_train = np.reshape(y_train, (y_train.shape[0],1))\n\nprint(\"x_train.shape = \" + str(x_train.shape))\nprint(\"y_train.shape = \" + str(y_train.shape))\n", "x_train.shape = (260000, 1, 50, 4)\ny_train.shape = (260000, 1)\n" ], [ "#Load Predictor\npredictor_path = 'optimusRetrainedMain.hdf5'\n\npredictor = load_model(predictor_path)\n\npredictor.trainable = False\npredictor.compile(optimizer=keras.optimizers.SGD(lr=0.1), loss='mean_squared_error')\n", "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\nWARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\nWARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\n" ], [ "#Generate (original) predictions\n\npred_train = predictor.predict(x_train[:, 0, ...], batch_size=32)\n\ny_train = (y_train >= 0.)\ny_train = np.concatenate([1. - y_train, y_train], axis=1)\n\npred_train = (pred_train >= 0.)\npred_train = np.concatenate([1. - pred_train, pred_train], axis=1)\n", "_____no_output_____" ], [ "from keras.layers import Input, Dense, Multiply, Flatten, Reshape, Conv2D, MaxPooling2D, GlobalMaxPooling2D, Activation\nfrom keras.layers import BatchNormalization\nfrom keras.models import Sequential, Model\nfrom keras.optimizers import Adam\nfrom keras import regularizers\nfrom keras import backend as K\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom keras.layers import Layer, InputSpec\nfrom keras import initializers, regularizers, constraints\n\nclass InstanceNormalization(Layer):\n def __init__(self, axes=(1, 2), trainable=True, **kwargs):\n super(InstanceNormalization, self).__init__(**kwargs)\n self.axes = axes\n self.trainable = trainable\n def build(self, input_shape):\n self.beta = self.add_weight(name='beta',shape=(input_shape[-1],),\n initializer='zeros',trainable=self.trainable)\n self.gamma = self.add_weight(name='gamma',shape=(input_shape[-1],),\n initializer='ones',trainable=self.trainable)\n def call(self, inputs):\n mean, variance = tf.nn.moments(inputs, self.axes, keep_dims=True)\n return tf.nn.batch_normalization(inputs, mean, variance, self.beta, self.gamma, 1e-6)\n\ndef bernoulli_sampling (prob):\n \"\"\" Sampling Bernoulli distribution by given probability.\n\n Args:\n - prob: P(Y = 1) in Bernoulli distribution.\n\n Returns:\n - samples: samples from Bernoulli distribution\n \"\"\" \n\n n, x_len, y_len, d = prob.shape\n samples = np.random.binomial(1, prob, (n, x_len, y_len, d))\n\n return samples\n\nclass INVASE():\n \"\"\"INVASE class.\n\n Attributes:\n - x_train: training features\n - y_train: training labels\n - model_type: invase or invase_minus\n - model_parameters:\n - actor_h_dim: hidden state dimensions for actor\n - critic_h_dim: hidden state dimensions for critic\n - n_layer: the number of layers\n - batch_size: the number of samples in mini batch\n - iteration: the number of iterations\n - activation: activation function of models\n - learning_rate: learning rate of model training\n - lamda: hyper-parameter of INVASE\n \"\"\"\n\n def __init__(self, x_train, y_train, model_type, model_parameters):\n\n self.lamda = model_parameters['lamda']\n self.actor_h_dim = model_parameters['actor_h_dim']\n self.critic_h_dim = model_parameters['critic_h_dim']\n self.n_layer = model_parameters['n_layer']\n self.batch_size = model_parameters['batch_size']\n self.iteration = model_parameters['iteration']\n self.activation = model_parameters['activation']\n self.learning_rate = model_parameters['learning_rate']\n\n #Modified Code\n self.x_len = x_train.shape[1]\n self.y_len = x_train.shape[2]\n self.dim = x_train.shape[3] \n self.label_dim = y_train.shape[1]\n\n self.model_type = model_type\n\n optimizer = Adam(self.learning_rate)\n\n # Build and compile critic\n self.critic = self.build_critic()\n self.critic.compile(loss='categorical_crossentropy', \n optimizer=optimizer, metrics=['acc'])\n\n # Build and compile the actor\n self.actor = self.build_actor()\n self.actor.compile(loss=self.actor_loss, optimizer=optimizer)\n\n if self.model_type == 'invase':\n # Build and compile the baseline\n self.baseline = self.build_baseline()\n self.baseline.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['acc'])\n\n\n def actor_loss(self, y_true, y_pred):\n \"\"\"Custom loss for the actor.\n\n Args:\n - y_true:\n - actor_out: actor output after sampling\n - critic_out: critic output \n - baseline_out: baseline output (only for invase)\n - y_pred: output of the actor network\n\n Returns:\n - loss: actor loss\n \"\"\"\n \n y_pred = K.reshape(y_pred, (K.shape(y_pred)[0], self.x_len*self.y_len*1))\n y_true = y_true[:, 0, 0, :]\n\n # Actor output\n actor_out = y_true[:, :self.x_len*self.y_len*1]\n # Critic output\n critic_out = y_true[:, self.x_len*self.y_len*1:(self.x_len*self.y_len*1+self.label_dim)]\n\n if self.model_type == 'invase':\n # Baseline output\n baseline_out = \\\n y_true[:, (self.x_len*self.y_len*1+self.label_dim):(self.x_len*self.y_len*1+2*self.label_dim)]\n # Ground truth label\n y_out = y_true[:, (self.x_len*self.y_len*1+2*self.label_dim):] \n elif self.model_type == 'invase_minus':\n # Ground truth label\n y_out = y_true[:, (self.x_len*self.y_len*1+self.label_dim):] \n\n # Critic loss\n critic_loss = -tf.reduce_sum(y_out * tf.log(critic_out + 1e-8), axis = 1) \n\n if self.model_type == 'invase': \n # Baseline loss\n baseline_loss = -tf.reduce_sum(y_out * tf.log(baseline_out + 1e-8), \n axis = 1) \n # Reward\n Reward = -(critic_loss - baseline_loss)\n elif self.model_type == 'invase_minus':\n Reward = -critic_loss\n\n # Policy gradient loss computation. \n custom_actor_loss = \\\n Reward * tf.reduce_sum(actor_out * K.log(y_pred + 1e-8) + \\\n (1-actor_out) * K.log(1-y_pred + 1e-8), axis = 1) - \\\n self.lamda * tf.reduce_mean(y_pred, axis = 1)\n\n # custom actor loss\n custom_actor_loss = tf.reduce_mean(-custom_actor_loss)\n\n return custom_actor_loss\n\n\n def build_actor(self):\n \"\"\"Build actor.\n\n Use feature as the input and output selection probability\n \"\"\"\n actor_model = Sequential()\n \n actor_model.add(Conv2D(self.actor_h_dim, (1, 7), padding='same', activation='linear'))\n actor_model.add(InstanceNormalization())\n actor_model.add(Activation(self.activation))\n for _ in range(self.n_layer - 2):\n actor_model.add(Conv2D(self.actor_h_dim, (1, 7), padding='same', activation='linear'))\n actor_model.add(InstanceNormalization())\n actor_model.add(Activation(self.activation))\n actor_model.add(Conv2D(1, (1, 1), padding='same', activation='sigmoid'))\n\n feature = Input(shape=(self.x_len, self.y_len, self.dim), dtype='float32')\n selection_probability = actor_model(feature)\n\n return Model(feature, selection_probability)\n\n\n def build_critic(self):\n \"\"\"Build critic.\n\n Use selected feature as the input and predict labels\n \"\"\"\n critic_model = Sequential()\n \n critic_model.add(Conv2D(self.critic_h_dim, (1, 7), padding='same', activation='linear'))\n critic_model.add(InstanceNormalization())\n critic_model.add(Activation(self.activation))\n for _ in range(self.n_layer - 2):\n critic_model.add(Conv2D(self.critic_h_dim, (1, 7), padding='same', activation='linear'))\n critic_model.add(InstanceNormalization())\n critic_model.add(Activation(self.activation))\n critic_model.add(Flatten())\n critic_model.add(Dense(self.critic_h_dim, activation=self.activation))\n critic_model.add(Dropout(0.2))\n critic_model.add(Dense(self.label_dim, activation ='softmax'))\n\n ## Inputs\n # Features\n feature = Input(shape=(self.x_len, self.y_len, self.dim), dtype='float32')\n # Binary selection\n selection = Input(shape=(self.x_len, self.y_len, 1), dtype='float32') \n\n # Element-wise multiplication\n critic_model_input = Multiply()([feature, selection])\n y_hat = critic_model(critic_model_input)\n\n return Model([feature, selection], y_hat)\n\n\n def build_baseline(self):\n \"\"\"Build baseline.\n\n Use the feature as the input and predict labels\n \"\"\"\n baseline_model = Sequential()\n \n baseline_model.add(Conv2D(self.critic_h_dim, (1, 7), padding='same', activation='linear'))\n baseline_model.add(InstanceNormalization())\n baseline_model.add(Activation(self.activation))\n for _ in range(self.n_layer - 2):\n baseline_model.add(Conv2D(self.critic_h_dim, (1, 7), padding='same', activation='linear'))\n baseline_model.add(InstanceNormalization())\n baseline_model.add(Activation(self.activation))\n baseline_model.add(Flatten())\n baseline_model.add(Dense(self.critic_h_dim, activation=self.activation))\n baseline_model.add(Dropout(0.2))\n baseline_model.add(Dense(self.label_dim, activation ='softmax'))\n\n # Input\n feature = Input(shape=(self.x_len, self.y_len, self.dim), dtype='float32') \n # Output \n y_hat = baseline_model(feature)\n\n return Model(feature, y_hat)\n\n\n def train(self, x_train, y_train):\n \"\"\"Train INVASE.\n\n Args:\n - x_train: training features\n - y_train: training labels\n \"\"\"\n\n for iter_idx in range(self.iteration):\n\n ## Train critic\n # Select a random batch of samples\n idx = np.random.randint(0, x_train.shape[0], self.batch_size)\n x_batch = x_train[idx,:]\n y_batch = y_train[idx,:]\n\n # Generate a batch of selection probability\n selection_probability = self.actor.predict(x_batch) \n # Sampling the features based on the selection_probability\n selection = bernoulli_sampling(selection_probability) \n # Critic loss\n critic_loss = self.critic.train_on_batch([x_batch, selection], y_batch) \n # Critic output\n critic_out = self.critic.predict([x_batch, selection])\n\n # Baseline output\n if self.model_type == 'invase': \n # Baseline loss\n baseline_loss = self.baseline.train_on_batch(x_batch, y_batch) \n # Baseline output\n baseline_out = self.baseline.predict(x_batch)\n\n ## Train actor\n # Use multiple things as the y_true: \n # - selection, critic_out, baseline_out, and ground truth (y_batch)\n if self.model_type == 'invase':\n y_batch_final = np.concatenate((np.reshape(selection, (y_batch.shape[0], -1)), \n np.asarray(critic_out), \n np.asarray(baseline_out), \n y_batch), axis = 1)\n elif self.model_type == 'invase_minus':\n y_batch_final = np.concatenate((np.reshape(selection, (y_batch.shape[0], -1)), \n np.asarray(critic_out), \n y_batch), axis = 1)\n\n y_batch_final = y_batch_final[:, None, None, :]\n \n # Train the actor\n actor_loss = self.actor.train_on_batch(x_batch, y_batch_final)\n\n if self.model_type == 'invase':\n # Print the progress\n dialog = 'Iterations: ' + str(iter_idx) + \\\n ', critic accuracy: ' + str(critic_loss[1]) + \\\n ', baseline accuracy: ' + str(baseline_loss[1]) + \\\n ', actor loss: ' + str(np.round(actor_loss,4))\n elif self.model_type == 'invase_minus':\n # Print the progress\n dialog = 'Iterations: ' + str(iter_idx) + \\\n ', critic accuracy: ' + str(critic_loss[1]) + \\\n ', actor loss: ' + str(np.round(actor_loss,4))\n\n if iter_idx % 100 == 0:\n print(dialog)\n\n def importance_score(self, x):\n \"\"\"Return featuer importance score.\n\n Args:\n - x: feature\n\n Returns:\n - feature_importance: instance-wise feature importance for x\n \"\"\" \n feature_importance = self.actor.predict(x) \n return np.asarray(feature_importance)\n\n\n def predict(self, x):\n \"\"\"Predict outcomes.\n\n Args:\n - x: feature\n\n Returns:\n - y_hat: predictions \n \"\"\" \n # Generate a batch of selection probability\n selection_probability = self.actor.predict(x) \n # Sampling the features based on the selection_probability\n selection = bernoulli_sampling(selection_probability) \n # Prediction \n y_hat = self.critic.predict([x, selection])\n\n return np.asarray(y_hat)\n", "_____no_output_____" ], [ "#Gradient saliency/backprop visualization\n\nimport matplotlib.collections as collections\nimport operator\nimport matplotlib.pyplot as plt\n\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n\nimport matplotlib as mpl\nfrom matplotlib.text import TextPath\nfrom matplotlib.patches import PathPatch, Rectangle\nfrom matplotlib.font_manager import FontProperties\nfrom matplotlib import gridspec\nfrom matplotlib.ticker import FormatStrFormatter\n\ndef plot_importance_scores(importance_scores, ref_seq, figsize=(12, 2), score_clip=None, sequence_template='', plot_start=0, plot_end=96) :\n\n end_pos = ref_seq.find(\"#\")\n \n fig = plt.figure(figsize=figsize)\n \n ax = plt.gca()\n \n if score_clip is not None :\n importance_scores = np.clip(np.copy(importance_scores), -score_clip, score_clip)\n \n max_score = np.max(np.sum(importance_scores[:, :], axis=0)) + 0.01\n\n for i in range(0, len(ref_seq)) :\n mutability_score = np.sum(importance_scores[:, i])\n dna_letter_at(ref_seq[i], i + 0.5, 0, mutability_score, ax)\n\n plt.sca(ax)\n plt.xlim((0, len(ref_seq)))\n plt.ylim((0, max_score))\n plt.axis('off')\n plt.yticks([0.0, max_score], [0.0, max_score], fontsize=16)\n \n for axis in fig.axes :\n axis.get_xaxis().set_visible(False)\n axis.get_yaxis().set_visible(False)\n \n plt.tight_layout()\n \n plt.show()\n", "_____no_output_____" ], [ "#Execute INVASE benchmark on synthetic datasets\n\nmask_penalty = 0.5#0.1\nhidden_dims = 32\nn_layers = 4\nepochs = 25\nbatch_size = 128\n\nmodel_parameters = {\n 'lamda': mask_penalty,\n 'actor_h_dim': hidden_dims, \n 'critic_h_dim': hidden_dims,\n 'n_layer': n_layers,\n 'batch_size': batch_size,\n 'iteration': int(x_train.shape[0] * epochs / batch_size), \n 'activation': 'relu', \n 'learning_rate': 0.0001\n}\n\nencoder = isol.OneHotEncoder(50)\n\nscore_clip = None\n\nallFiles = [\"optimus5_synthetic_random_insert_if_uorf_1_start_1_stop_variable_loc_512.csv\",\n \"optimus5_synthetic_random_insert_if_uorf_1_start_2_stop_variable_loc_512.csv\",\n \"optimus5_synthetic_random_insert_if_uorf_2_start_1_stop_variable_loc_512.csv\",\n \"optimus5_synthetic_random_insert_if_uorf_2_start_2_stop_variable_loc_512.csv\",\n \"optimus5_synthetic_examples_3.csv\"]\n\n#Train INVASE\ninvase_model = INVASE(x_train, pred_train, 'invase', model_parameters)\n\ninvase_model.train(x_train, pred_train) \n\nfor csv_to_open in allFiles :\n \n #Load dataset for benchmarking \n dataset_name = csv_to_open.replace(\".csv\", \"\")\n benchmarkSet = pd.read_csv(csv_to_open)\n \n seq_e_test = one_hot_encode(benchmarkSet, seq_len=50)\n x_test = seq_e_test[:, None, ...]\n \n print(x_test.shape)\n \n pred_test = predictor.predict(x_test[:, 0, ...], batch_size=32)\n y_test = pred_test\n\n y_test = (y_test >= 0.)\n y_test = np.concatenate([1. - y_test, y_test], axis=1)\n\n pred_test = (pred_test >= 0.)\n pred_test = np.concatenate([1. - pred_test, pred_test], axis=1) \n\n importance_scores_test = invase_model.importance_score(x_test)\n \n #Evaluate INVASE model on train and test data\n\n invase_pred_train = invase_model.predict(x_train)\n invase_pred_test = invase_model.predict(x_test)\n\n print(\"Training Accuracy = \" + str(np.sum(np.argmax(invase_pred_train, axis=1) == np.argmax(pred_train, axis=1)) / float(pred_train.shape[0])))\n print(\"Test Accuracy = \" + str(np.sum(np.argmax(invase_pred_test, axis=1) == np.argmax(pred_test, axis=1)) / float(pred_test.shape[0])))\n\n for plot_i in range(0, 3) :\n\n print(\"Test sequence \" + str(plot_i) + \":\")\n\n plot_dna_logo(x_test[plot_i, 0, :, :], sequence_template='N'*50, plot_sequence_template=True, figsize=(12, 1), plot_start=0, plot_end=50)\n plot_importance_scores(np.maximum(importance_scores_test[plot_i, 0, :, :].T, 0.), encoder.decode(x_test[plot_i, 0, :, :]), figsize=(12, 1), score_clip=score_clip, sequence_template='N'*50, plot_start=0, plot_end=50)\n\n #Save predicted importance scores\n\n model_name = \"invase_\" + dataset_name + \"_conv_full_data\"\n\n np.save(model_name + \"_importance_scores_test\", importance_scores_test)\n", "Iterations: 0, critic accuracy: 0.390625, baseline accuracy: 0.609375, actor loss: -11.6949\nIterations: 100, critic accuracy: 0.6171875, baseline accuracy: 0.640625, actor loss: -1.1258\nIterations: 200, critic accuracy: 0.6484375, baseline accuracy: 0.6640625, actor loss: -0.1016\nIterations: 300, critic accuracy: 0.5546875, baseline accuracy: 0.59375, actor loss: -0.5168\nIterations: 400, critic accuracy: 0.59375, baseline accuracy: 0.5703125, actor loss: -1.1017\nIterations: 500, critic accuracy: 0.65625, baseline accuracy: 0.609375, actor loss: -0.796\nIterations: 600, critic accuracy: 0.6015625, baseline accuracy: 0.6171875, actor loss: -1.1892\nIterations: 700, critic accuracy: 0.6171875, baseline accuracy: 0.6015625, actor loss: -0.5415\nIterations: 800, critic accuracy: 0.6484375, baseline accuracy: 0.6796875, actor loss: -0.5854\nIterations: 900, critic accuracy: 0.609375, baseline accuracy: 0.578125, actor loss: -0.7383\nIterations: 1000, critic accuracy: 0.671875, baseline accuracy: 0.78125, actor loss: -2.6702\nIterations: 1100, critic accuracy: 0.6015625, baseline accuracy: 0.71875, actor loss: -1.918\nIterations: 1200, critic accuracy: 0.5703125, baseline accuracy: 0.6875, actor loss: -2.127\nIterations: 1300, critic accuracy: 0.609375, baseline accuracy: 0.6953125, actor loss: -2.5803\nIterations: 1400, critic accuracy: 0.5546875, baseline accuracy: 0.703125, actor loss: -2.9973\nIterations: 1500, critic accuracy: 0.65625, baseline accuracy: 0.8046875, actor loss: -5.6637\nIterations: 1600, critic accuracy: 0.6171875, baseline accuracy: 0.765625, actor loss: -4.6882\nIterations: 1700, critic accuracy: 0.6171875, baseline accuracy: 0.8828125, actor loss: -6.1885\nIterations: 1800, critic accuracy: 0.625, baseline accuracy: 0.875, actor loss: -6.5106\nIterations: 1900, critic accuracy: 0.640625, baseline accuracy: 0.875, actor loss: -7.0292\nIterations: 2000, critic accuracy: 0.578125, baseline accuracy: 0.8515625, actor loss: -5.8939\nIterations: 2100, critic accuracy: 0.5703125, baseline accuracy: 0.90625, actor loss: -7.6099\nIterations: 2200, critic accuracy: 0.625, baseline accuracy: 0.8671875, actor loss: -6.8492\nIterations: 2300, critic accuracy: 0.59375, baseline accuracy: 0.8984375, actor loss: -7.5056\nIterations: 2400, critic accuracy: 0.6640625, baseline accuracy: 0.921875, actor loss: -7.237\nIterations: 2500, critic accuracy: 0.625, baseline accuracy: 0.890625, actor loss: -5.3026\nIterations: 2600, critic accuracy: 0.6484375, baseline accuracy: 0.890625, actor loss: -6.5486\nIterations: 2700, critic accuracy: 0.546875, baseline accuracy: 0.8984375, actor loss: -6.223\nIterations: 2800, critic accuracy: 0.578125, baseline accuracy: 0.859375, actor loss: -6.8185\nIterations: 2900, critic accuracy: 0.5859375, baseline accuracy: 0.90625, actor loss: -6.8197\nIterations: 3000, critic accuracy: 0.625, baseline accuracy: 0.921875, actor loss: -6.7912\nIterations: 3100, critic accuracy: 0.6015625, baseline accuracy: 0.9375, actor loss: -6.8764\nIterations: 3200, critic accuracy: 0.6015625, baseline accuracy: 0.8515625, actor loss: -5.4695\nIterations: 3300, critic accuracy: 0.5703125, baseline accuracy: 0.9140625, actor loss: -6.5216\nIterations: 3400, critic accuracy: 0.609375, baseline accuracy: 0.8984375, actor loss: -5.6773\nIterations: 3500, critic accuracy: 0.6875, baseline accuracy: 0.9296875, actor loss: -5.1542\nIterations: 3600, critic accuracy: 0.59375, baseline accuracy: 0.8828125, actor loss: -5.1956\nIterations: 3700, critic accuracy: 0.625, baseline accuracy: 0.9296875, actor loss: -5.6007\nIterations: 3800, critic accuracy: 0.65625, baseline accuracy: 0.8828125, actor loss: -4.5584\nIterations: 3900, critic accuracy: 0.625, baseline accuracy: 0.921875, actor loss: -5.4642\nIterations: 4000, critic accuracy: 0.625, baseline accuracy: 0.9453125, actor loss: -6.1186\nIterations: 4100, critic accuracy: 0.5703125, baseline accuracy: 0.8984375, actor loss: -4.9557\nIterations: 4200, critic accuracy: 0.5703125, baseline accuracy: 0.9375, actor loss: -5.6133\nIterations: 4300, critic accuracy: 0.625, baseline accuracy: 0.9453125, actor loss: -5.0569\nIterations: 4400, critic accuracy: 0.6484375, baseline accuracy: 0.90625, actor loss: -4.8592\nIterations: 4500, critic accuracy: 0.6328125, baseline accuracy: 0.8984375, actor loss: -4.5599\nIterations: 4600, critic accuracy: 0.5390625, baseline accuracy: 0.953125, actor loss: -5.5266\nIterations: 4700, critic accuracy: 0.59375, baseline accuracy: 0.9140625, actor loss: -4.9825\nIterations: 4800, critic accuracy: 0.578125, baseline accuracy: 0.9609375, actor loss: -5.0746\nIterations: 4900, critic accuracy: 0.703125, baseline accuracy: 0.90625, actor loss: -4.6208\nIterations: 5000, critic accuracy: 0.6328125, baseline accuracy: 0.90625, actor loss: -3.8968\nIterations: 5100, critic accuracy: 0.59375, baseline accuracy: 0.9375, actor loss: -4.678\nIterations: 5200, critic accuracy: 0.609375, baseline accuracy: 0.8828125, actor loss: -3.6911\nIterations: 5300, critic accuracy: 0.546875, baseline accuracy: 0.9140625, actor loss: -4.1596\nIterations: 5400, critic accuracy: 0.65625, baseline accuracy: 0.9296875, actor loss: -3.6451\nIterations: 5500, critic accuracy: 0.5859375, baseline accuracy: 0.9453125, actor loss: -4.4246\nIterations: 5600, critic accuracy: 0.6328125, baseline accuracy: 0.96875, actor loss: -4.284\nIterations: 5700, critic accuracy: 0.625, baseline accuracy: 0.9296875, actor loss: -4.0006\nIterations: 5800, critic accuracy: 0.5390625, baseline accuracy: 0.9375, actor loss: -4.5173\nIterations: 5900, critic accuracy: 0.65625, baseline accuracy: 0.9296875, actor loss: -3.8275\nIterations: 6000, critic accuracy: 0.6015625, baseline accuracy: 0.953125, actor loss: -4.0728\nIterations: 6100, critic accuracy: 0.65625, baseline accuracy: 0.9609375, actor loss: -4.1116\nIterations: 6200, critic accuracy: 0.6640625, baseline accuracy: 0.96875, actor loss: -3.5808\nIterations: 6300, critic accuracy: 0.6015625, baseline accuracy: 0.9296875, actor loss: -3.7118\nIterations: 6400, critic accuracy: 0.65625, baseline accuracy: 0.9375, actor loss: -3.3352\nIterations: 6500, critic accuracy: 0.640625, baseline accuracy: 0.96875, actor loss: -4.1158\nIterations: 6600, critic accuracy: 0.7109375, baseline accuracy: 0.9609375, actor loss: -3.5784\nIterations: 6700, critic accuracy: 0.6328125, baseline accuracy: 0.9140625, actor loss: -3.5007\nIterations: 6800, critic accuracy: 0.4921875, baseline accuracy: 0.9453125, actor loss: -4.053\nIterations: 6900, critic accuracy: 0.6015625, baseline accuracy: 0.9453125, actor loss: -3.4824\nIterations: 7000, critic accuracy: 0.6640625, baseline accuracy: 0.9140625, actor loss: -3.1074\nIterations: 7100, critic accuracy: 0.546875, baseline accuracy: 0.953125, actor loss: -3.521\nIterations: 7200, critic accuracy: 0.5859375, baseline accuracy: 0.9140625, actor loss: -2.9394\nIterations: 7300, critic accuracy: 0.609375, baseline accuracy: 0.953125, actor loss: -3.8403\nIterations: 7400, critic accuracy: 0.6328125, baseline accuracy: 0.9140625, actor loss: -2.6146\nIterations: 7500, critic accuracy: 0.59375, baseline accuracy: 0.9375, actor loss: -3.491\nIterations: 7600, critic accuracy: 0.6328125, baseline accuracy: 0.9375, actor loss: -2.9986\nIterations: 7700, critic accuracy: 0.625, baseline accuracy: 0.9609375, actor loss: -3.3308\nIterations: 7800, critic accuracy: 0.625, baseline accuracy: 0.9296875, actor loss: -3.4045\nIterations: 7900, critic accuracy: 0.59375, baseline accuracy: 0.9375, actor loss: -3.2892\nIterations: 8000, critic accuracy: 0.5859375, baseline accuracy: 0.9453125, actor loss: -2.9703\nIterations: 8100, critic accuracy: 0.625, baseline accuracy: 0.90625, actor loss: -2.4756\nIterations: 8200, critic accuracy: 0.578125, baseline accuracy: 0.9375, actor loss: -3.5445\nIterations: 8300, critic accuracy: 0.6015625, baseline accuracy: 0.953125, actor loss: -3.3239\nIterations: 8400, critic accuracy: 0.640625, baseline accuracy: 0.9375, actor loss: -3.0634\nIterations: 8500, critic accuracy: 0.59375, baseline accuracy: 0.9609375, actor loss: -3.2403\nIterations: 8600, critic accuracy: 0.6484375, baseline accuracy: 0.9765625, actor loss: -3.0564\nIterations: 8700, critic accuracy: 0.625, baseline accuracy: 0.90625, actor loss: -2.5869\n" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/iVibudh/TensorFlow-for-DeepLearning/blob/main/08-Time-Series-Forecasting/moving_average.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Moving average", "_____no_output_____" ], [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n <td>\n <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c03_moving_average.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n </td>\n <td>\n <a target=\"_blank\" href=\"https://github.com/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l08c03_moving_average.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n </td>\n</table>", "_____no_output_____" ], [ "## Setup", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\nkeras = tf.keras", "_____no_output_____" ], [ "def plot_series(time, series, format=\"-\", start=0, end=None, label=None):\n plt.plot(time[start:end], series[start:end], format, label=label)\n plt.xlabel(\"Time\")\n plt.ylabel(\"Value\")\n if label:\n plt.legend(fontsize=14)\n plt.grid(True)\n \ndef trend(time, slope=0):\n return slope * time\n\ndef seasonal_pattern(season_time):\n \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n return np.where(season_time < 0.4,\n np.cos(season_time * 2 * np.pi),\n 1 / np.exp(3 * season_time))\n\ndef seasonality(time, period, amplitude=1, phase=0):\n \"\"\"Repeats the same pattern at each period\"\"\"\n season_time = ((time + phase) % period) / period\n return amplitude * seasonal_pattern(season_time)\n\ndef white_noise(time, noise_level=1, seed=None):\n rnd = np.random.RandomState(seed)\n return rnd.randn(len(time)) * noise_level", "_____no_output_____" ] ], [ [ "## Trend and Seasonality", "_____no_output_____" ] ], [ [ "time = np.arange(4 * 365 + 1)\n\nslope = 0.05\nbaseline = 10\namplitude = 40\nseries = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n\nnoise_level = 5\nnoise = white_noise(time, noise_level, seed=42)\n\nseries += noise\n\nplt.figure(figsize=(10, 6))\nplot_series(time, series)\nplt.show()", "_____no_output_____" ] ], [ [ "## Naive Forecast", "_____no_output_____" ] ], [ [ "split_time = 1000\ntime_train = time[:split_time]\nx_train = series[:split_time]\ntime_valid = time[split_time:]\nx_valid = series[split_time:]\n\nnaive_forecast = series[split_time - 1:-1]\n\nplt.figure(figsize=(10, 6))\nplot_series(time_valid, x_valid, start=0, end=150, label=\"Series\")\nplot_series(time_valid, naive_forecast, start=1, end=151, label=\"Forecast\")", "_____no_output_____" ] ], [ [ "Now let's compute the mean absolute error between the forecasts and the predictions in the validation period:", "_____no_output_____" ] ], [ [ "keras.metrics.mean_absolute_error(x_valid, naive_forecast).numpy()", "_____no_output_____" ] ], [ [ "That's our baseline, now let's try a moving average.", "_____no_output_____" ], [ "## Moving Average", "_____no_output_____" ] ], [ [ "def moving_average_forecast(series, window_size):\n \"\"\"Forecasts the mean of the last few values.\n If window_size=1, then this is equivalent to naive forecast\"\"\"\n forecast = []\n for time in range(len(series) - window_size):\n forecast.append(series[time:time + window_size].mean())\n return np.array(forecast)", "_____no_output_____" ], [ "def moving_average_forecast(series, window_size):\n \"\"\"Forecasts the mean of the last few values.\n If window_size=1, then this is equivalent to naive forecast\n This implementation is *much* faster than the previous one\"\"\"\n mov = np.cumsum(series)\n mov[window_size:] = mov[window_size:] - mov[:-window_size]\n return mov[window_size - 1:-1] / window_size", "_____no_output_____" ], [ "moving_avg = moving_average_forecast(series, 30)[split_time - 30:]\n\nplt.figure(figsize=(10, 6))\nplot_series(time_valid, x_valid, label=\"Series\")\nplot_series(time_valid, moving_avg, label=\"Moving average (30 days)\")", "_____no_output_____" ], [ "keras.metrics.mean_absolute_error(x_valid, moving_avg).numpy()", "_____no_output_____" ] ], [ [ "That's worse than naive forecast! The moving average does not anticipate trend or seasonality, so let's try to remove them by using differencing. Since the seasonality period is 365 days, we will subtract the value at time *t* – 365 from the value at time *t*.", "_____no_output_____" ] ], [ [ "time_a = np.array(range(1, 21))\na = np.array([1.1, 1.5, 1.6, 1.4, 1.5, 1.6,1.7, 1.8, 1.9, 2, \n 2.1, 2.5, 2.6, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3])\nprint(\"a: \", a, len(a))\nprint(\"time_a: \", time_a, len (time_a))\n\nma_a = moving_average_forecast(a, 10)\nprint(\"ma_a: \", ma_a)\n\ndiff_a = (a[10:] - a[:-10]) ### This removes TREND + SEASONALITY, so, Only Noise is left\ndiff_time = time_a[10:]\nprint(\"diff_time: \", diff_time)\nprint(\"diff_a: \", diff_a)", "a: [1.1 1.5 1.6 1.4 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.5 2.6 2.4 2.5 2.6 2.7 2.8\n 2.9 3. ] 20\ntime_a: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20] 20\nma_a: [1.61 1.71 1.81 1.91 2.01 2.11 2.21 2.31 2.41 2.51]\ndiff_time: [11 12 13 14 15 16 17 18 19 20]\ndiff_a: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" ], [ "a[10:]", "_____no_output_____" ], [ "a[:-10]", "_____no_output_____" ], [ "diff_series = (series[365:] - series[:-365])\ndiff_time = time[365:]\n\nplt.figure(figsize=(10, 6))\nplot_series(diff_time, diff_series, label=\"Series(t) – Series(t–365)\")\nplt.show()", "_____no_output_____" ] ], [ [ "Focusing on the validation period:", "_____no_output_____" ] ], [ [ "plt.figure(figsize=(10, 6))\nplot_series(time_valid, diff_series[split_time - 365:], label=\"Series(t) – Series(t–365)\")\nplt.show()", "_____no_output_____" ] ], [ [ "Great, the trend and seasonality seem to be gone, so now we can use the moving average:", "_____no_output_____" ] ], [ [ "diff_moving_avg = moving_average_forecast(diff_series, 50)[split_time - 365 - 50:]\n\nplt.figure(figsize=(10, 6))\nplot_series(time_valid, diff_series[split_time - 365:], label=\"Series(t) – Series(t–365)\")\nplot_series(time_valid, diff_moving_avg, label=\"Moving Average of Diff\")\nplt.show()", "_____no_output_____" ] ], [ [ "Now let's bring back the trend and seasonality by adding the past values from t – 365:", "_____no_output_____" ] ], [ [ "diff_moving_avg_plus_past = series[split_time - 365:-365] + diff_moving_avg\n\nplt.figure(figsize=(10, 6))\nplot_series(time_valid, x_valid, label=\"Series\")\nplot_series(time_valid, diff_moving_avg_plus_past, label=\"Forecasts\")\nplt.show()", "_____no_output_____" ], [ "keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_past).numpy()", "_____no_output_____" ] ], [ [ "Better than naive forecast, good. However the forecasts look a bit too random, because we're just adding past values, which were noisy. Let's use a moving averaging on past values to remove some of the noise:", "_____no_output_____" ] ], [ [ "diff_moving_avg_plus_smooth_past = moving_average_forecast(series[split_time - 370:-359], 11) + diff_moving_avg\n# moving_average_forecast(series[split_time - 370:-359], 11) = Past series have been smoothed to reduce the effects of the past noise\n\nplt.figure(figsize=(10, 6))\nplot_series(time_valid, x_valid, label=\"Series\")\nplot_series(time_valid, diff_moving_avg_plus_smooth_past, label=\"Forecasts\")\nplt.show()", "_____no_output_____" ], [ "keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_smooth_past).numpy()", "_____no_output_____" ] ], [ [ "That's starting to look pretty good! Let's see if we can do better with a Machine Learning model.", "_____no_output_____" ] ] ]

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[ [ [ "# Approximate q-learning\n\nIn this notebook you will teach a __tensorflow__ neural network to do Q-learning.", "_____no_output_____" ], [ "__Frameworks__ - we'll accept this homework in any deep learning framework. This particular notebook was designed for tensorflow, but you will find it easy to adapt it to almost any python-based deep learning framework.", "_____no_output_____" ] ], [ [ "#XVFB will be launched if you run on a server\nimport os\nif os.environ.get(\"DISPLAY\") is not str or len(os.environ.get(\"DISPLAY\"))==0:\n !bash ../xvfb start\n %env DISPLAY=:1", "Starting virtual X frame buffer: Xvfb../xvfb: line 12: start-stop-daemon: command not found\n.\nenv: DISPLAY=:1\n" ], [ "import gym\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ], [ "env = gym.make(\"CartPole-v0\").env\nenv.reset()\nn_actions = env.action_space.n\nstate_dim = env.observation_space.shape\n\nplt.imshow(env.render(\"rgb_array\"))", "_____no_output_____" ], [ "state_dim", "_____no_output_____" ] ], [ [ "# Approximate (deep) Q-learning: building the network\n\nTo train a neural network policy one must have a neural network policy. Let's build it.\n\n\nSince we're working with a pre-extracted features (cart positions, angles and velocities), we don't need a complicated network yet. In fact, let's build something like this for starters:\n\n\n\nFor your first run, please only use linear layers (L.Dense) and activations. Stuff like batch normalization or dropout may ruin everything if used haphazardly. \n\nAlso please avoid using nonlinearities like sigmoid & tanh: agent's observations are not normalized so sigmoids may become saturated from init.\n\nIdeally you should start small with maybe 1-2 hidden layers with < 200 neurons and then increase network size if agent doesn't beat the target score.", "_____no_output_____" ] ], [ [ "import tensorflow as tf\nimport keras\nimport keras.layers as L\ntf.reset_default_graph()\nsess = tf.InteractiveSession()\nkeras.backend.set_session(sess)", "Using TensorFlow backend.\n" ], [ "network = keras.models.Sequential()\nnetwork.add(L.InputLayer(state_dim))\n\n# let's create a network for approximate q-learning following guidelines above\n# <YOUR CODE: stack more layers!!!1 >\nnetwork.add(L.Dense(128, activation='relu'))\nnetwork.add(L.Dense(196, activation='relu'))\nnetwork.add(L.Dense(n_actions, activation='linear'))", "_____no_output_____" ], [ "import numpy as np\nimport random\n\n\ndef get_action(state, epsilon=0):\n \"\"\"\n sample actions with epsilon-greedy policy\n recap: with p = epsilon pick random action, else pick action with highest Q(s,a)\n \"\"\"\n \n q_values = network.predict(state[None])[0]\n n = len(q_values)\n if random.random() < epsilon:\n return random.randint(0, n - 1)\n else:\n return np.argmax(q_values)\n\n# return <epsilon-greedily selected action>\n", "_____no_output_____" ], [ "assert network.output_shape == (None, n_actions), \"please make sure your model maps state s -> [Q(s,a0), ..., Q(s, a_last)]\"\nassert network.layers[-1].activation == keras.activations.linear, \"please make sure you predict q-values without nonlinearity\"\n\n# test epsilon-greedy exploration\ns = env.reset()\nassert np.shape(get_action(s)) == (), \"please return just one action (integer)\"\nfor eps in [0., 0.1, 0.5, 1.0]:\n state_frequencies = np.bincount([get_action(s, epsilon=eps) for i in range(10000)], minlength=n_actions)\n best_action = state_frequencies.argmax()\n assert abs(state_frequencies[best_action] - 10000 * (1 - eps + eps / n_actions)) < 200\n for other_action in range(n_actions):\n if other_action != best_action:\n assert abs(state_frequencies[other_action] - 10000 * (eps / n_actions)) < 200\n print('e=%.1f tests passed'%eps)", "e=0.0 tests passed\ne=0.1 tests passed\ne=0.5 tests passed\ne=1.0 tests passed\n" ] ], [ [ "### Q-learning via gradient descent\n\nWe shall now train our agent's Q-function by minimizing the TD loss:\n$$ L = { 1 \\over N} \\sum_i (Q_{\\theta}(s,a) - [r(s,a) + \\gamma \\cdot max_{a'} Q_{-}(s', a')]) ^2 $$\n\n\nWhere\n* $s, a, r, s'$ are current state, action, reward and next state respectively\n* $\\gamma$ is a discount factor defined two cells above.\n\nThe tricky part is with $Q_{-}(s',a')$. From an engineering standpoint, it's the same as $Q_{\\theta}$ - the output of your neural network policy. However, when doing gradient descent, __we won't propagate gradients through it__ to make training more stable (see lectures).\n\nTo do so, we shall use `tf.stop_gradient` function which basically says \"consider this thing constant when doingbackprop\".", "_____no_output_____" ] ], [ [ "# Create placeholders for the <s, a, r, s'> tuple and a special indicator for game end (is_done = True)\nstates_ph = keras.backend.placeholder(dtype='float32', shape=(None,) + state_dim)\nactions_ph = keras.backend.placeholder(dtype='int32', shape=[None])\nrewards_ph = keras.backend.placeholder(dtype='float32', shape=[None])\nnext_states_ph = keras.backend.placeholder(dtype='float32', shape=(None,) + state_dim)\nis_done_ph = keras.backend.placeholder(dtype='bool', shape=[None])", "_____no_output_____" ], [ "#get q-values for all actions in current states\npredicted_qvalues = network(states_ph)\n\n#select q-values for chosen actions\npredicted_qvalues_for_actions = tf.reduce_sum(predicted_qvalues * tf.one_hot(actions_ph, n_actions), axis=1)", "_____no_output_____" ], [ "gamma = 0.99\n\n# compute q-values for all actions in next states\n# predicted_next_qvalues = <YOUR CODE - apply network to get q-values for next_states_ph>\npredicted_next_qvalues = network(next_states_ph)\n\n# compute V*(next_states) using predicted next q-values\nnext_state_values = tf.reduce_max(predicted_next_qvalues, axis=1)\n\n# compute \"target q-values\" for loss - it's what's inside square parentheses in the above formula.\ntarget_qvalues_for_actions = rewards_ph + gamma * next_state_values\n\n# at the last state we shall use simplified formula: Q(s,a) = r(s,a) since s' doesn't exist\ntarget_qvalues_for_actions = tf.where(is_done_ph, rewards_ph, target_qvalues_for_actions)", "_____no_output_____" ], [ "#mean squared error loss to minimize\nloss = (predicted_qvalues_for_actions - tf.stop_gradient(target_qvalues_for_actions)) ** 2\nloss = tf.reduce_mean(loss)\n\n# training function that resembles agent.update(state, action, reward, next_state) from tabular agent\ntrain_step = tf.train.AdamOptimizer(1e-4).minimize(loss)", "_____no_output_____" ], [ "assert tf.gradients(loss, [predicted_qvalues_for_actions])[0] is not None, \"make sure you update q-values for chosen actions and not just all actions\"\nassert tf.gradients(loss, [predicted_next_qvalues])[0] is None, \"make sure you don't propagate gradient w.r.t. Q_(s',a')\"\nassert predicted_next_qvalues.shape.ndims == 2, \"make sure you predicted q-values for all actions in next state\"\nassert next_state_values.shape.ndims == 1, \"make sure you computed V(s') as maximum over just the actions axis and not all axes\"\nassert target_qvalues_for_actions.shape.ndims == 1, \"there's something wrong with target q-values, they must be a vector\"", "_____no_output_____" ] ], [ [ "### Playing the game", "_____no_output_____" ] ], [ [ "def generate_session(t_max=1000, epsilon=0, train=False):\n \"\"\"play env with approximate q-learning agent and train it at the same time\"\"\"\n total_reward = 0\n s = env.reset()\n \n for t in range(t_max):\n a = get_action(s, epsilon=epsilon) \n next_s, r, done, _ = env.step(a)\n \n if train:\n sess.run(train_step,{\n states_ph: [s], actions_ph: [a], rewards_ph: [r], \n next_states_ph: [next_s], is_done_ph: [done]\n })\n\n total_reward += r\n s = next_s\n if done: break\n \n return total_reward", "_____no_output_____" ], [ "epsilon = 0.5", "_____no_output_____" ], [ "for i in range(1000):\n session_rewards = [generate_session(epsilon=epsilon, train=True) for _ in range(100)]\n print(\"epoch #{}\\tmean reward = {:.3f}\\tepsilon = {:.3f}\".format(i, np.mean(session_rewards), epsilon))\n \n epsilon *= 0.99\n assert epsilon >= 1e-4, \"Make sure epsilon is always nonzero during training\"\n \n if np.mean(session_rewards) > 300:\n print (\"You Win!\")\n break\n", "epoch #0\tmean reward = 16.740\tepsilon = 0.500\nepoch #1\tmean reward = 16.610\tepsilon = 0.495\nepoch #2\tmean reward = 14.050\tepsilon = 0.490\nepoch #3\tmean reward = 15.400\tepsilon = 0.485\nepoch #4\tmean reward = 14.950\tepsilon = 0.480\nepoch #5\tmean reward = 19.480\tepsilon = 0.475\nepoch #6\tmean reward = 16.150\tepsilon = 0.471\nepoch #7\tmean reward = 17.380\tepsilon = 0.466\nepoch #8\tmean reward = 15.730\tepsilon = 0.461\nepoch #9\tmean reward = 25.080\tepsilon = 0.457\nepoch #10\tmean reward = 29.930\tepsilon = 0.452\nepoch #11\tmean reward = 37.970\tepsilon = 0.448\nepoch #12\tmean reward = 45.640\tepsilon = 0.443\nepoch #13\tmean reward = 46.730\tepsilon = 0.439\nepoch #14\tmean reward = 43.390\tepsilon = 0.434\nepoch #15\tmean reward = 79.620\tepsilon = 0.430\nepoch #16\tmean reward = 92.650\tepsilon = 0.426\nepoch #17\tmean reward = 125.850\tepsilon = 0.421\nepoch #18\tmean reward = 100.480\tepsilon = 0.417\nepoch #19\tmean reward = 150.340\tepsilon = 0.413\nepoch #20\tmean reward = 161.240\tepsilon = 0.409\nepoch #21\tmean reward = 131.490\tepsilon = 0.405\nepoch #22\tmean reward = 184.870\tepsilon = 0.401\nepoch #23\tmean reward = 191.210\tepsilon = 0.397\nepoch #24\tmean reward = 247.040\tepsilon = 0.393\nepoch #25\tmean reward = 207.300\tepsilon = 0.389\nepoch #26\tmean reward = 249.010\tepsilon = 0.385\nepoch #27\tmean reward = 252.960\tepsilon = 0.381\nepoch #28\tmean reward = 276.790\tepsilon = 0.377\nepoch #29\tmean reward = 376.700\tepsilon = 0.374\nYou Win!\n" ] ], [ [ "### How to interpret results\n\n\nWelcome to the f.. world of deep f...n reinforcement learning. Don't expect agent's reward to smoothly go up. Hope for it to go increase eventually. If it deems you worthy.\n\nSeriously though,\n* __ mean reward__ is the average reward per game. For a correct implementation it may stay low for some 10 epochs, then start growing while oscilating insanely and converges by ~50-100 steps depending on the network architecture. \n* If it never reaches target score by the end of for loop, try increasing the number of hidden neurons or look at the epsilon.\n* __ epsilon__ - agent's willingness to explore. If you see that agent's already at < 0.01 epsilon before it's is at least 200, just reset it back to 0.1 - 0.5.", "_____no_output_____" ], [ "### Record videos\n\nAs usual, we now use `gym.wrappers.Monitor` to record a video of our agent playing the game. Unlike our previous attempts with state binarization, this time we expect our agent to act ~~(or fail)~~ more smoothly since there's no more binarization error at play.\n\nAs you already did with tabular q-learning, we set epsilon=0 for final evaluation to prevent agent from exploring himself to death.", "_____no_output_____" ] ], [ [ "#record sessions\nimport gym.wrappers\nenv = gym.wrappers.Monitor(gym.make(\"CartPole-v0\"),directory=\"videos\",force=True)\nsessions = [generate_session(epsilon=0, train=False) for _ in range(100)]\nenv.close()\n", "_____no_output_____" ], [ "#show video\nfrom IPython.display import HTML\nimport os\n\nvideo_names = list(filter(lambda s:s.endswith(\".mp4\"),os.listdir(\"./videos/\")))\n\nHTML(\"\"\"\n<video width=\"640\" height=\"480\" controls>\n <source src=\"{}\" type=\"video/mp4\">\n</video>\n\"\"\".format(\"./videos/\"+video_names[-1])) #this may or may not be _last_ video. Try other indices", "_____no_output_____" ] ], [ [ "---", "_____no_output_____" ], [ "### Submit to coursera", "_____no_output_____" ] ], [ [ "%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "from submit2 import submit_cartpole\nsubmit_cartpole(generate_session, '[email protected]', 'gMmaBajRboD6YXKK')", "Submitted to Coursera platform. See results on assignment page!\n" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# NOAA Wave Watch 3 and NDBC Buoy Data Comparison", "_____no_output_____" ], [ "*Note: this notebook requires python3.*\n\nThis notebook demostrates how to compare [WaveWatch III Global Ocean Wave Model](http://data.planetos.com/datasets/noaa_ww3_global_1.25x1d:noaa-wave-watch-iii-nww3-ocean-wave-model?utm_source=github&utm_medium=notebook&utm_campaign=ndbc-wavewatch-iii-notebook) and [NOAA NDBC buoy data](http://data.planetos.com/datasets/noaa_ndbc_stdmet_stations?utm_source=github&utm_medium=notebook&utm_campaign=ndbc-wavewatch-iii-notebook) using the Planet OS API.\n\nAPI documentation is available at http://docs.planetos.com. If you have questions or comments, join the [Planet OS Slack community](http://slack.planetos.com/) to chat with our development team.\n\n\nFor general information on usage of IPython/Jupyter and Matplotlib, please refer to their corresponding documentation. https://ipython.org/ and http://matplotlib.org/. This notebook also makes use of the [matplotlib basemap toolkit.](http://matplotlib.org/basemap/index.html)", "_____no_output_____" ] ], [ [ "%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport dateutil.parser\nimport datetime\nfrom urllib.request import urlopen, Request\nimport simplejson as json\nfrom datetime import date, timedelta, datetime\nimport matplotlib.dates as mdates\nfrom mpl_toolkits.basemap import Basemap", "_____no_output_____" ] ], [ [ "**Important!** You'll need to replace apikey below with your actual Planet OS API key, which you'll find [on the Planet OS account settings page.](#http://data.planetos.com/account/settings/?utm_source=github&utm_medium=notebook&utm_campaign=ww3-api-notebook) and NDBC buoy station name in which you are intrested.", "_____no_output_____" ] ], [ [ "dataset_id = 'noaa_ndbc_stdmet_stations'\n## stations with wave height available: '46006', '46013', '46029'\n## stations without wave height: icac1', '41047', 'bepb6', '32st0', '51004'\n## stations too close to coastline (no point to compare to ww3)'sacv4', 'gelo1', 'hcef1'\nstation = '46029'\napikey = open('APIKEY').readlines()[0].strip() #'<YOUR API KEY HERE>'", "_____no_output_____" ] ], [ [ "Let's first query the API to see what stations are available for the [NDBC Standard Meteorological Data dataset.](http://data.planetos.com/datasets/noaa_ndbc_stdmet_stations?utm_source=github&utm_medium=notebook&utm_campaign=ndbc-wavewatch-iii-notebook)", "_____no_output_____" ] ], [ [ "API_url = 'http://api.planetos.com/v1/datasets/%s/stations?apikey=%s' % (dataset_id, apikey)\nrequest = Request(API_url)\nresponse = urlopen(request)\nAPI_data_locations = json.loads(response.read())\n# print(API_data_locations)", "_____no_output_____" ] ], [ [ "Now we'll use matplotlib to visualize the stations on a simple basemap.", "_____no_output_____" ] ], [ [ "m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\\\n llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')\nfig=plt.figure(figsize=(15,10))\nm.drawcoastlines()\n##m.fillcontinents()\nfor i in API_data_locations['station']:\n x,y=m(API_data_locations['station'][i]['SpatialExtent']['coordinates'][0],\n API_data_locations['station'][i]['SpatialExtent']['coordinates'][1])\n plt.scatter(x,y,color='r')\nx,y=m(API_data_locations['station'][station]['SpatialExtent']['coordinates'][0],\n API_data_locations['station'][station]['SpatialExtent']['coordinates'][1])\nplt.scatter(x,y,s=100,color='b')", "_____no_output_____" ] ], [ [ "Let's examine the last five days of data. For the WaveWatch III forecast, we'll use the reference time parameter to pull forecast data from the 18:00 model run from five days ago.", "_____no_output_____" ] ], [ [ "## Find suitable reference time values\natthemoment = datetime.utcnow()\natthemoment = atthemoment.strftime('%Y-%m-%dT%H:%M:%S') \n\nbefore5days = datetime.utcnow() - timedelta(days=5)\nbefore5days_long = before5days.strftime('%Y-%m-%dT%H:%M:%S')\nbefore5days_short = before5days.strftime('%Y-%m-%d')\n\nstart = before5days_long\nend = atthemoment\n\nreftime_start = str(before5days_short) + 'T18:00:00'\nreftime_end = reftime_start", "_____no_output_____" ] ], [ [ "API request for NOAA NDBC buoy station data", "_____no_output_____" ] ], [ [ "API_url = \"http://api.planetos.com/v1/datasets/{0}/point?station={1}&apikey={2}&start={3}&end={4}&count=1000\".format(dataset_id,station,apikey,start,end)\nprint(API_url)", "http://api.planetos.com/v1/datasets/noaa_ndbc_stdmet_stations/point?station=46029&apikey=8428878e4b944abeb84790e832c633fc&start=2018-06-30T08:33:58&end=2018-07-05T08:33:58&count=1000\n" ], [ "request = Request(API_url)\nresponse = urlopen(request)\nAPI_data_buoy = json.loads(response.read())", "_____no_output_____" ], [ "buoy_variables = []\nfor k,v in set([(j,i['context']) for i in API_data_buoy['entries'] for j in i['data'].keys()]):\n buoy_variables.append(k)", "_____no_output_____" ] ], [ [ "Find buoy station coordinates to use them later for finding NOAA Wave Watch III data", "_____no_output_____" ] ], [ [ "for i in API_data_buoy['entries']:\n #print(i['axes']['time'])\n if i['context'] == 'time_latitude_longitude':\n longitude = (i['axes']['longitude'])\n latitude = (i['axes']['latitude'])\n\nprint ('Latitude: '+ str(latitude))\nprint ('Longitude: '+ str(longitude))\n", "Latitude: 46.159000396728516\nLongitude: -124.51399993896484\n" ] ], [ [ "API request for NOAA WaveWatch III (NWW3) Ocean Wave Model near the point of selected station. Note that data may not be available at the requested reference time. If the response is empty, try removing the reference time parameters `reftime_start` and `reftime_end` from the query.", "_____no_output_____" ] ], [ [ "API_url = 'http://api.planetos.com/v1/datasets/noaa_ww3_global_1.25x1d/point?lat={0}&lon={1}&verbose=true&apikey={2}&count=100&end={3}&reftime_start={4}&reftime_end={5}'.format(latitude,longitude,apikey,end,reftime_start,reftime_end)\nrequest = Request(API_url)\nresponse = urlopen(request)\nAPI_data_ww3 = json.loads(response.read())\nprint(API_url)", "http://api.planetos.com/v1/datasets/noaa_ww3_global_1.25x1d/point?lat=46.159000396728516&lon=-124.51399993896484&verbose=true&apikey=8428878e4b944abeb84790e832c633fc&count=100&end=2018-07-05T08:33:58&reftime_start=2018-06-30T18:00:00&reftime_end=2018-06-30T18:00:00\n" ], [ "ww3_variables = []\nfor k,v in set([(j,i['context']) for i in API_data_ww3['entries'] for j in i['data'].keys()]):\n ww3_variables.append(k)", "_____no_output_____" ] ], [ [ "Manually review the list of WaveWatch and NDBC data variables to determine which parameters are equivalent for comparison.", "_____no_output_____" ] ], [ [ "print(ww3_variables)\nprint(buoy_variables)", "['Wind_speed_surface', 'v-component_of_wind_surface', 'u-component_of_wind_surface', 'Primary_wave_direction_surface', 'Significant_height_of_combined_wind_waves_and_swell_surface', 'Significant_height_of_swell_waves_ordered_sequence_of_data', 'Mean_period_of_swell_waves_ordered_sequence_of_data', 'Mean_period_of_wind_waves_surface', 'Direction_of_swell_waves_ordered_sequence_of_data', 'Primary_wave_mean_period_surface', 'Secondary_wave_direction_surface', 'Secondary_wave_mean_period_surface', 'Wind_direction_from_which_blowing_surface', 'Direction_of_wind_waves_surface', 'Significant_height_of_wind_waves_surface']\n['wind_spd', 'wave_height', 'air_temperature', 'dewpt_temperature', 'mean_wave_dir', 'average_wpd', 'dominant_wpd', 'sea_surface_temperature', 'air_pressure', 'wind_dir', 'water_level', 'visibility', 'gust']\n" ] ], [ [ "Next we'll build a dictionary of corresponding variables that we want to compare.", "_____no_output_____" ] ], [ [ "buoy_model = {'wave_height':'Significant_height_of_combined_wind_waves_and_swell_surface',\n 'mean_wave_dir':'Primary_wave_direction_surface',\n 'average_wpd':'Primary_wave_mean_period_surface',\n 'wind_spd':'Wind_speed_surface'}", "_____no_output_____" ] ], [ [ "Read data from the JSON responses and convert the values to floats for plotting. Note that depending on the dataset, some variables have different timesteps than others, so a separate time array for each variable is recommended.", "_____no_output_____" ] ], [ [ "def append_data(in_string):\n if in_string == None:\n return np.nan\n elif in_string == 'None':\n return np.nan\n else:\n return float(in_string)\n\nww3_data = {}\nww3_times = {}\nbuoy_data = {}\nbuoy_times = {}\nfor k,v in buoy_model.items():\n ww3_data[v] = []\n ww3_times[v] = []\n buoy_data[k] = []\n buoy_times[k] = []\n\nfor i in API_data_ww3['entries']:\n for j in i['data']:\n if j in buoy_model.values():\n ww3_data[j].append(append_data(i['data'][j]))\n ww3_times[j].append(dateutil.parser.parse(i['axes']['time']))\n \nfor i in API_data_buoy['entries']:\n for j in i['data']:\n if j in buoy_model.keys():\n buoy_data[j].append(append_data(i['data'][j]))\n buoy_times[j].append(dateutil.parser.parse(i['axes']['time']))\nfor i in ww3_data:\n ww3_data[i] = np.array(ww3_data[i])\n ww3_times[i] = np.array(ww3_times[i])", "_____no_output_____" ] ], [ [ "Finally, let's plot the data using matplotlib.", "_____no_output_____" ] ], [ [ "buoy_label = \"NDBC Station %s\" % station\nww3_label = \"WW3 at %s\" % reftime_start\nfor k,v in buoy_model.items():\n if np.abs(np.nansum(buoy_data[k]))>0:\n fig=plt.figure(figsize=(10,5))\n plt.title(k+' '+v)\n plt.plot(ww3_times[v],ww3_data[v], label=ww3_label)\n plt.plot(buoy_times[k],buoy_data[k],'*',label=buoy_label)\n plt.legend(bbox_to_anchor=(1.5, 0.22), loc=1, borderaxespad=0.)\n plt.xlabel('Time')\n plt.ylabel(k)\n fig.autofmt_xdate()\n plt.grid()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# SCEE and Interconnect", "_____no_output_____" ], [ "The SCEE module in SiPANN also has built in functionality to export any of it's models directly into a format readable by Lumerical Interconnect via the `export_interconnect()` function. This gives the user multiple options (Interconnect or Simphony) to cascade devices into complex structures. To export to a Interconnect file is as simple as a function call. First we declare all of our imports:", "_____no_output_____" ] ], [ [ "import numpy as np\nfrom SiPANN import scee", "_____no_output_____" ] ], [ [ "Then make our device and calculate it's scattering parameters (we arbitrarily choose a half ring resonator here)", "_____no_output_____" ] ], [ [ "r = 10000\nw = 500\nt = 220\nwavelength = np.linspace(1500, 1600)\ngap = 100\n\nhr = scee.HalfRing(w, t, r, gap)\nsparams = hr.sparams(wavelength)", "_____no_output_____" ] ], [ [ "And then export. Note `export_interconnect` takes in wavelengths in nms, but the Lumerical file will have frequency in meters, as is standard in Interconnect. To export:", "_____no_output_____" ] ], [ [ "filename = \"halfring_10microns_sparams.txt\"\nscee.export_interconnect(sparams, wavelength, filename)", "_____no_output_____" ] ], [ [ "As a final parameter, `export_interconnect` also has a `clear=True` parameter that will empty the file being written to before writing. If you'd like to append to an existing file, simply set `clear=False`.\n\nThis is available as a jupyter notebook [here](https://github.com/contagon/SiPANN/blob/master/examples/Tutorials/ExportToInterConnect.ipynb)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Aula 1", "_____no_output_____" ] ], [ [ "import pandas as pd", "_____no_output_____" ], [ "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'", "_____no_output_____" ], [ "dados = pd.read_csv(url_dados, compression = 'zip')\ndados", "_____no_output_____" ], [ "dados.head()", "_____no_output_____" ], [ "dados.shape", "_____no_output_____" ], [ "dados['tratamento']", "_____no_output_____" ], [ "dados['tratamento'].unique()", "_____no_output_____" ], [ "dados['tempo'].unique()", "_____no_output_____" ], [ "dados['dose'].unique()", "_____no_output_____" ], [ "dados['droga'].unique()", "_____no_output_____" ], [ "dados['g-0'].unique()", "_____no_output_____" ], [ "dados['tratamento'].value_counts()", "_____no_output_____" ], [ "dados['dose'].value_counts()", "_____no_output_____" ], [ "dados['tratamento'].value_counts(normalize = True)", "_____no_output_____" ], [ "dados['dose'].value_counts(normalize = True)", "_____no_output_____" ], [ "dados['tratamento'].value_counts().plot.pie()", "_____no_output_____" ], [ "dados['tempo'].value_counts().plot.pie()", "_____no_output_____" ], [ "dados['tempo'].value_counts().plot.bar()", "_____no_output_____" ], [ "dados_filtrados = dados[dados['g-0'] > 0]\ndados_filtrados.head()", "_____no_output_____" ] ], [ [ "#Desafios Aula 1", "_____no_output_____" ], [ "## Desafio 01: Investigar por que a classe tratamento é tão desbalanceada?", "_____no_output_____" ], [ "Dependendo o tipo de pesquisa é possível usar o mesmo controle para mais de um caso. Repare que o grupo de controle é um grupo onde não estamos aplicando o efeito de uma determinada droga. Então, esse mesmo grupo pode ser utilizado como controle para cada uma das drogas estudadas. ", "_____no_output_____" ], [ "Um ponto relevante da base de dados que estamos trabalhando é que todos os dados de controle estão relacionados ao estudo de apenas uma droga.", "_____no_output_____" ] ], [ [ "print(f\"Total de dados {len(dados['id'])}\\n\")\nprint(f\"Quantidade de drogas {len(dados.groupby(['droga', 'tratamento']).count()['id'])}\\n\")\ndisplay(dados.query('tratamento == \"com_controle\"').value_counts('droga'))\nprint()\ndisplay(dados.query('droga == \"cacb2b860\"').value_counts('tratamento'))\nprint()", "Total de dados 23814\n\nQuantidade de drogas 3289\n\n" ] ], [ [ "## Desafio 02: Plotar as 5 últimas linhas da tabela", "_____no_output_____" ] ], [ [ "dados.tail()", "_____no_output_____" ] ], [ [ "Outra opção seria usar o seguinte comando:", "_____no_output_____" ] ], [ [ "dados[-5:]", "_____no_output_____" ] ], [ [ "## Desafio 03: Proporção das classes tratamento.", "_____no_output_____" ] ], [ [ "dados['tratamento'].value_counts(normalize = True)", "_____no_output_____" ] ], [ [ "## Desafio 04: Quantas tipos de drogas foram investigadas.", "_____no_output_____" ] ], [ [ "dados['droga'].unique().shape[0]", "_____no_output_____" ] ], [ [ "Outra opção de solução:", "_____no_output_____" ] ], [ [ "len(dados['droga'].unique())", "_____no_output_____" ] ], [ [ "## Desafio 05: Procurar na documentação o método query(pandas).", "_____no_output_____" ], [ "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html", "_____no_output_____" ], [ "## Desafio 06: Renomear as colunas tirando o hífen.", "_____no_output_____" ] ], [ [ "dados.columns", "_____no_output_____" ], [ "nome_das_colunas = dados.columns", "_____no_output_____" ], [ "novo_nome_coluna = []\nfor coluna in nome_das_colunas:\n coluna = coluna.replace('-', '_')\n novo_nome_coluna.append(coluna)\ndados.columns = novo_nome_coluna \ndados.head()", "_____no_output_____" ] ], [ [ "Agora podemos comparar o resultado usando Query com o resultado usando máscara + slice", "_____no_output_____" ] ], [ [ "dados_filtrados = dados[dados['g_0'] > 0]\ndados_filtrados.head()", "_____no_output_____" ], [ "dados_filtrados = dados.query('g_0 > 0')\ndados_filtrados.head()", "_____no_output_____" ] ], [ [ "## Desafio 07: Deixar os gráficos bonitões. (Matplotlib.pyplot)", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt", "_____no_output_____" ], [ "valore_tempo = dados['tempo'].value_counts(ascending=True)\nvalore_tempo.sort_index()", "_____no_output_____" ], [ "plt.figure(figsize=(15, 10))\nvalore_tempo = dados['tempo'].value_counts(ascending=True)\nax = valore_tempo.sort_index().plot.bar()\nax.set_title('Janelas de tempo', fontsize=20)\nax.set_xlabel('Tempo', fontsize=18)\nax.set_ylabel('Quantidade', fontsize=18)\nplt.xticks(rotation = 0, fontsize=16)\nplt.yticks(fontsize=16)\nplt.show()", "_____no_output_____" ] ], [ [ "##Desafio 08: Resumo do que você aprendeu com os dados", "_____no_output_____" ], [ "Nesta aula utilizei a biblioteca Pandas, diversas funcionalidades da mesma para explorar dados. Durante a análise de dados, descobri fatores importantes para a obtenção de insights e também aprendi como plotar os gráficos de pizza e de colunas discutindo pontos positivos e negativos. ", "_____no_output_____" ], [ "Para mais informações a base dados estudada na imersão é uma versão simplificada [deste desafio](https://www.kaggle.com/c/lish-moa/overview/description) do Kaggle (em inglês).\n\n\nTambém recomendo acessar o\n[Connectopedia](https://clue.io/connectopedia/). O Connectopedia é um dicionário gratuito de termos e conceitos que incluem definições de viabilidade de células e expressão de genes. \n\n\nO desafio do Kaggle também está relacionado a estes artigos científicos:\n\nCorsello et al. “Discovering the anticancer potential of non-oncology drugs by systematic viability profiling,” Nature Cancer, 2020, https://doi.org/10.1038/s43018-019-0018-6\n\nSubramanian et al. “A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles,” Cell, 2017, https://doi.org/10.1016/j.cell.2017.10.049", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Part 2 - Lists\n\nWe learned about **variables** in Part 1. Sometimes it makes sense to group lots of items of information together in a **list**. This is a good idea when the items are all connected in some way.\n\nFor example, we might want to store the names of our friends. We could create several variables and assign the name of one friend to each of them (remember to do a Shift-Return on your keyboard, or click the Run button, to run the code):", "_____no_output_____" ] ], [ [ "friend_1 = \"Fred\"\nfriend_2 = \"Jane\"\nfriend_3 = \"Rob\"\nfriend_4 = \"Sophie\"\nfriend_5 = \"Rachel\"", "_____no_output_____" ] ], [ [ "This works OK. All the names are stored. But the problem is that they are all stored separately - Python does not know that they are all part of the same collection of friends.\n\nBut there's a really nice way to solve this problem: **lists**. We can create a list like this:", "_____no_output_____" ] ], [ [ "friends = [ \"Fred\", \"Jane\", \"Rob\", \"Sophie\", \"Rachel\" ]", "_____no_output_____" ] ], [ [ "We create a list using square brackets `[` `]`, with each item in the list separated by a comma `,`.\n\n### List methods\nThere are some special **list** tools (called **methods**) that we can use on our `friends` list. \n\nWe can add a name to the list using `append`:", "_____no_output_____" ] ], [ [ "friends.append(\"David\")\nprint(friends)", "_____no_output_____" ] ], [ [ "We can put the names in alphabetical order using `sort`:", "_____no_output_____" ] ], [ [ "friends.sort()\nprint(friends)", "_____no_output_____" ] ], [ [ "We can reverse the order using `reverse`:", "_____no_output_____" ] ], [ [ "friends.reverse()\nprint(friends)", "_____no_output_____" ] ], [ [ "These **list** **methods** allow us to do lots of things like this without having to write much code ourselves.\n\n### What is a list index?\n\nLet's make a new list. This time, we will start with an empty list, and then add to it:", "_____no_output_____" ] ], [ [ "shapes = []\nprint(shapes)", "_____no_output_____" ], [ "shapes.append(\"triangle\")\nshapes.append(\"square\")\nshapes.append(\"pentagon\")\nshapes.append(\"hexagon\")\nprint(shapes)", "_____no_output_____" ] ], [ [ "These lists are great, but we might not always want to operate on the whole list at once. Instead, we might want to pick out one particular item in the list. In a **list**, each item is numbered, *starting from zero*. This number is called an **index**. So, remembering that the numbering *starts from zero*, we can access the first item in the list like this:", "_____no_output_____" ] ], [ [ "print(shapes[0])", "_____no_output_____" ] ], [ [ "Notice that the **index** is surrounded by square brackets `[` `]`. We can get the second item in `shapes` like this:", "_____no_output_____" ] ], [ [ "print(shapes[1])", "_____no_output_____" ] ], [ [ "and the last one like this:", "_____no_output_____" ] ], [ [ "print(shapes[3])", "_____no_output_____" ] ], [ [ "Python also has a special way of indexing the last item in a list, and this is especially useful if you're not sure how many items there are in your list. You can use a negative index:", "_____no_output_____" ] ], [ [ "print(shapes[-1])", "_____no_output_____" ] ], [ [ "We can ask Python to tell us the index of an item in the list:", "_____no_output_____" ] ], [ [ "idx = shapes.index(\"square\")\nprint(idx)", "_____no_output_____" ] ], [ [ "Actually, the `index` method tells us the index of the first time that an item appears in a list.\n\n### Displaying our data in a bar chart", "_____no_output_____" ], [ "Let's have another look at our `shapes` list:", "_____no_output_____" ] ], [ [ "print(shapes)", "_____no_output_____" ] ], [ [ "and let's make another list that has the number of sides that each shape has:", "_____no_output_____" ] ], [ [ "sides=[3,4,5,6]\nprint(sides)", "_____no_output_____" ] ], [ [ "It would be nice to draw a bar chart showing the number of sides that each shape has. But drawing all those bars and axes would take quite a lot of code. Don't worry - someone has already done this, and we can use the code that they have written using the **import** command.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ] ], [ [ "(The `%matplotlib inline` is a Jupyter notebook command that means the plots we make will appear right here in our notebook)", "_____no_output_____" ], [ "Now we can plot our bar chart using only three lines of code:", "_____no_output_____" ] ], [ [ "plt.bar(shapes,sides)\nplt.xlabel(\"Shape\")\nplt.ylabel(\"Number of sides\");", "_____no_output_____" ] ], [ [ "That's a really nice, neat bar chart!", "_____no_output_____" ], [ "Try changing the lists and re-plotting the bar chart. You can add another shape, for example.", "_____no_output_____" ], [ "### What have we covered in this notebook?\nWe've learned about **lists** and the list **methods** that we can use to work with them. We've also learned about how to access an item in the list using an **index**. We then plotted a bar chart using code written by someone else - we used **import** to get this code.", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/osipov/edu/blob/master/pyt0/Demo_Data_Visualization.ipynb\" target=\"_blank\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a>", "_____no_output_____" ], [ "# Data Visualization\n", "_____no_output_____" ] ], [ [ "%matplotlib inline", "_____no_output_____" ], [ "import torch as pt\nimport matplotlib.pyplot as plt\n\nx = pt.linspace(0, 10, 100)\n\nfig = plt.figure()\nplt.plot(x, pt.sin(x), '-')\nplt.plot(x, pt.cos(x), '--')\nplt.show() # not needed in notebook, but needed in production", "_____no_output_____" ] ], [ [ "## You can save your plots...", "_____no_output_____" ] ], [ [ "fig.savefig('my_figure.png')", "_____no_output_____" ], [ "!ls -lh my_figure.png\n# For Windows, comment out the above and replace with below\n# On Windows, comment out above and uncomment below\n#!dir my_figure.png\"", "_____no_output_____" ] ], [ [ "## ...and reload saved images for display inside the notebook", "_____no_output_____" ] ], [ [ "from IPython.display import Image\nImage('my_figure.png')", "_____no_output_____" ], [ "# matplotlib supports many different file types\nfig.canvas.get_supported_filetypes()", "_____no_output_____" ] ], [ [ "## MATLAB-Style Interface", "_____no_output_____" ] ], [ [ "plt.figure() # create a plot figure\n\n# create the first of two panels and set current axis\nplt.subplot(2, 1, 1) # (rows, columns, panel number)\nplt.plot(x, pt.sin(x))\n\n# create the second panel and set current axis\nplt.subplot(2, 1, 2)\nplt.plot(x, pt.cos(x));", "_____no_output_____" ] ], [ [ "## Grids", "_____no_output_____" ] ], [ [ "plt.style.use('seaborn-whitegrid')\nfig = plt.figure()\nax = plt.axes()", "_____no_output_____" ] ], [ [ "## Draw a Function", "_____no_output_____" ] ], [ [ "plt.style.use('seaborn-whitegrid')\nfig = plt.figure()\nax = plt.axes()\nx = pt.linspace(0, 10, 1000)\nax.plot(x, pt.sin(x));", "_____no_output_____" ] ], [ [ "## Specify axes limits...\n", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x))\n\nplt.xlim(-1, 11)\nplt.ylim(-1.5, 1.5);", "_____no_output_____" ] ], [ [ "## Flipping the Axes Limits", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x))\n\nplt.xlim(10, 0)\nplt.ylim(1.2, -1.2);", "_____no_output_____" ] ], [ [ "## Axis", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x))\nplt.axis([-1, 11, -1.5, 1.5]);", "_____no_output_____" ] ], [ [ "## ...or let matplotlib \"tighten\" the axes...", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x))\nplt.axis('tight');", "_____no_output_____" ] ], [ [ "## ...or make the limits equal", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x))\nplt.axis('equal');", "_____no_output_____" ] ], [ [ "## Add titles and axis labels", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x))\nplt.title(\"A Sine Curve\")\nplt.xlabel(\"x\")\nplt.ylabel(\"sin(x)\");", "_____no_output_____" ] ], [ [ "## ...and a legend", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x), '-g', label='sin(x)')\nplt.plot(x, pt.cos(x), ':b', label='cos(x)')\nplt.axis('equal')\n\nplt.legend();", "_____no_output_____" ] ], [ [ "## Object-Oriented Interface", "_____no_output_____" ] ], [ [ "# First create a grid of plots\n# ax will be an array of two Axes objects\nfig, ax = plt.subplots(2)\n\n# Call plot() method on the appropriate object\nax[0].plot(x, pt.sin(x))\nax[1].plot(x, pt.cos(x));", "_____no_output_____" ] ], [ [ "## OO interface to axes", "_____no_output_____" ] ], [ [ "ax = plt.axes()\nax.plot(x, pt.sin(x))\nax.set(xlim=(0, 10), ylim=(-2, 2),\n xlabel='x', ylabel='sin(x)',\n title='A Simple Plot');", "_____no_output_____" ] ], [ [ "## Interface Differences\n| MATLAB-Style | OO Style |\n|--------------|-----------------|\n| plt.xlabel() | ax.set_xlabel() |\n| plt.ylabel() | ax.set_ylabel() |\n| plt.xlim() | ax.set_xlim() |\n| plt.ylim() | ax.set_ylim() |\n| plt.title() | ax.set_title() |", "_____no_output_____" ], [ "## Custom legends", "_____no_output_____" ] ], [ [ "x = pt.linspace(0, 10, 1000)\n\nplt.style.use('classic')\nplt.figure(figsize=(12,6))\n\nplt.rc('xtick', labelsize=20)\nplt.rc('ytick', labelsize=20)\n\nfig, ax = plt.subplots()\nax.plot(x, pt.sin(x), '-b', label='Sine')\nax.plot(x, pt.cos(x), '--r', label='Cosine')\nax.axis('equal')\nleg = ax.legend()", "_____no_output_____" ], [ "ax.legend(loc='upper left', frameon=False)\nfig", "_____no_output_____" ], [ "ax.legend(frameon=False, loc='lower center', ncol=2)\nfig", "_____no_output_____" ] ], [ [ "## Many ways to specify color...", "_____no_output_____" ] ], [ [ "plt.plot(x, pt.sin(x - 0), color='blue') # specify color by name\nplt.plot(x, pt.sin(x - 1), color='g') # short color code (rgbcmyk)\nplt.plot(x, pt.sin(x - 2), color='0.75') # Grayscale between 0 and 1\nplt.plot(x, pt.sin(x - 3), color='#FFDD44') # Hex code (RRGGBB from 00 to FF)\nplt.plot(x, pt.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\nplt.plot(x, pt.sin(x - 5), color='chartreuse'); # all HTML color names supported", "_____no_output_____" ] ], [ [ "## Specifying different line styles...", "_____no_output_____" ] ], [ [ "plt.plot(x, x + 0, linestyle='solid')\nplt.plot(x, x + 1, linestyle='dashed')\nplt.plot(x, x + 2, linestyle='dashdot')\nplt.plot(x, x + 3, linestyle='dotted');\n\n# For short, you can use the following codes:\nplt.plot(x, x + 4, linestyle='-') # solid\nplt.plot(x, x + 5, linestyle='--') # dashed\nplt.plot(x, x + 6, linestyle='-.') # dashdot\nplt.plot(x, x + 7, linestyle=':'); # dotted", "_____no_output_____" ] ], [ [ "## Specify different plot markers ", "_____no_output_____" ] ], [ [ "rnd1 = pt.manual_seed(0)\nrnd2 = pt.manual_seed(1)\nfor marker in 'o.,x+v^<>sd':\n plt.plot(pt.rand(5, generator = rnd1), pt.rand(5, generator = rnd2), marker,\n label='marker={}'.format(marker))\nplt.legend(numpoints=1)\nplt.xlim(0, 1.8);", "_____no_output_____" ] ], [ [ "## Scatterplots with Colors and Sizes", "_____no_output_____" ] ], [ [ "pt.manual_seed(0);\nx = pt.randn(100)\ny = pt.randn(100)\ncolors = pt.rand(100)\nsizes = 1000 * pt.rand(100)\n\nplt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\n cmap='viridis')\nplt.colorbar(); # show color scale", "_____no_output_____" ] ], [ [ "## Visualizing Multiple Dimensions", "_____no_output_____" ] ], [ [ "from sklearn.datasets import load_iris\niris = load_iris()\nfeatures = iris.data.T\n\nplt.scatter(features[0], features[1], alpha=0.2,\n s=100*features[3], c=iris.target, cmap='viridis')\nplt.xlabel(iris.feature_names[0])\nplt.ylabel(iris.feature_names[1]);", "_____no_output_____" ] ], [ [ "## Histograms", "_____no_output_____" ] ], [ [ "data = pt.randn(10000)\nplt.hist(data);", "_____no_output_____" ], [ "plt.hist(data, bins=30, alpha=0.5,\n histtype='stepfilled', color='steelblue',\n edgecolor='none')", "_____no_output_____" ] ], [ [ "## Display a grid of images", "_____no_output_____" ] ], [ [ "# load images of the digits 0 through 5 and visualize several of them\nfrom sklearn.datasets import load_digits\ndigits = load_digits(n_class=6)\n\nfig, ax = plt.subplots(8, 8, figsize=(6, 6))\nfor i, axi in enumerate(ax.flat):\n axi.imshow(digits.images[i], cmap='binary')\n axi.set(xticks=[], yticks=[])", "_____no_output_____" ] ], [ [ "Copyright 2020 CounterFactual.AI LLC. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.", "_____no_output_____" ] ] ]

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[ [ [ "# Classification", "_____no_output_____" ], [ "## MNIST", "_____no_output_____" ] ], [ [ "from sklearn.datasets import fetch_openml\n\nmnist = fetch_openml('mnist_784', version=1)\nmnist.keys()", "_____no_output_____" ], [ "X, y = mnist['data'], mnist['target']\nX.shape, y.shape", "_____no_output_____" ], [ "%matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nsome_digit = X[0]\nsome_digit_img = some_digit.reshape(28, 28)\n\nplt.imshow(some_digit_img, cmap='binary')\nplt.axis('off')\nplt.show()", "_____no_output_____" ], [ "y[0]", "_____no_output_____" ], [ "import numpy as np\n\ny = y.astype(np.uint8)\n\ny[0]", "_____no_output_____" ], [ "# MNIST is already split into training and test set\nX_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]", "_____no_output_____" ] ], [ [ "## Training a Binary Classifier\nFor start let's make a binary classifier that will indentify single digit - digit 5.", "_____no_output_____" ] ], [ [ "y_train_5, y_test_5 = (y_train == 5), (y_test == 5)", "_____no_output_____" ], [ "from sklearn.linear_model import SGDClassifier\n\nsgd_clf = SGDClassifier(random_state=42)\nsgd_clf.fit(X_train, y_train_5)\n\nsgd_clf.predict([some_digit])", "_____no_output_____" ] ], [ [ "## Performance Measures", "_____no_output_____" ], [ "### Measuring Accuracy Using Cross-Validation\n\n#### Implementing Cross-Validation\nFollowing code is roughly equivalent to *Scikit-Learn*'s function `cross_val_score`.", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import StratifiedKFold\nfrom sklearn.base import clone\n\nskfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n\nfor train_ix, test_ix in skfolds.split(X_train, y_train_5):\n clone_clf = clone(sgd_clf)\n \n X_train_folds = X_train[train_ix]\n y_train_folds = y_train_5[train_ix]\n \n X_test_folds = X_train[test_ix]\n y_test_folds = y_train_5[test_ix]\n \n clone_clf.fit(X_train_folds, y_train_folds)\n y_pred = clone_clf.predict(X_test_folds)\n n_correct = np.sum(y_pred == y_test_folds)\n print(n_correct / len(y_pred))", "0.9669\n0.91625\n0.96785\n" ], [ "from sklearn.model_selection import cross_val_score\n\ncross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring='accuracy')", "_____no_output_____" ] ], [ [ "This seems pretty good! However, let's check a classifier that always classifies an image as **not 5**.", "_____no_output_____" ] ], [ [ "from sklearn.base import BaseEstimator\n\nclass Never5Classifier(BaseEstimator):\n \n def fit(self, X, y=None):\n return self\n \n def predict(self, X):\n return np.zeros((len(X), 1), dtype=bool)\n\nnever_5_clf = Never5Classifier()\ncross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring='accuracy')", "_____no_output_____" ] ], [ [ "Over 90% accuracy! Well, the problem is that just about 10% of the whole dataset are images of 5 (there are 10 numbers in total). Hence the 90% accuracy.", "_____no_output_____" ], [ "### Confusion Matrix\nThe idea of a *confusion matrix* is to count the number of times class A is classified as class B and so on. \n\nTo compute the confusion matrix one must first get predicions (here on the train set, let's keep test set aside). We can take predictions for a cross-validation with `cross_val_predict` and pass them to `confusion_matrix`.\n\nFor a binary classification the confusion matrix looks like this:\n\n| | N | P |\n|-----|----|----|\n| N | TN | FP |\n| P | FN | TP |\n\nRows are the *actual* class and columns are the predicted class, furthermore\n* *P* - *positive* (class)\n* *N* - *negative* (class)\n* *TN* - *true negative*\n* *TP* - *true positive*\n* *FN* - *false negative*\n* *FP* - *false negative*", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import cross_val_predict\nfrom sklearn.metrics import confusion_matrix\n\ny_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)\nconfusion_matrix(y_train_5, y_train_pred)", "_____no_output_____" ], [ "y_train_perfect_predictions = y_train_5 # pretend we reached perfection\nconfusion_matrix(y_train_5, y_train_perfect_predictions)", "_____no_output_____" ] ], [ [ "### Precision and Recall\n\n**Precision** is the accuracy of positive predictions and is defined as $\\text{precision} = \\frac{TP}{TP + FP}$\n\n*Trivial way to ensure 100% precision is to make single prediction and make sure it's correct.*\n\n**Recall (sensitivity, true positive rate)** is the ratio of positive instances that are correctly detected and is defined as $\\text{recall} = \\frac{TP}{TP + FN}$\n\nIntuitive notion of precision and recall:\n* *precision* - how often is the predictor correct when the actual class is the positive one\n* *recall* - how likely does the predictor detect the positive class", "_____no_output_____" ] ], [ [ "from sklearn.metrics import precision_score, recall_score\n\nprecision = precision_score(y_train_5, y_train_pred)\nrecall = recall_score(y_train_5, y_train_pred)\nprecision, recall", "_____no_output_____" ] ], [ [ "Precision and recall are handy but it's even better to have single score based on which we can compare classifiers.\n\n$\\mathbf{F_1}$ score is the *harmonic mean* of precision and recall. Regular mean puts the same weight to all values, harmonic mean gives much more importance to lower values. So in order to have high $F_1$ score, both precision and mean must be high.\n\n$$\nF_1 = \\frac{2}{\\frac{1}{\\text{precision}} + \\frac{1}{\\text{recall}}} = 2 \\times \\frac{\\text{precision} \\times \\text{recall}}{\\text{precision} + \\text{recall}} = \\frac{TP}{TP + \\frac{FN + FP}{2}}\n$$", "_____no_output_____" ] ], [ [ "from sklearn.metrics import f1_score\n\nf1_score(y_train_5, y_train_pred)", "_____no_output_____" ] ], [ [ "### Precision/Recall Trade-off\n*Increasing precision reduces recall and vice versa.*\n\nHow does the classification work? The `SGDClassifier`, for instance, computes for each instance a score based on a *decision function*. If this score is greater than *decision threshold*, it assigns the instance to the positive class. Shifting this threshold will likely result a change in precision and recall.", "_____no_output_____" ] ], [ [ "y_scores = sgd_clf.decision_function([some_digit])\ny_scores", "_____no_output_____" ], [ "def predict_some_digit(threshold):\n return (y_scores > threshold)\n\n# Raising the threshold decreases recall\npredict_some_digit(threshold=0), predict_some_digit(threshold=8000)", "_____no_output_____" ] ], [ [ "From the example above, increasing the decision threshold decreases recall (`some_digit` is actually a 5 and with the increased thresholt is is no longer recognized).\n\nBut how to decide which threshold to use?", "_____no_output_____" ] ], [ [ "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method='decision_function')", "_____no_output_____" ], [ "from sklearn.metrics import precision_recall_curve\n\ndef plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n plt.plot(thresholds, precisions[:-1], 'b--', label='Precision')\n plt.plot(thresholds, recalls[:-1], 'g-', label='Recall')\n plt.xlabel('Threshold')\n plt.legend(loc='center right', fontsize=16)\n plt.grid(True)\n plt.axis([-50000, 50000, 0, 1])\n\nprecisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\n\nrecall_90_precision = recalls[np.argmax(precisions >= 0.9)]\nthreshold_90_precision = thresholds[np.argmax(precisions >= 0.9)]\n\nplt.figure(figsize=(8, 4))\n\n# plot precision and recall curves vs decision threshold\nplot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n\n# plot threshold corresponding to 90% precision\nplt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], 'r:')\n\n# plot precision level up to 90% precision threshold\nplt.plot([-50000, threshold_90_precision], [0.9, 0.9], 'r:')\n\n# plot recall level up to 90% precision threshold\nplt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], 'r:')\n\n# plot points on precision and recall curves corresponding to 90% precision threshold\nplt.plot([threshold_90_precision], [0.9], 'ro')\nplt.plot([threshold_90_precision], [recall_90_precision], 'ro')\n\nplt.show()", "_____no_output_____" ], [ "plt.figure(figsize=(8, 6))\n\n# plot precision vs recall\nplt.plot(recalls, precisions, \"b-\", linewidth=2)\nplt.xlabel('Precision', fontsize=16)\nplt.ylabel('Recall', fontsize=16)\n\n# style the plot\nplt.axis([0, 1, 0, 1])\nplt.grid(True)\nplt.title('Precision vs Recall')\n\n# plot 90% precision point\nplt.plot([recall_90_precision], [0.9], 'ro')\nplt.plot([recall_90_precision, recall_90_precision], [0., 0.9], 'r:')\nplt.plot([0.0, recall_90_precision], [0.9, 0.9], 'r:')\n\nplt.show()", "_____no_output_____" ], [ "y_train_pred_90 = (y_scores >= threshold_90_precision)\n\nprecision_90 = precision_score(y_train_5, y_train_pred_90)\nrecall_90_precision = recall_score(y_train_5, y_train_pred_90)\nprecision_90, recall_90_precision", "_____no_output_____" ] ], [ [ "### The ROC Curve\nThe **receiver operating characteristic** curve is similar to precesion-recall curve but instead plots *true positive rate (recall, sensitivity)* agains *false positive rate* (FPR). The FPR is 1 minus *true negative rate rate (specificity*. I.e. ROC curve plots *sensitivity* against 1 - *specificity*.", "_____no_output_____" ] ], [ [ "from sklearn.metrics import roc_curve\n\ndef plot_roc_curve(fpr, tpr, label=None):\n plt.plot(fpr, tpr, linewidth=2, label=label)\n plt.plot([0, 1], [0, 1], 'k--')\n plt.axis([0, 1, 0, 1])\n plt.xlabel('False Positive Rate', fontsize=16)\n plt.ylabel('True Positive Rate', fontsize=16)\n plt.grid(True)\n\nfpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\nfpr_90 = fpr[np.argmax(tpr >= recall_90_precision)]\n\nplt.figure(figsize=(8, 6))\n\n# plot the ROC curve\nplot_roc_curve(fpr, tpr)\n\n# plot point of 90% precision on the ROC curve\nplt.plot([fpr_90], [recall_90_precision], 'ro')\n\nplt.show()", "_____no_output_____" ] ], [ [ "Another way to compare classifiers is to measure the **area under the curve (AUC)**. Prfect classifier would have AUC score of 1 whereas completely random one would have 0.5 (this corresponds to the diagonal line in the ROC plot).", "_____no_output_____" ] ], [ [ "from sklearn.metrics import roc_auc_score\n\nroc_auc_score(y_train_5, y_scores)", "_____no_output_____" ] ], [ [ "As a rule of thumb, use PR curve when\n* positive class is rare\n* we care more about the false positives\n\notherwise ROC curve might be better.\n\n*For instance in the plot above, it might seem that the AUC is quite good but that's just because there's only few examples of the positive class (5s). In this case, the PR curve presents much more realistic view.*\n\nFollowing example shows a DT which does not have a `decision_function` method. Instead, it has `predict_proba` method returning class probabilities. In general *Scikit-Learn* models will have one or the other method or both.", "_____no_output_____" ] ], [ [ "from sklearn.ensemble import RandomForestClassifier\n\nforest_clf = RandomForestClassifier(random_state=42)\n\ny_proba_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method='predict_proba')\ny_scores_forest = y_proba_forest[:, 1] # score = probability of the positive class\n\nfpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)\nrecall_90_precision_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]\n\nplt.figure(figsize=(8, 6))\n\n# plot the ROC curve of the SGD\nplot_roc_curve(fpr, tpr, label='SGD')\n\n# plot the ROC curve of the Random Forest\nplot_roc_curve(fpr_forest, tpr_forest, label='Random Forest')\n\n# plot point of 90% precision on the SGD ROC curve\nplt.plot([fpr_90], [recall_90_precision], 'ro')\n\n# plot point of 90% precision on the Random Forest ROC curve\nplt.plot([fpr_90], [recall_90_precision_forest], 'ro')\n\nplt.legend(loc='lower right', fontsize=16)\n\nplt.show()", "_____no_output_____" ] ], [ [ "## Multiclass Classification\n\n**Multiclass (Multinominal) Classifiers**:\n* *Logistic Regression*\n* *Random Forrest*\n* *Naive Bayes*\n\n**Binary Classifiers**:\n* *SGD*\n* *SVM*\n\nStrategies to turn binary classifiers into multiclass:\n* **One-versus-the-rest (OvR)**: Train one classifier per class. When predicting class for new instance, get the score from each one and choose the class with the highest score.\n* **One-versus-one (OvO)**: Train one classifier for each pair of classes (for $N$ classes it's $N \\times (N - 1) / 2$ classifiers). When predicting, run the instance through all classifiers and choose class which wins the most duels. Main advantage is that each classifier needs only portion of the training set which contains it's pair of classes which is good for classifiers which don't scale well (e.g. SVM).", "_____no_output_____" ] ], [ [ "from sklearn.svm import SVC\n\nsvm_clf = SVC(gamma=\"auto\", random_state=42)\nsvm_clf.fit(X_train[:1000], y_train[:1000])\n\nsvm_clf.predict([some_digit])", "_____no_output_____" ], [ "some_digit_scores = svm_clf.decision_function([some_digit])\nsome_digit_scores", "_____no_output_____" ], [ "some_digit_class = np.argmax(some_digit_scores)\nsvm_clf.classes_[some_digit_class]", "_____no_output_____" ] ], [ [ "One can manually select the strategy by wrapping the model class into `OneVsRestClassifier` or `OneVsOneClassifier`.", "_____no_output_____" ] ], [ [ "from sklearn.multiclass import OneVsRestClassifier\n\novr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\novr_clf.fit(X_train[:1000], y_train[:1000])\n\novr_clf.predict([some_digit])", "_____no_output_____" ], [ "len(ovr_clf.estimators_)", "_____no_output_____" ] ], [ [ "`SGDClassifier` uses *OvR* under the hood", "_____no_output_____" ] ], [ [ "sgd_clf.fit(X_train, y_train)\nsgd_clf.predict([some_digit])", "_____no_output_____" ], [ "sgd_clf.decision_function([some_digit])", "_____no_output_____" ], [ "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring='accuracy')", "_____no_output_____" ] ], [ [ "CV on the SGD classifier shows pretty good accuracy compared to dummy (random) classifier which would have around 10%. This can be improved even further by simply scaling the input.", "_____no_output_____" ] ], [ [ "from sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n\ncross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring='accuracy')", "_____no_output_____" ] ], [ [ "### Error Analysis", "_____no_output_____" ] ], [ [ "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n\nconf_mx = confusion_matrix(y_train, y_train_pred)\nconf_mx", "_____no_output_____" ], [ "plt.matshow(conf_mx, cmap=plt.cm.gray)\nplt.title('Training set confusion matrix for the SGD classifier')\nplt.show()", "_____no_output_____" ] ], [ [ "Let's transform the confusion matrix a bit to focus on the errors:\n1. divide each value by the number of instances (images in this case) in that class\n1. fill diagonal with zeros to keep just the errors", "_____no_output_____" ] ], [ [ "row_sums = conf_mx.sum(axis=1, keepdims=True)\nnorm_conf_mx = conf_mx / row_sums\nnp.fill_diagonal(norm_conf_mx, 0)\n\nplt.matshow(norm_conf_mx, cmap=plt.cm.gray)\nplt.title('Class-normalized confusion matrix with 0 on diagonal')\nplt.show()", "_____no_output_____" ] ], [ [ "## Multilabel Classification\n*Multilabel classification* refers to a classification task where the classifier predicts multiple classes at once (output is a boolean vector).", "_____no_output_____" ] ], [ [ "from sklearn.neighbors import KNeighborsClassifier\n\ny_train_large = (y_train >= 7)\ny_train_odd = (y_train % 2 == 1)\ny_multilabel = np.c_[y_train_large, y_train_odd]\n\nknn_clf = KNeighborsClassifier()\nknn_clf.fit(X_train, y_multilabel)", "_____no_output_____" ], [ "knn_clf.predict([some_digit])", "_____no_output_____" ], [ "# This takes too long to evaluate but normally it would output the F1 score\n\n# y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3, n_jobs=-1)\n# f1_score(y_multilabel, y_train_knn_pred, average='macro')", "_____no_output_____" ] ], [ [ "## Multioutput Classification\n*Multioutput-multiclass* or just *multioutput classification* is a generalization of multilabel classification where each label can be multiclass (categorical, not just boolean).\n\nFollowing example removes noise from images. In this setup the output is one label per pixel (multilabel) and each pixel's label can have multiple values - pixel intensities (multioutput).", "_____no_output_____" ] ], [ [ "# modified training set\nnoise = np.random.randint(0, 100, (len(X_train), 784))\nX_train_mod = X_train + noise\n\n# modified test set\nnoise = np.random.randint(0, 100, (len(X_test), 784))\nX_test_mod = X_test + noise\n\n# targets are original images\ny_train_mod = X_train\ny_test_mod = X_test", "_____no_output_____" ], [ "some_index = 0\n\n# noisy image\nplt.subplot(121)\nplt.imshow(X_test_mod[some_index].reshape(28, 28), cmap='binary')\nplt.axis('off')\n\n# original image\nplt.subplot(122)\nplt.imshow(y_test_mod[some_index].reshape(28, 28), cmap='binary')\nplt.axis('off')\n\nplt.show()", "_____no_output_____" ], [ "knn_clf.fit(X_train_mod, y_train_mod)\n\nclean_digit = knn_clf.predict([X_test_mod[some_index]])\n\nplt.imshow(clean_digit.reshape(28, 28), cmap='binary')\nplt.axis('off')\nplt.show()", "_____no_output_____" ] ], [ [ "## Extra Material", "_____no_output_____" ], [ "### Dummy Classifier", "_____no_output_____" ] ], [ [ "from sklearn.dummy import DummyClassifier\n\ndummy_clf = DummyClassifier(strategy='prior')\n\ny_probas_dummy = cross_val_predict(dummy_clf, X_train, y_train_5, cv=3, method='predict_proba')\ny_scores_dummy = y_probas_dummy[:, 1]\n\nfprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dummy)\nplot_roc_curve(fprr, tprr)", "_____no_output_____" ] ], [ [ "## Exercises", "_____no_output_____" ], [ "### Data Augmentation", "_____no_output_____" ] ], [ [ "from scipy.ndimage.interpolation import shift\n\ndef shift_image(image, dx, dy):\n image = image.reshape((28, 28))\n shifted_image = shift(image, [dy, dx], cval=0, mode='constant')\n return shifted_image.reshape([-1])\n\nimage = X_train[1000]\nshifted_image_down = shift_image(image, 0, 5)\nshifted_image_left = shift_image(image, -5, 0)\n\nplt.figure(figsize=(12, 3))\n\n# original image\nplt.subplot(131)\nplt.title('Original', fontsize=14)\nplt.imshow(image.reshape(28, 28), interpolation='nearest', cmap='Greys')\n\n# image shifted down\nplt.subplot(132)\nplt.title('Shifted down', fontsize=14)\nplt.imshow(shifted_image_down.reshape(28, 28), interpolation='nearest', cmap='Greys')\n\n# image shifted left\nplt.subplot(133)\nplt.title('Shifted left', fontsize=14)\nplt.imshow(shifted_image_left.reshape(28, 28), interpolation='nearest', cmap='Greys')\n\nplt.show()", "_____no_output_____" ], [ "from sklearn.metrics import accuracy_score\n\nX_train_augmented = [image for image in X_train]\ny_train_augmented = [label for label in y_train]\n\nshifts = ((1, 0), (-1, 0), (0, 1), (0, -1))\n\nfor dx, dy in shifts:\n for image, label in zip(X_train, y_train):\n X_train_augmented.append(shift_image(image, dx, dy))\n y_train_augmented.append(label)\n\nX_train_augmented = np.array(X_train_augmented)\ny_train_augmented = np.array(y_train_augmented)\n\nshuffle_idx = np.random.permutation(len(X_train_augmented))\nX_train_augmented = X_train_augmented[shuffle_idx]\ny_train_augmented = y_train_augmented[shuffle_idx]\n\n# Best params without augmentation\nknn_clf = KNeighborsClassifier(n_neighbors=4, weights='distance')\nknn_clf.fit(X_train_augmented, y_train_augmented)\n\n# Accuracy without augmentation: 0.9714\ny_pred = knn_clf.predict(X_test)\naccuracy_score(y_test, y_pred)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "<center>\n <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n</center>\n", "_____no_output_____" ], [ "# **Hands-on Lab : Web Scraping**\n", "_____no_output_____" ], [ "Estimated time needed: **30 to 45** minutes\n", "_____no_output_____" ], [ "## Objectives\n", "_____no_output_____" ], [ "In this lab you will perform the following:\n", "_____no_output_____" ], [ "* Extract information from a given web site\n* Write the scraped data into a csv file.\n", "_____no_output_____" ], [ "## Extract information from the given web site\n\nYou will extract the data from the below web site: <br>\n", "_____no_output_____" ] ], [ [ "#this url contains the data you need to scrape\nurl = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/datasets/Programming_Languages.html\"", "_____no_output_____" ] ], [ [ "The data you need to scrape is the **name of the programming language** and **average annual salary**.<br> It is a good idea to open the url in your web broswer and study the contents of the web page before you start to scrape.\n", "_____no_output_____" ], [ "Import the required libraries\n", "_____no_output_____" ] ], [ [ "# Your code here\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd", "_____no_output_____" ] ], [ [ "Download the webpage at the url\n", "_____no_output_____" ] ], [ [ "#your code goes here\ndata = requests.get(url).text", "_____no_output_____" ] ], [ [ "Create a soup object\n", "_____no_output_____" ] ], [ [ "#your code goes here\nsoup = BeautifulSoup(data, 'html5lib')", "_____no_output_____" ] ], [ [ "Scrape the `Language name` and `annual average salary`.\n", "_____no_output_____" ] ], [ [ "#your code goes here\nlang_data = pd.DataFrame(columns=['Language', 'Avg_Salary'])\ntable = soup.find('table')\nfor row in table.find_all('tr'):\n \n cols = row.find_all('td')\n lang_name = cols[1].getText()\n avg_salary = cols[3].getText()\n lang_data = lang_data.append({\"Language\":lang_name, \"Avg_Salary\":avg_salary}, ignore_index=True)\n #print(\"{}----------{}\".format(lang_name, avg_salary))", "_____no_output_____" ] ], [ [ "Save the scrapped data into a file named *popular-languages.csv*\n", "_____no_output_____" ] ], [ [ "# your code goes here\n#Drop the first row\n#lang_data.drop(0, axis=0, inplace=True)\nlang_data.to_csv('popular-languages.csv', index=False)", "_____no_output_____" ] ], [ [ "## Authors\n", "_____no_output_____" ], [ "Ramesh Sannareddy\n", "_____no_output_____" ], [ "### Other Contributors\n", "_____no_output_____" ], [ "Rav Ahuja\n", "_____no_output_____" ], [ "## Change Log\n", "_____no_output_____" ], [ "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n| ----------------- | ------- | ----------------- | ---------------------------------- |\n| 2020-10-17 | 0.1 | Ramesh Sannareddy | Created initial version of the lab |\n", "_____no_output_____" ], [ "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2021-01-01).\n", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "Datashader provides a flexible series of processing stages that map from raw data into viewable images. As shown in the [Introduction](1-Introduction.ipynb), using datashader can be as simple as calling ``datashade()``, but understanding each of these stages will help you get the most out of the library. \n\nThe stages in a datashader pipeline are similar to those in a [3D graphics shading pipeline](https://en.wikipedia.org/wiki/Graphics_pipeline):\n\n\n\nHere the computational steps are listed across the top of the diagram, while the data structures or objects are listed along the bottom. Breaking up the computations in this way is what makes Datashader able to handle arbitrarily large datasets, because only one stage (Aggregation) requires access to the entire dataset. The remaining stages use a fixed-sized data structure regardless of the input dataset, allowing you to use any visualization or embedding methods you prefer without running into performance limitations.\n\nIn this notebook, we'll first put together a simple, artificial example to get some data, and then show how to configure and customize each of the data-processing stages involved:\n\n1. [Projection](#Projection)\n2. [Aggregation](#Aggregation)\n3. [Transformation](#Transformation)\n4. [Colormapping](#Colormapping)\n5. [Embedding](#Embedding)\n\n## Data\n\nFor an example, we'll construct a dataset made of five overlapping 2D Gaussian distributions with different &sigma;s (spatial scales). By default we'll have 10,000 datapoints from each category, but you should see sub-second response times even for 1 million datapoints per category if you increase `num`. ", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nfrom collections import OrderedDict as odict\n\nnum=10000\nnp.random.seed(1)\n\ndists = {cat: pd.DataFrame(odict([('x',np.random.normal(x,s,num)), \n ('y',np.random.normal(y,s,num)), \n ('val',val), \n ('cat',cat)])) \n for x, y, s, val, cat in \n [( 2, 2, 0.03, 10, \"d1\"), \n ( 2, -2, 0.10, 20, \"d2\"), \n ( -2, -2, 0.50, 30, \"d3\"), \n ( -2, 2, 1.00, 40, \"d4\"), \n ( 0, 0, 3.00, 50, \"d5\")] }\n\ndf = pd.concat(dists,ignore_index=True)\ndf[\"cat\"]=df[\"cat\"].astype(\"category\")", "_____no_output_____" ] ], [ [ "Datashader can work many different data objects provided by different data libraries depending on the type of data involved, such as columnar data in [Pandas](http://pandas.pydata.org) or [Dask](http://dask.pydata.org) dataframes, gridded multidimensional array data using [xarray](http://xarray.pydata.org), columnar data on GPUs using [cuDF](https://github.com/rapidsai/cudf), multidimensional arrays on GPUs using [CuPy](https://cupy.chainer.org/), and ragged arrays using [SpatialPandas](https://github.com/holoviz/spatialpandas) (see the [Performance User Guide](../10_Performance.ipynb) for a guide to selecting an appropriate library). Here, we're using a Pandas dataframe, with 50,000 rows by default:", "_____no_output_____" ] ], [ [ "df.tail()", "_____no_output_____" ] ], [ [ "To illustrate this dataset, we'll make a quick-and-dirty Datashader plot that dumps these x,y coordinates into an image:", "_____no_output_____" ] ], [ [ "import datashader as ds\nimport datashader.transfer_functions as tf\n\n%time tf.shade(ds.Canvas().points(df,'x','y'))", "_____no_output_____" ] ], [ [ "Without any special tweaking, datashader is able to reveal the overall shape of this distribution faithfully: four summed 2D normal distributions of different variances, arranged at the corners of a square, overlapping another very high-variance 2D normal distribution centered in the square. This immediately obvious structure makes a great starting point for exploring the data, and you can then customize each of the various stages involved as described below.\n\nOf course, this is just a static plot, and you can't see what the axes are, so we can instead embed this data into an interactive plot if we prefer:", "_____no_output_____" ], [ "Here, if you are running a live Python process, you can enable the \"wheel zoom\" tool on the right, zoom in anywhere in the distribution, and datashader will render a new image that shows the full distribution at that new location. If you are viewing this on a static web site, zooming will simply make the existing set of pixels larger, because this dynamic updating requires Python.\n\nNow that you can see the overall result, we'll unpack each of the steps in the Datashader pipeline and show how this image is constructed from the data.\n\n\n## Projection\n\nDatashader is designed to render datasets projected on to a 2D rectangular grid, eventually generating an image where each pixel corresponds to one cell in that grid. The ***Projection*** stage is primarily conceptual, as it consists of you deciding what you want to plot and how you want to plot it:\n\n- **Variables**: Select which variable you want to have on the *x* axis, and which one for the *y* axis. If those variables are not already columns in your dataframe (e.g. if you want to do a coordinate transformation), you'll need to create suitable columns mapping directly to *x* and *y* for use in the next step. For this example, the \"x\" and \"y\" columns are conveniently named `x` and `y` already, but any column name can be used for these axes.\n- **Ranges**: Decide what ranges of those values you want to map onto the scene. If you omit the ranges, datashader will calculate the ranges from the data values, but you will often wish to supply explicit ranges for three reasons:\n 1. Calculating the ranges requires a complete pass over the data, which takes nearly as much time as actually aggregating the data, so your plots will be about twice as fast if you specify the ranges.\n 2. Real-world datasets often have some outliers with invalid values, which can make it difficult to see the real data, so after your first plot you will often want to specify only the range that appears to have valid data.\n 3. Over the valid range of data, you will often be mainly interested in a specific region, allowing you to zoom in to that area (though with an interactive plot you can always do that as needed).\n- **Axis types**: Decide whether you want `'linear'` or `'log'` axes.\n- **Resolution**: Decide what size of aggregate array you are going to want. \n\nHere's an example of specifying a ``Canvas`` (a.k.a. \"Scene\") object for a 200x200-pixel image covering the range +/-8.0 on both axes:", "_____no_output_____" ] ], [ [ "canvas = ds.Canvas(plot_width=300, plot_height=300, \n x_range=(-8,8), y_range=(-8,8), \n x_axis_type='linear', y_axis_type='linear')", "_____no_output_____" ] ], [ [ "At this stage, no computation has actually been done -- the `canvas` object is a purely declarative, recording your preferences to be applied in the next stage. \n\n<!-- Need to move the Points/Lines/Rasters discussion into the section above once the API is rationalized, and rename Canvas to Scene. -->\n\n\n## Aggregation\n\n<!-- This section really belongs under Scene, above-->\n\nOnce a `Canvas` object has been specified, it can then be used to guide aggregating the data into a fixed-sized grid. Data is assumed to consist of a series of items, each of which has some visible representation (its rendering as a \"glyph\") that is combined with the representation of other items to produce an aggregate representation of the whole set of items in the rectangular grid. The available glyph types for representing a data item are currently:\n - **Canvas.points**: each data item is a coordinate location (an x,y pair), mapping into the single closest grid cell to that datapoint's location.\n - **Canvas.line**: each data item is a coordinate location, mapping into every grid cell falling between this point's location and the next in a straight line segment.\n - **Canvas.area**: each data item is a coordinate location, rendered as a shape filling the axis-aligned area between this point, the next point, and a baseline (e.g. zero, filling the area between a line and a base).\n - **Canvas.trimesh**: each data item is a triple of coordinate locations specifying a triangle, filling in the region bounded by that triangle.\n - **Canvas.polygons**: each data item is a sequence of coordinate locations specifying a polygon, filling in the region bounded by that polygon (minus holes if specified separately).\n - **Canvas.raster**: the collection of data items is an array specifying regularly spaced axis-aligned rectangles forming a regular grid; each cell in this array is rendered as a filled rectangle.\n - **Canvas.quadmesh**: the collection of data items is an array specifying irregularly spaced quadrilaterals forming a grid that is regular in the input space but can have arbitrary rectilinear or curvilinear shapes in the aggregate grid; each cell in this array is rendered as a filled quadrilateral.\n\nThese types are each covered in detail in the [User Guide](../user_guide/). Datashader can be extended to add additional types here and in each section below; see [Extending Datashader](../user_guide/9-Extending.ipynb) for more details. Many other plots like time series and network graphs can be constructed out of these basic primitives.\n\n\n<!-- (to here) -->", "_____no_output_____" ], [ "### Reductions\n\nOne you have determined your mapping, you'll next need to choose a reduction operator to use when aggregating multiple datapoints into a given pixel. For points, each datapoint is mapped into a single pixel, while the other glyphs have spatial extent and can thus map into multiple pixels, each of which operates the same way. All glyphs act like points if the entire glyph is contained within that pixel. Here we will talk only about \"datapoints\" for simplicity, which for an area-based glyph should be interpreted as \"the part of that glyph that falls into this pixel\".\n\nAll of the currently supported reduction operators are incremental, which means that we can efficiently process datasets in a single pass. Given an aggregate bin to update (typically corresponding to one eventual pixel) and a new datapoint, the reduction operator updates the state of the bin in some way. (Actually, datapoints are normally processed in batches for efficiency, but it's simplest to think about the operator as being applied per data point, and the mathematical result should be the same.) A large number of useful [reduction operators]((https://datashader.org/api.html#reductions) are supplied in `ds.reductions`, including:\n\n**`count(column=None)`**:\n increment an integer count each time a datapoint maps to this bin. The resulting aggregate array will be an unsigned integer type, allowing counts to be distinguished from the other types that are normally floating point.\n \n**`any(column=None)`**:\n the bin is set to 1 if any datapoint maps to it, and 0 otherwise.\n \n**`sum(column)`**:\n add the value of the given column for this datapoint to a running total for this bin.\n \n**`by(column, reduction)`**:\n given a bin with categorical data (i.e., [Pandas' `categorical` datatype](https://pandas-docs.github.io/pandas-docs-travis/categorical.html)), aggregate each category separately, accumulating the given datapoint in an appropriate category within this bin. These categories can later be collapsed into a single aggregate if needed; see examples below.\n \n**`summary(name1=op1,name2=op2,...)`**:\n allows multiple reduction operators to be computed in a single pass over the data; just provide a name for each resulting aggregate and the corresponding reduction operator to use when creating that aggregate. If multiple aggregates are needed for the same dataset and the same Canvas, using `summary` will generally be much more efficient than making multiple separate passes over the dataset.\n \nThe API documentation contains the complete list of [reduction operators]((https://datashader.org/api.html#reductions) provided, including `mean`, `min`, `max`, `var` (variance), `std` (standard deviation). The reductions are also imported into the ``datashader`` namespace for convenience, so that they can be accessed like ``ds.mean()`` here.\n\nFor the operators above, those accepting a `column` argument will only do the operation if the value of that column for this datapoint is not `NaN`. E.g. `count` with a column specified will count the datapoints having non-`NaN` values for that column.\n\nOnce you have selected your reduction operator, you can compute the aggregation for each pixel-sized aggregate bin:", "_____no_output_____" ] ], [ [ "canvas.points(df, 'x', 'y', agg=ds.count())", "_____no_output_____" ] ], [ [ "The result of will be an [xarray](http://xarray.pydata.org) `DataArray` data structure containing the bin values (typically one value per bin, but more for multiple category or multiple-aggregate operators) along with axis range and type information.\n\nWe can visualize this array in many different ways by customizing the pipeline stages described in the following sections, but for now we'll use HoloViews to render images using the default parameters to show the effects of a few different aggregate operators:", "_____no_output_____" ] ], [ [ "tf.Images(tf.shade( canvas.points(df,'x','y', ds.count()), name=\"count()\"),\n tf.shade( canvas.points(df,'x','y', ds.any()), name=\"any()\"),\n tf.shade( canvas.points(df,'x','y', ds.mean('y')), name=\"mean('y')\"),\n tf.shade(50-canvas.points(df,'x','y', ds.mean('val')), name=\"50- mean('val')\"))", "_____no_output_____" ] ], [ [ "Here ``count()`` renders each bin's count in a different color, to show the true distribution, while ``any()`` turns on a pixel if any point lands in that bin, and ``mean('y')`` averages the `y` column for every datapoint that falls in that bin. Of course, since ever datapoint falling into a bin happens to have the same `y` value, the mean reduction with `y` simply scales each pixel by its `y` location. \n\nFor the last image above, we specified that the `val` column should be used for the `mean` reduction, which in this case results in each category being assigned a different color, because in our dataset all items in the same category happen to have the same `val`. Here we also manipulated the result of the aggregation before displaying it by subtracting it from 50, as detailed in the next section.\n\n\n\n## Transformation\n\nNow that the data has been projected and aggregated into a gridded data structure, it can be processed in any way you like, before converting it to an image as will be described in the following section. At this stage, the data is still stored as bin data, not pixels, which makes a very wide variety of operations and transformations simple to express. \n\nFor instance, instead of plotting all the data, we can easily plot only those bins in the 99th percentile by count (left), or apply any [NumPy ufunc](http://docs.scipy.org/doc/numpy/reference/ufuncs.html) to the bin values (whether or not it makes any sense!):", "_____no_output_____" ] ], [ [ "agg = canvas.points(df, 'x', 'y')\n\ntf.Images(tf.shade(agg.where(agg>=np.percentile(agg,99)), name=\"99th Percentile\"),\n tf.shade(np.power(agg,2), name=\"Numpy square ufunc\"),\n tf.shade(np.sin(agg), name=\"Numpy sin ufunc\"))", "_____no_output_____" ] ], [ [ "The [xarray documentation](http://xarray.pydata.org/en/stable/computation.html) describes all the various transformations you can apply from within xarray, and of course you can always extract the data values and operate on them outside of xarray for any transformation not directly supported by xarray, then construct a suitable xarray object for use in the following stage. Once the data is in the aggregate array, you generally don't have to worry much about optimization, because it's a fixed-sized grid regardless of your data size, and so it is very straightforward to apply arbitrary transformations to the aggregates.\n\nThe above examples focus on a single aggregate, but there are many ways that you can use multiple data values per bin as well. For instance, you can apply any aggregation \"categorically\", aggregating `by` some categorical value so that datapoints for each unique value are aggregated independently:", "_____no_output_____" ] ], [ [ "aggc = canvas.points(df, 'x', 'y', ds.by('cat', ds.count()))\naggc", "_____no_output_____" ] ], [ [ "Here the `count()` aggregate has been collected into not just a 2D aggregate array, but a whole stack of aggregate arrays, one per `cat` value, making the aggregate be three dimensional (x,y,cat) rather than just two (x,y). With this 3D aggregate of counts per category, you can then select a specific category or subset of them for further processing, where `.sum(dim='cat')` will collapse across such a subset to give a single aggregate array:", "_____no_output_____" ] ], [ [ "agg_d3_d5=aggc.sel(cat=['d3', 'd5']).sum(dim='cat')\n\ntf.Images(tf.shade(aggc.sel(cat='d3'), name=\"Category d3\"),\n tf.shade(agg_d3_d5, name=\"Categories d3 and d5\")) ", "_____no_output_____" ] ], [ [ "You can also combine multiple aggregates however you like, as long as they were all constructed using the same Canvas object (which ensures that their aggregate arrays are the same size) and cover the same axis ranges:", "_____no_output_____" ] ], [ [ "tf.Images(tf.shade(agg_d3_d5.where(aggc.sel(cat='d3') == aggc.sel(cat='d5')), name=\"d3+d5 where d3==d5\"),\n tf.shade( agg.where(aggc.sel(cat='d3') == aggc.sel(cat='d5')), name=\"d1+d2+d3+d4+d5 where d3==d5\"))", "_____no_output_____" ] ], [ [ "The above two results are using the same mask (only those bins `where` the counts for 'd3' and 'd5' are equal), but applied to different aggregates (either just the `d3` and `d5` categories, or the entire set of counts).\n\n## Colormapping\n\nAs you can see above, the usual way to visualize an aggregate array is to map from each array bin into a color for a corresponding pixel in an image. The above examples use the `tf.shade()` method, which maps a scalar aggregate bin value into an RGB (color) triple and an alpha (opacity) value. By default, the colors are chosen from the colormap ['lightblue','darkblue'] (i.e., `#ADD8E6` to `#00008B`), with intermediate colors chosen as a linear interpolation independently for the red, green, and blue color channels (e.g. `AD` to `00` for the red channel, in this case). The alpha (opacity) value is set to 0 for empty bins and 1 for non-empty bins, allowing the page background to show through wherever there is no data. You can supply any colormap you like, including Bokeh palettes, Matplotlib colormaps, or a list of colors (using the color names from `ds.colors`, integer triples, or hexadecimal strings):", "_____no_output_____" ] ], [ [ "from bokeh.palettes import RdBu9\ntf.Images(tf.shade(agg,cmap=[\"darkred\", \"yellow\"], name=\"darkred, yellow\"),\n tf.shade(agg,cmap=[(230,230,0), \"orangered\", \"#300030\"], name=\"yellow, orange red, dark purple\"), \n tf.shade(agg,cmap=list(RdBu9), name=\"Bokeh RdBu9\"),\n tf.shade(agg,cmap=\"black\", name=\"Black\"))", "_____no_output_____" ] ], [ [ "As a special case (\"Black\", above), if you supply only a single color, the color will be kept constant at the given value but the alpha (opacity) channel will vary with the data.", "_____no_output_____" ], [ "#### Colormapping categorical data\n\nIf you want to use `tf.shade` with a categorical aggregate, you can use a colormap just as for a non-categorical aggregate if you first select a single category using something like `aggc.sel(cat='d3')` or else collapse all categories into a single aggregate using something like `aggc.sum(dim='cat')`. \n\nIf you want to visualize all the categories in one image, you can use `tf.shade` with the categorical aggregate directly, which will assign a color to each category and then calculate the transparency and color of each pixel according to each category's contribution to that pixel:", "_____no_output_____" ] ], [ [ "color_key = dict(d1='blue', d2='green', d3='red', d4='orange', d5='purple')\n\ntf.Images(tf.shade(aggc, name=\"Default color key\"),\n tf.shade(aggc, color_key=color_key, name=\"Custom color key\"))", "_____no_output_____" ] ], [ [ "Here the different colors mix not just visually due to blurring, but are actually mixed mathematically per pixel, with pixels that include data from multiple categories taking intermediate color values. The total (summed) data values across all categories are used to calculate the alpha channel, with the previously computed color being revealed to a greater or lesser extent depending on the value of the aggregate for that bin. See [Colormapping with negative values](#Colormapping-with-negative-values) below for more details on how these colors and transparencies are calculated.", "_____no_output_____" ], [ "The default color key for categorical data provides distinguishable colors for a couple of dozen categories, but you can provide an explicit color_key if you prefer. Choosing colors for different categories is more of an art than a science, because the colors not only need to be distinguishable, their combinations also need to be distinguishable if those categories ever overlap in nearby pixels, or else the results will be ambiguous. In practice, only a few categories can be reliably distinguished in this way, but [zooming in](3_Interactivity.ipynb) can be used to help disambiguate overlapping colors, as long as the basic set of colors is itself distinguishable.", "_____no_output_____" ], [ "#### Transforming data values for colormapping\n\nIn each of the above examples, you may have noticed that we were never required to specify any parameters about the data values; the plots just appear like magic. That magic is implemented in `tf.shade`. What `tf.shade` does for a 2D aggregate (non-categorical) is:\n\n1. **Mask** out all bins with a `NaN` value (for floating-point arrays) or a zero value (for the unsigned integer arrays that are returned from `count`); these bins will not have any effect on subsequent computations. \n\n2. **Transform** the bin values using a specified scalar function `how`. Calculates the value of that function for the difference between each bin value and the minimum non-masked bin value. E.g. for `how=\"linear\"`, simply returns the difference unchanged. Other `how` functions are discussed below.\n\n3. **Map** the resulting transformed data array into the provided colormap. First finds the value span (*l*,*h*) for the resulting transformed data array -- what are the lowest and highest non-masked values? -- and then maps the range (*l*,*h*) into the full range of the colormap provided. If a colormap is used, masked values are given a fully transparent alpha value, and non-masked ones are given a fully opaque alpha value. If a single color is used, the alpha value starts at `min_alpha` and increases proportionally to the mapped data value up to the full `alpha` value.\n\nThe result is thus auto-ranged to show whatever data values are found in the aggregate bins, with the `span` argument (described below) allowing you to override the range explicitly if you need to.\n\nAs described in [Plotting Pitfalls](../user_guide/1_Plotting_Pitfalls.ipynb), auto-ranging is only part of what is required to reveal the structure of the dataset; it's also crucial to automatically and potentially nonlinearly map from the aggregate values (e.g. bin counts) into the colormap. If we used a linear mapping, we'd see very little of the structure of the data:", "_____no_output_____" ] ], [ [ "tf.shade(agg,how='linear')", "_____no_output_____" ] ], [ [ "In the linear version, you can see that the bins that have zero count show the background color, since they have been masked out using the alpha channel of the image, and that the rest of the pixels have been mapped to colors near the bottom of the colormap. If you peer closely at it, you may even be able to see that one pixel (from the smallest Gaussian) has been mapped to the highest color in the colormap (here dark blue). But no other structure is visible, because the highest-count bin is so much higher than all of the other bins:", "_____no_output_____" ] ], [ [ "top15=agg.values.flat[np.argpartition(agg.values.flat, -15)[-15:]]\nprint(sorted(top15))\nprint(sorted(np.round(top15*255.0/agg.values.max()).astype(int)))", "_____no_output_____" ] ], [ [ "I.e., if using a colormap with 255 colors, the largest bin (`agg.values.max()`) is mapped to the highest color, but with a linear scale all of the other bins map to only the first 24 colors, leaving all intermediate colors unused. If we want to see any structure for these intermediate ranges, we need to transform these numerical values somehow before displaying them. For instance, if we take the logarithm of these large values, they will be mapped into a more tractable range:", "_____no_output_____" ] ], [ [ "print(np.log1p(sorted(top15)))", "_____no_output_____" ] ], [ [ "So we can plot the logarithms of the values (``how='log'``, below), which is an arbitrary transform but is appropriate for many types of data. Alternatively, we can make a histogram of the numeric values, then assign a pixel color to each equal-sized histogram bin to ensure even usage of every displayable color (``how='eq_hist'``; see [plotting pitfalls](../user_guide/1_Plotting_Pitfalls.ipynb). We can even supply any arbitrary transformation to the colormapper as a callable, such as a twenty-third root:", "_____no_output_____" ] ], [ [ "tf.Images(tf.shade(agg,how='log', name=\"log\"),\n tf.shade(agg,how='eq_hist', name=\"eq_hist\"),\n tf.shade(agg,how=lambda d, m: np.where(m, np.nan, d)**(1/23.), name=\"23rd root\"))", "_____no_output_____" ] ], [ [ "Usually, however, such custom operations are done directly on the aggregate during the ***Transformation*** stage; the `how` operations are meant for simple, well-defined transformations solely for the final steps of visualization, which allows the main aggregate array to stay in the original units and scale in which it was measured. Using `how` also helps simplify the subsequent ***Embedding*** stage, letting it provide one of a fixed set of legend types, either linear (for `how=linear`), logarithmic (for `how=log`) or percentile (for `how=eq_hist`). See the [shade docs](https://datashader.org/api.html#datashader.transfer_functions.shade) for more details on the `how` functions. \n\nFor categorical aggregates, the `shade` function works similarly to providing a single color to a non-categorical aggregate, with the alpha (opacity) calculated from the total value across all categories (and the color calculated as a weighted mixture of the colors for each category).", "_____no_output_____" ], [ "#### Controlling ranges for colormapping\n\nBy default, `shade` will autorange on the aggregate array, mapping the lowest and highest values of the aggregate array into the lowest and highest values of the colormap (or the available alpha values, for single colors). You can instead focus on a specific `span` of the aggregate data values, mapping that span into the available colors or the available alpha values:", "_____no_output_____" ] ], [ [ "tf.Images(tf.shade(agg,cmap=[\"grey\", \"blue\"], name=\"gb 0 20\", span=[0,20], how=\"linear\"),\n tf.shade(agg,cmap=[\"grey\", \"blue\"], name=\"gb 50 200\", span=[50,200], how=\"linear\"),\n tf.shade(agg,cmap=\"green\", name=\"Green 10 20\", span=[10,20], how=\"linear\"))", "_____no_output_____" ] ], [ [ "On the left, all counts above 20 are mapped to the highest value in the colormap (blue in this case), losing the ability to distinguish between values above 20 but providing the maximum color precision for the specific range 0 to 20. In the middle, all values 0 to 50 map to the first color in the colormap (grey in this case), and the colors are then linearly interpolated up to 200, with all values 200 and above mapping to the highest value in the colormap (blue in this case). With the single color mapping to alpha on the right, counts up to 10 are all mapped to `min_alpha`, counts 20 and above are all mapped to the specified `alpha` (255 in this case), and alpha is scaled linearly in between.", "_____no_output_____" ], [ "For plots that scale with alpha (i.e., categorical or single-color non-categorical plots), you can control the range of alpha values generated by setting `min_alpha` (lower bound) and `alpha` (upper bound), on a scale 0 to 255):", "_____no_output_____" ] ], [ [ "tf.Images(tf.shade(agg,cmap=\"green\", name=\"Green\"),\n tf.shade(agg,cmap=\"green\", name=\"No min_alpha\", min_alpha=0), \n tf.shade(agg,cmap=\"green\", name=\"Small alpha range\", min_alpha=50, alpha=80))", "_____no_output_____" ] ], [ [ "Here you can see that the faintest pixels are more visible with the default `min_alpha` (normally 40, left) than if you explicitly set the `min_alpha=0` (middle), which is why the `min_alpha` default is non-zero; otherwise low values would be indistinguishable from the background (see [Plotting Pitfalls](../user_guide/1_Plotting_Pitfalls.ipynb)).\n\nYou can combine `span` and `alpha` ranges to specifically control the data value range that maps to an opacity range, for single-color and categorical plotting:", "_____no_output_____" ] ], [ [ "tf.Images(tf.shade(agg,cmap=\"green\", name=\"g 0,20\", span=[ 0,20], how=\"linear\"),\n tf.shade(agg,cmap=\"green\", name=\"g 10,20\", span=[10,20], how=\"linear\"),\n tf.shade(agg,cmap=\"green\", name=\"g 10,20 0\", span=[10,20], how=\"linear\", min_alpha=0))", "_____no_output_____" ], [ "tf.Images(tf.shade(aggc, name=\"eq_hist\"),\n tf.shade(aggc, name=\"linear\", how='linear'),\n tf.shade(aggc, name=\"span 0,10\", how='linear', span=(0,10)), \n tf.shade(aggc, name=\"span 0,10\", how='linear', span=(0,20), min_alpha=0))", "_____no_output_____" ] ], [ [ "The categorical examples above focus on counts, but `ds.by` works on other aggregate types as well, colorizing by category but aggregating by sum, mean, etc. (but see the [following section](#Colormapping-with-negative-values) for details on how to interpret such colors):", "_____no_output_____" ] ], [ [ "agg_c = canvas.points(df,'x','y', ds.by('cat', ds.count()))\nagg_s = canvas.points(df,'x','y', ds.by(\"cat\", ds.sum(\"val\")))\nagg_m = canvas.points(df,'x','y', ds.by(\"cat\", ds.mean(\"val\")))\n\ntf.Images(tf.shade(agg_c), tf.shade(agg_s), tf.shade(agg_m))", "_____no_output_____" ] ], [ [ "#### Colormapping with negative values\n\nThe above examples all use positive data values to avoid confusion when there is no colorbar or other explicit indication of a z (color) axis range. Negative values are also supported, in which case for a non-categorical plot you should normally use a [diverging colormap](https://colorcet.holoviz.org/user_guide/Continuous.html#Diverging-colormaps,-for-plotting-magnitudes-increasing-or-decreasing-from-a-central-point:):", "_____no_output_____" ] ], [ [ "from colorcet import coolwarm, CET_D8\ndfn = df.copy()\ndfn.val.replace({20:-20, 30:0, 40:-40}, inplace=True)\naggn = ds.Canvas().points(dfn,'x','y', agg=ds.mean(\"val\"))\n\ntf.Images(tf.shade(aggn, name=\"Sequential\", cmap=[\"lightblue\",\"blue\"], how=\"linear\"),\n tf.shade(aggn, name=\"DivergingW\", cmap=coolwarm[::-1], span=(-50,50), how=\"linear\"),\n tf.shade(aggn, name=\"DivergingB\", cmap=CET_D8[::-1], span=(-50,50), how=\"linear\"))", "_____no_output_____" ] ], [ [ "In both of the above plots, values with no data are transparent as usual, showing white. With a sequential lightblue to blue colormap, increasing `val` numeric values are mapped to the colormap in order, with the smallest values (-40; large blob in the top left) getting the lowest color value (lightblue), less negative values (-20, blob in the bottom right) getting an intermediate color, and the largest average values (50, large distribution in the background) getting the highest color. Looking at such a plot, viewers have no easy way to determine which values are negative. Using a diverging colormap (right two plots) and forcing the span to be symmetric around zero ensures that negative values are plotted in one color range (reds) and positive are plotted in a clearly different range (blues). Note that when using a diverging colormap with transparent values, you should carefully consider what you want to happen around the zero point; here values with nearly zero average (blob in bottom left) disappear when using a white-centered diverging map (\"coolwarm\"), while they show up but in a neutral color when using a diverging map with a contrasting central color (\"CET_D8\").\n\nFor categorical plots of values that can be negative, the results are often quite difficult to interpret, for the same reason as for the Sequential case above:", "_____no_output_____" ] ], [ [ "agg_c = canvas.points(dfn,'x','y', ds.by('cat', ds.count()))\nagg_s = canvas.points(dfn,'x','y', ds.by(\"cat\", ds.sum(\"val\")))\nagg_m = canvas.points(dfn,'x','y', ds.by(\"cat\", ds.mean(\"val\")))\n\ntf.Images(tf.shade(agg_c, name=\"count\"), \n tf.shade(agg_s, name=\"sum\"), \n tf.shade(agg_s, name=\"sum baseline=0\", color_baseline=0))", "_____no_output_____" ] ], [ [ "Here a `count` aggregate ignores the negative values and thus works the same as when values were positive, but `sum` and other aggregates like `mean` take the negative values into account. By default, a pixel with the lowest value (whether negative or positive) maps to `min_alpha`, and the highest maps to `alpha`. The color is determined by how different each category's value is from the minimum value across all categories; categories with high values relative to the minimum contribute more to the color. There is not currently any way to tell which data values are positive or negative, as you can using a diverging colormap in the non-categorical case.\n\nInstead of using the default of the data minimum, you can pass a specific `color_baseline`, which is appropriate if your data has a well-defined reference value such as zero. Here, when we pass `color_baseline=0` the negative values are essentially ignored for color calculations, which can be seen on the green blob, where any orange data point is fully orange despite the presence of green-category datapoints; the middle plot `sum` shows a more appropriate color mixture in that case.", "_____no_output_____" ], [ "#### Spreading\n\nOnce an image has been created, it can be further transformed with a set of functions from `ds.transfer_functions`.\n\nFor instance, because it can be difficult to see individual dots, particularly for zoomed-in plots, you can transform the image to replace each non-transparent pixel with a shape, such as a circle (default) or square. This process is called spreading:", "_____no_output_____" ] ], [ [ "img = tf.shade(aggc, name=\"Original image\")\n \ntf.Images(img,\n tf.spread(img, name=\"spread 1px\"),\n tf.spread(img, px=2, name=\"spread 2px\"),\n tf.spread(img, px=3, shape='square', name=\"spread square\"))", "_____no_output_____" ] ], [ [ "As you can see, spreading is very effective for isolated datapoints, which is what it's normally used for, but it has overplotting-like effects for closely spaced points like in the green and purple regions above, and so it would not normally be used when the datapoints are dense.\n\nSpreading can be used with a custom mask, as long as it is square and an odd width and height (so that it will be centered over the original pixel):", "_____no_output_____" ] ], [ [ "mask = np.array([[1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1]])\n\ntf.spread(img, mask=mask)", "_____no_output_____" ] ], [ [ "To support interactive zooming, where spreading would be needed only in sparse regions of the dataset, we provide the dynspread function. `dynspread` will dynamically calculate the spreading size to use by counting the fraction of non-masked bins that have non-masked neighbors; see the\n[dynspread docs](https://datashader.org/api.html#datashader.transfer_functions.dynspread) for more details.\n\n\n#### Other image transfer_functions\n\nOther useful image operations are also provided, such as setting the background color or combining images:", "_____no_output_____" ] ], [ [ "tf.Images(tf.set_background(img,\"black\", name=\"Black bg\"),\n tf.stack(img,tf.shade(aggc.sel(cat=['d2', 'd3']).sum(dim='cat')), name=\"Sum d2 and d3 colors\"),\n tf.stack(img,tf.shade(aggc.sel(cat=['d2', 'd3']).sum(dim='cat')), how='saturate', name=\"d2+d3 saturated\")) ", "_____no_output_____" ] ], [ [ "See [the API docs](https://datashader.org/api.html#transfer-functions) for more details. Image composition operators to provide for the `how` argument of `tf.stack` (e.g. `over` (default), `source`, `add`, and `saturate`) are listed in [composite.py](https://raw.githubusercontent.com/holoviz/datashader/master/datashader/composite.py) and illustrated [here](http://cairographics.org/operators).\n\n## Moving on\n\nThe steps outlined above represent a complete pipeline from data to images, which is one way to use Datashader. However, in practice one will usually want to add one last additional step, which is to embed these images into a plotting program to be able to get axes, legends, interactive zooming and panning, etc. The [next notebook](3_Interactivity.ipynb) shows how to do such embedding.", "_____no_output_____" ] ] ]

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[ [ [ "Lambda School Data Science, Unit 2: Predictive Modeling\n\n# Applied Modeling, Module 1\n\nYou will use your portfolio project dataset for all assignments this sprint.\n\n## Assignment\n\nComplete these tasks for your project, and document your decisions.\n\n- [ ] Choose your target. Which column in your tabular dataset will you predict?\n- [ ] Choose which observations you will use to train, validate, and test your model. And which observations, if any, to exclude.\n- [ ] Determine whether your problem is regression or classification.\n- [ ] Choose your evaluation metric.\n- [ ] Begin with baselines: majority class baseline for classification, or mean baseline for regression, with your metric of choice.\n- [ ] Begin to clean and explore your data.\n- [ ] Begin to choose which features, if any, to exclude. Would some features \"leak\" information from the future?\n\n## Reading\n\n### ROC AUC\n- [Machine Learning Meets Economics](http://blog.mldb.ai/blog/posts/2016/01/ml-meets-economics/)\n- [ROC curves and Area Under the Curve explained](https://www.dataschool.io/roc-curves-and-auc-explained/)\n- [The philosophical argument for using ROC curves](https://lukeoakdenrayner.wordpress.com/2018/01/07/the-philosophical-argument-for-using-roc-curves/)\n\n### Imbalanced Classes\n- [imbalance-learn](https://github.com/scikit-learn-contrib/imbalanced-learn)\n- [Learning from Imbalanced Classes](https://www.svds.com/tbt-learning-imbalanced-classes/)\n\n### Last lesson\n- [Attacking discrimination with smarter machine learning](https://research.google.com/bigpicture/attacking-discrimination-in-ml/), by Google Research, with interactive visualizations. _\"A threshold classifier essentially makes a yes/no decision, putting things in one category or another. We look at how these classifiers work, ways they can potentially be unfair, and how you might turn an unfair classifier into a fairer one. As an illustrative example, we focus on loan granting scenarios where a bank may grant or deny a loan based on a single, automatically computed number such as a credit score.\"_\n- [How Shopify Capital Uses Quantile Regression To Help Merchants Succeed](https://engineering.shopify.com/blogs/engineering/how-shopify-uses-machine-learning-to-help-our-merchants-grow-their-business)\n- [Maximizing Scarce Maintenance Resources with Data: Applying predictive modeling, precision at k, and clustering to optimize impact](https://towardsdatascience.com/maximizing-scarce-maintenance-resources-with-data-8f3491133050), by Lambda DS3 student Michael Brady. His blog post extends the Tanzania Waterpumps scenario, far beyond what's in the lecture notebook.\n- [Notebook about how to calculate expected value from a confusion matrix by treating it as a cost-benefit matrix](https://github.com/podopie/DAT18NYC/blob/master/classes/13-expected_value_cost_benefit_analysis.ipynb)\n- [Simple guide to confusion matrix terminology](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) by Kevin Markham, with video\n- [Visualizing Machine Learning Thresholds to Make Better Business Decisions](https://blog.insightdatascience.com/visualizing-machine-learning-thresholds-to-make-better-business-decisions-4ab07f823415)", "_____no_output_____" ], [ "# Import the final Liverpool Football Club data file.", "_____no_output_____" ] ], [ [ "# import pandas library as pd.\nimport pandas as pd \n\n# read in the LiverpoolFootballClub_all csv file.\nLPFC = pd.read_csv('https://raw.githubusercontent.com/CVanchieri/LSDS-DataSets/master/EnglishPremierLeagueData/LiverpoolFootballClubData_EPL.csv')\n# show the data frame shape.\nprint(LPFC.shape)\n# show the data frame with headers.\nLPFC.head()", "(1003, 161)\n" ] ], [ [ "## Organizing & cleaning.", "_____no_output_____" ] ], [ [ "# group the columns we want to use.\ncolumns = [\"Div\", \"Date\", \"HomeTeam\", \"AwayTeam\", \"FTHG\", \"FTAG\", \"FTR\", \n \"HTHG\", \"HTAG\", \"HTR\", \"HS\", \"AS\", \"HST\", \"AST\", \"HHW\", \"AHW\", \n \"HC\", \"AC\", \"HF\", \"AF\", \"HO\", \"AO\", \"HY\", \"AY\", \"HR\", \"AR\", \"HBP\", \"ABP\"]\n# create a new data frame with just the grouped columns.\nLPFC = LPFC[columns]\n# show the data frame shape.\nprint(LPFC.shape)\n# show the data frame with headers.\nLPFC.head()", "(1003, 28)\n" ], [ "# relableing columns for better understanding.\nLPFC = LPFC.rename(columns={\"Div\": \"Division\", \"Date\": \"GameDate\", \"FTHG\": \"FullTimeHomeGoals\", \"FTAG\": \"FullTimeAwayGoals\", \"FTR\": \"FullTimeResult\", \"HTHG\": \"HalfTimeHomeGoals\", \n \"HTAG\": \"HalfTimeAwayGoals\", \"HTR\": \"HalfTimeResult\", \"HS\": \"HomeShots\", \"AS\": \"AwayShots\", \n \"HST\": \"HomeShotsOnTarget\", \"AST\": \"AwayShotsOnTarget\", \"HHW\": \"HomeShotsHitFrame\", \n \"AHW\": \"AwayShotsHitFrame\", \"HC\": \"HomeCorners\", \"AC\": \"AwayCorners\", \"HF\": \"HomeFouls\", \n \"AF\": \"AwayFouls\", \"HO\": \"HomeOffSides\", \"AO\": \"AwayOffSides\", \"HY\": \"HomeYellowCards\", \n \"AY\": \"AwayYellowCards\", \"HR\": \"HomeRedCards\", \"AR\": \"AwayRedCards\", \"HBP\": \"HomeBookingPoints_Y5_R10\", \n \"ABP\": \"AwayBookingPoints_Y5_R10\"})\n# show the data frame with headers.\nLPFC.head()", "_____no_output_____" ] ], [ [ "## Baseline accuracy score.", "_____no_output_____" ] ], [ [ "# import accuracy_score from sklearn.metrics library.\nfrom sklearn.metrics import accuracy_score\n\n# determine 'majority class' baseline starting point for every prediction.\n# single out the target, 'FullTimeResult' column.\ntarget = LPFC['FullTimeResult']\n# create the majority class with setting the 'mode' on the target data.\nmajority_class = target.mode()[0]\n# create the y_pred data.\ny_pred = [majority_class] * len(target)\n# accuracy score for the majority class baseline = frequency of the majority class.\nac = accuracy_score(target, y_pred)\nprint(\"'Majority Baseline' Accuracy Score =\", ac)", "'Majority Baseline' Accuracy Score = 0.4745762711864407\n" ] ], [ [ "## Train/test split the data frame, train/val/test.", "_____no_output_____" ] ], [ [ "df = LPFC.copy()", "_____no_output_____" ], [ "target = 'FullTimeResult'\ny = df[target]", "_____no_output_____" ], [ "# import train_test_split from sklearn.model_selection library.\nfrom sklearn.model_selection import train_test_split\n\ntarget = ['FullTimeResult']\ny = df[target]\n\n# split data into train, test.\nX_train, X_val, y_train, y_val = train_test_split(df, y, test_size=0.20,\n stratify=y, random_state=42)\n# show the data frame shapes.\nprint(\"train =\", X_train.shape, y_train.shape, \"val =\", X_val.shape, y_val.shape)", "train = (802, 28) (802, 1) val = (201, 28) (201, 1)\n" ] ], [ [ "## LogisticREgression model.", "_____no_output_____" ] ], [ [ "import numpy as np\nfrom datetime import datetime\n\ndef wrangle(X):\n \"\"\"Wrangle train, validate, and test sets in the same way\"\"\"\n \n # prevent SettingWithCopyWarning with a copy.\n X = X.copy()\n \n\n # make 'GameDate' useable with datetime.\n X['GameDate'] = pd.to_datetime(X['GameDate'], infer_datetime_format=True) \n \n # create new columns for 'YearOfGame', 'MonthOfGame', 'DayOfGame'.\n X['YearOfGame'] = X['GameDate'].dt.year\n X['MonthOfGame'] = X['GameDate'].dt.month\n X['DayOfGame'] = X['GameDate'].dt.day\n \n # removing 'FullTimeHomeGoals', 'FullTimeAwayGoals' as they directly coorelated to the result.\n dropped_columns = ['FullTimeHomeGoals', 'FullTimeAwayGoals', 'Division', 'GameDate']\n X = X.drop(columns=dropped_columns)\n \n # return the wrangled dataframe\n return X\n\nX_train = wrangle(X_train)\nX_val = wrangle(X_val)", "_____no_output_____" ], [ "# create the target as status_group.\ntarget = 'FullTimeResult'\n# set the features, remove target and id column.\ntrain_features = X_train.drop(columns=[target])\n# group all the numeric features.\nnumeric_features = train_features.select_dtypes(include='number').columns.tolist()\n# group the cardinality of the nonnumeric features.\ncardinality = train_features.select_dtypes(exclude='number').nunique()\n# group all categorical features with cardinality <= 100.\ncategorical_features = cardinality[cardinality <= 500].index.tolist()\n# create features with numeric + categorical\nfeatures = numeric_features + categorical_features\n# create the new vaules with the new features/target data.\nX_train = X_train[features]\nX_val = X_val[features]", "_____no_output_____" ], [ "!pip install category_encoders", "Collecting category_encoders\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/a0/52/c54191ad3782de633ea3d6ee3bb2837bda0cf3bc97644bb6375cf14150a0/category_encoders-2.1.0-py2.py3-none-any.whl (100kB)\n\r\u001b[K |███▎ | 10kB 16.1MB/s eta 0:00:01\r\u001b[K |██████▌ | 20kB 7.2MB/s eta 0:00:01\r\u001b[K |█████████▉ | 30kB 10.0MB/s eta 0:00:01\r\u001b[K |█████████████ | 40kB 6.4MB/s eta 0:00:01\r\u001b[K |████████████████▍ | 51kB 7.7MB/s eta 0:00:01\r\u001b[K |███████████████████▋ | 61kB 9.1MB/s eta 0:00:01\r\u001b[K |██████████████████████▉ | 71kB 10.3MB/s eta 0:00:01\r\u001b[K |██████████████████████████▏ | 81kB 11.6MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▍ | 92kB 12.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 102kB 9.2MB/s \n\u001b[?25hRequirement already satisfied: statsmodels>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.10.1)\nRequirement already satisfied: patsy>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.5.1)\nRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.16.5)\nRequirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.24.2)\nRequirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.21.3)\nRequirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.3.1)\nRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.1->category_encoders) (1.12.0)\nRequirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2.5.3)\nRequirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\nRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20.0->category_encoders) (0.14.0)\nInstalling collected packages: category-encoders\nSuccessfully installed category-encoders-2.1.0\n" ], [ "import category_encoders as ce\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\npipeline = make_pipeline(\n ce.OneHotEncoder(use_cat_names=True), \n SimpleImputer(strategy='median'), \n StandardScaler(),\n LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, n_jobs=-1)\n)\n\n# Fit on train, score on val\npipeline.fit(X_train, y_train)\nprint ('Training Accuracy', pipeline.score(X_train, y_train))\nprint('Validation Accuracy', pipeline.score(X_val, y_val))\ny_pred = pipeline.predict(X_val)", "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: 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" ] ] ]

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[ [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "___\n\n<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n___\n# Matplotlib Exercises \n\nWelcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along.\n\nAlso don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using seaborn and pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it!\n\n** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n\n# Exercises\n\nFollow the instructions to recreate the plots using this data:\n\n## Data", "_____no_output_____" ] ], [ [ "import numpy as np\nx = np.arange(0,100)\ny = x*2\nz = x**2", "_____no_output_____" ] ], [ [ "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ] ], [ [ "## Exercise 1\n\n** Follow along with these steps: **\n* ** Create a figure object called fig using plt.figure() **\n* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**", "_____no_output_____" ] ], [ [ "# Functional Method\nfig = plt.figure()\nax = fig.add_axes([0, 0, 1, 1])\nax.plot(x, y)\nax.set_title('title')\nax.set_xlabel('X')\nax.set_ylabel('Y')", "_____no_output_____" ] ], [ [ "## Exercise 2\n** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**", "_____no_output_____" ] ], [ [ "# create figure canvas\nfig = plt.figure()\n# create axes\nax1 = fig.add_axes([0,0,1,1])\nax2 = fig.add_axes([0.2,0.5,.2,.2])\n\nplt.xticks(np.arange(0, 1.2, step=0.2))\nplt.yticks(np.arange(0, 1.2, step=0.2))", "_____no_output_____" ] ], [ [ "** Now plot (x,y) on both axes. And call your figure object to show it.**", "_____no_output_____" ] ], [ [ "# create figure canvas\nfig = plt.figure()\n# create axes\nax1 = fig.add_axes([0,0,1,1])\nax2 = fig.add_axes([0.2,0.5,.2,.2])\n\nax1.set_xlabel('x1')\nax1.set_ylabel('y1')\nax2.set_xlabel('x2')\nax2.set_ylabel('y2')\n\nax1.plot(x, y, 'r-')\nax2.plot(x, y, 'b--')\n\nplt.xticks(np.arange(0, 120, step=20))\nplt.yticks(np.arange(0, 220, step=50))", "_____no_output_____" ] ], [ [ "## Exercise 3\n\n** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**", "_____no_output_____" ] ], [ [ "fig = plt.figure()\nax1 = fig.add_axes([0,0,1,1])\nax2 = fig.add_axes([0.2,0.5,.4,.4])", "_____no_output_____" ] ], [ [ "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**", "_____no_output_____" ] ], [ [ "fig = plt.figure()\nax1 = fig.add_axes([0,0,1,1])\nax2 = fig.add_axes([0.2,0.5,.4,.4])\nax1.plot(x, z)\nax2.plot(x, y, 'r--') # zoom using xlimit (20, 22), ylimit (30, 50)\nax2.set_xlim([20, 22])\nax2.set_ylim([30, 50])\n\nax2.set_title('zoom')\nax2.set_xlabel('X')\nax2.set_ylabel('Y')\n\nax1.set_xlabel('X')\nax1.set_ylabel('Z')\n", "_____no_output_____" ] ], [ [ "## Exercise 4\n\n** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**", "_____no_output_____" ] ], [ [ "fig, axes = plt.subplots(nrows=1, ncols=2)\n# axes object is an array of subplot axis.\nplt.tight_layout() # add space between rows & columns.", "_____no_output_____" ] ], [ [ "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**", "_____no_output_____" ] ], [ [ "fig, axes = plt.subplots(nrows=1, ncols=2)\n# axes object is an array of subplot axis.\n\naxes[0].plot(x, y, 'b--', lw=3)\naxes[1].plot(x, z, 'r-.', lw=2)\nplt.tight_layout() # add space between rows & columns.\n\n", "_____no_output_____" ] ], [ [ "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**", "_____no_output_____" ] ], [ [ "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 7))\n# axes object is an array of subplot axis.\n\naxes[0].plot(x, y, 'b--', lw=3)\naxes[1].plot(x, z, 'r-.', lw=2)\nplt.tight_layout() # add space between rows & columns.", "_____no_output_____" ] ], [ [ "# Great Job!", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Turn on Auto-Complete\n%config IPCompleter.greedy=True", "_____no_output_____" ], [ "# Start logging process at root level\nimport logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\nlogging.root.setLevel(level=logging.INFO)", "_____no_output_____" ], [ "# Load model and dictionary\n#model_id_current = 99999\n#model_path_current = \"models/enwiki-full-dict-\"+str(model_id_current)+\".model\"\n#model_path_99999 = \"models/enwiki-20190319-lemmatized-99999.model\"\nmodel_path_current =\"models/enwiki-20190409-lemmatized.model\"\n\ndictionary_full_wikien_lem_path = \"dictionaries/enwiki-20190409-dict-lemmatized.txt.bz2\"", "_____no_output_____" ], [ "# Load word2vec unlemmatized model\nfrom gensim.models import Word2Vec\nmodel = Word2Vec.load(model_path_current, mmap='r')", "2019-04-23 12:51:02,012 : INFO : 'pattern' package found; tag filters are available for English\n2019-04-23 12:51:02,021 : INFO : loading Word2Vec object from models/enwiki-20190409-lemmatized.model\n2019-04-23 12:52:19,106 : INFO : loading wv recursively from models/enwiki-20190409-lemmatized.model.wv.* with mmap=r\n2019-04-23 12:52:19,109 : INFO : loading vectors from models/enwiki-20190409-lemmatized.model.wv.vectors.npy with mmap=r\n2019-04-23 12:52:19,130 : INFO : setting ignored attribute vectors_norm to None\n2019-04-23 12:52:19,135 : INFO : loading vocabulary recursively from models/enwiki-20190409-lemmatized.model.vocabulary.* with mmap=r\n2019-04-23 12:52:19,137 : INFO : loading trainables recursively from models/enwiki-20190409-lemmatized.model.trainables.* with mmap=r\n2019-04-23 12:52:19,139 : INFO : loading syn1neg from models/enwiki-20190409-lemmatized.model.trainables.syn1neg.npy with mmap=r\n2019-04-23 12:52:19,143 : INFO : loading vectors_lockf from models/enwiki-20190409-lemmatized.model.trainables.vectors_lockf.npy with mmap=r\n2019-04-23 12:52:19,146 : INFO : setting ignored attribute cum_table to None\n2019-04-23 12:52:19,147 : INFO : loaded models/enwiki-20190409-lemmatized.model\n" ], [ "# Custom lemmatizer function to play with word\nfrom gensim.utils import lemmatize\n#vocabulary = set(wv.index2word)\ndef lem(word):\n try:\n return lemmatize(word)[0].decode(\"utf-8\")\n except:\n pass\n \nprint(lem(\"dog\"))\nprint(lem(\"that\"))", "dog/NN\nNone\n" ], [ "# Testing similarity\nprint(\"Most similar to\",\"woman\")\nprint(model.wv.most_similar(lem(\"woman\")))", "Most similar to woman\n[('man/NN', 0.6361385583877563), ('individual/NN', 0.5763572454452515), ('person/NN', 0.5568535327911377), ('girl/NN', 0.5561789274215698), ('female/JJ', 0.54877769947052), ('female/NN', 0.5409574508666992), ('lesbian/NN', 0.5204395055770874), ('gender/NN', 0.5188673734664917), ('child/NN', 0.4933265447616577), ('feminist/NN', 0.4769814610481262)]\n" ], [ "print(\"Most similar to\",\"doctor\")\nprint(model.wv.most_similar(lem(\"doctor\")))", "Most similar to doctor\n[('dentist/NN', 0.5610849261283875), ('nardole/NN', 0.5584279894828796), ('nurse/NN', 0.5565731525421143), ('physician/NN', 0.5523163080215454), ('dolittle/RB', 0.5519494414329529), ('psychiatrist/NN', 0.5512733459472656), ('veterinarian/NN', 0.523552417755127), ('naakudu/RB', 0.5198249816894531), ('zhivago/VB', 0.5178859233856201), ('senocak/RB', 0.5164788961410522)]\n" ], [ "# Saving some ram by using the KeyedVectors instance\nwv = model.wv\n#del model", "_____no_output_____" ], [ "# Testing similarity with KeyedVectors\nprint(\"Most similar to\",\"woman\")\nprint(wv.most_similar(lem(\"woman\")))\nprint(\"\\nMost similar to\",\"man\")\nprint(wv.most_similar(lem(\"man\")))\nprint(\"\\nMost similar to\",\"doctor\")\nprint(wv.most_similar(lem(\"doctor\")))\nprint(\"\\nMost similar to\",\"doctor\",\"cosmul\")\nprint(wv.most_similar_cosmul(positive=[lem(\"doctor\")]))", "Most similar to woman\n[('man/NN', 0.6361385583877563), ('individual/NN', 0.5763572454452515), ('person/NN', 0.5568535327911377), ('girl/NN', 0.5561789274215698), ('female/JJ', 0.54877769947052), ('female/NN', 0.5409574508666992), ('lesbian/NN', 0.5204395055770874), ('gender/NN', 0.5188673734664917), ('child/NN', 0.4933265447616577), ('feminist/NN', 0.4769814610481262)]\n\nMost similar to man\n[('woman/NN', 0.6361386179924011), ('boy/NN', 0.5653619170188904), ('person/NN', 0.5352815389633179), ('girl/NN', 0.5206164121627808), ('individual/NN', 0.49065500497817993), ('soldier/NN', 0.4820939302444458), ('spiderlongclose/VB', 0.46156802773475647), ('someone/NN', 0.4581664502620697), ('mcglurk/NN', 0.4518073499202728), ('one/NN', 0.4458999037742615)]\n\nMost similar to doctor\n[('dentist/NN', 0.5610849261283875), ('nardole/NN', 0.5584279894828796), ('nurse/NN', 0.5565731525421143), ('physician/NN', 0.5523163080215454), ('dolittle/RB', 0.5519494414329529), ('psychiatrist/NN', 0.5512733459472656), ('veterinarian/NN', 0.523552417755127), ('naakudu/RB', 0.5198249816894531), ('zhivago/VB', 0.5178859233856201), ('senocak/RB', 0.5164788961410522)]\n\nMost similar to doctor cosmul\n[('dentist/NN', 0.7805417776107788), ('nardole/NN', 0.7792133092880249), ('nurse/NN', 0.7782858610153198), ('physician/NN', 0.7761574387550354), ('dolittle/RB', 0.7759740352630615), ('psychiatrist/NN', 0.7756359577178955), ('veterinarian/NN', 0.7617754936218262), ('naakudu/RB', 0.7599117755889893), ('zhivago/VB', 0.7589422464370728), ('senocak/RB', 0.7582387328147888)]\n" ], [ "print(\"similarity of doctor + woman - man\")\nwv.most_similar(positive=[lem(\"doctor\"),lem(\"woman\")], negative=[lem(\"man\")])", "similarity of doctor + woman - man\n" ], [ "# Get cosmul of logic\nprint(\"cosmul of doctor + woman - man\")\nwv.most_similar_cosmul(positive=[lem(\"doctor\"),lem(\"woman\")], negative=[lem(\"man\")])", "cosmul of doctor + woman - man\n" ], [ "# Ways to retrive word vector\nprint(\"Get item dog\")\nvec_dog = wv.__getitem__(\"dog/NN\")\nvec_dog = wv.get_vector(\"dog/NN\")\nvec_dog = wv.word_vec(\"dog/NN\")\nprint(\"vec_dog\", vec_dog.shape, vec_dog[:10])", "Get item dog\nvec_dog (300,) [-0.13817333 -1.8090004 -0.45946687 -2.2184215 1.4197063 0.19401991\n -0.4230487 -2.7905297 -3.1192808 0.02542385]\n" ], [ "# Get similar words to vector\nprint(\"Similar by vector to dog vector at top 10\")\nprint(wv.similar_by_vector(vector=vec_dog, topn=10, restrict_vocab=None))\nprint(\"Most similar to dog vector\")\nprint(wv.most_similar(positive=[vec_dog]))\nprint(\"Similar to cat vector\")\nvec_cat = wv.word_vec(\"cat/NN\")\nprint(wv.most_similar(positive=[vec_cat]))", "Similar by vector to dog vector at top 10\n[('dog/NN', 1.0000001192092896), ('cat/NN', 0.7325705289840698), ('puppy/NN', 0.7017959356307983), ('dogs/NN', 0.6933757066726685), ('dachshund/NN', 0.6861976981163025), ('poodle/NN', 0.6616296768188477), ('rottweiler/JJ', 0.6588126420974731), ('hound/NN', 0.6473891139030457), ('pet/NN', 0.6361759901046753), ('pekingese/JJ', 0.6296164989471436)]\nMost similar to dog vector\n[('dog/NN', 1.0000001192092896), ('cat/NN', 0.7325705289840698), ('puppy/NN', 0.7017959356307983), ('dogs/NN', 0.6933757066726685), ('dachshund/NN', 0.6861976981163025), ('poodle/NN', 0.6616296768188477), ('rottweiler/JJ', 0.6588126420974731), ('hound/NN', 0.6473891139030457), ('pet/NN', 0.6361759901046753), ('pekingese/JJ', 0.6296164989471436)]\nSimilar to cat vector\n[('cat/NN', 1.0), ('dog/NN', 0.7325705885887146), ('meow/VB', 0.6924092769622803), ('kitten/NN', 0.6764312982559204), ('catjuan/JJ', 0.639890193939209), ('rabbit/NN', 0.6368660926818848), ('meow/NN', 0.635756254196167), ('fraidy/JJ', 0.6297948360443115), ('poodle/NN', 0.6230608224868774), ('pet/NN', 0.6119345426559448)]\n" ], [ "# closer to __ than __\nprint(\"closer to dog than cat\")\nprint(wv.words_closer_than(\"dog/NN\", \"cat/NN\"))\nprint(\"\\ncloser to cat than dog\")\nprint(wv.words_closer_than(\"cat/NN\", \"dog/NN\"))", "closer to dog than cat\n[]\n\ncloser to cat than dog\n[]\n" ], [ "# Normalized Vector\nvec_king_norm = wv.word_vec(\"king/NN\", use_norm=True)\nprint(\"vec_king_norm:\",vec_king_norm.shape, vec_king_norm[:10])\n# Not normalized vectore\nvec_king_unnorm = wv.word_vec(\"king/NN\", use_norm=False)\nprint(\"vec_king_unnorm:\",vec_king_norm.shape, vec_king_unnorm[:10])", "vec_king_norm: (300,) [ 0.02464886 0.09053605 0.00468578 -0.01604057 0.0808396 0.10550086\n 0.01262516 -0.0464116 -0.06513052 -0.08347644]\nvec_king_unnorm: (300,) [ 0.6677862 2.4528 0.12694712 -0.43457067 2.190104 2.8582263\n 0.34204054 -1.2573817 -1.764514 -2.2615411 ]\n" ], [ "wv.most_similar(positive=[vec_king_norm], negative=[vec_king_unnorm])", "_____no_output_____" ], [ "# Generate random vector\nimport numpy as np\nvec_random = np.random.rand(300,)\nvec_random_norm = vec_random / vec_random.max(axis=0)\nprint(\"similar to random vector\")\nprint(wv.most_similar(positive=[vec_random]))\nprint(\"\\n similar to nomalized random vector\")\nprint(wv.most_similar(positive=[vec_random_norm]))", "similar to random vector\n[('parigine/VB', 0.28092506527900696), ('nmcue/NN', 0.2804727852344513), ('mozart_kv/NN', 0.2786831855773926), ('tabuur/VB', 0.27440786361694336), ('tanantella/NN', 0.2741648852825165), ('kalfhani/NN', 0.27331894636154175), ('小法廷/VB', 0.27157384157180786), ('molagholi/VB', 0.2650672495365143), ('kriesinger/NN', 0.2646629810333252), ('shōhōtei/NN', 0.26364269852638245)]\n\n similar to nomalized random vector\n[('parigine/VB', 0.28092506527900696), ('nmcue/NN', 0.2804727852344513), ('mozart_kv/NN', 0.2786831855773926), ('tabuur/VB', 0.27440786361694336), ('tanantella/NN', 0.2741648852825165), ('kalfhani/NN', 0.27331894636154175), ('小法廷/VB', 0.27157384157180786), ('molagholi/VB', 0.2650672495365143), ('kriesinger/NN', 0.2646629810333252), ('shōhōtei/NN', 0.26364269852638245)]\n" ], [ "# Get similarity from a random vector and normilized king vector\nprint(\"similarity from a normalized random vector to normalized vector of king\")\nwv.most_similar(positive=[vec_random_norm,vec_king_norm])", "similarity from a normalized random vector to normalized vector of king\n" ], [ "# Get similarity from a random vector and unormalized king vector\nprint(\"similarity from a random vector to unormalized vector of king\")\nwv.most_similar(positive=[vec_random,vec_king_unnorm])", "similarity from a random vector to unormalized vector of king\n" ], [ "# Get cosine similarities from a vector to an array of vectors\nprint(\"cosine similarity from a random vector to unormalized vector of king\")\nwv.cosine_similarities(vec_random, [vec_king_unnorm])", "cosine similarity from a random vector to unormalized vector of king\n" ], [ "# Tests analogies based on a text file\nanalogy_scores = wv.accuracy('datasets/questions-words.txt')\n#print(analogy_scores)", "_____no_output_____" ], [ "# The the distance of two words\nprint(\"distance between dog and cat\")\nwv.distance(\"dog/NN\",\"cat/NN\")", "distance between dog and cat\n" ], [ "# Get the distance of a word for the list of word\nprint(\"distance from dog to king and cat\")\nwv.distances(\"dog/NN\",[\"king/NN\",\"cat/NN\"])", "distance from dog to king and cat\n" ], [ "# Evaluate pairs of words\n#wv.evaluate_word_pairs(\"datasets/SimLex-999.txt\")", "_____no_output_____" ], [ "# Get sentence similarities\n\nfrom gensim.models import KeyedVectors\nfrom gensim.utils import simple_preprocess \n\ndef tokemmized(sentence, vocabulary):\n tokens = [lem(word) for word in simple_preprocess(sentence)]\n return [word for word in tokens if word in vocabulary] \n\ndef compute_sentence_similarity(sentence_1, sentence_2, model_wv):\n vocabulary = set(model_wv.index2word)\n tokens_1 = tokemmized(sentence_1, vocabulary)\n tokens_2 = tokemmized(sentence_2, vocabulary)\n del vocabulary\n print(tokens_1, tokens_2)\n return model_wv.n_similarity(tokens_1, tokens_2)\n\nsimilarity = compute_sentence_similarity('this is a sentence', 'this is also a sentence', wv)\nprint(similarity,\"\\n\")\n\nsimilarity = compute_sentence_similarity('the cat is a mammal', 'the bird is a aves', wv)\nprint(similarity,\"\\n\")\n\nsimilarity = compute_sentence_similarity('the cat is a mammal', 'the dog is a mammal', wv)\nprint(similarity)", "['be/VB', 'sentence/NN'] ['be/VB', 'also/RB', 'sentence/NN']\n0.9267933550381176 \n\n['cat/NN', 'be/VB', 'mammal/NN'] ['bird/NN', 'be/VB', 'ave/NN']\n0.6503839221443558 \n\n['cat/NN', 'be/VB', 'mammal/NN'] ['dog/NN', 'be/VB', 'mammal/NN']\n0.9425444280677167\n" ], [ "# Analogy with not normalized vectors\nprint(\"france is to paris as berlin is to ?\")\nwv.most_similar([wv['france/NN'] - wv['paris/NN'] + wv['berlin/NN']])", "france is to paris as berlin is to ?\n" ], [ "# Analogy with normalized Vector\nvec_france_norm = wv.word_vec('france/NN', use_norm=True)\nvec_paris_norm = wv.word_vec('paris/NN', use_norm=True)\nvec_berlin_norm = wv.word_vec('berlin/NN', use_norm=True)\nvec_germany_norm = wv.word_vec('germany/NN', use_norm=True)\nvec_country_norm = wv.word_vec('country/NN', use_norm=True)\nprint(\"france is to paris as berlin is to ?\")\nwv.most_similar([vec_france_norm - vec_paris_norm + vec_berlin_norm])", "france is to paris as berlin is to ?\n" ], [ "# Cosine Similarities\nprint(\"cosine_similarities of france and paris\")\nprint(wv.cosine_similarities(vec_france_norm, [vec_paris_norm]),wv.distance(\"france/NN\", \"paris/NN\"))\nprint(\"cosine_similarities of france and berlin\")\nprint(wv.cosine_similarities(vec_france_norm, [vec_berlin_norm]),wv.distance(\"france/NN\", \"berlin/NN\"))\nprint(\"cosine_similarities of france and germany\")\nprint(wv.cosine_similarities(vec_france_norm, [vec_germany_norm]),wv.distance(\"france/NN\", \"germany/NN\"))\nprint(\"cosine_similarities of france and country\")\nprint(wv.cosine_similarities(vec_france_norm, [vec_country_norm]),wv.distance(\"france/NN\", \"country/NN\"))", "cosine_similarities of france and paris\n[0.62629485] 0.37370521250384203\ncosine_similarities of france and berlin\n[0.26217574] 0.7378242844644337\ncosine_similarities of france and germany\n[0.56096226] 0.4390377899399447\ncosine_similarities of france and country\n[0.35918537] 0.6408146341093731\n" ], [ "# Analogy\nprint(\"king is to man what woman is to ?\")\n#wv.most_similar([wv['man/NN'] - wv['woman/NN'] + wv['king/NN']])\nwv.most_similar([wv['king/NN'] - wv['man/NN'] + wv['woman/NN']])", "Man is to Woman what King is to ?\n" ], [ "# Analogy\nprint(\"paris is to france as germany is to ?\")\nwv.most_similar([wv['paris/NN'] - wv['france/NN'] + wv['germany/NN']])", "paris is to france as germany is to ?\n" ], [ "# Analogy\nprint(\"cat is to mammal as sparrow is to ?\")\nwv.most_similar([wv['cat/NN'] - wv['mammal/NN'] + wv['bird/NN']])", "cat is to mammal as sparrow is to ?\n" ], [ "# Analogy\nprint(\"grass is to green as sky is to ?\")\nwv.most_similar([wv['sky/NN'] - wv['blue/NN'] + wv['green/NN']])", "grass is to green as sky is to ?\n" ], [ "# Analogy\nprint(\"athens is to greece as baghdad is to ?\")\nwv.most_similar([wv['athens/NN'] - wv['greece/NN'] + wv['afghanistan/NN']])", "athens is to greece as baghdad is to ?\n" ], [ "wv.most_similar([wv[\"country/NN\"]])", "_____no_output_____" ], [ "wv.most_similar([wv[\"capital/NN\"]])", "_____no_output_____" ], [ "wv.most_similar([wv[\"paris/NN\"]-wv[\"capital/NN\"]])", "_____no_output_____" ], [ "wv.most_similar([wv[\"bern/NN\"]-wv[\"capital/NN\"]])", "_____no_output_____" ], [ "wv.most_similar([wv[\"switzerland/NN\"]-wv[\"bern/NN\"]])", "_____no_output_____" ], [ "wv.distance(\"dog/NN\", \"dogs/NN\")", "_____no_output_____" ], [ "wv.cosine_similarities(wv[\"dog/NN\"],[wv[\"dogs/NN\"]])", "_____no_output_____" ], [ "wv.distance(\"switzerland/NN\", \"bern/NN\")", "_____no_output_____" ], [ "wv.cosine_similarities(wv[\"switzerland/NN\"],[wv[\"bern/NN\"]])", "_____no_output_____" ], [ "wv.distance(\"paris/NN\", \"bern/NN\")", "_____no_output_____" ], [ "wv.cosine_similarities(wv[\"paris/NN\"],[wv[\"bern/NN\"]])", "_____no_output_____" ], [ "wv.cosine_similarities(wv[\"paris/NN\"],[wv[\"dog/NN\"]])", "_____no_output_____" ], [ "# Analogy\nprint(\"capital + science\")\nwv.most_similar([wv['capital/NN'] + wv['science/NN']])", "_____no_output_____" ], [ "wv.cosine_similarities(wv[\"education/NN\"], [wv[\"natality/NN\"],wv[\"salubrity/NN\"],wv[\"economy/NN\"]]\n\n#wv.distance(\"education\",\"natality\")\n\n# education, natality, salubrity, economy\n\n#wv.most_similar_cosmul(positive=[\"doctor\",\"woman\"], negative=[\"man\"])", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport psycopg2\nimport sqlalchemy\nimport config\nimport json\nimport numpy as np\nimport scrape", "_____no_output_____" ], [ "scrape.scrape_bls()", "Table 'blsdata' already exists.\n" ], [ "from sqlalchemy import create_engine\nfrom config import password\nEngine = create_engine(f\"postgresql://postgres:{password}@localhost:5432/Employee_Turnover\")\nConnection = Engine.connect()\ninitial_df=pd.read_sql(\"select * from turnover_data\", Connection)\nbls_df=pd.read_sql(\"select * from blsdata\", Connection)\nConnection.close()", "_____no_output_____" ], [ "bls_df.drop(bls_df.index[[35,36]])\nbls_df.drop(\"index\",axis=1,inplace=True)\nbls_df.head()", "_____no_output_____" ], [ "# Clean data: remove less helpful columns; rename values to be more user-friendly\ndf = initial_df.drop(['index','EducationField','EmployeeCount','EmployeeNumber','Education','StandardHours','JobRole','MaritalStatus',\n 'DailyRate','MonthlyRate','HourlyRate','Over18','OverTime','TotalWorkingYears'], axis=1).drop_duplicates()\ndf.rename(columns={'Attrition': 'Employment Status','BusinessTravel':'Business Travel','DistanceFromHome':'Commute (Miles)','EnvironmentSatisfaction':'Environment Satisfaction',\n 'JobInvolvement':'Job Involvement','JobLevel':'Job Level','JobSatisfaction':'Job Satisfaction',\n 'MonthlyIncome':'Monthly Income','NumCompaniesWorked':'Num Companies Worked','PercentSalaryHike':'Last Increase %',\n 'PerformanceRating':'Performance Rating','RelationshipSatisfaction':'Relationship Satisfaction','StockOptionLevel':'Stock Option Level',\n 'TrainingTimesLastYear':'Training Last Year','WorkLifeBalance':'Work/Life Balance','YearsAtCompany':'Tenure (Years)',\n 'YearsInCurrentRole':'Years in Role','YearsSinceLastPromotion':'Years Since Promotion','YearsWithCurrManager':'Years with Manager'}, inplace = True)\n\ndf['Employment Status'] = df['Employment Status'].replace(['No','Yes'],['Active','Terminated'])\ndf['Business Travel'] = df['Business Travel'].replace(['Travel_Rarely','Travel_Frequently','Non-Travel'],['Rare','Frequent','None'])\n\ncolumns = list(df)\nprint(columns)\ndf.head()", "_____no_output_____" ], [ "factors = ['Age', 'Business Travel', 'Department', 'Commute (Miles)', 'Environment Satisfaction', 'Gender', 'Job Involvement', 'Job Level', 'Job Satisfaction', 'Monthly Income', 'Performance Rating', 'Relationship Satisfaction', 'Stock Option Level', 'Training Last Year']\n", "_____no_output_____" ], [ "count18to29 = 0\ncount30to39 = 0\ncount40to49 = 0\ncount50to59 = 0\ncount60up = 0\n\n\nfor x in df['Age']:\n \n if x >=18 and x<30:\n count18to29 += 1 \n \n elif x >= 30 and x<40:\n count30to39 += 1\n \n elif x >= 40 and x<50:\n count40to49 += 1\n \n elif x >= 50 and x<60:\n count50to59 += 1\n \n elif x >= 60: \n count60up += 1\n \n else: pass\n ", "_____no_output_____" ], [ "age18to29_df= df.loc[(df['Age']>=18) & (df['Age']<30)]\nage18to29_act_df=age18to29_df.loc[(df['Employment Status'] == 'Active')]\nage18to29_term_df=age18to29_df.loc[(df['Employment Status'] == 'Terminated')]\n\nage30to39_df= df.loc[(df['Age']>=30) & (df['Age']<40)]\nage30to39_act_df=age30to39_df.loc[(df['Employment Status'] == 'Active')]\nage30to39_term_df=age30to39_df.loc[(df['Employment Status'] == 'Terminated')]\n\nage40to49_df= df.loc[(df['Age']>=40) & (df['Age']<50)]\nage40to49_act_df=age40to49_df.loc[(df['Employment Status'] == 'Active')]\nage40to49_term_df=age40to49_df.loc[(df['Employment Status'] == 'Terminated')]\n\nage50to59_df= df.loc[(df['Age']>=50) & (df['Age']<60)]\nage50to59_act_df=age50to59_df.loc[(df['Employment Status'] == 'Active')]\nage50to59_term_df=age50to59_df.loc[(df['Employment Status'] == 'Terminated')]\n\nage60up_df= df.loc[(df['Age']>=60)]\nage60up_act_df=age60up_df.loc[(df['Employment Status'] == 'Active')]\nage60up_term_df=age60up_df.loc[(df['Employment Status'] == 'Terminated')]\n\n\nper18to29Act=round((len(age18to29_act_df)/count18to29*100),1)\nper18to29Term=round((len(age18to29_term_df)/count18to29*100),1)\nper30to39Act=round((len(age30to39_act_df)/count30to39*100),1)\nper30to39Term=round((len(age30to39_term_df)/count30to39*100),1)\nper40to49Act=round((len(age40to49_act_df)/count40to49*100),1)\nper40to49Term=round((len(age40to49_term_df)/count40to49*100),1)\nper50to59Act=round((len(age50to59_act_df)/count50to59*100),1)\nper50to59Term=round((len(age50to59_term_df)/count50to59*100),1)\nper60upAct=round((len(age60up_act_df)/count60up*100),1)\nper60upTerm=round((len(age60up_term_df)/count60up*100),1)\n", "_____no_output_____" ], [ "age_dict={}\n\nage_dict['18_to_29Act'] = per18to29Act\nage_dict['18_to_29Term'] = per18to29Term\nage_dict['30_to_39Act'] = per30to39Act\nage_dict['30_to_39Term'] = per30to39Term\nage_dict['40_to_49Act'] = per40to49Act\nage_dict['40_to_49Term'] = per40to49Term\nage_dict['50_to_59Act'] = per50to59Act\nage_dict['50_to_59Term'] = per50to59Term\nage_dict['60upAct'] = per60upAct\nage_dict['60upTerm'] = per60upTerm\n\nparsed = json.loads(json.dumps(age_dict))\njson_age=json.dumps(parsed, indent=4, sort_keys=True)\n", "_____no_output_____" ], [ "new_df=df.groupby([\"Business Travel\",\"Employment Status\"]).count().reset_index()\nnew_df.head(8)", "_____no_output_____" ], [ "trav_df = new_df[\"Age\"].astype(float) \ntrav_df.head(6)", "_____no_output_____" ], [ "none_act=trav_df[2]\nnone_term=trav_df[3]\nnone_total=none_act + none_term\nrare_act=trav_df[4]\nrare_term=trav_df[5]\nrare_total=rare_act + rare_term\nfreq_act=trav_df[0]\nfreq_term=trav_df[1]\nfreq_total=freq_act+freq_term\n\nnone_act_rate=(none_act/none_total*100).round(1)\nnone_term_rate=(none_term/none_total*100).round(1)\nrare_act_rate=(rare_act/rare_total*100).round(1)\nrare__term_rate=(rare_term/rare_total*100).round(1)\nfreq_act_rate=(freq_act/freq_total*100).round(1)\nfreq_term_rate=(freq_term/freq_total*100).round(1)", "_____no_output_____" ], [ "trav_dict = {}\n\ntrav_dict['factor'] = 'Business Travel'\ntrav_dict['category'] = ['None','Rare','Frequent']\ntrav_dict['counts'] = [none_total,rare_total,freq_total]\ntrav_dict['Active'] = [none_act_rate,rare_act_rate,freq_act_rate]\ntrav_dict['Terminated'] = [none_term_rate, rare_term_rate, freq_term_rate]", "_____no_output_____" ], [ "new_dep_df=df.groupby([\"Department\",\"Employment Status\"]).count().reset_index()\nnew_dep_df.head()", "_____no_output_____" ], [ "dept_df = new_dep_df[\"Age\"].astype(float) \ndept_df.head(6)", "_____no_output_____" ], [ "hr_act=dept_df[0]\nhr_term=dept_df[1]\nhr_total=hr_act + hr_term\nrd_act=dept_df[2]\nrd_term=dept_df[3]\nrd_total=rd_act + rd_term\nsales_act=dept_df[4]\nsales_term=dept_df[5]\nsales_total=sales_act+sales_term\n\nhr_act_rate=(hr_act/hr_total*100).round(1)\nhr_term_rate=(hr_term/hr_total*100).round(1)\nrd_act_rate=(rd_act/rd_total*100).round(1)\nrd_term_rate=(rd_term/rd_total*100).round(1)\nsales_act_rate=(sales_act/sales_total*100).round(1)\nsales_term_rate=(sales_term/sales_total*100).round(1)", "_____no_output_____" ], [ "dept_dict = {}\n\ndept_dict['factor'] = 'Department'\ndept_dict['category'] = ['HR','R&D','Sales']\ndept_dict['counts'] = [hr_total,rd_total,sales_total]\ndept_dict['Active'] = [hr_act_rate,rd_act_rate,sales_act_rate]\ndept_dict['Terminated'] = [hr_term_rate, rd_term_rate, sales_term_rate]", "_____no_output_____" ], [ "jobIn_df=df.groupby([\"Job Involvement\",\"Employment Status\"]).count().reset_index() \n\njobIn_df.head(8)", "_____no_output_____" ], [ "JI=jobIn_df[\"Age\"].astype(float)\nJI.head(8)\ntype(JI)", "_____no_output_____" ], [ "JI1_act=JI[0]\nJI1_term=JI[1]\nJI1_total=(JI1_act + JI1_term)\nJI2_act=JI[2]\nJI2_term=JI[3]\nJI2_total=(JI2_act + JI2_term)\nJI3_act=JI[4]\nJI3_term=JI[5]\nJI3_total=(JI3_act + JI3_term)\nJI4_act=JI[6]\nJI4_term=JI[7]\nJI4_total=(JI4_act + JI4_term)\n\nrateJI1_act=(JI1_act/JI1_total*100).round(1)\nrateJI1_term=(JI1_term/JI1_total*100).round(1)\nrateJI2_act=(JI2_act/JI2_total*100).round(1)\nrateJI2_term=(JI2_term/JI2_total*100).round(1)\nrateJI3_act=(JI3_act/JI3_total*100).round(1)\nrateJI3_term=(JI3_term/JI3_total*100).round(1)\nrateJI4_act=(JI4_act/JI4_total*100).round(1)\nrateJI4_term=(JI4_term/JI4_total*100).round(1)\n\njobInvol_dict={}\n\njobInvol_dict['factor'] = 'Job Involvement'\njobInvol_dict['category'] = ['1','2','3','4']\njobInvol_dict['counts'] = [JI1_total, JI2_total, JI3_total, JI4_total]\njobInvol_dict['Active'] = [rateJI1_act, rateJI2_act, rateJI3_act, rateJI4_act]\njobInvol_dict['Terminated'] = [rateJI1_term, rateJI2_term, rateJI3_term, rateJI4_term]\n", "_____no_output_____" ], [ "gender_df=df.groupby([\"Gender\",\"Employment Status\"]).count().reset_index()\ngender_df = gender_df[\"Age\"].astype(float) \ngender_df.head()\n", "_____no_output_____" ], [ "fem_act_count=gender_df[0]\nfem_term_count=gender_df[1]\nfem_count=fem_act_count+fem_term_count\nmale_act_count=gender_df[2]\nmale_term_count=gender_df[3]\nmale_count=male_act_count+male_term_count\nfem_act_rate=(fem_act_count/fem_count*100).round(1)\nfem_term_rate=(fem_term_count/fem_count*100).round(1)\nmale_act_rate=(male_act_count/male_count*100).round(1)\nmale_term_rate=(male_term_count/male_count*100).round(1)\nprint(male_act_rate,male_term_rate)", "_____no_output_____" ], [ "gender_dict = {}\n\ngender_dict['factor'] = 'Gender'\ngender_dict['category'] = ['Male','Female']\ngender_dict['counts'] = [male_count, fem_count]\ngender_dict['Active'] = [male_act_rate, fem_act_rate]\ngender_dict['Terminated'] = [male_term_rate, fem_term_rate]", "_____no_output_____" ], [ "perf_df=df.groupby([\"Performance Rating\",\"Employment Status\"]).count().reset_index() \n", "_____no_output_____" ], [ "perf_df=perf_df[\"Age\"].astype(float)\n", "_____no_output_____" ], [ "count1_total=0\ncount2_total=0\ncount3_act=perf_df[0]\ncount3_term=perf_df[1]\ncount4_act=perf_df[2]\ncount4_term=perf_df[3]\ncount3_total=count3_act+count3_term\ncount4_total=count4_act+count4_term\n\nrate1_act=0\nrate1_term=0\nrate2_act=0\nrate2_term=0\nrate3_act=(count3_act/count3_total*100).round(1)\nrate3_term=(count3_term/count3_total*100).round(1)\nrate4_act=(count4_act/count4_total*100).round(1)\nrate4_term=(count4_term/count4_total*100).round(1)\n\nperf_dict={}\n\nperf_dict['factor'] = 'Performance Rating'\nperf_dict['category'] = ['1','2','3','4']\nperf_dict['counts'] = [count1_total, count2_total, count3_total, count4_total]\nperf_dict['Active'] = [rate1_act, rate2_act, rate3_act, rate4_act]\nperf_dict['Terminated'] = [rate1_term,rate2_term,rate3_term,rate4_term]\n", "_____no_output_____" ], [ "# Job Satisfaction \njs_df=df.groupby([\"Job Satisfaction\",\"Employment Status\"]).count().reset_index() \n", "_____no_output_____" ], [ "js=js_df[\"Age\"].astype(int)", "_____no_output_____" ], [ "actjs1 = js[0]\nterjs1 = js[1]\nalljs1 = js[0] + js[1]\nactjs2 = js[2]\nterjs2 = js[3]\nalljs2 = js[2] + js[3]\nactjs3 = js[4]\nterjs3 = js[5]\nalljs3 = js[4] + js[5]\nactjs4 = js[6]\nterjs4 = js[7]\nalljs4 = js[6] + js[7]\n\nrate_actjs1 = (actjs1/alljs1*100).round(1)\nrate_actjs2 = (actjs2/alljs2*100).round(1)\nrate_actjs3 = (actjs3/alljs3*100).round(1)\nrate_actjs4 = (actjs4/alljs4*100).round(1)\nrate_termjs1 = (terjs1/alljs1*100).round(1)\nrate_termjs2 = (terjs2/alljs2*100).round(1)\nrate_termjs3 = (terjs3/alljs3*100).round(1)\nrate_termjs4 = (terjs4/alljs4*100).round(1)\n\njs_dict = {}\n\njs_dict['factor'] = 'Job Satisfaction'\njs_dict['category'] = ['1','2','3','4']\njs_dict['counts'] = [alljs1,alljs2,alljs3,alljs4]\njs_dict['Active'] = [rate_actjs1, rate_actjs2, rate_actjs3, rate_actjs4]\njs_dict['Terminated'] = [rate_termjs1, rate_termjs2, rate_termjs3, rate_termjs4]\n", "_____no_output_____" ], [ "# Monthly Income\nbins = [1000, 3000, 5000, 7000, 20000]\nlabels=['<$3000','$3000-4999','$5000-6999','$7000 and up']\n\ngroups = df.groupby(['Employment Status', pd.cut(df['Monthly Income'], bins=bins, labels=labels)])\nmi=groups.size().reset_index().rename(columns={0:\"Count\"})", "_____no_output_____" ], [ "mi1_act=int(mi.iloc[0,2])\nmi1_term=int(mi.iloc[4,2])\nmi1_total=int((mi1_act + mi1_term))\nmi2_act=int(mi.iloc[1,2])\nmi2_term=int(mi.iloc[5,2])\nmi2_total=int((mi2_act + mi2_term))\nmi3_act=int(mi.iloc[2,2])\nmi3_term=int(mi.iloc[6,2])\nmi3_total=int((mi3_act + mi3_term))\nmi4_act=int(mi.iloc[3,2])\nmi4_term=int(mi.iloc[7,2])\nmi4_total=int((mi4_act + mi4_term))\n\nrate_mi1_act=(mi1_act/mi1_total*100)\nrate_mi1_term=(mi1_term/mi1_total*100)\nrate_mi2_act=(mi2_act/mi2_total*100)\nrate_mi2_term=(mi2_term/mi2_total*100)\nrate_mi3_act=(mi3_act/mi3_total*100)\nrate_mi3_term=(mi3_term/mi3_total*100)\nrate_mi4_act=(mi4_act/mi4_total*100)\nrate_mi4_term=(mi4_term/mi4_total*100)\n\nmi_dict={}\n\nmi_dict['factor'] = 'Monthly Income'\nmi_dict['category'] = [labels]\nmi_dict['counts'] = [mi1_total, mi2_total, mi3_total, mi4_total]\nmi_dict['Active'] = [rate_mi1_act, rate_mi2_act, rate_mi3_act, rate_mi4_act]\nmi_dict['Terminated'] = [rate_mi1_term, rate_mi2_term, rate_mi3_term, rate_mi4_term]", "_____no_output_____" ], [ "# Stock Options\nso_df=df.groupby([\"Stock Option Level\",\"Employment Status\"]).count().reset_index() ", "_____no_output_____" ], [ "so=so_df[\"Age\"].astype(float)\n", "_____no_output_____" ], [ "actso1 = so[0]\nterso1 = so[1]\nallso1 = so[0] + so[1]\nactso2 = so[2]\nterso2 = so[3]\nallso2 = so[2] + so[3]\nactso3 = so[4]\nterso3 = so[5]\nallso3 = so[4] + so[5]\nactso4 = so[6]\nterso4 = so[7]\nallso4 = so[6] + so[7]\n\nrate_actso1 = (actso1/allso1*100).round(1)\nrate_actso2 = (actso2/allso2*100).round(1)\nrate_actso3 = (actso3/allso3*100).round(1)\nrate_actso4 = (actso4/allso4*100).round(1)\nrate_termso1 = (terso1/allso1*100).round(1)\nrate_termso2 = (terso2/allso2*100).round(1)\nrate_termso3 = (terso3/allso3*100).round(1)\nrate_termso4 = (terso4/allso4*100).round(1)\n\nso_dict = {}\n\nso_dict['factor'] = 'Stock Option Level'\nso_dict['category'] = ['1','2','3','4']\nso_dict['counts'] = [allso1,allso2,allso3,allso4]\nso_dict['Active'] = [rate_actso1, rate_actso2, rate_actso3, rate_actso4]\nso_dict['Terminated'] = [rate_termso1, rate_termso2, rate_termso3, rate_termso4]", "_____no_output_____" ], [ "bins = [0,1,3,5,7]\nlabels=['None','1 or 2', '3 or 4', '5 or 6']\n\ngroupsT = df.groupby(['Employment Status', pd.cut(df['Training Last Year'], bins=bins, labels=labels)])\ntr=groupsT.size().reset_index().rename(columns={0:\"Count\",\"Training Last Year\":\"Trainings Last Year\"})\ntr.head(8)", "_____no_output_____" ], [ "tr1_act=int(tr.iloc[0,2])\ntr1_term=int(tr.iloc[4,2])\ntr1_total=int((tr1_act + tr1_term))\ntr2_act=int(tr.iloc[1,2])\ntr2_term=int(tr.iloc[5,2])\ntr2_total=int((tr2_act + tr2_term))\ntr3_act=int(tr.iloc[2,2])\ntr3_term=int(tr.iloc[6,2])\ntr3_total=int((tr3_act + tr3_term))\ntr4_act=int(tr.iloc[3,2])\ntr4_term=int(tr.iloc[7,2])\ntr4_total=int((tr4_act + tr4_term))\n\nrate_tr1_act=(tr1_act/tr1_total*100).round()\nrate_tr1_term=(tr1_term/tr1_total*100)\nrate_tr2_act=(tr2_act/tr2_total*100)\nrate_tr2_term=(tr2_term/tr2_total*100)\nrate_tr3_act=(tr3_act/tr3_total*100)\nrate_tr3_term=(tr3_term/tr3_total*100)\nrate_tr4_act=(tr4_act/tr4_total*100)\nrate_tr4_term=(tr4_term/tr4_total*100)\n\ntr_dict={}\n\ntr_dict['factor'] = 'Monthly Income'\ntr_dict['category'] = [labels]\ntr_dict['counts'] = [tr1_total, tr2_total, tr3_total, tr4_total]\ntr_dict['Active'] = [rate_tr1_act, rate_tr2_act, rate_tr3_act, rate_tr4_act]\ntr_dict['Terminated'] = [rate_tr1_term, rate_tr2_term, rate_tr3_term, rate_tr4_term]", "_____no_output_____" ], [ "bins = [0, 10, 20, 30]\nlabels=['<10 mi','10-19 mi','20-29 mi']\ngroups = df.groupby(['Employment Status', pd.cut(df['Commute (Miles)'], bins=bins, labels=labels)])\ncm=groups.size().reset_index().rename(columns={0:\"Count\"})\ncm.head(6)\n ", "_____no_output_____" ], [ "cm1_act=int(cm.iloc[0,2])\ncm1_term=int(cm.iloc[3,2])\ncm1_total=int((cm1_act + cm1_term))\ncm2_act=int(cm.iloc[1,2])\ncm2_term=int(cm.iloc[4,2])\ncm2_total=int((cm2_act + cm2_term))\ncm3_act=int(cm.iloc[2,2])\ncm3_term=int(cm.iloc[5,2])\ncm3_total=int((cm3_act + cm3_term))", "_____no_output_____" ], [ "rate_cm1_act=(cm1_act/cm1_total*100)\nrate_cm1_term=(cm1_term/cm1_total*100)\nrate_cm2_act=(cm2_act/cm2_total*100)\nrate_cm2_term=(cm2_term/cm2_total*100)\nrate_cm3_act=(cm3_act/cm3_total*100)\nrate_cm3_term=(cm3_term/cm3_total*100)\n ", "_____no_output_____" ], [ "cm_dict={}\n\ncm_dict['factor'] = 'Commute (Miles)'\ncm_dict['Category'] = [labels]\ncm_dict['Count'] = [cm1_total, cm2_total, cm3_total]\ncm_dict['Active'] = [rate_cm1_act, rate_cm2_act, rate_cm3_act]\ncm_dict['Terminated'] = [rate_cm1_term, rate_cm2_term, rate_cm3_term]", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Implementing the Gradient Descent Algorithm\n\nIn this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n#Some helper functions for plotting and drawing lines\n\ndef plot_points(X, y):\n admitted = X[np.argwhere(y==1)]\n rejected = X[np.argwhere(y==0)]\n plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'blue', edgecolor = 'k')\n plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'red', edgecolor = 'k')\n\ndef display(m, b, color='g--'):\n plt.xlim(-0.05,1.05)\n plt.ylim(-0.05,1.05)\n x = np.arange(-10, 10, 0.1)\n plt.plot(x, m*x+b, color)", "_____no_output_____" ] ], [ [ "## Reading and plotting the data", "_____no_output_____" ] ], [ [ "data = pd.read_csv('data.csv', header=None)\nX = np.array(data[[0,1]])\ny = np.array(data[2])\nplot_points(X,y)\nplt.show()", "_____no_output_____" ] ], [ [ "## TODO: Implementing the basic functions\nHere is your turn to shine. Implement the following formulas, as explained in the text.\n- Sigmoid activation function\n\n$$\\sigma(x) = \\frac{1}{1+e^{-x}}$$\n\n- Output (prediction) formula\n\n$$\\hat{y} = \\sigma(w_1 x_1 + w_2 x_2 + b)$$\n\n- Error function\n\n$$Error(y, \\hat{y}) = - y \\log(\\hat{y}) - (1-y) \\log(1-\\hat{y})$$\n\n- The function that updates the weights\n\n$$ w_i \\longrightarrow w_i + \\alpha (y - \\hat{y}) x_i$$\n\n$$ b \\longrightarrow b + \\alpha (y - \\hat{y})$$", "_____no_output_____" ] ], [ [ "# Implement the following functions\n\n# Activation (sigmoid) function\ndef sigmoid(x):\n exp = np.exp(-x)\n return 1/(1+exp)\n\n# Output (prediction) formula\ndef output_formula(features, weights, bias):\n return sigmoid(np.dot(features,weights)+bias)\n \n \n \n\n# Error (log-loss) formula\ndef error_formula(y, output):\n return -y*np.log(output)- (1 - y) * np.log(1-output)\n# Gradient descent step\ndef update_weights(x, y, weights, bias, learnrate):\n output = output_formula(x , weights , bias)\n error = y- output\n weights += learnrate*x*error\n bias += learnrate*error\n return weights, bias\n\n\n# # Activation (sigmoid) function\n# def sigmoid(x):\n# return 1 / (1 + np.exp(-x))\n\n# def output_formula(features, weights, bias):\n# return sigmoid(np.dot(features, weights) + bias)\n\n# def error_formula(y, output):\n# return - y*np.log(output) - (1 - y) * np.log(1-output)\n\n# def update_weights(x, y, weights, bias, learnrate):\n# output = output_formula(x, weights, bias)\n# d_error = y - output\n# weights += learnrate * d_error * x\n# bias += learnrate * d_error\n# return weights, bias\n", "_____no_output_____" ] ], [ [ "## Training function\nThis function will help us iterate the gradient descent algorithm through all the data, for a number of epochs. It will also plot the data, and some of the boundary lines obtained as we run the algorithm.", "_____no_output_____" ] ], [ [ "np.random.seed(44)\n\nepochs = 100\nlearnrate = 0.01\n\ndef train(features, targets, epochs, learnrate, graph_lines=False):\n \n errors = []\n n_records, n_features = features.shape\n last_loss = None\n weights = np.random.normal(scale=1 / n_features**.5, size=n_features)\n bias = 0\n for e in range(epochs):\n del_w = np.zeros(weights.shape)\n for x, y in zip(features, targets):\n output = output_formula(x, weights, bias)\n error = error_formula(y, output)\n weights, bias = update_weights(x, y, weights, bias, learnrate)\n \n # Printing out the log-loss error on the training set\n out = output_formula(features, weights, bias)\n loss = np.mean(error_formula(targets, out))\n errors.append(loss)\n if e % (epochs / 10) == 0:\n print(\"\\n========== Epoch\", e,\"==========\")\n if last_loss and last_loss < loss:\n print(\"Train loss: \", loss, \" WARNING - Loss Increasing\")\n else:\n print(\"Train loss: \", loss)\n last_loss = loss\n predictions = out > 0.5\n accuracy = np.mean(predictions == targets)\n print(\"Accuracy: \", accuracy)\n if graph_lines and e % (epochs / 100) == 0:\n display(-weights[0]/weights[1], -bias/weights[1])\n \n\n # Plotting the solution boundary\n plt.title(\"Solution boundary\")\n display(-weights[0]/weights[1], -bias/weights[1], 'black')\n\n # Plotting the data\n plot_points(features, targets)\n plt.show()\n\n # Plotting the error\n plt.title(\"Error Plot\")\n plt.xlabel('Number of epochs')\n plt.ylabel('Error')\n plt.plot(errors)\n plt.show()", "_____no_output_____" ] ], [ [ "## Time to train the algorithm!\nWhen we run the function, we'll obtain the following:\n- 10 updates with the current training loss and accuracy\n- A plot of the data and some of the boundary lines obtained. The final one is in black. Notice how the lines get closer and closer to the best fit, as we go through more epochs.\n- A plot of the error function. Notice how it decreases as we go through more epochs.", "_____no_output_____" ] ], [ [ "train(X, y, epochs, learnrate, True)", "\n========== Epoch 0 ==========\nTrain loss: 0.7135845195381633\nAccuracy: 0.4\n\n========== Epoch 10 ==========\nTrain loss: 0.6225835210454962\nAccuracy: 0.59\n\n========== Epoch 20 ==========\nTrain loss: 0.5548744083669508\nAccuracy: 0.74\n\n========== Epoch 30 ==========\nTrain loss: 0.501606141872473\nAccuracy: 0.84\n\n========== Epoch 40 ==========\nTrain loss: 0.4593334641861401\nAccuracy: 0.86\n\n========== Epoch 50 ==========\nTrain loss: 0.42525543433469976\nAccuracy: 0.93\n\n========== Epoch 60 ==========\nTrain loss: 0.39734615716713984\nAccuracy: 0.93\n\n========== Epoch 70 ==========\nTrain loss: 0.3741469765239074\nAccuracy: 0.93\n\n========== Epoch 80 ==========\nTrain loss: 0.3545997336816197\nAccuracy: 0.94\n\n========== Epoch 90 ==========\nTrain loss: 0.3379273658879921\nAccuracy: 0.94\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "CC0-1.0" ]

[ "CC0-1.0" ]

[ "CC0-1.0" ]

[ [ [ "from sklearn import tree\nclassifier = tree.DecisionTreeClassifier()", "_____no_output_____" ], [ "trainData = [[34],[35]]\ntrainLabel= [0, 1] # 0 - Fail, 1 - Pass\nclassifier.fit(trainData, trainLabel)", "_____no_output_____" ], [ "testData = [[25], [40], [90], [35], [34]]\ntestLabel = classifier.predict(testData)\nprint(testLabel)", "[0 1 1 1 0]\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import json\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import quad\nfrom scipy.special import comb\nfrom tabulate import tabulate\n\n%matplotlib inline", "_____no_output_____" ] ], [ [ "## Expected numbers on Table 3.", "_____no_output_____" ] ], [ [ "rows = []\ndatasets = {\n 'Binary': 2,\n 'AG news': 4,\n 'CIFAR10': 10,\n 'CIFAR100': 100,\n 'Wiki3029': 3029, \n}\n\n\ndef expectations(C: int) -> float:\n \"\"\"\n C is the number of latent classes.\n \"\"\"\n e = 0.\n\n for k in range(1, C + 1):\n e += C / k\n return e\n\n\nfor dataset_name, C in datasets.items():\n e = expectations(C)\n\n rows.append((dataset_name, C, np.ceil(e)))", "_____no_output_____" ], [ "# ImageNet is non-uniform label distribution on the training dataset\n\ndata = json.load(open(\"./imagenet_count.json\"))\ncounts = np.array(list(data.values()))\ntotal_num = np.sum(counts)\nprob = counts / total_num\n\n\ndef integrand(t: float, prob: np.ndarray) -> float:\n return 1. - np.prod(1 - np.exp(-prob * t))\n\n\nrows.append((\"ImageNet\", len(prob), np.ceil(quad(integrand, 0, np.inf, args=(prob))[0])))\n", "_____no_output_____" ], [ "print(tabulate(rows, headers=[\"Dataset\", \"\\# classes\", \"\\mathbb{E}[K+1]\"]))", "Dataset \\# classes \\mathbb{E}[K+1]\n--------- ------------ -----------------\nBinary 2 3\nAG news 4 9\nCIFAR10 10 30\nCIFAR100 100 519\nWiki3029 3029 26030\nImageNet 1000 7709\n" ] ], [ [ "## Probability $\\upsilon$", "_____no_output_____" ] ], [ [ "def prob(C, N):\n \"\"\"\n C: the number of latent class \n N: the number of samples to draw \n \"\"\"\n\n theoretical = []\n for n in range(C, N + 1):\n p = 0.\n for m in range(C - 1):\n p += comb(C - 1, m) * ((-1) ** m) * np.exp((n - 1) * np.log(1. - (m + 1) / C))\n\n theoretical.append((n, max(p, 0.)))\n\n return np.array(theoretical)\n", "_____no_output_____" ], [ "# example of CIFAR-10\n\nC = 10\nfor N in [32, 63, 128, 256, 512]:\n p = np.sum(prob(C, N).T[1])\n print(\"{:3d} {:.7f}\".format(N, p))\n", " 32 0.6909756\n 63 0.9869351\n128 0.9999861\n256 1.0000000\n512 1.0000000\n" ], [ "# example of CIFAR-100\n\nC = 100\nps = []\nns = []\n\nfor N in 128 * np.arange(1, 9):\n p = np.sum(prob(C, N).T[1])\n print(\"{:4d} {}\".format(N, p))\n ps.append(p)\n ns.append(N)", " 128 0.0004517171443332115\n 256 0.0005750103110269027\n 384 0.10845377001311465\n 512 0.5531327628081966\n 640 0.8510308810769567\n 768 0.956899070354311\n 896 0.9882414056661265\n1024 0.9970649738141432\n" ] ], [ [ "## Simulation", "_____no_output_____" ] ], [ [ "n_loop = 10\n\nrnd = np.random.RandomState(7)\nlabels = np.arange(C).repeat(100)\n\nresults = {}\n\nfor N in ns:\n\n num_iters = int(len(labels) / N)\n total_samples_for_bounds = float(num_iters * N * (n_loop))\n\n for _ in range(n_loop):\n rnd.shuffle(labels)\n\n for batch_id in range(len(labels) // N):\n\n if len(set(labels[N * batch_id:N * (batch_id + 1)])) == C:\n\n results[N] = results.get(N, 0.) + N / total_samples_for_bounds\n else:\n results[N] = results.get(N, 0.) + 0.\n\nxs = []\nys = []\nfor k, v in results.items():\n print(k, v)\n ys.append(v)\n xs.append(k)\n\n", "128 0.0\n256 0.0\n384 0.13076923076923075\n512 0.5789473684210534\n640 0.8733333333333351\n768 0.984615384615382\n896 0.9999999999999972\n1024 0.9999999999999984\n" ], [ "plt.plot(ns, ps, label=\"Theoretical\")\nplt.plot(xs, ys, label=\"Empirical\")\nplt.ylabel(\"probability\")\nplt.xlabel(\"$K+1$\")\nplt.title(\"CIFAR-100 simulation\")\nplt.legend()", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "### 3K Rice Genome GWAS Dataset Export Usage", "_____no_output_____" ], [ "Data for this was exported as single Hail MatrixTable (`.mt`) as well as individual variants (`csv.gz`), samples (`csv`), and call datasets (`zarr`).", "_____no_output_____" ] ], [ [ "from pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport hail as hl\nimport zarr\nhl.init()", "Running on Apache Spark version 2.4.4\nSparkUI available at http://8352602c2ab9:4041\nWelcome to\n __ __ <>__\n / /_/ /__ __/ /\n / __ / _ `/ / /\n /_/ /_/\\_,_/_/_/ version 0.2.32-a5876a0a2853\nLOGGING: writing to /home/eczech/repos/gwas-analysis/notebooks/organism/rice/hail-20200514-1737-0.2.32-a5876a0a2853.log\n" ], [ "path = Path('~/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export').expanduser()\npath", "_____no_output_____" ], [ "!du -sh {str(path)}/*", "582M\t/home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.calls.zarr\n336K\t/home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.cols.csv\n471M\t/home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.mt\n7.5M\t/home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.rows.csv.gz\n" ] ], [ [ "#### Hail", "_____no_output_____" ] ], [ [ "# The entire table with row, col, and call data:\nhl.read_matrix_table(str(path / 'rg-3k-gwas-export.mt')).describe()", "----------------------------------------\nGlobal fields:\n None\n----------------------------------------\nColumn fields:\n 's': str\n 'acc_seq_no': int64\n 'acc_stock_id': int64\n 'acc_gs_acc': float64\n 'acc_gs_variety_name': str\n 'acc_igrc_acc_src': int64\n 'pt_APANTH_REPRO': float64\n 'pt_APSH': float64\n 'pt_APCO_REV_POST': float64\n 'pt_APCO_REV_REPRO': float64\n 'pt_AWCO_LREV': float64\n 'pt_AWCO_REV': float64\n 'pt_AWDIST': float64\n 'pt_BLANTHPR_VEG': float64\n 'pt_BLANTHDI_VEG': float64\n 'pt_BLPUB_VEG': float64\n 'pt_BLSCO_ANTH_VEG': float64\n 'pt_BLSCO_REV_VEG': float64\n 'pt_CCO_REV_VEG': float64\n 'pt_CUAN_REPRO': float64\n 'pt_ENDO': float64\n 'pt_FLA_EREPRO': float64\n 'pt_FLA_REPRO': float64\n 'pt_INANTH': float64\n 'pt_LIGCO_REV_VEG': float64\n 'pt_LIGSH': float64\n 'pt_LPCO_REV_POST': float64\n 'pt_LPPUB': float64\n 'pt_LSEN': float64\n 'pt_NOANTH': float64\n 'pt_PEX_REPRO': float64\n 'pt_PTH': float64\n 'pt_SCCO_REV': float64\n 'pt_SECOND_BR_REPRO': float64\n 'pt_SLCO_REV': float64\n 'pt_SPKF': float64\n 'pt_SLLT_CODE': float64\n----------------------------------------\nRow fields:\n 'locus': locus<GRCh37>\n 'alleles': array<str>\n 'rsid': str\n 'cm_position': float64\n----------------------------------------\nEntry fields:\n 'GT': call\n----------------------------------------\nColumn key: ['s']\nRow key: ['locus', 'alleles']\n----------------------------------------\n" ] ], [ [ "### Pandas", "_____no_output_____" ], [ "Sample data contains phenotypes prefixed by `pt_` and `s` (sample_id) in the MatrixTable matches to the `s` in this table, as does the order:", "_____no_output_____" ] ], [ [ "pd.read_csv(path / 'rg-3k-gwas-export.cols.csv').head()", "_____no_output_____" ] ], [ [ "Variant data shouldn't be needed for much, but it's here:", "_____no_output_____" ] ], [ [ "pd.read_csv(path / 'rg-3k-gwas-export.rows.csv.gz').head()", "_____no_output_____" ] ], [ [ "### Zarr", "_____no_output_____" ], [ "Call data (dense and mean imputed in this case) can be sliced from a zarr array:", "_____no_output_____" ] ], [ [ "gt = zarr.open(str(path / 'rg-3k-gwas-export.calls.zarr'), mode='r')\n# Get calls for 10 variants and 5 samples\ngt[5:15, 5:10]", "_____no_output_____" ] ], [ [ "### Selecting Phenotypes", "_____no_output_____" ], [ "Pick a phenotype:\n \n- Definitions are in https://s3-ap-southeast-1.amazonaws.com/oryzasnp-atcg-irri-org/3kRG-phenotypes/3kRG_PhenotypeData_v20170411.xlsx\n - The \">2007 Dictionary\" sheet\n- Choose one with low sparsity\n", "_____no_output_____" ] ], [ [ "df = pd.read_csv(path / 'rg-3k-gwas-export.cols.csv')\ndf.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2113 entries, 0 to 2112\nData columns (total 37 columns):\ns 2113 non-null object\nacc_seq_no 2113 non-null int64\nacc_stock_id 2113 non-null int64\nacc_gs_acc 2113 non-null float64\nacc_gs_variety_name 2113 non-null object\nacc_igrc_acc_src 2113 non-null int64\npt_APANTH_REPRO 91 non-null float64\npt_APSH 133 non-null float64\npt_APCO_REV_POST 552 non-null float64\npt_APCO_REV_REPRO 2108 non-null float64\npt_AWCO_LREV 133 non-null float64\npt_AWCO_REV 2112 non-null float64\npt_AWDIST 30 non-null float64\npt_BLANTHPR_VEG 133 non-null float64\npt_BLANTHDI_VEG 13 non-null float64\npt_BLPUB_VEG 2112 non-null float64\npt_BLSCO_ANTH_VEG 133 non-null float64\npt_BLSCO_REV_VEG 2111 non-null float64\npt_CCO_REV_VEG 2110 non-null float64\npt_CUAN_REPRO 2111 non-null float64\npt_ENDO 1976 non-null float64\npt_FLA_EREPRO 133 non-null float64\npt_FLA_REPRO 2109 non-null float64\npt_INANTH 133 non-null float64\npt_LIGCO_REV_VEG 2111 non-null float64\npt_LIGSH 1430 non-null float64\npt_LPCO_REV_POST 2109 non-null float64\npt_LPPUB 1430 non-null float64\npt_LSEN 2110 non-null float64\npt_NOANTH 133 non-null float64\npt_PEX_REPRO 2112 non-null float64\npt_PTH 2109 non-null float64\npt_SCCO_REV 1985 non-null float64\npt_SECOND_BR_REPRO 1427 non-null float64\npt_SLCO_REV 1914 non-null float64\npt_SPKF 2111 non-null float64\npt_SLLT_CODE 2109 non-null float64\ndtypes: float64(32), int64(3), object(2)\nmemory usage: 610.9+ KB\n" ], [ "# First 1k variants with samples having data for this phenotype\nmask = df['pt_FLA_REPRO'].notnull()\ngtp = gt[:1000][:,mask]\ngtp.shape, gtp.dtype", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# PageRank Performance Benchmarking\n# Skip notebook test\n\nThis notebook benchmarks performance of running PageRank within cuGraph against NetworkX. NetworkX contains several implementations of PageRank. This benchmark will compare cuGraph versus the defaukt Nx implementation as well as the SciPy version\n\nNotebook Credits\n\n Original Authors: Bradley Rees\n Last Edit: 08/16/2020\n \nRAPIDS Versions: 0.15\n\nTest Hardware\n\n GV100 32G, CUDA 10,0\n Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz\n 32GB system memory\n \n", "_____no_output_____" ], [ "### Test Data\n\n| File Name | Num of Vertices | Num of Edges |\n|:---------------------- | --------------: | -----------: |\n| preferentialAttachment | 100,000 | 999,970 |\n| caidaRouterLevel | 192,244 | 1,218,132 |\n| coAuthorsDBLP | 299,067 | 1,955,352 |\n| dblp-2010 | 326,186 | 1,615,400 |\n| citationCiteseer | 268,495 | 2,313,294 |\n| coPapersDBLP | 540,486 | 30,491,458 |\n| coPapersCiteseer | 434,102 | 32,073,440 |\n| as-Skitter | 1,696,415 | 22,190,596 |\n\n\n", "_____no_output_____" ], [ "### Timing \nWhat is not timed: Reading the data\n\nWhat is timmed: (1) creating a Graph, (2) running PageRank\n\nThe data file is read in once for all flavors of PageRank. Each timed block will craete a Graph and then execute the algorithm. The results of the algorithm are not compared. If you are interested in seeing the comparison of results, then please see PageRank in the __notebooks__ repo. ", "_____no_output_____" ], [ "## NOTICE\n_You must have run the __dataPrep__ script prior to running this notebook so that the data is downloaded_\n\nSee the README file in this folder for a discription of how to get the data", "_____no_output_____" ], [ "## Now load the required libraries", "_____no_output_____" ] ], [ [ "# Import needed libraries\nimport gc\nimport time\nimport rmm\nimport cugraph\nimport cudf", "_____no_output_____" ], [ "# NetworkX libraries\nimport networkx as nx\nfrom scipy.io import mmread", "_____no_output_____" ], [ "try: \n import matplotlib\nexcept ModuleNotFoundError:\n os.system('pip install matplotlib')", "_____no_output_____" ], [ "import matplotlib.pyplot as plt; plt.rcdefaults()\nimport numpy as np", "_____no_output_____" ] ], [ [ "### Define the test data", "_____no_output_____" ] ], [ [ "# Test File\ndata = {\n 'preferentialAttachment' : './data/preferentialAttachment.mtx',\n 'caidaRouterLevel' : './data/caidaRouterLevel.mtx',\n 'coAuthorsDBLP' : './data/coAuthorsDBLP.mtx',\n 'dblp' : './data/dblp-2010.mtx',\n 'citationCiteseer' : './data/citationCiteseer.mtx',\n 'coPapersDBLP' : './data/coPapersDBLP.mtx',\n 'coPapersCiteseer' : './data/coPapersCiteseer.mtx',\n 'as-Skitter' : './data/as-Skitter.mtx'\n}", "_____no_output_____" ] ], [ [ "### Define the testing functions", "_____no_output_____" ] ], [ [ "# Data reader - the file format is MTX, so we will use the reader from SciPy\ndef read_mtx_file(mm_file):\n print('Reading ' + str(mm_file) + '...')\n M = mmread(mm_file).asfptype()\n \n return M", "_____no_output_____" ], [ "# CuGraph PageRank\n\ndef cugraph_call(M, max_iter, tol, alpha):\n\n gdf = cudf.DataFrame()\n gdf['src'] = M.row\n gdf['dst'] = M.col\n \n print('\\tcuGraph Solving... ')\n \n t1 = time.time()\n \n # cugraph Pagerank Call\n G = cugraph.DiGraph()\n G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)\n \n df = cugraph.pagerank(G, alpha=alpha, max_iter=max_iter, tol=tol)\n t2 = time.time() - t1\n \n return t2\n ", "_____no_output_____" ], [ "# Basic NetworkX PageRank\n\ndef networkx_call(M, max_iter, tol, alpha):\n nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n for nnz in range(M.getnnz()):\n nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n for nnz in range(M.getnnz()):\n M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n\n M = M.tocsr()\n if M is None:\n raise TypeError('Could not read the input graph')\n if M.shape[0] != M.shape[1]:\n raise TypeError('Shape is not square')\n\n # should be autosorted, but check just to make sure\n if not M.has_sorted_indices:\n print('sort_indices ... ')\n M.sort_indices()\n\n z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n \n print('\\tNetworkX Solving... ')\n \n # start timer\n t1 = time.time()\n \n Gnx = nx.DiGraph(M)\n\n pr = nx.pagerank(Gnx, alpha, z, max_iter, tol)\n \n t2 = time.time() - t1\n\n return t2", "_____no_output_____" ], [ "# SciPy PageRank\n\ndef networkx_scipy_call(M, max_iter, tol, alpha):\n nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}\n for nnz in range(M.getnnz()):\n nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]\n for nnz in range(M.getnnz()):\n M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])\n\n M = M.tocsr()\n if M is None:\n raise TypeError('Could not read the input graph')\n if M.shape[0] != M.shape[1]:\n raise TypeError('Shape is not square')\n\n # should be autosorted, but check just to make sure\n if not M.has_sorted_indices:\n print('sort_indices ... ')\n M.sort_indices()\n\n z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}\n\n # SciPy Pagerank Call\n print('\\tSciPy Solving... ')\n t1 = time.time()\n \n Gnx = nx.DiGraph(M) \n \n pr = nx.pagerank_scipy(Gnx, alpha, z, max_iter, tol)\n t2 = time.time() - t1\n\n return t2", "_____no_output_____" ] ], [ [ "### Run the benchmarks", "_____no_output_____" ] ], [ [ "# arrays to capture performance gains\ntime_cu = []\ntime_nx = []\ntime_sp = []\nperf_nx = []\nperf_sp = []\nnames = []\n\n# init libraries by doing a simple task \nv = './data/preferentialAttachment.mtx'\nM = read_mtx_file(v)\ntrapids = cugraph_call(M, 100, 0.00001, 0.85)\ndel M\n\n\nfor k,v in data.items():\n gc.collect()\n\n # Saved the file Name\n names.append(k)\n \n # read the data\n M = read_mtx_file(v)\n \n # call cuGraph - this will be the baseline\n trapids = cugraph_call(M, 100, 0.00001, 0.85)\n time_cu.append(trapids)\n \n # Now call NetworkX\n tn = networkx_call(M, 100, 0.00001, 0.85)\n speedUp = (tn / trapids)\n perf_nx.append(speedUp)\n time_nx.append(tn)\n \n # Now call SciPy\n tsp = networkx_scipy_call(M, 100, 0.00001, 0.85)\n speedUp = (tsp / trapids)\n perf_sp.append(speedUp) \n time_sp.append(tsp)\n \n print(\"cuGraph (\" + str(trapids) + \") Nx (\" + str(tn) + \") SciPy (\" + str(tsp) + \")\" )\n del M", "_____no_output_____" ] ], [ [ "### plot the output", "_____no_output_____" ] ], [ [ "%matplotlib inline\n\nplt.figure(figsize=(10,8))\n\nbar_width = 0.35\nindex = np.arange(len(names))\n\n_ = plt.bar(index, perf_nx, bar_width, color='g', label='vs Nx')\n_ = plt.bar(index + bar_width, perf_sp, bar_width, color='b', label='vs SciPy')\n\nplt.xlabel('Datasets')\nplt.ylabel('Speedup')\nplt.title('PageRank Performance Speedup')\nplt.xticks(index + (bar_width / 2), names)\nplt.xticks(rotation=90) \n\n# Text on the top of each barplot\nfor i in range(len(perf_nx)):\n plt.text(x = (i - 0.55) + bar_width, y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)\n\nfor i in range(len(perf_sp)):\n plt.text(x = (i - 0.1) + bar_width, y = perf_sp[i] + 25, s = round(perf_sp[i], 1), size = 12)\n\n\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "# Dump the raw stats", "_____no_output_____" ] ], [ [ "perf_nx", "_____no_output_____" ], [ "perf_sp", "_____no_output_____" ], [ "time_cu", "_____no_output_____" ], [ "time_nx", "_____no_output_____" ], [ "time_sp", "_____no_output_____" ] ], [ [ "___\nCopyright (c) 2020, NVIDIA CORPORATION.\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n___", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# My Notebook 2", "_____no_output_____" ] ], [ [ "import os", "_____no_output_____" ], [ "print(\"I am notebook 2\")", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "pip install contractions", "_____no_output_____" ], [ "import pandas as pd\nimport boto3, sys,sagemaker \nimport pandas as pd \nimport pandas as pd\nimport numpy as np\nimport nltk\nimport string\nimport contractions\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize import sent_tokenize\nfrom nltk.corpus import stopwords, wordnet\nfrom nltk.stem import WordNetLemmatizer\n# plt.xticks(rotation=70)\npd.options.mode.chained_assignment = None\npd.set_option('display.max_colwidth', 100)\n%matplotlib inline\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\n", "_____no_output_____" ], [ "df = pd.read_csv('cleanedText.csv', index_col=0)", "_____no_output_____" ], [ "df['no_punc'][1]", "_____no_output_____" ], [ "table = df.loc[:,['TEXT']]", "_____no_output_____" ], [ "nltk.download('punkt')\ntable['tokenizedSent'] = table['TEXT'].apply(sent_tokenize)\n", "_____no_output_____" ], [ "table['tokenizedSent'][1]", "_____no_output_____" ], [ "table['tokenized_words'] = table['tokenizedSent'].apply(lambda x: [word_tokenize(sent) for sent in x])\n# punc = string.punctuation\n# table['no_punc/numbers'] = table['tokenizedSent'].apply(lambda x: [word.lower() for word in x if word not in punc])", "_____no_output_____" ], [ "table['tokenized_words'][1]", "_____no_output_____" ], [ " table['no_punc/numbers'] = table['tokenized_words'].apply(lambda x: [[word.lower() for word in sent if word not in punc and word.isalpha()] for sent in x])", "_____no_output_____" ], [ " table['no_punc/numbers'][1]", "_____no_output_____" ], [ "nltk.download('stopwords')\nstop_words = set(stopwords.words('english'))\ntable['stopwords_removed'] = table['no_punc/numbers'].apply(lambda x: [[word for word in sent if word not in stop_words]for sent in x])", "_____no_output_____" ], [ "table['stopwords_removed'][1]", "_____no_output_____" ], [ "nltk.download('averaged_perceptron_tagger')\ntable['pos_tags'] = table['stopwords_removed'].apply(lambda x: [nltk.tag.pos_tag(sent) for sent in x if len(sent)> 0 ])", "_____no_output_____" ], [ "table['pos_tags'][1]", "_____no_output_____" ], [ "nltk.download('wordnet')\ndef get_wordnet_pos(tag):\n if tag.startswith('J'):\n return wordnet.ADJ\n elif tag.startswith('V'):\n return wordnet.VERB\n elif tag.startswith('N'):\n return wordnet.NOUN\n elif tag.startswith('R'):\n return wordnet.ADV\n else:\n return wordnet.NOUN\ntable['wordnet_pos'] = table['pos_tags'].apply(lambda x: [[(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in sent]for sent in x])", "_____no_output_____" ], [ "table['wordnet_pos'][1]", "_____no_output_____" ], [ "wnl = WordNetLemmatizer()\ntable['lemmatized'] = table['wordnet_pos'].apply(lambda x: [[wnl.lemmatize(word, tag) for word, tag in sent] for sent in x])\n", "_____no_output_____" ], [ "table.head()", "_____no_output_____" ], [ "table.to_csv('sentenceRowClean.csv')", "_____no_output_____" ], [ "pip install gensim", "_____no_output_____" ], [ "import logging\nfrom gensim.models.word2vec import Word2Vec\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\nw2v_model2 = Word2Vec(table['lemmatized'], vector_size=100, min_count=2)", "_____no_output_____" ], [ "y = list(table['lemmatized'])\nsentences = []\nfor row in y:\n for sent in row:\n sentences.append(sent)\n ", "_____no_output_____" ], [ "import logging\nfrom gensim.models.word2vec import Word2Vec\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\nw2v_model2 = Word2Vec(sentences, vector_size=100, min_count=2)", "_____no_output_____" ], [ "w2v_model2.wv.most_similar(['disease'])", "_____no_output_____" ], [ "w2v_model2.wv.most_similar(['cancer'])", "_____no_output_____" ], [ "w2v_model2.save('w2vec2')", "_____no_output_____" ], [ "model1[w]", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# prerequisite package imports\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sb\n\n%matplotlib inline\n\nfrom solutions_biv import violinbox_solution_1", "_____no_output_____" ] ], [ [ "We'll continue to make use of the fuel economy dataset in this workspace.", "_____no_output_____" ] ], [ [ "fuel_econ = pd.read_csv('./data/fuel_econ.csv')\nfuel_econ.head()", "_____no_output_____" ] ], [ [ "**Task**: What is the relationship between the size of a car and the size of its engine? The cars in this dataset are categorized into one of five different vehicle classes based on size. Starting from the smallest, they are: {Minicompact Cars, Subcompact Cars, Compact Cars, Midsize Cars, and Large Cars}. The vehicle classes can be found in the 'VClass' variable, while the engine sizes are in the 'displ' column (in liters). **Hint**: Make sure that the order of vehicle classes makes sense in your plot!", "_____no_output_____" ] ], [ [ "# YOUR CODE HERE\ncar_classes = ['Minicompact Cars', 'Subcompact Cars', 'Compact Cars', 'Midsize Cars', 'Large Cars']\nvclasses = pd.api.types.CategoricalDtype(ordered = True, categories = car_classes)\nfuel_econ['VClass'] = fuel_econ['VClass'].astype(vclasses)\nsb.violinplot(data = fuel_econ, x = 'VClass', y = 'displ')\nplt.xticks(rotation = 15)", "_____no_output_____" ], [ "# run this cell to check your work against ours\nviolinbox_solution_1()", "I used a violin plot to depict the data in this case; you might have chosen a box plot instead. One of the interesting things about the relationship between variables is that it isn't consistent. Compact cars tend to have smaller engine sizes than the minicompact and subcompact cars, even though those two vehicle sizes are smaller. The box plot would make it easier to see that the median displacement for the two smallest vehicle classes is greater than the third quartile of the compact car class.\n" ] ] ]

[ "code", "markdown", "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Langmuir-enhanced entrainment\n\nThis notebook reproduces Fig. 15 of [Li et al., 2019](https://doi.org/10.1029/2019MS001810).", "_____no_output_____" ] ], [ [ "import sys\nimport numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt \nfrom matplotlib import cm\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nsys.path.append(\"../../../gotmtool\")\nfrom gotmtool import *", "_____no_output_____" ], [ "def plot_hLL_dpedt(hLL, dpedt, casename_list, ax=None, xlabel_on=True):\n if ax is None: \n ax = plt.gca()\n idx_WD05 = [('WD05' in casename) for casename in casename_list]\n idx_WD08 = [('WD08' in casename) for casename in casename_list]\n idx_WD10 = [('WD10' in casename) for casename in casename_list]\n b0_str = [casename[2:4] for casename in casename_list]\n b0 = np.array([float(tmp[0])*100 if 'h' in tmp else float(tmp) for tmp in b0_str])\n b0_min = b0.min()\n b0_max = b0.max()\n ax.plot(hLL, dpedt, color='k', linewidth=1, linestyle=':', zorder=1)\n im = ax.scatter(hLL[idx_WD05], dpedt[idx_WD05], c=b0[idx_WD05], marker='d', edgecolors='k',\n linewidth=1, zorder=2, label='$U_{10}=5$ m s$^{-1}$', cmap='bone_r', vmin=b0_min, vmax=b0_max)\n ax.scatter(hLL[idx_WD08], dpedt[idx_WD08], c=b0[idx_WD08], marker='s', edgecolors='k',\n linewidth=1, zorder=2, label='$U_{10}=8$ m s$^{-1}$', cmap='bone_r', vmin=b0_min, vmax=b0_max)\n ax.scatter(hLL[idx_WD10], dpedt[idx_WD10], c=b0[idx_WD10], marker='^', edgecolors='k',\n linewidth=1, zorder=2, label='$U_{10}=10$ m s$^{-1}$', cmap='bone_r', vmin=b0_min, vmax=b0_max)\n ax.legend(loc='upper left')\n # add colorbar\n ax_inset = inset_axes(ax, width=\"30%\", height=\"3%\", loc='lower right',\n bbox_to_anchor=(-0.05, 0.1, 1, 1),\n bbox_transform=ax.transAxes,\n borderpad=0,)\n cb = plt.colorbar(im, cax=ax_inset, orientation='horizontal', shrink=0.35,\n ticks=[5, 100, 300, 500])\n cb.ax.set_xticklabels(['-5','-100','-300','-500'])\n ax.text(0.75, 0.2, '$Q_0$ (W m$^{-2}$)', color='black', transform=ax.transAxes,\n fontsize=10, va='top', ha='left')\n # get axes ratio\n ll, ur = ax.get_position() * plt.gcf().get_size_inches()\n width, height = ur - ll\n axes_ratio = height / width\n # add arrow and label\n add_arrow(ax, 0.6, 0.2, 0.3, 0.48, axes_ratio, color='gray', text='Increasing Convection')\n add_arrow(ax, 0.3, 0.25, -0.2, 0.1, axes_ratio, color='black', text='Increasing Langmuir')\n add_arrow(ax, 0.65, 0.75, -0.25, 0.01, axes_ratio, color='black', text='Increasing Langmuir')\n ax.set_xscale('log')\n ax.set_yscale('log')\n if xlabel_on:\n ax.set_xlabel('$h/\\kappa L$', fontsize=14)\n ax.set_ylabel('$d\\mathrm{PE}/dt$', fontsize=14)\n ax.set_xlim([3e-3, 4e1])\n ax.set_ylim([2e-4, 5e-2])\n # set the tick labels font\n for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n label.set_fontsize(14)\n\ndef plot_hLL_R(hLL, R, colors, legend_list, ax=None, xlabel_on=True):\n if ax is None: \n ax = plt.gca()\n ax.axhline(y=1, linewidth=1, color='black')\n nm = R.shape[0]\n for i in np.arange(nm):\n ax.scatter(hLL, R[i,:], color=colors[i], edgecolors='k', linewidth=0.5, zorder=10)\n ax.set_xscale('log')\n ax.set_xlim([3e-3, 4e1])\n if xlabel_on:\n ax.set_xlabel('$h/L_L$', fontsize=14)\n ax.set_ylabel('$R$', fontsize=14)\n # set the tick labels font\n for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n label.set_fontsize(14)\n # legend\n if nm > 1:\n xshift = 0.2 + 0.05*(11-nm)\n xx = np.arange(nm)+1\n xx = xx*0.06+xshift\n yy = np.ones(xx.size)*0.1\n for i in np.arange(nm):\n ax.text(xx[i], yy[i], legend_list[i], color='black', transform=ax.transAxes,\n fontsize=12, rotation=45, va='bottom', ha='left')\n ax.scatter(xx[i], 0.07, s=60, color=colors[i], edgecolors='k', linewidth=1, transform=ax.transAxes)\n\ndef add_arrow(ax, x, y, dx, dy, axes_ratio, color='black', text=None):\n ax.arrow(x, y, dx, dy, width=0.006, color=color, transform=ax.transAxes)\n if text is not None:\n dl = np.sqrt(dx**2+dy**2)\n xx = x + 0.5*dx + dy/dl*0.06\n yy = y + 0.5*dy - dx/dl*0.06\n angle = np.degrees(np.arctan(dy/dx*axes_ratio))\n ax.text(xx, yy, text, color=color, transform=ax.transAxes, fontsize=11,\n rotation=angle, va='center', ha='center')", "_____no_output_____" ] ], [ [ "### Load LF17 data", "_____no_output_____" ] ], [ [ "# load LF17 data\nlf17_data = np.load('LF17_dPEdt.npz')\nus0 = lf17_data['us0']\nb0 = lf17_data['b0']\nustar = lf17_data['ustar']\nhb = lf17_data['hb']\ndpedt = lf17_data['dpedt']\ncasenames = lf17_data['casenames']\nncase = len(casenames)\n\n# get parameter h/L_L= w*^3/u*^2/u^s(0)\ninds = us0==0\nus0[inds] = np.nan\nhLL = b0*hb/ustar**2/us0", "_____no_output_____" ] ], [ [ "### Compute the rate of change in potential energy in GOTM runs", "_____no_output_____" ] ], [ [ "turbmethods = [\n 'GLS-C01A',\n 'KPP-CVMix',\n 'KPPLT-VR12',\n 'KPPLT-LF17',\n ]\nntm = len(turbmethods)\ncmap = cm.get_cmap('rainbow')\nif ntm == 1:\n colors = ['gray']\nelse:\n colors = cmap(np.linspace(0,1,ntm))\n", "_____no_output_____" ], [ "m = Model(name='Entrainment-LF17', environ='../../.gotm_env.yaml')\ngotmdir = m.environ['gotmdir_run']+'/'+m.name\nprint(gotmdir)", "/Users/qingli/develop/dev_gotmtool/gotm/run/Entrainment-LF17\n" ], [ "# Coriolis parameter (s^{-1})\nf = 4*np.pi/86400*np.sin(np.pi/4)\n# Inertial period (s)\nTi = 2*np.pi/f\n# get dPEdt from GOTM run\nrdpedt = np.zeros([ntm, ncase])\nfor i in np.arange(ntm):\n print(turbmethods[i])\n for j in np.arange(ncase):\n sim = Simulation(path=gotmdir+'/'+casenames[j]+'/'+turbmethods[i])\n var_gotm = sim.load_data().Epot\n epot_gotm = var_gotm.data.squeeze()\n dtime = var_gotm.time - var_gotm.time[0]\n time_gotm = (dtime.dt.days*86400.+dtime.dt.seconds).data\n # starting index for the last inertial period\n t0_gotm = time_gotm[-1]-Ti\n tidx0_gotm = np.argmin(np.abs(time_gotm-t0_gotm))\n # linear fit\n xx_gotm = time_gotm[tidx0_gotm:]-time_gotm[tidx0_gotm]\n yy_gotm = epot_gotm[tidx0_gotm:]-epot_gotm[tidx0_gotm]\n slope_gotm, intercept_gotm, r_value_gotm, p_value_gotm, std_err_gotm = stats.linregress(xx_gotm,yy_gotm)\n rdpedt[i,j] = slope_gotm/dpedt[j]", "GLS-C01A\nKPP-CVMix\nKPPLT-VR12\nKPPLT-LF17\n" ], [ "fig, axarr = plt.subplots(2, 1, sharex='col')\nfig.set_size_inches(6, 7)\nplt.subplots_adjust(left=0.15, right=0.95, bottom=0.09, top=0.95, hspace=0.1)\nplot_hLL_dpedt(hLL, dpedt, casenames, ax=axarr[0])\nplot_hLL_R(hLL, rdpedt, colors, turbmethods, ax=axarr[1])\naxarr[0].text(0.04, 0.14, '(a)', color='black', transform=axarr[0].transAxes,\n fontsize=14, va='top', ha='left')\naxarr[1].text(0.88, 0.94, '(b)', color='black', transform=axarr[1].transAxes,\n fontsize=14, va='top', ha='left')", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "<center><h1><b><span style=\"color:blue\">Statistics</span></b></h1></center>\n\n\n#### **Quick intro to the following packages**\n- `hepstats`.\n\nI will not discuss here the `pyhf` package, which is very niche.\nPlease refer to the [GitHub repository](https://github.com/scikit-hep/pyhf) or related material at https://scikit-hep.org/resources.", "_____no_output_____" ], [ "## **`hepstats` - statistics tools and utilities**\n\nThe package contains 2 submodules:\n- `hypotests`: provides tools to do hypothesis tests such as discovery test and computations of upper limits or confidence intervals.\n- `modeling`: includes the Bayesian Block algorithm that can be used to improve the binning of histograms.\n\nNote: feel free to complement the introduction below with the several tutorials available from the [GitHub repository](https://github.com/scikit-hep/hepstats).", "_____no_output_____" ], [ "### **1. Adaptive binning determination**\n\nThe Bayesian Block algorithm produces histograms that accurately represent the underlying distribution while being robust to statistical fluctuations.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom hepstats.modeling import bayesian_blocks\n\ndata = np.append(np.random.laplace(size=10000), np.random.normal(5., 1., size=15000))\n\nbblocks = bayesian_blocks(data)\n\nplt.hist(data, bins=1000, label='Fine Binning', density=True)\nplt.hist(data, bins=bblocks, label='Bayesian Blocks', histtype='step', linewidth=2, density=True)\nplt.legend(loc=2);", "_____no_output_____" ] ], [ [ "### **2. Modelling**\n\nSo far the modelling functionality in the package makes use of the `zfit` package, see for example the `README` file and the `notebooks` directory in the [GitHub repository](https://github.com/scikit-hep/hepstats). Usage of `zfit` will be the topic of the last notebook in this tutorial, hence no further examples on `hepstats` are provided at this stage.", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown" ]

[ [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "##Tirmzi Analysis\nn=1000 m+=1000 nm-=120 istep= 4 min=150 max=700", "_____no_output_____" ] ], [ [ "import sys\nsys.path\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nfrom scipy import signal", "_____no_output_____" ], [ "ls", " Volume in drive C is Local Disk\n Volume Serial Number is AA1B-997A\n\n Directory of C:\\Users\\emaro\\OneDrive - University of Mount Union\\XSEDE Summer 2021\\xsede-spm\\data\n\n07/28/2021 11:41 AM <DIR> .\n07/28/2021 11:41 AM <DIR> ..\n07/28/2021 11:39 AM <DIR> .ipynb_checkpoints\n07/28/2021 11:37 AM 6,163 C' v. Z for 1nm thick sample 06-28-2021.png\n07/28/2021 11:37 AM 6,179 C' v. Z for varying sample thickness, 06-28-2021.png\n07/28/2021 11:37 AM 5,715 Cz v. Z for varying sample thickness, 06-28-2021.png\n07/28/2021 11:42 AM <DIR> FortranOutputTest\n07/28/2021 11:28 AM <DIR> PythonOutputTest\n07/28/2021 11:41 AM 91,187 Tirmzi_istep4-Copy2.ipynb\n 4 File(s) 109,244 bytes\n 5 Dir(s) 142,724,673,536 bytes free\n" ], [ "import capsol.newanalyzecapsol as ac", "_____no_output_____" ], [ "ac.get_gridparameters", "_____no_output_____" ], [ "import glob", "_____no_output_____" ], [ "folders = glob.glob(\"FortranOutputTest/*/\")\nfolders\n", "_____no_output_____" ], [ "all_data= dict() \nfor folder in folders:\n params = ac.get_gridparameters(folder + 'capsol.in')\n data = ac.np.loadtxt(folder + 'Z-U.dat')\n process_data = ac.process_data(params, data, smoothing=False, std=5*10**-9)\n all_data[folder]= (process_data)\nall_params= dict()\nfor folder in folders:\n params=ac.get_gridparameters(folder + 'capsol.in')\n all_params[folder]= (params)", "_____no_output_____" ], [ "all_data", "_____no_output_____" ], [ "all_data.keys()", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:\n data=all_data[key]\n thickness =all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')\n \n \nplt.title('C v. Z for 1nm thick sample') \nplt.ylabel(\"C(m)\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"C' v. Z for 1nm thick sample 06-28-2021.png\")", "No handles with labels found to put in legend.\n" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:\n data=all_data[key]\n thickness =all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')\n \n \nplt.title('C v. Z for 10nm thick sample') \nplt.ylabel(\"C(m)\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"C' v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:\n data=all_data[key]\n thickness =all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')\n \n \nplt.title('C v. Z for 100nm sample') \nplt.ylabel(\"C(m)\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"C' v. Z for varying sample thickness, 06-28-2021.png\")", "No handles with labels found to put in legend.\n" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:\n data=all_data[key]\n thickness =all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')\n \n \nplt.title('C v. Z for 500nm sample') \nplt.ylabel(\"C(m)\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"C' v. Z for varying sample thickness, 06-28-2021.png\")", "No handles with labels found to put in legend.\n" ] ], [ [ "cut off last experiment because capacitance was off the scale", "_____no_output_____" ] ], [ [ "for params in all_params.values():\n print(params['Thickness_sample'])\n print(params['m-'])", "10.0\n20\n" ], [ "all_params", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(4,-3)\n plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Cz vs. Z for 1.0nm') \nplt.ylabel(\"Cz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Cz v. Z for varying sample thickness, 06-28-2021.png\")", "No handles with labels found to put in legend.\n" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(4,-3)\n plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Cz vs. Z for 10.0nm') \nplt.ylabel(\"Cz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Cz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(4,-3)\n plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Cz vs. Z for 100.0nm') \nplt.ylabel(\"Cz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Cz v. Z for varying sample thickness, 06-28-2021.png\")", "No handles with labels found to put in legend.\n" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(4,-3)\n plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Cz vs. Z for 500.0nm') \nplt.ylabel(\"Cz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Cz v. Z for varying sample thickness, 06-28-2021.png\")", "No handles with labels found to put in legend.\n" ], [ "hoepker_data= np.loadtxt(\"Default Dataset (2).csv\" , delimiter= \",\")\nhoepker_data", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(5,-5)\n plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Czz vs. Z for 1.0nm') \nplt.ylabel(\"Czz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "params", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(5,-5)\n plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Czz vs. Z for 10.0nm') \nplt.ylabel(\"Czz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(5,-5)\n plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Czz vs. Z for 100.0nm') \nplt.ylabel(\"Czz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(5,-5)\n plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Czz vs. Z for 500.0 nm') \nplt.ylabel(\"Czz\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(8,-8)\n plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('alpha vs. Z for 1.0nm') \nplt.ylabel(\"$\\\\alpha$\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Alpha v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(8,-8)\n plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Alpha vs. Z for 10.0 nm') \nplt.ylabel(\"$\\\\alpha$\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(8,-8)\n plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Alpha vs. Z for 100.0nm') \nplt.ylabel(\"$\\\\alpha$\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:\n data=all_data[key]\n thickness=all_params[key]['Thickness_sample']\n rtip= all_params[key]['Rtip']\n er=all_params[key]['eps_r']\n s=slice(8,-8)\n plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )\n \nplt.title('Alpha vs. Z for 500.0nm')\nplt.ylabel(\"$\\\\alpha$\")\nplt.xlabel(\"Z(m)\")\nplt.legend()\nplt.savefig(\"Czz v. Z for varying sample thickness, 06-28-2021.png\")", "_____no_output_____" ], [ "data", "_____no_output_____" ], [ "from scipy.optimize import curve_fit\n", "_____no_output_____" ], [ "def Cz_model(z, a, n, b,):\n return(a*z**n + b)", "_____no_output_____" ], [ "all_data.keys()", "_____no_output_____" ], [ "data= all_data['capsol-calc\\\\0001-capsol\\\\']\nz= data['z'][1:-1]\ncz= data['cz'][1:-1]", "_____no_output_____" ], [ "popt, pcov= curve_fit(Cz_model, z, cz, p0=[cz[0]*z[0], -1, 0])\na=popt[0]\nn=popt[1]\nb=popt[2]\nstd_devs= np.sqrt(pcov.diagonal())\nsigma_a = std_devs[0]\nsigma_n = std_devs[1]\nmodel_output= Cz_model(z, a, n, b)\nrmse= np.sqrt(np.mean((cz - model_output)**2))\n", "_____no_output_____" ], [ "f\"a= {a} ± {sigma_a}\"", "_____no_output_____" ], [ "f\"n= {n}± {sigma_n}\"", "_____no_output_____" ], [ "model_output", "_____no_output_____" ], [ "\"Root Mean Square Error\"", "_____no_output_____" ], [ "rmse/np.mean(-cz)", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "import pandas as pd", "_____no_output_____" ], [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "_____no_output_____" ], [ "r = np.random.randn((1000))\nS0 = 1\nS = np.cumsum(r) + S0", "_____no_output_____" ], [ "T = 2\nmu = 0.\nsigma = 0.01\nS0 = 20\ndt = 0.01\nN = round(T/dt)\nt = np.linspace(0, T, N)\nW = np.random.standard_normal(size = N) \nW = np.cumsum(W)*np.sqrt(dt) ### standard brownian motion ###\nX = (mu-0.5*sigma**2)*t + sigma*W \nS = S0*np.exp(X) ### geometric brownian motion ###\nplt.plot(t, S)\nplt.show()", "_____no_output_____" ], [ "from ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets", "_____no_output_____" ], [ "from blackscholes import geometric_brownian_motion, blackScholes\nfrom scipy.stats import norm", "_____no_output_____" ], [ "geometric_brownian_motion(mu=0., sigma=0.01, s0=1, dt=0.01);", "_____no_output_____" ], [ "t = 2.\ndt = 0.01\nN = int(round(t / dt))\nnp.linspace(0, t, N)\ntt = np.linspace(0, t, N)\nW = norm((N))", "_____no_output_____" ], [ "@interact(mu=(-0.02, 0.05, 0.01), sigma=(0.01, 0.1, 0.005), S0=(1,100,10), dt=(0.001, 0.1, 0.001))\ndef plot_gbm(mu, sigma, S0, dt):\n s, t = geometric_brownian_motion(mu=mu, sigma=sigma, t=2, dt=dt, s0=S0)\n pd.Series(t, s).plot()\n plt.show()", "_____no_output_____" ], [ "df.ix[0.1:,:].gamma.plot()", "_____no_output_____" ], [ "tau = np.clip( np.linspace(1.0, .0, 101), 0.0000001, 100)\nS = 1.\nK = 1.\nsigma = 1\ndf = pd.DataFrame.from_dict(blackScholes(tau, S, K, sigma))\ndf.index = tau", "_____no_output_____" ], [ "@interact(mu=(-0.02, 0.05, 0.01), sigma=(0.01, 0.1, 0.005), S0=(1,100,10), dt=(0.001, 0.1, 0.001))\ndef plot_gbm(mu, sigma, S0, dt):\n s, t = geometric_brownian_motion(mu=mu, sigma=sigma, t=2, dt=dt, s0=S0)\n pd.Series(t, s).plot()\n plt.show()", "_____no_output_____" ] ], [ [ "## Q-learning", "_____no_output_____" ], [ "- Initialize $V(s)$ arbitrarily\n- Repeat for each episode\n- Initialize s\n- Repeat (for each step of episode)\n- - $\\alpha \\leftarrow$ action given by $\\pi$ for $s$\n- - Take action a, observe reward r, and next state s'\n- - $V(s) \\leftarrow V(s) + \\alpha [r = \\gamma V(s') - V(s)]$ \n- - $s \\leftarrow s'$\n- until $s$ is terminal", "_____no_output_____" ] ], [ [ "import td", "_____no_output_____" ], [ "import scipy as sp", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "α = 0.05\nγ = 0.1\ntd_learning = td.TD(α, γ)", "_____no_output_____" ] ], [ [ "## Black Scholes", "_____no_output_____" ], [ "$${\\displaystyle d_{1}={\\frac {1}{\\sigma {\\sqrt {T-t}}}}\\left[\\ln \\left({\\frac {S_{t}}{K}}\\right)+(r-q+{\\frac {1}{2}}\\sigma ^{2})(T-t)\\right]}$$", "_____no_output_____" ], [ "$${\\displaystyle C(S_{t},t)=e^{-r(T-t)}[FN(d_{1})-KN(d_{2})]\\,}$$", "_____no_output_____" ], [ "$${\\displaystyle d_{2}=d_{1}-\\sigma {\\sqrt {T-t}}={\\frac {1}{\\sigma {\\sqrt {T-t}}}}\\left[\\ln \\left({\\frac {S_{t}}{K}}\\right)+(r-q-{\\frac {1}{2}}\\sigma ^{2})(T-t)\\right]}$$", "_____no_output_____" ] ], [ [ "d_1 = lambda σ, T, t, S, K: 1. / σ / np.sqrt(T - t) * (np.log(S / K) + 0.5 * (σ ** 2) * (T-t))\nd_2 = lambda σ, T, t, S, K: 1. / σ / np.sqrt(T - t) * (np.log(S / K) - 0.5 * (σ ** 2) * (T-t))\n\ncall = lambda σ, T, t, S, K: S * sp.stats.norm.cdf( d_1(σ, T, t, S, K) ) - K * sp.stats.norm.cdf( d_2(σ, T, t, S, K) )", "_____no_output_____" ], [ "plt.plot(np.linspace(0.1, 4., 100), call(1., 1., .9, np.linspace(0.1, 4., 100), 1.))", "_____no_output_____" ], [ "d_1(1., 1., 0., 1.9, 1)", "_____no_output_____" ], [ "plt.plot(d_1(1., 1., 0., np.linspace(0.1, 2.9, 10), 1))", "_____no_output_____" ], [ "plt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.2, np.linspace(0.01, 1.9, 100), 1)))\nplt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.6, np.linspace(0.01, 1.9, 100), 1)))\nplt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.9, np.linspace(0.01, 1.9, 100), 1)))\nplt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.99, np.linspace(0.01, 1.9, 100), 1)))", "_____no_output_____" ], [ "def iterate_series(n=1000, S0 = 1):\n while True:\n r = np.random.randn((n))\n S = np.cumsum(r) + S0\n yield S, r", "_____no_output_____" ], [ "def iterate_world(n=1000, S0=1, N=5):\n for (s, r) in take(N, iterate_series(n=n, S0=S0)):\n t, t_0 = 0, 0\n for t in np.linspace(0, len(s)-1, 100):\n r = s[int(t)] / s[int(t_0)]\n yield r, s[int(t)]\n t_0 = t", "_____no_output_____" ], [ "from cytoolz import take", "_____no_output_____" ], [ "import gym\nimport gym_bs", "_____no_output_____" ], [ "from test_cem_future import *", "[2017-05-10 23:26:52,381] Making new env: fbs-v1\n" ], [ "import pandas as pd\nimport numpy as np", "_____no_output_____" ], [ "# df.iloc[3] = (0.2, 1, 3)\ndf", "_____no_output_____" ], [ "rwd, df, agent = noisy_evaluation(np.array([0.1, 0, 0]))\nrwd\ndf\nagent;", "_____no_output_____" ], [ "env.observation_space", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code" ], [ "markdown", "markdown", "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# Plotting with Matplotlib", "_____no_output_____" ], [ "IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling.", "_____no_output_____" ], [ "## matplotlib mode", "_____no_output_____" ], [ "To make plots using Matplotlib, you must first enable IPython's matplotlib mode.\n\nTo do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.\n\nThis magic takes an optional argument that specifies which Matplotlib backend should be used. Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:", "_____no_output_____" ] ], [ [ "%matplotlib inline", "_____no_output_____" ] ], [ [ "You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server. You can always call the `display` function to paste figures into the Notebook document.", "_____no_output_____" ], [ "## Making a simple plot", "_____no_output_____" ], [ "With matplotlib enabled, plotting should just work.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\nimport numpy as np", "_____no_output_____" ], [ "x = np.linspace(0, 3*np.pi, 500)\nplt.plot(x, np.sin(x**2))\nplt.title('A simple chirp');", "_____no_output_____" ] ], [ [ "These images can be resized by dragging the handle in the lower right corner. Double clicking will return them to their original size.", "_____no_output_____" ], [ "One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell.", "_____no_output_____" ], [ "## Loading Matplotlib demos with %load", "_____no_output_____" ], [ "IPython's `%load` magic can be used to load any Matplotlib demo by its URL:", "_____no_output_____" ] ], [ [ "# %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py\n\"\"\"\nPlot demonstrating the integral as the area under a curve.\n\nAlthough this is a simple example, it demonstrates some important tweaks:\n\n * A simple line plot with custom color and line width.\n * A shaded region created using a Polygon patch.\n * A text label with mathtext rendering.\n * figtext calls to label the x- and y-axes.\n * Use of axis spines to hide the top and right spines.\n * Custom tick placement and labels.\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\n\n\ndef func(x):\n return (x - 3) * (x - 5) * (x - 7) + 85\n\n\na, b = 2, 9 # integral limits\nx = np.linspace(0, 10)\ny = func(x)\n\nfig, ax = plt.subplots()\nplt.plot(x, y, 'r', linewidth=2)\nplt.ylim(bottom=0)\n\n# Make the shaded region\nix = np.linspace(a, b)\niy = func(ix)\nverts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\npoly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\nax.add_patch(poly)\n\nplt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n horizontalalignment='center', fontsize=20)\n\nplt.figtext(0.9, 0.05, '$x$')\nplt.figtext(0.1, 0.9, '$y$')\n\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nax.xaxis.set_ticks_position('bottom')\n\nax.set_xticks((a, b))\nax.set_xticklabels(('$a$', '$b$'))\nax.set_yticks([])\n\nplt.show()\n", "_____no_output_____" ] ], [ [ "Matplotlib 1.4 introduces an interactive backend for use in the notebook,\ncalled 'nbagg'. You can enable this with `%matplotlib notebook`.\n\nWith this backend, you will get interactive panning and zooming of matplotlib figures in your browser.", "_____no_output_____" ] ], [ [ "%matplotlib widget", "_____no_output_____" ], [ "plt.figure()\nx = np.linspace(0, 5 * np.pi, 1000)\nfor n in range(1, 4):\n plt.plot(np.sin(n * x))\nplt.show()", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/mjvakili/MLcourse/blob/master/day2/nn_qso_finder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "Let's start by importing the libraries that we need for this exercise.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom sklearn.model_selection import train_test_split\n#matplotlib settings\nmatplotlib.rcParams['xtick.major.size'] = 7\nmatplotlib.rcParams['xtick.labelsize'] = 'x-large'\nmatplotlib.rcParams['ytick.major.size'] = 7\nmatplotlib.rcParams['ytick.labelsize'] = 'x-large'\nmatplotlib.rcParams['xtick.top'] = False\nmatplotlib.rcParams['ytick.right'] = False\nmatplotlib.rcParams['ytick.direction'] = 'in'\nmatplotlib.rcParams['xtick.direction'] = 'in'\nmatplotlib.rcParams['font.size'] = 15\nmatplotlib.rcParams['figure.figsize'] = [7,7]", "_____no_output_____" ], [ "#We need the astroml library to fetch the photometric datasets of sdss qsos and stars\npip install astroml", "Requirement already satisfied: astroml in /usr/local/lib/python3.6/dist-packages (0.4.1)\nRequirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from astroml) (0.21.3)\nRequirement already satisfied: numpy>=1.4 in /usr/local/lib/python3.6/dist-packages (from astroml) (1.17.4)\nRequirement already satisfied: astropy>=1.2 in /usr/local/lib/python3.6/dist-packages (from astroml) (3.0.5)\nRequirement already satisfied: scipy>=0.11 in /usr/local/lib/python3.6/dist-packages (from astroml) (1.3.2)\nRequirement already satisfied: matplotlib>=0.99 in /usr/local/lib/python3.6/dist-packages (from astroml) (3.1.1)\nRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->astroml) (0.14.0)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=0.99->astroml) (0.10.0)\nRequirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=0.99->astroml) (2.6.1)\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=0.99->astroml) (2.4.5)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=0.99->astroml) (1.1.0)\nRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=0.99->astroml) (1.12.0)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=0.99->astroml) (41.6.0)\n" ], [ "from astroML.datasets import fetch_dr7_quasar\nfrom astroML.datasets import fetch_sdss_sspp\n\nquasars = fetch_dr7_quasar()\nstars = fetch_sdss_sspp()", "_____no_output_____" ], [ "# Data procesing taken from \n#https://www.astroml.org/book_figures/chapter9/fig_star_quasar_ROC.html by Jake Van der Plus\n\n# stack colors into matrix X\nNqso = len(quasars)\nNstars = len(stars)\nX = np.empty((Nqso + Nstars, 4), dtype=float)\n\nX[:Nqso, 0] = quasars['mag_u'] - quasars['mag_g']\nX[:Nqso, 1] = quasars['mag_g'] - quasars['mag_r']\nX[:Nqso, 2] = quasars['mag_r'] - quasars['mag_i']\nX[:Nqso, 3] = quasars['mag_i'] - quasars['mag_z']\n\nX[Nqso:, 0] = stars['upsf'] - stars['gpsf']\nX[Nqso:, 1] = stars['gpsf'] - stars['rpsf']\nX[Nqso:, 2] = stars['rpsf'] - stars['ipsf']\nX[Nqso:, 3] = stars['ipsf'] - stars['zpsf']\n\ny = np.zeros(Nqso + Nstars, dtype=int)\ny[:Nqso] = 1", "_____no_output_____" ], [ "X = X/np.max(X, axis=0)", "_____no_output_____" ], [ "# split into training and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.9)", "_____no_output_____" ], [ "#Now let's build a simple Sequential model in which fully connected layers come after one another\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Flatten(), #this flattens input\n tf.keras.layers.Dense(128, activation = \"relu\"),\n tf.keras.layers.Dense(64, activation = \"relu\"),\n tf.keras.layers.Dense(32, activation = \"relu\"),\n tf.keras.layers.Dense(32, activation = \"relu\"),\n tf.keras.layers.Dense(1, activation=\"sigmoid\")\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy')\nhistory = model.fit(X_train, y_train, validation_data = (X_test, y_test), batch_size = 32, epochs=20, verbose = 1)\n", "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\nInstructions for updating:\nIf using Keras pass *_constraint arguments to layers.\nWARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.where in 2.0, which has the same broadcast rule as np.where\nTrain on 389738 samples, validate on 43305 samples\nEpoch 1/20\n389738/389738 [==============================] - 24s 61us/sample - loss: 0.0495 - val_loss: 0.0281\nEpoch 2/20\n389738/389738 [==============================] - 23s 59us/sample - loss: 0.0311 - val_loss: 0.0275\nEpoch 3/20\n389738/389738 [==============================] - 23s 59us/sample - loss: 0.0290 - val_loss: 0.0336\nEpoch 4/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0278 - val_loss: 0.0261\nEpoch 5/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0269 - val_loss: 0.0238\nEpoch 6/20\n389738/389738 [==============================] - 25s 64us/sample - loss: 0.0263 - val_loss: 0.0231\nEpoch 7/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0259 - val_loss: 0.0250\nEpoch 8/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0257 - val_loss: 0.0230\nEpoch 9/20\n389738/389738 [==============================] - 23s 59us/sample - loss: 0.0254 - val_loss: 0.0224\nEpoch 10/20\n389738/389738 [==============================] - 23s 60us/sample - loss: 0.0251 - val_loss: 0.0284\nEpoch 11/20\n389738/389738 [==============================] - 23s 59us/sample - loss: 0.0248 - val_loss: 0.0223\nEpoch 12/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0245 - val_loss: 0.0234\nEpoch 13/20\n389738/389738 [==============================] - 23s 59us/sample - loss: 0.0245 - val_loss: 0.0228\nEpoch 14/20\n389738/389738 [==============================] - 23s 59us/sample - loss: 0.0243 - val_loss: 0.0269\nEpoch 15/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0243 - val_loss: 0.0227\nEpoch 16/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0242 - val_loss: 0.0230\nEpoch 17/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0239 - val_loss: 0.0248\nEpoch 18/20\n389738/389738 [==============================] - 23s 58us/sample - loss: 0.0239 - val_loss: 0.0221\nEpoch 19/20\n389738/389738 [==============================] - 25s 63us/sample - loss: 0.0239 - val_loss: 0.0217\nEpoch 20/20\n389738/389738 [==============================] - 23s 60us/sample - loss: 0.0236 - val_loss: 0.0235\n" ], [ "loss = history.history['loss']\nval_loss = history.history['val_loss']\nepochs = range(len(loss))\nplt.plot(epochs, loss, lw = 5, label='Training loss')\nplt.plot(epochs, val_loss, lw = 5, label='validation loss')\n\nplt.title('Loss')\nplt.legend(loc=0)\nplt.show()", "_____no_output_____" ], [ "prob = model.predict_proba(X_test) #model probabilities", "_____no_output_____" ], [ "from sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import roc_curve", "_____no_output_____" ], [ "fpr, tpr, thresholds = roc_curve(y_test, prob)", "_____no_output_____" ], [ "plt.loglog(fpr, tpr, lw = 4)\nplt.xlabel('false positive rate')\nplt.ylabel('true positive rate')\nplt.xlim(0.0, 0.15)\nplt.ylim(0.6, 1.01)\nplt.show()\n\nplt.plot(thresholds, tpr, lw = 4)\nplt.plot(thresholds, fpr, lw = 4)\nplt.xlim(0,1)\nplt.yscale(\"log\")\nplt.show()\n#plt.xlabel('false positive rate')\n#plt.ylabel('true positive rate')\n##plt.xlim(0.0, 0.15)\n#plt.ylim(0.6, 1.01)", "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\nInvalid limit will be ignored.\n after removing the cwd from sys.path.\n" ], [ "#Now let's look at the confusion matrix\ny_pred = model.predict(X_test)\nz_pred = np.zeros(y_pred.shape[0], dtype = int)\nmask = np.where(y_pred>.5)[0]\nz_pred[mask] = 1\nconfusion_matrix(y_test, z_pred.astype(int))", "_____no_output_____" ], [ "import os, signal\nos.kill(os.getpid(), signal.SIGKILL)", "_____no_output_____" ] ], [ [ "#Exercise1:\n\nTry to change the number of layers, batchsize, as well as the default learning rate, one at a time. See which one can make a more significant impact on the performance of the model.\n\n#Exercise 2:\nWrite a simple function for visualizing the predicted decision boundaries in the feature space. Try to identify the regions of the parameter space which contribute significantly to the false positive rates.\n\n#Exercise 3:\nThis dataset is a bit imbalanced in that the QSOs are outnumbered by the stars. Can you think of a wighting scheme to pass to the loss function, such that the detection rate of QSOs increases?", "_____no_output_____" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/nealonleo9/SQL/blob/main/PandasQureys.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ] ], [ [ "df1.query('age == 10')", "_____no_output_____" ], [ "You can also achieve this result via the traditional filtering method.\r\n\r\nfilter_1 = df['Mon'] > df['Tues']\r\ndf[filter_1]", "_____no_output_____" ], [ "If needed you can also use an environment variable to filter your data.\r\n Make sure to put an \"@\" sign in front of your variable within the string.\r\n\r\ndinner_limit=120\r\ndf.query('Thurs > @dinner_limit')", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

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[ [ [ "# Udacity PyTorch Scholarship Final Lab Challenge Guide \n**A hands-on guide to get 90% + accuracy and complete the challenge**", "_____no_output_____" ], [ "**By [Soumya Ranjan Behera](https://www.linkedin.com/in/soumya044)**", "_____no_output_____" ], [ "## This Tutorial will be divided into Two Parts, \n### [1. Model Building and Training](https://www.kaggle.com/soumya044/udacity-pytorch-final-lab-guide-part-1/)\n### [2. Submit in Udcaity's Workspace for evaluation](https://www.kaggle.com/soumya044/udacity-pytorch-final-lab-guide-part-2/)", "_____no_output_____" ], [ "**Note:** This tutorial is like a template or guide for newbies to overcome the fear of the final lab challenge. My intent is not to promote plagiarism or any means of cheating. Users are encourage to take this tutorial as a baseline and build their own better model. Cheers!", "_____no_output_____" ], [ "**Fork this Notebook and Run it from Top-To-Bottom Step by Step**", "_____no_output_____" ], [ "# Part 1: Build and Train a Model", "_____no_output_____" ], [ "**Credits:** The dataset credit goes to [Lalu Erfandi Maula Yusnu](https://www.kaggle.com/nunenuh)", "_____no_output_____" ], [ "## 1. Import Data set and visualiza some data", "_____no_output_____" ] ], [ [ "import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nprint(os.listdir(\"../input/\"))\n\n# Any results you write to the current directory are saved as output.", "_____no_output_____" ] ], [ [ "**Import some visualization Libraries**", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\n%matplotlib inline\nimport cv2", "_____no_output_____" ], [ "# Set Train and Test Directory Variables\nTRAIN_DATA_DIR = \"../input/flower_data/flower_data/train/\"\nVALID_DATA_DIR = \"../input/flower_data/flower_data/valid/\"", "_____no_output_____" ], [ "#Visualiza Some Images of any Random Directory-cum-Class\nFILE_DIR = str(np.random.randint(1,103))\nprint(\"Class Directory: \",FILE_DIR)\nfor file_name in os.listdir(os.path.join(TRAIN_DATA_DIR, FILE_DIR))[1:3]:\n img_array = cv2.imread(os.path.join(TRAIN_DATA_DIR, FILE_DIR, file_name))\n img_array = cv2.resize(img_array,(224, 224), interpolation = cv2.INTER_CUBIC)\n plt.imshow(img_array)\n plt.show()\n print(img_array.shape)", "_____no_output_____" ] ], [ [ "## 2. Data Preprocessing (Image Augmentation)", "_____no_output_____" ], [ "**Import PyTorch libraries**", "_____no_output_____" ] ], [ [ "import torch\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport torch.nn as nn\ntorch.__version__", "_____no_output_____" ] ], [ [ "**Note:** **Look carefully! Kaggle uses v1.0.0 while Udcaity workspace has v0.4.0 (Some issues may arise but we'll solve them)**", "_____no_output_____" ] ], [ [ "# check if CUDA is available\ntrain_on_gpu = torch.cuda.is_available()\n\nif not train_on_gpu:\n print('CUDA is not available. Training on CPU ...')\nelse:\n print('CUDA is available! Training on GPU ...')", "_____no_output_____" ] ], [ [ "**Make a Class Variable i.e a list of Target Categories (List of 102 species) **", "_____no_output_____" ] ], [ [ "# I used os.listdir() to maintain the ordering \nclasses = os.listdir(VALID_DATA_DIR)", "_____no_output_____" ] ], [ [ "**Load and Transform (Image Augmentation)** \nSoucre: https://github.com/udacity/deep-learning-v2-pytorch/blob/master/convolutional-neural-networks/cifar-cnn/cifar10_cnn_augmentation.ipynb", "_____no_output_____" ] ], [ [ "# Load and transform data using ImageFolder\n\n# VGG-16 Takes 224x224 images as input, so we resize all of them\ndata_transform = transforms.Compose([transforms.RandomResizedCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])])\n\ntrain_data = datasets.ImageFolder(TRAIN_DATA_DIR, transform=data_transform)\ntest_data = datasets.ImageFolder(VALID_DATA_DIR, transform=data_transform)\n\n# print out some data stats\nprint('Num training images: ', len(train_data))\nprint('Num test images: ', len(test_data))", "_____no_output_____" ] ], [ [ "### Find more on Image Transforms using PyTorch Here (https://pytorch.org/docs/stable/torchvision/transforms.html)", "_____no_output_____" ], [ "## 3. Make a DataLoader", "_____no_output_____" ] ], [ [ "# define dataloader parameters\nbatch_size = 32\nnum_workers=0\n\n# prepare data loaders\ntrain_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, \n num_workers=num_workers, shuffle=True)\ntest_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n num_workers=num_workers, shuffle=True)", "_____no_output_____" ] ], [ [ "**Visualize Sample Images**", "_____no_output_____" ] ], [ [ "# Visualize some sample data\n\n# obtain one batch of training images\ndataiter = iter(train_loader)\nimages, labels = dataiter.next()\nimages = images.numpy() # convert images to numpy for display\n\n# plot the images in the batch, along with the corresponding labels\nfig = plt.figure(figsize=(25, 4))\nfor idx in np.arange(20):\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n ax.set_title(classes[labels[idx]])", "_____no_output_____" ] ], [ [ "**Here plt.imshow() clips our data into [0,....,255] range to show the images. The Warning message is due to our Transform Function. We can Ignore it.**", "_____no_output_____" ], [ "## 4. Use a Pre-Trained Model (VGG16) \nHere we used a VGG16. You can experiment with other models. \nReferences: https://github.com/udacity/deep-learning-v2-pytorch/blob/master/transfer-learning/Transfer_Learning_Solution.ipynb", "_____no_output_____" ], [ "**Try More Models: ** https://pytorch.org/docs/stable/torchvision/models.html", "_____no_output_____" ] ], [ [ "# Load the pretrained model from pytorch\nmodel = models.<ModelNameHere>(pretrained=True) \nprint(model)", "_____no_output_____" ] ], [ [ "### We can see from above output that the last ,i.e, 6th Layer is a Fully-connected Layer with in_features=4096, out_features=1000", "_____no_output_____" ] ], [ [ "print(model.classifier[6].in_features) \nprint(model.classifier[6].out_features)\n# The above lines work for vgg only. For other models refer to print(model) and look for last FC layer", "_____no_output_____" ] ], [ [ "**Freeze Training for all 'Features Layers', Only Train Classifier Layers**", "_____no_output_____" ] ], [ [ "# Freeze training for all \"features\" layers\nfor param in model.features.parameters():\n param.requires_grad = False\n\n\n#For models like ResNet or Inception use the following,\n\n# Freeze training for all \"features\" layers\n# for _, param in model.named_parameters():\n# param.requires_grad = False", "_____no_output_____" ] ], [ [ "## Let's Add our own Last Layer which will have 102 out_features for 102 species", "_____no_output_____" ] ], [ [ "# VGG16 \nn_inputs = model.classifier[6].in_features\n\n#Others\n# n_inputs = model.fc.in_features\n\n# add last linear layer (n_inputs -> 102 flower classes)\n# new layers automatically have requires_grad = True\nlast_layer = nn.Linear(n_inputs, len(classes))\n\n# VGG16\nmodel.classifier[6] = last_layer\n\n# Others\n#model.fc = last_layer\n\n# if GPU is available, move the model to GPU\nif train_on_gpu:\n model.cuda()\n\n# check to see that your last layer produces the expected number of outputs\n\n#VGG\nprint(model.classifier[6].out_features)\n#Others\n#print(model.fc.out_features)", "_____no_output_____" ] ], [ [ "# 5. Specify our Loss Function and Optimzer", "_____no_output_____" ] ], [ [ "import torch.optim as optim\n\n# specify loss function (categorical cross-entropy)\ncriterion = #TODO\n\n# specify optimizer (stochastic gradient descent) and learning rate = 0.01 or 0.001\noptimizer = #TODO", "_____no_output_____" ] ], [ [ "# 6. Train our Model and Save necessary checkpoints", "_____no_output_____" ] ], [ [ "# Define epochs (between 50-200)\nepochs = 20\n# initialize tracker for minimum validation loss\nvalid_loss_min = np.Inf # set initial \"min\" to infinity\n\n# Some lists to keep track of loss and accuracy during each epoch\nepoch_list = []\ntrain_loss_list = []\nval_loss_list = []\ntrain_acc_list = []\nval_acc_list = []\n# Start epochs\nfor epoch in range(epochs):\n \n #adjust_learning_rate(optimizer, epoch)\n \n # monitor training loss\n train_loss = 0.0\n val_loss = 0.0\n \n ###################\n # train the model #\n ###################\n # Set the training mode ON -> Activate Dropout Layers\n model.train() # prepare model for training\n # Calculate Accuracy \n correct = 0\n total = 0\n \n # Load Train Images with Labels(Targets)\n for data, target in train_loader:\n \n if train_on_gpu:\n data, target = data.cuda(), target.cuda()\n \n # clear the gradients of all optimized variables\n optimizer.zero_grad()\n # forward pass: compute predicted outputs by passing inputs to the model\n output = model(data)\n \n if type(output) == tuple:\n output, _ = output\n \n # Calculate Training Accuracy \n predicted = torch.max(output.data, 1)[1] \n # Total number of labels\n total += len(target)\n # Total correct predictions\n correct += (predicted == target).sum()\n \n # calculate the loss\n loss = criterion(output, target)\n # backward pass: compute gradient of the loss with respect to model parameters\n loss.backward()\n # perform a single optimization step (parameter update)\n optimizer.step()\n # update running training loss\n train_loss += loss.item()*data.size(0)\n \n # calculate average training loss over an epoch\n train_loss = train_loss/len(train_loader.dataset)\n \n # Avg Accuracy\n accuracy = 100 * correct / float(total)\n \n # Put them in their list\n train_acc_list.append(accuracy)\n train_loss_list.append(train_loss)\n \n \n # Implement Validation like K-fold Cross-validation \n \n # Set Evaluation Mode ON -> Turn Off Dropout\n model.eval() # Required for Evaluation/Test\n\n # Calculate Test/Validation Accuracy \n correct = 0\n total = 0\n with torch.no_grad():\n for data, target in test_loader:\n\n\n if train_on_gpu:\n data, target = data.cuda(), target.cuda()\n\n # Predict Output\n output = model(data)\n if type(output) == tuple:\n output, _ = output\n\n # Calculate Loss\n loss = criterion(output, target)\n val_loss += loss.item()*data.size(0)\n # Get predictions from the maximum value\n predicted = torch.max(output.data, 1)[1]\n\n # Total number of labels\n total += len(target)\n\n # Total correct predictions\n correct += (predicted == target).sum()\n \n # calculate average training loss and accuracy over an epoch\n val_loss = val_loss/len(test_loader.dataset)\n accuracy = 100 * correct/ float(total)\n \n # Put them in their list\n val_acc_list.append(accuracy)\n val_loss_list.append(val_loss)\n \n # Print the Epoch and Training Loss Details with Validation Accuracy \n print('Epoch: {} \\tTraining Loss: {:.4f}\\t Val. acc: {:.2f}%'.format(\n epoch+1, \n train_loss,\n accuracy\n ))\n # save model if validation loss has decreased\n if val_loss <= valid_loss_min:\n print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(\n valid_loss_min,\n val_loss))\n # Save Model State on Checkpoint\n torch.save(model.state_dict(), 'model.pt')\n valid_loss_min = val_loss\n # Move to next epoch\n epoch_list.append(epoch + 1)", "_____no_output_____" ] ], [ [ "## Load Model State from Checkpoint", "_____no_output_____" ] ], [ [ "model.load_state_dict(torch.load('model.pt'))", "_____no_output_____" ] ], [ [ "## Save the whole Model (Pickling)", "_____no_output_____" ] ], [ [ "#Save/Pickle the Model\ntorch.save(model, 'classifier.pth')", "_____no_output_____" ] ], [ [ "# 7. Visualize Model Training and Validation", "_____no_output_____" ] ], [ [ "# Training / Validation Loss\nplt.plot(epoch_list,train_loss_list)\nplt.plot(val_loss_list)\nplt.xlabel(\"Epochs\")\nplt.ylabel(\"Loss\")\nplt.title(\"Training/Validation Loss vs Number of Epochs\")\nplt.legend(['Train', 'Valid'], loc='upper right')\nplt.show()", "_____no_output_____" ], [ "# Train/Valid Accuracy\nplt.plot(epoch_list,train_acc_list)\nplt.plot(val_acc_list)\nplt.xlabel(\"Epochs\")\nplt.ylabel(\"Training/Validation Accuracy\")\nplt.title(\"Accuracy vs Number of Epochs\")\nplt.legend(['Train', 'Valid'], loc='best')\nplt.show()", "_____no_output_____" ] ], [ [ "From the above graphs we get some really impressive results", "_____no_output_____" ], [ "**Overall Accuracy\n**", "_____no_output_____" ] ], [ [ "val_acc = sum(val_acc_list[:]).item()/len(val_acc_list)\nprint(\"Validation Accuracy of model = {} %\".format(val_acc))", "_____no_output_____" ] ], [ [ "# 8. Test our Model Performance ", "_____no_output_____" ] ], [ [ "# obtain one batch of test images\ndataiter = iter(test_loader)\nimages, labels = dataiter.next()\nimg = images.numpy()\n\n# move model inputs to cuda, if GPU available\nif train_on_gpu:\n images = images.cuda()\n\nmodel.eval() # Required for Evaluation/Test\n# get sample outputs\noutput = model(images)\nif type(output) == tuple:\n output, _ = output\n# convert output probabilities to predicted class\n_, preds_tensor = torch.max(output, 1)\npreds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())\n\n# plot the images in the batch, along with predicted and true labels\nfig = plt.figure(figsize=(20, 5))\nfor idx in np.arange(12):\n ax = fig.add_subplot(3, 4, idx+1, xticks=[], yticks=[])\n plt.imshow(np.transpose(img[idx], (1, 2, 0)))\n ax.set_title(\"Pr: {} Ac: {}\".format(classes[preds[idx]], classes[labels[idx]]),\n color=(\"green\" if preds[idx]==labels[idx].item() else \"red\"))", "_____no_output_____" ] ], [ [ "**We can see that the Correctly Classifies Results are Marked in \"Green\" and the misclassifies ones are \"Red\"**", "_____no_output_____" ], [ "## 8.1 Test our Model Performance with Gabriele Picco's Program", "_____no_output_____" ], [ "**Credits: ** **Gabriele Picco** (https://github.com/GabrielePicco/deep-learning-flower-identifier)", "_____no_output_____" ], [ "**Special Instruction:** \n1. **Uncomment the following two code cells while running the notebook.**\n2. Comment these two blocks while **Commit**, otherwise you will get an error \"Too many Output Files\" in Kaggle Only.\n3. If you find a solution to this then let me know.", "_____no_output_____" ] ], [ [ "# !git clone https://github.com/GabrielePicco/deep-learning-flower-identifier\n# !pip install airtable\n# import sys\n# sys.path.insert(0, 'deep-learning-flower-identifier')", "_____no_output_____" ], [ "# from test_model_pytorch_facebook_challenge import calc_accuracy\n# calc_accuracy(model, input_image_size=224, use_google_testset=False)", "_____no_output_____" ] ], [ [ "## **Congrats! We got almost 90% accuracy with just a simple configuration!** \n(We will get almost 90% accuracy in Gabriele's Test Suite. Just Uncomment above two code cells and see.)", "_____no_output_____" ], [ "# 9. Export our Model Checkpoint File or Model Pickle File", "_____no_output_____" ], [ "**Just Right-click on Below link and Copy the Link** \n**And Proceed to [Part 2 Tutorial](https://www.kaggle.com/soumya044/udacity-pytorch-final-lab-guide-part-2/)**", "_____no_output_____" ], [ "## Links Here: \n**Model State Checkpoint File: [model.pt](./model.pt)** (Preferred) \n**Classifier Pickle File: [classifier.pth](./classifier.pth)** \n(Right-click on model.pt and copy the link address) \n\n* If the links don't work then just modify the (link) as ./model.pt or ./classifier.pth", "_____no_output_____" ], [ "# **Proceed To Part 2: [Click Here](https://www.kaggle.com/soumya044/udacity-pytorch-final-lab-guide-part-2/)**", "_____no_output_____" ], [ "# Thank You \n\nIf you liked this kernel please **Upvote**. Don't forget to drop a comment or suggestion. \n\n### *Soumya Ranjan Behera*\nLet's stay Connected! [LinkedIn](https://www.linkedin.com/in/soumya044) \n\n**Happy Coding !**", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nfrom scipy import pi\nimport matplotlib.pyplot as plt\nimport pickle as cPickle\n#Sine wave\n\nN = 128\n\ndef get_sine_wave():\n x_sin = np.array([0.0 for i in range(N)])\n # print(x_sin)\n for i in range(N):\n # print(\"h\")\n x_sin[i] = np.sin(2.0*pi*i/16.0)\n\n plt.plot(x_sin)\n plt.title('Sine wave')\n plt.show()\n\n y_sin = np.fft.fftshift(np.fft.fft(x_sin[:16], 16))\n plt.plot(abs(y_sin))\n plt.title('FFT sine wave')\n plt.show()\n\n return x_sin\n\ndef get_bpsk_carrier():\n x = np.fromfile('gnuradio_dumps/bpsk_carrier', dtype = 'float32')\n x_bpsk_carrier = x[9000:9000+N]\n plt.plot(x_bpsk_carrier)\n plt.title('BPSK carrier')\n plt.show()\n\n # y_bpsk_carrier = np.fft.fft(x_bpsk_carrier, N)\n # plt.plot(abs(y_bpsk_carrier))\n # plt.title('FFT BPSK carrier')\n # plt.show()\n\ndef get_qpsk_carrier():\n x = np.fromfile('gnuradio_dumps/qpsk_carrier', dtype = 'float32')\n x_qpsk_carrier = x[12000:12000+N]\n plt.plot(x_qpsk_carrier)\n plt.title('QPSK carrier')\n plt.show()\n\n\n # y_qpsk_carrier = np.fft.fft(x_qpsk_carrier, N)\n # plt.plot(abs(y_qpsk_carrier))\n # plt.title('FFT QPSK carrier')\n # plt.show()\n\ndef get_bpsk():\n x = np.fromfile('gnuradio_dumps/bpsk', dtype = 'complex64')\n x_bpsk = x[9000:9000+N]\n plt.plot(x_bpsk.real)\n plt.plot(x_bpsk.imag)\n plt.title('BPSK')\n plt.show()\n\n\n\n # y_bpsk = np.fft.fft(x_bpsk, N)\n # plt.plot(abs(y_bpsk))\n # plt.title('FFT BPSK')\n # plt.show()\n\ndef get_qpsk():\n x = np.fromfile('gnuradio_dumps/qpsk', dtype = 'complex64')\n x_qpsk = x[11000:11000+N]\n plt.plot(x_qpsk.real)\n plt.plot(x_qpsk.imag)\n plt.title('QPSK')\n plt.show()\n\n\n\n # y_qpsk = np.fft.fft(x_bpsk, N)\n # plt.plot(abs(y_bqsk))\n # plt.title('FFT QPSK')\n # plt.show()\n\ndef load_dataset(location=\"../../datasets/radioml.dat\"):\n f = open(location, \"rb\")\n ds = cPickle.load(f, encoding = 'latin-1')\n return ds\n\n\ndef get_from_dataset(dataset, key):\n \"\"\"Returns complex version of dataset[key][500]\"\"\"\n xr = dataset[key][500][0]\n xi = dataset[key][500][1]\n plt.plot(xr)\n plt.plot(xi)\n plt.title(key)\n plt.show()\n return xr\n\nx_sin = get_sine_wave()\nx_bpsk_carrier = get_bpsk_carrier()\nx_qpsk_carrier = get_qpsk_carrier()\nx_bpsk = get_bpsk()\nx_qpsk = get_qpsk()\n\nds = load_dataset()\nx_amssb = get_from_dataset(dataset=ds, key=('AM-SSB', 16))\nx_amdsb = get_from_dataset(dataset=ds, key= ('AM-DSB', 18))\nx_gfsk = get_from_dataset(dataset=ds, key=('GFSK', 18))\n\n", "_____no_output_____" ], [ "nfft = 16\ncyclo_averaging = 8\noffsets = [0,1,2,3,4,5,6,7]\n\ndef compute_cyclo_fft(data, nfft): \n \n data_reshape = np.reshape(data, (-1, nfft)) \n y = np.fft.fftshift(np.fft.fft(data_reshape, axis=1), axes=1) \n \n \n return y.T\n\ndef compute_cyclo_ifft(data, nfft):\n return np.fft.fftshift(np.fft.fft(data))\n\ndef single_fft_cyclo(fft, offset):\n left = np.roll(fft, -offset)\n right = np.roll(fft, offset)\n spec = right * np.conj(left)\n return spec\n\n\ndef create_sc(spec, offset):\n left = np.roll(spec, -offset)\n right = np.roll(spec, offset)\n denom = left * right \n denom_norm = np.sqrt(denom)\n return np.divide(spec, denom_norm)\n\n\ndef cyclo_stationary(data):\n # fft\n cyc_fft = compute_cyclo_fft(data, nfft)\n \n # average\n num_ffts = int(cyc_fft.shape[0])\n cyc_fft = cyc_fft[:num_ffts] \n \n cyc_fft = np.mean(np.reshape(cyc_fft, (nfft, cyclo_averaging)), axis=1)\n \n print(cyc_fft)\n plt.title('cyc_fft')\n plt.plot(abs(cyc_fft))\n plt.show()\n\n specs = np.zeros((len(offsets)*16), dtype=np.complex64)\n scs = np.zeros((len(offsets)*16), dtype=np.complex64)\n cdp = {offset: 0 for offset in offsets}\n for j, offset in enumerate(offsets):\n spec = single_fft_cyclo(cyc_fft, offset)\n print(spec)\n plt.plot(abs(spec))\n plt.title(offset)\n plt.show()\n sc = create_sc(spec, offset)\n specs[j*16:j*16+16] = spec\n scs[j*16:j*16+16] = sc\n cdp[offset] = max(sc)\n return specs, scs, cdp\n\n\nspecs, scs, cdp = cyclo_stationary(x_sin)\nplt.plot(np.arange(128), scs.real)\nplt.plot(np.arange(128), scs.imag)\n\nplt.show()\n", "[ 6.69535287e-17 +0.00000000e+00j 6.19398243e-16 -3.33066907e-16j\n 3.40853099e-16 +1.04444071e-15j -2.53085959e-15 -3.55271368e-15j\n 6.63698404e-16 -3.30291350e-15j 3.97353638e-16 +5.27355937e-16j\n 7.92254681e-16 -2.53602855e-15j -5.60213720e-16 +8.00000000e+00j\n 8.71865222e-16 +0.00000000e+00j -1.07603878e-14 -8.00000000e+00j\n 7.92254681e-16 +2.53602855e-15j -2.75290419e-15 +3.10862447e-15j\n 6.63698404e-16 +3.30291350e-15j 6.19398243e-16 -1.94289029e-16j\n 3.40853099e-16 -1.04444071e-15j -3.28026013e-15 -5.55111512e-17j]\n" ] ] ]

[ "code" ]

[ [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Exercise: Find correspondences between old and modern english ", "_____no_output_____" ], [ "The purpose of this execise is to use two vecsigrafos, one built on UMBC and Wordnet and another one produced by directly running Swivel against a corpus of Shakespeare's complete works, to try to find corelations between old and modern English, e.g. \"thou\" -> \"you\", \"dost\" -> \"do\", \"raiment\" -> \"clothing\". For example, you can try to pick a set of 100 words in \"ye olde\" English corpus and see how they correlate to UMBC over WordNet. \n\n", "_____no_output_____" ], [ "Next, we prepare the embeddings from the Shakespeare corpus and load a UMBC vecsigrafo, which will provide the two vector spaces to correlate.", "_____no_output_____" ], [ "## Download a small text corpus", "_____no_output_____" ], [ "First, we download the corpus into our environment. We will use the Shakespeare's complete works corpus, published as part of Project Gutenberg and pbublicly available.", "_____no_output_____" ] ], [ [ "import os", "_____no_output_____" ], [ "%ls", "\u001b[0m\u001b[01;34m5class\u001b[0m/ figures_5class.h5\n5class.zip figures_5class_weights.h5\ncaptions_5class_cross.h5 quality5class.h5\ncaptions_5class_cross_weights.h5 qualityMix5class.h5\ncaptions_5class.h5 qualityUni5class.h5\ncaptions_5class_weights.h5 \u001b[01;34msample_data\u001b[0m/\ncross.h5 title_abstract_5class.h5\ncross_weights.h5 title_abstract_5class_weights.h5\nfigures_5class_cross.h5 \u001b[01;34mtutorial\u001b[0m/\nfigures_5class_cross_weights.h5\n" ], [ "#!rm -r tutorial\n!git clone https://github.com/HybridNLP2018/tutorial", "fatal: destination path 'tutorial' already exists and is not an empty directory.\n" ] ], [ [ "Let us see if the corpus is where we think it is:", "_____no_output_____" ] ], [ [ "%cd tutorial/lit\n%ls ", "/content/tutorial/lit\n\u001b[0m\u001b[01;34mcoocs\u001b[0m/ shakespeare_complete_works.txt \u001b[01;34mswivel\u001b[0m/ wget-log\n" ] ], [ [ "Downloading Swivel", "_____no_output_____" ] ], [ [ "!wget http://expertsystemlab.com/hybridNLP18/swivel.zip\n!unzip swivel.zip\n!rm swivel/*\n!rm swivel.zip", "\nRedirecting output to ‘wget-log.1’.\nArchive: swivel.zip\n inflating: swivel/analogy.cc \n inflating: swivel/distributed.sh \n inflating: swivel/eval.mk \n inflating: swivel/fastprep.cc \n inflating: swivel/fastprep.mk \n inflating: swivel/glove_to_shards.py \n inflating: swivel/nearest.py \n inflating: swivel/prep.py \n inflating: swivel/README.md \n inflating: swivel/swivel.py \n inflating: swivel/text2bin.py \n inflating: swivel/vecs.py \n inflating: swivel/wordsim.py \n" ] ], [ [ "## Learn the Swivel embeddings over the Old Shakespeare corpus", "_____no_output_____" ], [ "### Calculating the co-occurrence matrix", "_____no_output_____" ] ], [ [ "corpus_path = '/content/tutorial/lit/shakespeare_complete_works.txt'\ncoocs_path = '/content/tutorial/lit/coocs'\nshard_size = 512\nfreq=3\n!python /content/tutorial/scripts/swivel/prep.py --input={corpus_path} --output_dir={coocs_path} --shard_size={shard_size} --min_count={freq}", "running with flags \n/content/tutorial/scripts/swivel/prep.py:\n --bufsz: The number of co-occurrences to buffer\n (default: '16777216')\n (an integer)\n --input: The input text.\n (default: '')\n --max_vocab: The maximum vocabulary size\n (default: '1048576')\n (an integer)\n --min_count: The minimum number of times a word should occur to be included in\n the vocabulary\n (default: '5')\n (an integer)\n --output_dir: Output directory for Swivel data\n (default: '/tmp/swivel_data')\n --shard_size: The size for each shard\n (default: '4096')\n (an integer)\n --vocab: Vocabulary to use instead of generating one\n (default: '')\n --window_size: The window size\n (default: '10')\n (an integer)\n\ntensorflow.python.platform.app:\n -h,--[no]help: show this help\n (default: 'false')\n --[no]helpfull: show full help\n (default: 'false')\n --[no]helpshort: show this help\n (default: 'false')\n\nabsl.flags:\n --flagfile: Insert flag definitions from the given file into the command line.\n (default: '')\n --undefok: comma-separated list of flag names that it is okay to specify on\n the command line even if the program does not define a flag with that name.\n IMPORTANT: flags in this list that have arguments MUST use the --flag=value\n format.\n (default: '')\n\nvocabulary contains 23552 tokens\nComputing co-occurrences: 140000..., last lid 1820, sum(1820)=188.256746\nwriting shard 2116/2116\nWrote vocab and sum files to /content/tutorial/lit/coocs\nWrote vocab and sum files to /content/tutorial/lit/coocs\ndone!\n" ], [ "%ls {coocs_path} | head -n 10", "col_sums.txt\ncol_vocab.txt\nrow_sums.txt\nrow_vocab.txt\nshard-000-000.pb\nshard-000-001.pb\nshard-000-002.pb\nshard-000-003.pb\nshard-000-004.pb\nshard-000-005.pb\n" ] ], [ [ "### Learning the embeddings from the matrix", "_____no_output_____" ] ], [ [ "vec_path = '/content/tutorial/lit/vec/'\n!python /content/tutorial/scripts/swivel/swivel.py --input_base_path={coocs_path} \\\n --output_base_path={vec_path} \\\n --num_epochs=20 --dim=300 \\\n --submatrix_rows={shard_size} --submatrix_cols={shard_size}", "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:187: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nTo construct input pipelines, use the `tf.data` module.\nWARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:187: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nTo construct input pipelines, use the `tf.data` module.\nWARNING:tensorflow:From /content/tutorial/scripts/swivel/swivel.py:495: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease switch to tf.train.MonitoredTrainingSession\nWARNING:tensorflow:Issue encountered when serializing global_step.\nType is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n'Tensor' object has no attribute 'to_proto'\n2018-10-08 13:14:16.156023: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2018-10-08 13:14:16.156566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties: \nname: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\npciBusID: 0000:00:04.0\ntotalMemory: 11.17GiB freeMemory: 11.10GiB\n2018-10-08 13:14:16.156611: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0\n2018-10-08 13:14:18.064223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix:\n2018-10-08 13:14:18.064387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] 0 \n2018-10-08 13:14:18.064482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0: N \n2018-10-08 13:14:18.064823: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n2018-10-08 13:14:18.069298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10759 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\nINFO:tensorflow:Running local_init_op.\nINFO:tensorflow:Done running local_init_op.\nINFO:tensorflow:Starting standard services.\nINFO:tensorflow:Saving checkpoint to path /content/tutorial/lit/vec/model.ckpt\nINFO:tensorflow:Starting queue runners.\nINFO:tensorflow:global_step/sec: 0\nWARNING:tensorflow:Issue encountered when serializing global_step.\nType is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n'Tensor' object has no attribute 'to_proto'\nINFO:tensorflow:Recording summary at step 0.\nINFO:tensorflow:local_step=10 global_step=10 loss=19.0, 0.0% complete\nINFO:tensorflow:local_step=20 global_step=20 loss=18.7, 0.0% complete\nINFO:tensorflow:local_step=30 global_step=30 loss=19.0, 0.1% complete\nINFO:tensorflow:local_step=40 global_step=40 loss=18.1, 0.1% complete\nINFO:tensorflow:local_step=50 global_step=50 loss=19.5, 0.1% complete\nINFO:tensorflow:local_step=60 global_step=60 loss=17.5, 0.1% complete\nINFO:tensorflow:local_step=70 global_step=70 loss=17.1, 0.2% complete\nINFO:tensorflow:local_step=80 global_step=80 loss=17.3, 0.2% complete\nINFO:tensorflow:local_step=90 global_step=90 loss=18.9, 0.2% complete\nINFO:tensorflow:local_step=100 global_step=100 loss=16.0, 0.2% complete\nINFO:tensorflow:local_step=110 global_step=110 loss=153.8, 0.3% complete\nINFO:tensorflow:local_step=120 global_step=120 loss=12.1, 0.3% complete\nINFO:tensorflow:local_step=130 global_step=130 loss=27.2, 0.3% complete\nINFO:tensorflow:local_step=140 global_step=140 loss=20.3, 0.3% complete\nINFO:tensorflow:local_step=150 global_step=150 loss=18.5, 0.4% complete\nINFO:tensorflow:local_step=160 global_step=160 loss=14.9, 0.4% complete\nINFO:tensorflow:local_step=170 global_step=170 loss=17.0, 0.4% complete\nINFO:tensorflow:local_step=180 global_step=180 loss=14.8, 0.4% complete\nINFO:tensorflow:local_step=190 global_step=190 loss=13.7, 0.4% complete\nINFO:tensorflow:local_step=200 global_step=200 loss=17.0, 0.5% complete\nINFO:tensorflow:local_step=210 global_step=210 loss=22.4, 0.5% complete\nINFO:tensorflow:local_step=220 global_step=220 loss=21.1, 0.5% complete\nINFO:tensorflow:local_step=230 global_step=230 loss=15.8, 0.5% complete\nINFO:tensorflow:local_step=240 global_step=240 loss=14.4, 0.6% complete\nINFO:tensorflow:local_step=250 global_step=250 loss=27.9, 0.6% complete\nINFO:tensorflow:local_step=260 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complete\nINFO:tensorflow:local_step=400 global_step=400 loss=23.0, 0.9% complete\nINFO:tensorflow:local_step=410 global_step=410 loss=23.7, 1.0% complete\nINFO:tensorflow:local_step=420 global_step=420 loss=22.8, 1.0% complete\nINFO:tensorflow:local_step=430 global_step=430 loss=20.2, 1.0% complete\nINFO:tensorflow:local_step=440 global_step=440 loss=22.3, 1.0% complete\nINFO:tensorflow:local_step=450 global_step=450 loss=24.1, 1.1% complete\nINFO:tensorflow:local_step=460 global_step=460 loss=23.8, 1.1% complete\nINFO:tensorflow:local_step=470 global_step=470 loss=22.3, 1.1% complete\nINFO:tensorflow:local_step=480 global_step=480 loss=24.3, 1.1% complete\nINFO:tensorflow:local_step=490 global_step=490 loss=23.4, 1.2% complete\nINFO:tensorflow:local_step=500 global_step=500 loss=23.1, 1.2% complete\nINFO:tensorflow:local_step=510 global_step=510 loss=18.4, 1.2% complete\nINFO:tensorflow:local_step=520 global_step=520 loss=22.5, 1.2% complete\nINFO:tensorflow:local_step=530 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complete\nINFO:tensorflow:local_step=13070 global_step=13070 loss=18.6, 30.9% complete\nINFO:tensorflow:local_step=13080 global_step=13080 loss=18.4, 30.9% complete\nINFO:tensorflow:local_step=13090 global_step=13090 loss=18.0, 30.9% complete\nINFO:tensorflow:local_step=13100 global_step=13100 loss=18.0, 31.0% complete\nINFO:tensorflow:local_step=13110 global_step=13110 loss=19.0, 31.0% complete\nINFO:tensorflow:local_step=13120 global_step=13120 loss=17.7, 31.0% complete\nINFO:tensorflow:local_step=13130 global_step=13130 loss=17.8, 31.0% complete\nINFO:tensorflow:local_step=13140 global_step=13140 loss=17.9, 31.0% complete\nINFO:tensorflow:local_step=13150 global_step=13150 loss=18.9, 31.1% complete\nINFO:tensorflow:local_step=13160 global_step=13160 loss=19.1, 31.1% complete\nINFO:tensorflow:local_step=13170 global_step=13170 loss=18.2, 31.1% complete\nINFO:tensorflow:local_step=13180 global_step=13180 loss=18.7, 31.1% complete\nINFO:tensorflow:local_step=13190 global_step=13190 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complete\nINFO:tensorflow:local_step=13450 global_step=13450 loss=18.8, 31.8% complete\nINFO:tensorflow:local_step=13460 global_step=13460 loss=17.7, 31.8% complete\nINFO:tensorflow:local_step=13470 global_step=13470 loss=18.8, 31.8% complete\nINFO:tensorflow:local_step=13480 global_step=13480 loss=18.3, 31.9% complete\nINFO:tensorflow:local_step=13490 global_step=13490 loss=18.8, 31.9% complete\nINFO:tensorflow:local_step=13500 global_step=13500 loss=18.5, 31.9% complete\nINFO:tensorflow:local_step=13510 global_step=13510 loss=17.9, 31.9% complete\nINFO:tensorflow:local_step=13520 global_step=13520 loss=19.6, 31.9% complete\nINFO:tensorflow:local_step=13530 global_step=13530 loss=17.8, 32.0% complete\nINFO:tensorflow:local_step=13540 global_step=13540 loss=18.9, 32.0% complete\nINFO:tensorflow:local_step=13550 global_step=13550 loss=18.2, 32.0% complete\nINFO:tensorflow:local_step=13560 global_step=13560 loss=18.4, 32.0% complete\nINFO:tensorflow:local_step=13570 global_step=13570 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complete\nINFO:tensorflow:local_step=13830 global_step=13830 loss=19.4, 32.7% complete\nINFO:tensorflow:local_step=13840 global_step=13840 loss=19.0, 32.7% complete\nINFO:tensorflow:local_step=13850 global_step=13850 loss=18.4, 32.7% complete\nINFO:tensorflow:local_step=13860 global_step=13860 loss=18.3, 32.8% complete\nINFO:tensorflow:local_step=13870 global_step=13870 loss=19.3, 32.8% complete\nINFO:tensorflow:local_step=13880 global_step=13880 loss=18.8, 32.8% complete\nINFO:tensorflow:local_step=13890 global_step=13890 loss=17.6, 32.8% complete\nINFO:tensorflow:local_step=13900 global_step=13900 loss=18.0, 32.8% complete\nINFO:tensorflow:local_step=13910 global_step=13910 loss=17.7, 32.9% complete\nINFO:tensorflow:local_step=13920 global_step=13920 loss=18.1, 32.9% complete\nINFO:tensorflow:local_step=13930 global_step=13930 loss=17.9, 32.9% complete\nINFO:tensorflow:local_step=13940 global_step=13940 loss=19.8, 32.9% complete\nINFO:tensorflow:local_step=13950 global_step=13950 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complete\nINFO:tensorflow:local_step=14590 global_step=14590 loss=19.1, 34.5% complete\nINFO:tensorflow:local_step=14600 global_step=14600 loss=18.9, 34.5% complete\nINFO:tensorflow:local_step=14610 global_step=14610 loss=19.0, 34.5% complete\nINFO:tensorflow:local_step=14620 global_step=14620 loss=17.9, 34.5% complete\nINFO:tensorflow:local_step=14630 global_step=14630 loss=19.0, 34.6% complete\nINFO:tensorflow:local_step=14640 global_step=14640 loss=19.7, 34.6% complete\nINFO:tensorflow:local_step=14650 global_step=14650 loss=18.1, 34.6% complete\nINFO:tensorflow:local_step=14660 global_step=14660 loss=18.2, 34.6% complete\nINFO:tensorflow:local_step=14670 global_step=14670 loss=15.4, 34.7% complete\nINFO:tensorflow:local_step=14680 global_step=14680 loss=19.2, 34.7% complete\nINFO:tensorflow:local_step=14690 global_step=14690 loss=18.8, 34.7% complete\nINFO:tensorflow:local_step=14700 global_step=14700 loss=18.5, 34.7% complete\nINFO:tensorflow:local_step=14710 global_step=14710 loss=18.8, 34.8% complete\nINFO:tensorflow:local_step=14720 global_step=14720 loss=18.3, 34.8% complete\nINFO:tensorflow:local_step=14730 global_step=14730 loss=13.5, 34.8% complete\nINFO:tensorflow:local_step=14740 global_step=14740 loss=302.7, 34.8% complete\nINFO:tensorflow:local_step=14750 global_step=14750 loss=18.6, 34.9% complete\nINFO:tensorflow:local_step=14760 global_step=14760 loss=18.4, 34.9% complete\nINFO:tensorflow:local_step=14770 global_step=14770 loss=18.6, 34.9% complete\nINFO:tensorflow:local_step=14780 global_step=14780 loss=18.6, 34.9% complete\nINFO:tensorflow:local_step=14790 global_step=14790 loss=19.5, 34.9% complete\nINFO:tensorflow:local_step=14800 global_step=14800 loss=18.0, 35.0% complete\nINFO:tensorflow:local_step=14810 global_step=14810 loss=17.9, 35.0% complete\nINFO:tensorflow:local_step=14820 global_step=14820 loss=18.4, 35.0% complete\nINFO:tensorflow:local_step=14830 global_step=14830 loss=17.3, 35.0% complete\nINFO:tensorflow:local_step=14840 global_step=14840 loss=18.7, 35.1% complete\nINFO:tensorflow:local_step=14850 global_step=14850 loss=17.8, 35.1% complete\nINFO:tensorflow:local_step=14860 global_step=14860 loss=17.9, 35.1% complete\nINFO:tensorflow:local_step=14870 global_step=14870 loss=18.0, 35.1% complete\nINFO:tensorflow:local_step=14880 global_step=14880 loss=18.8, 35.2% complete\nINFO:tensorflow:local_step=14890 global_step=14890 loss=18.7, 35.2% complete\nINFO:tensorflow:local_step=14900 global_step=14900 loss=18.2, 35.2% complete\nINFO:tensorflow:local_step=14910 global_step=14910 loss=18.4, 35.2% complete\nINFO:tensorflow:local_step=14920 global_step=14920 loss=17.9, 35.3% complete\nINFO:tensorflow:local_step=14930 global_step=14930 loss=18.2, 35.3% complete\nINFO:tensorflow:local_step=14940 global_step=14940 loss=18.7, 35.3% complete\nINFO:tensorflow:local_step=14950 global_step=14950 loss=18.4, 35.3% complete\nINFO:tensorflow:Recording summary at step 14956.\nINFO:tensorflow:global_step/sec: 126.049\nINFO:tensorflow:local_step=14960 global_step=14960 loss=18.0, 35.3% complete\nINFO:tensorflow:local_step=14970 global_step=14970 loss=19.5, 35.4% complete\nINFO:tensorflow:local_step=14980 global_step=14980 loss=18.6, 35.4% complete\nINFO:tensorflow:local_step=14990 global_step=14990 loss=17.9, 35.4% complete\nINFO:tensorflow:local_step=15000 global_step=15000 loss=18.5, 35.4% complete\nINFO:tensorflow:local_step=15010 global_step=15010 loss=17.1, 35.5% complete\nINFO:tensorflow:local_step=15020 global_step=15020 loss=18.2, 35.5% complete\nINFO:tensorflow:local_step=15030 global_step=15030 loss=22.4, 35.5% complete\nINFO:tensorflow:local_step=15040 global_step=15040 loss=18.4, 35.5% complete\nINFO:tensorflow:local_step=15050 global_step=15050 loss=18.1, 35.6% complete\nINFO:tensorflow:local_step=15060 global_step=15060 loss=18.0, 35.6% complete\nINFO:tensorflow:local_step=15070 global_step=15070 loss=18.4, 35.6% complete\nINFO:tensorflow:local_step=15080 global_step=15080 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complete\nINFO:tensorflow:local_step=20280 global_step=20280 loss=18.3, 47.9% complete\nINFO:tensorflow:local_step=20290 global_step=20290 loss=17.7, 47.9% complete\nINFO:tensorflow:local_step=20300 global_step=20300 loss=17.7, 48.0% complete\nINFO:tensorflow:local_step=20310 global_step=20310 loss=18.6, 48.0% complete\nINFO:tensorflow:local_step=20320 global_step=20320 loss=18.8, 48.0% complete\nINFO:tensorflow:local_step=20330 global_step=20330 loss=18.2, 48.0% complete\nINFO:tensorflow:local_step=20340 global_step=20340 loss=17.2, 48.1% complete\nINFO:tensorflow:local_step=20350 global_step=20350 loss=18.3, 48.1% complete\nINFO:tensorflow:local_step=20360 global_step=20360 loss=19.6, 48.1% complete\nINFO:tensorflow:local_step=20370 global_step=20370 loss=19.2, 48.1% complete\nINFO:tensorflow:local_step=20380 global_step=20380 loss=18.6, 48.2% complete\nINFO:tensorflow:local_step=20390 global_step=20390 loss=19.3, 48.2% complete\nINFO:tensorflow:local_step=20400 global_step=20400 loss=17.7, 48.2% complete\nINFO:tensorflow:local_step=20410 global_step=20410 loss=17.8, 48.2% complete\nINFO:tensorflow:local_step=20420 global_step=20420 loss=18.0, 48.3% complete\nINFO:tensorflow:local_step=20430 global_step=20430 loss=18.1, 48.3% complete\nINFO:tensorflow:local_step=20440 global_step=20440 loss=19.5, 48.3% complete\nINFO:tensorflow:local_step=20450 global_step=20450 loss=18.0, 48.3% complete\nINFO:tensorflow:local_step=20460 global_step=20460 loss=17.8, 48.3% complete\nINFO:tensorflow:local_step=20470 global_step=20470 loss=18.7, 48.4% complete\nINFO:tensorflow:local_step=20480 global_step=20480 loss=18.3, 48.4% complete\nINFO:tensorflow:local_step=20490 global_step=20490 loss=17.8, 48.4% complete\nINFO:tensorflow:local_step=20500 global_step=20500 loss=18.5, 48.4% complete\nINFO:tensorflow:local_step=20510 global_step=20510 loss=18.3, 48.5% complete\nINFO:tensorflow:local_step=20520 global_step=20520 loss=18.7, 48.5% complete\nINFO:tensorflow:local_step=20530 global_step=20530 loss=18.6, 48.5% complete\nINFO:tensorflow:local_step=20540 global_step=20540 loss=17.6, 48.5% complete\nINFO:tensorflow:local_step=20550 global_step=20550 loss=19.3, 48.6% complete\nINFO:tensorflow:local_step=20560 global_step=20560 loss=18.4, 48.6% complete\nINFO:tensorflow:local_step=20570 global_step=20570 loss=18.9, 48.6% complete\nINFO:tensorflow:local_step=20580 global_step=20580 loss=18.2, 48.6% complete\nINFO:tensorflow:local_step=20590 global_step=20590 loss=18.5, 48.7% complete\nINFO:tensorflow:local_step=20600 global_step=20600 loss=18.2, 48.7% complete\nINFO:tensorflow:local_step=20610 global_step=20610 loss=18.8, 48.7% complete\nINFO:tensorflow:local_step=20620 global_step=20620 loss=18.1, 48.7% complete\nINFO:tensorflow:local_step=20630 global_step=20630 loss=18.6, 48.7% complete\nINFO:tensorflow:local_step=20640 global_step=20640 loss=18.1, 48.8% complete\nINFO:tensorflow:local_step=20650 global_step=20650 loss=18.6, 48.8% complete\nINFO:tensorflow:local_step=20660 global_step=20660 loss=18.5, 48.8% complete\nINFO:tensorflow:local_step=20670 global_step=20670 loss=18.9, 48.8% complete\nINFO:tensorflow:local_step=20680 global_step=20680 loss=19.0, 48.9% complete\nINFO:tensorflow:local_step=20690 global_step=20690 loss=19.9, 48.9% complete\nINFO:tensorflow:local_step=20700 global_step=20700 loss=19.5, 48.9% complete\nINFO:tensorflow:local_step=20710 global_step=20710 loss=18.4, 48.9% complete\nINFO:tensorflow:local_step=20720 global_step=20720 loss=18.3, 49.0% complete\nINFO:tensorflow:local_step=20730 global_step=20730 loss=18.8, 49.0% complete\nINFO:tensorflow:local_step=20740 global_step=20740 loss=18.0, 49.0% complete\nINFO:tensorflow:local_step=20750 global_step=20750 loss=19.1, 49.0% complete\nINFO:tensorflow:local_step=20760 global_step=20760 loss=276.3, 49.1% complete\nINFO:tensorflow:local_step=20770 global_step=20770 loss=18.8, 49.1% complete\nINFO:tensorflow:local_step=20780 global_step=20780 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global_step=20910 loss=18.8, 49.4% complete\nINFO:tensorflow:local_step=20920 global_step=20920 loss=18.3, 49.4% complete\nINFO:tensorflow:local_step=20930 global_step=20930 loss=18.0, 49.5% complete\nINFO:tensorflow:local_step=20940 global_step=20940 loss=18.3, 49.5% complete\nINFO:tensorflow:local_step=20950 global_step=20950 loss=15.3, 49.5% complete\nINFO:tensorflow:local_step=20960 global_step=20960 loss=17.7, 49.5% complete\nINFO:tensorflow:local_step=20970 global_step=20970 loss=18.6, 49.6% complete\nINFO:tensorflow:local_step=20980 global_step=20980 loss=18.2, 49.6% complete\nINFO:tensorflow:local_step=20990 global_step=20990 loss=18.8, 49.6% complete\nINFO:tensorflow:local_step=21000 global_step=21000 loss=18.7, 49.6% complete\nINFO:tensorflow:local_step=21010 global_step=21010 loss=17.9, 49.6% complete\nINFO:tensorflow:local_step=21020 global_step=21020 loss=18.2, 49.7% complete\nINFO:tensorflow:local_step=21030 global_step=21030 loss=19.0, 49.7% complete\nINFO:tensorflow:local_step=21040 global_step=21040 loss=18.9, 49.7% complete\nINFO:tensorflow:local_step=21050 global_step=21050 loss=18.1, 49.7% complete\nINFO:tensorflow:local_step=21060 global_step=21060 loss=17.7, 49.8% complete\nINFO:tensorflow:local_step=21070 global_step=21070 loss=19.6, 49.8% complete\nINFO:tensorflow:local_step=21080 global_step=21080 loss=19.2, 49.8% complete\nINFO:tensorflow:local_step=21090 global_step=21090 loss=300.9, 49.8% complete\nINFO:tensorflow:local_step=21100 global_step=21100 loss=15.4, 49.9% complete\nINFO:tensorflow:local_step=21110 global_step=21110 loss=17.1, 49.9% complete\nINFO:tensorflow:local_step=21120 global_step=21120 loss=18.2, 49.9% complete\nINFO:tensorflow:local_step=21130 global_step=21130 loss=21.0, 49.9% complete\nINFO:tensorflow:local_step=21140 global_step=21140 loss=18.3, 50.0% complete\nINFO:tensorflow:local_step=21150 global_step=21150 loss=18.1, 50.0% complete\nINFO:tensorflow:local_step=21160 global_step=21160 loss=19.5, 50.0% complete\nINFO:tensorflow:local_step=21170 global_step=21170 loss=17.3, 50.0% complete\nINFO:tensorflow:local_step=21180 global_step=21180 loss=17.5, 50.0% complete\nINFO:tensorflow:local_step=21190 global_step=21190 loss=17.9, 50.1% complete\nINFO:tensorflow:local_step=21200 global_step=21200 loss=18.5, 50.1% complete\nINFO:tensorflow:local_step=21210 global_step=21210 loss=18.7, 50.1% complete\nINFO:tensorflow:local_step=21220 global_step=21220 loss=17.5, 50.1% complete\nINFO:tensorflow:local_step=21230 global_step=21230 loss=18.7, 50.2% complete\nINFO:tensorflow:local_step=21240 global_step=21240 loss=18.2, 50.2% complete\nINFO:tensorflow:local_step=21250 global_step=21250 loss=18.3, 50.2% complete\nINFO:tensorflow:local_step=21260 global_step=21260 loss=18.1, 50.2% complete\nINFO:tensorflow:local_step=21270 global_step=21270 loss=17.4, 50.3% complete\nINFO:tensorflow:local_step=21280 global_step=21280 loss=17.7, 50.3% complete\nINFO:tensorflow:local_step=21290 global_step=21290 loss=18.4, 50.3% complete\nINFO:tensorflow:local_step=21300 global_step=21300 loss=18.7, 50.3% complete\nINFO:tensorflow:local_step=21310 global_step=21310 loss=18.8, 50.4% complete\nINFO:tensorflow:local_step=21320 global_step=21320 loss=17.9, 50.4% complete\nINFO:tensorflow:local_step=21330 global_step=21330 loss=15.5, 50.4% complete\nINFO:tensorflow:local_step=21340 global_step=21340 loss=18.5, 50.4% complete\nINFO:tensorflow:local_step=21350 global_step=21350 loss=17.4, 50.4% complete\nINFO:tensorflow:local_step=21360 global_step=21360 loss=18.0, 50.5% complete\nINFO:tensorflow:local_step=21370 global_step=21370 loss=17.9, 50.5% complete\nINFO:tensorflow:local_step=21380 global_step=21380 loss=18.1, 50.5% complete\nINFO:tensorflow:local_step=21390 global_step=21390 loss=18.1, 50.5% complete\nINFO:tensorflow:local_step=21400 global_step=21400 loss=18.1, 50.6% complete\nINFO:tensorflow:local_step=21410 global_step=21410 loss=18.6, 50.6% complete\nINFO:tensorflow:local_step=21420 global_step=21420 loss=287.3, 50.6% complete\nINFO:tensorflow:local_step=21430 global_step=21430 loss=18.3, 50.6% complete\nINFO:tensorflow:local_step=21440 global_step=21440 loss=19.2, 50.7% complete\nINFO:tensorflow:local_step=21450 global_step=21450 loss=18.4, 50.7% complete\nINFO:tensorflow:local_step=21460 global_step=21460 loss=18.1, 50.7% complete\nINFO:tensorflow:local_step=21470 global_step=21470 loss=17.9, 50.7% complete\nINFO:tensorflow:local_step=21480 global_step=21480 loss=17.8, 50.8% complete\nINFO:tensorflow:local_step=21490 global_step=21490 loss=18.2, 50.8% complete\nINFO:tensorflow:local_step=21500 global_step=21500 loss=17.2, 50.8% complete\nINFO:tensorflow:local_step=21510 global_step=21510 loss=21.6, 50.8% complete\nINFO:tensorflow:local_step=21520 global_step=21520 loss=19.0, 50.9% complete\nINFO:tensorflow:local_step=21530 global_step=21530 loss=18.5, 50.9% complete\nINFO:tensorflow:local_step=21540 global_step=21540 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global_step=21670 loss=17.8, 51.2% complete\nINFO:tensorflow:local_step=21680 global_step=21680 loss=17.8, 51.2% complete\nINFO:tensorflow:local_step=21690 global_step=21690 loss=18.2, 51.3% complete\nINFO:tensorflow:local_step=21700 global_step=21700 loss=18.6, 51.3% complete\nINFO:tensorflow:local_step=21710 global_step=21710 loss=19.1, 51.3% complete\nINFO:tensorflow:local_step=21720 global_step=21720 loss=17.7, 51.3% complete\nINFO:tensorflow:local_step=21730 global_step=21730 loss=18.8, 51.3% complete\nINFO:tensorflow:local_step=21740 global_step=21740 loss=17.8, 51.4% complete\nINFO:tensorflow:local_step=21750 global_step=21750 loss=18.3, 51.4% complete\nINFO:tensorflow:local_step=21760 global_step=21760 loss=18.2, 51.4% complete\nINFO:tensorflow:local_step=21770 global_step=21770 loss=16.2, 51.4% complete\nINFO:tensorflow:local_step=21780 global_step=21780 loss=18.5, 51.5% complete\nINFO:tensorflow:local_step=21790 global_step=21790 loss=18.5, 51.5% complete\nINFO:tensorflow:local_step=21800 global_step=21800 loss=18.6, 51.5% complete\nINFO:tensorflow:local_step=21810 global_step=21810 loss=18.2, 51.5% complete\nINFO:tensorflow:local_step=21820 global_step=21820 loss=18.1, 51.6% complete\nINFO:tensorflow:local_step=21830 global_step=21830 loss=18.6, 51.6% complete\nINFO:tensorflow:local_step=21840 global_step=21840 loss=18.4, 51.6% complete\nINFO:tensorflow:local_step=21850 global_step=21850 loss=17.8, 51.6% complete\nINFO:tensorflow:local_step=21860 global_step=21860 loss=19.0, 51.7% complete\nINFO:tensorflow:local_step=21870 global_step=21870 loss=17.8, 51.7% complete\nINFO:tensorflow:local_step=21880 global_step=21880 loss=18.9, 51.7% complete\nINFO:tensorflow:local_step=21890 global_step=21890 loss=18.5, 51.7% complete\nINFO:tensorflow:local_step=21900 global_step=21900 loss=18.1, 51.7% complete\nINFO:tensorflow:local_step=21910 global_step=21910 loss=18.8, 51.8% complete\nINFO:tensorflow:local_step=21920 global_step=21920 loss=17.7, 51.8% complete\nINFO:tensorflow:local_step=21930 global_step=21930 loss=17.8, 51.8% complete\nINFO:tensorflow:local_step=21940 global_step=21940 loss=18.0, 51.8% complete\nINFO:tensorflow:local_step=21950 global_step=21950 loss=19.3, 51.9% complete\nINFO:tensorflow:local_step=21960 global_step=21960 loss=19.2, 51.9% complete\nINFO:tensorflow:local_step=21970 global_step=21970 loss=19.8, 51.9% complete\nINFO:tensorflow:local_step=21980 global_step=21980 loss=18.3, 51.9% complete\nINFO:tensorflow:local_step=21990 global_step=21990 loss=16.2, 52.0% complete\nINFO:tensorflow:local_step=22000 global_step=22000 loss=15.8, 52.0% complete\nINFO:tensorflow:local_step=22010 global_step=22010 loss=16.6, 52.0% complete\nINFO:tensorflow:local_step=22020 global_step=22020 loss=18.3, 52.0% complete\nINFO:tensorflow:local_step=22030 global_step=22030 loss=18.1, 52.1% complete\nINFO:tensorflow:local_step=22040 global_step=22040 loss=18.8, 52.1% complete\nINFO:tensorflow:local_step=22050 global_step=22050 loss=17.9, 52.1% complete\nINFO:tensorflow:local_step=22060 global_step=22060 loss=19.4, 52.1% complete\nINFO:tensorflow:local_step=22070 global_step=22070 loss=17.6, 52.2% complete\nINFO:tensorflow:local_step=22080 global_step=22080 loss=18.6, 52.2% complete\nINFO:tensorflow:local_step=22090 global_step=22090 loss=19.3, 52.2% complete\nINFO:tensorflow:local_step=22100 global_step=22100 loss=18.1, 52.2% complete\nINFO:tensorflow:local_step=22110 global_step=22110 loss=19.9, 52.2% complete\nINFO:tensorflow:local_step=22120 global_step=22120 loss=18.5, 52.3% complete\nINFO:tensorflow:local_step=22130 global_step=22130 loss=17.9, 52.3% complete\nINFO:tensorflow:local_step=22140 global_step=22140 loss=19.7, 52.3% complete\nINFO:tensorflow:local_step=22150 global_step=22150 loss=17.7, 52.3% complete\nINFO:tensorflow:local_step=22160 global_step=22160 loss=18.1, 52.4% complete\nINFO:tensorflow:local_step=22170 global_step=22170 loss=19.1, 52.4% complete\nINFO:tensorflow:local_step=22180 global_step=22180 loss=18.2, 52.4% complete\nINFO:tensorflow:local_step=22190 global_step=22190 loss=17.8, 52.4% complete\nINFO:tensorflow:local_step=22200 global_step=22200 loss=19.1, 52.5% complete\nINFO:tensorflow:local_step=22210 global_step=22210 loss=18.2, 52.5% complete\nINFO:tensorflow:local_step=22220 global_step=22220 loss=18.5, 52.5% complete\nINFO:tensorflow:local_step=22230 global_step=22230 loss=19.5, 52.5% complete\nINFO:tensorflow:local_step=22240 global_step=22240 loss=18.3, 52.6% complete\nINFO:tensorflow:local_step=22250 global_step=22250 loss=18.2, 52.6% complete\nINFO:tensorflow:local_step=22260 global_step=22260 loss=18.4, 52.6% complete\nINFO:tensorflow:local_step=22270 global_step=22270 loss=18.6, 52.6% complete\nINFO:tensorflow:local_step=22280 global_step=22280 loss=17.7, 52.6% complete\nINFO:tensorflow:local_step=22290 global_step=22290 loss=18.0, 52.7% complete\nINFO:tensorflow:local_step=22300 global_step=22300 loss=18.6, 52.7% complete\nINFO:tensorflow:local_step=22310 global_step=22310 loss=17.9, 52.7% complete\nINFO:tensorflow:local_step=22320 global_step=22320 loss=19.2, 52.7% complete\nINFO:tensorflow:local_step=22330 global_step=22330 loss=18.1, 52.8% complete\nINFO:tensorflow:local_step=22340 global_step=22340 loss=17.9, 52.8% complete\nINFO:tensorflow:local_step=22350 global_step=22350 loss=18.6, 52.8% complete\nINFO:tensorflow:local_step=22360 global_step=22360 loss=17.8, 52.8% complete\nINFO:tensorflow:local_step=22370 global_step=22370 loss=19.4, 52.9% complete\nINFO:tensorflow:local_step=22380 global_step=22380 loss=17.9, 52.9% complete\nINFO:tensorflow:local_step=22390 global_step=22390 loss=18.4, 52.9% complete\nINFO:tensorflow:local_step=22400 global_step=22400 loss=17.9, 52.9% complete\nINFO:tensorflow:local_step=22410 global_step=22410 loss=19.8, 53.0% complete\nINFO:tensorflow:local_step=22420 global_step=22420 loss=17.8, 53.0% complete\nINFO:tensorflow:local_step=22430 global_step=22430 loss=18.3, 53.0% complete\nINFO:tensorflow:local_step=22440 global_step=22440 loss=232.0, 53.0% complete\nINFO:tensorflow:local_step=22450 global_step=22450 loss=229.4, 53.0% complete\nINFO:tensorflow:local_step=22460 global_step=22460 loss=18.5, 53.1% complete\nINFO:tensorflow:local_step=22470 global_step=22470 loss=18.9, 53.1% complete\nINFO:tensorflow:Recording summary at step 22475.\nINFO:tensorflow:global_step/sec: 125.318\nINFO:tensorflow:local_step=22480 global_step=22480 loss=18.2, 53.1% complete\nINFO:tensorflow:local_step=22490 global_step=22490 loss=19.2, 53.1% complete\nINFO:tensorflow:local_step=22500 global_step=22500 loss=18.9, 53.2% complete\nINFO:tensorflow:local_step=22510 global_step=22510 loss=19.0, 53.2% complete\nINFO:tensorflow:local_step=22520 global_step=22520 loss=19.1, 53.2% complete\nINFO:tensorflow:local_step=22530 global_step=22530 loss=18.1, 53.2% complete\nINFO:tensorflow:local_step=22540 global_step=22540 loss=18.7, 53.3% complete\nINFO:tensorflow:local_step=22550 global_step=22550 loss=17.9, 53.3% complete\nINFO:tensorflow:local_step=22560 global_step=22560 loss=19.1, 53.3% complete\nINFO:tensorflow:local_step=22570 global_step=22570 loss=19.1, 53.3% complete\nINFO:tensorflow:local_step=22580 global_step=22580 loss=18.4, 53.4% complete\nINFO:tensorflow:local_step=22590 global_step=22590 loss=19.1, 53.4% complete\nINFO:tensorflow:local_step=22600 global_step=22600 loss=267.8, 53.4% complete\nINFO:tensorflow:local_step=22610 global_step=22610 loss=256.1, 53.4% complete\nINFO:tensorflow:local_step=22620 global_step=22620 loss=18.0, 53.4% complete\nINFO:tensorflow:local_step=22630 global_step=22630 loss=18.7, 53.5% complete\nINFO:tensorflow:local_step=22640 global_step=22640 loss=18.3, 53.5% complete\nINFO:tensorflow:local_step=22650 global_step=22650 loss=18.3, 53.5% complete\nINFO:tensorflow:local_step=22660 global_step=22660 loss=18.0, 53.5% complete\nINFO:tensorflow:local_step=22670 global_step=22670 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global_step=22800 loss=18.4, 53.9% complete\nINFO:tensorflow:local_step=22810 global_step=22810 loss=18.2, 53.9% complete\nINFO:tensorflow:local_step=22820 global_step=22820 loss=18.7, 53.9% complete\nINFO:tensorflow:local_step=22830 global_step=22830 loss=19.0, 53.9% complete\nINFO:tensorflow:local_step=22840 global_step=22840 loss=19.9, 54.0% complete\nINFO:tensorflow:local_step=22850 global_step=22850 loss=18.5, 54.0% complete\nINFO:tensorflow:local_step=22860 global_step=22860 loss=18.3, 54.0% complete\nINFO:tensorflow:local_step=22870 global_step=22870 loss=18.4, 54.0% complete\nINFO:tensorflow:local_step=22880 global_step=22880 loss=18.9, 54.1% complete\nINFO:tensorflow:local_step=22890 global_step=22890 loss=18.4, 54.1% complete\nINFO:tensorflow:local_step=22900 global_step=22900 loss=18.3, 54.1% complete\nINFO:tensorflow:local_step=22910 global_step=22910 loss=18.0, 54.1% complete\nINFO:tensorflow:local_step=22920 global_step=22920 loss=17.6, 54.2% complete\nINFO:tensorflow:local_step=22930 global_step=22930 loss=18.3, 54.2% complete\nINFO:tensorflow:local_step=22940 global_step=22940 loss=18.1, 54.2% complete\nINFO:tensorflow:local_step=22950 global_step=22950 loss=19.7, 54.2% complete\nINFO:tensorflow:local_step=22960 global_step=22960 loss=18.5, 54.3% complete\nINFO:tensorflow:local_step=22970 global_step=22970 loss=18.3, 54.3% complete\nINFO:tensorflow:local_step=22980 global_step=22980 loss=18.9, 54.3% complete\nINFO:tensorflow:local_step=22990 global_step=22990 loss=18.9, 54.3% complete\nINFO:tensorflow:local_step=23000 global_step=23000 loss=19.6, 54.3% complete\nINFO:tensorflow:local_step=23010 global_step=23010 loss=17.8, 54.4% complete\nINFO:tensorflow:local_step=23020 global_step=23020 loss=18.7, 54.4% complete\nINFO:tensorflow:local_step=23030 global_step=23030 loss=19.5, 54.4% complete\nINFO:tensorflow:local_step=23040 global_step=23040 loss=18.4, 54.4% complete\nINFO:tensorflow:local_step=23050 global_step=23050 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complete\nINFO:tensorflow:local_step=23310 global_step=23310 loss=18.3, 55.1% complete\nINFO:tensorflow:local_step=23320 global_step=23320 loss=17.8, 55.1% complete\nINFO:tensorflow:local_step=23330 global_step=23330 loss=19.1, 55.1% complete\nINFO:tensorflow:local_step=23340 global_step=23340 loss=18.1, 55.2% complete\nINFO:tensorflow:local_step=23350 global_step=23350 loss=18.6, 55.2% complete\nINFO:tensorflow:local_step=23360 global_step=23360 loss=17.8, 55.2% complete\nINFO:tensorflow:local_step=23370 global_step=23370 loss=18.9, 55.2% complete\nINFO:tensorflow:local_step=23380 global_step=23380 loss=18.4, 55.2% complete\nINFO:tensorflow:local_step=23390 global_step=23390 loss=15.1, 55.3% complete\nINFO:tensorflow:local_step=23400 global_step=23400 loss=18.0, 55.3% complete\nINFO:tensorflow:local_step=23410 global_step=23410 loss=18.7, 55.3% complete\nINFO:tensorflow:local_step=23420 global_step=23420 loss=18.1, 55.3% complete\nINFO:tensorflow:local_step=23430 global_step=23430 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global_step=30020 loss=18.4, 70.9% complete\nINFO:tensorflow:local_step=30030 global_step=30030 loss=17.4, 71.0% complete\nINFO:tensorflow:local_step=30040 global_step=30040 loss=18.7, 71.0% complete\nINFO:tensorflow:local_step=30050 global_step=30050 loss=18.9, 71.0% complete\nINFO:tensorflow:local_step=30060 global_step=30060 loss=18.9, 71.0% complete\nINFO:tensorflow:Recording summary at step 30060.\nINFO:tensorflow:global_step/sec: 126.417\nINFO:tensorflow:local_step=30070 global_step=30070 loss=18.1, 71.1% complete\nINFO:tensorflow:local_step=30080 global_step=30080 loss=18.2, 71.1% complete\nINFO:tensorflow:local_step=30090 global_step=30090 loss=18.9, 71.1% complete\nINFO:tensorflow:local_step=30100 global_step=30100 loss=17.8, 71.1% complete\nINFO:tensorflow:local_step=30110 global_step=30110 loss=18.2, 71.1% complete\nINFO:tensorflow:local_step=30120 global_step=30120 loss=18.1, 71.2% complete\nINFO:tensorflow:local_step=30130 global_step=30130 loss=17.9, 71.2% 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complete\nINFO:tensorflow:local_step=30520 global_step=30520 loss=18.7, 72.1% complete\nINFO:tensorflow:local_step=30530 global_step=30530 loss=18.8, 72.1% complete\nINFO:tensorflow:local_step=30540 global_step=30540 loss=18.6, 72.2% complete\nINFO:tensorflow:local_step=30550 global_step=30550 loss=18.4, 72.2% complete\nINFO:tensorflow:local_step=30560 global_step=30560 loss=18.5, 72.2% complete\nINFO:tensorflow:local_step=30570 global_step=30570 loss=18.1, 72.2% complete\nINFO:tensorflow:local_step=30580 global_step=30580 loss=18.3, 72.3% complete\nINFO:tensorflow:local_step=30590 global_step=30590 loss=18.4, 72.3% complete\nINFO:tensorflow:local_step=30600 global_step=30600 loss=18.6, 72.3% complete\nINFO:tensorflow:local_step=30610 global_step=30610 loss=17.6, 72.3% complete\nINFO:tensorflow:local_step=30620 global_step=30620 loss=17.4, 72.4% complete\nINFO:tensorflow:local_step=30630 global_step=30630 loss=18.9, 72.4% complete\nINFO:tensorflow:local_step=30640 global_step=30640 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complete\nINFO:tensorflow:local_step=37360 global_step=37360 loss=19.3, 88.3% complete\nINFO:tensorflow:local_step=37370 global_step=37370 loss=17.4, 88.3% complete\nINFO:tensorflow:local_step=37380 global_step=37380 loss=18.1, 88.3% complete\nINFO:tensorflow:local_step=37390 global_step=37390 loss=18.3, 88.4% complete\nINFO:tensorflow:local_step=37400 global_step=37400 loss=18.6, 88.4% complete\nINFO:tensorflow:local_step=37410 global_step=37410 loss=19.1, 88.4% complete\nINFO:tensorflow:local_step=37420 global_step=37420 loss=18.5, 88.4% complete\nINFO:tensorflow:local_step=37430 global_step=37430 loss=18.5, 88.4% complete\nINFO:tensorflow:local_step=37440 global_step=37440 loss=18.9, 88.5% complete\nINFO:tensorflow:local_step=37450 global_step=37450 loss=17.4, 88.5% complete\nINFO:tensorflow:local_step=37460 global_step=37460 loss=21.6, 88.5% complete\nINFO:tensorflow:local_step=37470 global_step=37470 loss=15.0, 88.5% complete\nINFO:tensorflow:local_step=37480 global_step=37480 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global_step=37610 loss=19.2, 88.9% complete\nINFO:tensorflow:local_step=37620 global_step=37620 loss=18.3, 88.9% complete\nINFO:tensorflow:local_step=37630 global_step=37630 loss=18.6, 88.9% complete\nINFO:tensorflow:local_step=37640 global_step=37640 loss=18.0, 88.9% complete\nINFO:tensorflow:local_step=37650 global_step=37650 loss=19.7, 89.0% complete\nINFO:tensorflow:local_step=37660 global_step=37660 loss=17.7, 89.0% complete\nINFO:tensorflow:Recording summary at step 37662.\nINFO:tensorflow:global_step/sec: 126.7\nINFO:tensorflow:local_step=37670 global_step=37670 loss=18.9, 89.0% complete\nINFO:tensorflow:local_step=37680 global_step=37680 loss=18.0, 89.0% complete\nINFO:tensorflow:local_step=37690 global_step=37690 loss=18.0, 89.1% complete\nINFO:tensorflow:local_step=37700 global_step=37700 loss=18.1, 89.1% complete\nINFO:tensorflow:local_step=37710 global_step=37710 loss=18.6, 89.1% complete\nINFO:tensorflow:local_step=37720 global_step=37720 loss=18.2, 89.1% complete\nINFO:tensorflow:local_step=37730 global_step=37730 loss=18.4, 89.2% complete\nINFO:tensorflow:local_step=37740 global_step=37740 loss=17.7, 89.2% complete\nINFO:tensorflow:local_step=37750 global_step=37750 loss=19.2, 89.2% complete\nINFO:tensorflow:local_step=37760 global_step=37760 loss=17.4, 89.2% complete\nINFO:tensorflow:local_step=37770 global_step=37770 loss=18.2, 89.2% complete\nINFO:tensorflow:local_step=37780 global_step=37780 loss=17.7, 89.3% complete\nINFO:tensorflow:local_step=37790 global_step=37790 loss=21.2, 89.3% complete\nINFO:tensorflow:local_step=37800 global_step=37800 loss=17.8, 89.3% complete\nINFO:tensorflow:local_step=37810 global_step=37810 loss=18.8, 89.3% complete\nINFO:tensorflow:local_step=37820 global_step=37820 loss=19.3, 89.4% complete\nINFO:tensorflow:local_step=37830 global_step=37830 loss=19.4, 89.4% complete\nINFO:tensorflow:local_step=37840 global_step=37840 loss=15.6, 89.4% complete\nINFO:tensorflow:local_step=37850 global_step=37850 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complete\nINFO:tensorflow:local_step=41910 global_step=41910 loss=18.1, 99.0% complete\nINFO:tensorflow:local_step=41920 global_step=41920 loss=17.7, 99.1% complete\nINFO:tensorflow:local_step=41930 global_step=41930 loss=19.9, 99.1% complete\nINFO:tensorflow:local_step=41940 global_step=41940 loss=17.6, 99.1% complete\nINFO:tensorflow:local_step=41950 global_step=41950 loss=21.5, 99.1% complete\nINFO:tensorflow:local_step=41960 global_step=41960 loss=18.5, 99.1% complete\nINFO:tensorflow:local_step=41970 global_step=41970 loss=19.3, 99.2% complete\nINFO:tensorflow:local_step=41980 global_step=41980 loss=18.4, 99.2% complete\nINFO:tensorflow:local_step=41990 global_step=41990 loss=17.8, 99.2% complete\nINFO:tensorflow:local_step=42000 global_step=42000 loss=18.1, 99.2% complete\nINFO:tensorflow:local_step=42010 global_step=42010 loss=18.6, 99.3% complete\nINFO:tensorflow:local_step=42020 global_step=42020 loss=17.9, 99.3% complete\nINFO:tensorflow:local_step=42030 global_step=42030 loss=18.3, 99.3% complete\nINFO:tensorflow:local_step=42040 global_step=42040 loss=18.1, 99.3% complete\nINFO:tensorflow:local_step=42050 global_step=42050 loss=17.6, 99.4% complete\nINFO:tensorflow:local_step=42060 global_step=42060 loss=19.1, 99.4% complete\nINFO:tensorflow:local_step=42070 global_step=42070 loss=17.6, 99.4% complete\nINFO:tensorflow:local_step=42080 global_step=42080 loss=18.6, 99.4% complete\nINFO:tensorflow:local_step=42090 global_step=42090 loss=19.0, 99.5% complete\nINFO:tensorflow:local_step=42100 global_step=42100 loss=18.5, 99.5% complete\nINFO:tensorflow:local_step=42110 global_step=42110 loss=19.0, 99.5% complete\nINFO:tensorflow:local_step=42120 global_step=42120 loss=17.8, 99.5% complete\nINFO:tensorflow:local_step=42130 global_step=42130 loss=18.7, 99.6% complete\nINFO:tensorflow:local_step=42140 global_step=42140 loss=18.7, 99.6% complete\nINFO:tensorflow:local_step=42150 global_step=42150 loss=17.8, 99.6% complete\nINFO:tensorflow:local_step=42160 global_step=42160 loss=15.4, 99.6% complete\nINFO:tensorflow:local_step=42170 global_step=42170 loss=18.5, 99.6% complete\nINFO:tensorflow:local_step=42180 global_step=42180 loss=18.8, 99.7% complete\nINFO:tensorflow:local_step=42190 global_step=42190 loss=18.5, 99.7% complete\nINFO:tensorflow:local_step=42200 global_step=42200 loss=18.0, 99.7% complete\nINFO:tensorflow:local_step=42210 global_step=42210 loss=18.9, 99.7% complete\nINFO:tensorflow:local_step=42220 global_step=42220 loss=18.9, 99.8% complete\nINFO:tensorflow:local_step=42230 global_step=42230 loss=17.8, 99.8% complete\nINFO:tensorflow:local_step=42240 global_step=42240 loss=18.8, 99.8% complete\nINFO:tensorflow:local_step=42250 global_step=42250 loss=17.9, 99.8% complete\nINFO:tensorflow:local_step=42260 global_step=42260 loss=17.9, 99.9% complete\nINFO:tensorflow:local_step=42270 global_step=42270 loss=18.0, 99.9% complete\nINFO:tensorflow:local_step=42280 global_step=42280 loss=21.3, 99.9% complete\nINFO:tensorflow:local_step=42290 global_step=42290 loss=17.8, 99.9% complete\nINFO:tensorflow:local_step=42300 global_step=42300 loss=18.9, 100.0% complete\nINFO:tensorflow:local_step=42310 global_step=42310 loss=18.2, 100.0% complete\nINFO:tensorflow:local_step=42320 global_step=42320 loss=18.7, 100.0% complete\nWARNING:tensorflow:Issue encountered when serializing global_step.\nType is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n'Tensor' object has no attribute 'to_proto'\n" ] ], [ [ "Checking the context of the 'vec' directory. Should contain checkpoints of the model plus tsv files for column and row embeddings.", "_____no_output_____" ] ], [ [ "os.listdir(vec_path)\n", "_____no_output_____" ] ], [ [ "Converting tsv to bin:", "_____no_output_____" ] ], [ [ "!python /content/tutorial/scripts/swivel/text2bin.py --vocab={vec_path}vocab.txt --output={vec_path}vecs.bin \\\n {vec_path}row_embedding.tsv \\\n {vec_path}col_embedding.tsv", "executing text2bin\nmerging files ['/content/tutorial/lit/vec/row_embedding.tsv', '/content/tutorial/lit/vec/col_embedding.tsv'] into output bin\n" ], [ "%ls {vec_path}", "checkpoint\ncol_embedding.tsv\nevents.out.tfevents.1539004459.46972dad0a54\ngraph.pbtxt\nmodel.ckpt-0.data-00000-of-00001\nmodel.ckpt-0.index\nmodel.ckpt-0.meta\nmodel.ckpt-42320.data-00000-of-00001\nmodel.ckpt-42320.index\nmodel.ckpt-42320.meta\nrow_embedding.tsv\nvecs.bin\nvocab.txt\n" ] ], [ [ "### Read stored binary embeddings and inspect them", "_____no_output_____" ] ], [ [ "import importlib.util\n\nspec = importlib.util.spec_from_file_location(\"vecs\", \"/content/tutorial/scripts/swivel/vecs.py\")\nm = importlib.util.module_from_spec(spec)\nspec.loader.exec_module(m)\nshakespeare_vecs = m.Vecs(vec_path + 'vocab.txt', vec_path + 'vecs.bin')", "Opening vector with expected size 23552 from file /content/tutorial/lit/vec/vocab.txt\nvocab size 23552 (unique 23552)\nread rows\n" ] ], [ [ "##Basic method to print the k nearest neighbors for a given word", "_____no_output_____" ] ], [ [ "def k_neighbors(vec, word, k=10):\n res = vec.neighbors(word)\n if not res:\n print('%s is not in the vocabulary, try e.g. %s' % (word, vecs.random_word_in_vocab()))\n else:\n for word, sim in res[:10]:\n print('%0.4f: %s' % (sim, word))", "_____no_output_____" ], [ "k_neighbors(shakespeare_vecs, 'strife')", "1.0000: strife\n0.4599: tutors\n0.3981: tumultuous\n0.3530: future\n0.3368: daughters’\n0.3229: cease\n0.3018: Nought\n0.2866: strike.\n0.2852: War\n0.2775: nature.\n" ], [ "k_neighbors(shakespeare_vecs,'youth')", "1.0000: youth\n0.3436: tall,\n0.3350: vanity,\n0.2945: idleness.\n0.2929: womb;\n0.2847: tall\n0.2823: suffering\n0.2742: stillness\n0.2671: flow'ring\n0.2671: observation\n" ] ], [ [ "## Load vecsigrafo from UMBC over WordNet", "_____no_output_____" ] ], [ [ "%ls ", "\u001b[0m\u001b[01;34mcoocs\u001b[0m/ shakespeare_complete_works.txt \u001b[01;34mswivel\u001b[0m/ \u001b[01;34mvec\u001b[0m/ wget-log wget-log.1\n" ], [ "!wget https://zenodo.org/record/1446214/files/vecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz\n%ls", "\nRedirecting output to ‘wget-log.2’.\n\u001b[0m\u001b[01;34mcoocs\u001b[0m/\nshakespeare_complete_works.txt\n\u001b[01;34mswivel\u001b[0m/\n\u001b[01;34mvec\u001b[0m/\nvecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz\nwget-log\nwget-log.1\nwget-log.2\n" ], [ "!tar -xvzf vecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz\n!rm vecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz\n", "vecsi_tlgs_wnscd_ls_f_6e_160d/row_embedding.tsv\n" ], [ "umbc_wn_vec_path = '/content/tutorial/lit/vecsi_tlgs_wnscd_ls_f_6e_160d/'", "_____no_output_____" ] ], [ [ "Extracting the vocabulary from the .tsv file:", "_____no_output_____" ] ], [ [ "with open(umbc_wn_vec_path + 'vocab.txt', 'w', encoding='utf_8') as f:\n with open(umbc_wn_vec_path + 'row_embedding.tsv', 'r', encoding='utf_8') as vec_lines:\n vocab = [line.split('\\t')[0].strip() for line in vec_lines]\n for word in vocab:\n print(word, file=f)", "_____no_output_____" ] ], [ [ "Converting tsv to bin:", "_____no_output_____" ] ], [ [ "!python /content/tutorial/scripts/swivel/text2bin.py --vocab={umbc_wn_vec_path}vocab.txt --output={umbc_wn_vec_path}vecs.bin \\\n {umbc_wn_vec_path}row_embedding.tsv ", "executing text2bin\nmerging files ['/content/tutorial/lit/vecsi_tlgs_wnscd_ls_f_6e_160d/row_embedding.tsv'] into output bin\n" ], [ "%ls", "\u001b[0m\u001b[01;34mcoocs\u001b[0m/ \u001b[01;34mvec\u001b[0m/ wget-log.1\nshakespeare_complete_works.txt \u001b[01;34mvecsi_tlgs_wnscd_ls_f_6e_160d\u001b[0m/ wget-log.2\n\u001b[01;34mswivel\u001b[0m/ wget-log\n" ], [ "umbc_wn_vecs = m.Vecs(umbc_wn_vec_path + 'vocab.txt', umbc_wn_vec_path + 'vecs.bin')", "Opening vector with expected size 1499136 from file /content/tutorial/lit/vecsi_tlgs_wnscd_ls_f_6e_160d/vocab.txt\nvocab size 1499136 (unique 1499125)\nread rows\n" ], [ "k_neighbors(umbc_wn_vecs, 'lem_California')", "1.0000: lem_California\n0.6301: lem_Central Valley\n0.5959: lem_University of California\n0.5542: lem_Southern California\n0.5254: lem_Santa Cruz\n0.5241: lem_Astro Aerospace\n0.5168: lem_San Francisco Bay\n0.5092: lem_San Diego County\n0.5074: lem_Santa Barbara\n0.5069: lem_Santa Rosa\n" ] ], [ [ "# Add your solution to the proposed exercise here", "_____no_output_____" ], [ "Follow the instructions given in the prvious lesson (*Vecsigrafos for curating and interlinking knowledge graphs*) to find correlation between terms in old Enlgish extracted from the Shakespeare corpus and terms in modern English extracted from UMBC. You will need to generate a dictionary relating pairs of lemmas between the two vocabularies and use to produce a pair of translation matrices to transform vectors from one vector space to the other. Then apply the k_neighbors method to identify the correlations.", "_____no_output_____" ], [ "# Conclusion", "_____no_output_____" ], [ "This notebook proposes the use of Shakespeare's complete works and UMBC to provide the student with embeddings that can be exploited for different operations between the two vector spaces. Particularly, we propose to identify terms and their correlations over such spaces.", "_____no_output_____" ], [ "# Acknowledgements", "_____no_output_____" ], [ "In memory of Dr. Jack Brandabur, whose passion for Shakespeare and Cervantes inspired this notebook.", "_____no_output_____" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_04_3_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# T81-558: Applications of Deep Neural Networks\n**Module 4: Training for Tabular Data**\n* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/).", "_____no_output_____" ], [ "# Module 4 Material\n\n* Part 4.1: Encoding a Feature Vector for Keras Deep Learning [[Video]](https://www.youtube.com/watch?v=Vxz-gfs9nMQ&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_1_feature_encode.ipynb)\n* Part 4.2: Keras Multiclass Classification for Deep Neural Networks with ROC and AUC [[Video]](https://www.youtube.com/watch?v=-f3bg9dLMks&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_2_multi_class.ipynb)\n* **Part 4.3: Keras Regression for Deep Neural Networks with RMSE** [[Video]](https://www.youtube.com/watch?v=wNhBUC6X5-E&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_3_regression.ipynb)\n* Part 4.4: Backpropagation, Nesterov Momentum, and ADAM Neural Network Training [[Video]](https://www.youtube.com/watch?v=VbDg8aBgpck&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_4_backprop.ipynb)\n* Part 4.5: Neural Network RMSE and Log Loss Error Calculation from Scratch [[Video]](https://www.youtube.com/watch?v=wmQX1t2PHJc&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_5_rmse_logloss.ipynb)", "_____no_output_____" ], [ "# Google CoLab Instructions\n\nThe following code ensures that Google CoLab is running the correct version of TensorFlow.", "_____no_output_____" ] ], [ [ "try:\n %tensorflow_version 2.x\n COLAB = True\n print(\"Note: using Google CoLab\")\nexcept:\n print(\"Note: not using Google CoLab\")\n COLAB = False", "Note: not using Google CoLab\n" ] ], [ [ "# Part 4.3: Keras Regression for Deep Neural Networks with RMSE\n\nRegression results are evaluated differently than classification. Consider the following code that trains a neural network for regression on the data set **jh-simple-dataset.csv**. ", "_____no_output_____" ] ], [ [ "import pandas as pd\nfrom scipy.stats import zscore\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\n\n# Read the data set\ndf = pd.read_csv(\n \"https://data.heatonresearch.com/data/t81-558/jh-simple-dataset.csv\",\n na_values=['NA','?'])\n\n# Generate dummies for job\ndf = pd.concat([df,pd.get_dummies(df['job'],prefix=\"job\")],axis=1)\ndf.drop('job', axis=1, inplace=True)\n\n# Generate dummies for area\ndf = pd.concat([df,pd.get_dummies(df['area'],prefix=\"area\")],axis=1)\ndf.drop('area', axis=1, inplace=True)\n\n# Generate dummies for product\ndf = pd.concat([df,pd.get_dummies(df['product'],prefix=\"product\")],axis=1)\ndf.drop('product', axis=1, inplace=True)\n\n# Missing values for income\nmed = df['income'].median()\ndf['income'] = df['income'].fillna(med)\n\n# Standardize ranges\ndf['income'] = zscore(df['income'])\ndf['aspect'] = zscore(df['aspect'])\ndf['save_rate'] = zscore(df['save_rate'])\ndf['subscriptions'] = zscore(df['subscriptions'])\n\n# Convert to numpy - Classification\nx_columns = df.columns.drop('age').drop('id')\nx = df[x_columns].values\ny = df['age'].values\n\n# Create train/test\nx_train, x_test, y_train, y_test = train_test_split( \n x, y, test_size=0.25, random_state=42)", "_____no_output_____" ], [ "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Activation\nfrom tensorflow.keras.callbacks import EarlyStopping\n\n# Build the neural network\nmodel = Sequential()\nmodel.add(Dense(25, input_dim=x.shape[1], activation='relu')) # Hidden 1\nmodel.add(Dense(10, activation='relu')) # Hidden 2\nmodel.add(Dense(1)) # Output\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmonitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, \n patience=5, verbose=1, mode='auto', restore_best_weights=True)\nmodel.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor],verbose=2,epochs=1000)\n", "Train on 1500 samples, validate on 500 samples\nEpoch 1/1000\n1500/1500 - 1s - loss: 1905.4454 - val_loss: 1628.1341\nEpoch 2/1000\n1500/1500 - 0s - loss: 1331.4213 - val_loss: 889.0575\nEpoch 3/1000\n1500/1500 - 0s - loss: 554.8426 - val_loss: 303.7261\nEpoch 4/1000\n1500/1500 - 0s - loss: 276.2087 - val_loss: 241.2495\nEpoch 5/1000\n1500/1500 - 0s - loss: 232.2832 - val_loss: 208.2143\nEpoch 6/1000\n1500/1500 - 0s - loss: 198.5331 - val_loss: 179.5262\nEpoch 7/1000\n1500/1500 - 0s - loss: 169.0791 - val_loss: 154.5270\nEpoch 8/1000\n1500/1500 - 0s - loss: 144.1286 - val_loss: 132.8691\nEpoch 9/1000\n1500/1500 - 0s - loss: 122.9873 - val_loss: 115.0928\nEpoch 10/1000\n1500/1500 - 0s - loss: 104.7249 - val_loss: 98.7375\nEpoch 11/1000\n1500/1500 - 0s - loss: 89.8292 - val_loss: 86.2749\nEpoch 12/1000\n1500/1500 - 0s - loss: 77.3071 - val_loss: 75.0022\nEpoch 13/1000\n1500/1500 - 0s - loss: 67.0604 - val_loss: 66.1396\nEpoch 14/1000\n1500/1500 - 0s - loss: 58.9584 - val_loss: 58.4367\nEpoch 15/1000\n1500/1500 - 0s - loss: 51.2491 - val_loss: 52.7136\nEpoch 16/1000\n1500/1500 - 0s - loss: 45.1765 - val_loss: 46.5179\nEpoch 17/1000\n1500/1500 - 0s - loss: 39.8843 - val_loss: 41.3721\nEpoch 18/1000\n1500/1500 - 0s - loss: 35.1468 - val_loss: 37.2132\nEpoch 19/1000\n1500/1500 - 0s - loss: 31.1755 - val_loss: 33.0697\nEpoch 20/1000\n1500/1500 - 0s - loss: 27.6307 - val_loss: 30.3131\nEpoch 21/1000\n1500/1500 - 0s - loss: 24.8457 - val_loss: 26.9474\nEpoch 22/1000\n1500/1500 - 0s - loss: 22.4056 - val_loss: 24.3656\nEpoch 23/1000\n1500/1500 - 0s - loss: 20.3071 - val_loss: 22.1642\nEpoch 24/1000\n1500/1500 - 0s - loss: 18.5446 - val_loss: 20.4782\nEpoch 25/1000\n1500/1500 - 0s - loss: 17.1571 - val_loss: 18.8670\nEpoch 26/1000\n1500/1500 - 0s - loss: 15.9407 - val_loss: 17.6862\nEpoch 27/1000\n1500/1500 - 0s - loss: 14.9866 - val_loss: 16.5275\nEpoch 28/1000\n1500/1500 - 0s - loss: 14.1251 - val_loss: 15.6342\nEpoch 29/1000\n1500/1500 - 0s - loss: 13.4655 - val_loss: 14.8625\nEpoch 30/1000\n1500/1500 - 0s - loss: 12.8994 - val_loss: 14.2826\nEpoch 31/1000\n1500/1500 - 0s - loss: 12.5566 - val_loss: 13.6121\nEpoch 32/1000\n1500/1500 - 0s - loss: 12.0077 - val_loss: 13.3087\nEpoch 33/1000\n1500/1500 - 0s - loss: 11.5357 - val_loss: 12.6593\nEpoch 34/1000\n1500/1500 - 0s - loss: 11.2365 - val_loss: 12.1849\nEpoch 35/1000\n1500/1500 - 0s - loss: 10.8074 - val_loss: 11.9388\nEpoch 36/1000\n1500/1500 - 0s - loss: 10.5593 - val_loss: 11.4006\nEpoch 37/1000\n1500/1500 - 0s - loss: 10.2093 - val_loss: 10.9751\nEpoch 38/1000\n1500/1500 - 0s - loss: 9.8386 - val_loss: 10.8651\nEpoch 39/1000\n1500/1500 - 0s - loss: 9.5938 - val_loss: 10.5728\nEpoch 40/1000\n1500/1500 - 0s - loss: 9.1488 - val_loss: 9.8661\nEpoch 41/1000\n1500/1500 - 0s - loss: 8.8920 - val_loss: 9.5228\nEpoch 42/1000\n1500/1500 - 0s - loss: 8.5156 - val_loss: 9.1506\nEpoch 43/1000\n1500/1500 - 0s - loss: 8.2628 - val_loss: 8.9486\nEpoch 44/1000\n1500/1500 - 0s - loss: 7.9219 - val_loss: 8.5034\nEpoch 45/1000\n1500/1500 - 0s - loss: 7.7077 - val_loss: 8.0760\nEpoch 46/1000\n1500/1500 - 0s - loss: 7.3165 - val_loss: 7.6620\nEpoch 47/1000\n1500/1500 - 0s - loss: 7.0259 - val_loss: 7.4933\nEpoch 48/1000\n1500/1500 - 0s - loss: 6.7422 - val_loss: 7.0583\nEpoch 49/1000\n1500/1500 - 0s - loss: 6.5163 - val_loss: 6.8024\nEpoch 50/1000\n1500/1500 - 0s - loss: 6.2633 - val_loss: 7.3045\nEpoch 51/1000\n1500/1500 - 0s - loss: 6.0029 - val_loss: 6.2712\nEpoch 52/1000\n1500/1500 - 0s - loss: 5.6791 - val_loss: 5.9342\nEpoch 53/1000\n1500/1500 - 0s - loss: 5.4798 - val_loss: 6.0110\nEpoch 54/1000\n1500/1500 - 0s - loss: 5.2115 - val_loss: 5.3928\nEpoch 55/1000\n1500/1500 - 0s - loss: 4.9592 - val_loss: 5.2215\nEpoch 56/1000\n1500/1500 - 0s - loss: 4.7189 - val_loss: 5.0103\nEpoch 57/1000\n1500/1500 - 0s - loss: 4.4683 - val_loss: 4.7098\nEpoch 58/1000\n1500/1500 - 0s - loss: 4.2650 - val_loss: 4.5259\nEpoch 59/1000\n1500/1500 - 0s - loss: 4.0953 - val_loss: 4.4263\nEpoch 60/1000\n1500/1500 - 0s - loss: 3.8027 - val_loss: 4.1103\nEpoch 61/1000\n1500/1500 - 0s - loss: 3.5759 - val_loss: 3.7770\nEpoch 62/1000\n1500/1500 - 0s - loss: 3.3755 - val_loss: 3.5737\nEpoch 63/1000\n1500/1500 - 0s - loss: 3.1781 - val_loss: 3.4833\nEpoch 64/1000\n1500/1500 - 0s - loss: 3.0001 - val_loss: 3.2246\nEpoch 65/1000\n1500/1500 - 0s - loss: 2.7691 - val_loss: 3.1021\nEpoch 66/1000\n1500/1500 - 0s - loss: 2.6227 - val_loss: 2.8215\nEpoch 67/1000\n1500/1500 - 0s - loss: 2.4682 - val_loss: 2.7528\nEpoch 68/1000\n1500/1500 - 0s - loss: 2.3243 - val_loss: 2.5394\nEpoch 69/1000\n1500/1500 - 0s - loss: 2.1664 - val_loss: 2.3886\nEpoch 70/1000\n1500/1500 - 0s - loss: 2.0377 - val_loss: 2.2536\nEpoch 71/1000\n1500/1500 - 0s - loss: 1.8845 - val_loss: 2.2354\nEpoch 72/1000\n1500/1500 - 0s - loss: 1.7931 - val_loss: 2.0831\nEpoch 73/1000\n1500/1500 - 0s - loss: 1.6889 - val_loss: 1.8866\nEpoch 74/1000\n1500/1500 - 0s - loss: 1.5820 - val_loss: 1.7964\nEpoch 75/1000\n1500/1500 - 0s - loss: 1.5085 - val_loss: 1.7138\nEpoch 76/1000\n1500/1500 - 0s - loss: 1.4159 - val_loss: 1.6468\nEpoch 77/1000\n1500/1500 - 0s - loss: 1.3606 - val_loss: 1.5906\nEpoch 78/1000\n1500/1500 - 0s - loss: 1.2652 - val_loss: 1.5063\nEpoch 79/1000\n1500/1500 - 0s - loss: 1.1937 - val_loss: 1.4506\nEpoch 80/1000\n1500/1500 - 0s - loss: 1.1180 - val_loss: 1.4817\nEpoch 81/1000\n1500/1500 - 0s - loss: 1.1412 - val_loss: 1.2800\nEpoch 82/1000\n1500/1500 - 0s - loss: 1.0385 - val_loss: 1.2412\nEpoch 83/1000\n1500/1500 - 0s - loss: 0.9846 - val_loss: 1.1891\nEpoch 84/1000\n1500/1500 - 0s - loss: 0.9937 - val_loss: 1.1322\nEpoch 85/1000\n1500/1500 - 0s - loss: 0.8915 - val_loss: 1.0847\nEpoch 86/1000\n1500/1500 - 0s - loss: 0.8562 - val_loss: 1.1110\nEpoch 87/1000\n1500/1500 - 0s - loss: 0.8468 - val_loss: 1.0686\nEpoch 88/1000\n1500/1500 - 0s - loss: 0.7947 - val_loss: 0.9805\nEpoch 89/1000\n1500/1500 - 0s - loss: 0.7807 - val_loss: 0.9463\nEpoch 90/1000\n1500/1500 - 0s - loss: 0.7502 - val_loss: 0.9965\nEpoch 91/1000\n1500/1500 - 0s - loss: 0.7529 - val_loss: 0.9532\nEpoch 92/1000\n1500/1500 - 0s - loss: 0.6857 - val_loss: 0.8712\nEpoch 93/1000\n1500/1500 - 0s - loss: 0.6717 - val_loss: 0.8498\nEpoch 94/1000\n1500/1500 - 0s - loss: 0.6869 - val_loss: 0.8518\nEpoch 95/1000\n1500/1500 - 0s - loss: 0.6626 - val_loss: 0.8275\nEpoch 96/1000\n1500/1500 - 0s - loss: 0.6308 - val_loss: 0.7850\nEpoch 97/1000\n1500/1500 - 0s - loss: 0.6056 - val_loss: 0.7708\nEpoch 98/1000\n1500/1500 - 0s - loss: 0.5991 - val_loss: 0.7643\nEpoch 99/1000\n1500/1500 - 0s - loss: 0.6102 - val_loss: 0.8104\nEpoch 100/1000\n1500/1500 - 0s - loss: 0.5647 - val_loss: 0.7227\nEpoch 101/1000\n1500/1500 - 0s - loss: 0.5474 - val_loss: 0.7107\nEpoch 102/1000\n1500/1500 - 0s - loss: 0.5395 - val_loss: 0.6847\nEpoch 103/1000\n1500/1500 - 0s - loss: 0.5350 - val_loss: 0.7383\nEpoch 104/1000\n1500/1500 - 0s - loss: 0.5551 - val_loss: 0.6698\nEpoch 105/1000\n1500/1500 - 0s - loss: 0.5032 - val_loss: 0.6520\nEpoch 106/1000\n1500/1500 - 0s - loss: 0.5418 - val_loss: 0.7518\nEpoch 107/1000\n1500/1500 - 0s - loss: 0.4949 - val_loss: 0.6307\nEpoch 108/1000\n1500/1500 - 0s - loss: 0.5166 - val_loss: 0.6741\nEpoch 109/1000\n1500/1500 - 0s - loss: 0.4992 - val_loss: 0.6195\nEpoch 110/1000\n1500/1500 - 0s - loss: 0.4610 - val_loss: 0.6268\nEpoch 111/1000\n1500/1500 - 0s - loss: 0.4554 - val_loss: 0.5956\nEpoch 112/1000\n1500/1500 - 0s - loss: 0.4704 - val_loss: 0.5977\nEpoch 113/1000\n1500/1500 - 0s - loss: 0.4687 - val_loss: 0.5736\nEpoch 114/1000\n1500/1500 - 0s - loss: 0.4497 - val_loss: 0.5817\nEpoch 115/1000\n1500/1500 - 0s - loss: 0.4326 - val_loss: 0.5833\nEpoch 116/1000\n1500/1500 - 0s - loss: 0.4181 - val_loss: 0.5738\nEpoch 117/1000\n1500/1500 - 0s - loss: 0.4252 - val_loss: 0.5688\nEpoch 118/1000\n1500/1500 - 0s - loss: 0.4675 - val_loss: 0.5680\nEpoch 119/1000\n1500/1500 - 0s - loss: 0.4328 - val_loss: 0.5463\nEpoch 120/1000\n1500/1500 - 0s - loss: 0.4091 - val_loss: 0.5912\nEpoch 121/1000\n1500/1500 - 0s - loss: 0.4047 - val_loss: 0.5459\nEpoch 122/1000\n1500/1500 - 0s - loss: 0.4456 - val_loss: 0.5509\nEpoch 123/1000\n1500/1500 - 0s - loss: 0.4081 - val_loss: 0.5540\nEpoch 124/1000\nRestoring model weights from the end of the best epoch.\n1500/1500 - 0s - loss: 0.4353 - val_loss: 0.5538\nEpoch 00124: early stopping\n" ] ], [ [ "### Mean Square Error\n\nThe mean square error is the sum of the squared differences between the prediction ($\\hat{y}$) and the expected ($y$). MSE values are not of a particular unit. If an MSE value has decreased for a model, that is good. However, beyond this, there is not much more you can determine. Low MSE values are desired.\n\n$ \\mbox{MSE} = \\frac{1}{n} \\sum_{i=1}^n \\left(\\hat{y}_i - y_i\\right)^2 $\n", "_____no_output_____" ] ], [ [ "from sklearn import metrics\n\n# Predict\npred = model.predict(x_test)\n\n# Measure MSE error. \nscore = metrics.mean_squared_error(pred,y_test)\nprint(\"Final score (MSE): {}\".format(score))", "Final score (MSE): 0.5463447829677607\n" ] ], [ [ "### Root Mean Square Error\n\nThe root mean square (RMSE) is essentially the square root of the MSE. Because of this, the RMSE error is in the same units as the training data outcome. Low RMSE values are desired.\n\n$ \\mbox{RMSE} = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n \\left(\\hat{y}_i - y_i\\right)^2} $", "_____no_output_____" ] ], [ [ "import numpy as np\n\n# Measure RMSE error. RMSE is common for regression.\nscore = np.sqrt(metrics.mean_squared_error(pred,y_test))\nprint(\"Final score (RMSE): {}\".format(score))", "Final score (RMSE): 0.7391513938076291\n" ] ], [ [ "### Lift Chart\n\n\nTo generate a lift chart, perform the following activities:\n\n* Sort the data by expected output. Plot the blue line above.\n* For every point on the x-axis plot the predicted value for that same data point. This is the green line above.\n* The x-axis is just 0 to 100% of the dataset. The expected always starts low and ends high.\n* The y-axis is ranged according to the values predicted.\n\nReading a lift chart:\n\n* The expected and predict lines should be close. Notice where one is above the ot other.\n* The below chart is the most accurate on lower age.", "_____no_output_____" ] ], [ [ "# Regression chart.\ndef chart_regression(pred, y, sort=True):\n t = pd.DataFrame({'pred': pred, 'y': y.flatten()})\n if sort:\n t.sort_values(by=['y'], inplace=True)\n plt.plot(t['y'].tolist(), label='expected')\n plt.plot(t['pred'].tolist(), label='prediction')\n plt.ylabel('output')\n plt.legend()\n plt.show()", "_____no_output_____" ], [ "\n# Plot the chart\nchart_regression(pred.flatten(),y_test)", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import csv", "_____no_output_____" ], [ "MonthlySales = []", "_____no_output_____" ], [ "with open('data/MonthlySales.csv', 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n MonthlySales.append(row)", "_____no_output_____" ], [ "for a in MonthlySales:\n print a", "_____no_output_____" ], [ "#print keys\nfor a in MonthlySales:\n print a.keys()", "_____no_output_____" ], [ "#print keys and values\nfor a in MonthlySales:\n for key, value in a.items():\n print key + \": \", value\n print '\\n'", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "#https://dbader.org/blog/python-generators", "_____no_output_____" ], [ "def repeat_n_times(value,max_rep):\n for i in range(max_rep):\n yield value + str(i)", "_____no_output_____" ], [ "for a in repeat_n_times('hello',10):\n print(a)", "hello0\nhello1\nhello2\nhello3\nhello4\nhello5\nhello6\nhello7\nhello8\nhello9\n" ], [ "#Hard coding\ndef repeat_three_times(value):\n yield value\n yield value\n yield value", "_____no_output_____" ], [ "for i in repeat_three_times(\"hi\"):\n print(i)", "hi\nhi\nhi\n" ], [ "iterator=repeat_three_times(\"hello\")\nprint(next(iterator))\nprint(next(iterator))\nprint(next(iterator))\nprint(next(iterator))", "hello\nhello\nhello\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# flake8: noqa\n##########################################################\n# Relative Imports\n##########################################################\nimport sys\nfrom os.path import isfile\nfrom os.path import join\n\n\ndef find_pkg(name: str, depth: int):\n if depth <= 0:\n ret = None\n else:\n d = [\"..\"] * depth\n path_parts = d + [name, \"__init__.py\"]\n\n if isfile(join(*path_parts)):\n ret = d\n else:\n ret = find_pkg(name, depth - 1)\n return ret\n\n\ndef find_and_ins_syspath(name: str, depth: int):\n path_parts = find_pkg(name, depth)\n if path_parts is None:\n raise RuntimeError(\"Could not find {}. Try increasing depth.\".format(name))\n path = join(*path_parts)\n if path not in sys.path:\n sys.path.insert(0, path)\n\n\ntry:\n import caldera\nexcept ImportError:\n find_and_ins_syspath(\"caldera\", 3)\n\n##########################################################\n# Main\n##########################################################\n\nimport copy\nimport hydra\nfrom examples.traversals.training import TrainingModule\nfrom examples.traversals.data import DataGenerator, DataConfig\nfrom examples.traversals.configuration import Config\nfrom examples.traversals.configuration.data import Uniform, DiscreteUniform\nfrom typing import TypeVar\nfrom pytorch_lightning import Trainer\nfrom examples.traversals.loggers import logger\nfrom omegaconf import DictConfig, OmegaConf\nfrom rich.panel import Panel\nfrom rich import print\nfrom rich.syntax import Syntax\n\n\nC = TypeVar(\"C\")\n\n\ndef prime_the_model(model: TrainingModule, config: Config):\n logger.info(\"Priming the model with data\")\n config_copy: DataConfig = copy.deepcopy(config.data)\n config_copy.train.num_graphs = 10\n config_copy.eval.num_graphs = 0\n data_copy = DataGenerator(config_copy, progress_bar=False)\n for a, b in data_copy.train_loader():\n model.model.forward(a, 10)\n break\n\n\ndef print_title():\n print(Panel(\"Training Example: [red]Traversal\", title=\"[red]caldera\"))\n\n\ndef print_model(model: TrainingModule):\n print(Panel(\"Network\", expand=False))\n print(model)\n\n\ndef print_yaml(cfg: Config):\n print(Panel(\"Configuration\", expand=False))\n print(Syntax(OmegaConf.to_yaml(cfg), \"yaml\"))\n\n# def config_override(cfg: DictConfig):\n# # defaults\n# cfg.hyperparameters.lr = 1e-3\n# cfg.hyperparameters.train_core_processing_steps = 10\n# cfg.hyperparameters.eval_core_processing_steps = 10\n#\n# cfg.data.train.num_graphs = 5000\n# cfg.data.train.num_nodes = DiscreteUniform(10, 100)\n# cfg.data.train.density = Uniform(0.01, 0.03)\n# cfg.data.train.path_length = DiscreteUniform(5, 10)\n# cfg.data.train.composition_density = Uniform(0.01, 0.02)\n# cfg.data.train.batch_size = 512\n# cfg.data.train.shuffle = False\n#\n# cfg.data.eval.num_graphs = 500\n# cfg.data.eval.num_nodes = DiscreteUniform(10, 100)\n# cfg.data.eval.density = Uniform(0.01, 0.03)\n# cfg.data.eval.path_length = DiscreteUniform(5, 10)\n# cfg.data.eval.composition_density = Uniform(0.01, 0.02)\n# cfg.data.eval.batch_size = \"${data.eval.num_graphs}\"\n# cfg.data.eval.shuffle = False\n\n# @hydra.main(config_path=\"conf\", config_name=\"config\")\n# def main(hydra_cfg: DictConfig):\n\n# print_title()\n\n\n# logger.setLevel(hydra_cfg.log_level)\n# if hydra_cfg.log_level.upper() == 'DEBUG':\n# verbose = True\n# else:\n# verbose = False\n# # really unclear why hydra has so many unclear validation issues with structure configs using ConfigStore\n# # this correctly assigns the correct structured config\n# # and updates from the passed hydra config\n# # annoying... but this resolves all these issues\n# cfg = OmegaConf.structured(Config())\n# cfg.update(hydra_cfg)\n\n# # debug\n# if verbose:\n# print_yaml(cfg)\n\n# from pytorch_lightning.loggers import WandbLogger\n# wandb_logger = WandbLogger(project='pytorchlightning')\n\n# # explicitly convert the DictConfig back to Config object\n# # has the added benefit of performing validation upfront\n# # before any expensive training or logging initiates\n# config = Config.from_dict_config(cfg)\n\n# # initialize the training module\n# training_module = TrainingModule(config)\n\n# logger.info(\"Priming the model with data\")\n# prime_the_model(training_module, config)\n# logger.debug(Panel(\"Model\", expand=False))\n\n# if verbose:\n# print_model(training_module)\n\n# logger.info(\"Generating data...\")\n# data = DataGenerator(config.data)\n# data.init()\n\n# logger.info(\"Beginning training...\")\n# trainer = Trainer(gpus=config.gpus, logger=wandb_logger)\n\n# trainer.fit(\n# training_module,\n# train_dataloader=data.train_loader(),\n# val_dataloaders=data.eval_loader(),\n# )\n\n\n# if __name__ == \"__main__\":\n# main()\n", "_____no_output_____" ], [ "from examples.traversals.configuration import get_config\n\nconfig = get_config( as_config_class=True)\n\ndata = DataGenerator(config.data)\ndata.init()\n\ntraining_module = TrainingModule(config)\n\nlogger.info(\"Priming the model with data\")\nprime_the_model(training_module, config)", "/home/justin/anaconda3/envs/caldera/lib/python3.7/site-packages/omegaconf/omegaconf.py:579: UserWarning: update() merge flag is is not specified, defaulting to False.\nFor more details, see https://github.com/omry/omegaconf/issues/367\n stacklevel=1,\n" ], [ "dir(data)", "_____no_output_____" ], [ "from torch import optim\nfrom tqdm.auto import tqdm\nimport torch\nfrom caldera.data import GraphTuple\n\n\ndef mse_tuple(criterion, device, a, b):\n loss = torch.tensor(0.0, dtype=torch.float32, device=device)\n assert len(a) == len(b)\n for i, (_a, _b) in enumerate(zip(a, b)):\n assert _a.shape == _b.shape\n l = criterion(_a, _b)\n loss += l\n return loss\n\n\ndef train(network, loader, cuda: bool = False):\n device = 'cpu'\n if cuda and torch.cuda.is_available():\n device = 'cuda:' + str(torch.cuda.current_device())\n\n network.eval()\n network.to(device)\n input_batch, target_batch = loader.first()\n input_batch = input_batch.detach()\n input_batch.to(device)\n network(input_batch, 1)\n \n optimizer = optim.AdamW(network.parameters(), lr=1e-2)\n loss_func = torch.nn.MSELoss()\n \n losses = []\n for epoch in range(20):\n print(epoch)\n running_loss = 0.\n network.train()\n for input_batch, target_batch in loader:\n optimizer.zero_grad()\n out_batch = network(input_batch, 5)[-1]\n out_tuple = GraphTuple(out_batch.e, out_batch.x, out_batch.g)\n target_tuple = GraphTuple(target_batch.e, target_batch.x, target_batch.g)\n \n loss = mse_tuple(loss_func, device, out_tuple, target_tuple)\n loss.backward()\n running_loss = running_loss + loss.item()\n optimizer.step()\n print(running_loss)\n losses.append(running_loss)\n return losses\n\n\n# loader = DataLoaders.sigmoid_circuit(1000, 10)\ntrain(training_module.model, data.train_loader())\n", "_____no_output_____" ], [ "inp, targ = data.eval_loader().first()", "_____no_output_____" ], [ "from caldera.transforms.networkx import NetworkxAttachNumpyBool\n\ng = targ.to_networkx_list()[0]\n\nto_bool = NetworkxAttachNumpyBool('node', 'features', 'x')\ngraphs = to_bool(targ.to_networkx_list())\ngraphs[0].nodes(data=True)", "_____no_output_____" ], [ "from matplotlib import cm\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport networkx as nx\n%matplotlib inline\n", "_____no_output_____" ], [ "def edge_colors(g, key, cmap):\n edgecolors = list()\n edgelist = list(g.edges)\n edgefeat = list()\n for e in edgelist:\n edata = g.edges[e]\n edgefeat.append(edata[key][0].item())\n edgefeat = np.array(edgefeat)\n edgecolors = cmap(edgefeat)\n return edgecolors\n nx.draw_networkx_edges(g, pos=pos, edge_color=edgecolors, arrows=False)\n\ndef node_colors(g, key, cmap):\n nodecolors = list()\n nodelist = list(g.nodes)\n nodefeat = list()\n for n in nodelist:\n ndata = g.nodes[n]\n nodefeat.append(ndata[key][0].item())\n nodefeat = np.array(nodefeat)\n nodecolors = cmap(nodefeat)\n return nodecolors\n nx.draw_networkx_nodes(g, pos=pos, node_size=10, node_color=nodecolors)\n\ndef plot_graph(g, ax, cmap, key='features', seed=1):\n pos = nx.layout.spring_layout(g, seed=seed)\n nx.draw_networkx_edges(g, ax=ax, pos=pos, edge_color=edge_colors(g, key, cmap), arrows=False);\n nx.draw_networkx_nodes(g, ax=ax, pos=pos, node_size=10, node_color=node_colors(g, key, cmap))\n \n \ndef comparison_plot(out_g, expected_g):\n fig, axes = plt.subplots(1, 2, figsize=(5, 2.5))\n axes[0].axis('off')\n axes[1].axis('off')\n \n axes[0].set_title(\"out\")\n plot_graph(out_g, axes[0], cm.plasma)\n \n axes[1].set_title(\"expected\")\n plot_graph(expected_g, axes[1], cm.plasma)\n return fig, axes\n\ndef validate_compare_plot(trainer, plmodel):\n eval_loader = trainer.val_dataloaders[0]\n for x, y in eval_loader:\n break\n plmodel.eval()\n y_hat = plmodel.model.forward(x, 10)[-1]\n y_graphs = y.to_networkx_list()\n y_hat_graphs = y_hat.to_networkx_list()\n\n idx = 0\n yg = y_graphs[idx]\n yhg = y_hat_graphs[idx]\n return comparison_plot(yhg, yg)\n\nfig, axes = validate_compare_plot(trainer, training_module)", "_____no_output_____" ], [ "from pytorch_lightning.loggers import WandbLogger\nwandb_logger = WandbLogger(project='pytorchlightning')", "_____no_output_____" ], [ "wandb_logger.experiment", "_____no_output_____" ], [ "wandb.Image?", "_____no_output_____" ], [ "import wandb\nimport io\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\ndef fig_to_pil(fig):\n buf = io.BytesIO()\n fig.savefig(buf, format='png')\n buf.seek(0)\n im = Image.open(buf)\n# buf.close()\n return im\n\nwandb_logger.experiment.log({'s': [wandb.Image(fig_to_pil(fig))]} )", "Failed to query for notebook name, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable\n\u001b[34m\u001b[1mwandb\u001b[0m: Wandb version 0.10.4 is available! To upgrade, please run:\n\u001b[34m\u001b[1mwandb\u001b[0m: $ pip install wandb --upgrade\n" ], [ "wandb_logger.experiment.log", "_____no_output_____" ], [ "import io\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\nbuf = io.BytesIO()\nfig.savefig(buf, format='png')\nbuf.seek(0)\nim = Image.open(buf)\nim.show()\nbuf.close()\n\nstr(buf)", "_____no_output_____" ], [ "x.to_networkx_list()[0].nodes(data=True)\n", "_____no_output_____" ], [ "def comparison_plot(out_g, expected_g):\n fig, axes = plt.subplots(1, 2, figsize=(5, 2.5))\n axes[0].axis('off')\n axes[1].axis('off')\n \n axes[0].set_title(\"out\")\n plot_graph(out_g, axes[0], cm.plasma)\n \n axes[1].set_title(\"expected\")\n plot_graph(expected_g, axes[1], cm.plasma)\nx, y = data.eval_loader().first()\ny_hat = training_module.model.forward(x, 10)[-1]\ny_graphs = y.to_networkx_list()\ny_hat_graphs = y_hat.to_networkx_list()\n\nidx = 0\nyg = y_graphs[idx]\nyhg = y_hat_graphs[idx]\ncomparison_plot(yhg, yg)", "_____no_output_____" ], [ "g = random_graph((100, 150), d=(0.01, 0.03), e=None)\n\n\nannotate_shortest_path(g)\n# nx.draw(g)\npos = nx.layout.spring_layout(g)\nnodelist = list(g.nodes)\nnode_color = []\nfor n in nodelist:\n node_color.append(g.nodes[n]['target'][0])\nedge_list = []\nedge_color = []\nfor n1, n2, edata in g.edges(data=True):\n edge_list.append((n1, n2))\n edge_color.append(edata['target'][0])\nprint(node_color)\nnx.draw_networkx_edges(g, pos=pos, width=0.5, edge_color=edge_color)\nnx.draw_networkx_nodes(g, pos=pos, node_color=node_color, node_size=10)\n", "_____no_output_____" ], [ "NetworkxAttachNumpyBool?", "_____no_output_____" ], [ "g.nodes(data=True)", "_____no_output_____" ], [ "from caldera.transforms.networkx import NetworkxApplyToFeature\n\nNetworkxApplyToFeature('features', edge_func= lambda x: list(x))(g)", "_____no_output_____" ], [ "import time\n\nfrom rich.progress import Progress as RichProgress\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass\n\n@dataclass\nclass TaskEvent:\n task_id: int\n name: str\n\n\nclass TaskProgress(object):\n DEFAULT_REFRESH_PER_SECOND = 4.\n\n def __init__(self,\n progress = None,\n task_id: int = None,\n refresh_rate_per_second: int = DEFAULT_REFRESH_PER_SECOND,\n parent = None):\n self.task_id = task_id\n self.children = []\n self.parent = parent\n self.progress = progress or RichProgress()\n self.last_updated = time.time()\n self.refresh_rate_per_second = refresh_rate_per_second\n\n def self_task(self, *args, **kwargs):\n task_id = self.progress.add_task(*args, **kwargs)\n self.task_id = task_id\n\n def add_task(self, *args, **kwargs):\n task_id = self.progress.add_task(*args, **kwargs)\n new_task = self.__class__(self.progress, task_id, self.refresh_rate_per_second, parent=self)\n self.children.append(new_task)\n return new_task\n\n @property\n def _task(self):\n return self.progress.tasks[self.task_id]\n\n def listen(self, event: TaskEvent):\n if event.name == 'refresh':\n completed = sum(t._task.completed for t in self.children)\n total = sum(t._task.total for t in self.children)\n self.update(completed=completed/total, total=1., refresh=True)\n elif event.name == 'finished':\n self.finish()\n\n def emit_up(self, event_name):\n if self.parent:\n self.parent.listen(TaskEvent(task_id=self.task_id, name=event_name))\n\n def emit_down(self, event_name: TaskEvent):\n for child in self.children:\n print(\"sending to child\")\n child.listen(TaskEvent(task_id=self.task_id, name=event_name))\n\n def update(self, *args, **kwargs):\n now = time.time()\n if 'refresh' not in kwargs:\n if now - self.last_updated > 1. / self.refresh_rate_per_second:\n kwargs['refresh'] = True\n else:\n kwargs['refresh'] = False\n \n if kwargs['refresh']:\n self.emit_up('refresh')\n self.last_updated = now\n self.progress.update(self.task_id, *args, **kwargs)\n\n def is_complete(self):\n return self.completed >= self.task.total\n\n def finish(self):\n self.progress.update(self.task_id, completed=self._task.total, refresh=True)\n self.emit_down('finished')\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.progress.__exit__(exc_type, exc_val, exc_tb)\n self.finish()\n\n\nwith TaskProgress() as progress:\n progress.self_task('main', total=10)\n bar1 = progress.add_task('bar1', total=10)\n bar2 = progress.add_task('bar2', total=10)\n for _ in range(10):\n bar1.update(advance=1)\n time.sleep(0.1)\n for _ in range(10):\n bar2.update(advance=1)\n time.sleep(0.1)\n", "_____no_output_____" ], [ "bar.progress.tasks[0].completed", "_____no_output_____" ], [ "import torch\n\n", "_____no_output_____" ], [ "target = torch.ones([1, 64], dtype=torch.float32) # 64 classes, batch size = 10\noutput = torch.full([1, 64], 1.5) # A prediction (logit)\n\nprint(target)\nprint(output)\n# pos_weight = torch.ones([64]) # All weights are equal to 1\ncriterion = torch.nn.BCEWithLogitsLoss()\ncriterion(output, target) # -log(sigmoid(1.5))", "_____no_output_____" ], [ "from caldera.data import GraphBatch\n\nbatch = GraphBatch.random_batch(2, 5, 4, 3)\ngraphs = batch.to_networkx_list()\n\nimport networkx as nx\n\nnx.draw(graphs[0])\n\nexpected = torch.randn(batch.x.shape)\nx = batch.x\nx = torch.nn.Softmax()(x)\nprint(x.sum(axis=1))\nx, expected\nx = torch.nn.BCELoss()(x, expected)\nx", "/home/justin/anaconda3/envs/caldera/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n if sys.path[0] == '':\n" ], [ "import torch\n\nx = torch.randn(10, 10)\n\ntorch.stack([x, x]).shape", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "cd ..", "/Users/nguyen/projects/ab-framework\n" ], [ "from src.plot import *", "_____no_output_____" ] ], [ [ "## Test zplot", "_____no_output_____" ] ], [ [ "zplot()", "_____no_output_____" ], [ "zplot(area=0.80, two_tailed=False)", "_____no_output_____" ], [ "zplot(area=0.80, two_tailed=False, align_right=True)", "_____no_output_____" ] ], [ [ "## Test abplot", "_____no_output_____" ] ], [ [ "abplot(n=4000, bcr=0.11, d_hat=0.03, show_alpha=True)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import openslide\nfrom openslide import deepzoom", "_____no_output_____" ], [ "def extract_patches(osr_obj, tile_size):\n \"\"\"Description: Uses openslide to extract patches at closest zoom size\n INPUT: an openslide object (the .svs image)\n OUTPUT: a list of tiles as image objects\n \"\"\"\n list_of_tiles = []\n \n t = openslide.deepzoom.DeepZoomGenerator(osr_obj, tile_size=tile_size, overlap=0, limit_bounds=False)\n max_level = t.level_count\n \n addresses = t.level_tiles[max_level-1] # zoomed in to furthest amount\n \n for x_coord in range(addresses[0]):\n for y_coord in range(addresses[1]):\n list_of_tiles.append(t.get_tile(max_level - 1, (x_coord, y_coord)))\n \n return list_of_tiles", "_____no_output_____" ], [ "def check_if_white(pixel):\n \"\"\"DESCRIPTION: Checks to see if a given pixel is an off-white or white pixel\n INPUT: 3x1 tuple, where each element is in range [0,255]\n OUTPUT: 1 : is a whitespace\n 0 : is not a whitespace\n \"\"\"\n for x in pixel:\n if x < 230:\n return 0\n return 1", "_____no_output_____" ], [ "def determine_quality(img, WHITESPACE_CUTOFF):\n \"\"\"Description: Determines quality of a histology tile image\n INPUT: Tile (Image object)\n OUTPUT: 0 : too much whitespace\n 1 : acceptable image\n \n \"\"\"\n p = img.load()\n img_size = img.size\n \n total = 0\n \n for y in range(img_size[1]):\n for x in range(0,img_size[0],3):\n if check_if_white(p[x,y]):\n total += 1\n\n if total / (img_size[0] * img_size[1] / 3) > WHITESPACE_CUTOFF:\n return 0 # too much whitespace\n else:\n return 1 # acceptable", "_____no_output_____" ], [ "def pad(i, n):\n \"\"\" PADS AN INTEGER\n INPUT: i : [int] number to be padded\n n : [int] total length of padded number\n OUTPUT: str\n \"\"\"\n i = str(i)\n l = len(i)\n \n if l > n:\n raise ValueError('Integer is bigger than padding specifications')\n else:\n return ('0' * (n-l)) + i", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# About this Notebook\n\nIn this notebook, we provide the tensor factorization implementation using an iterative Alternating Least Square (ALS), which is a good starting point for understanding tensor factorization.", "_____no_output_____" ] ], [ [ "import numpy as np\nfrom numpy.linalg import inv as inv", "_____no_output_____" ] ], [ [ "# Part 1: Matrix Computation Concepts\n\n## 1) Kronecker product\n\n- **Definition**:\n\nGiven two matrices $A\\in\\mathbb{R}^{m_1\\times n_1}$ and $B\\in\\mathbb{R}^{m_2\\times n_2}$, then, the **Kronecker product** between these two matrices is defined as\n\n$$A\\otimes B=\\left[ \\begin{array}{cccc} a_{11}B & a_{12}B & \\cdots & a_{1m_2}B \\\\ a_{21}B & a_{22}B & \\cdots & a_{2m_2}B \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ a_{m_11}B & a_{m_12}B & \\cdots & a_{m_1m_2}B \\\\ \\end{array} \\right]$$\nwhere the symbol $\\otimes$ denotes Kronecker product, and the size of resulted $A\\otimes B$ is $(m_1m_2)\\times (n_1n_2)$ (i.e., $m_1\\times m_2$ columns and $n_1\\times n_2$ rows).\n\n- **Example**:\n\nIf $A=\\left[ \\begin{array}{cc} 1 & 2 \\\\ 3 & 4 \\\\ \\end{array} \\right]$ and $B=\\left[ \\begin{array}{ccc} 5 & 6 & 7\\\\ 8 & 9 & 10 \\\\ \\end{array} \\right]$, then, we have\n\n$$A\\otimes B=\\left[ \\begin{array}{cc} 1\\times \\left[ \\begin{array}{ccc} 5 & 6 & 7\\\\ 8 & 9 & 10\\\\ \\end{array} \\right] & 2\\times \\left[ \\begin{array}{ccc} 5 & 6 & 7\\\\ 8 & 9 & 10\\\\ \\end{array} \\right] \\\\ 3\\times \\left[ \\begin{array}{ccc} 5 & 6 & 7\\\\ 8 & 9 & 10\\\\ \\end{array} \\right] & 4\\times \\left[ \\begin{array}{ccc} 5 & 6 & 7\\\\ 8 & 9 & 10\\\\ \\end{array} \\right] \\\\ \\end{array} \\right]$$\n\n$$=\\left[ \\begin{array}{cccccc} 5 & 6 & 7 & 10 & 12 & 14 \\\\ 8 & 9 & 10 & 16 & 18 & 20 \\\\ 15 & 18 & 21 & 20 & 24 & 28 \\\\ 24 & 27 & 30 & 32 & 36 & 40 \\\\ \\end{array} \\right]\\in\\mathbb{R}^{4\\times 6}.$$\n", "_____no_output_____" ], [ "## 2) Khatri-Rao product (`kr_prod`)\n\n- **Definition**:\n\nGiven two matrices $A=\\left( \\boldsymbol{a}_1,\\boldsymbol{a}_2,...,\\boldsymbol{a}_r \\right)\\in\\mathbb{R}^{m\\times r}$ and $B=\\left( \\boldsymbol{b}_1,\\boldsymbol{b}_2,...,\\boldsymbol{b}_r \\right)\\in\\mathbb{R}^{n\\times r}$ with same number of columns, then, the **Khatri-Rao product** (or **column-wise Kronecker product**) between $A$ and $B$ is given as follows,\n\n$$A\\odot B=\\left( \\boldsymbol{a}_1\\otimes \\boldsymbol{b}_1,\\boldsymbol{a}_2\\otimes \\boldsymbol{b}_2,...,\\boldsymbol{a}_r\\otimes \\boldsymbol{b}_r \\right)\\in\\mathbb{R}^{(mn)\\times r},$$\nwhere the symbol $\\odot$ denotes Khatri-Rao product, and $\\otimes$ denotes Kronecker product.\n\n- **Example**:\n\nIf $A=\\left[ \\begin{array}{cc} 1 & 2 \\\\ 3 & 4 \\\\ \\end{array} \\right]=\\left( \\boldsymbol{a}_1,\\boldsymbol{a}_2 \\right) $ and $B=\\left[ \\begin{array}{cc} 5 & 6 \\\\ 7 & 8 \\\\ 9 & 10 \\\\ \\end{array} \\right]=\\left( \\boldsymbol{b}_1,\\boldsymbol{b}_2 \\right) $, then, we have\n\n$$A\\odot B=\\left( \\boldsymbol{a}_1\\otimes \\boldsymbol{b}_1,\\boldsymbol{a}_2\\otimes \\boldsymbol{b}_2 \\right) $$\n\n$$=\\left[ \\begin{array}{cc} \\left[ \\begin{array}{c} 1 \\\\ 3 \\\\ \\end{array} \\right]\\otimes \\left[ \\begin{array}{c} 5 \\\\ 7 \\\\ 9 \\\\ \\end{array} \\right] & \\left[ \\begin{array}{c} 2 \\\\ 4 \\\\ \\end{array} \\right]\\otimes \\left[ \\begin{array}{c} 6 \\\\ 8 \\\\ 10 \\\\ \\end{array} \\right] \\\\ \\end{array} \\right]$$\n\n$$=\\left[ \\begin{array}{cc} 5 & 12 \\\\ 7 & 16 \\\\ 9 & 20 \\\\ 15 & 24 \\\\ 21 & 32 \\\\ 27 & 40 \\\\ \\end{array} \\right]\\in\\mathbb{R}^{6\\times 2}.$$", "_____no_output_____" ] ], [ [ "def kr_prod(a, b):\n return np.einsum('ir, jr -> ijr', a, b).reshape(a.shape[0] * b.shape[0], -1)", "_____no_output_____" ], [ "A = np.array([[1, 2], [3, 4]])\nB = np.array([[5, 6], [7, 8], [9, 10]])\nprint(kr_prod(A, B))", "[[ 5 12]\n [ 7 16]\n [ 9 20]\n [15 24]\n [21 32]\n [27 40]]\n" ] ], [ [ "## 3) CP decomposition\n\n### CP Combination (`cp_combination`)\n\n- **Definition**:\n\nThe CP decomposition factorizes a tensor into a sum of outer products of vectors. For example, for a third-order tensor $\\mathcal{Y}\\in\\mathbb{R}^{m\\times n\\times f}$, the CP decomposition can be written as\n\n$$\\hat{\\mathcal{Y}}=\\sum_{s=1}^{r}\\boldsymbol{u}_{s}\\circ\\boldsymbol{v}_{s}\\circ\\boldsymbol{x}_{s},$$\nor element-wise,\n\n$$\\hat{y}_{ijt}=\\sum_{s=1}^{r}u_{is}v_{js}x_{ts},\\forall (i,j,t),$$\nwhere vectors $\\boldsymbol{u}_{s}\\in\\mathbb{R}^{m},\\boldsymbol{v}_{s}\\in\\mathbb{R}^{n},\\boldsymbol{x}_{s}\\in\\mathbb{R}^{f}$ are columns of factor matrices $U\\in\\mathbb{R}^{m\\times r},V\\in\\mathbb{R}^{n\\times r},X\\in\\mathbb{R}^{f\\times r}$, respectively. The symbol $\\circ$ denotes vector outer product.\n\n- **Example**:\n\nGiven matrices $U=\\left[ \\begin{array}{cc} 1 & 2 \\\\ 3 & 4 \\\\ \\end{array} \\right]\\in\\mathbb{R}^{2\\times 2}$, $V=\\left[ \\begin{array}{cc} 1 & 2 \\\\ 3 & 4 \\\\ 5 & 6 \\\\ \\end{array} \\right]\\in\\mathbb{R}^{3\\times 2}$ and $X=\\left[ \\begin{array}{cc} 1 & 5 \\\\ 2 & 6 \\\\ 3 & 7 \\\\ 4 & 8 \\\\ \\end{array} \\right]\\in\\mathbb{R}^{4\\times 2}$, then if $\\hat{\\mathcal{Y}}=\\sum_{s=1}^{r}\\boldsymbol{u}_{s}\\circ\\boldsymbol{v}_{s}\\circ\\boldsymbol{x}_{s}$, then, we have\n\n$$\\hat{Y}_1=\\hat{\\mathcal{Y}}(:,:,1)=\\left[ \\begin{array}{ccc} 31 & 42 & 65 \\\\ 63 & 86 & 135 \\\\ \\end{array} \\right],$$\n$$\\hat{Y}_2=\\hat{\\mathcal{Y}}(:,:,2)=\\left[ \\begin{array}{ccc} 38 & 52 & 82 \\\\ 78 & 108 & 174 \\\\ \\end{array} \\right],$$\n$$\\hat{Y}_3=\\hat{\\mathcal{Y}}(:,:,3)=\\left[ \\begin{array}{ccc} 45 & 62 & 99 \\\\ 93 & 130 & 213 \\\\ \\end{array} \\right],$$\n$$\\hat{Y}_4=\\hat{\\mathcal{Y}}(:,:,4)=\\left[ \\begin{array}{ccc} 52 & 72 & 116 \\\\ 108 & 152 & 252 \\\\ \\end{array} \\right].$$", "_____no_output_____" ] ], [ [ "def cp_combine(U, V, X):\n return np.einsum('is, js, ts -> ijt', U, V, X)", "_____no_output_____" ], [ "U = np.array([[1, 2], [3, 4]])\nV = np.array([[1, 3], [2, 4], [5, 6]])\nX = np.array([[1, 5], [2, 6], [3, 7], [4, 8]])\nprint(cp_combine(U, V, X))\nprint()\nprint('tensor size:')\nprint(cp_combine(U, V, X).shape)", "[[[ 31 38 45 52]\n [ 42 52 62 72]\n [ 65 82 99 116]]\n\n [[ 63 78 93 108]\n [ 86 108 130 152]\n [135 174 213 252]]]\n\ntensor size:\n(2, 3, 4)\n" ] ], [ [ "## 4) Tensor Unfolding (`ten2mat`)\n\nUsing numpy reshape to perform 3rd rank tensor unfold operation. [[**link**](https://stackoverflow.com/questions/49970141/using-numpy-reshape-to-perform-3rd-rank-tensor-unfold-operation)]", "_____no_output_____" ] ], [ [ "def ten2mat(tensor, mode):\n return np.reshape(np.moveaxis(tensor, mode, 0), (tensor.shape[mode], -1), order = 'F')", "_____no_output_____" ], [ "X = np.array([[[1, 2, 3, 4], [3, 4, 5, 6]], \n [[5, 6, 7, 8], [7, 8, 9, 10]], \n [[9, 10, 11, 12], [11, 12, 13, 14]]])\nprint('tensor size:')\nprint(X.shape)\nprint('original tensor:')\nprint(X)\nprint()\nprint('(1) mode-1 tensor unfolding:')\nprint(ten2mat(X, 0))\nprint()\nprint('(2) mode-2 tensor unfolding:')\nprint(ten2mat(X, 1))\nprint()\nprint('(3) mode-3 tensor unfolding:')\nprint(ten2mat(X, 2))", "tensor size:\n(3, 2, 4)\noriginal tensor:\n[[[ 1 2 3 4]\n [ 3 4 5 6]]\n\n [[ 5 6 7 8]\n [ 7 8 9 10]]\n\n [[ 9 10 11 12]\n [11 12 13 14]]]\n\n(1) mode-1 tensor unfolding:\n[[ 1 3 2 4 3 5 4 6]\n [ 5 7 6 8 7 9 8 10]\n [ 9 11 10 12 11 13 12 14]]\n\n(2) mode-2 tensor unfolding:\n[[ 1 5 9 2 6 10 3 7 11 4 8 12]\n [ 3 7 11 4 8 12 5 9 13 6 10 14]]\n\n(3) mode-3 tensor unfolding:\n[[ 1 5 9 3 7 11]\n [ 2 6 10 4 8 12]\n [ 3 7 11 5 9 13]\n [ 4 8 12 6 10 14]]\n" ] ], [ [ "# Part 2: Tensor CP Factorization using ALS (TF-ALS)\n\nRegarding CP factorization as a machine learning problem, we could perform a learning task by minimizing the loss function over factor matrices, that is,\n\n$$\\min _{U, V, X} \\sum_{(i, j, t) \\in \\Omega}\\left(y_{i j t}-\\sum_{r=1}^{R}u_{ir}v_{jr}x_{tr}\\right)^{2}.$$\n\nWithin this optimization problem, multiplication among three factor matrices (acted as parameters) makes this problem difficult. Alternatively, we apply the ALS algorithm for CP factorization.\n\nIn particular, the optimization problem for each row $\\boldsymbol{u}_{i}\\in\\mathbb{R}^{R},\\forall i\\in\\left\\{1,2,...,M\\right\\}$ of factor matrix $U\\in\\mathbb{R}^{M\\times R}$ is given by\n\n$$\\min _{\\boldsymbol{u}_{i}} \\sum_{j,t:(i, j, t) \\in \\Omega}\\left[y_{i j t}-\\boldsymbol{u}_{i}^\\top\\left(\\boldsymbol{x}_{t}\\odot\\boldsymbol{v}_{j}\\right)\\right]\\left[y_{i j t}-\\boldsymbol{u}_{i}^\\top\\left(\\boldsymbol{x}_{t}\\odot\\boldsymbol{v}_{j}\\right)\\right]^\\top.$$\n\nThe least square for this optimization is\n\n$$u_{i} \\Leftarrow\\left(\\sum_{j, t, i, j, t ) \\in \\Omega} \\left(x_{t} \\odot v_{j}\\right)\\left(x_{t} \\odot v_{j}\\right)^{\\top}\\right)^{-1}\\left(\\sum_{j, t :(i, j, t) \\in \\Omega} y_{i j t} \\left(x_{t} \\odot v_{j}\\right)\\right), \\forall i \\in\\{1,2, \\ldots, M\\}.$$\n\nThe alternating least squares for $V\\in\\mathbb{R}^{N\\times R}$ and $X\\in\\mathbb{R}^{T\\times R}$ are\n\n$$\\boldsymbol{v}_{j}\\Leftarrow\\left(\\sum_{i,t:(i,j,t)\\in\\Omega}\\left(\\boldsymbol{x}_{t}\\odot\\boldsymbol{u}_{i}\\right)\\left(\\boldsymbol{x}_{t}\\odot\\boldsymbol{u}_{i}\\right)^\\top\\right)^{-1}\\left(\\sum_{i,t:(i,j,t)\\in\\Omega}y_{ijt}\\left(\\boldsymbol{x}_{t}\\odot\\boldsymbol{u}_{i}\\right)\\right),\\forall j\\in\\left\\{1,2,...,N\\right\\},$$\n\n$$\\boldsymbol{x}_{t}\\Leftarrow\\left(\\sum_{i,j:(i,j,t)\\in\\Omega}\\left(\\boldsymbol{v}_{j}\\odot\\boldsymbol{u}_{i}\\right)\\left(\\boldsymbol{v}_{j}\\odot\\boldsymbol{u}_{i}\\right)^\\top\\right)^{-1}\\left(\\sum_{i,j:(i,j,t)\\in\\Omega}y_{ijt}\\left(\\boldsymbol{v}_{j}\\odot\\boldsymbol{u}_{i}\\right)\\right),\\forall t\\in\\left\\{1,2,...,T\\right\\}.$$\n", "_____no_output_____" ] ], [ [ "def CP_ALS(sparse_tensor, rank, maxiter):\n dim1, dim2, dim3 = sparse_tensor.shape\n dim = np.array([dim1, dim2, dim3])\n \n U = 0.1 * np.random.rand(dim1, rank)\n V = 0.1 * np.random.rand(dim2, rank)\n X = 0.1 * np.random.rand(dim3, rank)\n \n pos = np.where(sparse_tensor != 0)\n binary_tensor = np.zeros((dim1, dim2, dim3))\n binary_tensor[pos] = 1\n tensor_hat = np.zeros((dim1, dim2, dim3))\n \n for iters in range(maxiter):\n for order in range(dim.shape[0]):\n if order == 0:\n var1 = kr_prod(X, V).T\n elif order == 1:\n var1 = kr_prod(X, U).T\n else:\n var1 = kr_prod(V, U).T\n var2 = kr_prod(var1, var1)\n var3 = np.matmul(var2, ten2mat(binary_tensor, order).T).reshape([rank, rank, dim[order]])\n var4 = np.matmul(var1, ten2mat(sparse_tensor, order).T)\n for i in range(dim[order]):\n var_Lambda = var3[ :, :, i]\n inv_var_Lambda = inv((var_Lambda + var_Lambda.T)/2 + 10e-12 * np.eye(rank))\n vec = np.matmul(inv_var_Lambda, var4[:, i])\n if order == 0:\n U[i, :] = vec.copy()\n elif order == 1:\n V[i, :] = vec.copy()\n else:\n X[i, :] = vec.copy()\n\n tensor_hat = cp_combine(U, V, X)\n mape = np.sum(np.abs(sparse_tensor[pos] - tensor_hat[pos])/sparse_tensor[pos])/sparse_tensor[pos].shape[0]\n rmse = np.sqrt(np.sum((sparse_tensor[pos] - tensor_hat[pos]) ** 2)/sparse_tensor[pos].shape[0])\n \n if (iters + 1) % 100 == 0:\n print('Iter: {}'.format(iters + 1))\n print('Training MAPE: {:.6}'.format(mape))\n print('Training RMSE: {:.6}'.format(rmse))\n print()\n \n return tensor_hat, U, V, X", "_____no_output_____" ] ], [ [ "# Part 3: Data Organization\n\n## 1) Matrix Structure\n\nWe consider a dataset of $m$ discrete time series $\\boldsymbol{y}_{i}\\in\\mathbb{R}^{f},i\\in\\left\\{1,2,...,m\\right\\}$. The time series may have missing elements. We express spatio-temporal dataset as a matrix $Y\\in\\mathbb{R}^{m\\times f}$ with $m$ rows (e.g., locations) and $f$ columns (e.g., discrete time intervals),\n\n$$Y=\\left[ \\begin{array}{cccc} y_{11} & y_{12} & \\cdots & y_{1f} \\\\ y_{21} & y_{22} & \\cdots & y_{2f} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ y_{m1} & y_{m2} & \\cdots & y_{mf} \\\\ \\end{array} \\right]\\in\\mathbb{R}^{m\\times f}.$$\n\n## 2) Tensor Structure\n\nWe consider a dataset of $m$ discrete time series $\\boldsymbol{y}_{i}\\in\\mathbb{R}^{nf},i\\in\\left\\{1,2,...,m\\right\\}$. The time series may have missing elements. We partition each time series into intervals of predifined length $f$. We express each partitioned time series as a matrix $Y_{i}$ with $n$ rows (e.g., days) and $f$ columns (e.g., discrete time intervals per day),\n\n$$Y_{i}=\\left[ \\begin{array}{cccc} y_{11} & y_{12} & \\cdots & y_{1f} \\\\ y_{21} & y_{22} & \\cdots & y_{2f} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ y_{n1} & y_{n2} & \\cdots & y_{nf} \\\\ \\end{array} \\right]\\in\\mathbb{R}^{n\\times f},i=1,2,...,m,$$\n\ntherefore, the resulting structure is a tensor $\\mathcal{Y}\\in\\mathbb{R}^{m\\times n\\times f}$.", "_____no_output_____" ], [ "**How to transform a data set into something we can use for time series imputation?**\n", "_____no_output_____" ], [ "# Part 4: Experiments on Guangzhou Data Set", "_____no_output_____" ] ], [ [ "import scipy.io\n\ntensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/tensor.mat')\ndense_tensor = tensor['tensor']\nrandom_matrix = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_matrix.mat')\nrandom_matrix = random_matrix['random_matrix']\nrandom_tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_tensor.mat')\nrandom_tensor = random_tensor['random_tensor']\n\nmissing_rate = 0.2\n\n# =============================================================================\n### Random missing (RM) scenario:\nbinary_tensor = np.round(random_tensor + 0.5 - missing_rate)\n# =============================================================================\n\n# =============================================================================\n### Non-random missing (NM) scenario:\n# binary_tensor = np.zeros(dense_tensor.shape)\n# for i1 in range(dense_tensor.shape[0]):\n# for i2 in range(dense_tensor.shape[1]):\n# binary_tensor[i1, i2, :] = np.round(random_matrix[i1, i2] + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ] ], [ [ "**Question**: Given only the partially observed data $\\mathcal{Y}\\in\\mathbb{R}^{m\\times n\\times f}$, how can we impute the unknown missing values?\n\nThe main influential factors for such imputation model are:\n\n- `rank`.\n\n- `maxiter`.\n", "_____no_output_____" ] ], [ [ "import time\nstart = time.time()\nrank = 80\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.0809251\nTraining RMSE: 3.47736\n\nIter: 200\nTraining MAPE: 0.0805399\nTraining RMSE: 3.46261\n\nIter: 300\nTraining MAPE: 0.0803688\nTraining RMSE: 3.45631\n\nIter: 400\nTraining MAPE: 0.0802661\nTraining RMSE: 3.45266\n\nIter: 500\nTraining MAPE: 0.0801768\nTraining RMSE: 3.44986\n\nIter: 600\nTraining MAPE: 0.0800948\nTraining RMSE: 3.44755\n\nIter: 700\nTraining MAPE: 0.0800266\nTraining RMSE: 3.4456\n\nIter: 800\nTraining MAPE: 0.0799675\nTraining RMSE: 3.44365\n\nIter: 900\nTraining MAPE: 0.07992\nTraining RMSE: 3.4419\n\nIter: 1000\nTraining MAPE: 0.079885\nTraining RMSE: 3.44058\n\nFinal Imputation MAPE: 0.0833307\nFinal Imputation RMSE: 3.59283\n\nRunning time: 2908 seconds\n" ] ], [ [ "**Experiment results** of missing data imputation using TF-ALS:\n\n| scenario |`rank`| `maxiter`| mape | rmse |\n|:----------|-----:|---------:|-----------:|----------:|\n|**20%, RM**| 80 | 1000 | **0.0833** | **3.5928**|\n|**40%, RM**| 80 | 1000 | **0.0837** | **3.6190**|\n|**20%, NM**| 10 | 1000 | **0.1027** | **4.2960**|\n|**40%, NM**| 10 | 1000 | **0.1028** | **4.3274**|\n", "_____no_output_____" ], [ "# Part 5: Experiments on Birmingham Data Set", "_____no_output_____" ] ], [ [ "import scipy.io\n\ntensor = scipy.io.loadmat('../datasets/Birmingham-data-set/tensor.mat')\ndense_tensor = tensor['tensor']\nrandom_matrix = scipy.io.loadmat('../datasets/Birmingham-data-set/random_matrix.mat')\nrandom_matrix = random_matrix['random_matrix']\nrandom_tensor = scipy.io.loadmat('../datasets/Birmingham-data-set/random_tensor.mat')\nrandom_tensor = random_tensor['random_tensor']\n\nmissing_rate = 0.3\n\n# =============================================================================\n### Random missing (RM) scenario:\nbinary_tensor = np.round(random_tensor + 0.5 - missing_rate)\n# =============================================================================\n\n# =============================================================================\n### Non-random missing (NM) scenario:\n# binary_tensor = np.zeros(dense_tensor.shape)\n# for i1 in range(dense_tensor.shape[0]):\n# for i2 in range(dense_tensor.shape[1]):\n# binary_tensor[i1, i2, :] = np.round(random_matrix[i1,i2] + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 30\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.0509401\nTraining RMSE: 15.3163\n\nIter: 200\nTraining MAPE: 0.0498774\nTraining RMSE: 14.9599\n\nIter: 300\nTraining MAPE: 0.0490062\nTraining RMSE: 14.768\n\nIter: 400\nTraining MAPE: 0.0481006\nTraining RMSE: 14.6343\n\nIter: 500\nTraining MAPE: 0.0474233\nTraining RMSE: 14.5365\n\nIter: 600\nTraining MAPE: 0.0470442\nTraining RMSE: 14.4642\n\nIter: 700\nTraining MAPE: 0.0469617\nTraining RMSE: 14.4082\n\nIter: 800\nTraining MAPE: 0.0470459\nTraining RMSE: 14.3623\n\nIter: 900\nTraining MAPE: 0.0472333\nTraining RMSE: 14.3235\n\nIter: 1000\nTraining MAPE: 0.047408\nTraining RMSE: 14.2898\n\nFinal Imputation MAPE: 0.0583358\nFinal Imputation RMSE: 18.9148\n\nRunning time: 38 seconds\n" ] ], [ [ "**Experiment results** of missing data imputation using TF-ALS:\n\n| scenario |`rank`| `maxiter`| mape | rmse |\n|:----------|-----:|---------:|-----------:|-----------:|\n|**10%, RM**| 30 | 1000 | **0.0615** | **18.5005**|\n|**30%, RM**| 30 | 1000 | **0.0583** | **18.9148**|\n|**10%, NM**| 10 | 1000 | **0.1447** | **41.6710**|\n|**30%, NM**| 10 | 1000 | **0.1765** | **63.8465**|\n", "_____no_output_____" ], [ "# Part 6: Experiments on Hangzhou Data Set", "_____no_output_____" ] ], [ [ "import scipy.io\n\ntensor = scipy.io.loadmat('../datasets/Hangzhou-data-set/tensor.mat')\ndense_tensor = tensor['tensor']\nrandom_matrix = scipy.io.loadmat('../datasets/Hangzhou-data-set/random_matrix.mat')\nrandom_matrix = random_matrix['random_matrix']\nrandom_tensor = scipy.io.loadmat('../datasets/Hangzhou-data-set/random_tensor.mat')\nrandom_tensor = random_tensor['random_tensor']\n\nmissing_rate = 0.4\n\n# =============================================================================\n### Random missing (RM) scenario:\nbinary_tensor = np.round(random_tensor + 0.5 - missing_rate)\n# =============================================================================\n\n# =============================================================================\n### Non-random missing (NM) scenario:\n# binary_tensor = np.zeros(dense_tensor.shape)\n# for i1 in range(dense_tensor.shape[0]):\n# for i2 in range(dense_tensor.shape[1]):\n# binary_tensor[i1, i2, :] = np.round(random_matrix[i1, i2] + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 50\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.176548\nTraining RMSE: 17.0263\n\nIter: 200\nTraining MAPE: 0.174888\nTraining RMSE: 16.8609\n\nIter: 300\nTraining MAPE: 0.175056\nTraining RMSE: 16.7835\n\nIter: 400\nTraining MAPE: 0.174988\nTraining RMSE: 16.7323\n\nIter: 500\nTraining MAPE: 0.175013\nTraining RMSE: 16.6942\n\nIter: 600\nTraining MAPE: 0.174928\nTraining RMSE: 16.6654\n\nIter: 700\nTraining MAPE: 0.174722\nTraining RMSE: 16.6441\n\nIter: 800\nTraining MAPE: 0.174565\nTraining RMSE: 16.6284\n\nIter: 900\nTraining MAPE: 0.174454\nTraining RMSE: 16.6159\n\nIter: 1000\nTraining MAPE: 0.174409\nTraining RMSE: 16.6054\n\nFinal Imputation MAPE: 0.209776\nFinal Imputation RMSE: 100.315\n\nRunning time: 279 seconds\n" ] ], [ [ "**Experiment results** of missing data imputation using TF-ALS:\n\n| scenario |`rank`| `maxiter`| mape | rmse |\n|:----------|-----:|---------:|-----------:|----------:|\n|**20%, RM**| 50 | 1000 | **0.1991** |**111.303**|\n|**40%, RM**| 50 | 1000 | **0.2098** |**100.315**|\n|**20%, NM**| 5 | 1000 | **0.2837** |**42.6136**|\n|**40%, NM**| 5 | 1000 | **0.2811** |**38.4201**|\n", "_____no_output_____" ], [ "# Part 7: Experiments on New York Data Set", "_____no_output_____" ] ], [ [ "import scipy.io\n\ntensor = scipy.io.loadmat('../datasets/NYC-data-set/tensor.mat')\ndense_tensor = tensor['tensor']\nrm_tensor = scipy.io.loadmat('../datasets/NYC-data-set/rm_tensor.mat')\nrm_tensor = rm_tensor['rm_tensor']\nnm_tensor = scipy.io.loadmat('../datasets/NYC-data-set/nm_tensor.mat')\nnm_tensor = nm_tensor['nm_tensor']\n\nmissing_rate = 0.1\n\n# =============================================================================\n### Random missing (RM) scenario\n### Set the RM scenario by:\n# binary_tensor = np.round(rm_tensor + 0.5 - missing_rate)\n# =============================================================================\n\n# =============================================================================\n### Non-random missing (NM) scenario\n### Set the NM scenario by:\nbinary_tensor = np.zeros(dense_tensor.shape)\nfor i1 in range(dense_tensor.shape[0]):\n for i2 in range(dense_tensor.shape[1]):\n for i3 in range(61):\n binary_tensor[i1, i2, i3 * 24 : (i3 + 1) * 24] = np.round(nm_tensor[i1, i2, i3] + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 30\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.511739\nTraining RMSE: 4.07981\n\nIter: 200\nTraining MAPE: 0.501094\nTraining RMSE: 4.0612\n\nIter: 300\nTraining MAPE: 0.504264\nTraining RMSE: 4.05578\n\nIter: 400\nTraining MAPE: 0.507211\nTraining RMSE: 4.05119\n\nIter: 500\nTraining MAPE: 0.509956\nTraining RMSE: 4.04623\n\nIter: 600\nTraining MAPE: 0.51046\nTraining RMSE: 4.04129\n\nIter: 700\nTraining MAPE: 0.509797\nTraining RMSE: 4.03294\n\nIter: 800\nTraining MAPE: 0.509531\nTraining RMSE: 4.02976\n\nIter: 900\nTraining MAPE: 0.509265\nTraining RMSE: 4.02861\n\nIter: 1000\nTraining MAPE: 0.508873\nTraining RMSE: 4.02796\n\nFinal Imputation MAPE: 0.540363\nFinal Imputation RMSE: 5.66633\n\nRunning time: 742 seconds\n" ] ], [ [ "**Experiment results** of missing data imputation using TF-ALS:\n\n| scenario |`rank`| `maxiter`| mape | rmse |\n|:----------|-----:|---------:|-----------:|----------:|\n|**10%, RM**| 30 | 1000 | **0.5262** | **6.2444**|\n|**30%, RM**| 30 | 1000 | **0.5488** | **6.8968**|\n|**10%, NM**| 30 | 1000 | **0.5170** | **5.9863**|\n|**30%, NM**| 30 | 100 | **-** | **-**|\n", "_____no_output_____" ], [ "# Part 8: Experiments on Seattle Data Set", "_____no_output_____" ] ], [ [ "import pandas as pd\n\ndense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)\nRM_mat = pd.read_csv('../datasets/Seattle-data-set/RM_mat.csv', index_col = 0)\ndense_mat = dense_mat.values\nRM_mat = RM_mat.values\ndense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])\nRM_tensor = RM_mat.reshape([RM_mat.shape[0], 28, 288])\n\nmissing_rate = 0.2\n\n# =============================================================================\n### Random missing (RM) scenario\n### Set the RM scenario by:\nbinary_tensor = np.round(RM_tensor + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 50\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.0749497\nTraining RMSE: 4.47036\n\nIter: 200\nTraining MAPE: 0.0745197\nTraining RMSE: 4.44713\n\nIter: 300\nTraining MAPE: 0.0741685\nTraining RMSE: 4.43496\n\nIter: 400\nTraining MAPE: 0.0739049\nTraining RMSE: 4.42523\n\nIter: 500\nTraining MAPE: 0.0737243\nTraining RMSE: 4.41692\n\nIter: 600\nTraining MAPE: 0.0735726\nTraining RMSE: 4.40994\n\nIter: 700\nTraining MAPE: 0.073415\nTraining RMSE: 4.4034\n\nIter: 800\nTraining MAPE: 0.0732484\nTraining RMSE: 4.3976\n\nIter: 900\nTraining MAPE: 0.0731012\nTraining RMSE: 4.393\n\nIter: 1000\nTraining MAPE: 0.0729675\nTraining RMSE: 4.38843\n\nFinal Imputation MAPE: 0.0741996\nFinal Imputation RMSE: 4.49292\n\nRunning time: 2594 seconds\n" ], [ "import pandas as pd\n\ndense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)\nRM_mat = pd.read_csv('../datasets/Seattle-data-set/RM_mat.csv', index_col = 0)\ndense_mat = dense_mat.values\nRM_mat = RM_mat.values\ndense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])\nRM_tensor = RM_mat.reshape([RM_mat.shape[0], 28, 288])\n\nmissing_rate = 0.4\n\n# =============================================================================\n### Random missing (RM) scenario\n### Set the RM scenario by:\nbinary_tensor = np.round(RM_tensor + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 50\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.0747491\nTraining RMSE: 4.4544\n\nIter: 200\nTraining MAPE: 0.074195\nTraining RMSE: 4.42842\n\nIter: 300\nTraining MAPE: 0.0738864\nTraining RMSE: 4.4162\n\nIter: 400\nTraining MAPE: 0.0735955\nTraining RMSE: 4.40699\n\nIter: 500\nTraining MAPE: 0.0733999\nTraining RMSE: 4.40083\n\nIter: 600\nTraining MAPE: 0.0732636\nTraining RMSE: 4.3959\n\nIter: 700\nTraining MAPE: 0.0731835\nTraining RMSE: 4.39241\n\nIter: 800\nTraining MAPE: 0.0731367\nTraining RMSE: 4.3899\n\nIter: 900\nTraining MAPE: 0.0730982\nTraining RMSE: 4.38779\n\nIter: 1000\nTraining MAPE: 0.0730573\nTraining RMSE: 4.38571\n\nFinal Imputation MAPE: 0.0757934\nFinal Imputation RMSE: 4.5574\n\nRunning time: 2706 seconds\n" ], [ "import pandas as pd\n\ndense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)\nNM_mat = pd.read_csv('../datasets/Seattle-data-set/NM_mat.csv', index_col = 0)\ndense_mat = dense_mat.values\nNM_mat = NM_mat.values\ndense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])\n\nmissing_rate = 0.2\n\n# =============================================================================\n### Non-random missing (NM) scenario\n### Set the NM scenario by:\nbinary_tensor = np.zeros((dense_mat.shape[0], 28, 288))\nfor i1 in range(binary_tensor.shape[0]):\n for i2 in range(binary_tensor.shape[1]):\n binary_tensor[i1, i2, :] = np.round(NM_mat[i1, i2] + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 10\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.100191\nTraining RMSE: 5.60051\n\nIter: 200\nTraining MAPE: 0.0981896\nTraining RMSE: 5.51405\n\nIter: 300\nTraining MAPE: 0.0969386\nTraining RMSE: 5.46377\n\nIter: 400\nTraining MAPE: 0.0967974\nTraining RMSE: 5.45581\n\nIter: 500\nTraining MAPE: 0.0966243\nTraining RMSE: 5.44397\n\nIter: 600\nTraining MAPE: 0.0960368\nTraining RMSE: 5.42168\n\nIter: 700\nTraining MAPE: 0.0958292\nTraining RMSE: 5.41295\n\nIter: 800\nTraining MAPE: 0.0957371\nTraining RMSE: 5.40865\n\nIter: 900\nTraining MAPE: 0.0956582\nTraining RMSE: 5.40568\n\nIter: 1000\nTraining MAPE: 0.095595\nTraining RMSE: 5.40339\n\nFinal Imputation MAPE: 0.0994999\nFinal Imputation RMSE: 5.63311\n\nRunning time: 351 seconds\n" ], [ "import pandas as pd\n\ndense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)\nNM_mat = pd.read_csv('../datasets/Seattle-data-set/NM_mat.csv', index_col = 0)\ndense_mat = dense_mat.values\nNM_mat = NM_mat.values\ndense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])\n\nmissing_rate = 0.4\n\n# =============================================================================\n### Non-random missing (NM) scenario\n### Set the NM scenario by:\nbinary_tensor = np.zeros((dense_mat.shape[0], 28, 288))\nfor i1 in range(binary_tensor.shape[0]):\n for i2 in range(binary_tensor.shape[1]):\n binary_tensor[i1, i2, :] = np.round(NM_mat[i1, i2] + 0.5 - missing_rate)\n# =============================================================================\n\nsparse_tensor = np.multiply(dense_tensor, binary_tensor)", "_____no_output_____" ], [ "import time\nstart = time.time()\nrank = 10\nmaxiter = 1000\ntensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)\npos = np.where((dense_tensor != 0) & (sparse_tensor == 0))\nfinal_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]\nfinal_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])\nprint('Final Imputation MAPE: {:.6}'.format(final_mape))\nprint('Final Imputation RMSE: {:.6}'.format(final_rmse))\nprint()\nend = time.time()\nprint('Running time: %d seconds'%(end - start))", "Iter: 100\nTraining MAPE: 0.0996282\nTraining RMSE: 5.55963\n\nIter: 200\nTraining MAPE: 0.0992568\nTraining RMSE: 5.53825\n\nIter: 300\nTraining MAPE: 0.0986723\nTraining RMSE: 5.51806\n\nIter: 400\nTraining MAPE: 0.0967838\nTraining RMSE: 5.46447\n\nIter: 500\nTraining MAPE: 0.0962312\nTraining RMSE: 5.44762\n\nIter: 600\nTraining MAPE: 0.0961017\nTraining RMSE: 5.44322\n\nIter: 700\nTraining MAPE: 0.0959531\nTraining RMSE: 5.43927\n\nIter: 800\nTraining MAPE: 0.0958815\nTraining RMSE: 5.43619\n\nIter: 900\nTraining MAPE: 0.0958781\nTraining RMSE: 5.4344\n\nIter: 1000\nTraining MAPE: 0.0958921\nTraining RMSE: 5.43266\n\nFinal Imputation MAPE: 0.10038\nFinal Imputation RMSE: 5.7034\n\nRunning time: 304 seconds\n" ] ], [ [ "**Experiment results** of missing data imputation using TF-ALS:\n\n| scenario |`rank`| `maxiter`| mape | rmse |\n|:----------|-----:|---------:|-----------:|----------:|\n|**20%, RM**| 50 | 1000 | **0.0742** |**4.4929**|\n|**40%, RM**| 50 | 1000 | **0.0758** |**4.5574**|\n|**20%, NM**| 10 | 1000 | **0.0995** |**5.6331**|\n|**40%, NM**| 10 | 1000 | **0.1004** |**5.7034**|\n", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "import panel as pn\nimport holoviews as hv\nimport geoviews as gv\nfrom adhui import CreateMesh, ConceptualModelEditor\nfrom earthsim.annotators import PolyAnnotator\nfrom earthsim.grabcut import GrabCutPanel, SelectRegionPanel\n\nhv.extension('bokeh')", "_____no_output_____" ], [ "# create stages for pipeline\nstages = [\n ('Select Region', SelectRegionPanel),\n ('Grabcut', GrabCutPanel),\n ('Path Editor', ConceptualModelEditor),\n ('Mesh', CreateMesh)\n]\n\n# create the pipeline\npipeline = pn.pipeline.Pipeline(stages, debug=True)\n \n# return a display of the pipeline\npipeline.layout.servable()", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import os\nos.environ['CUDA_VISIBLE_DEVICES']='0,1,2,3,4,5'", "_____no_output_____" ], [ "%run -p train_cnf_augmented.py --data mnist --dims 64,64,64 --strides 1,1,1,1 --num_blocks 2 --layer_type concat --multiscale True --rademacher True --batch_size 900 --test_batch_size 500 --save ../experiments_published/cnf_published_conditional_bs900_1 --seed 1 --conditional True --controlled_tol False --train_mode sup --concat_size 3 --log_freq 10 --weight_y 0.5", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "\n\"\"\"nector is green in colour\nplease go green\"\"\"\n", "_____no_output_____" ], [ "print(\"amrit's specail green\")", "amrit's specail green\n" ], [ "def green():\n print(\"in the green\")", "_____no_output_____" ], [ "\n# __name__ = \"amrit\"", "_____no_output_____" ], [ "print(__name__)", "__main__\n" ], [ "if __name__ == \"__main__\":\n def peach():\n print('peach is a fruit')", "_____no_output_____" ], [ "#peach()", "peach is a fruit\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "Let's look at:\nNumber of labels per image (histogram)\nQuality score per image for images with multiple labels (sigmoid?)\n", "_____no_output_____" ] ], [ [ "import csv\nfrom itertools import islice\nfrom collections import defaultdict\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport torch\nimport torchvision\nimport numpy as np", "_____no_output_____" ], [ "CSV_PATH = 'wgangp_data.csv'", "_____no_output_____" ], [ "realness = {}\n# real_votes = defaultdict(int)\n# fake_votes = defaultdict(int)\ntotal_votes = defaultdict(int)\ncorrect_votes = defaultdict(int)\n\n\n\n\nwith open(CSV_PATH) as f:\n dictreader = csv.DictReader(f)\n for line in dictreader:\n img_name = line['img']\n assert(line['realness'] in ('True', 'False'))\n assert(line['correctness'] in ('True', 'False'))\n \n realness[img_name] = line['realness'] == 'True'\n if line['correctness'] == 'True':\n correct_votes[img_name] += 1\n total_votes[img_name] += 1\n \n ", "_____no_output_____" ], [ "pdx = pd.read_csv(CSV_PATH)\npdx", "_____no_output_____" ], [ "pdx[pdx.groupby('img').count() > 50]\npdx\n#df.img\n# print(df.columns)\n# print(df['img'])", "_____no_output_____" ], [ "# How much of the time do people guess \"fake\"? Slightly more than half!\npdx[pdx.correctness != pdx.realness].count()/pdx.count()", "_____no_output_____" ], [ "# How much of the time do people guess right? 94.4%\npdx[pdx.correctness].count()/pdx.count()", "_____no_output_____" ], [ "#90.3% of the time, real images are correctly labeled as real\npdx[pdx.realness][pdx.correctness].count()/pdx[pdx.realness].count()", "/Users/alexkolchinski/anaconda3/envs/wgan_experiment/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n \"\"\"Entry point for launching an IPython kernel.\n" ], [ "#98.5% of the time, fake images are correctly labeled as fake\npdx[~pdx.realness][pdx.correctness].count()/pdx[~pdx.realness].count()", "/Users/alexkolchinski/anaconda3/envs/wgan_experiment/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n \n" ], [ "len(total_votes.values())", "_____no_output_____" ], [ "img_dict = {img: [realness[img], correct_votes[img], total_votes[img], correct_votes[img]/total_votes[img]] for img in realness }\n# print(img_dict.keys())\n#img_dict['celeba500/005077_crop.jpg']\nplt.hist([v[3] for k,v in img_dict.items() if 'celeb' in k])", "_____no_output_____" ], [ "def getVotesDict(img_dict):\n\n votes_dict = defaultdict(int)\n for img in total_votes:\n votes_dict[img_dict[img][2]] += 1\n return votes_dict\n \nvotes_dict = getVotesDict(img_dict)\nfor i in sorted(votes_dict.keys()):\n print(i, votes_dict[i])", "1 2460\n2 1283\n3 400\n4 95\n5 11\n6 2\n129 2\n130 1\n131 4\n132 4\n133 8\n134 8\n135 10\n136 8\n137 3\n138 2\n" ], [ "selected_img_dict = {img:value for img, value in img_dict.items() if img_dict[img][2] > 10}\nless_than_50_dict = {img:value for img, value in img_dict.items() if img_dict[img][2] < 10}\nimgs_over_50 = list(selected_img_dict.keys())\n# print(len(selected_img_dict))\n# print(imgs_over_50)", "_____no_output_____" ], [ "pdx_50 = pdx[pdx.img.apply(lambda x: x in imgs_over_50)]\nlen(pdx_50)", "_____no_output_____" ], [ "pdx_under_50 = pdx[pdx.img.apply(lambda x: x not in imgs_over_50)]\nlen(pdx_under_50)", "_____no_output_____" ], [ "len(pdx_under_50[pdx_under_50.img.apply(lambda x: 'wgan' not in x)])", "_____no_output_____" ], [ "correctness = sorted([value[3] for key, value in selected_img_dict.items()])\nprint(correctness)\nplt.hist(correctness)\nplt.show()", "[0.6204379562043796, 0.7164179104477612, 0.7557251908396947, 0.7898550724637681, 0.8059701492537313, 0.8120300751879699, 0.8248175182481752, 0.8283582089552238, 0.837037037037037, 0.8449612403100775, 0.8455882352941176, 0.8712121212121212, 0.875, 0.8787878787878788, 0.8805970149253731, 0.8823529411764706, 0.8897058823529411, 0.8962962962962963, 0.9, 0.9022556390977443, 0.9083969465648855, 0.9097744360902256, 0.9111111111111111, 0.9130434782608695, 0.9197080291970803, 0.924812030075188, 0.924812030075188, 0.9259259259259259, 0.9259259259259259, 0.9312977099236641, 0.937984496124031, 0.9398496240601504, 0.9473684210526315, 0.9481481481481482, 0.9552238805970149, 0.9555555555555556, 0.9558823529411765, 0.9618320610687023, 0.9621212121212122, 0.9701492537313433, 0.9701492537313433, 0.9703703703703703, 0.9703703703703703, 0.9703703703703703, 0.9779411764705882, 0.9779411764705882, 0.9848484848484849, 0.9849624060150376, 0.9852941176470589, 0.9925373134328358]\n" ], [ "correctness = sorted([value[3] for key, value in less_than_50_dict.items()])\n# print(correctness)\nplt.hist(correctness)\nplt.show()\n\n", "_____no_output_____" ], [ "ct = []\n# selected_img = [img in total_votes.keys() if total_votes[img] > 1 ]", "_____no_output_____" ], [ "discriminator = torch.load('discriminator.pt', map_location='cpu')\n# torch.load_state_dict('discriminator.pt')", "_____no_output_____" ], [ "discriminator(torch.zeros(64,64,3))", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Naive Bayes Classifier\n\nPredicting positivty/negativity of movie reviews using Naive Bayes algorithm", "_____no_output_____" ], [ "## 1. Import Dataset\n\nLabels:\n* 0 : Negative review\n* 1 : Positive review", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport warnings\nwarnings.filterwarnings('ignore')\n\nreviews = pd.read_csv('ratings_train.txt', delimiter='\\t')\nreviews.head(10)", "_____no_output_____" ], [ "#divide between negative and positive reviews with more than 30 words in length\nneg = reviews[(reviews.document.str.len() >= 30) & (reviews.label == 0)].sample(3000, random_state=43)\npos = reviews[(reviews.document.str.len() >= 30) & (reviews.label == 1)].sample(3000, random_state=43)", "_____no_output_____" ], [ "pos.head()", "_____no_output_____" ], [ "#NLP method\nimport re\nimport konlpy\nfrom konlpy.tag import Twitter\n\nokt = Twitter()", "_____no_output_____" ], [ "def parse(s):\n s = re.sub(r'[?$.!,-_\\'\\\"(){}~]+', '', s)\n try:\n return okt.nouns(s)\n except:\n return []\n \n#okt.morphs is another option", "_____no_output_____" ], [ "neg['parsed_doc'] = neg.document.apply(parse)\npos['parsed_doc'] = pos.document.apply(parse)", "_____no_output_____" ], [ "neg.head()", "_____no_output_____" ], [ "pos.head()", "_____no_output_____" ], [ "# create 5800 training data / 200 test data\nneg_train = neg[:2900]\npos_train = pos[:2900]\nneg_test = neg[2900:]\npos_test = pos[2900:]", "_____no_output_____" ] ], [ [ "## 2. Create Corpus", "_____no_output_____" ] ], [ [ "neg_corpus = set(neg_train.parsed_doc.sum())\npos_corpus = set(pos_train.parsed_doc.sum())", "_____no_output_____" ], [ "corpus = list((neg_corpus).union(pos_corpus))\nprint('corpus length : ', len(corpus))", "corpus length : 9836\n" ], [ "corpus[:10]", "_____no_output_____" ] ], [ [ "## 3. Create Bag of Words", "_____no_output_____" ] ], [ [ "from collections import OrderedDict", "_____no_output_____" ], [ "neg_bow_vecs = []\nfor _, doc in neg.parsed_doc.items():\n bow_vecs = OrderedDict()\n for w in corpus:\n if w in doc:\n bow_vecs[w] = 1\n else:\n bow_vecs[w] = 0\n neg_bow_vecs.append(bow_vecs)", "_____no_output_____" ], [ "pos_bow_vecs = []\nfor _, doc in pos.parsed_doc.items():\n bow_vecs = OrderedDict()\n for w in corpus:\n if w in doc:\n bow_vecs[w] = 1\n else:\n bow_vecs[w] = 0\n pos_bow_vecs.append(bow_vecs)", "_____no_output_____" ], [ "#bag of word vector example\n#this length is equal to the length of the corpus\nneg_bow_vecs[0].values()", "_____no_output_____" ] ], [ [ "## 4. Model Training", "_____no_output_____" ], [ "$n$ is the dimension of each document, in other words, the length of corpus <br>\n\n$$\\large p(pos|doc) = \\Large \\frac{p(doc|pos) \\cdot p(pos)}{p(doc)}$$\n<br>\n$$\\large p(neg|doc) = \\Large \\frac{p(doc|neg) \\cdot p(neg)}{p(doc)}$$\n\n<br><br>\n**Likelihood functions:** <br><br>\n$p(word_{i}|pos) = \\large \\frac{\\text{the number of positive documents that contain the word}}{\\text{the number of positive documents}}$\n\n$p(word_{i}|neg) = \\large \\frac{\\text{the number of negative documents that contain the word}}{\\text{the number of negative documents}}$", "_____no_output_____" ] ], [ [ "import numpy as np", "_____no_output_____" ], [ "corpus[:5]", "_____no_output_____" ], [ "list(neg_train.parsed_doc.items())[0]", "_____no_output_____" ], [ "#this counts how many times a word in corpus appeares in neg_train data\nneg_words_likelihood_cnts = {}\nfor w in corpus:\n cnt = 0\n for _, doc in neg_train.parsed_doc.items():\n if w in doc:\n cnt += 1\n neg_words_likelihood_cnts[w] = cnt", "_____no_output_____" ], [ "#this counts how many times a word in corpus appeares in pos_train data : p(neg)\npos_words_likelihood_cnts = {}\nfor w in corpus:\n cnt = 0\n for _, doc in pos_train.parsed_doc.items():\n if w in doc:\n cnt += 1\n pos_words_likelihood_cnts[w] = cnt", "_____no_output_____" ], [ "import operator", "_____no_output_____" ], [ "sorted(neg_words_likelihood_cnts.items(), key=operator.itemgetter(1), reverse=True)[:10]", "_____no_output_____" ], [ "sorted(pos_words_likelihood_cnts.items(), key=operator.itemgetter(1), reverse=True)[:10]", "_____no_output_____" ] ], [ [ "## 5. Classifier\n\n* We represent each documents in terms of bag of words. If the size of Corpus is $n$, this means that each bag of word of document is $n-dimensional$\n* When there isn't a word, we use **Laclacian Smoothing**", "_____no_output_____" ] ], [ [ "test_data = pd.concat([neg_test, pos_test], axis=0)", "_____no_output_____" ], [ "test_data.head()", "_____no_output_____" ], [ "def predict(doc):\n pos_prior, neg_prior = 1/2, 1/2 #because we have equal number of pos and neg training documents\n\n # Posterior of pos\n pos_prob = np.log(1)\n for word in corpus:\n if word in doc:\n # the word is in the current document and has appeared in pos documents\n if word in pos_words_likelihood_cnts:\n pos_prob += np.log((pos_words_likelihood_cnts[word] + 1) / (len(pos_train) + len(corpus)))\n else:\n # the word is in the current document, but has never appeared in pos documents : Laplacian\n pos_prob += np.log(1 / (len(pos_train) + len(corpus)))\n else:\n # the word is not in the current document, but has appeared in pos documents \n # we can find the possibility that the word is not in pos\n if word in pos_words_likelihood_cnts:\n pos_prob += \\\n np.log((len(pos_train) - pos_words_likelihood_cnts[word] + 1) / (len(pos_train) + len(corpus)))\n else:\n # the word is not in the current document, and has never appeared in pos documents : Laplacian\n pos_prob += np.log((len(pos_train) + 1) / (len(pos_train) + len(corpus)))\n pos_prob += np.log(pos_prior)\n \n # Posterior of neg\n neg_prob = 1\n for word in corpus:\n if word in doc:\n # 단어가 현재 문장에 존재하고, neg 문장에 나온적이 있는 경우\n if word in neg_words_likelihood_cnts:\n neg_prob += np.log((neg_words_likelihood_cnts[word] + 1) / (len(neg_train) + len(corpus)))\n else:\n # 단어가 현재 문장에 존재하고, neg 문장에서 한 번도 나온적이 없는 경우 : 라플라시안 스무딩\n neg_prob += np.log(1 / (len(neg_train) + len(corpus)))\n else:\n # 단어가 현재 문장에 존재하지 않고, neg 문장에 나온적이 있는 경우 (neg에서 해당단어가 없는 확률을 구할 수 있다.)\n if word in neg_words_likelihood_cnts:\n neg_prob += \\\n np.log((len(neg_train) - neg_words_likelihood_cnts[word] + 1) / (len(neg_train) + len(corpus)))\n else:\n # 단어가 현재 문장에 존재하지 않고, pos 문장에서 단 한 번도 나온적이 없는 경우 : 라플라시안 스무딩\n neg_prob += np.log((len(neg_train) + 1) / (len(neg_train) + len(corpus)))\n neg_prob += np.log(neg_prior)\n \n if pos_prob >= neg_prob:\n return 1\n else:\n return 0", "_____no_output_____" ], [ "test_data['pred'] = test_data.parsed_doc.apply(predict)", "_____no_output_____" ], [ "test_data.head()", "_____no_output_____" ], [ "test_data.shape", "_____no_output_____" ], [ "sum(test_data.label ^ test_data.pred)", "_____no_output_____" ] ], [ [ "There are a total of 200 test documents, and of these 200 tests only 46 were different", "_____no_output_____" ] ], [ [ "1 - sum(test_data.label ^ test_data.pred) / len(test_data)", "_____no_output_____" ] ], [ [ "We have about 77% accuracy rate, which is relatively high", "_____no_output_____" ] ] ]

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[ [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom pylab import rcParams\nrcParams['figure.figsize'] = 16, 9\n\nfig, ax = plt.subplots(1,1)\nplt.axis('equal')\n\nax.set_xlim([0, 2*np.pi])\nax.set_ylim([-2.5, 2.5])\n\n# sine function plot\nx = np.linspace(0, 2*np.pi, 100)\ny = np.sin(x)\n\n# x axis path\nax.plot(3*x - 3, 0*y, linewidth=1, color='#dddddd')\n# y axis path\nax.plot(0*x, 2.5*y, linewidth=1, color='#dddddd')\n\n# unit circle path\nax.plot(np.cos(x), np.sin(x), linewidth=1)\n# sine path\nax.plot(x + 1, np.sin(x), linewidth=1)\n\n\n# ------ anim -------\nsineLine, = ax.plot([], [], linewidth=4)\nsineDot, = ax.plot([], [], 'o', color='#ff0000')\n\ncircleLine, = ax.plot([], [],linewidth=4)\ncircleDot, = ax.plot([], [], 'o', color='black')\n\ndef sineAnim(i):\n # sine anim\n sineLine.set_data(x[:i] + 1,y[:i])\n sineDot.set_data(x[i] + 1, y[i])\n # circle anim\n circleLine.set_data(np.cos(x[:i]), np.sin(x[:i]))\n circleDot.set_data(np.cos(x[i]), np.sin(x[i]))\n\nanim = animation.FuncAnimation(fig, sineAnim, frames=len(x), interval=50)\n# -------------\n\nanim.save('sine-py-effect.mp4', writer='ffmpeg')\n\n# plt.show()\n# HTML(anim.to_html5_video())", "_____no_output_____" ] ] ]

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[ [ [ "# Auditing a dataframe\nIn this notebook, we shall demonstrate how to use `privacypanda` to _audit_ the privacy of your data. `privacypanda` provides a simple function which prints the names of any columns which break privacy. Currently, these are:\n- Addresses\n - E.g. \"10 Downing Street\"; \"221b Baker St\"; \"EC2R 8AH\"\n- Phonenumbers (UK mobile)\n - E.g. \"+447123456789\"\n- Email addresses\n - Ending in \".com\", \".co.uk\", \".org\", \".edu\" (to be expanded soon)", "_____no_output_____" ] ], [ [ "%load_ext watermark\n%watermark -n -p pandas,privacypanda -g", "Sun Mar 08 2020 \n\npandas 1.0.1\nprivacypanda 0.1.0.dev0\nGit hash: 7d1343dc13973da5c265a5a2bcf1915384c3e131\n" ], [ "import pandas as pd\nimport privacypanda as pp", "_____no_output_____" ] ], [ [ "---\n## Firstly, we need data", "_____no_output_____" ] ], [ [ "data = pd.DataFrame(\n {\n \"user ID\": [\n 1665,\n 1,\n 5287,\n 42,\n ],\n \"User email\": [\n \"xxxxxxxxxxxxx\",\n \"xxxxxxxx\",\n \"I'm not giving you that\",\n \"[email protected]\",\n ],\n \"User address\": [\n \"AB1 1AB\",\n \"\",\n \"XXX XXX\",\n \"EC2R 8AH\",\n ],\n \"Likes raclette\": [\n 1,\n 0,\n 1,\n 1,\n ],\n }\n)", "_____no_output_____" ] ], [ [ "You will notice two things about this dataframe:\n1. _Some_ of the data has already been anonymized, for example by replacing characters with \"x\"s. However, the person who collected this data has not been fastidious with its cleaning as there is still some raw, potentially problematic private information. As the dataset grows, it becomes easier to miss entries with private information\n2. Not all columns expose privacy: \"Likes raclette\" is pretty benign information (but be careful, lots of benign information can be combined to form a unique fingerprint identifying an individual - let's not worry about this at the moment, though), and \"user ID\" is already an anonymized labelling of an individual.", "_____no_output_____" ], [ "---\n# Auditing the data's privacy\nAs a data scientist, we want a simple way to tell which columns, if any break privacy. More importantly, _how_ they break privacy determines how we deal with them. For example, emails will likely be superfluous information for analysis and can therefore be removed from the data, but age may be important and so we may wish instead to apply differential privacy to the dataset.\n\nWe can use `privacypanda`'s `report_privacy` function to see which data is problematic.", "_____no_output_____" ] ], [ [ "report = pp.report_privacy(data)\nprint(report)", "User address: ['address']\nUser email: ['email']\n\n" ] ], [ [ "`report_privacy` returns a `Report` object which stores the privacy issues of each column in the data. \n\nAs `privacypanda` is in active development, \nthis is currently only a simple dictionary of binary \"breaks\"/\"doesn't break\" privacy for each column. \nWe aim to make this information _cell-level_, \ni.e. removing/replacing the information in individual cells in order to protect privacy with less information loss.", "_____no_output_____" ] ] ]

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[ [ [ "import pandas as pd\nimport numpy as np", "_____no_output_____" ] ], [ [ "## read datafiles\n- C-18 for language population\n- C-13 for particular age-range population from a state", "_____no_output_____" ] ], [ [ "c18=pd.read_excel('datasets/C-18.xlsx',skiprows=6,header=None,engine='openpyxl')", "_____no_output_____" ], [ "c13=pd.read_excel('datasets/C-13.xls',skiprows=7,header=None)", "_____no_output_____" ] ], [ [ "### particular age groups are\n- 5-9\n- 10-14\n- 15-19\n- 20-24\n- 25-29\n- 30-49\n- 50-69\n- 70+\n- Age not stated", "_____no_output_____" ], [ "## obtain useful data from C-13 and C-18 for age-groups\n- first get particular state names for identifying specific states\n- get particular age-groups from C-18 file\n- make list of particular age group row/col for a particular states\n- now just simply iterate through each state to get relevent data and store it into a csv file\n - to get total pop of particular age-range I have used C-13 file\n - to get total pop that speaks more than 3 languages from a state in a particular age-range I have used C-18 file ", "_____no_output_____" ] ], [ [ "# STATE_NAMES=[list(np.unique(c18.iloc[:,2].values))]\nSTATE_NAMES=[]\nfor state in c18.iloc[:,2].values:\n if not (state in STATE_NAMES):\n STATE_NAMES.append(state)\nAGE_GROUPS=list(c18.iloc[1:10,4].values)\n# although it is a bit of manual work but it is worth the efforts\nAGE_GROUP_RANGES=[list(range(5,10)),list(range(10,15)),list(range(15,20)),list(range(20,25)),list(range(25,30)),list(range(30,50)),list(range(50,70)),list(range(70,100))+['100+'],['Age not stated']]", "_____no_output_____" ], [ "useful_data=[]\nfor i,state in enumerate(STATE_NAMES):\n for j,age_grp in enumerate(AGE_GROUPS):\n # this list is to get only the years in the particular age-group\n\n true_false_list=[]\n for single_year_age in c13.iloc[:,4].values:\n if single_year_age in AGE_GROUP_RANGES[j]:\n true_false_list.append(True)\n else:\n true_false_list.append(False)\n\n # here i is the state code\n male_pop=c13[(c13.loc[:,1]==i) & (true_false_list)].iloc[:,6].values.sum()\n female_pop=c13[(c13.loc[:,1]==i) & (true_false_list)].iloc[:,7].values.sum()\n \n # tri\n tri_male=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,9]\n tri_female=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,10]\n\n #bi\n bi_male=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,6] - tri_male\n bi_female=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,7] - tri_female\n\n #uni\n uni_male=male_pop-bi_male-tri_male\n uni_female=female_pop-bi_female-tri_female\n\n item={\n 'state-code':i,\n 'state-name':state,\n 'age-group':age_grp,\n 'age-group-male-pop':male_pop,\n 'age-group-female-pop':female_pop,\n 'tri-male-ratio':tri_male/male_pop,\n 'tri-female-ratio':tri_female/female_pop,\n 'bi-male-ratio':bi_male/male_pop,\n 'bi-female-ratio':bi_female/female_pop,\n 'uni-male-ratio':uni_male/male_pop,\n 'uni-female-ratio':uni_female/female_pop\n }\n\n useful_data.append(item)", "_____no_output_____" ], [ "df=pd.DataFrame(useful_data)", "_____no_output_____" ] ], [ [ "## age-analysis \n- get highest ratio age-group for a state and store it into csv file\n- above process can be repeated for all parts of the question", "_____no_output_____" ] ], [ [ "tri_list=[]\nbi_list=[]\nuni_list=[]\nfor i in range(36):\n male_values=df[df['state-code']==i].sort_values(by='tri-male-ratio',ascending=False).iloc[0,[2,5]].values\n female_values=df[df['state-code']==i].sort_values(by='tri-male-ratio',ascending=False).iloc[0,[2,6]].values\n tri_item={\n 'state/ut':i,\n 'age-group-males':male_values[0],\n 'ratio-males':male_values[1],\n 'age-group-females':female_values[0],\n 'ratio-females':female_values[1]\n }\n\n tri_list.append(tri_item)\n\n male_values=df[df['state-code']==i].sort_values(by='bi-male-ratio',ascending=False).iloc[0,[2,7]].values\n female_values=df[df['state-code']==i].sort_values(by='bi-male-ratio',ascending=False).iloc[0,[2,8]].values\n bi_item={\n 'state/ut':i,\n 'age-group-males':male_values[0],\n 'ratio-males':male_values[1],\n 'age-group-females':female_values[0],\n 'ratio-females':female_values[1]\n }\n\n bi_list.append(bi_item)\n\n male_values=df[df['state-code']==i].sort_values(by='uni-male-ratio',ascending=False).iloc[0,[2,9]].values\n female_values=df[df['state-code']==i].sort_values(by='uni-male-ratio',ascending=False).iloc[0,[2,10]].values\n uni_item={\n 'state/ut':i,\n 'age-group-males':male_values[0],\n 'ratio-males':male_values[1],\n 'age-group-females':female_values[0],\n 'ratio-females':female_values[1]\n }\n\n uni_list.append(uni_item)", "_____no_output_____" ] ], [ [ "- convert into pandas dataframes and store into CSVs", "_____no_output_____" ] ], [ [ "tri_df=pd.DataFrame(tri_list)\nbi_df=pd.DataFrame(bi_list)\nuni_df=pd.DataFrame(uni_list)", "_____no_output_____" ], [ "tri_df.to_csv('outputs/age-gender-a.csv',index=False)\nbi_df.to_csv('outputs/age-gender-b.csv',index=False)\nuni_df.to_csv('outputs/age-gender-c.csv',index=False)", "_____no_output_____" ] ], [ [ "## observations\n\n- in almost all states(and all cases) both highest ratio female and male age-groups are same.\n- interestingly in only one language case for all states '5-9' age group dominates, and it is also quite intutive; since at that early stage in life children only speak their mother toung only ", "_____no_output_____" ] ], [ [ "uni_df", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Neo4J graph example\n# author: Gressling, T\n# license: MIT License # code: github.com/gressling/examples\n# activity: single example # index: 25-2 ", "_____no_output_____" ], [ "# https://gist.github.com/korakot/328aaac51d78e589b4a176228e4bb06f\n# download 3.5.8 or neo4j-enterprise-4.0.0-alpha09mr02-unix\n!curl https://neo4j.com/artifact.php?name=neo4j-community-3.5.8-unix.tar.gz -v -o neo4j.tar.gz\n#!curl https://s3-eu-west-1.amazonaws.com/dist.neo4j.org/neo4j-community-3.5.8-unix.tar.gz?x-amz-security-token=IQoJb3JpZ2luX2VjEBgaCXVzLWVhc3QtMSJIMEYCIQC8JQ87qLW8MutNDC7kLf%2F8lCgTEeFw6XMHe0g6JGiLwQIhALrPjMot9j4eV1EiWsysYUamjICHutsaKG%2Fa%2B05ZJKD%2BKr0DCJD%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQABoMMTI4OTE2Njc5MzMwIgyekSOUHQOH4V1oebkqkQMSnlGj83iqgQ1X%2Bb9lDsjjfh%2FGggoIuvkn8RO9Ur3Ws24VznIHWrxQTECnTtQfsjhruOUbGJKv%2FlKBy9VU0aLu0zdrNcxeWZedOW09We0xVS4QTBipwW4i0UubWw%2FuDp1vAKPc1wLIq3vuvgflB4sXmTgvkQ%2FcT2%2BoIrvflqmSQ%2Fr9SB9Cqj9iACjxNQZrLs3qv2WgWxUNSsVjJYGXUx1yzx0ckCtxKYZ%2BzVBvqGuG1yULIodkGo4Kfbk5bh7s69gk8N4Gli7cQvYc9ajSFGg5IHXJU7%2BvRWeekX%2F2o7JlCRQogSNlW8kvv7o9ioD6Uj1mkOnR6rMsEv4Xo%2B2buKg7LqaPobmZwyjGnMBvZdndLXq37lAT7%2BP1i%2BVNCC7rak4Aqb3HtFMDZ%2F0nmdAitcKDWG1Am1mnaXiL3s6MZQ88SoU8h5RK0k01M%2FBxU9ZPwfi%2Bm8OAC%2Bgh6QyP9f7CPqSdI%2Fy8BSthxARcwWxl2ZwhHtUu7jqFf601aTu0iof%2FP2tH9oxty4kdH%2BI64qo7JCr9%2BzDx4OT9BTrqAfGlw5dReXwyh%2BSnYxW%2BB42cGs2JDcrFohn6UGdG3BtI%2FsjAFymH0vkCtXfN3irUPPzOoFzx216v%2F4GFfGebIpWqr85hT2f%2F28fck2XPbXiv%2BXSeffQdc8UkSL7dMHbquZ%2BmdOtCNlMhOWpnov5J7aICj9uY4AR60kNUSW3N4nra3CXjNEJWy%2B8ft49e6lnR9iKlVFxdwoXb1YAEx4egpctFcffoiaIEk2GinHjShAQgApOZFgOLe%2FDC9%2BnIwhxL7rSUfP7Ox%2FbEJF%2Br6VNYJddoD6D8xF2OVo%2FxZzv4M6eyw6Squ5r6i4LM7g%3D%3D&AWSAccessKeyId=ASIAR4BAINKRGKUIBRUS&Expires=1605973931&Signature=gzC025ItqNNdXpCJkGsm%2FvQt2WU%3D -o neo4j.tar.gz\n", "_____no_output_____" ], [ "# decompress and rename\n!tar -xf neo4j.tar.gz # or --strip-components=1\n!mv neo4j-community-3.5.8 nj", "_____no_output_____" ], [ "# disable password, and start server\n!sed -i '/#dbms.security.auth_enabled/s/^#//g' nj/conf/neo4j.conf\n!nj/bin/neo4j start", "_____no_output_____" ], [ "# from neo4j import GraphDatabase\n# !pip install py2neo", "_____no_output_____" ], [ "from py2neo import Graph\ngraph = Graph(\"bolt://localhost:7687\", auth=(\"neo4j\", \"password\"))\ngraph.delete_all()", "_____no_output_____" ], [ "# define the entities of the graph (nodes)\nfrom py2neo import Node", "_____no_output_____" ], [ "laboratory = Node(\"Laboratory\", name=\"Laboratory 1\")\nlab1 = Node(\"Person\", name=\"Peter\", employee_ID=2)\nlab2 = Node(\"Person\", name=\"Susan\", employee_ID=4)\nsample1 = Node(\"Sample\", name=\"A-12213\", weight=45.7)\nsample2 = Node(\"Sample\", name=\"B-33443\", weight=48.0)\n\n# shared sample between two experiments\nsample3 = Node(\"Sample\", name=\"AB-33443\", weight=24.3)\nexperiment1 = Node(\"Experiment\", name=\"Screening-45\")\nexperiment2 = Node(\"Experiment\", name=\"Screening/w/Sol\")", "_____no_output_____" ], [ "graph.create(laboratory | lab1 | lab2 | sample1 | sample2 | experiment1 |\nexperiment2)", "_____no_output_____" ], [ "# Define the relationships of the graph (edges)\nfrom py2neo import Relationship", "_____no_output_____" ], [ "graph.create(Relationship(lab1, \"works in\", laboratory))\ngraph.create(Relationship(lab2, \"works in\", laboratory))\ngraph.create(Relationship(lab1, \"performs\", sample1))\ngraph.create(Relationship(lab2, \"performs\", sample2))\ngraph.create(Relationship(lab2, \"performs\", sample3))\ngraph.create(Relationship(sample1, \"partof\", experiment1))\ngraph.create(Relationship(sample2, \"partof\", experiment2))\ngraph.create(Relationship(sample3, \"partof\", experiment2))\ngraph.create(Relationship(sample3, \"partof\", experiment1))", "_____no_output_____" ], [ "import neo4jupyter\nneo4jupyter.init_notebook_mode()\nneo4jupyter.draw(graph)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]