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__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
edge_convert.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import platform
import json
import shutil
import logging
import sys
from AionConfigManager import AionConfigManager
from sklearn.externals import joblib
class edgeformats:
def __init__(self,deploy_path):
self.deploy_path = deploy_path
self.edge_deploy_path = os.path.join(deploy_path,"edge")
os.mkdir(self.edge_deploy_path)
def converttoedgedeployment(self,saved_model,edge_format,xtrain,model_type,iterName,iterVersion,features,profiled_data_file):
if edge_format == 'onnx':
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
xtrain = xtrain[features]
initial_type = [('float_input', FloatTensorType([None, xtrain.shape[1]]))]
filename = os.path.join(self.deploy_path,saved_model)
loaded_model = joblib.load(filename)
onx = convert_sklearn(loaded_model, initial_types=initial_type)
onnx_filename = os.path.join(self.edge_deploy_path, model_type + '_' + iterName + '_' + iterVersion + '.onnx')
with open(onnx_filename, "wb") as f:
f.write(onx.SerializeToString())
self.createedgeruntimeFile(onnx_filename,profiled_data_file,features)
def createedgeruntimeFile(self,onnx_filename,datafilepath,features):
runtimefilecontent = ''
runtimefilecontent += 'import pandas'
runtimefilecontent += '\n'
runtimefilecontent += 'import numpy'
runtimefilecontent += '\n'
runtimefilecontent += 'import sys'
runtimefilecontent += '\n'
runtimefilecontent += 'import onnxruntime as rt'
runtimefilecontent += '\n'
runtimefilecontent += 'def onnx_runtime_validation():'
runtimefilecontent += '\n'
runtimefilecontent += ' modelfile = r"'+str(onnx_filename)+'"'
runtimefilecontent += '\n'
runtimefilecontent += ' datafile = r"'+str(datafilepath)+'"'
runtimefilecontent += '\n'
runtimefilecontent += ' dataframe = pandas.read_csv(datafile)'
runtimefilecontent += '\n'
runtimefilecontent += ' dataframe = dataframe['+str(features)+']'
runtimefilecontent += '\n'
runtimefilecontent += ' df = dataframe.head(8)'
runtimefilecontent += '\n'
runtimefilecontent += ' dataset = df.values'
runtimefilecontent += '\n'
runtimefilecontent += ' sess = rt.InferenceSession(modelfile)'
runtimefilecontent += '\n'
runtimefilecontent += ' input_name = sess.get_inputs()[0].name'
runtimefilecontent += '\n'
runtimefilecontent += ' label_name = sess.get_outputs()[0].name'
runtimefilecontent += '\n'
runtimefilecontent += ' inputsize=sess.get_inputs()[0].shape'
runtimefilecontent += '\n'
runtimefilecontent += ' XYZ = dataset[:,0:inputsize[1]].astype(float)'
runtimefilecontent += '\n'
runtimefilecontent += ' pred_onx = sess.run([label_name], {input_name: XYZ.astype(numpy.float32)[0:8]})[0]'
runtimefilecontent += '\n'
runtimefilecontent += ' df[\'predictions\'] = pred_onx'
runtimefilecontent += '\n'
runtimefilecontent += ' result = df.to_json(orient="records")'
runtimefilecontent += '\n'
runtimefilecontent += ' return(result)'
runtimefilecontent += '\n'
runtimefilecontent += 'if __name__ == "__main__":'
runtimefilecontent += '\n'
runtimefilecontent += ' output = onnx_runtime_validation()'
runtimefilecontent += '\n'
runtimefilecontent += ' print("predictions:",output)'
filename = os.path.join(self.edge_deploy_path,'onnxvalidation.py')
f = open(filename, "w")
f.write(str(runtimefilecontent))
f.close()
|
common.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
from pathlib import Path
from AION.prediction_package.imports import importModule
from AION.prediction_package import utility
from AION.prediction_package.utility import TAB_CHAR
from importlib.metadata import version
"""
This file provide the functionality which is common for most of the
problem types deployment.
"""
def main_code():
return """
class predict():
def __init__(self):
self.profiler = inputprofiler()
self.selector = selector()
self.trainer = trainer()
self.formatter = output_format()
def run(self, data):
try:
df = self._parse_data(data)
raw_df = df.copy()
df = self.profiler.run(df)
df = self.selector.run(df)
df = self.trainer.run(df)
output = self.formatter.run(raw_df, df)
print("predictions:",output)
return (output)
except Exception as e:
output = {"status":"FAIL","message":str(e).strip('"')}
print("predictions:",json.dumps(output))
return (json.dumps(output))
def _parse_data(self, data):
file_path = Path(data)
if file_path.suffix == ".tsv":
df = pd.read_csv(data,encoding='utf-8',sep='\\t',skipinitialspace = True,na_values=['-','?'])
elif file_path.suffix in [".csv", ".dat"]:
df=pd.read_csv(data,encoding='utf-8',skipinitialspace = True,na_values=['-','?'])
elif file_path.suffix in [".gz"] and file_path.stem.endswith('.csv'):
df=pd.read_csv(data,encoding='utf-8',skipinitialspace = True,na_values=['-','?'])
elif file_path.suffix == ".json":
with open(data,'r',encoding='utf-8') as f:
jsonData = json.load(f)
df = pd.json_normalize(jsonData)
else:
jsonData = json.loads(data)
df = pd.json_normalize(jsonData)
return df
import sys
if __name__ == "__main__":
output = predict().run(sys.argv[1])
"""
def profiler_code(params, indent=0):
"""
This will create the profiler file based on the config file.
separated file is created as profiler is required for input drift also.
"""
imported_modules = [
{'module': 'json', 'mod_from': None, 'mod_as': None},
{'module': 'scipy', 'mod_from': None, 'mod_as': None},
{'module': 'joblib', 'mod_from': None, 'mod_as': None},
{'module': 'numpy', 'mod_from': None, 'mod_as': 'np'},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
{'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}
]
importer = importModule()
utility.import_modules(importer, imported_modules)
code = """
class inputprofiler():
"""
init_code = """
def __init__(self):
"""
if params.get('text_features'):
imported_modules.append({'module':'importlib.util'})
init_code += """
# preprocessing
preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl'
if not preprocess_path.exists():
raise ValueError(f'Preprocess model file not found: {preprocess_path}')
self.profiler = joblib.load(preprocess_path)
"""
run_code = """
def run(self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
"""
if params.get('input_features_type'):
imported_modules.append({'module':'dtype','mod_from':'numpy'})
run_code += f"""
df = df.astype({params.get('input_features_type')})
"""
if params.get('word2num_features'):
imported_modules.append({'module':'w2n','mod_from':'word2number'})
run_code += f"""
def s2n(value):
try:
x=eval(value)
return x
except:
try:
return w2n.word_to_num(value)
except:
return np.nan
df[{params['word2num_features']}] = df[{params['word2num_features']}].apply(lambda x: s2n(x))"""
if params.get('unpreprocessed_columns'):
run_code += f"""
unpreprocessed_data = df['{params['unpreprocessed_columns'][0]}']
df.drop(['{params['unpreprocessed_columns'][0]}'], axis=1,inplace=True)
"""
if params.get('force_numeric_conv'):
run_code += f"""
df[{params['force_numeric_conv']}] = df[{params['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')"""
if params.get('conversion_method','').lower() == 'glove':
code_text, modules = __profiler_glove_code(params)
imported_modules.extend( modules)
init_code += code_text
elif params.get('conversion_method','').lower() == 'fasttext':
init_code += __profiler_fasttext_code(params)
run_code += __profiler_main_code(params)
if params.get('unpreprocessed_columns'):
run_code += f"""
df['{params.get('unpreprocessed_columns')[0]}'] = unpreprocessed_data
"""
utility.import_modules(importer, imported_modules)
import_code = importer.getCode()
return import_code + code + init_code + run_code
def __profiler_glove_code(params, indent=2):
modules = []
modules.append({'module':'load_pretrained','mod_from':'text.Embedding'})
modules.append({'module':'TextProcessing','mod_from':'text'})
code = """
model_path = TextProcessing.checkAndDownloadPretrainedModel('glove')
embed_size, pretrained_model = load_pretrained(model_path)
self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model)
"""
return code.replace('\n', '\n'+(indent * TAB_CHAR)), modules
def __profiler_fasttext_code(params, indent=2):
code = """
def get_pretrained_model_path():
try:
from AION.appbe.dataPath import DATA_DIR
modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing'
except:
modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing'
if not modelsPath.exists():
modelsPath.mkdir(parents=True, exist_ok=True)
return modelsPath
if not importlib.util.find_spec('fasttext'):
raise ValueError('fastText not installed')
else:
import os
import fasttext
import fasttext.util
cwd = os.getcwd()
os.chdir(get_pretrained_model_path())
fasttext.util.download_model('en', if_exists='ignore')
pretrained_model = fasttext.load_model('cc.en.300.bin')
os.chdir(cwd)
self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model)
self.profiler.set_params(text_process__vectorizer__external_model_type = 'binary')
"""
return code.replace('\n', '\n'+(indent * TAB_CHAR))
def __profiler_main_code(params, indent=2):
code = f"""
df = self.profiler.transform(df)
columns = {params['output_features']}
if isinstance(df, scipy.sparse.spmatrix):
df = pd.DataFrame(df.toarray(), columns=columns)
else:
df = pd.DataFrame(df, columns=columns)
return df
"""
return code.replace('\n', '\n'+(indent * TAB_CHAR))
def feature_selector_code( params, indent=0):
modules = [
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'}
]
code = """
class selector():
# this class
def __init__(self):
pass
def run(self, df):"""
code +=f"""
return df[{params['output_features']}]
"""
return code, modules
def feature_reducer_code( params, indent=0):
modules = [
{'module': 'joblib', 'mod_from': None, 'mod_as': None},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
{'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}
]
code = f"""
class selector():
def __init__(self):
reducer_file = (Path(__file__).parent/"model")/"{params['reducer_file']}"
if not reducer_file.exists():
raise ValueError(f'Failed to load Feature Engineering model file: {{reducer_file}}')
self.model = joblib.load(reducer_file)
def run(self, df):
reducer_input = {params['input_features']}
reducer_output = {params['output_features']}
df = self.model.transform(df[reducer_input])
return pd.DataFrame(df,columns=reducer_output)
"""
if indent:
code = code.replace('\n', '\n'+(indent * TAB_CHAR))
return code, modules
def create_feature_list(config=None, target_feature=None, deploy_path=None):
featurelist = []
if 'profiler' in config:
if 'input_features_type' in config['profiler']:
input_features = config['profiler']['input_features_type']
for x in input_features:
featurelt={}
featurelt['feature'] = x
if x == target_feature:
featurelt['Type'] = 'Target'
else:
if input_features[x] in ['int','int64','float','float64']:
featurelt['Type'] = 'Numeric'
elif input_features[x] == 'object':
featurelt['Type'] = 'Text'
elif input_features[x] == 'category':
featurelt['Type'] = 'Category'
else:
featurelt['Type'] = 'Unknown'
featurelist.append(featurelt)
featurefile = f"""
import json
def getfeatures():
try:
features = {featurelist}
outputjson = {{"status":"SUCCESS","features":features}}
output = json.dumps(outputjson)
print("Features:",output)
return(output)
except Exception as e:
output = {{"status":"FAIL","message":str(e).strip(\'"\')}}
print("Features:",json.dumps(output))
return (json.dumps(output))
if __name__ == "__main__":
output = getfeatures()
"""
with open( deploy_path/'featureslist.py', 'wb') as f:
f.write( str(featurefile).encode('utf8'))
def requirement_file(deploy_path,model,textFeatures,learner_type='ML'):
modules = ['pandas','numpy','alibi','matplotlib','joblib','shap','ipython','category_encoders','scikit-learn','word2number','flask_restful','evidently','Flask-Cors']
requires = ''
for mod in modules:
requires += f"{mod}=={version(mod)}\n"
if len(textFeatures) > 0:
tmodules = ['spacy','nltk','textblob','demoji','beautifulsoup4','text-unidecode','pyspellchecker','contractions','protobuf']
for mod in tmodules:
requires += f"{mod}=={version(mod)}\n"
if model == 'Extreme Gradient Boosting (XGBoost)':
mmodules = ['xgboost']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model == 'Light Gradient Boosting (LightGBM)':
mmodules = ['lightgbm']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model == 'Categorical Boosting (CatBoost)':
mmodules = ['catboost']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'arima':
mmodules = ['pmdarima']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'fbprophet':
mmodules = ['prophet']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'lstm' or model.lower() == 'mlp' or learner_type =='DL':
mmodules = ['tensorflow']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() in ['cox', 'kaplanmeierfitter']: #bug 12833
mmodules = ['lifelines']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'sentencetransformer': #bug 12833
mmodules = ['sentence_transformers']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
with open( deploy_path/'requirements.txt', 'wb') as f:
f.write(str(requires).encode('utf8'))
def create_readme_file(deploy_path,modelfile,features):
data = json.dumps([{x:x+'_value'} for x in features])
backslash_data = data.replace('"', '\\"')
content = f"""
========== Files Structures ==========
{modelfile} ------ Trained Model
aion_prediction.py --> Python package entry point
script/inputprofiler.py --> Profiling like FillNA and Category to Numeric
========== How to call the model ==========
============== From Windows Terminal ==========
python aion_prediction.py "{backslash_data}"
============== From Linux Terminal ==========
python aion_prediction.py "{data}"
============== Output ==========
{{"status":"SUCCESS","data":[{{"Data1":"Value","prediction":"Value"}}]}} ## for single Row/Record
{{"status":"SUCCESS","data":[{{"Data1":"Value","prediction":"Value"}},{{"Data1":"Value","prediction":"Value"}}]}} ## For Multiple Row/Record
{{"status":"ERROR","message":"description"}} ## In Case Exception or Error
"""
filename = deploy_path/'readme.txt'
with open(filename, 'w') as f:
f.write(content)
def create_util_folder(deploy_path):
import tarfile
ext_path = Path(__file__).parent.parent/'utilities'
for x in ext_path.iterdir():
if x.suffix == '.tar':
if x.name not in ['scikit_surprise-1.1.1.dist-info.tar','surprise.tar']:
my_tar = tarfile.open(x)
my_tar.extractall(deploy_path)
my_tar.close()
|
model_deploy.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os,sys
import platform
import json
import shutil
import logging
from pathlib import Path
from prediction_package import production
from prediction_package import prediction_transformation as cs
class DeploymentManager:
def __init__(self):
self.requirementfile=''
self.modelfile=''
self.s2i_environmentfile=''
self.selectorfile=''
self.profilerfile=''
self.readmepackagename=''
self.pythonpackage=''
self.log = logging.getLogger('eion')
def include_import_file(self,learner_type,method,scoreParam,model_type,model):
if((learner_type == 'DL') or (learner_type == 'TextDL')):
self.modelfile += 'from tensorflow.keras.models import load_model'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras import backend as K'
self.modelfile += '\n'
self.modelfile += 'import tensorflow as tf'
self.modelfile += '\n'
if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and model.lower() == 'autoencoder'):
self.modelfile += 'import joblib'
self.modelfile += '\n'
self.modelfile += 'import os'
self.modelfile += '\n'
self.modelfile += 'import pandas as pd'
self.modelfile += '\n'
self.modelfile += 'import numpy as np'
self.modelfile += '\n'
self.modelfile += 'from pathlib import Path'
self.modelfile += '\n'
self.modelfile += 'import tensorflow as tf'
self.modelfile += '\n'
self.modelfile += 'from keras.models import load_model'
self.modelfile += '\n'
self.modelfile += 'import warnings'
self.modelfile += '\n'
self.modelfile += 'from sklearn.preprocessing import StandardScaler'
self.modelfile += '\n'
self.modelfile += 'warnings.filterwarnings("ignore")'
self.modelfile += '\n'
if(learner_type == 'ImageClassification'):
self.modelfile += 'import os'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.models import Sequential'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.layers import Dense, Dropout, Flatten'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.preprocessing import image'
self.modelfile += '\n'
self.modelfile += 'import numpy as np'
self.modelfile += '\n'
self.modelfile += 'import tensorflow as tf'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.layers import Input'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.models import Model'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.optimizers import Adam'
self.modelfile += '\n'
self.modelfile += 'import cv2'
self.modelfile += '\n'
if(learner_type == 'objectDetection'):
self.modelfile += 'import os\n'
self.modelfile += 'from object_detection.utils import label_map_util\n'
self.modelfile += 'from object_detection.utils import config_util\n'
self.modelfile += 'from object_detection.utils import visualization_utils as viz_utils\n'
self.modelfile += 'from object_detection.builders import model_builder\n'
self.modelfile += 'import tensorflow as tf\n'
self.modelfile += 'import numpy as np\n'
self.modelfile += 'from PIL import Image\n'
self.modelfile += 'import matplotlib.pyplot as plt\n'
self.modelfile += 'import pandas as pd\n'
self.modelfile += 'from pathlib import Path\n'
if(learner_type == 'Text Similarity'):
self.modelfile += 'from tensorflow.keras.models import load_model'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras import backend as K'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.preprocessing.sequence import pad_sequences'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras.preprocessing.text import Tokenizer'
self.modelfile += '\n'
self.modelfile += 'import tensorflow as tf'
self.modelfile += '\n'
if(model == 'Neural Architecture Search'):
self.modelfile += 'from tensorflow.keras.models import load_model'
self.modelfile += '\n'
self.modelfile += 'from tensorflow.keras import backend as K'
self.modelfile += '\n'
self.modelfile += 'import tensorflow as tf'
self.modelfile += '\n'
self.modelfile += 'import joblib'
self.modelfile += '\n'
self.modelfile += 'import os'
self.modelfile += '\n'
self.modelfile += 'import pandas as pd'
self.modelfile += '\n'
self.modelfile += 'from sklearn.decomposition import LatentDirichletAllocation\n'
self.modelfile += 'import numpy as np\n'
self.modelfile += 'from pathlib import Path\n'
if model.lower() == 'deep q network' or model.lower() == 'dueling deep q network':
self.modelfile += 'from tensorflow import constant'
self.modelfile += '\n'
self.modelfile += 'from tf_agents.trajectories import time_step'
self.modelfile += '\n'
self.requirementfile += 'tensorflow==2.5.0'
if model.lower() == 'lstm' or model.lower() == 'mlp':
self.modelfile += 'from tensorflow.keras.models import load_model'
self.modelfile += '\n'
self.requirementfile += 'tensorflow==2.5.0'
if(learner_type == 'Text Similarity'):
self.modelfile += 'def cosine_distance(vests):'
self.modelfile += '\n';
self.modelfile += ' x, y = vests'
self.modelfile += '\n';
self.modelfile += ' x = K.l2_normalize(x, axis=-1)'
self.modelfile += '\n';
self.modelfile += ' y = K.l2_normalize(y, axis=-1)'
self.modelfile += '\n';
self.modelfile += ' return -K.mean(x * y, axis=-1, keepdims=True)'
self.modelfile += '\n';
self.modelfile += 'def cos_dist_output_shape(shapes):'
self.modelfile += '\n';
self.modelfile += ' shape1, shape2 = shapes'
self.modelfile += '\n';
self.modelfile += ' return (shape1[0],1)'
self.modelfile += '\n';
if(learner_type == 'TextDL' or learner_type == 'DL'):
if(scoreParam.lower() == 'recall' or scoreParam.lower() == 'f1_score'):
self.modelfile += 'def recall_m(y_true, y_pred):'
self.modelfile += '\n';
self.modelfile += ' true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))'
self.modelfile += '\n';
self.modelfile += ' possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))'
self.modelfile += '\n';
self.modelfile += ' recall = true_positives / (possible_positives + K.epsilon())'
self.modelfile += '\n';
self.modelfile += ' return recall'
self.modelfile += '\n';
if(scoreParam.lower() == 'precision' or scoreParam.lower() == 'f1_score'):
self.modelfile += 'def precision_m(y_true, y_pred):'
self.modelfile += '\n';
self.modelfile += ' true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))'
self.modelfile += '\n';
self.modelfile += ' predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))'
self.modelfile += '\n';
self.modelfile += ' precision = true_positives / (predicted_positives + K.epsilon())'
self.modelfile += '\n';
self.modelfile += ' return precision'
self.modelfile += '\n';
if(scoreParam.lower() == 'f1_score'):
self.modelfile += 'def f1_m(y_true, y_pred):'
self.modelfile += '\n';
self.modelfile += ' precision = precision_m(y_true, y_pred)'
self.modelfile += '\n';
self.modelfile += ' recall = recall_m(y_true, y_pred)'
self.modelfile += '\n';
self.modelfile += ' return 2*((precision*recall)/(precision+recall+K.epsilon()))'
self.modelfile += '\n';
if(scoreParam.lower() == 'rmse'):
self.modelfile += 'def rmse_m(y_true, y_pred):'
self.modelfile += '\n';
self.modelfile += ' return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))'
self.modelfile += '\n';
if(scoreParam.lower() =='r2'):
self.modelfile += 'def r_square(y_true, y_pred):'
self.modelfile += '\n';
self.modelfile += ' SS_res = K.sum(K.square(y_true-y_pred))'
self.modelfile += '\n';
self.modelfile += ' SS_tot = K.sum(K.square(y_true-K.mean(y_true)))'
self.modelfile += '\n';
self.modelfile += ' return (1 - SS_res/(SS_tot+K.epsilon()))'
self.modelfile += '\n';
if(learner_type.lower() in ['similarityidentification','contextualsearch']):
self.modelfile += 'from pathlib import Path\n'
if model_type == 'BM25':
self.modelfile += 'from rank_bm25 import BM25Okapi\n'
elif scoreParam == 'VectorDB Cosine':
self.modelfile += 'import chromadb\n'
else:
self.modelfile += 'from sklearn.metrics.pairwise import cosine_similarity\n'
self.pythonpackage += '========== Python Packags Requires ========='
self.pythonpackage += '\n'
self.pythonpackage += 'scikit-learn'
self.pythonpackage += '\n'
self.pythonpackage += 'scipy'
self.pythonpackage += '\n'
self.pythonpackage += 'numpy'
self.pythonpackage += '\n'
if((learner_type == 'DL') or (learner_type =='TextDL')):
self.modelfile += 'import numpy as np'
self.modelfile += '\n'
self.requirementfile += 'scikit-learn==0.21.3'
self.requirementfile += '\n'
self.requirementfile += 'scipy==1.3.3'
self.requirementfile += '\n'
self.requirementfile += 'numpy==1.17.4'
self.requirementfile += '\n'
if(learner_type == 'TextML'):
self.requirementfile += 'spacy==2.2.3'
self.requirementfile += '\n'
self.requirementfile += 'https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz'
self.requirementfile += '\n'
if(learner_type == 'DL' or learner_type == 'TextDL'):
self.requirementfile += 'keras==2.3.1'
self.requirementfile += '\n'
self.requirementfile += 'tensorflow==2.0.0b1'
self.requirementfile += '\n'
if(learner_type == 'RecommenderSystem'):
self.requirementfile += 'surprise'
self.requirementfile += '\n'
if(method == 'package'):
self.modelfile += 'import surprise'
self.modelfile += '\n'
self.modelfile += 'import statsmodels'
self.modelfile += '\n'
self.requirementfile += 'statsmodels==0.10.2'
self.requirementfile += '\n'
def crate_readme_file(self,deploy_path,modelfile,features,method,single_file=False):
self.readme='========== Files Structures =========='
self.readme+='\n'
self.readme+=modelfile+' ------ Trained Model'
self.readme+='\n'
self.readme+='aion_prediction.py --> Python package entry point'
self.readme+='\n'
if not single_file:
self.readme+='script/inputprofiler.py --> Profiling like FillNA and Category to Numeric'
self.readme+='\n'
self.readme+='script/selector.py --> Feature Selection'
self.readme+='\n'
self.readme+='script/trained_model.py --> Read the model file and call the prediction'
self.readme+='\n'
self.readme+='script/output_format.py --> Output formatter file'
self.readme+='\n'
self.readme+= self.pythonpackage
self.readme+= '========== How to call the model =========='
self.readme+='\n'
self.readme+= '============== From Windows Terminal =========='
self.readme+='\n'
if method == 'optimus_package':
self.readme += 'python aion_prediction.py filename.json'
self.readme +='\n'
self.readme += '========== Embedded Methods =========='
self.readme +='\n'
self.readme += 'Function Name: predict_from_json - When input is Json Data'
self.readme +='\n'
self.readme += 'Function Name: predict_from_file - When input is Json File'
self.readme +='\n'
else:
callpython = 'python aion_prediction.py "[{'
for x in features:
if(callpython != 'python prediction.py "[{'):
callpython += ','
callpython += '\\\"'+str(x)+'\\\"'+':'+'\\\"'+str(x)+'_value'+'\\\"'
callpython += '}]"'
self.readme += callpython
self.readme+='\n'
self.readme+= '============== From Linux Terminal =========='
self.readme+='\n'
callpython = 'python aion_prediction.py \'[{'
temp =callpython
for x in features:
if(callpython != temp):
callpython += ','
callpython += '"'+str(x)+'"'+':'+'"'+str(x)+'_value'+'"'
callpython += '}]\''
self.readme += callpython
self.readme+='\n'
self.readme+= '============== Output =========='
self.readme+='\n'
self.readme+= '{"status":"SUCCESS","data":[{"Data1":"Value","prediction":"Value"}]}' ## For Single Row/Record'
self.readme+='\n'
self.readme+= '{"status":"SUCCESS","data":[{"Data1":"Value","prediction":"Value"},{"Data1":"Value","prediction":"Value"}]} ## For Multiple Row/Record'
self.readme+='\n'
self.readme+= '{"status":"ERROR","message":"description"} ## In Case Exception or Error'
self.readme+='\n'
#print(self.readme)
filename = os.path.join(deploy_path,'readme.txt')
self.log.info('-------> Readme File Location: '+filename)
f = open(filename, "wb")
f.write(str(self.readme).encode('utf8'))
f.close()
def create_class(self,classname):
#self.modelfile += 'class '+classname+'(object):'
self.modelfile += 'class trained_model(object):'
self.modelfile += '\n'
def profiler_code(self,model_type,model,output_columns, features, text_feature,wordToNumericFeatures=[], deploy={},datetimeFeature=''):
profiler = deploy.get('profiler',{})
if isinstance(features, str):
features = features.split(',')
code = f"""
import scipy
import joblib
import numpy as np
import pandas as pd
from pathlib import Path
"""
if text_feature:
code += """
import importlib.util\n"""
if wordToNumericFeatures:
code += """
from word2number import w2n
def s2n(value):
try:
x=eval(value)
return x
except:
try:
return w2n.word_to_num(value)
except:
return np.nan
"""
if 'code' in deploy.get('preprocess',{}).keys():
code += deploy['preprocess']['code']
if profiler.get('conversion_method','').lower() == 'glove':
code += """
class inputprofiler(object):
def __init__(self):
self.model = None
preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl'
if preprocess_path.exists():
self.model = joblib.load(preprocess_path)
from text.Embedding import load_pretrained
from text import TextProcessing
model_path = TextProcessing.checkAndDownloadPretrainedModel('glove')
embed_size, loaded_model = load_pretrained(model_path)
self.model.set_params(text_process__vectorizer__external_model = loaded_model)
else:
raise ValueError('Preprocess model not found')
def apply_profiler(self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
"""
elif profiler.get('conversion_method','').lower() == 'fasttext':
code += """
def get_pretrained_model_path():
try:
from AION.appbe.dataPath import DATA_DIR
modelsPath = Path(DATA_DIR)/'PreTrainedModels'/'TextProcessing'
except:
modelsPath = Path('aion')/'PreTrainedModels'/'TextProcessing'
if not modelsPath.exists():
modelsPath.mkdir(parents=True, exist_ok=True)
return modelsPath
class inputprofiler(object):
def __init__(self):
self.model = None
preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl'
if preprocess_path.exists():
self.model = joblib.load(preprocess_path)
if not importlib.util.find_spec('fasttext'):
raise ValueError('fastText not installed')
else:
import os
import fasttext
import fasttext.util
cwd = os.getcwd()
os.chdir(get_pretrained_model_path())
fasttext.util.download_model('en', if_exists='ignore')
loaded_model = fasttext.load_model('cc.en.300.bin')
os.chdir(cwd)
self.model.set_params(text_process__vectorizer__external_model = loaded_model)
self.model.set_params(text_process__vectorizer__external_model_type = 'binary')
else:
raise ValueError('Preprocess model not found')
def apply_profiler(self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
"""
else:
code += """
class inputprofiler(object):
def __init__(self):
self.model = None
preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl'
if preprocess_path.exists():
self.model = joblib.load(preprocess_path)
else:
raise ValueError('Preprocess model not found')
def apply_profiler(self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
"""
if 'code' in deploy.get('preprocess',{}).keys():
code += " df = preprocess( df)\n"
if wordToNumericFeatures:
code += f"""
df[{wordToNumericFeatures}] = df[{wordToNumericFeatures}].apply(lambda x: s2n(x))"""
if profiler.get('unpreprocessed_columns'):
code += f"""
unpreprocessed_data = df['{profiler['unpreprocessed_columns'][0]}']
df.drop(['{profiler['unpreprocessed_columns'][0]}'], axis=1,inplace=True)
"""
if profiler.get('force_numeric_conv'):
code += f"""
df[{profiler['force_numeric_conv']}] = df[{profiler['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')
"""
code += f"""
if self.model:
df = self.model.transform(df)"""
code += f"""
columns = {output_columns}
if isinstance(df, scipy.sparse.spmatrix):
df = pd.DataFrame(df.toarray(), columns=columns)
else:
df = pd.DataFrame(df, columns=columns)
"""
##The below if loop for avoiding unpreprocessed column variable storing which is not used for anomaly detection
if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na':
pass
else:
if profiler.get('unpreprocessed_columns'):
code += f"""
df['{profiler.get('unpreprocessed_columns')[0]}'] = unpreprocessed_data
"""
if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na':
##This below set_index is wrong, because we drop datetimefeature before profiling and doing set_index. So commented now.
# code += f"""
# df.set_index('{datetimeFeature}', inplace=True)"""
code += f"""
return(df,'{datetimeFeature}')\n"""
else:
code += f"""
return(df)"""
return code
def no_profiling_code(self, features):
if isinstance(features, str):
features = features.split(',')
return f"""
import pandas as pd
import numpy as np
class inputprofiler(object):
def apply_profiler(self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
return df[{features}]
"""
def create_profiler_file(self,learner_type,deploy_path,profiler,features,numericToLabel_json,column_merge_flag,text_features,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder,model, config=None,datetimeFeature=''):
filename = str(Path(deploy_path)/'script'/'inputprofiler.py')
if 'profiler' in config:
if model_type == 'BM25':
code = self.profiler_code(model_type,model,['tokenize'],features, text_features,config['profiler']['word2num_features'])
elif model == 'KaplanMeierFitter':
code = self.no_profiling_code(features)
elif model.lower() in ['arima', 'fbprophet']: #task 12627
code = self.no_profiling_code('noofforecasts')
else:
code = self.profiler_code(model_type,model,config['profiler']['output_features'],features, text_features,config['profiler']['word2num_features'],config,datetimeFeature)
if code:
with open(filename,'w',encoding="utf-8") as f:
f.write(code)
self.log.info('-------> Profiler File Location :'+filename)
return
self.profilerfile += 'import pandas as pd'
self.profilerfile += '\n'
self.profilerfile += 'import joblib'
self.profilerfile += '\n'
self.profilerfile += 'import os'
self.profilerfile += '\n'
self.profilerfile += 'from word2number import w2n'
self.profilerfile += '\n'
self.profilerfile += 'import numpy as np'
self.profilerfile += '\nfrom pathlib import Path\n'
#print("1")
#print(profiler)
if(learner_type == 'Text Similarity' or len(text_features) > 0):
self.profilerfile += 'from text import TextProcessing'
self.profilerfile += '\n'
self.profilerfile += 'def textCleaning(textCorpus):'
self.profilerfile += '\n'
self.profilerfile += ' textProcessor = TextProcessing.TextProcessing()'
self.profilerfile += '\n'
self.profilerfile += ' textCorpus = textProcessor.transform(textCorpus)'
self.profilerfile += '\n'
self.profilerfile += ' return(textCorpus)'
self.profilerfile += '\n'
self.profilerfile += 'class inputprofiler(object):'
self.profilerfile += '\n'
self.profilerfile += ' def s2n(self,value):'
self.profilerfile += '\n'
self.profilerfile += ' try:'
self.profilerfile += '\n'
self.profilerfile += ' x=eval(value)'
self.profilerfile += '\n'
self.profilerfile += ' return x'
self.profilerfile += '\n'
self.profilerfile += ' except:'
self.profilerfile += '\n'
self.profilerfile += ' try:'
self.profilerfile += '\n'
self.profilerfile += ' return w2n.word_to_num(value)'
self.profilerfile += '\n'
self.profilerfile += ' except:'
self.profilerfile += '\n'
self.profilerfile += ' return np.nan '
self.profilerfile += '\n'
self.profilerfile += ' def apply_profiler(self,df):'
self.profilerfile += '\n'
if(len(wordToNumericFeatures) > 0):
for w2nFeature in wordToNumericFeatures:
if w2nFeature not in features:
continue
self.profilerfile += " df['"+w2nFeature+"']=df['"+w2nFeature+"'].apply(lambda x: self.s2n(x))"
self.profilerfile += '\n'
self.profilerfile += " df = df.replace(r'^\s*$', np.NaN, regex=True)"
self.profilerfile += '\n'
self.profilerfile += ' try:'
self.profilerfile += '\n'
self.profilerfile += ' df.dropna(how="all",axis=1,inplace=True)'
self.profilerfile += '\n'
self.profilerfile += ' except:'
self.profilerfile += '\n'
self.profilerfile += ' df.fillna(0)'
self.profilerfile += '\n'
if model_type.lower() != 'timeseriesforecasting': #task 11997
self.profilerfile += ' preprocess_path = Path(__file__).parent.parent/"model"/"preprocess_pipe.pkl"\n'
self.profilerfile += ' if preprocess_path.exists():\n'
self.profilerfile += ' model = joblib.load(preprocess_path)\n'
if model_type.lower()=='anomaly_detection' and model.lower() == 'autoencoder':
self.profilerfile += f" df[{features}] = model.transform(df[{features}])\n"
else:
self.profilerfile += f" df = model.transform(df)\n"
if 'operation' in profiler:
y = profiler['operation']
for action in y:
feature = action['feature']
#if feature not in features:
# continue
operation = action['Action']
if(operation == 'Drop'):
self.profilerfile += " if '"+feature+"' in df.columns:"
self.profilerfile += '\n'
self.profilerfile += " df.drop(columns=['"+feature+"'],inplace = True)"
self.profilerfile += '\n'
if(operation == 'FillValue'):
self.profilerfile += " if '"+feature+"' in df.columns:"
self.profilerfile += '\n'
fvalue = action['value']
self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].fillna(value='"+fvalue+"')"
self.profilerfile += '\n'
if(operation == 'Encoder'):
value = action['value']
value = value.replace("\n", "\\n")
self.profilerfile += " if '"+feature+"' in df.columns:"
self.profilerfile += '\n'
self.profilerfile += " le_dict="+str(value)
self.profilerfile += '\n'
self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].apply(lambda x: le_dict.get(x,-1))"
self.profilerfile += '\n'
self.profilerfile += " if -1 in df['"+feature+"'].values:"
self.profilerfile += '\n'
self.profilerfile += " raise Exception('Category value of "+feature+" not present in training data')"
self.profilerfile += '\n'
if 'conversion' in profiler:
catergoryConverton = profiler['conversion']
#print(catergoryConverton)
if (catergoryConverton['categoryEncoding'].lower() in ['targetencoding','onehotencoding']) and ('features' in catergoryConverton):
self.profilerfile += " encoder = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','categoryEncoder.pkl'))"
self.profilerfile += '\n'
self.profilerfile += " CategoryFeatures = "+str(catergoryConverton['features'])
self.profilerfile += '\n'
if catergoryConverton['categoryEncoding'].lower() == 'onehotencoding':
self.profilerfile += " transformed_data = encoder.transform(df[CategoryFeatures]).toarray()"
self.profilerfile += '\n'
self.profilerfile += " feature_labels = encoder.get_feature_names(CategoryFeatures)"
self.profilerfile += '\n'
self.profilerfile += " transformed_data = pd.DataFrame(transformed_data,columns=feature_labels) "
self.profilerfile += '\n'
else:
self.profilerfile += " transformed_data = encoder.transform(df[CategoryFeatures])"
self.profilerfile += '\n'
self.profilerfile += " dataColumns=list(df.columns)"
self.profilerfile += '\n'
self.profilerfile += " nonNormFeatures=list(set(dataColumns) - set(CategoryFeatures))"
self.profilerfile += '\n'
self.profilerfile += " dataArray=df[nonNormFeatures]"
self.profilerfile += '\n'
self.profilerfile += " df = pd.concat([dataArray, transformed_data],axis=1)"
self.profilerfile += '\n'
y = json.loads(numericToLabel_json)
for feature_details in y:
feature = feature_details['feature']
if feature not in features:
continue
label = feature_details['Labels']
bins = feature_details['Bins']
self.profilerfile += " if '"+feature+"' in df.columns:"
self.profilerfile += '\n'
self.profilerfile += " cut_bins="+str(bins)
self.profilerfile += '\n'
self.profilerfile += " cut_labels="+str(label)
self.profilerfile += '\n'
self.profilerfile += " df['"+feature+"'] = pd.cut(df['"+feature+"'],bins=cut_bins,labels=cut_labels)"
self.profilerfile += '\n'
self.profilerfile += " df['"+feature+"'] = df['"+feature+"'].fillna(value=0)"
self.profilerfile += '\n'
if(len(text_features) > 0):
if(len(text_features) > 1):
self.profilerfile += ' merge_features = '+str(text_features)
self.profilerfile += '\n'
self.profilerfile += ' df[\'combined\'] = df[merge_features].apply(lambda row: \' \'.join(row.values.astype(str)), axis=1)'
self.profilerfile += '\n'
self.profilerfile += ' features = [\'combined\']'
self.profilerfile += '\n'
else:
self.profilerfile += " features = "+str(text_features)
self.profilerfile += '\n'
if model_type == 'BM25':
self.profilerfile += """\
df_text = df[features[0]]
pipe = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{preprocessing_pipe}'))
df['tokenize'] = pipe.transform(df_text)\n""".format(preprocessing_pipe=preprocessing_pipe)
elif conversion_method == 'sentenceTransformer':
self.profilerfile += """\
df_text = df[features[0]]
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(\'sentence-transformers/msmarco-distilroberta-base-v2\')
df_vect = model.encode(df_text)
for empCol in {text_features}:
df = df.drop(columns=[empCol])
if isinstance(df_vect, np.ndarray):
df1 = pd.DataFrame(df_vect)
else:
df1 = pd.DataFrame(df_vect.toarray(),columns = pipe.named_steps[\'vectorizer\'].get_feature_names())
df1 = df1.add_suffix(\'_vect\')
df = pd.concat([df, df1],axis=1)\n""".format(preprocessing_pipe=preprocessing_pipe, text_features=text_features)
else:
self.profilerfile += """\
df_text = df[features[0]]
pipe = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','{preprocessing_pipe}'))
df_vect=pipe.transform(df_text)
for empCol in {text_features}:
df = df.drop(columns=[empCol])
if isinstance(df_vect, np.ndarray):
df1 = pd.DataFrame(df_vect)
else:
df1 = pd.DataFrame(df_vect.toarray(),columns = pipe.named_steps[\'vectorizer\'].get_feature_names())
df1 = df1.add_suffix(\'_vect\')
df = pd.concat([df, df1],axis=1)\n""".format(preprocessing_pipe=preprocessing_pipe, text_features=text_features)
if(learner_type == 'Text Similarity'):
self.profilerfile += ' df[\''+firstDocFeature+'\'] = textCleaning(df[\''+firstDocFeature+'\'])'
self.profilerfile += '\n'
self.profilerfile += ' df[\''+secondDocFeature+'\'] = textCleaning(df[\''+secondDocFeature+'\'])'
self.profilerfile += '\n'
if len(normFeatures) > 0 and normalizer != '':
self.profilerfile += " normFeatures = "+str(normFeatures)
self.profilerfile += '\n'
self.profilerfile += ' normalizepipe = joblib.load(os.path.join(os.path.dirname(os.path.abspath(__file__)),\'..\',\'model\',\''+normalizer+'\'))'
self.profilerfile += '\n'
self.profilerfile += ' dataColumns=list(df.columns)'
self.profilerfile += '\n'
self.profilerfile += ' nonNormFeatures=list(set(dataColumns) - set(normFeatures))'
self.profilerfile += '\n'
self.profilerfile += ' dataframe=df[normFeatures]'
self.profilerfile += '\n'
self.profilerfile += ' transDf = normalizepipe.transform(dataframe)'
self.profilerfile += '\n'
self.profilerfile += ' nontransDF=df[nonNormFeatures].values'
self.profilerfile += '\n'
self.profilerfile += ' dataColumns=normFeatures+nonNormFeatures'
self.profilerfile += '\n'
self.profilerfile += ' scaledDf = pd.DataFrame(np.hstack((transDf, nontransDF)),columns=dataColumns)'
self.profilerfile += '\n'
self.profilerfile += ' df=scaledDf'
self.profilerfile += '\n'
else:
self.profilerfile += ' df=df.dropna()\n'
self.profilerfile += ' return(df)'
filename = os.path.join(deploy_path,'script','inputprofiler.py')
self.log.info('-------> Profiler File Location :'+filename)
f = open(filename, "w",encoding="utf-8")
f.write(str(self.profilerfile))
f.close()
def isEnglish(self, s):
try:
s.encode(encoding='utf-8').decode('ascii')
except UnicodeDecodeError:
return False
else:
return True
def create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config=None):
cs.create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config)
def create_init_function_for_regression(self,modelfile):
self.modelfile += ' def __init__(self):'
self.modelfile += '\n'
self.modelfile += " self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
def create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig):
cs.create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig)
def create_predict_proba(self,learner_type,method):
self.modelfile += ' def predict(self,X,features_names):'
self.modelfile += '\n'
self.modelfile += ' return self.model.predict_proba(X)'
def create_forcast(self,method,no_of_prediction):
self.modelfile += ' def predict(self,X,features_names):'
self.modelfile += '\n'
self.modelfile += ' no_of_prediction = '+str(no_of_prediction)
self.modelfile += '\n'
self.modelfile += ' lag_order = self.model.k_ar'
self.modelfile += '\n'
self.modelfile += ' return self.model.forecast(X.values[-lag_order:],steps=no_of_prediction)'
def create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam=None):
scorePrm = scoreParam
cs.create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scorePrm)
def save_model_deploy(self,outputfolder,modelname):
#filename = outputfolder+modelname+'.py'
filename = os.path.join(outputfolder,'script','trained_model.py')
self.log.info('-------> Model File Location :'+filename)
f = open(filename, "w",encoding="utf-8")
f.write(str(self.modelfile))
f.close()
def create_TextCleaner(self,outputfolder):
profilerPath = os.path.join(outputfolder,'profiler')
try:
os.makedirs(profilerPath)
except OSError:
self.log.info("ProfilePath Folder Already Exists")
try:
textprofileFileLocation = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','profiler','textDataProfiler.py')
initFileLocation = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','profiler','__init__.py')
shutil.copy2(textprofileFileLocation,profilerPath)
shutil.copy2(initFileLocation,profilerPath)
'''
if(platform.system() == 'Windows'):
shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'\\..\\profiler\\textDataProfiler.py',profilerPath)
shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'\\..\\profiler\\__init__.py',profilerPath)
else:
shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'/../profiler/textDataProfiler.py',profilerPath)
shutil.copy2(os.path.dirname(os.path.abspath(__file__))+'/../profiler/__init__.py',profilerPath)
'''
except OSError:
self.log.info("Copy to Profiler Path Failed")
def listToString(self,s):
str1='['
for feature in s:
if(str1 != '['):
str1 += ','
str1 += '"'+feature+'"'
str1+=']'
return str1
def print_files(self):
self.log.info(self.modelfile)
def create_util_folder(self, deploy_path,learner_type):
import tarfile
ext_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..','utilities'))
for x in os.listdir(ext_path):
if x.endswith('.tar'):
if x not in ['scikit_surprise-1.1.1.dist-info.tar','surprise.tar']:
tarPackage = os.path.join(ext_path, x)
my_tar = tarfile.open(tarPackage)
my_tar.extractall(deploy_path)
my_tar.close()
else:
if learner_type == 'RecommenderSystem':
tarPackage = os.path.join(ext_path, x)
my_tar = tarfile.open(tarPackage)
my_tar.extractall(deploy_path)
my_tar.close()
def deploy_model(self,deploy_name,deployJson,learner_type,model_type,model,scoreParam,saved_model,deploy_path,features,profiler,datalocation,output_label,column_merge_flag,textFeatures,numericalFeatures,nonNumericFeatures,preprocessing_pipe,numericToLabel_json,threshold,loss_matrix,optimizer,firstDocFeature,secondDocFeature,padding_length,trained_data_file,dictDiffCount,targetFeature,normalizer,normFeatures,pcaModel_pickle_file,bpca_features,apca_features,optimizationmethod,deployFolder,iterName,iterVersion,wordToNumericFeatures,imageconfig,sessonal_freq,additional_regressors,grouperbyjson,rowfilterexpression,xtrain,profiled_data_file,conversion_method,modelFeatures,indexFeature,lag_order,scalertransformationFile,no_of_prediction,preprocess_pipe,preprocess_out_columns, label_encoder,datetimeFeature,usecaseLocation,config=None):
try:
serviceName = '{}{}{}'.format(iterName, '_' if iterVersion != '' else '', iterVersion)
self.log.info('-------> Deploy Location :'+deploy_path)
if production.is_supported(model_type.lower()):
if learner_type == 'Text Similarity':
coder = production.get_deployer(learner_type)
coder.create_file(deploy_path, preprocessing_pipe, saved_model, firstDocFeature, secondDocFeature)
elif model_type.lower() in ['classification', 'regression','clustering','timeseriesforecasting']:
params = {}
params['usecase_name']= iterName
params['usecase_ver']= iterVersion
params['features']={}
params['features']['input_feat'] = config['profiler']['input_features']
params['features']['target_feat'] = targetFeature
params['features']['text_feat'] = textFeatures
params['paths']={}
params['paths']['deploy'] = Path(deploy_path)
params['paths']['usecase'] = params['paths']['deploy'].parent
params['profiler']=config['profiler']
if 'code' in config.get('preprocess',{}).keys():
params['profiler']['preprocess']=config['preprocess']
params['selector']={}
params['selector']['reducer']=True if pcaModel_pickle_file else False
params['selector']['reducer_file']=pcaModel_pickle_file
if pcaModel_pickle_file:
params['selector']['input_features']=bpca_features
params['selector']['output_features']=apca_features
else:
params['selector']['input_features']=config['profiler']['input_features']
params['selector']['output_features']=features
params['training']={}
params['training']['algo']= model
params['training']['model_file']=saved_model
if model_type.lower() == 'timeseriesforecasting':
if params['training']['algo'] in ['LSTM','MLP','ENCODER_DECODER_LSTM_MVI_UVO']:
params['training']['lag_order'] = int(lag_order)
params['training']['scaler_file'] = Path(scalertransformationFile).name
elif params['training']['algo'] == 'VAR':
params['training']['dictDiffCount'] = dictDiffCount
params['training']['no_of_prediction'] = no_of_prediction
elif params['training']['algo'] == 'FBPROPHET':
params['training']['sessonal_freq'] = sessonal_freq
params['training']['additional_regressors'] = additional_regressors
self.log.info(params)
deployer = production.get_deployer(model_type.lower(), params=params)
deployer.run( )
self.log.info('Status:- |... Model deployment files created')
self.log.info('Status:- |... Model deployment completed')
return
else:
# for output_formatter.py
from prediction_package.output_formatter import outputFormatter
outputObj = outputFormatter()
outputObj.crate_output_format_file(deploy_path, learner_type, model_type, model, output_label,
threshold, trained_data_file, dictDiffCount, targetFeature, features,datetimeFeature)
#for aion_predict.py
from prediction_package.aion_prediction import aionPrediction
predictionObj = aionPrediction()
# print(deploy_path)
predictionObj.create_prediction_file(deploy_name, deploy_path, learner_type, grouperbyjson,rowfilterexpression,model_type,datetimeFeature)
# for aion_service.py
predictionObj.create_model_service(deploy_path, serviceName, model_type)
# for aion_publish.py
predictionObj.create_publish_service(usecaseLocation, iterName, iterVersion, model_type)
if learner_type.lower()=="recommendersystem":
# Task 11190---
#For recommender system
from prediction_package.recommender_code import generate_recommender_code
generate_recommender_code(deploy_path)
return
#self.create_TextCleaner(deploy_path)
if(len(textFeatures) > 0):
self.create_TextCleaner(deploy_path)
self.include_import_file(learner_type,deployJson['method'],scoreParam, model_type,model)
if((learner_type == 'TS' and model.lower() not in ['lstm','mlp','var']) or learner_type == 'RecommenderSystem'):
features=[]
self.create_class(deploy_name)
if len(bpca_features) != 0:
self.create_profiler_file(learner_type,deploy_path,profiler,bpca_features,numericToLabel_json,column_merge_flag,textFeatures,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder, model, config,datetimeFeature)
else:
self.create_profiler_file(learner_type,deploy_path,profiler,features,numericToLabel_json,column_merge_flag,textFeatures,preprocessing_pipe,firstDocFeature,secondDocFeature,normalizer,normFeatures,wordToNumericFeatures,conversion_method,model_type,preprocess_pipe,preprocess_out_columns, label_encoder, model, config,datetimeFeature)
self.create_selector_file(deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature,model_type, model,config)
self.create_init_function_for_classification(saved_model,'classes',learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,model,model_type,imageconfig)
except Exception as e:
print(e)
import traceback
exception_type, exception_object, exception_traceback = sys.exc_info()
filename = exception_traceback.tb_frame.f_code.co_filename
line_number = exception_traceback.tb_lineno
self.log.info("Exception type: ", exception_type)
self.log.info("File name: ", filename)
self.log.info("Line number: ", line_number)
self.log.info("multivariate model build error traceback: \n"+str(traceback.print_exc()))
raise Exception(e)
#print(model)
if(model.lower() == 'var'):
self.log.info("Create Forecast Function")
self.create_forcast(deployJson['method'],no_of_prediction)
else:
self.create_predict(learner_type,deployJson['method'],model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,features,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam)
self.save_model_deploy(deploy_path,deploy_name)
if(len(textFeatures) > 0):
if model_type.lower() == 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting':
predictionObj.create_text_drift_file(deploy_path,textFeatures,targetFeature,model_type)
if model_type.lower() == 'classification':
predictionObj.create_classification_text_performance_file(deploy_path,textFeatures,targetFeature)
elif model_type.lower() == 'regression':
predictionObj.create_regression_text_performance_file(deploy_path,textFeatures,targetFeature)
else:
if model_type.lower() == 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting': #task 11997
predictionObj.create_drift_file(deploy_path,features,targetFeature,model_type)
if model_type.lower() == 'classification':
predictionObj.create_classification_performance_file(deploy_path,features,targetFeature)
elif model_type.lower() == 'regression':
predictionObj.create_regression_performance_file(deploy_path,features,targetFeature)
self.log.info('Status:- |... Model deployment files created')
self.crate_readme_file(deploy_path,saved_model,features,deployJson['method'])
from prediction_package.requirements import requirementfile
requirementfile(deploy_path,model,textFeatures,learner_type)
os.chdir(deploy_path)
textdata = False
if(learner_type == 'Text Similarity' or len(textFeatures) > 0):
textdata = True
self.create_util_folder(deploy_path,learner_type)
self.log.info('Status:- |... Model deployment completed')
def deployTSum(self,deploy_path,preTrainedModellocation):
def create_predict(preTrainedModellocation):
text = f"""
import sys
import json
def predict(data):
try:
import pandas as pd
import numpy as np
from pathlib import Path
keywordsFile =Path(__file__).parent/'data'/'keywordDataBase.csv'
outputSumFile =Path(__file__).parent/'data'/'summarizedOutput.csv'
fileName=data
#print("fileName---",fileName)
inputDataFileFrame = pd.DataFrame()
inputDataFileFrame['Sentences']=""
rowIndex=0
if fileName.endswith(".pdf"):
from pypdf import PdfReader
reader = PdfReader(fileName)
number_of_pages = len(reader.pages)
text=""
textOutputForFile=""
OrgTextOutputForFile=""
for i in range(number_of_pages) :
page = reader.pages[i]
text1 = page.extract_text()
text=text+text1
import nltk
tokens = nltk.sent_tokenize(text)
for sentence in tokens:
sentence=sentence.replace("\\n", " ")
if (len(sentence.split()) < 4 ) or (len(str(sentence.split(',')).split()) < 8)or (any(chr.isdigit() for chr in sentence)) :
continue
inputDataFileFrame.at[rowIndex,'Sentences']=str(sentence.strip())
rowIndex=rowIndex+1
if fileName.endswith(".txt"):
data=[]
with open(fileName, "r",encoding="utf-8") as f:
data.append(f.read())
str1 = ""
for ele in data:
str1 += ele
sentences=str1.split(".")
count=0
for sentence in sentences:
count += 1
inputDataFileFrame.at[rowIndex,'Sentences']=str(sentence.strip())
rowIndex=rowIndex+1
inputDataFileFrame['LabelByKw']=0
#print(inputDataFileFrame)
keywordsFileFrame=pd.read_csv(keywordsFile,encoding='utf-8')
Keyword_list = keywordsFileFrame['Keyword'].tolist()
for i in inputDataFileFrame.index:
for x in Keyword_list:
if (str(inputDataFileFrame["Sentences"][i])).lower().find(x) != -1:
inputDataFileFrame['LabelByKw'][i]=1
break
import pickle
from sklearn.preprocessing import LabelEncoder
pkl_filename='classificationModel.sav'
pkl_filename =Path(__file__).parent/'model'/'classificationModel.sav'
with open(pkl_filename, 'rb') as file:
pickle_model = pickle.load(file)
testsample=inputDataFileFrame[["Sentences"]]
labelencoder = LabelEncoder()
testsample["Sentences"] = labelencoder.fit_transform(testsample["Sentences"])
y_predicted = pickle_model.predict_proba(testsample)
df=pd.DataFrame({{"SectionName":np.nan,"Sentences":np.nan, "Predicted_Prob":y_predicted[:,1]}})
df['LabelByModel']=df['Predicted_Prob'].apply(lambda x: 0 if x <= 0.5 else 1 )
inputDataFileFrame['LabelByModel']= df['LabelByModel']
textToSum=""
for i in inputDataFileFrame.index:
if (inputDataFileFrame['LabelByModel'][i] or inputDataFileFrame['LabelByKw'][i]) :
textToSum=textToSum+" "+inputDataFileFrame["Sentences"][i]
stdir=r"{preTrainedModellocation}"
stdir = stdir.replace('\\\\', '\\\\\\\\')
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
modelbert = AutoModelForSeq2SeqLM.from_pretrained(stdir,local_files_only=True)
tokenizer = AutoTokenizer.from_pretrained(stdir,local_files_only=True)
inputs = tokenizer("summarize: " + textToSum, return_tensors="pt", max_length=512, truncation=True)
outputs = modelbert.generate(inputs["input_ids"], max_length=512, min_length=140, length_penalty=2.0, num_beams=4, early_stopping=True)
summarizedOutputOfSection= tokenizer.decode(outputs[0])
summarizedOutputOfSection=summarizedOutputOfSection.replace("</s>","")
summarizedOutputOfSection=summarizedOutputOfSection.replace("<s>","")
sumDatadata = [summarizedOutputOfSection]
df = pd.DataFrame(sumDatadata, columns=['textSum'])
df.to_csv(outputSumFile,encoding='utf-8')
outputjson = {{"status":"SUCCESS","msg":"Press Download button to download summarized output","data":summarizedOutputOfSection}}
print("predictions:",json.dumps(outputjson))
return (json.dumps(outputjson))
except KeyError as e:
output = {{"status":"FAIL","message":str(e).strip('"')}}
print("predictions:",json.dumps(output))
return (json.dumps(output))
except Exception as e:
output = {{"status":"FAIL","message":str(e).strip('"')}}
print("predictions:",json.dumps(output))
return (json.dumps(output))
if __name__ == "__main__":
output = predict(sys.argv[1])
"""
return text
deploy_path = Path(deploy_path)
aion_prediction = deploy_path/'aion_predict.py'
with open(aion_prediction, 'w') as f:
f.write(create_predict(preTrainedModellocation))
|
recommender_code.py | #task 11190: Item based Recommender system---Usnish
import os
def generate_recommender_code(deployPath):
code = """
import pandas as pd
import numpy as np
import os
ITEMID = 'itemId'
DATA_FOLDER = 'data'
USER_ITEM_MATRIX = 'user_item_matrix.csv'
ITEM_SIMILARITY_MATRIX = 'item_similarity_matrix.csv'
RATING = 'rating'
SIMILARITY_SCORE = 'similarity_score'
class collaborative_filter(object):
def __init__(self):
self.matrix = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', DATA_FOLDER, USER_ITEM_MATRIX),index_col=0)
self.matrix.index.name = ITEMID
self.item_similarity_cosine = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', DATA_FOLDER, ITEM_SIMILARITY_MATRIX))
self.item_similarity_cosine.index.name = ITEMID
self.item_similarity_cosine.columns.name = ITEMID
def item_based_rec(self,picked_userid, number_of_recommendations,number_of_similar_items=5):
import operator
if not isinstance(picked_userid,str):
picked_userid = str(picked_userid)
if picked_userid not in self.matrix.columns:
raise KeyError("UserID Does Not Exist")
# Movies that the target user has not watched
try:
picked_userid_unwatched = pd.DataFrame(self.matrix[picked_userid].isna()).reset_index()
picked_userid_unwatched = picked_userid_unwatched[picked_userid_unwatched[picked_userid] == True][ITEMID].values.tolist()
# Movies that the target user has watched
picked_userid_watched = pd.DataFrame(self.matrix[picked_userid].dropna(axis=0, how='all') \
.sort_values(ascending=False)) \
.reset_index() \
.rename(columns={picked_userid: 'rating'})
# Dictionary to save the unwatched movie and predicted rating pair
rating_prediction = {}
# Loop through unwatched movies
for picked_movie in picked_userid_unwatched:
if not isinstance(picked_movie,str):
picked_movie = str(picked_movie)
# Calculate the similarity score of the picked movie with other movies
try:
picked_movie_similarity_score = self.item_similarity_cosine[[picked_movie]].reset_index().rename(
columns={picked_movie: SIMILARITY_SCORE})
# Rank the similarities between the picked user watched movie and the picked unwatched movie.
picked_userid_watched_similarity = pd.merge(left=picked_userid_watched,
right=picked_movie_similarity_score,
on=ITEMID,
how='inner') \
.sort_values(SIMILARITY_SCORE, ascending=False)[
:number_of_similar_items]
# Calculate the predicted rating using weighted average of similarity scores and the ratings from picked user
try:
predicted_rating = round(np.average(picked_userid_watched_similarity[RATING],weights=picked_userid_watched_similarity[SIMILARITY_SCORE]), 6)
except Exception as e:
predicted_rating = 0
# Save the predicted rating in the dictionary
rating_prediction[picked_movie] = predicted_rating
except Exception as e:
rating_prediction[picked_movie] = 0
# Return the top recommended movies
return sorted(rating_prediction.items(), key=operator.itemgetter(1), reverse=True)[:number_of_recommendations]
except Exception as e:
print(e)
raise KeyError(str(e))
def predict(self,X):
predictions = []
for index,row in X.iterrows():
score = self.item_based_rec(int(row["uid"]),int(row["numberOfRecommendation"]))
df = pd.DataFrame(score,columns=['ItemId','Ratings'])
predictions.append(df)
return predictions"""
filename = os.path.join(deployPath, 'script', 'item_recommendation.py')
# print(deploy_path)
f = open(filename, "wb")
f.write(str(code).encode('utf8'))
f.close()
|
aion_prediction.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import platform
import json
import shutil
import logging
class aionPrediction:
def __init__(self):
self.log = logging.getLogger('eion')
def create_optimus_prediction_file (self,classname,deploy_path,learner_type):
self.predictionFile = 'import warnings'
self.predictionFile += '\n'
self.predictionFile += 'warnings.filterwarnings("ignore")'
self.predictionFile += '\n'
self.predictionFile += 'import json'
self.predictionFile += '\n'
self.predictionFile += 'import os'
self.predictionFile += '\n'
self.predictionFile += 'import sys'
self.predictionFile += '\n'
self.predictionFile += 'import pandas as pd'
self.predictionFile += '\n'
self.predictionFile += 'from pandas import json_normalize'
self.predictionFile += '\n'
self.predictionFile += 'from importlib import import_module'
self.predictionFile += '\n'
self.predictionFile += 'import importlib.util'
self.predictionFile += '\n'
self.predictionFile += 'class prediction:'
self.predictionFile += '\n'
self.predictionFile += ' def predict_from_json(self,json_data):'
self.predictionFile += '\n'
self.predictionFile += ' data = json.loads(json_data)'
self.predictionFile += '\n'
self.predictionFile += ' output=self.predict(data)'
self.predictionFile += '\n'
self.predictionFile += ' print("predictions:",output)'
self.predictionFile += '\n'
self.predictionFile += '\n'
self.predictionFile += ' def predict_from_file(self,filename):'
self.predictionFile += '\n'
self.predictionFile += ' with open(filename,\'r\',encoding=\'utf-8\') as f:'
self.predictionFile += '\n'
self.predictionFile += ' data = json.load(f)'
self.predictionFile += '\n'
self.predictionFile += ' output=self.predict(data)'
self.predictionFile += '\n'
self.predictionFile += ' print("predictions:",output)'
self.predictionFile += '\n'
self.predictionFile += '\n'
self.predictionFile += ' def predict(self,json_data):'
self.predictionFile += '\n'
self.predictionFile += ' try:'
self.predictionFile += '\n'
#self.predictionFile += ' jsonData = json.loads(json_data)'
self.predictionFile += ' jsonData=json_data'
self.predictionFile += '\n'
self.predictionFile += ' model_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/trained_model.py")'
self.predictionFile += '\n'
self.predictionFile += ' model = importlib.util.module_from_spec(model_obj)'
self.predictionFile += '\n'
self.predictionFile += ' model_obj.loader.exec_module(model)'
self.predictionFile += '\n'
#if(learner_type != 'TextML'):
self.predictionFile += ' profiler_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/inputprofiler.py")'
self.predictionFile += '\n'
self.predictionFile += ' inputprofiler = importlib.util.module_from_spec(profiler_obj)'
self.predictionFile += '\n'
self.predictionFile += ' profiler_obj.loader.exec_module(inputprofiler)'
self.predictionFile += '\n'
self.predictionFile += ' selector_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/selector.py")'
self.predictionFile += '\n'
self.predictionFile += ' selector = importlib.util.module_from_spec(selector_obj)'
self.predictionFile += '\n'
self.predictionFile += ' selector_obj.loader.exec_module(selector)'
self.predictionFile += '\n'
self.predictionFile += ' output_format_obj = importlib.util.spec_from_file_location("module.name", os.path.dirname(os.path.abspath(__file__))+"/output_format.py")'
self.predictionFile += '\n'
self.predictionFile += ' output_format = importlib.util.module_from_spec(output_format_obj)'
self.predictionFile += '\n'
self.predictionFile += ' output_format_obj.loader.exec_module(output_format)'
self.predictionFile += '\n'
self.predictionFile += ' df = json_normalize(jsonData)'
self.predictionFile += '\n'
self.predictionFile += ' df0 = df.copy()'
self.predictionFile += '\n'
#if(learner_type != 'TextML'):
self.predictionFile += ' profilerobj = inputprofiler.inputprofiler()'
self.predictionFile += '\n'
self.predictionFile += ' df = profilerobj.apply_profiler(df)'
self.predictionFile += '\n'
self.predictionFile += ' selectobj = selector.selector()'
self.predictionFile += '\n'
self.predictionFile += ' df = selectobj.apply_selector(df)'
self.predictionFile += '\n'
self.predictionFile += ' output = model.trained_model().predict(df,"")'
self.predictionFile += '\n'
self.predictionFile += ' outputobj = output_format.output_format()'
self.predictionFile += '\n'
self.predictionFile += ' output = outputobj.apply_output_format(df0,output)'
#self.predictionFile += '\n'
#self.predictionFile += ' print(output)'
self.predictionFile += '\n'
self.predictionFile += ' return output'
self.predictionFile += '\n'
self.predictionFile += ' except KeyError as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' return json.dumps(output)'
self.predictionFile += '\n'
self.predictionFile += ' except Exception as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' return json.dumps(output)'
self.predictionFile += '\n'
self.predictionFile += '\n'
self.predictionFile += 'if __name__ == "__main__":'
self.predictionFile += '\n'
self.predictionFile += ' predictobj = prediction()'
self.predictionFile += '\n'
self.predictionFile += ' predictobj.predict_from_file(sys.argv[1])'
self.predictionFile += '\n'
filename = os.path.join(deploy_path,'prediction.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_text_drift_file(self,deploy_path,features,target,model_type): #task-14549
self.predictionFile = 'import warnings'
self.predictionFile += '\n'
self.predictionFile += 'warnings.filterwarnings("ignore")'
self.predictionFile += '\n'
self.predictionFile += 'import json'
self.predictionFile += '\n'
self.predictionFile += 'import os'
self.predictionFile += '\n'
self.predictionFile += 'import sys'
self.predictionFile += '\n'
self.predictionFile += 'import pandas as pd'
self.predictionFile += '\n'
self.predictionFile += 'from monitoring import check_drift'
self.predictionFile += '\n'
self.predictionFile += 'def drift(data):'
self.predictionFile += '\n'
self.predictionFile += ' try:'
self.predictionFile += '\n'
self.predictionFile += ' if os.path.splitext(data)[1] == ".json":'
self.predictionFile += '\n'
self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:'
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.load(f)'
self.predictionFile += '\n'
self.predictionFile += ' else:'
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.loads(data)'
self.predictionFile += '\n'
self.predictionFile += ' jsonData[\'features\'] = \''+",".join([feature for feature in features])+'\''
self.predictionFile += '\n'
self.predictionFile += ' jsonData[\'target\'] = \''+target+'\''
self.predictionFile += '\n'
if model_type.lower() != 'timeseriesforecasting': #task 11997
self.predictionFile += ' htmlfilepath=evidently_details(jsonData)'
self.predictionFile += '\n'
else:
self.predictionFile += ' htmlfilepath=\'\''
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.dumps(jsonData)'
self.predictionFile += '\n'
self.predictionFile += ' output = check_drift(jsonData)'
self.predictionFile += '\n'
self.predictionFile += ' output = json.loads(output)'
self.predictionFile += '\n'
self.predictionFile += ' output[\'htmlPath\'] = str(htmlfilepath)'
self.predictionFile += '\n'
self.predictionFile += ' print("drift:", json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return(output)'
self.predictionFile += '\n'
self.predictionFile += ' except KeyError as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' print("drift:",json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return (json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' except Exception as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' print("drift:",json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return (json.dumps(output))'
self.predictionFile += '\n'
if model_type.lower() != 'timeseriesforecasting': #task 11997
self.predictionFile += 'def evidently_details(deployJson):'
self.predictionFile += '\n'
self.predictionFile += ' features = deployJson[\'features\'].split(\',\')'
self.predictionFile += '\n'
self.predictionFile += ' target = deployJson[\'target\']'
self.predictionFile += '\n'
self.predictionFile += """\
try:
from evidently.report import Report
from evidently.metrics import TextDescriptorsDriftMetric, ColumnDriftMetric
from evidently.pipeline.column_mapping import ColumnMapping
from sklearn.preprocessing import LabelEncoder
historicaldataFrame=pd.read_csv(deployJson['trainingDataLocation'],skipinitialspace = True,na_values=['-','?'])
currentdataFrame=pd.read_csv(deployJson['currentDataLocation'],skipinitialspace = True,na_values=['-','?'])
historicaldataFrame.columns = historicaldataFrame.columns.str.strip()
currentdataFrame.columns = currentdataFrame.columns.str.strip()
hdf = historicaldataFrame.dropna(subset=features)
cdf = currentdataFrame.dropna(subset=features)
hdf['Text_Features'] = hdf[features].apply("-".join, axis=1)
cdf['Text_Features'] = cdf[features].apply("-".join, axis=1)
hdf['target'] = historicaldataFrame[target]
cdf['target'] = currentdataFrame[target]
le = LabelEncoder()
le.fit(hdf['target'])
hdf['target'] = le.transform(hdf['target'])
le.fit(cdf['target'])
cdf['target'] = le.transform(cdf['target'])
hd = hdf[['Text_Features', 'target']]
cd = cdf[['Text_Features', 'target']]
column_mapping = ColumnMapping()
column_mapping.target = 'target'
column_mapping.prediction = 'target'
column_mapping.text_features = ['Text_Features']
column_mapping.numerical_features = []
column_mapping.categorical_features = []
performance_report = Report(metrics=[ColumnDriftMetric('target'),TextDescriptorsDriftMetric(column_name='Text_Features')])
performance_report.run(reference_data=hd, current_data=cd,column_mapping=column_mapping)
report = os.path.join(os.path.dirname(os.path.abspath(__file__)),"log","My_report.html")
performance_report.save_html(report)
return(report)
except Exception as e:
print('Error: ', e)
return('NA')"""
self.predictionFile += '\n'
self.predictionFile += 'if __name__ == "__main__":'
self.predictionFile += '\n'
self.predictionFile += ' output = drift(sys.argv[1])'
filename = os.path.join(deploy_path,'aion_ipdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_drift_file(self,deploy_path,features,target,model_type):
self.predictionFile = 'import warnings'
self.predictionFile += '\n'
self.predictionFile += 'warnings.filterwarnings("ignore")'
self.predictionFile += '\n'
self.predictionFile += 'import json'
self.predictionFile += '\n'
self.predictionFile += 'import os'
self.predictionFile += '\n'
self.predictionFile += 'import sys'
self.predictionFile += '\n'
self.predictionFile += 'import pandas as pd'
self.predictionFile += '\n'
self.predictionFile += 'from monitoring import check_drift'
self.predictionFile += '\n'
self.predictionFile += 'from pandas import json_normalize'
self.predictionFile += '\n'
self.predictionFile += 'from script.inputprofiler import inputprofiler'
self.predictionFile += '\n'
self.predictionFile += 'def drift(data):'
self.predictionFile += '\n'
self.predictionFile += ' try:'
self.predictionFile += '\n'
self.predictionFile += ' if os.path.splitext(data)[1] == ".json":'
self.predictionFile += '\n'
self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:'
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.load(f)'
self.predictionFile += '\n'
self.predictionFile += ' else:'
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.loads(data)'
self.predictionFile += '\n'
self.predictionFile += ' jsonData[\'features\'] = \''+",".join([feature for feature in features])+'\''
self.predictionFile += '\n'
self.predictionFile += ' jsonData[\'target\'] = \''+target+'\''
self.predictionFile += '\n'
if model_type.lower() != 'timeseriesforecasting': #task 11997
self.predictionFile += ' htmlfilepath=evidently_details(jsonData)'
self.predictionFile += '\n'
else:
self.predictionFile += ' htmlfilepath=\'\''
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.dumps(jsonData)'
self.predictionFile += '\n'
self.predictionFile += ' output = check_drift(jsonData)'
self.predictionFile += '\n'
self.predictionFile += ' output = json.loads(output)'
self.predictionFile += '\n'
self.predictionFile += ' output[\'htmlPath\'] = str(htmlfilepath)'
self.predictionFile += '\n'
self.predictionFile += ' output = json.dumps(output)'
self.predictionFile += '\n'
self.predictionFile += ' print("drift:",output)'
self.predictionFile += '\n'
self.predictionFile += ' return(output)'
self.predictionFile += '\n'
self.predictionFile += ' except KeyError as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' print("drift:",json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return (json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' except Exception as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' print("drift:",json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return (json.dumps(output))'
self.predictionFile += '\n'
if model_type.lower() != 'timeseriesforecasting': #task 11997
self.predictionFile += 'def evidently_details(deployJson):'
self.predictionFile += '\n'
self.predictionFile += ' features = deployJson[\'features\'].split(\',\')'
self.predictionFile += '\n'
self.predictionFile += ' target = deployJson[\'target\']'
self.predictionFile += '\n'
self.predictionFile += """\
try:
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
historicaldataFrame=pd.read_csv(deployJson['trainingDataLocation'],skipinitialspace = True,na_values=['-','?'])
currentdataFrame=pd.read_csv(deployJson['currentDataLocation'],skipinitialspace = True,na_values=['-','?'])
historicaldataFrame.columns = historicaldataFrame.columns.str.strip()
currentdataFrame.columns = currentdataFrame.columns.str.strip()
profilerobj = inputprofiler()
historicaldataFramep = profilerobj.run(historicaldataFrame)
currentdataFramep = profilerobj.run(currentdataFrame)
hdf = historicaldataFramep[features]
cdf = currentdataFramep[features]
hdf['target'] = historicaldataFrame[target]
cdf['target'] = currentdataFrame[target]
data_drift_report = Report(metrics = [DataDriftPreset()])
data_drift_report.run(reference_data=hdf,current_data=cdf,column_mapping = None)
report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','my_report.html')
data_drift_report.save_html(report)
return(report)
except Exception as e:
print('Error')
return('NA')"""
self.predictionFile += '\n'
self.predictionFile += 'if __name__ == "__main__":'
self.predictionFile += '\n'
self.predictionFile += ' output = drift(sys.argv[1])'
filename = os.path.join(deploy_path,'aion_ipdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_prediction_file(self,classname,deploy_path,learner_type,grouperbyjson,rowfilterexpression,model_type,datetimeFeature):
self.predictionFile = 'import warnings'
self.predictionFile += '\n'
self.predictionFile += 'warnings.filterwarnings("ignore")'
self.predictionFile += '\n'
self.predictionFile += 'import json'
self.predictionFile += '\n'
self.predictionFile += 'import os'
self.predictionFile += '\n'
self.predictionFile += 'import sys'
self.predictionFile += '\n'
self.predictionFile += 'import pandas as pd'
self.predictionFile += '\n'
self.predictionFile += 'from pandas import json_normalize'
self.predictionFile += '\n'
if(learner_type.lower() != 'recommendersystem'): #task 11190
self.predictionFile += 'from script.selector import selector'
self.predictionFile += '\n'
self.predictionFile += 'from script.inputprofiler import inputprofiler'
self.predictionFile += '\n'
#self.predictionFile += 'from '+classname+' import '+classname
self.predictionFile += 'from script.trained_model import trained_model'
self.predictionFile += '\n'
else:
self.predictionFile += 'from script.item_recommendation import collaborative_filter'
self.predictionFile += '\n'
self.predictionFile += 'from script.output_format import output_format'
self.predictionFile += '\n'
if (learner_type != 'RecommenderSystem'): #task 11190
self.predictionFile += 'profilerobj = inputprofiler()'
self.predictionFile += '\n'
self.predictionFile += 'selectobj = selector()'
self.predictionFile += '\n'
self.predictionFile += 'modelobj = trained_model()'
self.predictionFile += '\n'
else:
self.predictionFile += 'colabobj = collaborative_filter()'
self.predictionFile += '\n'
self.predictionFile += 'outputobj = output_format()'
self.predictionFile += '\n'
self.predictionFile += 'def predict(data):'
self.predictionFile += '\n'
self.predictionFile += ' try:'
self.predictionFile += '\n'
self.predictionFile += ' if os.path.splitext(data)[1] == ".tsv":'
self.predictionFile += '\n'
self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',sep=\'\\t\',skipinitialspace = True,na_values=[\'-\',\'?\'])'
self.predictionFile += '\n'
self.predictionFile += ' elif os.path.splitext(data)[1] == ".csv":'
self.predictionFile += '\n'
self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',skipinitialspace = True,na_values=[\'-\',\'?\'])'
self.predictionFile += '\n'
self.predictionFile += ' elif os.path.splitext(data)[1] == ".dat":'
self.predictionFile += '\n'
self.predictionFile += ' df=pd.read_csv(data,encoding=\'utf-8\',skipinitialspace = True,na_values=[\'-\',\'?\'])'
self.predictionFile += '\n'
self.predictionFile += ' else:'
self.predictionFile += '\n'
self.predictionFile += ' if os.path.splitext(data)[1] == ".json":'
self.predictionFile += '\n'
self.predictionFile += ' with open(data,\'r\',encoding=\'utf-8\') as f:'
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.load(f)'
self.predictionFile += '\n'
self.predictionFile += ' else:'
self.predictionFile += '\n'
self.predictionFile += ' jsonData = json.loads(data)'
self.predictionFile += '\n'
self.predictionFile += ' df = json_normalize(jsonData)'
self.predictionFile += '\n'
self.predictionFile += ' df.rename(columns=lambda x: x.strip(), inplace=True)'
self.predictionFile += '\n'
if str(rowfilterexpression) != '':
self.predictionFile += ' filterexpression = "'+rowfilterexpression+'"'
self.predictionFile += '\n'
self.predictionFile += ' df = df.query(filterexpression)'
self.predictionFile += '\n'
#print(grouperbyjson)
if str(grouperbyjson) != '':
datetime = grouperbyjson['datetime']
unit = grouperbyjson['unit']
if unit == '':
self.predictionFile += ' df[\'date\'] = pd.to_datetime(df[\''+datetime+'\'])'
self.predictionFile += '\n'
else:
self.predictionFile += ' df[\'date\'] = pd.to_datetime(df[\''+datetime+'\'],unit=\''+unit+'\')'
self.predictionFile += '\n'
self.predictionFile += ' df = df.reset_index()'
self.predictionFile += '\n'
self.predictionFile += ' df.set_index(\'date\',inplace=True)'
self.predictionFile += '\n'
self.predictionFile += ' df = df.'+grouperbyjson['groupbystring']
self.predictionFile += '\n'
self.predictionFile += ' df.columns = df.columns.droplevel(0)'
self.predictionFile += '\n'
self.predictionFile += ' df = df.reset_index()'
self.predictionFile += '\n'
self.predictionFile += ' df0 = df.copy()'
self.predictionFile += '\n'
if(learner_type != 'RecommenderSystem'): #task 11190
if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na':
self.predictionFile += ' df,datetimeFeature = profilerobj.apply_profiler(df)'
self.predictionFile += '\n'
else:
self.predictionFile += ' df = profilerobj.apply_profiler(df)'
self.predictionFile += '\n'
self.predictionFile += ' df = selectobj.apply_selector(df)'
self.predictionFile += '\n'
#self.predictionFile += ' modelobj = '+classname+'()'
self.predictionFile += ' output = modelobj.predict(df,"")'
self.predictionFile += '\n'
else:
self.predictionFile += ' output = colabobj.predict(df)'
self.predictionFile += '\n'
if model_type.lower() == 'anomaly_detection' and datetimeFeature != '' and datetimeFeature.lower() != 'na':
self.predictionFile += ' output = outputobj.apply_output_format(df0,output,datetimeFeature)'
self.predictionFile += '\n'
else:
self.predictionFile += ' output = outputobj.apply_output_format(df0,output)'
self.predictionFile += '\n'
self.predictionFile += ' print("predictions:",output)'
self.predictionFile += '\n'
self.predictionFile += ' return(output)'
self.predictionFile += '\n'
self.predictionFile += ' except KeyError as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' print("predictions:",json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return (json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' except Exception as e:'
self.predictionFile += '\n'
self.predictionFile += ' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
self.predictionFile += '\n'
self.predictionFile += ' print("predictions:",json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += ' return (json.dumps(output))'
self.predictionFile += '\n'
self.predictionFile += 'if __name__ == "__main__":'
self.predictionFile += '\n'
self.predictionFile += ' output = predict(sys.argv[1])'
filename = os.path.join(deploy_path,'aion_predict.py')
f = open(filename, "w")
f.write(str(self.predictionFile))
f.close()
def create_classification_text_performance_file(self,deploy_path,features,target):
features = ",".join([feature for feature in features])
self.predictionFile = """\
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
import json
import os
import sys
from pandas import json_normalize
# from evidently.dashboard import Dashboard
# from evidently.tabs import ClassificationPerformanceTab
from evidently.pipeline.column_mapping import ColumnMapping
from aion_predict import predict
from evidently.report import Report
from evidently.pipeline.column_mapping import ColumnMapping
from evidently.metric_preset import ClassificationPreset
def odrift(data):
try:
"""
self.predictionFile += ' features = \''+features+'\''
self.predictionFile += '\n'
self.predictionFile += ' target = \''+target+'\''
self.predictionFile += '\n'
self.predictionFile +="""\
if os.path.splitext(data)[1] == ".json":
with open(data,'r',encoding='utf-8') as f:
jsonData = json.load(f)
else:
jsonData = json.loads(data)
production = predict().run(jsonData['currentDataLocation'])
reference = predict().run(jsonData['trainingDataLocation'])
production = json.loads(production)
reference = json.loads(reference)
if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'):
production = production['data']
production = json_normalize(production)
reference = reference['data']
reference = json_normalize(reference)
production['target'] = production[target]
reference['target'] = reference[target]
column_mapping = ColumnMapping()
column_mapping.target = target
column_mapping.prediction = 'prediction'
column_mapping.datetime = None
column_mapping.text_features = features.split(',')
iris_model_performance_dashboard = Report(metrics=[ClassificationPreset()])
iris_model_performance_dashboard.run(reference_data=reference, current_data=production,column_mapping=column_mapping)
report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html')
iris_model_performance_dashboard.save_html(report)
metrics_output = iris_model_performance_dashboard.as_dict()
output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']}
print("drift:",json.dumps(output))
return (json.dumps(output))
except KeyError as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
except Exception as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
if __name__ == "__main__":
output = odrift(sys.argv[1])"""
filename = os.path.join(deploy_path,'aion_opdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_classification_performance_file(self,deploy_path,features,target):
features = ",".join([feature for feature in features])
self.predictionFile = """\
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
import json
import os
import sys
from pandas import json_normalize
from evidently.report import Report
from evidently.metric_preset import ClassificationPreset
from evidently.pipeline.column_mapping import ColumnMapping
from aion_predict import predict
def odrift(data):
try:
"""
self.predictionFile += ' features = \''+features+'\''
self.predictionFile += '\n'
self.predictionFile += ' target = \''+target+'\''
self.predictionFile += '\n'
self.predictionFile +="""\
if os.path.splitext(data)[1] == ".json":
with open(data,'r',encoding='utf-8') as f:
jsonData = json.load(f)
else:
jsonData = json.loads(data)
production = predict().run(jsonData['currentDataLocation'])
reference = predict().run(jsonData['trainingDataLocation'])
production = json.loads(production)
reference = json.loads(reference)
if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'):
production = production['data']
production = json_normalize(production)
reference = reference['data']
reference = json_normalize(reference)
production['target'] = production[target]
reference['target'] = reference[target]
column_mapping = ColumnMapping()
column_mapping.target = target
column_mapping.prediction = 'prediction'
column_mapping.datetime = None
column_mapping.numerical_features = features.split(',')
model_performance_dashboard = Report(metrics = [ClassificationPreset()])
model_performance_dashboard.run(reference_data =reference, current_data =production, column_mapping = column_mapping)
report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html')
model_performance_dashboard.save_html(report)
metrics_output = model_performance_dashboard.as_dict()
output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']}
print("drift:",json.dumps(output))
return (json.dumps(output))
else:
output = {"status":"SUCCESS","htmlPath":'NA'}
print("drift:",json.dumps(output))
return (json.dumps(output))
except KeyError as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
except Exception as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
if __name__ == "__main__":
output = odrift(sys.argv[1])"""
filename = os.path.join(deploy_path,'aion_opdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_model_service(self,deploy_path,serviceName,problemType):
filedata = """
from flask import Flask, jsonify, request
from flask_restful import Resource, Api
from aion_predict import predict"""
if problemType.lower() == 'classification' or problemType.lower() == 'regression':
filedata += """
from aion_xai import local_analysis
from aion_ipdrift import drift
from aion_opdrift import odrift"""
filedata += """
import json
import os
import pandas as pd
import io
import argparse
from pathlib import Path
from flask_cors import CORS, cross_origin
app = Flask(__name__)
#cross origin resource from system arguments
parser = argparse.ArgumentParser()
parser.add_argument('-ip', '--ipaddress', help='IP Address')
parser.add_argument('-p', '--port', help='Port Number')
parser.add_argument("-cors", type=str, required=False)
d = vars(parser.parse_args())
modelPath = Path(__file__).parent
try:
with open( (modelPath/'etc')/'display.json', 'r') as f:
disp_data = json.load(f)
is_explainable = not disp_data.get('textFeatures')
except:
disp_data = {}
is_explainable = True
if "cors" in d.keys():
if d["cors"] != '' and d["cors"] != None:
d["cors"] = [s.strip() for s in d["cors"].split(",")]
#cors = CORS(app, resources={r"/AION/*": {"origins": ["http://localhost", "http://localhost:5000"]}})
cors = CORS(app, resources={r"/AION/*": {"origins": d["cors"]}})
api = Api(app)
class predictapi(Resource):
def get(self):
features = disp_data.get('modelFeatures')
if features:
msg=\"""
RequestType: POST
Content-Type=application/json
Body: {displaymsg}
\""".format(displaymsg={ x:'Value' for x in features})
else:
displaymsg='Data in JSON Format'
return jsonify(displaymsg)
def post(self):
data = request.get_json()
output = predict().run(json.dumps(data))
return jsonify(json.loads(output))
class predictfileapi(Resource):
def post(self):
if 'file' in request.files:
file = request.files['file']
urlData = file.read()
rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8')))
data = rawData.to_json(orient='records')
output = predict().run(data)
return jsonify(json.loads(output))
else:
displaymsg='File is mising'
return jsonify(displaymsg)
def get(self):
msg=\"""
RequestType: POST
Body:send file content in body\"""
return jsonify(msg)
"""
if problemType.lower() == 'classification' or problemType.lower() == 'regression':
filedata += """
class explainapi(Resource):
def get(self):
features = disp_data.get('modelFeatures')
if features:
msg=\"""
RequestType: POST
Content-Type=application/json
Body: {displaymsg}
\""".format(displaymsg={ x:'Value' for x in features})
else:
displaymsg='Data in JSON Format'
return jsonify(displaymsg)
def post(self):
data = request.get_json()
if is_explainable:
output = local_analysis(json.dumps(data))
else:
output = json.dumps({"status":"FAIL","data":"explain api is not supported when text features are used for training"})
return jsonify(json.loads(output))
class monitoringapi(Resource):
def get(self):
return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'})
def post(self):
data = request.get_json()
output = drift(json.dumps(data))
return jsonify(json.loads(output))
class performanceapi(Resource):
def get(self):
return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'})
def post(self):
data = request.get_json()
output = odrift(json.dumps(data))
return jsonify(json.loads(output))
"""
filedata += """
api.add_resource(predictapi, '/AION/{serviceName}/predict')""".format(serviceName=serviceName)
filedata += """
api.add_resource(predictfileapi, '/AION/{serviceName}/predict_file')""".format(serviceName=serviceName)
if problemType.lower() == 'classification' or problemType.lower() == 'regression':
filedata += """
api.add_resource(explainapi, '/AION/{serviceName}/explain')
api.add_resource(monitoringapi, '/AION/{serviceName}/monitoring')
api.add_resource(performanceapi, '/AION/{serviceName}/performance')""".format(serviceName=serviceName)
filedata += """
if __name__ == '__main__':
args = parser.parse_args()
app.run(args.ipaddress,port = args.port,debug = True)"""
filename = os.path.join(deploy_path,'aion_service.py')
f = open(filename, "wb")
f.write(str(filedata).encode('utf8'))
f.close()
def create_regression_performance_file(self,deploy_path,features,target):
features = ",".join([feature for feature in features])
self.predictionFile = """\
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
import json
import os
import sys
from pandas import json_normalize
from evidently.report import Report
from evidently.metric_preset import RegressionPreset
from evidently.pipeline.column_mapping import ColumnMapping
from aion_predict import predict
def odrift(data):
try:
"""
self.predictionFile += ' features = \''+features+'\''
self.predictionFile += '\n'
self.predictionFile += ' target = \''+target+'\''
self.predictionFile += '\n'
self.predictionFile +="""\
if os.path.splitext(data)[1] == ".json":
with open(data,'r',encoding='utf-8') as f:
jsonData = json.load(f)
else:
jsonData = json.loads(data)
production = predict().run(jsonData['currentDataLocation'])
reference = predict().run(jsonData['trainingDataLocation'])
production = json.loads(production)
reference = json.loads(reference)
if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'):
production = production['data']
production = json_normalize(production)
reference = reference['data']
reference = json_normalize(reference)
production['target'] = production[target]
reference['target'] = reference[target]
column_mapping = ColumnMapping()
column_mapping.target = target
column_mapping.prediction = 'prediction'
column_mapping.datetime = None
column_mapping.numerical_features = features.split(',')
iris_model_performance_dashboard = Report(metrics=[RegressionPreset()])
iris_model_performance_dashboard.run(reference_data = reference, current_data = production, column_mapping = column_mapping)
report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html')
iris_model_performance_dashboard.save_html(report)
metrics_output = iris_model_performance_dashboard.as_dict()
output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']}
print("drift:",json.dumps(output))
return (json.dumps(output))
else:
output = {"status":"SUCCESS","htmlPath":'NA'}
print("drift:",json.dumps(output))
return (json.dumps(output))
except KeyError as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
except Exception as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
if __name__ == "__main__":
output = odrift(sys.argv[1])"""
filename = os.path.join(deploy_path,'aion_opdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_regression_text_performance_file(self,deploy_path,features,target):
features = ",".join([feature for feature in features])
self.predictionFile = """\
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
import json
import os
import sys
from pandas import json_normalize
from aion_predict import predict
from evidently.report import Report
from evidently.pipeline.column_mapping import ColumnMapping
from evidently.metric_preset import RegressionPreset
def odrift(data):
try:
"""
self.predictionFile += ' features = \''+features+'\''
self.predictionFile += '\n'
self.predictionFile += ' target = \''+target+'\''
self.predictionFile += '\n'
self.predictionFile +="""\
if os.path.splitext(data)[1] == ".json":
with open(data,'r',encoding='utf-8') as f:
jsonData = json.load(f)
else:
jsonData = json.loads(data)
production = predict().run(jsonData['currentDataLocation'])
reference = predict().run(jsonData['trainingDataLocation'])
production = json.loads(production)
reference = json.loads(reference)
if (production['status'] == 'SUCCESS' and reference['status'] == 'SUCCESS'):
production = production['data']
production = json_normalize(production)
reference = reference['data']
reference = json_normalize(reference)
production['target'] = production[target]
reference['target'] = reference[target]
column_mapping = ColumnMapping()
column_mapping.target = target
column_mapping.prediction = 'prediction'
column_mapping.datetime = None
column_mapping.numerical_features = features.split(',')
iris_model_performance_dashboard = Report(metrics=[RegressionPreset()])
iris_model_performance_dashboard.run(reference_data=reference, current_data=production,column_mapping=column_mapping)
report = os.path.join(os.path.dirname(os.path.abspath(__file__)),'log','performance.html')
iris_model_performance_dashboard.save_html(report)
metrics_output = iris_model_performance_dashboard.as_dict()
output = {"status":"SUCCESS","htmlPath":report, 'drift_details':metrics_output['metrics']}
print("drift:",json.dumps(output))
return (json.dumps(output))
else:
output = {"status":"SUCCESS","htmlPath":'NA'}
print("drift:",json.dumps(output))
return (json.dumps(output))
except KeyError as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
except Exception as e:
print(e)
output = {"status":"FAIL","message":str(e).strip('"')}
print("drift:",json.dumps(output))
return (json.dumps(output))
if __name__ == "__main__":
output = odrift(sys.argv[1])"""
filename = os.path.join(deploy_path,'aion_opdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_publish_service(self,datalocation,usecaseid,version,problemType):
filename = os.path.join(datalocation,'aion_publish_service.py')
if not os.path.exists(filename):
filedata = """
import sys
import json
import time
import sqlite3
import argparse
import pandas as pd
import io
from pathlib import Path
from datetime import datetime
filename = Path(__file__).parent/'config.json'
with open (filename, "r") as f:
data = json.loads(f.read())
modelVersion = str(data['version'])
modelPath = Path(__file__).parent/modelVersion
sys.path.append(str(modelPath))
try:
with open( (modelPath/'etc')/'display.json', 'r') as f:
disp_data = json.load(f)
is_explainable = not disp_data.get('textFeatures')
except:
disp_data = {}
is_explainable = True
from flask import Flask, jsonify, request
from flask_restful import Resource, Api
from flask_cors import CORS, cross_origin
from flask import Response
from aion_predict import predict
"""
if problemType.lower() == 'classification' or problemType.lower() == 'regression':
filedata += """
from aion_ipdrift import drift
from aion_opdrift import odrift
if is_explainable:
from aion_xai import local_analysis
"""
filedata += """
dataPath = Path(__file__).parent/'data'
dataPath.mkdir(parents=True, exist_ok=True)
app = Flask(__name__)
#cross origin resource from system arguments
parser = argparse.ArgumentParser()
parser.add_argument('-ip', '--ipaddress', help='IP Address')
parser.add_argument('-p', '--port', help='Port Number')
parser.add_argument("-cors", type=str, required=False)
d = vars(parser.parse_args())
if "cors" in d.keys():
if d["cors"] != '' and d["cors"] != None:
d["cors"] = [s.strip() for s in d["cors"].split(",")]
#cors = CORS(app, resources={r"/AION/*": {"origins": ["http://localhost", "http://localhost:5000"]}})
cors = CORS(app, resources={r"/AION/*": {"origins": d["cors"]}})
api = Api(app)
class sqlite_db():
def __init__(self, location, database_file=None):
if not isinstance(location, Path):
location = Path(location)
if database_file:
self.database_name = database_file
else:
self.database_name = location.stem + '.db'
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
self.tables = []
def table_exists(self, name):
if name in self.tables:
return True
elif name:
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
listOfTables = self.cursor.execute(query).fetchall()
if len(listOfTables) > 0 :
self.tables.append(name)
return True
return False
def read(self, table_name,condition=''):
if condition == '':
return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
else:
return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn)
def create_table(self,name, columns, dtypes):
query = f'CREATE TABLE IF NOT EXISTS {name} ('
for column, data_type in zip(columns, dtypes):
query += f"'{column}' TEXT,"
query = query[:-1]
query += ');'
self.conn.execute(query)
return True
def update(self,table_name,updates,condition):
update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}'
self.cursor.execute(update_query)
self.conn.commit()
return True
def write(self,data, table_name):
if not self.table_exists(table_name):
self.create_table(table_name, data.columns, data.dtypes)
tuple_data = list(data.itertuples(index=False, name=None))
insert_query = f'INSERT INTO {table_name} VALUES('
for i in range(len(data.columns)):
insert_query += '?,'
insert_query = insert_query[:-1] + ')'
self.cursor.executemany(insert_query, tuple_data)
self.conn.commit()
return True
def delete(self, name):
pass
def close(self):
self.conn.close()"""
filedata += """
app = Flask(__name__)
api = Api(app)
class predictapi(Resource):
def get(self):
features = disp_data.get('modelFeatures')
if features:
msg=\"""
RequestType: POST
Content-Type=application/json
Body: {displaymsg}
\""".format(displaymsg={ x:'Value' for x in features})
else:
displaymsg='Data in JSON Format'
return jsonify(displaymsg)
def post(self):
sqlite_dbObj = sqlite_db(dataPath,'data.db')
if not sqlite_dbObj.table_exists('metrices'):
data = {'noOfPredictCalls':'0','noOfDriftCalls':'0',"noOfActualCalls":'0',"mid":'0'}
data = pd.DataFrame(data, index=[0])
sqlite_dbObj.create_table('metrices',data.columns, data.dtypes)
data = request.get_json()
output = predict().run(json.dumps(data))
outputobj = json.loads(output)
if outputobj['status'] == 'SUCCESS':
try:
df2 = pd.read_json(json.dumps(outputobj['data']), orient ='records')
if not sqlite_dbObj.table_exists('prodData'):
sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes)
sqlite_dbObj.write(df2,'prodData')
except:
pass
try:
data = sqlite_dbObj.read('metrices')
#print(data)
if len(data) == 0:
data = [{'mid':'0','noOfPredictCalls':'1','noOfDriftCalls':'0',"noOfActualCalls":'0'}]
data = pd.read_json(json.dumps(data), orient ='records')
sqlite_dbObj.write(data,'metrices')
else:
noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1
sqlite_dbObj.update('metrices',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","mid = 0")
except Exception as e:
print(e)
pass
return jsonify(json.loads(output))
class predictfileapi(Resource):
def post(self):
sqlite_dbObj = sqlite_db(dataPath,'data.db')
if not sqlite_dbObj.table_exists('metrices'):
data = {'noOfPredictCalls':'0','noOfDriftCalls':'0',"noOfActualCalls":'0',"mid":'0'}
data = pd.DataFrame(data, index=[0])
sqlite_dbObj.create_table('metrices',data.columns, data.dtypes)
if 'file' in request.files:
file = request.files['file']
urlData = file.read()
rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8')))
data = rawData.to_json(orient='records')
output = predict().run(data)
outputobj = json.loads(output)
if outputobj['status'] == 'SUCCESS':
try:
df2 = pd.read_json(json.dumps(outputobj['data']), orient ='records')
if not sqlite_dbObj.table_exists('prodData'):
sqlite_dbObj.create_table('prodData',df2.columns, df2.dtypes)
sqlite_dbObj.write(df2,'prodData')
except:
pass
try:
data = sqlite_dbObj.read('metrices')
#print(data)
if len(data) == 0:
data = [{'mid':'0','noOfPredictCalls':'1','noOfDriftCalls':'0',"noOfActualCalls":'0'}]
data = pd.read_json(json.dumps(data), orient ='records')
sqlite_dbObj.write(data,'metrices')
else:
noofPredictCalls = int(data['noOfPredictCalls'].iloc[0])+1
sqlite_dbObj.update('metrices',"noOfPredictCalls = '"+str(noofPredictCalls)+"'","mid = 0")
except Exception as e:
print(e)
pass
return jsonify(json.loads(output))
else:
output = {'status':'error','msg':'File is missing'}
return jsonify(output)
"""
if problemType.lower() == 'classification' or problemType.lower() == 'regression':
filedata += """
class explainapi(Resource):
def get(self):
features = disp_data.get('modelFeatures')
if features:
msg=\"""
RequestType: POST
Content-Type=application/json
Body: {displaymsg}
\""".format(displaymsg={ x:'Value' for x in features})
else:
displaymsg='Data in JSON Format'
return jsonify(displaymsg)
def post(self):
data = request.get_json()
if is_explainable:
output = local_analysis(json.dumps(data))
else:
output = json.dumps({"status":"FAIL","data":"explain api is not supported when text features are used for training"})
return jsonify(json.loads(output))
class monitoringapi(Resource):
def get(self):
return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'})
def post(self):
sqlite_dbObj = sqlite_db(dataPath,'data.db')
if not sqlite_dbObj.table_exists('monitoring'):
data = {'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'}
data = pd.DataFrame(data, index=[0])
sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes)
trainingDataPath = (modelPath/'data')/'preprocesseddata.csv.gz'
if not sqlite_dbObj.table_exists('prodData'):
return jsonify({'status':'Error','msg':'Prod data not available'})
data = sqlite_dbObj.read('prodData')
filetimestamp = str(int(time.time()))
dataFile = dataPath/('AION_' + filetimestamp+'.csv')
data.to_csv(dataFile, index=False)
data = request.get_json()
data={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile}
output = drift(json.dumps(data))
outputData = json.loads(output)
status = outputData['status']
if status == 'SUCCESS':
Msg = str(outputData['data'])
else:
Msg = 'Error during drift analysis'
now = datetime.now() # current date and time
date_time = now.strftime("%m/%d/%Y, %H:%M:%S")
data = {'status':status,'Msg':Msg,'RecordTime':date_time,'version':modelVersion}
data = pd.DataFrame(data, index=[0])
sqlite_dbObj.write(data,'monitoring')
return jsonify(json.loads(output))"""
filedata += """
class matricesapi(Resource):
def get(self):
sqlite_dbObj = sqlite_db(dataPath,'data.db')
if sqlite_dbObj.table_exists('metrices'):
df1 = sqlite_dbObj.read('metrices')
else:
df1 = pd.DataFrame()
#print(df1)
if sqlite_dbObj.table_exists('monitoring'):
df2 = sqlite_dbObj.read('monitoring')
else:
df2 = pd.DataFrame()
msg = {'Deployed Version':str(modelVersion)}
if df1.shape[0] > 0:
msg.update({'noOfPredictCalls':str(df1['noOfPredictCalls'].iloc[0])})
else:
msg.update({'noOfPredictCalls':'0'})
driftDetails = []
for idx in reversed(df2.index):
driftd = {'version':str(df2.version[idx]),'status':str(df2.status[idx]),'recordTime':str(df2.RecordTime[idx]),'msg':str(df2.Msg[idx])}
driftDetails.append(driftd)
msg.update({'driftDetails':driftDetails})
return jsonify(msg)
class performanceapi(Resource):
def get(self):
return jsonify({'trainingDataLocation':'Training File Location','currentDataLocation':'production Location'})
def post(self):
sqlite_dbObj = sqlite_db(dataPath,'data.db')
if not sqlite_dbObj.table_exists('monitoring'):
data = {'status':'No Drift','Msg':'No Input Drift Found','RecordTime':'Time','version':'1'}
data = pd.DataFrame(data, index=[0])
sqlite_dbObj.create_table('monitoring',data.columns, data.dtypes)
trainingDataPath = (modelPath/'data')/'preprocesseddata.csv.gz'
if not sqlite_dbObj.table_exists('prodData'):
return jsonify({'status':'Error','msg':'Prod data not available'})
data = sqlite_dbObj.read('prodData')
filetimestamp = str(int(time.time()))
dataFile = dataPath/('AION_' + filetimestamp+'.csv')
data.to_csv(dataFile, index=False)
data = request.get_json()
data={'trainingDataLocation':trainingDataPath,'currentDataLocation':dataFile}
output = odrift(json.dumps(data))
return jsonify(json.loads(output))
"""
filedata += """
api.add_resource(predictapi, '/AION/{serviceName}/predict')
api.add_resource(predictfileapi, '/AION/{serviceName}/predict_file')
api.add_resource(matricesapi, '/AION/{serviceName}/metrices')""".format(serviceName=usecaseid)
if problemType.lower() == 'classification' or problemType.lower() == 'regression':
filedata += """
api.add_resource(explainapi, '/AION/{serviceName}/explain')
api.add_resource(monitoringapi, '/AION/{serviceName}/monitoring')
api.add_resource(performanceapi, '/AION/{serviceName}/performance')
""".format(serviceName=usecaseid)
filedata += """
if __name__ == '__main__':
args = parser.parse_args()
app.run(args.ipaddress,port = args.port,debug = True)"""
f = open(filename, "wb")
f.write(str(filedata).encode('utf8'))
f.close()
data = {'version':version}
filename = os.path.join(datalocation,'config.json')
with open(filename, "w") as outfile:
json.dump(data, outfile)
outfile.close() |
utility.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
TAB_CHAR = ' ' * 4
def import_modules(importer, modules_list):
for module in modules_list:
mod_from = module.get('mod_from',None)
mod_as = module.get('mod_as',None)
importer.addModule(module['module'], mod_from=mod_from, mod_as=mod_as)
|
imports.py | """
/**
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* © Copyright HCL Technologies Ltd. 2021, 2022
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*/
"""
from importlib.metadata import version
import sys
class importModule():
def __init__(self):
self.importModule = {}
self.stdlibModule = []
self.localModule = {}
def addLocalModule(self,module, mod_from=None, mod_as=None):
if module == '*':
if module not in self.localModule.keys():
self.localModule[module]= [mod_from]
else:
self.localModule[module].append(mod_from)
elif module not in self.localModule.keys():
self.localModule[module] = {'from':mod_from, 'as':mod_as}
def addModule(self, module, mod_from=None, mod_as=None):
if module not in self.importModule.keys():
self.importModule[module] = {'from':mod_from, 'as':mod_as}
if module in sys.stdlib_module_names:
self.stdlibModule.append(module)
elif isinstance(self.importModule[module], list):
if mod_as not in [x['as'] for x in self.importModule[module]]:
self.importModule[module].append({'from':mod_from, 'as':mod_as})
elif mod_as not in [x['from'] for x in self.importModule[module]]:
self.importModule[module].append({'from':mod_from, 'as':mod_as})
elif mod_as != self.importModule[module]['as']:
as_list = [self.importModule[module]]
as_list.append({'from':mod_from, 'as':mod_as})
self.importModule[module] = as_list
elif mod_from != self.importModule[module]['from']:
as_list = [self.importModule[module]]
as_list.append({'from':mod_from, 'as':mod_as})
self.importModule[module] = as_list
def getModules(self):
return (self.importModule, self.stdlibModule)
def getBaseModule(self, extra_importers=[]):
modules_alias = { 'sklearn':'scikit-learn',
'genetic_selection':'sklearn-genetic',
'google': 'google-cloud-storage',
'azure':'azure-storage-file-datalake'}
local_modules = {'AIX':'/app/AIX-0.1-py3-none-any.whl'}
modules = []
require = ""
if extra_importers:
extra_importers = [importer.importModule for importer in extra_importers if isinstance(importer, importModule)]
importers_module = [self.importModule] + extra_importers
for importer_module in importers_module:
for k,v in importer_module.items():
if v['from']:
mod = v['from'].split('.')[0]
else:
mod = k
if mod in modules_alias.keys():
mod = modules_alias[mod]
modules.append(mod)
modules = list(set(modules))
for mod in modules:
try:
if mod in local_modules.keys():
require += f"{local_modules[mod]}\n"
else:
require += f"{mod}=={version(mod)}\n"
except :
if mod not in sys.stdlib_module_names:
raise
return require
def getCode(self):
def to_string(k, v):
mod = ''
if v['from']:
mod += 'from {} '.format(v['from'])
mod += 'import {}'.format(k)
if v['as']:
mod += ' as {} '.format(v['as'])
return mod
modules = ""
local_modules = ""
std_lib_modules = ""
third_party_modules = ""
for k,v in self.importModule.items():
if k in self.stdlibModule:
std_lib_modules = std_lib_modules + '\n' + to_string(k, v)
elif isinstance(v, dict):
third_party_modules = third_party_modules + '\n' + to_string(k, v)
elif isinstance(v, list):
for alias in v:
third_party_modules = third_party_modules + '\n' + to_string(k, alias)
for k,v in self.localModule.items():
if k != '*':
local_modules = local_modules + '\n' + to_string(k, v)
else:
for mod_from in v:
local_modules = local_modules + '\n' + f'from {mod_from} import {k}'
if std_lib_modules:
modules = modules + "\n#Standard Library modules" + std_lib_modules
if third_party_modules:
modules = modules + "\n\n#Third Party modules" + third_party_modules
if local_modules:
modules = modules + "\n\n#local modules" + local_modules + '\n'
return modules
def copyCode(self, importer):
self.importModule, self.stdlibModule = importer.getModules()
|
EncryptPythonSourceCode.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import subprocess
import os
import glob
import sys
import python_minifier
def encrypt_files(path):
cwd = os.getcwd()
secure_path = os.path.join(path,'SecuredScripts')
try:
if not os.path.exists(secure_path):
os.mkdir(secure_path)
files = [f for f in glob.glob(path + "/*.py")]
for file in files:
#encrypted_file_details[0] = file
#file = files[0]
#print(file)
#filename_w_dir = os.path.splitext(file)
filename_w_ext = os.path.basename(file)
filename, file_extension = os.path.splitext(filename_w_ext)
file_folder_path = os.path.join(secure_path,filename)
#print(file_folder_path)
if not os.path.exists(file_folder_path):
os.mkdir(file_folder_path)
# Minify python source code
minify_file = os.path.join(file_folder_path,filename+'_minify.py')
pythonfolder,_ = os.path.split(sys.executable)
pyminify_script = os.path.join(pythonfolder,'Scripts','pyminify.exe')
minify_command = "\""+sys.executable+"\" \""+pyminify_script+ "\" \"" + file + "\" > \"" + minify_file+"\""
subprocess.call(minify_command, shell=True)
# Change directory to folder path
os.chdir(file_folder_path)
# Obfuscate minified file
pyarmor_script = os.path.join(pythonfolder,'Scripts','pyarmor.exe')
obfusc_commmand = "\""+sys.executable+"\" \""+pyarmor_script+"\" obfuscate \"" + minify_file+"\""
#print(obfusc_commmand)
subprocess.call(obfusc_commmand, shell=True)
# Change directory to dist path
obfusc_file = os.path.join(file_folder_path,'dist',filename+'_minify.py')
#print(obfusc_file)
chdirpath = os.path.join(file_folder_path,'dist')
os.chdir(chdirpath)
# Compress obfuscated file
compressed_file = os.path.join(file_folder_path,'dist',filename+'_compressed.py')
#print(compressed_file)
pyminifier_script = os.path.join(pythonfolder,'Scripts','pyminifier.exe')
compress_command = "\""+sys.executable+"\" \""+pyminifier_script+"\" --gzip -o \"" +compressed_file + "\" \"" + obfusc_file+"\""
#print(compress_command)
subprocess.call(compress_command, shell=True)
#compile_command = sys.executable+'-m py_compile "' + compressed_file+'"'
#print(compile_command)
#subprocess.call(compile_command , shell=True)
#encrypted_file_details['compiled_file'] = file
#compiled_file = os.path.join(file_folder_path,'dist','__pycache__',filename+'_compressed.cpython-37.pyc')
#encrypted_file_details[1] = compiled_file
#encrypted_file_list.append(encrypted_file_details)
#encrypted_file = filename + '_compressed.cpython-37_encrypted.pyc'
#encrypt_command = "python " + cwd + "\\Encrypt_Key_Dcrypt.py " + compiled_file + ' ' + encrypted_file + " --g -e"
#print(encrypt_command)
#subprocess.call(encrypt_command, shell=True)
#encrypted_file_list += ']'
#return(encrypted_file_list)
os.chdir(path)
except OSError as err:
print ("Creation of the directory %s failed "+str(err))
# Driver function
if __name__=="__main__":
path = sys.argv[1]
encrypt_files(path)
#(base) C:\Himanshu\DataPreprocessing>pyminify DataPreprocessing.py > DataPreprocessing_minify.py
#Obfuscate
#(base) C:\Himanshu\DataPreprocessing>pyarmor obfuscate C:\Himanshu\DataPreprocessing\DataPreprocessing_minify.py
#Compression
#(base) C:\Himanshu\DataPreprocessing>pyminifier --gzip -o C:\Himanshu\DataPreprocessing\dist\DataPreprocessing_compressed.py C:\Himanshu\DataPreprocessing\dist\DataPreprocessing_minify.py
#(base) C:\Himanshu\DataPreprocessing>cd dist
#(base) C:\Himanshu\DataPreprocessing\dist>python DataPreprocessing_compressed.py "DocumentText" "Label" 90 ".csv" "C:\Himanshu\DataAcquisition\ClassificationDataNewBalanced.csv"
#Compiling compressed .py to .pyc file
#(base) C:\Himanshu\DataPreprocessing\dist>python -m py_compile DataPreprocessing_compressed.py
#Encrypt .pyc file
#(base) C:\Himanshu\DataPreprocessing\dist>python C:\Himanshu\Encrypt_Key_Dcrypt.py C:\Himanshu\DataPreprocessing\dist\__pycache__\DataPreprocessing_compressed.cpython-36.pyc DataPreprocessing_compressed.cpython-36_encrypted.pyc --g -e
#Decrypt file
#(base) C:\Himanshu\DataPreprocessing\dist>python C:\Himanshu\Encrypt_Key_Dcrypt.py DataPreprocessing_compressed.cpython-36_encrypted.pyc DataPreprocessing_compressed.cpython-36_decrypted.pyc --d
#Run decrypted file
#(base) C:\Himanshu\DataPreprocessing\dist>python DataPreprocessing_compressed.cpython-36_decrypted.pyc "DocumentText" "Label" 90 ".csv" "C:\Himanshu\DataAcquisition\ClassificationDataNewBalanced.csv" |
create_docker.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import shutil
import subprocess
from os.path import expanduser
import platform
deploymentfolder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'HCLT','AION','target')
modelname='AION_12'
version='1'
def createDockerImage(deploymentfolder,modelname,version,learner_type,textdata):
modelPath = os.path.join(deploymentfolder)
filename = os.path.join(deploymentfolder,'docker_image')
modelservice = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','run_modelService.py')
shellscript = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','start_modelservice.sh')
aix = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','AIX-0.1-py3-none-any.whl')
drift = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','Drift-0.1-py3-none-any.whl')
sitepackage = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','..','..','..','extensions','site-packages')
model_dockerSetup = os.path.join(os.path.dirname(os.path.abspath(__file__)),'dockersetup','docker_'+modelname + '_' + version)
docker_setup = os.path.join(model_dockerSetup,modelname + '_' + version)
model_sitepackage = os.path.join(model_dockerSetup,'site-packages')
model_dockerSetupservicefile = os.path.join(model_dockerSetup,'run_modelService.py')
model_dockershellscript = os.path.join(model_dockerSetup,'start_modelservice.sh')
model_aix = os.path.join(model_dockerSetup,'AIX-0.1-py3-none-any.whl')
model_drift = os.path.join(model_dockerSetup,'Drift-0.1-py3-none-any.whl')
try:
os.mkdir(model_dockerSetup)
except Exception as e:
print("Error in creating Setup directpry "+str(e))
pass
shutil.copytree(modelPath, docker_setup)
if textdata:
shutil.copytree(sitepackage, model_sitepackage)
modelpretrainpath=os.path.join(model_dockerSetup,'HCLT','AION','PreTrainedModels','TextProcessing')
'''
try:
os.makedirs(modelpretrainpath, exist_ok=True)
except Exception as e:
print("Error in creating Setup directpry "+str(e))
pass
'''
home = expanduser("~")
if platform.system() == 'Windows':
hostpretrainpath = os.path.join(home,'AppData','Local','HCLT','AION','PreTrainedModels','TextProcessing')
else:
hostpretrainpath = os.path.join(home,'HCLT','AION','PreTrainedModels','TextProcessing')
shutil.copytree(hostpretrainpath, modelpretrainpath)
shutil.copyfile(modelservice, model_dockerSetupservicefile)
shutil.copyfile(shellscript, model_dockershellscript)
shutil.copyfile(aix, model_aix)
shutil.copyfile(drift,model_drift)
try:
os.mkdir(filename)
except:
pass
requirementfilename = os.path.join(model_dockerSetup,'requirements.txt')
installfilename = os.path.join(model_dockerSetup,'install.py')
dockerfile = os.path.join(model_dockerSetup,'Dockerfile')
dockerdata='FROM python:3.8-slim-buster'
dockerdata+='\n'
if textdata:
dockerdata+='WORKDIR /root'
dockerdata+='\n'
dockerdata+='COPY HCLT HCLT'
dockerdata+='\n'
dockerdata+='WORKDIR /app'
dockerdata+='\n'
dockerdata+='COPY requirements.txt requirements.txt'
dockerdata+='\n'
dockerdata+='COPY '+modelname+'_'+version+' '+modelname+'_'+version
dockerdata+='\n'
if textdata:
dockerdata+='COPY site-packages site-packages'
dockerdata+='\n'
dockerdata+='COPY install.py install.py'
dockerdata+='\n'
dockerdata+='COPY run_modelService.py run_modelService.py'
dockerdata+='\n'
dockerdata+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3-none-any.whl'
dockerdata+='\n'
dockerdata+='COPY Drift-0.1-py3-none-any.whl Drift-0.1-py3-none-any.whl'
dockerdata+='\n'
dockerdata+='COPY start_modelservice.sh start_modelservice.sh'
dockerdata+='\n'
if textdata:
dockerdata+='''RUN apt-get update \
&& apt-get install -y build-essential manpages-dev \
&& python -m pip install --no-cache-dir --upgrade pip \
&& python -m pip install --no-cache-dir pandas==1.2.4 \
&& python -m pip install --no-cache-dir numpy==1.19.5 \
&& python -m pip install --no-cache-dir joblib==1.0.1 \
&& python -m pip install --no-cache-dir Cython==0.29.23 \
&& mv site-packages/* /usr/local/lib/python3.8/site-packages \
&& python -m pip install --no-cache-dir scipy==1.6.3 \
&& python -m pip install --no-cache-dir AIX-0.1-py3-none-any.whl \
&& python -m pip install --no-cache-dir Drift-0.1-py3-none-any.whl \
&& python -m pip install --no-cache-dir scikit-learn==0.24.2 \
&& python -m pip install --no-cache-dir spacy==2.2.3 \
&& python -m pip install --no-cache-dir nltk==3.6.2 \
&& python -m pip install --no-cache-dir textblob==0.15.3 \
&& python -m pip install --no-cache-dir gensim==3.8.3 \
&& python -m pip install --no-cache-dir demoji==1.1.0 \
&& python -m pip install --no-cache-dir lxml==4.6.3 \
&& python -m pip install --no-cache-dir Beautifulsoup4==4.9.3 \
&& python -m pip install --no-cache-dir Unidecode==1.2.0 \
&& python -m pip install --no-cache-dir pyspellchecker==0.6.2 \
&& python -m pip install --no-cache-dir pycontractions==2.0.1 \
&& python -m pip install --no-cache-dir tensorflow==2.4.1 \
&& python -m pip install --no-cache-dir nltk==3.6.2 \
&& python -m pip install --no-cache-dir -r requirements.txt \
&& python install.py \
&& chmod +x start_modelservice.sh
ENTRYPOINT ["./start_modelservice.sh"]
'''
else:
dockerdata+='''RUN apt-get update \
&& apt-get install -y build-essential manpages-dev \
&& python -m pip install --no-cache-dir --upgrade pip \
&& python -m pip install --no-cache-dir pandas==1.2.4 \
&& python -m pip install --no-cache-dir numpy==1.19.5 \
&& python -m pip install --no-cache-dir joblib==1.0.1 \
&& python -m pip install --no-cache-dir Cython==0.29.23 \
&& python -m pip install --no-cache-dir scipy==1.6.3 \
&& python -m pip install --no-cache-dir AIX-0.1-py3-none-any.whl \
&& python -m pip install --no-cache-dir Drift-0.1-py3-none-any.whl \
&& python -m pip install --no-cache-dir scikit-learn==0.24.2 \
&& python -m pip install --no-cache-dir -r requirements.txt \
&& chmod +x start_modelservice.sh
ENTRYPOINT ["./start_modelservice.sh"]
'''
f = open(dockerfile, "w")
f.write(str(dockerdata))
f.close()
requirementdata=''
requirementdata+='word2number==1.1'
if learner_type == 'DL':
requirementdata+='\n'
requirementdata+='tensorflow==2.5.0'
f = open(requirementfilename, "w")
f.write(str(requirementdata))
f.close()
if textdata:
installfile='''
import nltk
import ssl
try:
_create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
pass
else:
ssl._create_default_https_context = _create_unverified_https_context
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')'''
f = open(installfilename, "w")
f.write(str(installfile))
f.close()
try:
command = 'docker pull python:3.8-slim-buster'
os.system(command);
#subprocess.check_call(["chmod", "+x", "start_modelservice.sh"], cwd=model_dockerSetup)
subprocess.check_call(["docker", "build", "-t",modelname.lower()+":"+version,"."], cwd=model_dockerSetup)
subprocess.check_call(["docker", "save", "-o",modelname.lower()+"_"+version+".tar",modelname.lower()+":"+version], cwd=model_dockerSetup)
dockerfilepath = os.path.join(model_dockerSetup,modelname.lower()+"_"+version+".tar")
shutil.copyfile(dockerfilepath, os.path.join(filename,modelname.lower()+"_"+version+".tar"))
shutil.rmtree(model_dockerSetup)
return 'Success','SUCCESSFULLY'
except Exception as e:
print("Error: "+str(e))
shutil.rmtree(model_dockerSetup)
return 'Error',str(e)
#createDockerImage(deploymentfolder,modelname,version) |
requirements.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from importlib.metadata import version
import sys
import os
def requirementfile(deploy_path,model,textFeatures,learner_type):
print('hola', model)
modules = ['pandas','numpy','alibi','matplotlib','joblib','shap','ipython','category_encoders','scikit-learn','word2number','flask_restful','evidently','Flask-Cors']
requires = ''
for mod in modules:
requires += f"{mod}=={version(mod)}\n"
if len(textFeatures) > 0:
tmodules = ['spacy','nltk','textblob','demoji','beautifulsoup4','text-unidecode','pyspellchecker','contractions','protobuf']
for mod in tmodules:
requires += f"{mod}=={version(mod)}\n"
if model == 'Extreme Gradient Boosting (XGBoost)':
mmodules = ['xgboost']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model == 'Light Gradient Boosting (LightGBM)':
mmodules = ['lightgbm']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model == 'Categorical Boosting (CatBoost)':
mmodules = ['catboost']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'arima':
mmodules = ['pmdarima']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'fbprophet':
mmodules = ['prophet']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'lstm' or model.lower() == 'mlp' or learner_type =='DL':
mmodules = ['tensorflow']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() in ['cox', 'kaplanmeierfitter']: #bug 12833
mmodules = ['lifelines']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
if model.lower() == 'sentencetransformer': #bug 12833
mmodules = ['sentence_transformers']
for mod in mmodules:
requires += f"{mod}=={version(mod)}\n"
filename = os.path.join(deploy_path,'requirements.txt')
f = open(filename, "wb")
f.write(str(requires).encode('utf8'))
f.close()
|
eion_compress.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import platform
import sys
import subprocess
import glob
import shutil
import time
from aion_deployment.EncryptPythonSourceCode import encrypt_files
import json
def encrypt(alldirs):
for dir in alldirs:
try:
encrypt_files(dir)
except Exception as error_obj:
print("Exception in encrypting", error_obj)
print("-"*50)
def replace_by_compressed(alldirs):
for dir in alldirs:
try:
#print("Processing dir", dir)
files = [f for f in glob.glob(dir + "/*.py")]
secure_path = os.path.join(dir, 'SecuredScripts')
time.sleep(6)
for file in files:
try:
filename_w_ext = os.path.basename(file)
filename, file_extension = os.path.splitext(filename_w_ext)
if filename == "__init__":
continue
#print("Processing file", file)
file_folder_path = os.path.join(secure_path, filename, 'dist')
compressed_file_path = os.path.join(file_folder_path, filename+'_compressed.py')
shutil.copy(compressed_file_path, dir)
os.remove(file)
new_compressed_file_path = os.path.join(dir, filename+'_compressed.py')
target_file_path = os.path.join(dir, filename_w_ext)
os.rename(new_compressed_file_path, target_file_path)
if filename == 'aion_prediction':
shutil.copytree(os.path.join(file_folder_path, 'pytransform'), os.path.join(dir, 'pytransform'))
except Exception as error_obj:
print("Exception in file ", error_obj)
shutil.rmtree(secure_path)
except Exception as error_obj:
print("Exception in dir ", error_obj)
def start_Obfuscate(path):
project_path = path
subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path.join(project_path,dI))]
alldirs = [
project_path,
]
for subdir in subdirs:
if(subdir != 'pytransform'):
alldirs.append(os.path.join(project_path, subdir))
encrypt(alldirs)
replace_by_compressed(alldirs)
if __name__=="__main__":
project_path = sys.argv[1]
print("project_path", project_path)
subdirs = [dI for dI in os.listdir(project_path) if os.path.isdir(os.path.join(project_path,dI))]
alldirs = [
project_path,
]
for subdir in subdirs:
alldirs.append(os.path.join(project_path, subdir))
encrypt(alldirs)
print("*"*50)
replace_by_compressed(alldirs)
# python eion_compress.py "C:\Users\ashwani.s\Desktop\22April\22April\Mohita" "C:\Users\ashwani.s\Desktop\eion\eion" > logfile.log
|
production.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from pathlib import Path
from AION.prediction_package.imports import importModule
from AION.prediction_package.aion_prediction import aionPrediction
from AION.prediction_package.utility import TAB_CHAR
from AION.prediction_package import utility
from AION.prediction_package import common
from AION.prediction_package.base import deployer
def is_supported(problem_type, algo=None):
"""
Return True if problem_type supported otherwise False
"""
supported = ['classification','regression','clustering','timeseriesforecasting','Text Similarity']
return problem_type in supported
def get_deployer(problem_type, algo=None, params={}):
"""
Return deployer class object based on problem type
Raise error if no class is associated with problem type
"""
params['problem_type'] = problem_type
if problem_type == 'classification':
return classification( params)
elif problem_type == 'regression':
return regression( params)
elif problem_type == 'clustering':
return clustering( params)
elif problem_type == 'timeseriesforecasting':
from AION.prediction_package.time_series import forecasting
return forecasting.get_deployer( params)
elif problem_type == 'Text Similarity':
return textSimilarity( params)
else:
raise ValueError('deployment is not supported')
class classification( deployer):
def __init__(self, params={}):
super().__init__( params)
self.feature_reducer = False
if not self.name:
self.name = 'classification'
def create_idrift(self):
obj = aionPrediction()
if self.params['features']['text_feat']:
obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name)
else:
obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],self.name)
def create_odrift(self):
obj = aionPrediction()
if self.params['features']['text_feat']:
obj.create_classification_text_performance_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'])
else:
obj.create_classification_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'])
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
code = f"""
class trainer():
"""
init_code, run_code = self._get_train_code()
return code + init_code + run_code
def _get_train_code(self):
init_code = f"""
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')"""
run_code = f"""
def run(self, df):\
"""
if self.params['training']['algo'] in ['Neural Network']:
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
init_code += f"""
self.model = load_model(model_file)
"""
run_code += """
df = df.astype(np.float32)
return pd.DataFrame(np.argmax(self.model.predict(df),axis=1))
"""
elif self.params['training']['algo'] in ['Neural Architecture Search']:
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
self.importer.addModule(module='autokeras',mod_as='ak')
init_code += f"""
self.model = load_model(model_file,custom_objects=ak.CUSTOM_OBJECTS)
"""
run_code += """
df = df.astype(np.float32)
return pd.DataFrame(self.model.predict(df))
"""
elif self.params['training']['algo'] in ['Deep Q Network','Dueling Deep Q Network']:
self.importer.addModule('joblib')
self.importer.addModule(module='numpy',mod_as='np')
self.importer.addModule(module='constant',mod_from='tensorflow')
self.importer.addModule(module='time_step',mod_from='tf_agents.trajectories')
init_code += f"""
self.model = joblib.load(model_file)
"""
run_code += """
df = df.astype(np.float32)
q, _ = self.model(np.array(df), step_type=constant([time_step.StepType.FIRST] * np.array(df).shape[0]), training=False)
return pd.DataFrame(q.numpy())
"""
elif self.params['training']['algo'] in ['Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']:
self.importer.addModule(module='numpy',mod_as='np')
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
init_code += f"""
self.model = load_model(model_file)
"""
run_code += """
df = np.expand_dims(df, axis=2)
df = df.astype(np.float32)
return pd.DataFrame(np.argmax(self.model.predict(df),axis=1))
"""
else:
self.importer.addModule(module='joblib')
self.importer.addModule(module='numpy',mod_as='np')
init_code += f"""
self.model = joblib.load(model_file)
"""
run_code += """
df = df.astype(np.float32)
return pd.DataFrame(self.model.predict_proba(df), columns=self.model.classes_)
"""
return init_code, run_code
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('joblib')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__(self):
pass
def run(self, raw_df, output):
output = round(output,2)
encoder_file = (Path(__file__).parent/"model")/"label_encoder.pkl"
if encoder_file.exists():
encoder = joblib.load(encoder_file)
output.rename(columns=dict(zip(output.columns, encoder.inverse_transform(list(output.columns)))), inplace=True)
raw_df['prediction'] = output.idxmax(axis=1)
raw_df['probability'] = output.max(axis=1).round(2)
raw_df['remarks'] = output.apply(lambda x: x.to_json(double_precision=2), axis=1)
outputjson = raw_df.to_json(orient='records',double_precision=5)
outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}
return(json.dumps(outputjson))
"""
class regression( deployer):
def __init__(self, params={}):
super().__init__( params)
self.feature_reducer = False
if not self.name:
self.name = 'regression'
def create_idrift(self):
obj = aionPrediction()
if self.params['features']['text_feat']:
obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name)
else:
obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],self.name)
def create_odrift(self):
obj = aionPrediction()
if self.params['features']['text_feat']:
obj.create_regression_text_performance_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'])
else:
obj.create_regression_performance_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'])
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
code = f"""
class trainer():
"""
init_code = f"""
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
"""
run_code = f"""
def run(self, df):\
"""
if self.params['training']['algo'] in ['Neural Architecture Search']:
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
self.importer.addModule(module='autokeras',mod_as='ak')
init_code += f"""
self.model = load_model(model_file,custom_objects=ak.CUSTOM_OBJECTS)
"""
run_code += """
df = df.astype(np.float32)
return self.model.predict(df).reshape(1, -1)
"""
elif self.params['training']['algo'] in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']:
self.importer.addModule(module='numpy',mod_as='np')
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
init_code += f"""
self.model = load_model(model_file)
"""
run_code += """
df = np.expand_dims(df, axis=2)
df = df.astype(np.float32)
return self.model.predict(df).reshape(1, -1)
"""
else:
self.importer.addModule('joblib')
init_code += f"""
self.model = joblib.load(model_file)
"""
run_code += """
df = df.astype(np.float32)
return self.model.predict(df).reshape(1, -1)
"""
return code + init_code + run_code
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__(self):
pass
def run(self, raw_df, output):
raw_df['prediction'] = output[0]
raw_df['prediction'] = raw_df['prediction'].round(2)
outputjson = raw_df.to_json(orient='records',double_precision=5)
outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}
return(json.dumps(outputjson))
"""
class clustering( deployer):
def __init__(self, params={}):
super().__init__( params)
self.feature_reducer = False
if not self.name:
self.name = 'clustering'
def training_code( self):
self.importer.addModule('joblib')
self.importer.addModule(module='pandas',mod_as='pd')
code = f"""
class trainer():
"""
init_code = f"""
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
"""
run_code = f"""
def run(self, df):\
"""
if self.params['training']['algo'] == 'DBSCAN':
init_code += f"""
self.model = joblib.load(model_file)
"""
run_code += """
return self.model.fit_predict(df)
"""
else:
init_code += f"""
self.model = joblib.load(model_file)
"""
run_code += """
return self.model.predict(df).reshape(1, -1)
"""
return code + init_code + run_code
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__(self):
pass
def run(self, raw_df, output):
raw_df['prediction'] = output[0]
raw_df['prediction'] = raw_df['prediction'].round(2)
outputjson = raw_df.to_json(orient='records',double_precision=2)
outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}
return(json.dumps(outputjson))
"""
return code
if __name__ == '__main__':
config = {'usecase_name': 'AI0110', 'usecase_ver': '1', 'features': {'input_feat': ['v2'], 'target_feat': 'v1', 'text_feat': ['v2']}, 'paths': {'deploy': r'C:/Users/vashistah/AppData/Local/Programs/HCLTech/AION/data/target/AI0110/1', 'usecase': r'C:/Users/vashistah/AppData/Local/Programs/HCLTech/AION/data/target/AI0110'}, 'profiler': {'input_features': ['v2'], 'output_features': ['07xxxxxxxxx_vect', '08700621170150p_vect', '08702840625comuk_vect', '08718726270150gbpmtmsg18_vect', '1000s_vect', '10am7pm_vect', '10k_vect', '10p_vect', '10pmin_vect', '10ppm_vect', '11mths_vect', '125gift_vect', '12hrs_vect', '12mths_vect', '150p_vect', '150perwksub_vect', '150pm_vect', '150pmin_vect', '150pmsg_vect', '150pmsgrcvdhgsuite3422landsroww1j6hl_vect', '150pmtmsgrcvd18_vect', '150ppm_vect', '150ptone_vect', '150pwk_vect', '150week_vect', '16only_vect', '18only_vect', '1hr_vect', '1minmobsmorelkpobox177hp51fl_vect', '1st_vect', '1x150pwk_vect', '20p_vect', '20pmin_vect', '21st_vect', '220cm2_vect', '24hrs_vect', '25p_vect', '26th_vect', '2day_vect', '2find_vect', '2geva_vect', '2go_vect', '2marrow_vect', '2mrw_vect', '2nd_vect', '2nite_vect', '2optout_vect', '2p_vect', '2u_vect', '2waxsto_vect', '2wks_vect', '300p_vect', '31pmsg_vect', '3510i_vect', '3d_vect', '3g_vect', '3gbp_vect', '3hrs_vect', '3mins_vect', '3qxj9_vect', '3rd_vect', '3ss_vect', '3u_vect', '3uz_vect', '3wk_vect', '40gb_vect', '4a_vect', '4d_vect', '4eva_vect', '4get_vect', '4info_vect', '4mths_vect', '4th_vect', '4u_vect', '50p_vect', '5min_vect', '5pm_vect', '5wb_vect', '5we_vect', '60pmin_vect', '6hrs_vect', '6months_vect', '6pm_vect', '7250i_vect', '7ish_vect', '8am_vect', '8pm_vect', '8th_vect', '8wp_vect', '9ae_vect', '9ja_vect', '9pm_vect', '9t_vect', 'aathi_vect', 'abi_vect', 'ability_vect', 'abiola_vect', 'able_vect', 'abt_vect', 'abta_vect', 'aburo_vect', 'ac_vect', 'academic_vect', 'acc_vect', 'accept_vect', 'access_vect', 'accident_vect', 'accidentally_vect', 'accordingly_vect', 'account_vect', 'ache_vect', 'across_vect', 'acted_vect', 'action_vect', 'activate_vect', 'activities_vect', 'actor_vect', 'actual_vect', 'actually_vect', 'ad_vect', 'adam_vect', 'add_vect', 'added_vect', 'addicted_vect', 'addie_vect', 'address_vect', 'admin_vect', 'administrator_vect', 'admirer_vect', 'admit_vect', 'adore_vect', 'adoring_vect', 'ads_vect', 'adult_vect', 'advance_vect', 'adventure_vect', 'advice_vect', 'advise_vect', 'affair_vect', 'affairs_vect', 'affectionate_vect', 'afraid_vect', 'aft_vect', 'afternoon_vect', 'aftr_vect', 'agalla_vect', 'age_vect', 'age16_vect', 'ages_vect', 'ago_vect', 'agree_vect', 'ah_vect', 'aha_vect', 'ahead_vect', 'ahmad_vect', 'ai_vect', 'aight_vect', 'aint_vect', 'air_vect', 'airport_vect', 'airtel_vect', 'aiya_vect', 'aiyah_vect', 'aiyar_vect', 'aiyo_vect', 'al_vect', 'album_vect', 'alert_vect', 'alex_vect', 'alfie_vect', 'ali_vect', 'allah_vect', 'allow_vect', 'allowed_vect', 'almost_vect', 'alone_vect', 'along_vect', 'already_vect', 'alright_vect', 'alrite_vect', 'also_vect', 'always_vect', 'alwys_vect', 'amazing_vect', 'american_vect', 'among_vect', 'amount_vect', 'amp_vect', 'amt_vect', 'andros_vect', 'angry_vect', 'annie_vect', 'anniversary_vect', 'announcement_vect', 'anot_vect', 'another_vect', 'ans_vect', 'ansr_vect', 'answer_vect', 'answered_vect', 'answering_vect', 'answers_vect', 'anthony_vect', 'anti_vect', 'anybody_vect', 'anymore_vect', 'anyone_vect', 'anything_vect', 'anytime_vect', 'anyway_vect', 'anyways_vect', 'apartment_vect', 'app_vect', 'apparently_vect', 'applebees_vect', 'apply_vect', 'appointment_vect', 'appreciate_vect', 'appreciated_vect', 'approx_vect', 'apps_vect', 'appt_vect', 'april_vect', 'ar_vect', 'arcade_vect', 'ard_vect', 'area_vect', 'argh_vect', 'argument_vect', 'arm_vect', 'armand_vect', 'arms_vect', 'around_vect', 'arrange_vect', 'arrested_vect', 'arrive_vect', 'arsenal_vect', 'art_vect', 'arun_vect', 'asap_vect', 'ashley_vect', 'ask_vect', 'askd_vect', 'asked_vect', 'askin_vect', 'asking_vect', 'asks_vect', 'asleep_vect', 'ass_vect', 'assume_vect', 'ate_vect', 'atlanta_vect', 'atlast_vect', 'atm_vect', 'attached_vect', 'attempt_vect', 'attend_vect', 'auction_vect', 'august_vect', 'aunt_vect', 'aunty_vect', 'auto_vect', 'av_vect', 'available_vect', 'avatar_vect', 'ave_vect', 'avent_vect', 'avoid_vect', 'await_vect', 'awaiting_vect', 'awake_vect', 'award_vect', 'awarded_vect', 'away_vect', 'awesome_vect', 'aww_vect', 'b4_vect', 'ba_vect', 'babe_vect', 'babes_vect', 'babies_vect', 'baby_vect', 'back_vect', 'bad_vect', 'bag_vect', 'bags_vect', 'bahamas_vect', 'bak_vect', 'balance_vect', 'bank_vect', 'banks_vect', 'bar_vect', 'barely_vect', 'basic_vect', 'basically_vect', 'bat_vect', 'bath_vect', 'bathe_vect', 'bathing_vect', 'battery_vect', 'bay_vect', 'bb_vect', 'bc_vect', 'bck_vect', 'bcoz_vect', 'bday_vect', 'be_vect', 'bears_vect', 'beautiful_vect', 'beauty_vect', 'bec_vect', 'become_vect', 'becoz_vect', 'bed_vect', 'bedrm_vect', 'bedroom_vect', 'beer_vect', 'befor_vect', 'beg_vect', 'begin_vect', 'behave_vect', 'behind_vect', 'bein_vect', 'believe_vect', 'bell_vect', 'belly_vect', 'belovd_vect', 'best_vect', 'bet_vect', 'better_vect', 'beyond_vect', 'bf_vect', 'bid_vect', 'bids_vect', 'big_vect', 'bigger_vect', 'biggest_vect', 'bill_vect', 'billed_vect', 'billion_vect', 'bills_vect', 'bin_vect', 'biola_vect', 'birds_vect', 'birla_vect', 'birth_vect', 'birthdate_vect', 'birthday_vect', 'bishan_vect', 'bit_vect', 'bitch_vect', 'bite_vect', 'black_vect', 'blackberry_vect', 'blah_vect', 'blake_vect', 'blank_vect', 'bleh_vect', 'bless_vect', 'blessing_vect', 'bloo_vect', 'blood_vect', 'bloody_vect', 'blue_vect', 'bluetooth_vect', 'bluff_vect', 'boat_vect', 'body_vect', 'bold_vect', 'bone_vect', 'bonus_vect', 'boo_vect', 'book_vect', 'booked_vect', 'booking_vect', 'books_vect', 'boost_vect', 'booty_vect', 'bored_vect', 'boring_vect', 'born_vect', 'boss_vect', 'boston_vect', 'bother_vect', 'bottom_vect', 'bought_vect', 'bout_vect', 'bowl_vect', 'box_vect', 'box326_vect', 'box334sk38ch_vect', 'box97n7qp_vect', 'boy_vect', 'boye_vect', 'boyfriend_vect', 'boys_vect', 'boytoy_vect', 'brah_vect', 'brand_vect', 'bread_vect', 'break_vect', 'breathe_vect', 'bright_vect', 'brilliant_vect', 'bring_vect', 'bringing_vect', 'brings_vect', 'british_vect', 'bro_vect', 'broad_vect', 'broke_vect', 'broken_vect', 'bros_vect', 'brothas_vect', 'brother_vect', 'brought_vect', 'bruv_vect', 'bslvyl_vect', 'bt_vect', 'btnationalrate_vect', 'btw_vect', 'bucks_vect', 'bud_vect', 'budget_vect', 'buff_vect', 'buffet_vect', 'bugis_vect', 'building_vect', 'buns_vect', 'burger_vect', 'burns_vect', 'bus_vect', 'buses_vect', 'business_vect', 'busy_vect', 'butt_vect', 'buy_vect', 'buying_vect', 'buzz_vect', 'bx420_vect', 'bx420ip45we_vect', 'bye_vect', 'ca_vect', 'cabin_vect', 'cafe_vect', 'cake_vect', 'cal_vect', 'calculation_vect', 'calicut_vect', 'california_vect', 'call_vect', 'call2optout674_vect', 'callback_vect', 'callcost_vect', 'called_vect', 'caller_vect', 'callers_vect', 'callertune_vect', 'callin_vect', 'calling_vect', 'calls_vect', 'callså_vect', 'cam_vect', 'camcorder_vect', 'came_vect', 'camera_vect', 'cameravideo_vect', 'campus_vect', 'can_vect', 'canada_vect', 'canal_vect', 'canary_vect', 'cancel_vect', 'cancelled_vect', 'cancer_vect', 'cant_vect', 'captain_vect', 'car_vect', 'card_vect', 'cardiff_vect', 'care_vect', 'cared_vect', 'career_vect', 'careful_vect', 'carefully_vect', 'caring_vect', 'carlos_vect', 'caroline_vect', 'cars_vect', 'cartoon_vect', 'case_vect', 'cash_vect', 'cashbalance_vect', 'cashin_vect', 'castor_vect', 'cat_vect', 'catch_vect', 'catching_vect', 'caught_vect', 'cause_vect', 'cbe_vect', 'cc_vect', 'cd_vect', 'cdgt_vect', 'cds_vect', 'celebrate_vect', 'celebration_vect', 'cell_vect', 'center_vect', 'centre_vect', 'certainly_vect', 'cha_vect', 'chain_vect', 'challenge_vect', 'chance_vect', 'change_vect', 'changed_vect', 'changes_vect', 'channel_vect', 'character_vect', 'charge_vect', 'charged_vect', 'charges_vect', 'charity_vect', 'charles_vect', 'chase_vect', 'chasing_vect', 'chat_vect', 'chatting_vect', 'cheap_vect', 'cheaper_vect', 'cheat_vect', 'chechi_vect', 'check_vect', 'checked_vect', 'checking_vect', 'cheers_vect', 'chennai_vect', 'cherish_vect', 'chest_vect', 'chicken_vect', 'chikku_vect', 'child_vect', 'childish_vect', 'children_vect', 'chill_vect', 'chillin_vect', 'china_vect', 'chinese_vect', 'chip_vect', 'chocolate_vect', 'choice_vect', 'choose_vect', 'chosen_vect', 'christ_vect', 'christmas_vect', 'church_vect', 'cine_vect', 'cinema_vect', 'citizen_vect', 'city_vect', 'claim_vect', 'claims_vect', 'claire_vect', 'class_vect', 'classes_vect', 'clean_vect', 'cleaning_vect', 'clear_vect', 'clearly_vect', 'click_vect', 'clock_vect', 'close_vect', 'closed_vect', 'closer_vect', 'closes_vect', 'clothes_vect', 'club_vect', 'cn_vect', 'co_vect', 'coast_vect', 'coat_vect', 'cochin_vect', 'code_vect', 'coffee_vect', 'coin_vect', 'coins_vect', 'cold_vect', 'colleagues_vect', 'collect_vect', 'collected_vect', 'collecting_vect', 'collection_vect', 'college_vect', 'colour_vect', 'come_vect', 'comedy_vect', 'comes_vect', 'comin_vect', 'coming_vect', 'commercial_vect', 'common_vect', 'community_vect', 'comp_vect', 'company_vect', 'competition_vect', 'complete_vect', 'completed_vect', 'completely_vect', 'complimentary_vect', 'computer_vect', 'concentrate_vect', 'concert_vect', 'conditions_vect', 'conducts_vect', 'confidence_vect', 'confirm_vect', 'congrats_vect', 'congratulations_vect', 'connection_vect', 'consider_vect', 'considering_vect', 'constant_vect', 'constantly_vect', 'contact_vect', 'contacted_vect', 'contacts_vect', 'content_vect', 'contents_vect', 'continue_vect', 'contract_vect', 'control_vect', 'convey_vect', 'convinced_vect', 'cool_vect', 'coping_vect', 'copy_vect', 'cornwall_vect', 'correct_vect', 'cos_vect', 'cost_vect', 'costa_vect', 'costs_vect', 'costå_vect', 'could_vect', 'count_vect', 'countin_vect', 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'user_vect', 'usf_vect', 'using_vect', 'usual_vect', 'usually_vect', 'vale_vect', 'valentine_vect', 'valentines_vect', 'valid_vect', 'valid12hrs_vect', 'valuable_vect', 'value_vect', 'valued_vect', 'vary_vect', 've_vect', 'vegas_vect', 'verify_vect', 'version_vect', 'via_vect', 'vid_vect', 'video_vect', 'videochat_vect', 'videophones_vect', 'vijay_vect', 'vikky_vect', 'village_vect', 'violated_vect', 'violence_vect', 'vip_vect', 'virgin_vect', 'visit_vect', 'vivek_vect', 'vl_vect', 'voda_vect', 'vodafone_vect', 'vodka_vect', 'voice_vect', 'voicemail_vect', 'vomit_vect', 'vote_vect', 'voucher_vect', 'vouchers_vect', 'vry_vect', 'vth_vect', 'w45wq_vect', 'wa_vect', 'wah_vect', 'wait_vect', 'waited_vect', 'waitin_vect', 'waiting_vect', 'wake_vect', 'waking_vect', 'wales_vect', 'walk_vect', 'walked_vect', 'walking_vect', 'walmart_vect', 'wan_vect', 'wana_vect', 'want_vect', 'wanted_vect', 'wanting_vect', 'wants_vect', 'wap_vect', 'warm_vect', 'warner_vect', 'waste_vect', 'wasted_vect', 'wat_vect', 'watch_vect', 'watching_vect', 'water_vect', 'wats_vect', 'way_vect', 'wc1n3xx_vect', 'we_vect', 'weak_vect', 'wear_vect', 'wearing_vect', 'weather_vect', 'web_vect', 'website_vect', 'wed_vect', 'wedding_vect', 'wednesday_vect', 'wee_vect', 'weed_vect', 'week_vect', 'weekend_vect', 'weekends_vect', 'weekly_vect', 'weeks_vect', 'weigh_vect', 'weight_vect', 'weird_vect', 'welcome_vect', 'well_vect', 'welp_vect', 'wen_vect', 'went_vect', 'west_vect', 'wet_vect', 'what_vect', 'whatever_vect', 'whats_vect', 'whenever_vect', 'whenevr_vect', 'wherever_vect', 'whether_vect', 'white_vect', 'whn_vect', 'whole_vect', 'whos_vect', 'whose_vect', 'wid_vect', 'widelivecomindex_vect', 'wif_vect', 'wife_vect', 'wil_vect', 'willing_vect', 'win_vect', 'wind_vect', 'wine_vect', 'winner_vect', 'winning_vect', 'wins_vect', 'wipro_vect', 'wisdom_vect', 'wise_vect', 'wish_vect', 'wishes_vect', 'wishing_vect', 'wit_vect', 'within_vect', 'without_vect', 'wiv_vect', 'wk_vect', 'wkend_vect', 'wkg_vect', 'wkly_vect', 'wks_vect', 'wld_vect', 'wml_vect', 'wn_vect', 'wnt_vect', 'wo_vect', 'woke_vect', 'woken_vect', 'woman_vect', 'women_vect', 'wonder_vect', 'wonderful_vect', 'wondering_vect', 'wont_vect', 'woot_vect', 'word_vect', 'words_vect', 'work_vect', 'workin_vect', 'working_vect', 'works_vect', 'world_vect', 'worried_vect', 'worries_vect', 'worry_vect', 'worse_vect', 'worst_vect', 'worth_vect', 'wot_vect', 'would_vect', 'wow_vect', 'write_vect', 'wrong_vect', 'wtf_vect', 'wud_vect', 'wuld_vect', 'wun_vect', 'www4tcbiz_vect', 'wwwcomuknet_vect', 'wwwetlpcoukexpressoffer_vect', 'wwwgetzedcouk_vect', 'wwwldewcom_vect', 'wwwldewcom1win150ppmx3age16_vect', 'wwwmovietriviatv_vect', 'wwwringtonescouk_vect', 'wwwsmsconet_vect', 'wwwtxttowincouk_vect', 'wwwurawinnercom_vect', 'wylie_vect', 'xchat_vect', 'xmas_vect', 'xuhui_vect', 'xx_vect', 'xxx_vect', 'xxxx_vect', 'xxxxx_vect', 'xy_vect', 'ya_vect', 'yahoo_vect', 'yan_vect', 'yar_vect', 'yay_vect', 'yck_vect', 'yeah_vect', 'year_vect', 'years_vect', 'yelling_vect', 'yellow_vect', 'yep_vect', 'yes_vect', 'yest_vect', 'yesterday_vect', 'yet_vect', 'yetunde_vect', 'yijue_vect', 'ym_vect', 'yo_vect', 'yoga_vect', 'yogasana_vect', 'yor_vect', 'you_vect', 'yr_vect', 'yrs_vect', 'yummy_vect', 'yun_vect', 'yuo_vect', 'yup_vect', 'zed_vect', 'zindgi_vect', 'ìï_vect', 'ûò_vect']}, 'training': {'algo': 'Logistic Regression', 'model_file': 'AI0110_1.sav'}}
deployer = get_deployer('classification',params=config)
deployer.run( ) |
output_formatter.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import platform
import json
import shutil
import logging
class outputFormatter:
def __init__(self):
self.log = logging.getLogger('eion')
self.log.info('========> Inside Output Formatter')
def crate_output_format_file(self,deploy_path,learner_type,modelType,model,output_label,threshold,trained_data_file,dictDiffCount,targetFeature,features,datetimeFeature):
self.output_formatfile = 'import json'
self.output_formatfile += '\n'
self.output_formatfile += 'import numpy as np'
self.output_formatfile += '\n'
self.output_formatfile += 'import pandas as pd'
self.output_formatfile += '\n'
self.output_formatfile += 'import os'
self.output_formatfile += '\n'
self.output_formatfile += 'from pathlib import Path'
self.output_formatfile += '\n'
if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"):
self.output_formatfile += 'from script.aion_granularity import aion_gettimegranularity'
self.output_formatfile += '\n'
self.output_formatfile += 'class output_format(object):'
self.output_formatfile += '\n'
if(model == 'VAR'):
self.output_formatfile += ' def invertTransformation(self,predictions):'
self.output_formatfile += '\n'
self.output_formatfile += ' datasetdf = pd.read_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)),"..","data","trainingdata.csv"))'
self.output_formatfile += '\n'
self.output_formatfile += ' dictDiffCount = '+str(dictDiffCount)
self.output_formatfile += '\n'
self.output_formatfile += ' targetFeature = "'+str(targetFeature)+'"'
self.output_formatfile += '\n'
self.output_formatfile += ' columns = targetFeature.split(",")'
self.output_formatfile += '\n'
self.output_formatfile += ' pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns)'
self.output_formatfile += '\n'
self.output_formatfile += ' for j in range(0,len(columns)):'
self.output_formatfile += '\n'
self.output_formatfile += ' for i in range(0, len(predictions)):'
self.output_formatfile += '\n'
self.output_formatfile += ' pred.iloc[i][j] = round(predictions[i][j],2)'
self.output_formatfile += '\n'
self.output_formatfile += ' prediction = pred'
self.output_formatfile += '\n'
self.output_formatfile += ' for col in columns:'
self.output_formatfile += '\n'
self.output_formatfile += ' if col in dictDiffCount:'
self.output_formatfile += '\n'
self.output_formatfile += ' if dictDiffCount[col]==2:'
self.output_formatfile += '\n'
self.output_formatfile += ' prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum()'
self.output_formatfile += '\n'
self.output_formatfile += ' prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum()'
self.output_formatfile += '\n'
self.output_formatfile += ' prediction = pred'
self.output_formatfile += '\n'
self.output_formatfile += ' return(prediction)'
self.output_formatfile += '\n'
self.log.info("op:modelType: \n"+str(modelType))
if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"):
# if modelType == 'anomaly_detection':
self.output_formatfile += ' def find_point_subsequence_anomalies(self,datetime_column,dataframe=None):'
self.output_formatfile += '\n'
self.output_formatfile += ' try:'
self.output_formatfile += '\n'
self.output_formatfile += ' dataframe[datetime_column] = pd.to_datetime(dataframe[datetime_column]) '
self.output_formatfile += '\n'
self.output_formatfile += ' aion_gettimegranularity_obj=aion_gettimegranularity(dataframe,datetime_column) '
self.output_formatfile += '\n'
self.output_formatfile += ' anomaly_info_df=aion_gettimegranularity_obj.get_granularity() '
self.output_formatfile += '\n'
self.output_formatfile += ' except Exception as e:'
self.output_formatfile += '\n'
self.output_formatfile += ' print(f"find_point_subsequence_anomalies,: aion_gettimegranularity err msg:{e} ")\n'
self.output_formatfile += ' return anomaly_info_df'
self.output_formatfile += '\n'
if((model.lower() in ['autoencoder','dbscan']) and modelType.lower()=="anomaly_detection"):
if (datetimeFeature!='' and datetimeFeature!='NA'):
self.output_formatfile += ' def apply_output_format(self,df,modeloutput,datetimeFeature):'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' def apply_output_format(self,df,modeloutput):'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' def apply_output_format(self,df,modeloutput):'
self.output_formatfile += '\n'
if modelType.lower() == 'classification':
self.output_formatfile += ' modeloutput = round(modeloutput,2)'
self.output_formatfile += '\n'
if(learner_type == 'ImageClassification'):
if(str(output_label) != '{}'):
inv_mapping_dict = {v: k for k, v in output_label.items()}
self.output_formatfile += ' le_dict = '+ str(inv_mapping_dict)
self.output_formatfile += '\n'
self.output_formatfile += ' predictions = []'
self.output_formatfile += '\n'
self.output_formatfile += ' for x in modeloutput:'
self.output_formatfile += '\n'
self.output_formatfile += ' x = le_dict[x]'
self.output_formatfile += '\n'
self.output_formatfile += ' predictions.append(x)'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' predictions=modeloutput'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = predictions'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = df.to_json(orient=\'records\')'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}'
self.output_formatfile += '\n'
elif(learner_type == 'Text Similarity'):
self.output_formatfile += ' df[\'prediction\'] = np.where(modeloutput > '+str(threshold)+',1,0)'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'probability\'] = modeloutput'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}'
self.output_formatfile += '\n'
elif(learner_type == 'TS'):
if(model == 'VAR'):
self.output_formatfile += ' modeloutput = self.invertTransformation(modeloutput)'
self.output_formatfile += '\n'
self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\',double_precision=2)'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}'
elif(model.lower() == 'fbprophet'):
self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\')'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}'
elif((model.lower() == 'lstm' or model.lower() == 'mlp') and len(features) >= 1):
self.output_formatfile += ' modeloutput = modeloutput.round(2)\n'
self.output_formatfile += ' modeloutput = modeloutput.to_json(orient=\'records\')\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(modeloutput)}\n'
else:
self.output_formatfile += ' modeloutput = modeloutput.round(2)'
self.output_formatfile += '\n'
self.output_formatfile += ' modeloutput = json.dumps(modeloutput.tolist())'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":eval(modeloutput)}'
self.output_formatfile += '\n'
elif(learner_type in ['RecommenderSystem','similarityIdentification','contextualSearch']):
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = modeloutput'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}'
self.output_formatfile += '\n'
else:
if(modelType == 'Classification' or modelType == 'TLClassification' or modelType == 'anomaly_detection'):
if(modelType == 'Classification' or modelType == 'TLClassification' or modelType == 'anomaly_detection'):
if(str(output_label) != '{}'):
inv_mapping_dict = {v: k for k, v in output_label.items()}
self.output_formatfile += ' le_dict = '+ str(inv_mapping_dict)
self.output_formatfile += '\n'
'''
if(model in ['SGDClassifier']):
self.output_formatfile += ' modeloutput = modeloutput.replace({"predict_class": le_dict})'
else:
self.output_formatfile += ' modeloutput = modeloutput.rename(columns=le_dict)'
'''
if modelType != 'anomaly_detection':
self.output_formatfile += ' modeloutput = modeloutput.rename(columns=le_dict)'
self.output_formatfile += '\n'
if(threshold != -1):
'''
if(model in ['SGDClassifier']):
self.output_formatfile += ' df[\'prediction\'] = np.where(modeloutput[\'probability\'] > '+str(threshold)+',1,0)'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'probability\'] = modeloutput[\'probability\']'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'remarks\'] = ""'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' predictedData = modeloutput.iloc[:,1]'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = np.where(predictedData > '+str(threshold)+',modeloutput.columns[1],modeloutput.columns[0])'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'probability\'] = np.where(df[\'prediction\'] == modeloutput.columns[1],modeloutput.iloc[:,1],modeloutput.iloc[:,0])'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)'
self.output_formatfile += '\n'
'''
self.output_formatfile += ' predictedData = modeloutput.iloc[:,1]'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = np.where(predictedData > '+str(threshold)+',modeloutput.columns[1],modeloutput.columns[0])'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'probability\'] = np.where(df[\'prediction\'] == modeloutput.columns[1],modeloutput.iloc[:,1],modeloutput.iloc[:,0])'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)'
self.output_formatfile += '\n'
else:
'''
if(model in ['SGDClassifier']):
self.output_formatfile += ' df[\'prediction\'] = modeloutput[\'predict_class\']'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'probability\'] = ""'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'remarks\'] = "NA"'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' df[\'prediction\'] = modeloutput.idxmax(axis=1)'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'probability\'] = modeloutput.max(axis=1)'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)'
self.output_formatfile += '\n'
'''
if modelType == 'anomaly_detection':
# if (model.lower()=='autoencoder'):
if model.lower() in ['autoencoder']:
if (datetimeFeature != '' and datetimeFeature.lower() != 'na'):
self.output_formatfile += ' df[modeloutput.columns] = modeloutput\n'
self.output_formatfile += ' anomaly_df=df[df[\'anomaly\'] == True]\n'
self.output_formatfile += ' anomaly_prediction_df=self.find_point_subsequence_anomalies(datetimeFeature,anomaly_df)\n'
self.output_formatfile += ' new_dir = str(Path(__file__).parent.parent/\'data\')\n'
self.output_formatfile += ' anomaly_prediction_df.to_csv(f"{new_dir}/anomaly_data.csv")\n'
self.output_formatfile += ' try:\n'
self.output_formatfile += ' anomaly_prediction_df[datetimeFeature]=pd.to_datetime(anomaly_prediction_df[datetimeFeature])\n'
self.output_formatfile += ' df[datetimeFeature]=pd.to_datetime(df[datetimeFeature])\n'
self.output_formatfile += ' anomaly_prediction_df.drop("Time_diff",axis=1,inplace=True)\n'
self.output_formatfile += ' except:\n'
self.output_formatfile += ' pass\n'
self.output_formatfile += ' try:\n'
self.output_formatfile += ' df_out = pd.merge(df, anomaly_prediction_df, on=df.columns.values.tolist(), how=\'left\')\n'
self.output_formatfile += ' df_out[\'anomaly\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n'
self.output_formatfile += ' df_out[\'anomalyType\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n'
self.output_formatfile += ' df_out.to_csv(f"{new_dir}/overall_ad_output.csv") \n'
self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str) \n'
self.output_formatfile += ' df_out.drop("time_diff",axis=1,inplace=True)\n'
self.output_formatfile += ' except Exception as e:\n'
self.output_formatfile += ' print("anomaly data updated issue",e)\n'
self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n'
self.output_formatfile += ' df=df_out \n'
else:
self.output_formatfile += ' df[modeloutput.columns] = modeloutput\n'
elif (model.lower()=='dbscan'):
if (datetimeFeature != '' and datetimeFeature.lower() != 'na'):
self.output_formatfile += ' df[\'anomaly\'] = modeloutput[\'cluster\']== -1\n'
self.output_formatfile += ' anomaly_df=df[df[\'anomaly\'] == True]\n'
self.output_formatfile += ' anomaly_prediction_df=self.find_point_subsequence_anomalies(datetimeFeature,anomaly_df)\n'
self.output_formatfile += ' new_dir = str(Path(__file__).parent.parent/\'data\')\n'
self.output_formatfile += ' try:\n'
self.output_formatfile += ' anomaly_prediction_df[datetimeFeature]=pd.to_datetime(anomaly_prediction_df[datetimeFeature])\n'
self.output_formatfile += ' df[datetimeFeature]=pd.to_datetime(df[datetimeFeature])\n'
self.output_formatfile += ' except:\n'
self.output_formatfile += ' pass\n'
self.output_formatfile += ' try:\n'
self.output_formatfile += ' df_out = pd.merge(df, anomaly_prediction_df, on=df.columns.values.tolist(), how=\'left\')\n'
self.output_formatfile += ' df_out[\'anomaly\'].replace([\'None\', \'NaN\', np.nan], "Normal", inplace=True)\n'
self.output_formatfile += ' df_out.to_csv(f"{new_dir}/overall_ad_output.csv") \n'
self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n'
self.output_formatfile += ' except Exception as e:\n'
self.output_formatfile += ' print("anomaly data updated.")\n'
self.output_formatfile += ' df_out[datetimeFeature]=df_out[datetimeFeature].astype(str)\n'
self.output_formatfile += ' df=df_out \n'
else:
self.output_formatfile += ' df[\'anomaly\'] = modeloutput[\'cluster\']== -1\n'
self.output_formatfile += ' df.sort_values(by=[\'anomaly\'], ascending=False, inplace=True)\n'
else:
self.output_formatfile += ' df[\'prediction\'] = modeloutput'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' df[\'prediction\'] = modeloutput.idxmax(axis=1)'
self.output_formatfile += '\n'
if learner_type != 'DL':
self.output_formatfile += ' df[\'probability\'] = modeloutput.max(axis=1).round(2)'
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'remarks\'] = modeloutput.apply(lambda x: x.to_json(double_precision=2), axis=1)'
self.output_formatfile += '\n'
else:
if model == 'COX':
self.output_formatfile += '\n'
self.output_formatfile += ' modeloutput[0] = modeloutput[0].round(2)'
self.output_formatfile += '\n'
#self.output_formatfile += ' modeloutput = modeloutput[0].to_json(orient=\'records\',double_precision=2)'
#self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = modeloutput'
self.output_formatfile += '\n'
else:
self.output_formatfile += ' df[\'prediction\'] = modeloutput[0]'
if(learner_type == 'objectDetection'):
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = df[\'prediction\']'
else:
self.output_formatfile += '\n'
self.output_formatfile += ' df[\'prediction\'] = df[\'prediction\'].round(2)'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = df.to_json(orient=\'records\',double_precision=2)'
self.output_formatfile += '\n'
self.output_formatfile += ' outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}'
self.output_formatfile += '\n'
self.output_formatfile += ' return(json.dumps(outputjson))'
filename = os.path.join(deploy_path,'script','output_format.py')
#print(deploy_path)
f = open(filename, "wb")
self.log.info('-------> Output Mapping File Location :'+filename)
f.write(str(self.output_formatfile).encode('utf8'))
f.close() |
inputdrift.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import pandas as pd
import numpy as np
import scipy
import warnings
import scipy.stats as st
import logging
import json
class inputdrift():
def __init__(self,conf):
self.log = logging.getLogger('eion')
def get_input_drift(self,ndf,hdf,outputfolder):
selectedColumns = self.features.split(',')
dataalertcount=0
distributionChangeColumns=""
distributionChangeMessage=[]
for i in range(0,len(selectedColumns)):
data1=hdf[selectedColumns[i]]
data2=ndf[selectedColumns[i]]
if(data1.dtype !="str" and data2.dtype !="str" ):
cumulativeData=data1.append(data2)
teststaticValue=teststatic(self,data1,data2)
if (teststaticValue < 0.05):
distributionName1,sse1=DistributionFinder(self,data1)
distributionName2,sse2=DistributionFinder(self,data2)
if(distributionName1 == distributionName2):
dataalertcount = dataalertcount
else:
dataalertcount = dataalertcount+1
distributionChangeColumns=distributionChangeColumns+selectedColumns[i]+","
changedColumn = {}
changedColumn['Feature'] = selectedColumns[i]
changedColumn['KS_Training'] = teststaticValue
changedColumn['Training_Distribution'] = distributionName1
changedColumn['New_Distribution'] = distributionName2
distributionChangeMessage.append(changedColumn)
else :
dataalertcount = dataalertcount
else :
response ="Selected Columns should be Numerical Values"
if(dataalertcount == 0):
resultStatus="Model is working as expected"
else :
resultStatus=json.dumps(distributionChangeMessage)
return(dataalertcount,resultStatus)
def DistributionFinder(self,data):
try:
distributionName =""
sse =0.0
KStestStatic=0.0
dataType=""
if(data.dtype == "float64"):
dataType ="Continuous"
elif(data.dtype =="int"):
dataType="Discrete"
elif(data.dtype =="int64"):
dataType="Discrete"
if(dataType == "Discrete"):
distributions= [st.bernoulli,st.binom,st.geom,st.nbinom,st.poisson]
index, counts = np.unique(data.astype(int),return_counts=True)
if(len(index)>=2):
best_sse = np.inf
y1=[]
total=sum(counts)
mean=float(sum(index*counts))/total
variance=float((sum(index**2*counts) -total*mean**2))/(total-1)
dispersion=mean/float(variance)
theta=1/float(dispersion)
r=mean*(float(theta)/1-theta)
for j in counts:
y1.append(float(j)/total)
pmf1=st.bernoulli.pmf(index,mean)
pmf2=st.binom.pmf(index,len(index),p=mean/len(index))
pmf3=st.geom.pmf(index,1/float(1+mean))
pmf4=st.nbinom.pmf(index,mean,r)
pmf5=st.poisson.pmf(index,mean)
sse1 = np.sum(np.power(y1 - pmf1, 2.0))
sse2 = np.sum(np.power(y1 - pmf2, 2.0))
sse3 = np.sum(np.power(y1 - pmf3, 2.0))
sse4 = np.sum(np.power(y1 - pmf4, 2.0))
sse5 = np.sum(np.power(y1- pmf5, 2.0))
sselist=[sse1,sse2,sse3,sse4,sse5]
for i in range(0,len(sselist)):
if best_sse > sselist[i] > 0:
best_distribution = distributions[i].name
best_sse = sselist[i]
elif (len(index) == 1):
best_distribution = "Constant Data-No Distribution"
best_sse = 0.0
distributionName =best_distribution
sse=best_sse
elif(dataType == "Continuous"):
distributions = [st.uniform,st.expon,st.weibull_max,st.weibull_min,st.chi,st.norm,st.lognorm,st.t,st.gamma,st.beta]
best_distribution = st.norm.name
best_sse = np.inf
datamin=data.min()
datamax=data.max()
nrange=datamax-datamin
y, x = np.histogram(data.astype(float), bins='auto', density=True)
x = (x + np.roll(x, -1))[:-1] / 2.0
for distribution in distributions:
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
params = distribution.fit(data.astype(float))
# Separate parts of parameters
arg = params[:-2]
loc = params[-2]
scale = params[-1]
# Calculate fitted PDF and error with fit in distribution
pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)
sse = np.sum(np.power(y - pdf, 2.0))
if(best_sse >sse > 0):
best_distribution = distribution.name
best_sse = sse
distributionName =best_distribution
sse=best_sse
except:
response = str(sys.exc_info()[0])
message='Job has Failed'+response
print(message)
return distributionName,sse
##KStestStatic -pvalue finding
def teststatic(self,data1,data2):
try:
teststatic =st.ks_2samp(data1,data2)
pValue=0.0
scipyVersion =scipy.__version__
if(scipyVersion <= "0.14.1"):
pValue =teststatic[1]
else:
pValue =teststatic.pvalue
except:
response = str(sys.exc_info()[0])
print("Input Drift Job Failed "+response)
return pValue
|
prediction_transformation.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os,sys
import platform
import json
import shutil
import logging
from pathlib import Path
def create_selector_file(self,deploy_path,features,pcaModel_pickle_file,bpca_features,apca_features,textFeatures,nonNumericFeatures,numericalFeatures,profiler,targetFeature, model_type,model,config=None):
self.selectorfile += 'import pandas as pd'
self.selectorfile += '\n'
self.selectorfile += 'import joblib'
self.selectorfile += '\n'
self.selectorfile += 'import os'
self.selectorfile += '\n'
self.selectorfile += 'import numpy as np'
self.selectorfile += '\n'
self.selectorfile += 'class selector(object):'
self.selectorfile += '\n'
self.selectorfile += ' def apply_selector(self,df):'
self.selectorfile += '\n'
if pcaModel_pickle_file != '':
self.selectorfile += " pcaModel = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+pcaModel_pickle_file+"'))"
self.selectorfile += '\n'
self.selectorfile += ' bpca_features = '+str(bpca_features)
self.selectorfile += '\n'
self.selectorfile += ' apca_features = '+str(apca_features)
self.selectorfile += '\n'
self.selectorfile += ' df = pcaModel.transform(df[bpca_features])'
self.selectorfile += '\n'
self.selectorfile += ' df = pd.DataFrame(df,columns=apca_features)'
self.selectorfile += '\n'
if(len(features) != 0) and model_type != 'BM25':
if model_type.lower()!='anomaly_detection' and model.lower() != 'autoencoder':
self.selectorfile += ' df = df['+str(features)+']'
self.selectorfile += '\n'
self.selectorfile += ' return(df)'
filename = os.path.join(deploy_path,'script','selector.py')
f = open(filename, "wb")
self.log.info('-------> Feature Selector File Location :'+filename)
f.write(str(self.selectorfile).encode('utf8'))
f.close()
featurefile = 'import json'
featurefile +='\n'
featurefile += 'def getfeatures():'
featurefile +='\n'
featurefile +=' try:'
featurefile +='\n'
featurelist = []
if 'profiler' in config:
if 'input_features_type' in config['profiler']:
inputfeatures = config['profiler']['input_features_type']
for x in inputfeatures:
featurelt={}
featurelt['feature'] = x
print(x,inputfeatures[x])
if x == targetFeature:
featurelt['Type'] = 'Target'
else:
if inputfeatures[x] in ['int','int64','float','float64']:
featurelt['Type'] = 'Numeric'
elif inputfeatures[x] == 'object':
featurelt['Type'] = 'Text'
elif inputfeatures[x] == 'category':
featurelt['Type'] = 'Category'
else:
featurelt['Type'] = 'Unknown'
featurelist.append(featurelt)
featurefile +=' features = '+str(featurelist)
featurefile +='\n'
featurefile +=' outputjson = {"status":"SUCCESS","features":features}'
featurefile +='\n'
featurefile +=' output = json.dumps(outputjson)'
featurefile +='\n'
featurefile +=' print("Features:",output)'
featurefile +='\n'
featurefile +=' return(output)'
featurefile +='\n'
featurefile +=' except Exception as e:'
featurefile +='\n'
featurefile +=' output = {"status":"FAIL","message":str(e).strip(\'"\')}'
featurefile +='\n'
featurefile +=' print("Features:",json.dumps(output))'
featurefile +='\n'
featurefile +=' return (json.dumps(output))'
featurefile +='\n'
featurefile +='if __name__ == "__main__":'
featurefile +='\n'
featurefile +=' output = getfeatures()'
filename = os.path.join(deploy_path,'featureslist.py')
f = open(filename, "wb")
f.write(str(featurefile).encode('utf8'))
f.close()
def create_init_function_for_classification(self,modelfile,classes,learner_type,scoreParam,loss_matrix,optimizer,preprocessing_pipe,modelName,model_type,imageconfig):
self.modelfile += ' def __init__(self):'
self.modelfile += '\n'
if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and modelName.lower()=="autoencoder"):
modelfile=modelfile.replace('.sav','')
self.modelfile+=" self.model = tf.keras.models.load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
elif(learner_type == 'TextDL' or learner_type == 'DL'):
if modelName.lower() == 'googlemodelsearch':
self.modelfile += ' import autokeras as ak'
self.modelfile += '\n'
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','modelsearch_rootdir','saved_model_onnx.onnx'))"
self.modelfile += '\n'
else:
if scoreParam == 'recall':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'recall': recall_m},compile=False)"
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[recall_m])'
self.modelfile += '\n'
elif scoreParam == 'precision':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'precision': precision_m},compile=False)"
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[precision_m])'
self.modelfile += '\n'
elif scoreParam == 'roc_auc':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),compile=False)"
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[tf.keras.metrics.AUC()])'
self.modelfile += '\n'
elif scoreParam == 'f1_score':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'f1_score': f1_m},compile=False)"
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[f1_m])'
self.modelfile += '\n'
elif scoreParam == 'r2':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'r2': r_square},compile=False)"
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[r_square])'
self.modelfile += '\n'
elif scoreParam == 'rmse':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects={'rmse': rmse_m},compile=False)"
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss=\''+loss_matrix+'\',optimizer=\''+optimizer+'\', metrics=[rmse_m])'
self.modelfile += '\n'
elif scoreParam == 'mse':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
elif scoreParam == 'mae':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
elif scoreParam == 'accuracy':
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
else:
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
elif(learner_type == 'Text Similarity'):
self.modelfile += " self.preprocessing = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+preprocessing_pipe+"'))"
self.modelfile += '\n'
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'), custom_objects={'cosine_distance': cosine_distance, 'cos_dist_output_shape': cos_dist_output_shape})"
self.modelfile += '\n'
elif(learner_type in ['similarityIdentification','contextualSearch']):
if scoreParam == 'VectorDB Cosine':
vectorfiledbname = 'trainingdataVecDB'
self.modelfile += f"\
\n persist_directory = os.path.join(os.path.dirname(__file__),'..','data')\
\n client = chromadb.PersistentClient(path=persist_directory)\
\n self.collection_name = '{vectorfiledbname}'\
\n self.collection = client.get_collection(self.collection_name)\n"
else:
self.modelfile += " self.train_input = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','data','trainingdata.csv'))\n\n"
elif(learner_type == 'ImageClassification'):
self.modelfile += ' self.config='+str(imageconfig)
self.modelfile += '\n'
if(modelName.lower() == 'densenet'):
self.modelfile += ' baseModel = tf.keras.applications.DenseNet121(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.config[\'img_width\'],self.config[\'img_height\'],self.config[\'img_channel\'])))'
else:
self.modelfile += ' baseModel = tensorflow.keras.applications.InceptionV3(weights="imagenet", include_top=False, input_tensor=Input(shape=(self.config[\'img_width\'],self.config[\'img_height\'],self.config[\'img_channel\'])))'
self.modelfile += '\n'
self.modelfile += ' headModel = baseModel.output'
self.modelfile += '\n'
self.modelfile += ' headModel = Flatten(name="flatten")(headModel)'
self.modelfile += '\n'
self.modelfile += ' headModel = Dense(1024, activation=\'relu\')(headModel)'
self.modelfile += '\n'
self.modelfile += ' headModel = Dropout(0.5)(headModel)'
self.modelfile += '\n'
self.modelfile += ' headModel = Dense(2, activation=\'sigmoid\')(headModel)'
self.modelfile += '\n'
self.modelfile += ' headModel = self.model = Model(inputs=baseModel.input, outputs=headModel)'
self.modelfile += '\n'
self.modelfile += ' opt = Adam(lr=self.config[\'lr\'])'
self.modelfile += '\n'
self.modelfile += ' self.model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])'
self.modelfile += '\n'
self.modelfile += " self.model.load_weights(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
elif(learner_type == 'objectDetection'):
self.modelfile += " self.MODEL_LOCATION = os.path.join(os.path.dirname(__file__),'..','model')\n"
self.modelfile += ' PATH_TO_CFG = self.MODEL_LOCATION+"/export/pipeline.config"\n'
self.modelfile += ' PATH_TO_CKPT = self.MODEL_LOCATION+"/export/checkpoint/"\n'
self.modelfile += ' PATH_TO_LABELS = self.MODEL_LOCATION+"/export/label_map.pbtxt"\n'
self.modelfile += ' configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\n'
self.modelfile += ' self.detection_model = model_builder.build(model_config=configs["model"], is_training=False)\n'
self.modelfile += ' ckpt = tf.compat.v2.train.Checkpoint(model=self.detection_model)\n'
self.modelfile += ' ckpt.restore(os.path.join(PATH_TO_CKPT, "ckpt-0")).expect_partial()\n'
self.modelfile += ' self.category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\
use_display_name=True)\n'
elif learner_type == 'TS' and (modelName.lower() == 'lstm' or modelName.lower() == 'mlp'):
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
elif modelName.lower() == 'neural architecture search':
self.modelfile += ' import autokeras as ak'
self.modelfile += '\n'
self.modelfile += " self.model = load_model(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'),custom_objects=ak.CUSTOM_OBJECTS)"
self.modelfile += '\n'
else:
self.modelfile += " self.model = joblib.load(os.path.join(os.path.dirname(__file__),'..','model','"+modelfile+"'))"
self.modelfile += '\n'
def create_predict(self,learner_type,method,model,model_type,threshold,firstDocFeature,secondDocFeature,padding_length,optimizationmethod,sessonal_freq,additional_regressors,feature,modelFeatures,indexFeature,lag_order,scalertransformationFile,datetimeFeature,scoreParam=None):
self.modelfile += ' def predict(self,X,features_names):'
self.modelfile += '\n'
if (learner_type == 'ML' and model_type.lower()=='anomaly_detection' and model.lower()=="autoencoder"):
self.modelfile += f" X=X[{feature}]\n"
self.modelfile += f" X = np.asarray(X).astype('float32')\n"
self.modelfile += f" reconstructed = self.model.predict(X)\n"
self.modelfile += f" predict_loss = tf.keras.losses.mae(reconstructed,X)\n"
self.modelfile += ' max_threshold = np.mean(predict_loss) + 2*np.std(predict_loss)\n'
self.modelfile += ' min_threshold = np.mean(predict_loss) - 2*np.std(predict_loss)\n'
self.modelfile += ' prediction_df = pd.DataFrame()\n'
self.modelfile += ' prediction_df["loss"] = predict_loss\n'
self.modelfile += ' prediction_df["max_threshold"] = max_threshold\n'
self.modelfile += ' prediction_df["min_threshold"] = min_threshold\n'
self.modelfile += ' prediction_df["anomaly"] = np.where((prediction_df["loss"] > prediction_df["max_threshold"]) | (prediction_df["loss"] <= prediction_df["min_threshold"]), True, False)\n'
self.modelfile += ' return prediction_df\n'
elif(learner_type == 'RecommenderSystem'):
self.modelfile += ' predictions = []'
self.modelfile += '\n'
self.modelfile += ' for index,row in X.iterrows():'
self.modelfile += '\n'
self.modelfile += ' score = self.model.predict(int(row["uid"]),int(row["iid"]))'
self.modelfile += '\n'
self.modelfile += ' predictions.append(score.est)'
self.modelfile += '\n'
self.modelfile += ' return predictions'
elif(learner_type in ['similarityIdentification','contextualSearch']):
tfeatures = list(modelFeatures.split(","))
if indexFeature != '' and indexFeature != 'NA':
ifeatures = indexFeature.split(",")
for ifes in ifeatures:
if ifes not in tfeatures:
tfeatures.append(ifes)
if model_type == 'BM25':
self.modelfile += f"\n\
tokenized_corpus =[doc.split(' ') for doc in self.train_input.tokenize]\n\
bm25 = BM25Okapi(tokenized_corpus)\n\
tokenized_query = [doc.split(' ') for doc in X.tokenize]\n\
logcnt = 5\n\
output = []\n\
for query in tokenized_query:\n\
doc_scores = bm25.get_scores(query)\n\
related_docs_indices = np.argsort(doc_scores)[::-1][:logcnt]\n\
x = self.train_input[{tfeatures}].loc[self.train_input.index[related_docs_indices]]\n\
x['Score'] = doc_scores[related_docs_indices]\n\
x['Score'] = round(x['Score'],2).astype(str)+'%'\n\
output.append(x)\n\
return output\n"
elif scoreParam == 'VectorDB Cosine':
featuresVecDB = modelFeatures.split(",")
self.modelfile += ' logcnt = 5\n'
self.modelfile += f" columns = {featuresVecDB}\n"
self.modelfile += f"\
\n output = []\
\n for rowindex, row in X.iterrows():\
\n queryembedding = X.iloc[rowindex:rowindex+1].to_numpy()\
\n results = self.collection.query(\
\n query_embeddings=queryembedding.tolist(),\
\n n_results=logcnt\
\n )\
\n x = pd.DataFrame(columns=columns)\
\n for i in range(0, len(results['ids'][0])):\
\n documentAry = results['documents'][0][i]\
\n documentAry = documentAry.split(' ~&~ ')\
\n for j in range(0, len(documentAry)):\
\n x.at[i,columns[j]] = documentAry[j]\
\n x.at[i,'Score'] = results['distances'][0][i]\
\n output.append(x)\
\n return output"
else:
self.modelfile += ' columns = self.train_input.columns.tolist()\n'
self.modelfile += ' logcnt = 5\n'
self.modelfile += f" train_input = self.train_input[{tfeatures}]\n"
for tf in tfeatures:
self.modelfile += f" columns.remove('{tf}')\n"
self.modelfile += f"\
\n results = cosine_similarity(self.train_input[columns],X)\
\n output = []\
\n for i in range(results.shape[1]):\
\n related_docs_indices = results[:,i].argsort(axis=0)[:-(int(logcnt) + 1):-1]\
\n x=self.train_input[{tfeatures}].loc[self.train_input.index[related_docs_indices]]\
\n scores = []\
\n for j in range(0,logcnt):\
\n scores.append(str(round((results[related_docs_indices][j][i])*100))+'%')\
\n x['Score'] = scores\
\n output.append(x)\
\n return output"
elif(learner_type == 'Text Similarity'):
self.modelfile += ' X["'+firstDocFeature+'"] = X["'+firstDocFeature+'"].astype(str)'
self.modelfile += '\n'
self.modelfile += ' X["'+secondDocFeature+'"] = X["'+secondDocFeature+'"].astype(str)'
self.modelfile += '\n'
self.modelfile += ' test_sentence1 = self.preprocessing.texts_to_sequences(X["'+firstDocFeature+'"].values)'
self.modelfile += '\n'
self.modelfile += ' test_sentence2 = self.preprocessing.texts_to_sequences(X["'+secondDocFeature+'"].values)'
self.modelfile += '\n'
self.modelfile += ' test_sentence1 = pad_sequences(test_sentence1, maxlen='+str(padding_length)+', padding=\'post\')'
self.modelfile += '\n'
self.modelfile += ' test_sentence2 = pad_sequences(test_sentence2, maxlen='+str(padding_length)+', padding=\'post\')'
self.modelfile += '\n'
self.modelfile += ' prediction = self.model.predict([test_sentence1, test_sentence2 ])'
self.modelfile += '\n'
self.modelfile += ' return(prediction)'
self.modelfile += '\n'
elif(learner_type == 'ImageClassification'):
self.modelfile += ' predictions = []'
self.modelfile += '\n'
self.modelfile += ' for index, row in X.iterrows(): '
self.modelfile += '\n'
self.modelfile += ' img = cv2.imread(row[\'imagepath\'])'
self.modelfile += '\n'
self.modelfile += ' img = cv2.resize(img, (self.config[\'img_width\'],self.config[\'img_height\']))'
self.modelfile += '\n'
self.modelfile += ' img = image.img_to_array(img)'
self.modelfile += '\n'
self.modelfile += ' img = np.expand_dims(img, axis=0)'
self.modelfile += '\n'
self.modelfile += ' img = img/255'
self.modelfile += '\n'
self.modelfile += ' prediction = self.model.predict(img)'
self.modelfile += '\n'
self.modelfile += ' prediction = np.argmax(prediction,axis=1)'
self.modelfile += '\n'
self.modelfile += ' predictions.append(prediction[0])'
self.modelfile += '\n'
self.modelfile += ' return(predictions)'
self.modelfile += '\n'
elif(learner_type == 'objectDetection'):
self.modelfile += ' @tf.function\n'
self.modelfile += ' def detect_fn(image):\n'
self.modelfile += ' image, shapes = self.detection_model.preprocess(image)\n'
self.modelfile += ' prediction_dict = self.detection_model.predict(image, shapes)\n'
self.modelfile += ' detections = self.detection_model.postprocess(prediction_dict, shapes)\n'
self.modelfile += ' return detections\n'
self.modelfile += ' def load_image_into_numpy_array(path):\n'
self.modelfile += ' return np.array(Image.open(path))\n'
self.modelfile += ' imageLocation = []\n'
self.modelfile += ' for i, row in X.iterrows():\n'
self.modelfile += ' if ("confidance" in row) and row["confidance"] <= 1.0:\n'
self.modelfile += ' confidance = row["confidance"]\n'
self.modelfile += ' else:\n'
self.modelfile += ' confidance = 0.8\n'
self.modelfile += ' imageName = str(Path(row["imagepath"]).stem)+"_output"+str(Path(row["imagepath"]).suffix)\n'
self.modelfile += ' image_np = load_image_into_numpy_array(row["imagepath"])\n'
self.modelfile += ' input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n'
self.modelfile += ' detections = detect_fn(input_tensor)\n'
self.modelfile += ' num_detections = int(detections.pop("num_detections"))\n'
self.modelfile += ' detections = {key: value[0, :num_detections].numpy()\n\
for key, value in detections.items()}\n'
self.modelfile += ' detections["num_detections"] = num_detections\n'
self.modelfile += ' detections["detection_classes"] = detections["detection_classes"].astype(np.int64)\n'
self.modelfile += ' label_id_offset = 1\n'
self.modelfile += ' image_np_with_detections = image_np.copy()\n'
self.modelfile += ' viz_utils.visualize_boxes_and_labels_on_image_array(\n\
image_np_with_detections,\n\
detections["detection_boxes"],\n\
detections["detection_classes"]+label_id_offset,\n\
detections["detection_scores"],\n\
self.category_index,\n\
use_normalized_coordinates=True,\n\
max_boxes_to_draw=200,\n\
min_score_thresh=confidance,\n\
agnostic_mode=False)\n'
self.modelfile += ' plt.figure()\n'
self.modelfile += ' plt.imsave(os.path.join(self.MODEL_LOCATION,imageName), image_np_with_detections)\n'
self.modelfile += ' imageLocation.append(os.path.join(self.MODEL_LOCATION,imageName))\n'
self.modelfile += ' plt.show()\n'
self.modelfile += ' return imageLocation\n'
else:
if(learner_type == 'DL' and model != 'Neural Network'):
self.modelfile += ' X = np.expand_dims(X, axis=2)'
self.modelfile += '\n'
if(learner_type == 'TextDL'):
self.modelfile += ' return pd.DataFrame(np.argmax(self.model.predict(X),axis=1))'
self.modelfile += '\n'
elif(learner_type == 'TextML'):
self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X),columns=self.model.classes_)'
self.modelfile += '\n'
elif(learner_type == 'DL' and model_type == 'Classification'):
self.modelfile += ' X = X.astype(np.float32)'
self.modelfile += '\n'
self.modelfile += ' return pd.DataFrame(np.argmax(self.model.predict(X),axis=1))'
self.modelfile += '\n'
else:
if(model_type == 'Classification' or model_type == 'TLClassification'):
if model == 'Neural Architecture Search':
self.modelfile += ' X = X.astype(np.float32)'
self.modelfile += '\n'
self.modelfile += ' return pd.DataFrame(self.model.predict(X))'
self.modelfile += '\n'
else:
if optimizationmethod == 'genetic':
self.modelfile += '\n'
self.modelfile += ' try:'
self.modelfile += '\n'
self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X))'
self.modelfile += '\n'
self.modelfile += ' except:'
self.modelfile += '\n'
self.modelfile += ' return pd.DataFrame(self.model.predict(X))'
else:
self.modelfile += ' X = X.astype(np.float32)'
self.modelfile += '\n'
if model.lower() == 'deep q network' or model.lower() == 'dueling deep q network':
self.modelfile += ' q, _ = self.model(np.array(X), step_type=constant([time_step.StepType.FIRST] * np.array(X).shape[0]), training=False)'
self.modelfile += '\n'
self.modelfile += ' return pd.DataFrame(q.numpy())'
else:
self.modelfile += ' return pd.DataFrame(self.model.predict_proba(X), columns=self.model.classes_)'
self.modelfile += '\n'
elif model_type == 'Regression' and model == 'NAS':
self.modelfile += \
"""
X = X.astype(np.float32)
return self.model.predict(X)
"""
elif(learner_type == 'TS'):
if model.lower() == 'fbprophet':
self.modelfile += ' sessonal_freq="'+str(sessonal_freq)+'"'
self.modelfile += '\n'
self.modelfile += ' ts_prophet_future = self.model.make_future_dataframe(periods=int(X["noofforecasts"][0]),freq=sessonal_freq,include_history = False)'
self.modelfile += '\n'
if (additional_regressors):
self.modelfile += '\n'
self.modelfile += ' additional_regressors='+str(additional_regressors)
self.modelfile += '\n'
self.modelfile += ' ts_prophet_future[additional_regressors] = dataFrame[additional_regressors]'
self.modelfile += '\n'
self.modelfile += ' ts_prophet_future.reset_index(drop=True)'
self.modelfile += '\n'
self.modelfile += ' ts_prophet_future=ts_prophet_future.dropna()'
self.modelfile += '\n'
self.modelfile += ' train_forecast = self.model.predict(ts_prophet_future)'
self.modelfile += '\n'
self.modelfile += ' prophet_forecast_tail=train_forecast[[\'ds\', \'yhat\', \'yhat_lower\',\'yhat_upper\']].tail( int(X["noofforecasts"][0]))'
self.modelfile += '\n'
self.modelfile += ' return(prophet_forecast_tail)'
elif model.lower() == 'lstm' or model.lower() == 'mlp':
self.modelfile += ' lag_order='+str(lag_order)
self.modelfile += '\n'
self.modelfile += ' xt = X.values'
self.modelfile += '\n'
scalertransformationFile = scalertransformationFile.split('\\')[-1]
self.modelfile += ' loaded_scaler_model = joblib.load(os.path.join(os.path.dirname(__file__),\'..\',\'model\',\''+scalertransformationFile+'\'))'
self.modelfile += '\n'
self.modelfile += ' xt = xt.astype(\'float32\')'
self.modelfile += '\n'
self.modelfile += ' xt = loaded_scaler_model.transform(xt)'
self.modelfile += '\n'
self.modelfile += ' noOfPredictions = 10'
self.modelfile += '\n'
self.modelfile += ' pred_data = xt'
self.modelfile += '\n'
self.modelfile += ' y_future = []'
self.modelfile += '\n'
self.modelfile += ' for i in range(noOfPredictions):'
self.modelfile += '\n'
if len(feature) == 1:
self.modelfile += ' pred_data = pred_data[-lag_order:]'
self.modelfile += '\n'
if model.lower() == 'mlp':
self.modelfile += ' pred_data = pred_data.reshape((1,lag_order))'
else:
self.modelfile += ' pred_data = pred_data.reshape((1,lag_order,1))'
self.modelfile += '\n'
self.modelfile += ' pred = self.model.predict(pred_data)'
self.modelfile += '\n'
self.modelfile += ' predoutput = loaded_scaler_model.inverse_transform(pred) '
self.modelfile += '\n'
self.modelfile += ' y_future.append(predoutput.flatten()[-1])'
self.modelfile += '\n'
self.modelfile += ' pred_data = np.append(pred_data,pred)'
self.modelfile += '\n'
self.modelfile += ' pred = pd.DataFrame(index=range(0,len(y_future)),columns='+str(feature)+')'
self.modelfile += '\n'
self.modelfile += ' for i in range(0, len(y_future)):'
self.modelfile += '\n'
self.modelfile += ' pred.iloc[i] = y_future[i]'
self.modelfile += '\n'
self.modelfile += ' return pred'
else:
self.modelfile += ' pdata = pred_data[-lag_order:]'
self.modelfile += '\n'
self.modelfile += ' pdata = pdata.reshape((1,lag_order,'+str(len(feature))+'))'
self.modelfile += '\n'
self.modelfile += ' pred = self.model.predict(pdata)'
self.modelfile += '\n'
self.modelfile += ' predoutput = loaded_scaler_model.inverse_transform(pred) '
self.modelfile += '\n'
self.modelfile += ' y_future.append(predoutput)'
self.modelfile += '\n'
self.modelfile += ' pred_data = np.append(pred_data,pred,axis=0)'
self.modelfile += '\n'
self.modelfile += ' pred = pd.DataFrame(index=range(0,len(y_future)),columns='+str(feature)+')'
self.modelfile += '\n'
self.modelfile += ' for i in range(0, len(y_future)):'
self.modelfile += '\n'
self.modelfile += ' pred.iloc[i] = y_future[i]'
self.modelfile += '\n'
self.modelfile += ' return pred'
else:
self.modelfile += ' return self.model.predict(n_periods=int(X["noofforecasts"][0]))'
else:
if model == 'KaplanMeierFitter':
self.modelfile += '\n'
self.modelfile += ' res = self.model.predict(X[\''+feature[0]+'\'].astype(int))'
self.modelfile += '\n'
self.modelfile += ' if isinstance(res, pd.DataFrame):\n'
self.modelfile += ' return res.values.reshape(1,-1)\n'
self.modelfile += ' else:\n'
self.modelfile += ' return np.array([res])\n'
elif model == 'COX':
self.modelfile += ' res = []\n'
self.modelfile += ' for idx,row in X.iterrows():\n'
self.modelfile += ' res.append(self.model.predict_survival_function(X, times=row[self.model.duration_col])[idx].values[0])\n'
self.modelfile += ' return pd.DataFrame(res)'
#self.modelfile += ' return self.model.predict_survival_function(X, times=X[self.model.duration_col])'
self.modelfile += '\n'
elif(learner_type == 'DL' and model_type in ['Classification','Regression']):
self.modelfile += ' X = X.astype(np.float32)'
self.modelfile += '\n'
self.modelfile += ' return self.model.predict(X).reshape(1, -1)'
self.modelfile += '\n'
elif (model_type == 'Clustering' and model == 'DBSCAN'):
self.modelfile += ' return self.model.fit_predict(X)'
elif(model_type.lower() == 'anomaly_detection' and model.lower() == 'dbscan'):
self.modelfile += " pred=self.model.fit_predict(X)\n"
self.modelfile += " X.loc[:,'cluster'] = self.model.labels_ \n"
self.modelfile += ' return X\n'
elif model_type.lower() == 'anomaly_detection':
self.modelfile += ' X = X.astype(np.float32)\n'
self.modelfile += ' return self.model.predict(X)'
else:
if model_type != 'Clustering':
self.modelfile += ' X = X.astype(np.float32)'
self.modelfile += '\n'
#self.modelfile += ' return self.model.predict(X).reshape(1, -1)'
self.modelfile += \
"""
if isinstance(self.model, LatentDirichletAllocation):
output = np.matrix(self.model.transform(X)).argmax(axis=1)
return output.flatten().tolist()
return self.model.predict(X).reshape(1, -1)
"""
|
base.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from pathlib import Path
from AION.prediction_package.imports import importModule
from AION.prediction_package.aion_prediction import aionPrediction
from AION.prediction_package.utility import TAB_CHAR
from AION.prediction_package import utility
from AION.prediction_package import common
def file_header( usecase=''):
return ''
class deployer():
"""
base deployer class which can be used to generate the deployemnt code.
This class will be inherited by deployer specific to problem type.
"""
def __init__(self, params={}):
if not params['paths']['deploy']:
raise ValueError('Deploy path is not provided')
self.deploy_path = Path(params['paths']['deploy'])
if not self.deploy_path.exists():
self.deploy_path.mkdir(parents=True, exist_ok=True)
self.name = params.get('problem_type', '')
self.params = params
self.importer = importModule()
self.feature_reducer = False
def profiler_code(self):
return common.profiler_code(self.params['profiler'])
def feature_engg_code(self):
if self.params['selector'].get('reducer',False):
code, modules = common.feature_reducer_code(self.params['selector'])
else:
code, modules = common.feature_selector_code(self.params['selector'])
utility.import_modules(self.importer, modules)
return code
def training_code(self):
return common.training_code(self.params['training'])
def formatter_code(self):
return ''
def run(self):
"""
run function will be called to start the deployment process.
This function will create following files
inputprofiler.py for preprocessing the input
aion_predict.py for prediction
model service file
"""
code = self.predict_code( )
with open(self.deploy_path/'aion_predict.py', 'w') as f:
f.write(code)
profiler_code = self.profiler_code()
with open(self.deploy_path/'script'/'inputprofiler.py', 'w') as f:
f.write(profiler_code)
self.create_model_service( )
self.create_publish_service()
self.create_idrift()
self.create_odrift()
common.create_feature_list(self.params, self.params['features']['target_feat'], self.deploy_path)
common.requirement_file(self.deploy_path,self.params['training']['algo'],self.params['features']['text_feat'])
common.create_readme_file(self.deploy_path, self.params['training']['model_file'], self.params['features']['input_feat'])
self.create_utils_folder()
def predict_code(self):
imported_modules = [
{'module': 'json', 'mod_from': None, 'mod_as': None},
{'module': 'joblib', 'mod_from': None, 'mod_as': None},
{'module': 'numpy', 'mod_from': None, 'mod_as': 'np'},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
{'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}
]
utility.import_modules(self.importer, imported_modules)
self.importer.addLocalModule(module='inputprofiler',mod_from='script.inputprofiler')
code_text = ""
code_text += self.feature_engg_code()
code_text += self.training_code()
code_text += self.formatter_code()
code_text += common.main_code()
code = file_header()
code += self.importer.getCode()
return code + code_text
def create_model_service(self):
service_name = '{}{}{}'.format(self.params['usecase_name'], '_' if self.params['usecase_ver'] != '' else '', self.params['usecase_ver'])
obj = aionPrediction()
obj.create_model_service(self.deploy_path, service_name, self.name)
def create_publish_service(self):
obj = aionPrediction()
obj.create_publish_service(self.params['paths']['usecase'], self.params['usecase_name'],self.params['usecase_ver'], self.name)
def create_idrift(self):
pass
def create_odrift(self):
pass
def create_utils_folder(self):
common.create_util_folder(self.deploy_path)
|
forecasting.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from pathlib import Path
from AION.prediction_package.imports import importModule
from AION.prediction_package.aion_prediction import aionPrediction
from AION.prediction_package.utility import TAB_CHAR
from AION.prediction_package import utility
from AION.prediction_package.base import deployer
from AION.prediction_package import common
import numpy as np
def get_deployer( params):
if params['training']['algo'] == 'ARIMA':
return arima(params)
elif params['training']['algo'] == 'LSTM':
return lstm(params)
elif params['training']['algo'] == 'ENCODER_DECODER_LSTM_MVI_UVO':
return lstmencdec_mviuvo(params)
elif params['training']['algo'] == 'MLP':
return mlp(params)
elif params['training']['algo'] == 'VAR':
return var(params)
elif params['training']['algo'] == 'FBPROPHET':
return fbprophet(params)
else:
raise ValueError(f"Algorithm {params['training']['algo']} for time series forecasting is not supported")
def _profiler_code(params, importer):
"""
This will create the profiler file based on the config file.
separated file is created as profiler is required for input drift also.
"""
imported_modules = [
{'module': 'json', 'mod_from': None, 'mod_as': None},
{'module': 'scipy', 'mod_from': None, 'mod_as': None},
{'module': 'joblib', 'mod_from': None, 'mod_as': None},
{'module': 'numpy', 'mod_from': None, 'mod_as': 'np'},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
{'module': 'Path', 'mod_from': 'pathlib', 'mod_as': None}
]
utility.import_modules(importer, imported_modules)
if 'code' in params['profiler'].get('preprocess',{}).keys():
code = params['profiler']['preprocess']['code']
else:
code = ""
code += """
class inputprofiler():
"""
init_code = """
def __init__(self):
"""
init_code += """
# preprocessing
preprocess_path = Path(__file__).parent.parent/'model'/'preprocess_pipe.pkl'
if not preprocess_path.exists():
raise ValueError(f'Preprocess model file not found: {preprocess_path}')
self.profiler = joblib.load(preprocess_path)
"""
run_code = """
def run(self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
"""
if 'code' in params['profiler'].get('preprocess',{}).keys():
run_code += """
df = preprocess( df)"""
if params['profiler'].get('unpreprocessed_columns'):
run_code += f"""
unpreprocessed_data = df['{params['profiler']['unpreprocessed_columns'][0]}']
df.drop(['{params['profiler']['unpreprocessed_columns'][0]}'], axis=1,inplace=True)
"""
if params['profiler'].get('force_numeric_conv'):
run_code += f"""
df[{params['profiler']['force_numeric_conv']}] = df[{params['profiler']['force_numeric_conv']}].apply(pd.to_numeric,errors='coerce')"""
run_code += _profiler_main_code(params)
if params['profiler'].get('unpreprocessed_columns'):
run_code += f"""
df['{params['profiler'].get('unpreprocessed_columns')[0]}'] = unpreprocessed_data
"""
run_code += """ return df
"""
utility.import_modules(importer, imported_modules)
import_code = importer.getCode()
return import_code + code + init_code + run_code
def _profiler_main_code(params):
code = f"""
df = self.profiler.transform(df)
columns = {params['profiler']['output_features']}
if isinstance(df, scipy.sparse.spmatrix):
df = pd.DataFrame(df.toarray(), columns=columns)
else:
df = pd.DataFrame(df, columns=columns)
"""
return code
class arima( deployer):
def __init__(self, params={}):
super().__init__( params)
self.name = 'timeseriesforecasting'
def profiler_code( self):
imported_modules = [
{'module': 'numpy', 'mod_from': None, 'mod_as': 'np'},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
]
importer = importModule()
utility.import_modules(importer, imported_modules)
code = """
class inputprofiler():
def __init__(self):
pass
def run( self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
return df[['noofforecasts']]
"""
return importer.getCode() + code
def feature_engg_code(self):
self.importer.addModule(module='pandas',mod_as='pd')
return f"""
class selector():
def __init__(self):
pass
def run(self, df):
return df
"""
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
self.importer.addModule(module='Path',mod_from='pathlib')
self.importer.addModule(module='numpy',mod_as='np')
self.importer.addModule(module='joblib')
return f"""
class trainer():
def __init__(self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
self.model = joblib.load(model_file)
def run(self,df):
return self.model.predict(n_periods=int(df["noofforecasts"][0]))
"""
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__( self):
pass
def run(self,raw_df,df):
df = df.round(2)
df = json.dumps(df.tolist())
outputjson = {"status":"SUCCESS","data":eval(df)}
return(json.dumps(outputjson))
"""
class lstm( deployer):
def __init__(self, params={}):
super().__init__( params)
self.name = 'timeseriesforecasting'
def profiler_code(self):
importer = importModule()
return _profiler_code( self.params, importer)
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
self.importer.addModule(module='Path',mod_from='pathlib')
code = f"""
class trainer():
"""
init_code, run_code = self._get_train_code()
return code + init_code + run_code
def _get_train_code(self):
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
init_code = f"""
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
self.model = load_model(model_file)
"""
run_code = f"""
def run(self, df):
lag_order={self.params['training']['lag_order']}
xt = df.values
scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}"
if not scaler_file.exists():
raise ValueError(f'Scaling file not found: {{scaler_file}}')
loaded_scaler_model = joblib.load(scaler_file)
xt = xt.astype('float32')
xt = loaded_scaler_model.transform(xt)
noOfPredictions = 10
pred_data = xt
y_future = []
for i in range(noOfPredictions):
"""
if len(self.params['selector']['output_features']) == 1:
run_code += f"""
pred_data = pred_data[-lag_order:]
pred_data = pred_data.reshape((1,lag_order,1))
pred = self.model.predict(pred_data)
predoutput = loaded_scaler_model.inverse_transform(pred)
y_future.append(predoutput.flatten()[-1])
pred_data = np.append(pred_data,pred)
pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']})
for i in range(0, len(y_future)):
pred.iloc[i] = y_future[i]
return pred
"""
else:
run_code += f"""
pdata = pred_data[-lag_order:]
pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])}))
pred = self.model.predict(pdata)
predoutput = loaded_scaler_model.inverse_transform(pred)
y_future.append(predoutput)
pred_data = np.append(pred_data,pred,axis=0)
pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']})
for i in range(0, len(y_future)):
pred.iloc[i] = y_future[i]
return pred
"""
return init_code, run_code
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__( self):
pass
def run(self,raw_df,df):
df = df.round(2)
df = df.to_json(orient='records')
outputjson = {"status":"SUCCESS","data":json.loads(df)}
return(json.dumps(outputjson))
"""
class lstmencdec_mviuvo( deployer):
def __init__(self, params={}):
super().__init__( params)
self.name = 'timeseriesforecasting'
outputFeatrues = params['profiler']['output_features']
self.targetColIndx = outputFeatrues.index(params['features']['target_feat'])
selectedColDict = params['selector']['output_features']
self.selectedCols = list()
for col in selectedColDict:
self.selectedCols.append(col)
def profiler_code(self):
importer = importModule()
return _profiler_code( self.params, importer)
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
self.importer.addModule(module='Path',mod_from='pathlib')
code = f"""
class trainer():
"""
init_code, run_code = self._get_train_code()
return code + init_code + run_code
def _get_train_code(self):
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
init_code = f"""
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
self.model = load_model(model_file)
"""
run_code = f"""
def run(self, df):
targetColIndx = {self.targetColIndx}
lag_order={self.params['training']['lag_order']}
xt = df.values
scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}"
if not scaler_file.exists():
raise ValueError(f'Scaling file not found: {{scaler_file}}')
loaded_scaler_model = joblib.load(scaler_file)
xt = xt.astype('float32')
xt = loaded_scaler_model.transform(xt)
noOfPredictions = 10
pred_data = xt
y_future = []
pdata = pred_data[-lag_order:]
pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])}))
pred = self.model.predict(pdata)
pred_1d = pred.ravel()
pdata_2d = pdata.ravel().reshape(len(pdata) * lag_order, {len(self.params['selector']['output_features'])})
pdata_2d[:,targetColIndx] = pred_1d
pred_2d_inv = loaded_scaler_model.inverse_transform(pdata_2d)
predout = pred_2d_inv[:, targetColIndx]
predout = predout.reshape(len(pred_1d),1)
pred = pd.DataFrame(index=range(0,len(predout)),columns=['{self.params['features']['target_feat']}'])
for i in range(0, len(predout)):
pred.iloc[i] = predout[i]
return pred
"""
return init_code, run_code
def feature_engg_code(self):
self.importer.addModule(module='pandas',mod_as='pd')
return f"""
class selector():
def __init__(self):
pass
def run(self, df):
return df[{self.selectedCols}]
"""
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__( self):
pass
def run(self,raw_df,df):
df = df.round(2)
df = df.to_json(orient='records')
outputjson = {"status":"SUCCESS","data":json.loads(df)}
return(json.dumps(outputjson))
"""
class mlp( lstm):
def __init__(self, params={}):
super().__init__( params)
self.name = 'timeseriesforecasting'
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
self.importer.addModule(module='Path',mod_from='pathlib')
code = f"""
class trainer():
"""
init_code, run_code = self._get_train_code()
return code + init_code + run_code
def _get_train_code(self):
self.importer.addModule(module='load_model',mod_from='tensorflow.keras.models')
init_code = f"""
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
self.model = load_model(model_file)"""
run_code = f"""
def run(self, df):
lag_order={self.params['training']['lag_order']}
xt = df.values
scaler_file = (Path(__file__).parent/"model")/"{self.params['training']['scaler_file']}"
if not scaler_file.exists():
raise ValueError(f'Scaling file not found: {{scaler_file}}')
loaded_scaler_model = joblib.load(scaler_file)
xt = xt.astype('float32')
xt = loaded_scaler_model.transform(xt)
noOfPredictions = 10
pred_data = xt
y_future = []
for i in range(noOfPredictions):
"""
if len(self.params['selector']['output_features']) == 1:
run_code += f"""
pred_data = pred_data[-lag_order:]
pred_data = pred_data.reshape((1,lag_order))
pred = self.model.predict(pred_data)
predoutput = loaded_scaler_model.inverse_transform(pred)
y_future.append(predoutput.flatten()[-1])
pred_data = np.append(pred_data,pred)
pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']})
for i in range(0, len(y_future)):
pred.iloc[i] = y_future[i]
return pred
"""
else:
run_code += f"""
pdata = pred_data[-lag_order:]
pdata = pdata.reshape((1,lag_order,{len(self.params['selector']['output_features'])}))
pred = self.model.predict(pdata)
predoutput = loaded_scaler_model.inverse_transform(pred)
y_future.append(predoutput)
pred_data = np.append(pred_data,pred,axis=0)
pred = pd.DataFrame(index=range(0,len(y_future)),columns={self.params['selector']['output_features']})
for i in range(0, len(y_future)):
pred.iloc[i] = y_future[i]
return pred
"""
return init_code, run_code
class var( deployer):
def __init__(self, params={}):
super().__init__( params)
self.name = 'timeseriesforecasting'
def profiler_code(self):
importer = importModule()
code = _profiler_code( self.params, importer)
return code
def feature_engg_code(self):
self.importer.addModule(module='pandas',mod_as='pd')
return f"""
class selector():
def __init__(self):
pass
def run(self, df):
return df[{self.params['selector']['output_features']}]
"""
def training_code( self):
self.importer.addModule(module='joblib')
return f"""
class trainer():
def __init__( self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
self.model = joblib.load(model_file)
def run(self,df):
lag_order = self.model.k_ar
return self.model.forecast(df.values[-lag_order:],steps={self.params['training']['no_of_prediction']})
"""
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return f"""
class output_format():
def __init__( self):
pass
def invertTransformation(self,predictions):
datasetdf = pd.read_csv((Path(__file__).parent/"data")/"trainingdata.csv")
dictDiffCount = {self.params['training']['dictDiffCount']}
target_features = "{self.params['features']['target_feat']}"
columns = target_features.split(',')
pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns)
for j in range(0,len(columns)):
for i in range(0, len(predictions)):
pred.iloc[i][j] = round(predictions[i][j],2)
prediction = pred
for col in columns:
if col in dictDiffCount:
if dictDiffCount[col]==2:
prediction[col] = (datasetdf[col].iloc[-1]-datasetdf[col].iloc[-2]) + prediction[col].cumsum()
prediction[col] = datasetdf[col].iloc[-1] + prediction[col].cumsum()
prediction = pred
return(prediction)
def run(self,raw_df,df):
df = self.invertTransformation(df)
df = df.to_json(orient='records',double_precision=2)
outputjson = {{"status":"SUCCESS","data":json.loads(df)}}
return(json.dumps(outputjson))
"""
class fbprophet( deployer):
def __init__(self, params={}):
super().__init__( params)
self.name = 'timeseriesforecasting'
def profiler_code( self):
imported_modules = [
{'module': 'numpy', 'mod_from': None, 'mod_as': 'np'},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
]
importer = importModule()
utility.import_modules(importer, imported_modules)
code = """
class inputprofiler():
def __init__(self):
pass
def run( self,df):
df = df.replace(r'^\s*$', np.NaN, regex=True)
return df[['noofforecasts']]
"""
return importer.getCode() + code
def feature_engg_code(self):
self.importer.addModule(module='pandas',mod_as='pd')
return f"""
class selector():
def __init__(self):
pass
def run(self, df):
return df
"""
def training_code( self):
self.importer.addModule(module='pandas',mod_as='pd')
self.importer.addModule(module='Path',mod_from='pathlib')
self.importer.addModule(module='joblib')
code = f"""
class trainer():
def __init__(self):
model_file = (Path(__file__).parent/"model")/"{self.params['training']['model_file']}"
if not model_file.exists():
raise ValueError(f'Trained model file not found: {{model_file}}')
self.model = joblib.load(model_file)
"""
code += f"""
def run(self,df):
sessonal_freq = '{self.params['training']['sessonal_freq']}'
ts_prophet_future = self.model.make_future_dataframe(periods=int(df["noofforecasts"][0]),freq=sessonal_freq,include_history = False)
"""
if (self.params['training']['additional_regressors']):
code += f"""
additional_regressors={self.params['training']['additional_regressors']}
ts_prophet_future[additional_regressors] = dataFrame[additional_regressors]
ts_prophet_future.reset_index(drop=True)
ts_prophet_future=ts_prophet_future.dropna()
"""
code += """
train_forecast = self.model.predict(ts_prophet_future)
prophet_forecast_tail=train_forecast[[\'ds\', \'yhat\', \'yhat_lower\',\'yhat_upper\']].tail( int(df["noofforecasts"][0]))
return(prophet_forecast_tail)"""
return code
def formatter_code(self):
self.importer.addModule('json')
self.importer.addModule('pandas', mod_as='pd')
return """
class output_format():
def __init__( self):
pass
def run(self,raw_df,df):
df = df.to_json(orient='records')
outputjson = {"status":"SUCCESS","data":json.loads(df)}
return(json.dumps(outputjson))
"""
|
local_pipeline.py | import docker
import json
import logging
def read_json(file_path):
data = None
with open(file_path,'r') as f:
data = json.load(f)
return data
def run_pipeline(inputconfig):
inputconfig = json.loads(inputconfig)
logfilepath = inputconfig['logfilepath']
logging.basicConfig(level=logging.INFO,filename =logfilepath)
usecasename = inputconfig['usecase']
logging.info("UseCaseName :"+str(usecasename))
version = inputconfig['version']
logging.info("version :"+str(version))
config = inputconfig['dockerlist']
persistancevolume = inputconfig['persistancevolume']
logging.info("PersistanceVolume :"+str(persistancevolume))
datasetpath = inputconfig['datasetpath']
logging.info("DataSet Path :"+str(datasetpath))
config = read_json(config)
client = docker.from_env()
inputconfig = {'modelName':usecasename,'modelVersion':str(version),'dataLocation':datasetpath}
inputconfig = json.dumps(inputconfig)
inputconfig = inputconfig.replace('"', '\\"')
logging.info("===== Model Monitoring Container Start =====")
outputStr = client.containers.run(config['ModelMonitoring'],'python code.py -i'+datasetpath,volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('ModelMonitoring: '+str(outputStr))
print('ModelMonitoring: '+str(outputStr))
logging.info("===== ModelMonitoring Stop =====")
logging.info("===== Data Ingestion Container Start =====")
outputStr = client.containers.run(config['DataIngestion'],'python code.py',volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('DataIngestion: '+str(outputStr))
print('DataIngestion: '+str(outputStr))
logging.info("===== Data Ingestion Container Stop =====")
outputStr = outputStr.strip()
decoded_data = json.loads(outputStr)
status = decoded_data['Status']
if status != 'Success':
output = {'Status':'Error','Msg':'Data Ingestion Fails'}
logging.info("===== Transformation Container Start =====")
outputStr = client.containers.run(config['DataTransformation'],'python code.py',volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('Data Transformations: '+str(outputStr))
print('Data Transformations: '+str(outputStr))
logging.info("===== Transformation Container Done =====")
outputStr = outputStr.strip()
decoded_data = json.loads(outputStr)
status = decoded_data['Status']
if status != 'Success':
output = {'Status':'Error','Msg':'Data Transformations Fails'}
logging.info("===== Feature Engineering Container Start =====")
outputStr = client.containers.run(config['FeatureEngineering'],'python code.py',volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('FeatureEngineering: '+str(outputStr))
print('FeatureEngineering: '+str(outputStr))
logging.info("===== Feature Engineering Container Done =====")
outputStr = outputStr.strip()
decoded_data = json.loads(outputStr)
status = decoded_data['Status']
modeltraining = config['ModelTraining']
for mt in modeltraining:
logging.info("===== Training Container Start =====")
outputStr = client.containers.run(mt['Training'],'python code.py',volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('ModelTraining: '+str(outputStr))
print('ModelTraining: '+str(outputStr))
logging.info("===== Training Container Done =====")
outputStr = outputStr.strip()
try:
decoded_data = json.loads(outputStr)
status = decoded_data['Status']
except Exception as inst:
logging.info(inst)
logging.info("===== Model Registry Start =====")
outputStr = client.containers.run(config['ModelRegistry'],'python code.py',volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('ModelRegistry: '+str(outputStr))
print('ModelRegistry: '+str(outputStr))
logging.info("===== ModelRegistry Done =====")
logging.info("===== ModelServing Start =====")
outputStr = client.containers.run(config['ModelServing'],'python code.py',volumes=[persistancevolume+':/aion'])
outputStr = outputStr.decode('utf-8')
logging.info('Prediction: '+str(outputStr))
print('Prediction: '+str(outputStr))
logging.info("===== ModelServing Done =====") |
build_container.py | import os
import shutil
import sys
import subprocess
from os.path import expanduser
import platform
import json
def createDockerImage(model_name,model_version,module,folderpath):
command = 'docker pull python:3.8-slim-buster'
os.system(command);
subprocess.check_call(["docker", "build", "-t",module+'_'+model_name.lower()+":"+model_version,"."], cwd=folderpath)
def local_docker_build(config):
print(config)
config = json.loads(config)
model_name = config['usecase']
model_version = config['version']
mlaac__code_path = config['mlacPath']
docker_images = {}
docker_images['ModelMonitoring'] = 'modelmonitoring'+'_'+model_name.lower()+':'+model_version
dataset_addr = os.path.join(mlaac__code_path,'ModelMonitoring')
createDockerImage(model_name,model_version,'modelmonitoring',dataset_addr)
docker_images['DataIngestion'] = 'dataingestion'+'_'+model_name.lower()+':'+model_version
dataset_addr = os.path.join(mlaac__code_path,'DataIngestion')
createDockerImage(model_name,model_version,'dataingestion',dataset_addr)
transformer_addr = os.path.join(mlaac__code_path,'DataTransformation')
docker_images['DataTransformation'] = 'datatransformation'+'_'+model_name.lower()+':'+model_version
createDockerImage(model_name,model_version,'datatransformation',transformer_addr)
featureengineering_addr = os.path.join(mlaac__code_path,'FeatureEngineering')
docker_images['FeatureEngineering'] = 'featureengineering'+'_'+model_name.lower()+':'+model_version
createDockerImage(model_name,model_version,'featureengineering',featureengineering_addr)
from os import listdir
arr = [filename for filename in os.listdir(mlaac__code_path) if filename.startswith("ModelTraining")]
docker_training_images = []
for x in arr:
dockertraing={}
dockertraing['Training'] = str(x).lower()+'_'+model_name.lower()+':'+model_version
docker_training_images.append(dockertraing)
training_addri = os.path.join(mlaac__code_path,x)
createDockerImage(model_name,model_version,str(x).lower(),training_addri)
docker_images['ModelTraining'] = docker_training_images
docker_images['ModelRegistry'] = 'modelregistry'+'_'+model_name.lower()+':'+model_version
deploy_addr = os.path.join(mlaac__code_path,'ModelRegistry')
createDockerImage(model_name,model_version,'modelregistry',deploy_addr)
docker_images['ModelServing'] = 'modelserving'+'_'+model_name.lower()+':'+model_version
deploy_addr = os.path.join(mlaac__code_path,'ModelServing')
createDockerImage(model_name,model_version,'modelserving',deploy_addr)
outputjsonFile = os.path.join(mlaac__code_path,'dockerlist.json')
with open(outputjsonFile, 'w') as f:
json.dump(docker_images, f)
f.close()
output = {'Status':'Success','Msg':outputjsonFile}
output = json.dumps(output)
print("aion_build_container:",output) |
git_upload.py | import os
import sys
import json
from pathlib import Path
import subprocess
import shutil
import argparse
def create_and_save_yaml(git_storage_path, container_label,usecasepath):
file_name_prefix = 'gh-acr-'
yaml_file = f"""\
name: gh-acr-{container_label}
on:
push:
branches: main
paths: {container_label}/**
workflow_dispatch:
jobs:
gh-acr-build-push:
runs-on: ubuntu-latest
steps:
- name: 'checkout action'
uses: actions/checkout@main
- name: 'azure login'
uses: azure/login@v1
with:
creds: ${{{{ secrets.AZURE_CREDENTIALS }}}}
- name: 'build and push image'
uses: azure/docker-login@v1
with:
login-server: ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}
username: ${{{{ secrets.REGISTRY_USERNAME }}}}
password: ${{{{ secrets.REGISTRY_PASSWORD }}}}
- run: |
docker build ./{container_label}/ModelMonitoring -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label}
docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelmonitoring:{container_label}
docker build ./{container_label}/DataIngestion -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label}
docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/dataingestion:{container_label}
docker build ./{container_label}/DataTransformation -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label}
docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/datatransformation:{container_label}
docker build ./{container_label}/FeatureEngineering -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label}
docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/featureengineering:{container_label}
docker build ./{container_label}/ModelRegistry -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label}
docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelregistry:{container_label}
docker build ./{container_label}/ModelServing -t ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label}
docker push ${{{{ secrets.REGISTRY_LOGIN_SERVER }}}}/modelserving:{container_label}
"""
arr = [filename for filename in os.listdir(usecasepath) if filename.startswith("ModelTraining")]
for x in arr:
yaml_file+=' docker build ./'+container_label+'/'+x+' -t ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label
yaml_file+='\n'
yaml_file+=' docker push ${{ secrets.REGISTRY_LOGIN_SERVER }}/'+x.lower()+':'+container_label
yaml_file+='\n'
with open(Path(git_storage_path)/(file_name_prefix + container_label + '.yaml'), 'w') as f:
f.write(yaml_file)
def run_cmd(cmd):
try:
subprocess.check_output(cmd, stderr=subprocess.PIPE)
except subprocess.CalledProcessError as e:
if e.stderr:
if isinstance(e.stderr, bytes):
err_msg = e.stderr.decode(sys.getfilesystemencoding())
else:
err_msg = e.stderr
elif e.output:
if isinstance(e.output, bytes):
err_msg = e.output.decode(sys.getfilesystemencoding())
else:
err_msg = e.output
else:
err_msg = str(e)
return False, err_msg
return True, ""
def validate_config(config):
non_null_keys = ['url','username', 'token', 'location', 'gitFolderLocation', 'email', 'modelName']
missing_keys = [k for k in non_null_keys if k not in config.keys()]
if missing_keys:
raise ValueError(f"following fields are missing in config file: {missing_keys}")
for k,v in config.items():
if k in non_null_keys and not v:
raise ValueError(f"Please provide value for '{k}' in config file.")
def upload(config):
validate_config(config)
url_type = config.get('url_type','https')
if url_type == 'https':
https_str = "https://"
url = https_str + config['username'] + ":" + config['token'] + "@" + config['url'][len(https_str):]
else:
url = config['url']
model_location = Path(config['location'])
git_folder_location = Path(config['gitFolderLocation'])
git_folder_location.mkdir(parents=True, exist_ok=True)
(git_folder_location/'.github'/'workflows').mkdir(parents=True, exist_ok=True)
if not model_location.exists():
raise ValueError('Trained model data not found')
os.chdir(str(git_folder_location))
(git_folder_location/config['modelName']).mkdir(parents=True, exist_ok=True)
shutil.copytree(model_location, git_folder_location/config['modelName'], dirs_exist_ok=True)
create_and_save_yaml((git_folder_location/'.github'/'workflows'), config['modelName'],config['location'])
if (Path(git_folder_location)/'.git').exists():
first_upload = False
else:
first_upload = True
if first_upload:
cmd = ['git','init']
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
cmd = ['git','config','user.name',config['username']]
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
cmd = ['git','config','user.email',config['email']]
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
cmd = ['git','add', '-A']
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
cmd = ['git','commit','-m',f"commit {config['modelName']}"]
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
cmd = ['git','branch','-M','main']
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
if first_upload:
cmd = ['git','remote','add','origin', url]
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
cmd = ['git','push','-f','-u','origin', 'main']
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
else:
cmd = ['git','push']
status, msg = run_cmd(cmd)
if not status:
raise ValueError(msg)
return json.dumps({'Status':'SUCCESS'})
if __name__ == '__main__':
try:
if shutil.which('git') is None:
raise ValueError("git is not installed on this system")
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', help='Config file location or as a string')
args = parser.parse_args()
if Path(args.config).is_file() and Path(args.config).suffix == '.json':
with open(args.config,'r') as f:
config = json.load(f)
else:
config = json.loads(args.config)
print(upload(config))
except Exception as e:
status = {'Status':'Failure','msg':str(e)}
print(json.dumps(status)) |
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
kafka_consumer.py | from kafka import KafkaConsumer
from json import loads
import pandas as pd
import json
import os,sys
import time
import multiprocessing
from os.path import expanduser
import platform
import datetime
modelDetails = {}
class Process(multiprocessing.Process):
def __init__(self, modelSignature,jsonData,predictedData,modelpath):
super(Process, self).__init__()
self.config = jsonData
self.modelSignature = modelSignature
self.data = predictedData
self.modelpath = modelpath
def run(self):
#data = pd.json_normalize(self.data)
minotoringService = self.config['minotoringService']['url']
trainingdatalocation = self.config['trainingDataLocation'][self.modelSignature]
#filetimestamp = 'AION_'+str(int(time.time()))+'.csv'
#data.to_csv(dataFile, index=False)
inputFieldsJson = {"trainingDataLocation":trainingdatalocation,"currentDataLocation":self.data}
inputFieldsJson = json.dumps(inputFieldsJson)
ser_url = minotoringService+self.modelSignature+'/monitoring'
driftTime = datetime.datetime.now()
import requests
try:
response = requests.post(ser_url, data=inputFieldsJson,headers={"Content-Type":"application/json",})
outputStr=response.content
outputStr = outputStr.decode('utf-8')
outputStr = outputStr.strip()
decoded_data = json.loads(outputStr)
print(decoded_data)
status = decoded_data['status']
msg = decoded_data['data']
except Exception as inst:
if 'Failed to establish a new connection' in str(inst):
status = 'Fail'
msg = 'AION Service needs to be started'
else:
status = 'Fail'
msg = 'Error during Drift Analysis'
statusFile = os.path.join(self.modelpath,self.modelSignature+'_status.csv')
df = pd.DataFrame(columns = ['dateTime', 'status', 'msg'])
df = df.append({'dateTime' : driftTime, 'status' : status, 'msg' : msg},ignore_index = True)
print(df)
if (os.path.exists(statusFile)):
df.to_csv(statusFile, mode='a', header=False,index=False)
else:
df.to_csv(statusFile, header=True,index=False)
def launch_kafka_consumer():
from appbe.dataPath import DATA_DIR
configfile = os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf')
with open(configfile,'r',encoding='utf-8') as f:
jsonData = json.load(f)
f.close()
kafkaIP=jsonData['kafkaCluster']['ip']
kafkaport = jsonData['kafkaCluster']['port']
topic = jsonData['kafkaCluster']['topic']
kafkaurl = kafkaIP+':'+kafkaport
if jsonData['database']['csv'] == 'True':
database = 'csv'
elif jsonData['database']['mySql'] == 'True':
database = 'mySql'
else:
database = 'csv'
kafkaPath = os.path.join(DATA_DIR,'kafka')
if not (os.path.exists(kafkaPath)):
try:
os.makedirs(kafkaPath)
except OSError as e:
pass
consumer = KafkaConsumer(topic,bootstrap_servers=[kafkaurl],auto_offset_reset='earliest',enable_auto_commit=True,group_id='my-group',value_deserializer=lambda x: loads(x.decode('utf-8')))
for message in consumer:
message = message.value
data = message['data']
data = pd.json_normalize(data)
modelname = message['usecasename']
version = message['version']
modelSignature = modelname+'_'+str(version)
modelpath = os.path.join(kafkaPath,modelSignature)
try:
os.makedirs(modelpath)
except OSError as e:
pass
secondsSinceEpoch = time.time()
if modelSignature not in modelDetails:
modelDetails[modelSignature] = {}
modelDetails[modelSignature]['startTime'] = secondsSinceEpoch
if database == 'csv':
csvfile = os.path.join(modelpath,modelSignature+'.csv')
if (os.path.exists(csvfile)):
data.to_csv(csvfile, mode='a', header=False,index=False)
else:
data.to_csv(csvfile, header=True,index=False)
modelTimeFrame = jsonData['timeFrame'][modelSignature]
currentseconds = time.time()
print(currentseconds - modelDetails[modelSignature]['startTime'])
if (currentseconds - modelDetails[modelSignature]['startTime']) >= float(modelTimeFrame):
csv_path = os.path.join(modelpath,modelSignature+'.csv')
#predictedData = pd.read_csv(csv_path)
##predictedData = predictedData.to_json(orient="records")
index = Process(modelSignature,jsonData,csv_path,modelpath)
index.start()
modelDetails[modelSignature]['startTime'] = secondsSinceEpoch
|
pattern.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import pandas as pd
import numpy as np
import sys
import math
import markov_clustering as mc
import os
import networkx as nx
import logging
import json
## How far you'd like your random-walkers to go (bigger number -> more walking)
EXPANSION_POWER = 2
## How tightly clustered you'd like your final picture to be (bigger number -> more clusters)
INFLATION_POWER = 2
## If you can manage 100 iterations then do so - otherwise, check you've hit a stable end-point.
ITERATION_COUNT = 100
def normalize(matrix):
return matrix/np.sum(matrix, axis=0)
def expand(matrix, power):
return np.linalg.matrix_power(matrix, power)
def inflate(matrix, power):
for entry in np.nditer(matrix, op_flags=['readwrite']):
entry[...] = math.pow(entry, power)
return matrix
class pattern:
def __init__(self,modelFeatures,targetFeature):
self.modelFeatures = modelFeatures.split(',')
self.targetFeature = targetFeature
self.log = logging.getLogger('eion')
def training(self,df,outputLocation):
df["code"] = df[self.targetFeature].astype("category")
df['code'] = df.code.cat.codes
df2 = df[[self.targetFeature,'code']]
df2 = df2.drop_duplicates()
code_book = df2.to_dict('records')
size = len(code_book)
if self.targetFeature in self.modelFeatures:
self.modelFeatures.remove(self.targetFeature)
df['prev_code'] = df.groupby(self.modelFeatures)['code'].shift()
df['prev_activity'] = df.groupby(self.modelFeatures)[self.targetFeature].shift()
print(self.modelFeatures)
df = df.dropna(axis=0, subset=['prev_code'])
df['prev_code'] = df['prev_code'].astype('int32')
matrix = np.zeros((size, size),float)
np.set_printoptions(suppress=True)
for index, row in df.iterrows():
matrix[int(row['prev_code'])][int(row['code'])] += 1
np.fill_diagonal(matrix, 1)
matrix = normalize(matrix)
pmatrix = matrix
i = 0
records = []
for row in matrix:
j = 0
for val in row:
for event in code_book:
if event['code'] == i:
page = event[self.targetFeature]
if event['code'] == j:
nextpage = event[self.targetFeature]
record = {}
record['State'] = page
record['NextState'] = nextpage
record['Probability'] = round(val,2)
records.append(record)
j = j+1
i = i+1
df_probability = pd.DataFrame(records)
self.log.info('Status:- |... StateTransition Probability Matrix')
for _ in range(ITERATION_COUNT):
matrix = normalize(inflate(expand(matrix, EXPANSION_POWER), INFLATION_POWER))
result = mc.run_mcl(matrix) # run MCL with default parameters
c = 0
clusters = mc.get_clusters(matrix) # get clusters
self.log.info('Status:- |... StateTransition Algorithm applied: MarkovClustering')
clusterrecords = []
for cluster in clusters:
clusterid = c
clusterlist = ''
for pageid in cluster:
for event in code_book:
if event['code'] == pageid:
page = event[self.targetFeature]
if clusterlist != '':
clusterlist = clusterlist+','
clusterlist = clusterlist+page
record = {}
record['clusterid'] = c
record['clusterlist'] = clusterlist
clusterrecords.append(record)
c = c+1
df_cluster = pd.DataFrame(clusterrecords)
probabilityoutputfile = os.path.join(outputLocation, 'stateTransitionProbability.csv')
self.log.info('-------> State Transition Probability Matrix:' + probabilityoutputfile)
df_probability.to_csv(probabilityoutputfile,index=False)
clusteringoutputfile = os.path.join(outputLocation, 'stateClustering.csv')
self.log.info('-------> State Transition Probability Grouping:' + clusteringoutputfile)
df_cluster.to_csv(clusteringoutputfile,index=False)
datadetailsfile = os.path.join(outputLocation, 'datadetails.json')
dataanalytics = {}
dataanalytics['activity'] = self.targetFeature
dataanalytics['sessionid'] = self.modelFeatures[0]
updatedConfig = json.dumps(dataanalytics)
with open(datadetailsfile, "w") as fpWrite:
fpWrite.write(updatedConfig)
fpWrite.close()
evaulatemodel = '{"Model":"MarkovClustering","Score":0}'
return(evaulatemodel,probabilityoutputfile,clusteringoutputfile)
|
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
runtime_utility.py |
class aionRunTimeUtility:
# def __init__(self):
# print("AI.ON ConversionUtility function init...")
def executeOnRuntime(self,inputModelName,inputDataSet):
# print("AI.ON ConversionUtility function starts...")
RuntimeType = inputModelName.rsplit('.', 1)[1]
inputDataType = inputDataSet.rsplit('.', 1)[1]
if((RuntimeType == 'ONNX' or RuntimeType == 'onnx') and (inputDataType.lower()=='json')):
# print("Inference through ONNX Runtime started [ML]")
import pandas
import json
with open(inputDataSet) as datafile:
data = json.load(datafile)
dataframe = pandas.DataFrame(data,index=[0])
import numpy
import onnxruntime as rt
sess = rt.InferenceSession(inputModelName)
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
inputsize=sess.get_inputs()[0].shape
first_n_column = dataframe.iloc[: , :inputsize[1]]
dataset = first_n_column.values
if(inputsize[1]!=len(dataframe.columns)):
print("Error : Input Data size does not match")
return 0
pred_onx = sess.run([label_name], {input_name: dataset.astype(numpy.float32)[0:1]})[0]
# for i in range(0, 1):
#print("ONNX Runtime Prediction [csv]: ",pred_onx)
output = numpy.squeeze(pred_onx)
predictions = numpy.squeeze(output)
prediction = numpy.argmax(predictions)
return(prediction)
# print("Inference through ONNX modelcompleted ")
if((RuntimeType == 'ONNX' or RuntimeType == 'onnx') and (inputDataType!='json')):
import numpy as np
import onnxruntime as rt
from tensorflow.keras.preprocessing import image
sess = rt.InferenceSession(inputModelName)
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
inputsize=sess.get_inputs()[0].shape
img = image.load_img(inputDataSet, target_size=(inputsize[1], inputsize[2]))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
pred_onx = sess.run([label_name], {input_name: x.astype(np.float32)[0:1]})[0]
output = np.squeeze(pred_onx)
predictions = np.squeeze(output)
return(pred_onx)
if((RuntimeType == 'TFLITE' or RuntimeType == 'tflite')and (inputDataType=='json')):
import numpy as np
import tensorflow as tf
import pandas
from numpy import asarray
interpreter = tf.lite.Interpreter(model_path=inputModelName)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
import pandas
import json
with open(inputDataSet) as datafile:
data = json.load(datafile)
dataframe = pandas.DataFrame(data,index=[0])
dataset = dataframe.values
XYZ = dataset[:,0:input_shape[1]].astype(float)
input_data = asarray(XYZ[0]).reshape((1, input_shape[1]))
for i in range(0, 1):
input_data = asarray(XYZ[i]).reshape((1,input_shape[1]))
interpreter.set_tensor(input_details[0]['index'], input_data.astype(np.float32)[0:1])
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
predictions = np.squeeze(output_data)
prediction = np.argmax(predictions)
return(prediction)
if((RuntimeType == 'TFLITE' or RuntimeType == 'tflite') and (inputDataType!='json')):
import numpy as np
from tensorflow.keras.preprocessing import image
import os
import tensorflow as tf
import pandas
from numpy import asarray
interpreter = tf.lite.Interpreter(model_path=inputModelName)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
img = image.load_img(inputDataSet, target_size=(input_shape[1], input_shape[2]))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
interpreter.set_tensor(input_details[0]['index'], x.astype(np.float32)[0:1])
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
predictions = np.squeeze(output_data)
prediction = np.argmax(predictions)
return(prediction)
def runTimeTesting(inputModelName,inputDataSet):
objRunTimeUtility=aionRunTimeUtility()
return(objRunTimeUtility.executeOnRuntime(inputModelName,inputDataSet))
|
model_convertions.py | import os
import sys
import logging
import json
import joblib
from pathlib import Path
import platform
from datetime import datetime as dt
import time
import argparse
log = None
def get_true_option(d, default_value=None):
if isinstance(d, dict):
for k,v in d.items():
if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True):
return k
return default_value
def convert_keras2onnx(input_model, output_file):
import tensorflow as tf
import tf2onnx
from tensorflow.keras.models import load_model
model = load_model(input_model)
config = model.get_config()
modelInputShape=config["layers"][0]["config"]["batch_input_shape"]
spec = (tf.TensorSpec(modelInputShape, tf.float32, name="input"),)
model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13, output_path=output_file)
def convert_sklearn2onnx(input_model, file_path, input_shape=1):
#print('Instead convert_sklearn2onnx')
from skl2onnx import convert_sklearn
#print('Instead convert_sklearn2onnx')
from skl2onnx.common.data_types import FloatTensorType
sklearn_model = joblib.load(input_model)
sklearn_model_name= str(type(sklearn_model)).split(".")[-1][:-2]
initial_type = [('float_input', FloatTensorType([None, input_shape]))]
model = convert_sklearn(sklearn_model, initial_types=initial_type)
with open(file_path, "wb") as f:
f.write(model.SerializeToString())
def convert_xgboost2onnx(input_model, file_path, input_shape=1):
from onnxmltools.convert import convert_xgboost
from onnxmltools.convert.common.data_types import FloatTensorType
xgBoost_model = joblib.load(input_model)
if not xgBoost_model.n_estimators:
xgBoost_model.n_estimators = xgBoost_model.get_num_boosting_rounds()
n_features = xgBoost_model.n_features_in_
xgBoost_model.get_booster().feature_names = [f'f{x}' for x in range(n_features)]
initial_type = [('float_input', FloatTensorType([None, xgBoost_model.n_features_in_]))]
model = convert_xgboost(xgBoost_model, initial_types=initial_type)
with open(file_path, "wb") as f:
f.write(model.SerializeToString())
def convert_lightgbm2onnx(input_model, file_path):
from onnxmltools.convert import convert_lightgbm
from onnxmltools.convert.common.data_types import FloatTensorType
lightGBM_model = joblib.load(input_model)
initial_type = [('float_input', FloatTensorType([None, lightGBM_model.n_features_in_]))]
model = convert_lightgbm(lightGBM_model, initial_types=initial_type, zipmap=False)
with open(file_path, "wb") as f:
f.write(model.SerializeToString())
def convert_coreml2onnx(input_model, file_path):
import coremltools
import onnxmltools
coreml_model = coremltools.utils.load_spec(input_model)
onnx_model = onnxmltools.convert_coreml(coreml_model)
onnxmltools.utils.save_model(onnx_model, file_path)
def convert_tflite2onnx(input_model, file_path):
cmd = f"{sys.executable} -m tf2onnx.convert --opset 13 --tflite {str(input_model)} --output {str(file_path)}"
os.system(cmd)
def convert_tensorflow2onnx(input_model, file_path):
import subprocess
cmd = [sys.executable, '-m','tf2onnx.convert','--saved-model',str(input_model),'--output',str(file_path)]
result = subprocess.check_output(cmd)
result = result.decode('utf-8')
def convert_libsvm2onnx(input_model, file_path):
import onnxmltools
import libsvm.svmutil as svmutil
from onnxmltools.convert.libsvm import convert
from onnxmltools.convert.common.data_types import FloatTensorType
loaded_model=svmutil.svm_load_model(str(input_model))
model = convert(loaded_model, "node", [('input', FloatTensorType())])
onnxmltools.utils.save_model(model, file_path)
def optimize_onnx(onnx_model_file, output_file_path):
from onnxruntime.quantization import quantize_dynamic, QuantType
quantize_dynamic(onnx_model_file, output_file_path, weight_type=QuantType.QUInt8)
return True
def convert_keras2tflite(input_model, file_path, optimized=False):
import tensorflow as tf
converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file(input_model)
if optimized:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
model = converter.convert()
with open(file_path, 'wb') as f:
f.write(model)
def convert_tensorflow2tflite(input_model, file_path, optimized=False):
import tensorflow as tf
modelpath=str(input_model)
#converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(input_model)
converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(modelpath)
if optimized:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
model = converter.convert()
with open(file_path, 'wb') as f:
f.write(model)
class model_converter():
def __init__(self, model_path, output_path,input_format,output_format, shape=None):
if not self.is_conversion_supported(input_format,output_format):
raise ValueError(f"{input_format} to {output_format} is not supported")
if not Path(model_path).exists():
raise ValueError(f"Model doen't exists at: {model_path}")
self.model_path = Path(model_path)
self.output_path = Path(output_path)
self.output_path.mkdir(parents=True, exist_ok=True)
self.input_format = input_format
self.output_format = output_format
self.shape = shape
def is_conversion_supported(self, model_format, output_format):
onnx_formats = ['onnx_standard','onnx_optimized']
tflite_formats = ['tflite_standard','tflite_optimized']
sagemaker_formats = ['sagemaker']
all_formats = onnx_formats + tflite_formats + sagemaker_formats
formats = {'sklearn':onnx_formats + sagemaker_formats, 'keras':onnx_formats + tflite_formats,
'tensorflow':onnx_formats + tflite_formats, 'tflite':onnx_formats, 'lightgbm':onnx_formats,
'xgboost':onnx_formats, 'libsvm':onnx_formats,'coreml':['onnx_standard'] }
if model_format in list(formats.keys()) and output_format in all_formats:
if output_format in formats[model_format]:
return True
return False
def convert(self):
if self.output_format == 'onnx_standard':
output_file = self.output_path/(self.model_path.stem + '.onnx')
if self.input_format == 'sklearn':
model = convert_sklearn2onnx(self.model_path, output_file, self.shape)
elif self.input_format == 'keras':
convert_keras2onnx(self.model_path, output_file)
elif self.input_format == 'lightgbm':
convert_lightgbm2onnx(self.model_path, output_file)
elif self.input_format == 'xgboost':
convert_xgboost2onnx(self.model_path, output_file)
elif self.input_format == 'libsvm':
convert_libsvm2onnx(self.model_path, output_file)
elif self.input_format == 'coreml':
convert_coreml2onnx(self.model_path, output_file)
elif self.input_format == 'tflite':
convert_tflite2onnx(self.model_path, output_file)
elif self.input_format == 'tensorflow':
convert_tensorflow2onnx(self.model_path, output_file)
elif self.output_format == 'onnx_optimized':
onnx_std_file = self.output_path/(self.model_path.stem + '_unquant.onnx')
if onnx_std_file.exists():
onnx_std_file.unlink()
output_file = self.output_path/(self.model_path.stem + 'Opt.onnx')
if self.input_format == 'sklearn':
convert_sklearn2onnx(self.model_path, onnx_std_file, self.shape)
elif self.input_format == 'keras':
convert_keras2onnx(self.model_path, onnx_std_file)
elif self.input_format == 'lightgbm':
convert_lightgbm2onnx(self.model_path, onnx_std_file)
elif self.input_format == 'xgboost':
convert_xgboost2onnx(self.model_path, onnx_std_file)
elif self.input_format == 'libsvm':
convert_libsvm2onnx(self.model_path, onnx_std_file)
elif self.input_format == 'tflite':
convert_tflite2onnx(self.model_path, onnx_std_file)
elif self.input_format == 'tensorflow':
convert_tensorflow2onnx(self.model_path, onnx_std_file)
if onnx_std_file.exists():
try:
optimize_onnx(onnx_std_file, output_file)
except Exception as e:
raise
finally:
onnx_std_file.unlink()
temp_file = onnx_std_file.parent/(onnx_std_file.stem + '-opt.onnx')
if temp_file.exists():
temp_file.unlink()
elif self.output_format in ['tflite_standard', 'tflite_optimized']:
if self.output_format == 'tflite_optimized':
output_file = self.output_path/(self.model_path.stem + 'Opt.tflite')
optimized = True
else:
output_file = self.output_path/(self.model_path.stem + '.tflite')
optimized = False
if self.input_format == 'keras':
convert_keras2tflite(self.model_path, output_file, optimized)
elif self.input_format == 'tensorflow':
convert_tensorflow2tflite(self.model_path, output_file, optimized)
def run(model_path, output_path, input_format, output_format, input_shape=None):
from appbe.dataPath import LOG_LOCATION
input_format = input_format.lower()
output_format = output_format.lower()
log_file_path = Path(LOG_LOCATION)
log_file_path.mkdir(parents=True, exist_ok=True)
time_stamp = dt.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H-%M-%S')
fileName='modelConversion_'+time_stamp+'.log'
filehandler = logging.FileHandler(log_file_path/fileName, 'w','utf-8')
formatter = logging.Formatter('%(message)s')
filehandler.setFormatter(formatter)
log = logging.getLogger('modelConversionUtility')
log.propagate = False
for hdlr in log.handlers[:]: # remove the existing file handlers
if isinstance(hdlr,logging.FileHandler):
log.removeHandler(hdlr)
log.addHandler(filehandler)
log.setLevel(logging.INFO)
log.info('------------------ModelConversionUtility---------------------')
log.info(f'Input model path: {model_path}')
log.info(f'Output model path: {output_path}')
log.info(f'Input model format: {input_format}')
log.info(f'Output model format: {output_format}')
log.info(f'\nConverting {input_format} to {output_format} start:')
output ={}
output['logfiles'] = str(log_file_path/fileName)
log.info(f"\nExecution Start Time: {dt.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')}")
try:
start_time = time.time()
objConvUtility=model_converter(model_path,output_path,input_format,output_format,input_shape)
objConvUtility.convert()
end_time = time.time()
log.info(f"Time required for conversion: {end_time - start_time} sec")
log.info(f'\nConverting {input_format} to {output_format} Successful')
output['Convert'] = "Success"
except Exception as e:
output['Convert'] = "Error"
log.info('Error: ' + str(e))
log.error(e, exc_info=True)
if 'not supported' in str(e):
output['sub error'] = "Not supported"
output = json.dumps(output)
log.info(f'Output: {output}')
return output
def convert(config_file):
with open(config_file, 'r') as f:
config = json.load(f)
model_path = config['advance']['aionConversionUtility']['modelpath']
output_path = config['advance']['aionConversionUtility']['deployedlocation']
input_format = get_true_option(config['advance']['aionConversionUtility']['inputModelType'],'').lower()
output_format = get_true_option(config['advance']['aionConversionUtility']['outputModelType'],'').lower()
if input_format=="keras":
input_shape = int(config['advance']['aionConversionUtility']['inputShape'])
if input_format!="keras":
input_shape = config['advance']['aionConversionUtility']['numberoffeatures']
input_shape = int(input_shape) if input_shape else 0
#input_shape = int(config['advance']['aionConversionUtility']['numberoffeatures'])
output = run(model_path, output_path, input_format, output_format, input_shape)
print(output) |
run_onnxinference.py | import pandas
import numpy
import sys
import onnxruntime as rt
def onnx_runtime_validation(modelfile,datafile):
dataframe = pandas.read_csv(datafile)
df = dataframe.head(8)
dataset = df.values
sess = rt.InferenceSession(modelfile)
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
inputsize=sess.get_inputs()[0].shape
XYZ = dataset[:,0:inputsize[1]].astype(float)
pred_onx = sess.run([label_name], {input_name: XYZ.astype(numpy.float32)[0:8]})[0]
print("Prediction of AION generated/converted model on ONNX runtime for 8 sets of data")
for i in range(0, 8):
output = numpy.squeeze(pred_onx[i])
predictions = numpy.squeeze(output)
prediction = numpy.argmax(predictions)
df['predictions'] = predictions
result = df.to_json(orient="records")
return(result)
if __name__ == "__main__":
output = onnx_runtime_validation(sys.argv[1],sys.argv[2])
print("predictions:",output)
|
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
aionNAS.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import logging
logging.getLogger('tensorflow').disabled = True
import json
#from nltk.corpus import stopwords
from collections import Counter
from numpy import mean
from numpy import std
from pandas import read_csv
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from learner.machinelearning import machinelearning
# from sklearn.dummy import DummyClassifier
# create histograms of numeric input variables
import sys
import os
import re
import pandas as pd
import numpy as np
from learner.aion_matrix import aion_matrix
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
import autokeras as ak
# load the sonar dataset
from sklearn.model_selection import train_test_split
# from sklearn.metrics import cohen_kappa_score
# from sklearn.metrics import roc_auc_score
# from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve
from math import sqrt
from sklearn.metrics import mean_squared_error, explained_variance_score,mean_absolute_error
from sklearn import metrics
class aionNAS:
def __init__(self,nas_class,nas_params,xtrain1,xtest1,ytrain1,ytest1,deployLocation):
try:
self.dfFeatures=None
self.nas_class=nas_class
self.nas_params=nas_params
self.targetFeature=None
self.log = logging.getLogger('eion')
self.n_models=int(self.nas_params['n_models'])
self.n_epochs=int(self.nas_params['n_epochs'])
self.optimizer=self.nas_params['optimizer']
self.metrics=self.nas_params['metrics']
self.tuner=self.nas_params['tuner']
self.seed=int(self.nas_params['seed'])
self.xtrain = xtrain1
self.xtest = xtest1
self.ytrain = ytrain1
self.ytest = ytest1
#self.labelMaps = labelMaps
self.deployLocation=deployLocation
except Exception as e:
self.log.info('<!------------- NAS INIT Error ---------------> ')
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
def paramCheck(self):
try:
if not (self.nas_class):
self.log.info('<!------------- NAS class input Error ---------------> ')
if not (self.nas_params):
self.log.info('<!------------- NAS model hyperparameter input Error ---------------> ')
if not (self.targetFeature):
self.log.info('<!------------- NAS model targetFeature input Error ---------------> ')
if (self.n_models < 1):
self.n_models=1
if not (self.dfFeatures):
self.log.info('<!------------- NAS model features Error ---------------> ')
if (self.n_epochs < 1):
self.n_models=1
if not (self.optimizer):
self.optimizer="adam"
if not (self.tuner):
self.tuner="greedy"
if (self.seed < 1):
self.seed=0
if not (self.metrics):
self.metrics=None
except ValueError:
self.log.info('<------------------ NAS config file error. --------------->')
def recall_m(self,y_true, y_pred):
true_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1)))
possible_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + tf.keras.backend.epsilon())
return recall
def precision_m(self,y_true, y_pred):
true_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1)))
predicted_positives = tf.keras.metrics.Sum(tf.keras.backend.round(tf.keras.backend.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + tf.keras.backend.epsilon())
return precision
def f1_score(self,y_true, y_pred):
precision = self.precision_m(y_true, y_pred)
recall = self.recall_m(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+tf.keras.backend.epsilon()))
def nasStructdataPreprocess(self):
df=self.data
self.paramCheck()
target=df[self.targetFeature].values
counter = Counter(target)
for k,v in counter.items():
per = v / len(target) * 100
self.log.info('autokeras struct Class=%d, Count=%d, Percentage=%.3f%%' % (k, v, per))
# select columns with numerical data types
num_ix = df.select_dtypes(include=['int64', 'float64']).columns
subset = df[num_ix]
last_ix = len(df.columns) - 1
y=df[self.targetFeature]
X = df.drop(self.targetFeature, axis=1)
#Using Pearson Correlation
# plt.figure(figsize=(12,10))
# cor = df.corr()
# sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)
# plt.show()
# select categorical features
cat_ix = X.select_dtypes(include=['object', 'bool']).columns
# one hot encode cat features only
ct = ColumnTransformer([('o',OneHotEncoder(),cat_ix)], remainder='passthrough')
X = X.reset_index()
X=X.replace(to_replace="NULL",value=0)
X = X.dropna(how='any',axis=0)
X = ct.fit_transform(X)
from sklearn.preprocessing import scale
X = scale(X)
# label encode the target variable to have the classes 0 and 1
y = LabelEncoder().fit_transform(y)
# separate into train and test sets
X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=self.test_size,random_state=1)
return X_train, X_test, y_train, y_test
def nasStructClassification(self,scoreParam):
try:
objClf = aion_matrix()
X_train, X_test, y_train, y_test= self.xtrain, self.xtest, self.ytrain, self.ytest
modelName="nas_structdata_classifier"
self.log.info("Processing structured data block...\n")
s_in = ak.StructuredDataInput()
#s_in = Flatten()(s_in)
s_out = ak.StructuredDataBlock(categorical_encoding=True)(s_in)
self.log.info("Data pipe via autokeras Classification Dense layers ...\n")
s_out = ak.ClassificationHead()(s_out)
self.log.info("applying autokeras automodel to run different neural models...\n")
try:
tuner = str(self.tuner).lower()
except UnicodeEncodeError:
tuner = (self.tuner.encode('utf8')).lower()
nasclf = ak.AutoModel(
inputs=s_in,
outputs=s_out,
overwrite=True,
tuner=tuner,
max_trials=self.n_models,
seed=self.seed)
# compile the model
#nasclf.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc',self.f1_score,self.precision_m, self.recall_m])
nasclf.fit(X_train, y_train, epochs=self.n_epochs)
best_model = nasclf.export_model()
mpredict=best_model.predict(X_test)
mtpredict=best_model.predict(X_train)
#loss, accuracy, f1_score, precision, recall = nasclf.evaluate(X_test, y_test, verbose=0)
#from sklearn.metrics import classification_report
#Classification report
y_pred_bool = np.argmax(mpredict, axis=1)
y_train_pred_bool = np.argmax(mtpredict, axis=1)
score = objClf.get_score(scoreParam,y_test, y_pred_bool)
#best_model = nasclf.export_model()
best_model_summary=best_model.summary()
filename = os.path.join(self.deployLocation,'log','summary.txt')
with open(filename,'w') as f:
best_model.summary(print_fn=lambda x: f.write(x + '\n'))
f.close()
#self.log.info("==========")
#self.log.info(best_model_summary)
self.log.info("NAS struct data classification, best model summary: \n"+str(best_model.summary(print_fn=self.log.info)))
#self.log.info("==========")
#Save and load model
# # #try:
# try:
# best_model.save("model_class_autokeras", save_format="tf")
# except Exception:
# best_model.save("model_class_autokeras.h5")
# loaded_model = load_model("model_class_autokeras", custom_objects=ak.CUSTOM_OBJECTS)
# loadedmodel_predict=loaded_model.predict(X_test)
loss,accuracy_m=nasclf.evaluate(X_test, y_test)
#mpredict_classes = mpredict.argmax(axis=-1)
#accuracy = accuracy_score(y_test.astype(int), mpredict.astype(int))
# precision tp / (tp + fp)
#precision = precision_score(y_test.astype(int), mpredict.astype(int),average='macro')
# recall: tp / (tp + fn)
#recall = recall_score(y_test.astype(int), mpredict.astype(int),average='macro')
#f1score=f1_score(y_test.astype(int), mpredict.astype(int) , average="macro")
self.log.info("Autokeras struct data classification metrics: \n")
except Exception as inst:
self.log.info("Error: NAS failed "+str(inst))
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
print(inst)
return modelName,nasclf,score
def nasStructRegressor(self,scoreParam):
objClf = aion_matrix()
modelName="nas_struct_regressor"
#self.paramCheck()
X_train, X_test, y_train, y_test= self.xtrain, self.xtest, self.ytrain, self.ytest
# Autokeras alg
s_in = ak.StructuredDataInput()
#tf.keras.layers.GlobalMaxPooling2D()(s_in)
s_out = ak.StructuredDataBlock(categorical_encoding=True)(s_in)
self.log.info("Data pipe via autokeras Regression Dense layers ...\n")
s_out = ak.RegressionHead(loss='mse', metrics=['mae'])(s_out)
self.log.info("applying autokeras automodel to evaluate different neural models...\n")
try:
tuner = str(self.tuner).lower()
except UnicodeEncodeError:
tuner = (self.tuner.encode('utf8')).lower()
nas_reg = ak.AutoModel(
inputs=s_in,
outputs=s_out,
overwrite=True,
tuner=tuner,
max_trials=self.n_models)
nas_reg.fit(X_train, y_train, epochs=self.n_epochs)
best_model = nas_reg.export_model()
self.log.info("NAS struct data regression best model summary: \n")
best_model_summary=best_model.summary(print_fn=self.log.info)
self.log.info(best_model_summary)
predictm=best_model.predict(X_test)
mtpredict=best_model.predict(X_train)
score = objClf.get_score(scoreParam,y_test, predictm)
self.log.info("Autokeras struct data regression metrics: \n")
return modelName,nas_reg,score
def nasMain(self,scoreParam):
modelName = ""
nasclf=None
nas_reg=None
#text_reg_model=None
mse_value=0
reg_rmse=0
mape_reg=0
huber_loss_reg=0
accuracy=0
precision=0
recall=0
#Dummy values to return main for classification problems
dummy_score_1=int(0)
#dummy_score_2=int(0)
try:
if ((self.nas_class.lower() == "classification")):
modelName,nasclf,score=self.nasStructClassification(scoreParam)
self.log.info('NAS Struct Classification score: '+str(score))
best_model_nas = nasclf.export_model()
scoredetails = '{"Model":"NAS","Score":'+str(round(score,2))+'}'
return best_model_nas,self.nas_params,round(score,2),'NAS',-1,-1,-1
elif (self.nas_class.lower() == "regression"):
modelName,nas_reg,score =self.nasStructRegressor(scoreParam)
self.log.info('NAS Struct Regression score: '+str(score))
best_model_nas = nas_reg.export_model()
'''
filename = os.path.join(self.deployLocation,'model','autoKerasModel')
best_model_nas = nas_reg.export_model()
try:
best_model_nas.save(filename, save_format="tf")
modelName = 'autoKerasModel'
except Exception:
filename = os.path.join(self.deployLocation,'model','autoKerasModel.h5')
best_model_nas.save(filename)
modelName = 'autoKerasModel.h5'
'''
scoredetails = '{"Model":"NAS","Score":'+str(round(score,2))+'}'
'''
error_matrix = '"MSE":"'+str(round(mse_value,2))+'","RMSE":"'+str(round(reg_rmse,2))+'","MAPE":"'+str(round(mape_reg,2))+'","MSLE":"'+str(round(msle_reg,2))+'"'
'''
return best_model_nas,self.nas_params,score,'NAS'
else:
pass
except Exception as inst:
print(inst)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
output = {"status":"FAIL","message":str(inst).strip('"')}
output = json.dumps(output)
|
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
survival_analysis.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''# -*- coding: utf-8 -*-
"""
@author: satish_k
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statistics
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from lifelines import KaplanMeierFitter, CoxPHFitter
from lifelines.statistics import logrank_test
from scipy import stats
import logging
class SurvivalAnalysis(object):
def __init__(self, df, method, event_column, duration_column, fitter_param=None, df_negate=None ):
pd.options.display.width = 30
self.df = df
self.fitter_param = fitter_param
self.method = method
self.event_column = event_column
self.duration_column = duration_column
self.models = []
self.train = df.drop_duplicates().reset_index()
self.test = None
if isinstance(df_negate, pd.DataFrame):
self.df_n = df_negate.drop_duplicates().reset_index()
else:
self.df_n = None
self.log = logging.getLogger('eion')
self.plots = []
def learn(self):
self.log.info('\n---------- SurvivalAnalysis learner has started ----------')
self.log.info('\n---------- SurvivalAnalysis learner method is "%s" ----------'%self.method)
lifelines_univariate_models = ["AalenJohansenFitter", "BreslowFlemingHarringtonFitter", "ExponentialFitter", "GeneralizedGammaFitter",
"KaplanMeierFitter", "LogLogisticFitter", "LogNormalFitter", "MixtureCureFitter", "NelsonAalenFitter", "PiecewiseExponentialFitter",
"SplineFitter", "WeibullFitter"]
lifelines_regression_models = ["AalenAdditiveFitter", "CRCSplineFitter", "CoxPHFitter", "CoxTimeVaryingFitter", "GeneralizedGammaRegressionFitter",
"LogLogisticAFTFitter", "LogNormalAFTFitter", "PiecewiseExponentialRegressionFitter", "WeibullAFTFitter"]
if self.method.lower() in ['kaplanmeierfitter','kaplanmeier','kaplan-meier','kaplan meier','kaplan','km','kmf']:
self.log.info('\n---------- SurvivalAnalysis learner method "%s" has started ----------'%self.method)
#from lifelines.utils import find_best_parametric_model
#m,s = find_best_parametric_model(event_times=self.df[self.duration_column])
if not isinstance(self.df_n, pd.DataFrame):
kmf = KaplanMeierFitter()
self.log.info('\n Shape of training data - %s'%str(self.train.shape))
T = self.train[self.duration_column]
E = self.train[self.event_column]
self.log.info('\n T : \n%s'%str(T))
self.log.info('\n E : \n%s'%str(E))
K = kmf.fit(T, E)
ax = plt.subplot(111)
kmf_sf = K.survival_function_
ax = kmf_sf.plot(ax=ax)
kmf_sf_json = self.survival_probability_to_json(kmf_sf)
self.models.append(K)
plt.title("KM Survival Functions")
self.plots.append(plt)
self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method)
self.log.info('\n---------- SurvivalAnalysis learner has ended ----------')
return kmf_sf_json
else:
kmf1 = KaplanMeierFitter()
kmf2 = KaplanMeierFitter()
T1 = self.train[self.duration_column]
E1 = self.train[self.event_column]
#self.df_n = self.df_n.drop('fin', axis=1)
T2 = self.df_n[self.duration_column]
E2 = self.df_n[self.event_column]
ax = plt.subplot(111)
plt.title("KM Survival Functions - Filter vs Negation")
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has started----------'%self.method)
kmf1.fit(T1, E1)
ax = kmf1.plot(ax=ax, label='%s'%self.fitter_param)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has ended----------'%self.method)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has started----------'%self.method)
kmf2.fit(T2, E2)
ax = kmf2.plot(ax=ax, label='~%s'%self.fitter_param)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has ended----------'%self.method)
self.models.extend([kmf1,kmf2])
kmf1_sf = kmf1.survival_function_
kmf2_sf = kmf2.survival_function_
kmf1_sf_json = self.survival_probability_to_json(kmf1_sf)
self.plots.append(plt)
self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method)
self.log.info('\n---------- SurvivalAnalysis learner has ended ----------')
return kmf1_sf_json
elif self.method.lower() in ['coxphfitter','coxregression','cox-regression','cox regression','coxproportionalhazard','coxph','cox','cph']:
self.log.info('\n---------- SurvivalAnalysis learner method "%s" has started ----------'%self.method)
#from lifelines.utils import k_fold_cross_validation
if not isinstance(self.df_n, pd.DataFrame):
cph = CoxPHFitter()
C = cph.fit(self.train, self.duration_column, self.event_column, show_progress=True)
self.models.append(C)
cph_sf = C.baseline_survival_
ax = plt.subplot(111)
ax = C.plot(ax=ax)
cph_sf_json = self.survival_probability_to_json(cph_sf)
self.log.info('\n Summary : \n%s'%str(C.summary))
plt.title("COX hazard ratio")
self.plots.append(plt)
self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method)
self.log.info('\n---------- SurvivalAnalysis learner has ended ----------')
#plt.show()
return cph_sf_json
else:
cph1 = CoxPHFitter(penalizer=0.0001)
cph2 = CoxPHFitter(penalizer=0.0001)
ax = plt.subplot(211)
plt.title("COX hazard ratio - [%s](Top) vs [~(%s)](Bottom)"%(self.fitter_param,self.fitter_param))
#self.train = self.train.drop('fin',axis=1)
self.df_n = self.drop_constant_features(self.df_n)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has started----------'%self.method)
cph1.fit(self.train, self.duration_column, self.event_column, show_progress=True, step_size=0.4)
ax = cph1.plot(ax=ax, label='%s'%self.fitter_param)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for filter expression has ended----------'%self.method)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has started----------'%self.method)
cph2.fit(self.df_n, self.duration_column, self.event_column, show_progress=True, step_size=0.4)
ax = plt.subplot(212)
ax = cph2.plot(ax=ax, label='~%s'%self.fitter_param)
self.log.info('\n---------- SurvivalAnalysis learner "%s" fitting for negation has ended----------'%self.method)
self.models.extend([cph1,cph2])
cph1_sf = cph1.baseline_survival_
cph2_sf = cph2.baseline_survival_
cph1_sf_json = self.survival_probability_to_json(cph1_sf)
#plt.show()
plt.tight_layout()
self.plots.append(plt)
self.log.info('\n---------- SurvivalAnalysis learner method "%s" has ended ----------'%self.method)
self.log.info('\n---------- SurvivalAnalysis learner has ended ----------')
return cph1_sf_json
def survival_probability_to_json(self, sf):
'''
sf = Survival function i.e. KaplanMeierFitter.survival_function_ or CoxPHFitter.baseline_survival_
returns json of survival probabilities
'''
sf = sf[sf.columns[0]].apply(lambda x: "%4.2f"%(x*100))
self.log.info('\n Survival probabilities : \n%s'%str(sf))
sf = sf.reset_index()
sf = sf.sort_values(sf.columns[0])
sf_json = sf.to_json(orient='records')
self.log.info('\n Survival prbability json : \n%s'%str(sf_json))
return sf_json
def drop_constant_features(self, df):
for col in df.columns:
if len(df[col].unique()) == 1:
df.drop(col,inplace=True,axis=1)
return df
def predict(self):
if self.method == 'KaplanMeierFitter':
return self.model.predict(self.test[self.duration_column])
#kmf.predict()
#kmf.median_survival_time_
#from lifelines.utils import median_survival_times
#median_ci = median_survival_times(kmf.confidence_interval_)
elif self.method == 'CoxPHFitter':
#print('train score',self.model.score(self.train))
#print('test score',self.model.score(self.test))
return self.model.predict_survival_function(self.test)
#cph.predict_cumulative_hazard()
#cph.predict_expectation()
#cph.predict_log_partial_hazard()
#cph.predict_median()
#cph.predict_partial_hazard()
#cph.predict_percentile()
#cph.predict_survival_function()
#cph.predict_hazard()
#cph.score()
#cph.summary()
#if __name__ == "__main__":
# data_file = r"C:\Users\satish_k\Desktop\Work\input\echocardiogram.csv"
# #data_file = r"C:\Users\satish_k\Desktop\Work\input\lymphoma.csv"
# method = "CoxPHFitter"
# event_column = "alive"
# duration_column = "survival"
# sa = SurvivalAnalysis(data_file, method, event_column, duration_column)
# sa.profiler()
# model = sa.learn()
# print(sa.predict())
#print(model.survival_function_)
|
DebiasingManager.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import numpy as np
import logging
logging.getLogger('tensorflow').disabled = True
import aif360
from aif360.datasets import StandardDataset
from aif360.algorithms.preprocessing.reweighing import Reweighing
from aif360.algorithms.preprocessing import DisparateImpactRemover
class DebiasingManager:
def __init__(self):
self.data = ''
# ------------------------------- Debiasing Changes -------------------------------
def get_attributes(self, data, selected_attr=None):
unprivileged_groups = []
privileged_groups = []
if selected_attr == None:
selected_attr = data.protected_attribute_names
for attr in selected_attr:
idx = data.protected_attribute_names.index(attr)
privileged_groups.append({attr:data.privileged_protected_attributes[idx]})
unprivileged_groups.append({attr:data.unprivileged_protected_attributes[idx]})
return privileged_groups, unprivileged_groups
# ------------------------------- -------------------------------
def Bias_Mitigate(self, dataFrame, protected_feature, privileged_className, target_feature, algorithm):
# log = logging.getLogger('eion')
# log.propagate = False
data_encoded = dataFrame.copy()
categorical_names = {}
encoders = {}
dataFrame = dataFrame.replace('Unknown', 'NA')
dataFrame = dataFrame.replace(np.nan, 'NA')
try:
# Label-Encoding
for feature in dataFrame.columns:
le = LabelEncoder()
le.fit(data_encoded[feature])
data_encoded[feature] = le.transform(data_encoded[feature])
categorical_names[feature] = le.classes_
encoders[feature] = le
privileged_class = np.where(categorical_names[protected_feature] == privileged_className)[0]
target_feature_count = len(data_encoded[target_feature].value_counts())
# Check if it's BinaryLabel
if target_feature_count == 2:
binaryLabelDataset = aif360.datasets.BinaryLabelDataset(
favorable_label='1',
unfavorable_label='0',
df=data_encoded,
label_names=[target_feature],
protected_attribute_names=[protected_feature])
data_orig = binaryLabelDataset
# Check if it's Non-BinaryLabel
if target_feature_count > 2:
data_orig = StandardDataset(data_encoded,
label_name=target_feature,
favorable_classes=[1],
protected_attribute_names=[protected_feature],
privileged_classes=[privileged_class])
if algorithm == 'DIR':
DIR = DisparateImpactRemover(repair_level=0.9)
data_transf_train = DIR.fit_transform(data_orig)
# log.info('Status:-|... DIR applied on input dataset')
else:
privileged_groups, unprivileged_groups = self.get_attributes(data_orig, selected_attr=[protected_feature])
RW = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)
data_transf_train = RW.fit_transform(data_orig)
# log.info('Status:-|... Reweighing applied on input dataset')
transf_dataFrame = data_transf_train.convert_to_dataframe()[0]
data_decoded = transf_dataFrame.copy().astype('int')
for column in data_decoded.columns:
data_decoded[column] = encoders[column].inverse_transform(data_decoded[column])
debiased_dataFrame = data_decoded
except Exception as e:
print(e)
debiased_dataFrame = dataFrame
return debiased_dataFrame
|
actian.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import pyodbc as pyodbc
import pandas as pd
import json
def simple_select(c, sql_query, bind_params=None, display_sql=False):
"""where c is a cursor"""
if bind_params is None:
c.execute(sql_query)
else:
if display_sql:
c.execute(sql_query, bind_params)
headers = []
if c.description is not None:
# We have a SELECT statement
for x in c.description:
headers.append(x[0])
row_count = 0
row = c.fetchone()
data=[]
while row:
row_count += 1
xrow={}
for i in range(len(row)):
xrow[headers[i]] = row[i]
data.append(xrow)
row = c.fetchone()
#df = pd.DataFrame(data)
return(data)
def validatequery(request,query):
resultdata = []
try:
server_url = request.session['server_url']
username_actian = request.session['username']
password_actian = request.session['password']
database_actian = request.session['database']
conn = get_connection(server_url,username_actian,password_actian,database_actian)
sql_text = query
cur = conn.cursor()
resultdata = simple_select(cur, query)
cur.close()
if len(resultdata) > 0:
return "Query executed successfully"
else:
return "No rows returned"
except Exception as e:
print(e)
return str(e)
def executequery(request,query):
resultdata = []
try:
server_url = request.session['server_url']
username_actian = request.session['username']
password_actian = request.session['password']
database_actian = request.session['database']
conn = get_connection(server_url,username_actian,password_actian,database_actian)
sql_text = query
cur = conn.cursor()
resultdata = simple_select(cur, query)
cur.close()
return(resultdata)
except Exception as e:
print(e)
return(resultdata)
def list_tables_fields(request,table_list):
table_field_obj = {}
table_field_obj['data'] = []
try:
server_url = request.session['server_url']
username_actian = request.session['username']
password_actian = request.session['password']
database_actian = request.session['database']
table_list = json.loads(table_list)
conn = get_connection(server_url,username_actian,password_actian,database_actian)
for table in table_list:
tf_obj = {}
tf_obj['TableName'] = str(table).strip()
tf_obj['Fields']= []
field_list = []
sql_text = "SELECT column_name, false as is_select FROM iicolumns WHERE table_name='"+table+"'"
cur = conn.cursor()
field_list = simple_select(cur, sql_text)
cur.close()
print(field_list)
tf_obj['Fields'] = field_list
table_field_obj['data'].append(tf_obj)
print("----------------------")
print(table_field_obj)
print(json.dumps(table_field_obj))
print("----------------------")
return json.dumps(table_field_obj)
except Exception as e:
print("Something went wrong "+str(e))
return table_field_obj
def list_tables(request):
server_url = request.session['server_url']
username_actian = request.session['username']
password_actian = request.session['password']
database_actian = request.session['database']
dt_list = []
try:
conn = get_connection(server_url,username_actian,password_actian,database_actian)
sql_text = "select table_name from iitables where table_type='T' and table_owner='"+username_actian+"'"
cur = conn.cursor()
dt_list = simple_select(cur, sql_text)
cur.close()
return dt_list
except:
print("Something went wrong")
return dt_list
def get_connection(server_url,username_actian,password_actian,database_actian):
conn = pyodbc.connect("driver=Ingres;servertype=ingres;server=@"+str(server_url)+",tcp_ip,VW;uid="+str(username_actian)+";pwd="+str(password_actian)+";database="+str(database_actian))
print("connected")
return conn
def getDataFromActianAvalanche(request):
server_url = request.POST.get('server_url')
username_actian = request.POST.get('username')
password_actian = request.POST.get('password')
database_actian = request.POST.get('database')
table_actian = request.POST.get('table')
conn = get_connection(server_url,username_actian,password_actian,database_actian)
c = conn.cursor()
sql_text = "select * from "+str(table_actian)
data = simple_select(c, sql_text)
df = pd.DataFrame(data)
return(df) |
dataPath.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
from os.path import expanduser
import platform
DEFAULT_FILE_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),'conf')
cur_dir = os.path.dirname(os.path.abspath(__file__))
home = expanduser("~")
if platform.system() == 'Windows':
DATA_DIR = os.path.normpath(os.path.join(cur_dir,'..','..','..','..','..','..','data'))
DATA_FILE_PATH = os.path.join(DATA_DIR,'storage')
CONFIG_FILE_PATH = os.path.join(DATA_DIR,'config')
DEPLOY_LOCATION = os.path.join(DATA_DIR,'target')
LOG_LOCATION = os.path.join(DATA_DIR,'logs')
LOG_FILE = os.path.join(DATA_DIR,'logs','ux.log')
else:
DATA_DIR = os.path.join(home,'HCLT','data')
DATA_FILE_PATH = os.path.join(DATA_DIR,'storage')
CONFIG_FILE_PATH = os.path.join(DATA_DIR,'config')
DEPLOY_LOCATION = os.path.join(DATA_DIR,'target')
LOG_FILE = os.path.join(DATA_DIR,'logs','ux.log')
LOG_LOCATION = os.path.join(DATA_DIR,'logs') |
pages.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os,sys
from appbe import compute
from appbe.aion_config import kafka_setting
from appbe.aion_config import running_setting
from records import pushrecords
from appbe import service_url
import json
import time
import pandas as pd
from django.db.models import Max, F
from os.path import expanduser
import platform
from appbe.data_io import sqlite_db
import subprocess
from appbe.dataPath import DEFAULT_FILE_PATH
from appbe.dataPath import DATA_FILE_PATH
from appbe.dataPath import CONFIG_FILE_PATH
from appbe.dataPath import DEPLOY_LOCATION
from appbe.dataPath import DATA_DIR
DEPLOY_DATABASE_PATH = os.path.join(DATA_DIR,'sqlite')
def pushRecordForTraining():
from appbe.pages import getversion
AION_VERSION = getversion()
try:
status,msg = pushrecords.enterRecord(AION_VERSION)
except Exception as e:
print("Exception", e)
status = False
msg = str(e)
return status,msg
def getversion():
configFolder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config')
version = 'NA'
for file in os.listdir(configFolder):
if file.endswith(".var"):
version = file.rsplit('.', 1)
version = version[0]
break
return version
def getusercasestatus(request):
if 'UseCaseName' in request.session:
selected_use_case = request.session['UseCaseName']
else:
selected_use_case = 'Not Defined'
if 'ModelVersion' in request.session:
ModelVersion = request.session['ModelVersion']
else:
ModelVersion = 0
if 'ModelStatus' in request.session:
ModelStatus = request.session['ModelStatus']
else:
ModelStatus = 'Not Trained'
return selected_use_case,ModelVersion,ModelStatus
def getMLModels(configSettingsJson):
mlmodels =''
dlmodels = ''
problem_type = ""
problemtypes = configSettingsJson['basic']['analysisType']
for k in problemtypes.keys():
if configSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
sc = ""
if problemtypes in ['classification','regression','survivalAnalysis']:
scoringCreteria = configSettingsJson['basic']['scoringCriteria'][problem_type]
for k in scoringCreteria.keys():
if configSettingsJson['basic']['scoringCriteria'][problem_type][k] == 'True':
sc = k
break
else:
sc = 'NA'
if problem_type in ['classification','regression']:
algorihtms = configSettingsJson['basic']['algorithms'][problem_type]
#print(algorihtms)
for k in algorihtms.keys():
#print(configSettingsJson['basic']['algorithms'][problem_type][k])
if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True':
if k in ['SNN','RNN','CNN']:
if dlmodels != '':
dlmodels += ', '
dlmodels += k
else:
if mlmodels != '':
mlmodels += ', '
mlmodels += k
elif problem_type in ['videoForecasting','imageClassification','objectDetection']:
algorihtms = configSettingsJson['basic']['algorithms'][problem_type]
for k in algorihtms.keys():
if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True':
if dlmodels != '':
dlmodels += ', '
dlmodels += k
else:
algorihtms = configSettingsJson['basic']['algorithms'][problem_type]
for k in algorihtms.keys():
if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True':
if mlmodels != '':
mlmodels += ', '
mlmodels += k
displayProblemType = problem_type
selected_model_size = ''
if problem_type.lower() == 'llmfinetuning':
displayProblemType = 'LLM Fine-Tuning'
supported_model_types = configSettingsJson['basic']['modelSize'][problem_type][mlmodels]
for k in supported_model_types.keys():
if configSettingsJson['basic']['modelSize'][problem_type][mlmodels][k] == 'True':
selected_model_size = k
break
#print(selected_model_size)
if mlmodels == 'TF_IDF':
mlmodels = 'TF-IDF'
if mlmodels == 'LatentSemanticAnalysis':
mlmodels = 'Latent Semantic Analysis (LSA)'
if mlmodels == 'SentenceTransformer_distilroberta':
mlmodels = 'SentenceTransformer (DistilRoBERTa)'
if mlmodels == 'SentenceTransformer_miniLM':
mlmodels = 'SentenceTransformer (MiniLM)'
if mlmodels == 'SentenceTransformer_mpnet':
mlmodels = 'SentenceTransformer (MPNet)'
return(problem_type,displayProblemType,sc,mlmodels,dlmodels,selected_model_size)
def get_usecase_page(request,usecasedetails,Existusecases,usecaseId = None,search_text=None):
try:
x = request.build_absolute_uri().split("http://")
y = x[1].split("/")
url = y[0].split(":")
tacking_url = url[0]
except:
tacking_url = '127.0.0.1'
computeinfrastructure = compute.readComputeConfig()
ruuningSetting = running_setting()
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
status = 'SUCCESS'
ser_url = service_url.read_service_url_params(request)
hosturl =request.get_host()
hosturl = hosturl.split(':')
hosturl = hosturl[0]
packagetip='''
Call From Command Line
1. Click AION Shell
2. python {packageAbsolutePath}/aion_prediction.py {json_data}
Call As a Package
1. Go To package_path\WHEELfile
2. python -m pip install {packageName}-py3-none-any.whl
Call the predict function after wheel package installation
1. from {packageName} import aion_prediction as p1
2. p1.predict({json_data})'''
models = Existusecases.objects.filter(Status='SUCCESS').order_by('-id')
usecase = usecasedetails.objects.all().order_by('-id')
usecase = landing_page(usecasedetails,Existusecases,hosturl,usecaseId,search_text)
if len(usecase) > 0:
nouc = usecasedetails.objects.latest('id')
nouc = (nouc.id)+1
nouc = str(nouc).zfill(4)
else:
nouc = 1
nouc = str(nouc).zfill(4)
description_text = 'This is a usecase for AI' + str(nouc)
context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc, 'models': models, 'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'tacking_url':tacking_url,
'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'computeinfrastructure':computeinfrastructure,'ruuningSetting':ruuningSetting}
return status,context,'usecases.html'
def checkText(configPath):
isText='False'
with open(configPath) as config:
data = json.load(config)
for feature in data['advance']['profiler']['featureDict'] :
if feature['type']=='text':
isText = 'True';
break;
return isText
# For Task ID 12393
# For BUG ID 13161
def checkFE(configPath):
isFE = 'False'
with open(configPath) as config:
data = json.load(config)
is_selection_method = data.get('advance', {}).get('selector', {}).get('selectionMethod', {}).get('featureEngineering','False')
feature_dict= data.get('advance', {}).get('selector', {}).get('featureEngineering', {})
if 'null' in feature_dict.keys():
feature_dict.pop('null')
if is_selection_method == 'True' or 'True' in list(feature_dict.values()):
isFE = 'True'
return isFE
def get_model(Existusecases,usercaseid,version=-1):
from django.core import serializers
if version == -1:
models = Existusecases.objects.filter(ModelName=usercaseid).order_by('-id')
else:
models = Existusecases.objects.filter(ModelName=usercaseid,Version=version).order_by('-id')
for model in models:
model.scoringCreteria = 'NA'
model.score = 'NA'
model.deploymodel = 'NA'
model.problemType = 'NA'
model.maacsupport = 'False'
model.flserversupport = 'False'
model.onlinelerningsupport = 'False'
model.oltrainingdetails=''
model.xplain = 'True'
model.isText = 'False'
problemTypeNames = {'topicmodelling':'TopicModelling','anomalydetection':'AnomalyDetection'}
if model.Status == 'SUCCESS':
if os.path.isdir(str(model.DeployPath)):
modelPath = os.path.join(str(model.DeployPath),'etc','output.json')
try:
with open(modelPath) as file:
outputconfig = json.load(file)
file.close()
if outputconfig['status'] == 'SUCCESS':
model.scoringCreteria = outputconfig['data']['ScoreType']
model.score = outputconfig['data']['BestScore']
model.deploymodel = outputconfig['data']['BestModel']
model.problemType = outputconfig['data']['ModelType']
if model.problemType in ['topicmodelling','anomalydetection']:
model.problemType = problemTypeNames[model.problemType]
model.featuresused = outputconfig['data']['featuresused']
model.targetFeature = outputconfig['data']['targetFeature']
if 'params' in outputconfig['data']:
model.modelParams = outputconfig['data']['params']
model.modelType = outputconfig['data']['ModelType']
model.isText = checkText(str(model.ConfigPath))
model.isFeatureEng = checkFE(str(model.ConfigPath))#task id 12393
model.dataPath = os.path.join(str(model.DeployPath),'data', 'postprocesseddata.csv.gz')
mlacSupportedModel = ["Logistic Regression","Naive Bayes","Decision Tree","Random Forest",
"Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)",
"Categorical Boosting (CatBoost)","Linear Regression","Lasso","Ridge","MLP","LSTM"]
if model.problemType.lower() in ['classification','regression','timeseriesforecasting']: #task 11997
if model.deploymodel in mlacSupportedModel:
model.maacsupport = 'True'
if model.problemType.lower() not in ['classification','regression']:
model.xplain = 'False'
elif model in ["Neural Architecture Search"]:
model.xplain = 'False'
model.flserversupport = 'False'
model.onlinelerningsupport = 'False'
supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"]
if model.deploymodel in supportedmodels:
model.flserversupport = 'True'
else:
model.flserversupport = 'False'
supportedmodels = ["Extreme Gradient Boosting (XGBoost)"]
if model.deploymodel in supportedmodels:
model.encryptionsupport = 'True'
else:
model.encryptionsupport = 'False'
supportedmodels = ["Online Decision Tree Classifier","Online Logistic Regression","Online Linear Regression","Online Decision Tree Regressor","Online KNN Regressor","Online Softmax Regression","Online KNN Classifier"]
if model.deploymodel in supportedmodels:
model.onlinelerningsupport = 'True'
onlineoutputPath = os.path.join(str(model.DeployPath),'production','Config.json')
with open(onlineoutputPath) as file:
onlineoutputPath = json.load(file)
file.close()
details = {'Score' :onlineoutputPath['metricList'],'DataSize':onlineoutputPath['trainRowsList']}
dfonline = pd.DataFrame(details)
model.oltrainingdetails = dfonline
else:
model.onlinelerningsupport = 'False'
except Exception as e:
print(e)
pass
return models
def landing_page(usecasedetails,Existusecases,hosturl,usecaseId = None,search_text=None):
sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db')
if usecaseId:
usecase = usecasedetails.objects.filter(id=usecaseId)
else:
if search_text:
usecase = usecasedetails.objects.filter(UsecaseName__contains=str(search_text)).order_by('-id')
else:
#usecase = usecasedetails.objects.all().order_by('-id')[:100] #top 100 records
usecase = usecasedetails.objects.all().order_by('-id') #top 100 records
usecaselist=[]
if not usecaseId:
for x in usecase:
problemType= 'NA'
publish_url = ''
otherModel = {}
models = Existusecases.objects.filter(Status='SUCCESS',publishStatus='Published',ModelName=x.id).order_by('-id')
if len(models) > 0:
#print(models[0])
version = models[0].Version
if os.path.isdir(str(models[0].DeployPath)):
modelPath = os.path.join(str(models[0].DeployPath),'etc','output.json')
with open(modelPath) as file:
outputconfig = json.load(file)
problemType = outputconfig['data']['ModelType']
#print(problemType.lower())
if problemType.lower() == "llm fine-tuning":
cloudconfig = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'compute_conf.json'))
print(cloudconfig)
from appbe.models import get_instance
hypervisor,instanceid,region,image,status = get_instance(x.usecaseid+ '_' + str(version))
from llm.llm_inference import get_ip
instanceip = get_ip(cloudconfig,instanceid,hypervisor,region,image) #usnish__ server maynot running
if instanceip != '':
publish_url = 'http://' + instanceip + ':' + '8000' + '/generate'
else:
publish_url = 'service not available'
else:
publish_url = 'http://'+hosturl+':'+str(models[0].portNo)+'/AION/'+x.usecaseid+'/predict'
publish_status = 'Published'
#publish_url = 'http://'+hosturl+':'+str(models[0].portNo)+'/AION/'+x.usecaseid+'/predict'
parentModel = get_model(Existusecases,x.id,int(version))
else:
models = Existusecases.objects.filter(Status='SUCCESS',ModelName=x.id).order_by('-id')
if len(models) > 0:
publish_status = 'Trained'
version = models[0].Version
parentModel = get_model(Existusecases,x.id,int(version))
else:
models = Existusecases.objects.filter(ModelName=x.id).order_by('-id')
if len(models)==0:
publish_status= 'Not Trained'
version = -1
else:
if models[0].Status == 'FAIL':
publish_status= 'Failed'
elif models[0].Status == 'Running':
publish_status = 'Running'
else:
publish_status='Not Trained'
problemType = models[0].ProblemType
version = models[0].Version
parentModel={}
usecasedetails = {'uuid':x.id,'description':x.Description,'usecaseid':x.usecaseid,'usecase':x.UsecaseName,'status':publish_status,'publish_url':publish_url,'version':version,'parentModel':parentModel,'otherModel':otherModel,'problemType':problemType}
usecaselist.append(usecasedetails)
else:
for x in usecase:
otherModel = get_model(Existusecases,x.id)
problemType = otherModel[0].problemType
usecasedetails = {'uuid':x.id,'description':x.Description,'usecase':x.UsecaseName,'status':'','version':'','parentModel':{},'otherModel':otherModel,'problemType':problemType}
usecaselist.append(usecasedetails)
return usecaselist
def get_landing_model(Existusecases):
models = Existusecases.objects.filter(Status='SUCCESS').order_by('-id')
for model in models:
model.scoringCreteria = 'NA'
model.score = 'NA'
model.deploymodel = 'NA'
if os.path.isdir(str(model.DeployPath)):
modelPath = os.path.join(str(model.DeployPath),'etc','output.json')
try:
with open(modelPath) as file:
outputconfig = json.load(file)
file.close()
if outputconfig['status'] == 'SUCCESS':
model.scoringCreteria = outputconfig['data']['ScoreType']
model.score = outputconfig['data']['BestScore']
model.deploymodel = outputconfig['data']['BestModel']
model.problemType = outputconfig['data']['ModelType']
model.maacsupport = 'True'
model.flserversupport = 'False'
model.onlinelerningsupport = 'False'
supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"]
if model.deploymodel in supportedmodels:
model.flserversupport = 'True'
else:
model.flserversupport = 'False'
supportedmodels = ["Extreme Gradient Boosting (XGBoost)"]
if model.deploymodel in supportedmodels:
model.encryptionsupport = 'True'
else:
model.encryptionsupport = 'False'
supportedmodels = ["Online Decision Tree Classifier","Online Logistic Regression"]
if model.deploymodel in supportedmodels:
model.onlinelerningsupport = 'True'
onlineoutputPath = os.path.join(str(model.DeployPath),'production','Config.json')
with open(onlineoutputPath) as file:
onlineoutputPath = json.load(file)
file.close()
details = {'Score' :onlineoutputPath['metricList'],'DataSize':onlineoutputPath['trainRowsList']}
dfonline = pd.DataFrame(details)
model.oltrainingdetails = dfonline
else:
model.onlinelerningsupport = 'False'
except Exception as e:
pass
return models
def usecase_page(request,usecasedetails,Existusecases,usecaseid,search_text):
try:
from appbe import read_service_url_params
tacking_url = read_service_url_params(request)
except:
tacking_url = '127.0.0.1'
hosturl =request.get_host()
hosturl = hosturl.split(':')
hosturl = hosturl[0]
computeinfrastructure = compute.readComputeConfig()
from appbe.aion_config import settings
usecasetab = settings()
kafkaSetting = kafka_setting()
ruuningSetting = running_setting()
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
status,msg = pushRecordForTraining()
if status == False:
context = {'msg':msg}
context['selected'] = 'License'
return status,context,'licenseexpired.html'
ser_url = service_url.read_service_url_params(request)
packagetip='''
Call From Command Line
1. Click AION Shell
2. python {packageAbsolutePath}/aion_predict.py {json_data}
Call As a Package
1. Go To package_path\publish\package
2. python -m pip install {packageName}-py3-none-any.whl
Call the predict function after wheel package installation
1. from {packageName} import aion_predict as p1
2. p1.predict({json_data})'''
if request.method == "POST":
usecasename = request.POST.get('UsecaseName')
description = request.POST.get('Description')
usecaseid = request.POST.get('usecaseid')
#print('1',usecasename)
if (usecasename == ''):
usecase = landing_page(usecasedetails,Existusecases,hosturl)
if len(usecase) > 0:
nouc = usecasedetails.objects.latest('id')
nouc = (nouc.id)+1
else:
nouc = 1
nouc = str(nouc).zfill(4)
description_text = 'This is a usecase for AI' + str(nouc)
context = {'description_text':description_text,'usecase':'usecase','Notallowed':'Usecasename is mandatory','ser_url':ser_url,'packagetip':packagetip,'usecasedetail': usecase,'nouc':nouc, 'ser_url':ser_url,'packagetip':packagetip, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'tacking_url':tacking_url,'usecasetab':usecasetab,
'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting}
return status,context,'usecases.html'
else:
usecase_count = usecasedetails.objects.filter(usecaseid=usecaseid).count()
usecasename_count = usecasedetails.objects.filter(UsecaseName=usecasename).count()
usecase = landing_page(usecasedetails,Existusecases,hosturl)
if (usecase_count > 0) or (usecasename_count > 0):
nouc = usecasedetails.objects.latest('id')
nouc = (nouc.id)+1
nouc = str(nouc).zfill(4)
Msg = 'Error in usecase creating, try again'
if usecase_count > 0:
Msg = 'Error in usecase creating, try again'
if usecasename_count > 0:
Msg = 'There is already a use case with same name, please provide unique name'
description_text = 'This is a usecase for AI' + str(nouc)
context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc,'Status':'error','Msg': Msg,'tacking_url':tacking_url,'usecasetab':usecasetab,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ser_url':ser_url,'packagetip':packagetip,
'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting}
return status,context,'usecases.html'
else:
clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id')
from appbe.s3bucketsDB import get_s3_bucket
from appbe.gcsbucketsDB import get_gcs_bucket
from appbe.azureStorageDB import get_azureStorage
p = usecasedetails(UsecaseName=usecasename,usecaseid=usecaseid,Description=description)
p.save()
s1 = Existusecases.objects.filter(ModelName=p.id).annotate(maxver=Max('ModelName__existusecases__Version'))
config_list = s1.filter(Version=F('maxver'))
if config_list.count() > 0:
Version = config_list[0].Version
Version = Version + 1
else:
Version = 1
ps = Existusecases(DataFilePath='', DeployPath='', Status='Not Trained',ConfigPath='', Version=Version, ModelName=p,TrainOuputLocation='')
ps.save()
request.session['ModelName'] = p.id
request.session['UseCaseName'] = usecasename
request.session['usecaseid'] = usecaseid
request.session['ModelVersion'] = Version
request.session['ModelStatus'] = 'Not Trained'
request.session['currentstate'] = 0
request.session['finalstate'] = 0
selected_use_case = usecasename
model_status = 'Not Trained'
ModelVersion = Version
from appbe.telemetry import UseCaseCreated
UseCaseCreated(usecaseid+'-'+str(Version))
if len(usecase) > 0:
nouc = usecasedetails.objects.latest('id')
nouc = (nouc.id)+1
else:
nouc = 1
nouc = str(nouc).zfill(4)
description_text = 'This is a usecase for AI' + str(nouc)
context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc, 'newusercase': usecasename,'tacking_url':tacking_url,'finalstate':request.session['finalstate'],
'description': description,'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'clusteringModels':clusteringModels,'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'usecasetab':usecasetab,'azurestorage':get_azureStorage(),
'ModelStatus': model_status, 'ModelVersion': ModelVersion, 'selected': 'modeltraning','computeinfrastructure':computeinfrastructure}
return status,context,'upload.html'
else:
models = get_landing_model(Existusecases)
usecase = landing_page(usecasedetails,Existusecases,hosturl,usecaseid,search_text)
if len(usecase) > 0:
nouc = usecasedetails.objects.latest('id')
nouc = (nouc.id)+1
else:
nouc = 1
nouc = str(nouc).zfill(4)
description_text = 'This is a usecase for AI' + str(nouc)
context = {'description_text':description_text,'usecasedetail': usecase, 'nouc': nouc, 'models': models, 'selected_use_case': selected_use_case,'ser_url':ser_url,'packagetip':packagetip,'tacking_url':tacking_url,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'usecasetab':usecasetab}
if usecaseid:
context.update({'ucdetails':'True'})
return status,context,'usecases.html'
def index_page(request,usecasedetails,Existusecases):
if 'ModelVersion' in request.session:
del request.session['ModelVersion']
if 'UseCaseName' in request.session:
del request.session['UseCaseName']
if 'ModelStatus' in request.session:
del request.session['ModelStatus']
if 'currentstate' in request.session:
del request.session['currentstate']
if 'finalstate' in request.session:
del request.session['finalstate']
return usecases_page(request,usecasedetails,Existusecases)
def usecases_page(request,usecasedetails,Existusecases,usecaseid=None,substring=None):
return usecase_page(request,usecasedetails,Existusecases,usecaseid,substring)
def mllite_page(request):
from appbe.aion_config import settings
usecasetab = settings()
status,msg = pushRecordForTraining()
if status == False:
context = {'selected':'mllite','lerror':msg}
return context
configFile = os.path.join(DEFAULT_FILE_PATH, 'model_converter.json')
f = open(configFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
context = {}
context = {'selected':'mllite','sagemaker':configSettingsJson,'usecasetab':usecasetab}
return context
def mltesting_page(request):
from appbe.aion_config import settings
usecasetab = settings()
status,msg = pushRecordForTraining()
if status == False:
context = {'lerror':msg}
return context
if request.method == "POST":
models = request.POST['model']
datap = request.POST['data']
if(os.path.isfile(models) and os.path.isfile(datap)):
request.session['datalocation'] = datap
df = pd.read_csv(datap,encoding='utf-8',skipinitialspace = True,encoding_errors= 'replace')
trainfea = df.columns.tolist()
featurs = request.POST.getlist('Training')
feature = ",".join(featurs)
filetimestamp = str(int(time.time()))
settingconfig = os.path.join(CONFIG_FILE_PATH, 'MLTest_' + filetimestamp + '.json')
request.session['MLTestResult'] = settingconfig
mltestresult={}
mltestresult['models'] = models
mltestresult['datap'] = datap
mltestresult['feature'] = feature
# features = ['PetalLengthCm','PetalWidthCm']
targ = request.POST['Target']
tar =[targ]
mltestresult['target'] = targ
mltestresult = json.dumps(mltestresult)
with open(settingconfig, "w") as fpWrite:
fpWrite.write(mltestresult)
fpWrite.close()
from pathlib import Path
mltest={}
if Path(models).is_file() and Path(datap).is_file():
try:
from mltest import baseline
outputStr = baseline.baseline_testing(models,datap, feature, targ)
#scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_mltest.py'))
#print(scriptPath, models, datap, feature, targ)
#outputStr = subprocess.check_output([sys.executable, scriptPath, models, datap, feature, targ])
#print(outputStr)
#outputStr = outputStr.decode('utf-8')
#outputStr= outputStr.replace('\'','\"')
#print('ou',outputStr)
#outputStr = outputStr.strip()
mltest = json.loads(outputStr)
Problemtype= mltest['Problemtype']
with open(request.session['MLTestResult'], 'r+') as f:
mltestresult = json.load(f)
f.close()
mltestresult['Problemtype'] = Problemtype
mltestresult['ProblemName'] = mltest['ProblemName']
status = mltest['Status']
if status == 'Fail':
errormsg= mltest['Msg']
context = {'error':errormsg,'mltest':'mltest'}
else:
if Problemtype == 'Classification':
mltestresult['Score'] = mltest['Accuracy']
mltestresult['Params'] = mltest['Params']
Problem= mltest['ProblemName']
Parameters= mltest['Params']
round_params = {}
for key, value in Parameters.items():
if isinstance(value, float):
round_params[key] = round(value,2)
else:
round_params[key] = value
matrixconfusion = mltest['Confusionmatrix']
classificationreport = mltest['classificationreport']
classificationreport = json.loads(classificationreport)
matrixconfusion = json.loads(matrixconfusion)
indexName =[]
columnName = []
for i in matrixconfusion.keys():
indexName.append("act:"+str(i))
for j in matrixconfusion[i].keys():
columnName.append("pre:"+str(j))
df3 = pd.DataFrame.from_dict(classificationreport)
df = df3.transpose()
df2 = pd.DataFrame.from_dict(matrixconfusion)
df1 = pd.DataFrame(df2.values,index=indexName,columns=columnName)
report = df.to_html()
report1 = df1.to_html()
recordone = mltest['onerecord']
recordsten = mltest['tenrecords']
recordshund = mltest['hundrecords']
context = {'modelname': models,'datapath':datap,'features':featurs,'target':tar,'Problemtype':Problem,'modeltype':Problemtype,'Parameter':round_params,'Onerecord':recordone,'Tenrecords':recordsten,'Hundrecords':recordshund,'matrixconfusion':report1,'classificationreport':report,'classification':'classification','df':df,'df1':df1,'basemltest':'basemltest','success':'success','trainfea':trainfea,'selected':'mltesting','usecasetab':usecasetab}
elif Problemtype == 'Regression':
Problem= mltest['ProblemName']
mltestresult['Params'] = mltest['Params']
mltestresult['Score'] = mltest['R2']
Parameters= mltest['Params']
round_params = {}
for key, value in Parameters.items():
if isinstance(value, float):
round_params[key] = round(value,2)
else:
round_params[key] = value
Mse = mltest['MSE']
Mae = mltest['MAE']
Rmse = mltest['RMSE']
R2 = mltest['R2']
recordone = mltest['onerecord']
recordsten = mltest['tenrecords']
recordshund = mltest['hundrecords']
context = {'modelname': models,'datapath':datap,'features':featurs,'target':tar, 'Problemtype':Problem,'Parameter':round_params,'Onerecord':recordone,'Tenrecords':recordsten,'Hundrecords':recordshund,'Mse':Mse,'Mae':Mae,'Rmse':Rmse,'R2Score':R2,'regression':'regression','success':"success",'selected': 'mltest','basemltest':'basemltest','usecasetab':usecasetab}
else:
errormsg= mltest['Msg']
context = {'error':errormsg,'mltest':'mltest'}
mltestresult = json.dumps(mltestresult)
with open(settingconfig, "w") as fpWrite:
fpWrite.write(mltestresult)
fpWrite.close()
except Exception as e:
print("-------------"+str(e)+'=================')
e = str(e).replace('\'','')
errormsg = 'Error: Exception '+str(e)
context = {'error':errormsg,'mltest':'mltest'}
else:
if not (Path(models).is_file() and Path(datap).is_file()):
context = {'error':"Please Check ModelPath & Datapath Format","result":"result",'selected':'mltesting','usecasetab':usecasetab}
elif not Path(models).is_file():
context = {'error':"Please Check ModelPath Format","result":"result",'selected':'mltesting','usecasetab':usecasetab}
elif not Path(datap).is_file():
context = {'error':"Please Check DataPath Format","result":"result",'selected':'mltesting','usecasetab':usecasetab}
else:
context = {'error':'Either model path or data path does not exist','mltest':'mltest','usecasetab':usecasetab}
else:
context = {'selected':'mltesting','usecasetab':usecasetab}
return context |
aion_config.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os,sys
import json
import platform
import subprocess
def kafka_setting():
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf'))
f = open(file_path, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
from os.path import expanduser
home = expanduser("~")
if platform.system() == 'Windows':
DEPLOY_LOCATION = os.path.join(home,'AppData','Local','HCLT','AION','target','kafka')
else:
DEPLOY_LOCATION = os.path.join(home,'HCLT','AION','target','kafka')
configSettingsJson['kafkalocation'] = DEPLOY_LOCATION
return(configSettingsJson)
def start_tracking():
from appbe.dataPath import DEPLOY_LOCATION
import platform
mlflowpath = os.path.normpath(os.path.join(os.path.dirname(__file__),'..','..','..','..','Scripts','mlflow.exe'))
script_path = os.path.normpath(os.path.join(os.path.dirname(__file__),'..','..','..','..','Scripts'))
#Updating path for system environment; Bug-13835
os.environ['PATH']= os.environ['PATH']+ ';'+ str(script_path)
DEPLOY_LOCATION = os.path.join(DEPLOY_LOCATION,'mlruns')
if platform.system() == 'Windows':
subprocess.Popen([sys.executable, mlflowpath,"ui", "--backend-store-uri","file:///"+DEPLOY_LOCATION])
else:
subprocess.Popen(['mlflow',"ui","-h","0.0.0.0","--backend-store-uri","file:///"+DEPLOY_LOCATION])
def aion_tracking():
status = 'Success'
import requests
try:
response = requests.get('http://localhost:5000')
if response.status_code != 200:
status = 'Error'
except Exception as inst:
print(inst)
status = 'Error'
return status
def aion_service():
try:
if platform.system() == 'Windows':
nooftasks = getrunningstatus('AION_Service')
else:
nooftasks = getrunningstatus('run_service')
if len(nooftasks):
status = 'Running'
else:
if platform.system() == 'Windows':
servicepath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','sbin','AION_Service.bat'))
os.system('start cmd /c "'+servicepath+'"')
else:
servicepath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','bin','run_service.py'))
subprocess.Popen([sys.executable,servicepath])
status = 'Started'
except Exception as inst:
print(inst)
status = 'Error'
return status
def getrunningstatus(name):
try:
taskdetails = []
if platform.system() == 'Windows':
r = ([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq conhost.exe"').decode('UTF-8').splitlines()])
r.append([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq cmd.exe"').decode('UTF-8').splitlines()])
else:
r = ([line.split() for line in subprocess.check_output("ps -ef | grep .py",shell=True).decode('UTF-8').splitlines()])
for i in range(len(r)):
s = r[i]
if any(name in j for j in s):
taskdetails.append('Yes')
break
return (taskdetails)
except Exception as inst:
print(inst)
status = 'Error'
return status
def getTasks(mlflow,consumer,service):
mlflowlist = []
consumerlist=[]
servicelist = []
#r = os.popen('tasklist /v').read().strip().split('\n')
try:
if platform.system() == 'Windows':
r = ([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq conhost.exe"').decode('UTF-8').splitlines()])
r.append([line.split() for line in subprocess.check_output('tasklist /v /FI "IMAGENAME eq cmd.exe"').decode('UTF-8').splitlines()])
else:
r = ([line.split() for line in subprocess.check_output("ps -ef | grep .py",shell=True).decode('UTF-8').splitlines()])
except Exception as e:
print(e)
r = []
#print(r)
#print ('# of tasks is %s' % (len(r)))
for i in range(len(r)):
s = r[i]
if any(mlflow in j for j in s):
mlflowlist.append('Yes')
if any(consumer in j for j in s):
consumerlist.append('Yes')
if any(service in j for j in s):
servicelist.append('Yes')
return (mlflowlist,consumerlist,servicelist)
def running_setting():
otherApps = {}
if platform.system() == 'Windows':
mlflowlist,consumerlist,servicelist = getTasks('AION_MLFlow','AION_Consumer','AION_Service')
else:
mlflowlist,consumerlist,servicelist = getTasks('run_mlflow','AION_Consumer','run_service')
if len(mlflowlist):
otherApps['modeltracking'] = 'Running'
else:
otherApps['modeltracking'] = 'Not Running'
#nooftasks = getTasks('AION_Consumer')
if len(consumerlist):
otherApps['consumer'] = 'Running'
else:
otherApps['consumer'] = 'Not Running'
#nooftasks = getTasks('AION_Service')
if len(servicelist):
otherApps['service'] = 'Running'
else:
otherApps['service'] = 'Not Running'
return(otherApps)
#EDA Performance change
# ----------------------------
def eda_setting():
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config','eda.config')
sample_size=''
try:
if(os.path.isfile(configfilepath)):
file = open(configfilepath, "r")
read = file.read()
file.close()
for line in read.splitlines():
if 'sample_size=' in line:
sample_size = line.split('=',1)[1]
except Exception as inst:
pass
return(sample_size)
def get_telemetryoptout():
telemetryoptuout = "No"
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
try:
if sqlite_obj.table_exists('settings'):
data = sqlite_obj.read_data('settings')
for values in data:
telemetryoptuout = values[7]
else:
telemetryoptuout = 'No'
except Exception as e:
print(e)
telemetryoptuout ='No'
return telemetryoptuout
def get_edafeatures():
No_of_Permissible_Features_EDA = ""
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
try:
if sqlite_obj.table_exists('settings'):
data = sqlite_obj.read_data('settings')
for values in data:
No_of_Permissible_Features_EDA = values[3]
else:
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config')
if (os.path.isfile(configfilepath)):
file = open(configfilepath, "r")
read = file.read()
file.close()
for line in read.splitlines():
if 'No_of_Permissible_Features_EDA=' in line:
No_of_Permissible_Features_EDA = line.split('=', 1)[1]
except Exception as e:
print(e)
No_of_Permissible_Features_EDA =20
return No_of_Permissible_Features_EDA
def get_graviton_data():
graviton_url = ""
graviton_userid = ""
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
try:
if sqlite_obj.table_exists('settings'):
data = sqlite_obj.read_data('settings')
for values in data:
graviton_url = values[0]
graviton_userid = values[1]
else:
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config')
if (os.path.isfile(configfilepath)):
file = open(configfilepath, "r")
read = file.read()
file.close()
for line in read.splitlines():
if 'graviton_url=' in line:
graviton_url = line.split('=', 1)[1]
if 'graviton_userid=' in line:
graviton_userid = line.split('=', 1)[1]
except Exception as e:
print(e)
graviton_url = ""
graviton_userid = ""
return graviton_url,graviton_userid
def get_llm_data():
apiKeyIdLLM = ""
apiUrlLLM = ""
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
try:
if sqlite_obj.table_exists('openai'):
data = sqlite_obj.read_data('openai')[0]
param_keys = ['api_type','api_key','api_base','api_version']
openai_data = dict((x,y) for x,y in zip(param_keys,data))
return openai_data['api_key'],openai_data['api_base'],openai_data['api_type'],openai_data['api_version']
except Exception as e:
print(e)
apiKeyIdLLM = ""
apiUrlLLM = ""
return apiKeyIdLLM,apiUrlLLM,"",""
def settings():
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config','aion.config')
usecase='disable'
graviton_url = ''
graviton_userid = ''
apiKeyIdLLM = ''
apiUrlLLM = ''
No_of_Permissible_Features_EDA = ''
try:
from appbe.sqliteUtility import sqlite_db
import pandas as pd
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('settings'):
column_names = sqlite_obj.column_names('settings')
data = sqlite_obj.read_data('settings')
if 'telemetryOptOut' not in column_names:
query = 'Alter Table settings ADD telemetryOptOut TEXT'
sqlite_obj.execute_query(query)
if 'No_of_Permissible_Features_EDA' not in column_names or 'apiKeyIdLLM' not in column_names:
sqlite_obj.drop_table('settings')
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config')
file = open(configfilepath, "r")
dataread = file.read()
for line in dataread.splitlines():
if 'usecase=' in line:
cusecase = line.split('=', 1)[1]
if 'graviton_url=' in line:
cgraviton_url = line.split('=', 1)[1]
if 'graviton_userid=' in line:
cgraviton_userid = line.split('=', 1)[1]
if 'No_of_Permissible_Features_EDA=' in line:
cNo_of_Permissible_Features_EDA = line.split('=', 1)[1]
if 'apiKeyIdLLM=' in line:
capiKeyIdLLM = ''
if 'apiUrlLLM=' in line:
capiUrlLLM = ''
file.close()
if 'apiKeyIdLLM' not in column_names:
apiKeyIdLLM = capiKeyIdLLM
if 'apiUrlLLM' not in column_names:
apiUrlLLM = capiUrlLLM
if 'No_of_Permissible_Features_EDA' not in column_names:
No_of_Permissible_Features_EDA = cNo_of_Permissible_Features_EDA
newdata = {}
newdata.update({'graviton_url':[data[0][0]],'graviton_userid': [data[0][1]],'usecase': [data[0][2]],'No_of_Permissible_Features_EDA':[No_of_Permissible_Features_EDA],'settingsid':['1'],'apiKeyIdLLM' :apiKeyIdLLM,'apiUrlLLM':apiUrlLLM,'telemetryOptOut':telemetryOptOut})
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'settings')
data = sqlite_obj.read_data('settings')
for values in data:
graviton_url = values[0]
graviton_userid = values[1]
usecase = values[2]
No_of_Permissible_Features_EDA = values[3]
telemetryOptOut = values[7]
else:
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'aion.config')
if (os.path.isfile(configfilepath)):
file = open(configfilepath, "r")
read = file.read()
file.close()
apiKeyIdLLM = ''
apiUrlLLM = ''
for line in read.splitlines():
if 'usecase=' in line:
usecase = line.split('=', 1)[1]
if 'graviton_url=' in line:
graviton_url = line.split('=', 1)[1]
if 'graviton_userid=' in line:
graviton_userid = line.split('=', 1)[1]
if 'No_of_Permissible_Features_EDA=' in line:
No_of_Permissible_Features_EDA = line.split('=', 1)[1]
newdata = {}
newdata.update({'graviton_url':[graviton_url],'graviton_userid': [graviton_userid],'usecase': [usecase],'No_of_Permissible_Features_EDA':[No_of_Permissible_Features_EDA],'settingsid':['1'],'apiKeyIdLLM' :'','apiUrlLLM':'','telemetryOptOut':['No']})
# --------else create table and update the data, write data will create a table if it does nt exists-----
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'settings')
return(usecase)
except Exception as e:
print(e)
configfilepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','config','aion.config')
try:
if(os.path.isfile(configfilepath)):
file = open(configfilepath, "r")
read = file.read()
file.close()
for line in read.splitlines():
if 'usecase=' in line:
usecase = line.split('=',1)[1]
if 'graviton_url=' in line:
graviton_url = line.split('=',1)[1]
if 'graviton_userid=' in line:
graviton_userid = line.split('=',1)[1]
if 'No_of_Permissible_Features_EDA=' in line:
No_of_Permissible_Features_EDA = line.split('=', 1)[1]
if 'apiKeyIdLLM=' in line:
apiKeyIdLLM = line.split('=', 1)[1]
if 'apiUrlLLM=' in line:
apiUrlLLM = line.split('=', 1)[1]
except Exception as inst:
pass
external_system = 'enable'
semantico = 'enable'
return(usecase)
def addKafkaModel(request,datalocation):
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','kafkaConfig.conf'))
f = open(file_path, "r+")
configSettings = f.read()
configSettingsJson = json.loads(configSettings)
modelSignature = request.POST.get('modelsignature')
timeframe = request.POST.get('timeframe')
command = request.POST.get('kafkasubmit')
if command.lower() == 'configure':
configSettingsJson['timeFrame'][modelSignature] = str(timeframe)
configSettingsJson['trainingDataLocation'][modelSignature] = datalocation
elif command.lower() == 'unconfigure':
del configSettingsJson['timeFrame'][modelSignature]
updatedConfigSettingsJson = json.dumps(configSettingsJson)
f.seek(0)
f.write(updatedConfigSettingsJson)
f.truncate()
f.close()
def saveopenaisettings(request):
try:
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('openai'):
updated_data = 'api_type="'+request.POST.get('api_type')+'",api_key="'+request.POST.get('apiKeyIdLLM')+'",api_base="'+request.POST.get('apiUrlLLM')+'",api_version="'+request.POST.get('api_version')+'"'
sqlite_obj.update_data(updated_data,'','openai')
else:
newdata = {}
newdata.update({'api_type':['azure'],'api_key': [request.POST.get('apiKeyIdLLM')],'api_base': [request.POST.get('apiUrlLLM')],'api_version':[request.POST.get('api_version')]})
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'openai')
except Exception as e:
print(e)
def savegravitonconfig(request):
try:
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
updated_data = 'graviton_url="'+request.POST.get('graviton_url')+'",graviton_userid="'+request.POST.get('graviton_userid')+'"'
sqlite_obj.update_data(updated_data,'settingsid=1','settings')
except Exception as e:
print(e)
def saveconfigfile(request):
try:
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
updated_data = 'usecase="'+request.POST.get('usecasetab')+'",No_of_Permissible_Features_EDA="'+request.POST.get('edefeatures')+'",telemetryOptOut="'+request.POST.get('telemetryOptOut')+'"'
print(updated_data)
sqlite_obj.update_data(updated_data,'settingsid=1','settings')
return request.POST.get('usecasetab')
except Exception as e:
print(e) |
reports.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import pandas as pd
import numpy as np
import json
import os
def downloadtrainingfile(request,Existusecases):
usename = request.session['UseCaseName'].replace(" ", "_") + '_' + str(request.session['ModelVersion'])
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r+", encoding="utf-8")
configSettingsData = f.read()
configSettingsJson = json.loads(configSettingsData)
modelName = request.session['UseCaseName']
modelVersion = request.session['ModelVersion']
modelStatus = request.session['ModelStatus']
model = Existusecases.objects.get(ModelName=request.session['ModelName'],Version=request.session['ModelVersion'])
output_train_json_filename = str(model.TrainOuputLocation)
f = open(output_train_json_filename, "r+")
training_output = f.read()
f.close()
dict = {'Attribute':[],
'Value':[]
}
training_output = json.loads(training_output)
dfdashbord = pd.DataFrame(dict)
dfdashbord.loc[len(dfdashbord.index)] = ['UseCaseName',modelName]
dfdashbord.loc[len(dfdashbord.index)] = ['ProblemType',training_output['data']['ModelType']]
dfdashbord.loc[len(dfdashbord.index)] = ['Version',str(modelVersion)]
dfdashbord.loc[len(dfdashbord.index)] = ['Status',modelStatus]
if 'vmDetails' in training_output['data']:
dfdashbord.loc[len(dfdashbord.index)] = ['DeployLocation', training_output['data']['vmDetails']]
else:
dfdashbord.loc[len(dfdashbord.index)] = ['DeployLocation',training_output['data']['deployLocation']]
dfdashbord.loc[len(dfdashbord.index)] = ['BestModel',training_output['data']['BestModel']]
dfdashbord.loc[len(dfdashbord.index)] = ['BestScore',training_output['data']['BestScore']]
dfdashbord.loc[len(dfdashbord.index)] = ['ScoringParam',training_output['data']['ScoreType']]
if training_output['data']['ModelType'] != 'LLM Fine-Tuning':
dfdashbord.loc[len(dfdashbord.index)] = ['Test%',configSettingsJson['advance']['testPercentage']]
dfdashbord.loc[len(dfdashbord.index)] = ['FeaturesUsed',training_output['data']['featuresused']]
from io import BytesIO as IO
excel_file = IO()
edaFileName = usename + '_training.xlsx'
excel_writer = pd.ExcelWriter(excel_file, engine="xlsxwriter")
dfdashbord.to_excel(excel_writer, sheet_name='Dashboard',index=False)
if training_output['data']['ModelType'].lower() != 'multimodellearning' and training_output['data']['ModelType'].lower() != 'multilabelprediction':
EvaluatedModels = training_output['data']['EvaluatedModels']
EvaluatedModels = pd.DataFrame(EvaluatedModels)
EvaluatedModels.to_excel(excel_writer, sheet_name='EvaluatedModels',startrow=0 , startcol=0)
if training_output['data']['ModelType'].lower() == 'classification':
#print(training_output['data']['matrix'])
row1 = 10
row2 = 10
if 'ConfusionMatrix' in training_output['data']['matrix']:
confusionMatrix = training_output['data']['matrix']['ConfusionMatrix']
confusionMatrix = pd.DataFrame(confusionMatrix)
confusionMatrix.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0)
row1 =confusionMatrix.shape[0]+5
if 'ConfusionMatrix' in training_output['data']['trainmatrix']:
confusionMatrix = training_output['data']['trainmatrix']['ConfusionMatrix']
confusionMatrix = pd.DataFrame(confusionMatrix)
confusionMatrix.to_excel(excel_writer, sheet_name='Training Matrix',startrow=0 , startcol=0)
if 'ClassificationReport' in training_output['data']['matrix']:
confusionMatrix = training_output['data']['matrix']['ClassificationReport']
confusionMatrix = pd.DataFrame(confusionMatrix)
confusionMatrix.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=row1 , startcol=0)
if 'ClassificationReport' in training_output['data']['trainmatrix']:
confusionMatrix = training_output['data']['trainmatrix']['ClassificationReport']
confusionMatrix = pd.DataFrame(confusionMatrix)
confusionMatrix.to_excel(excel_writer, sheet_name='Training Matrix',startrow=row2 , startcol=0)
if training_output['data']['ModelType'].lower() == 'regression':
dict = {'Attribute':[],'Value':[]}
testingDF = pd.DataFrame(dict)
try:
testingDF.loc[len(testingDF.index)] = ['MAE',training_output['data']['matrix']['MAE']]
testingDF.loc[len(testingDF.index)] = ['R2Score',training_output['data']['matrix']['R2Score']]
testingDF.loc[len(testingDF.index)] = ['MSE',training_output['data']['matrix']['MSE']]
testingDF.loc[len(testingDF.index)] = ['MAPE',training_output['data']['matrix']['MAPE']]
testingDF.loc[len(testingDF.index)] = ['RMSE',training_output['data']['matrix']['RMSE']]
except:
pass
testingDF.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0)
trainingDF = pd.DataFrame(dict)
try:
trainingDF.loc[len(trainingDF.index)] = ['MAE',training_output['data']['trainmatrix']['MAE']]
trainingDF.loc[len(trainingDF.index)] = ['R2Score',training_output['data']['trainmatrix']['R2Score']]
trainingDF.loc[len(trainingDF.index)] = ['MSE',training_output['data']['trainmatrix']['MSE']]
trainingDF.loc[len(trainingDF.index)] = ['MAPE',training_output['data']['trainmatrix']['MAPE']]
trainingDF.loc[len(trainingDF.index)] = ['RMSE',training_output['data']['trainmatrix']['RMSE']]
except:
pass
trainingDF.to_excel(excel_writer, sheet_name='Training Matrix',startrow=0 , startcol=0)
if training_output['data']['ModelType'].lower() == 'clustering':
dict = {'Attribute':[],'Value':[]}
trainingDF = pd.DataFrame(dict)
try:
trainingDF.loc[len(trainingDF.index)] = ['SilHouette_Avg',round(training_output['data']['trainmatrix']['SilHouette_Avg'],2)]
trainingDF.loc[len(trainingDF.index)] = ['DaviesBouldinScore',round(training_output['data']['trainmatrix']['DaviesBouldinScore'],2)]
trainingDF.loc[len(trainingDF.index)] = ['CalinskiHarabazScore',round(training_output['data']['trainmatrix']['CalinskiHarabazScore'],2)]
except:
pass
trainingDF.to_excel(excel_writer, sheet_name='Training Matrix',startrow=0 , startcol=0)
centroidpath = os.path.join(training_output['data']['deployLocation'],'centers.csv')
if(os.path.isfile(centroidpath)):
df_center = pd.read_csv(centroidpath)
df_center = df_center.rename(columns={"Unnamed: 0": "Cluster"})
df_center.to_excel(excel_writer, sheet_name='Centroid',startrow=0 , startcol=0)
if training_output['data']['ModelType'].lower() == 'timeseriesforecasting': #task 11997
if training_output['data']['BestModel'].lower() == 'var':
dict = {'Features':[],'Attribute':[],'Value':[]}
trainingDF = pd.DataFrame(dict)
FeaturesMatrix = training_output['data']['matrix']
for x in FeaturesMatrix:
try:
trainingDF.loc[len(trainingDF.index)] = [x['Features'],'MAE',x['MAE']]
trainingDF.loc[len(trainingDF.index)] = [x['Features'],'MSE',x['MSE']]
trainingDF.loc[len(trainingDF.index)] = [x['Features'],'MAPE',x['MAPE']]
trainingDF.loc[len(trainingDF.index)] = [x['Features'],'RMSE',x['RMSE']]
except:
pass
trainingDF.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0)
else:
dict = {'Attribute':[],'Value':[]}
trainingDF = pd.DataFrame(dict)
try:
trainingDF.loc[len(trainingDF.index)] = ['MAE',training_output['data']['matrix']['MAE']]
trainingDF.loc[len(trainingDF.index)] = ['MSE',training_output['data']['matrix']['MSE']]
trainingDF.loc[len(trainingDF.index)] = ['MAPE',training_output['data']['matrix']['MAPE']]
trainingDF.loc[len(trainingDF.index)] = ['RMSE',training_output['data']['matrix']['RMSE']]
except:
pass
trainingDF.to_excel(excel_writer, sheet_name='Testing Matrix',startrow=0 , startcol=0)
workbook = excel_writer.book
#excel_writer.save()
excel_writer.close()
excel_file.seek(0)
return edaFileName,excel_file
|
images_analysis.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
def analysis_images(folder_path):
from AIX import image_eda
qualityscore = image_eda.img_MeasureImageQuality(folder_path)
eda_result = image_eda.img_EDA(folder_path)
#Image Duplicate Finder
duplicate_img = image_eda.img_duplicatefinder(folder_path)
color_plt = image_eda.img_plot_colour_hist(folder_path)
return qualityscore,eda_result,duplicate_img,color_plt |
models.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os.path
from pathlib import Path
import time
import subprocess
import sys
import shutil
from appbe.aion_config import kafka_setting
from appbe.aion_config import running_setting
from appbe.publish import chech_publish_info
from llm.llm_tuning import update_sqllite_data
from appbe.data_io import sqlite_db
from appbe.dataPath import DATA_DIR
from appbe import installPackage
from appbe import compute
import json
import os
import signal
from os.path import expanduser
import platform
import pandas as pd
LOG_FILE_PATH = os.path.join(DATA_DIR,'logs')
GITHUB_FILE_PATH = os.path.join(DATA_DIR,'github')
PUBLISH_PATH = os.path.join(DATA_DIR,'target')
DEPLOY_DATABASE_PATH = os.path.join(DATA_DIR,'sqlite')
os.makedirs(LOG_FILE_PATH, exist_ok=True)
'''
def check_publish_info(usecase,version):
sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db')
if sqlite_dbObj.table_exists('publish'):
publishState= 'Published'
'''
def get_instance(modelID):
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if sqlite_obj.table_exists("LLMTuning"):
data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID)
if len(data) > 0:
return (data[3],data[2],data[5],data[6],data[4])
else:
return '','','','',''
else:
return '','','','',''
def startServices(request,usecasedetails,Existusecases):
try:
models = Existusecases.objects.filter(publishStatus='Published')
print(models)
if len(models) > 0:
for model in models:
try:
portNo = model.portNo
ppid = model.publishPID
if ppid == 0:
continue
try:
os.kill(int(model.publishPID), signal.SIGTERM)
except Exception as e:
print(e)
scriptPath = os.path.join(PUBLISH_PATH,model.ModelName.usecaseid,'aion_publish_service.py')
if os.path.exists(scriptPath):
outputStr = subprocess.Popen([sys.executable, scriptPath,'-ip','0.0.0.0','-p',str(portNo)])
model.publishStatus = 'Published'
model.publishPID = outputStr.pid
model.portNo = portNo
model.save()
else:
print("Pass")
pass
except Exception as e:
print(e)
except Exception as e:
print(e)
def publishmodel(request,usecaseid,version,Existusecases,usecasedetails):
portNo=0
usecased = usecasedetails.objects.get(usecaseid=usecaseid)
models = Existusecases.objects.filter(ModelName=usecased,publishStatus='Published')
if len(models) > 0:
for model in models:
try:
portNo = model.portNo
try:
os.kill(int(model.publishPID), signal.SIGTERM)
except Exception as e:
print(e)
mod = Existusecases.objects.get(id=model.id)
mod.publishStatus = ''
mod.publishPID = 0
mod.portNo = 0
mod.save()
except Exception as e:
print(e)
pass
missingNumbers = []
if portNo == 0:
models = Existusecases.objects.filter(publishStatus='Published')
usedPortNo=[]
for model in models:
usedPortNo.append(model.portNo)
startPortNo = 8091
endPortNo = 8091+5
missingNumbers = [ i for i in range(startPortNo,endPortNo) if i not in usedPortNo]
if len(missingNumbers) > 0:
portNo = missingNumbers[0]
if portNo != 0:
scriptPath = os.path.join(PUBLISH_PATH,usecaseid,'aion_publish_service.py')
model = Existusecases.objects.get(ModelName=usecased,Version=version)
isExist = os.path.exists(scriptPath)
if isExist:
configfile = os.path.join(PUBLISH_PATH,usecaseid,'config.json')
configdata = {'version': str(version)}
with open(configfile, "w") as outfile:
json.dump(configdata, outfile)
outfile.close()
outputStr = subprocess.Popen([sys.executable, scriptPath,'-ip','0.0.0.0','-p',str(portNo)])
model.publishStatus = 'Published'
model.publishPID = outputStr.pid
model.portNo = portNo
model.save()
Status = 'SUCCESS'
hosturl =request.get_host()
hosturl = hosturl.split(':')
url = 'http://'+hosturl[0]+':'+str(portNo)+'/AION/'+str(usecaseid)+'/predict'
Msg = 'Model Published Successfully'
else:
Status = 'Error'
Msg = 'Model Published Error'
url = ''
else:
Status = 'Error'
Msg = 'All ports are utilized'
url=''
return Status,Msg,url
def get_published_models(instanceid):
from appbe.sqliteUtility import sqlite_db
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if sqlite_obj.table_exists("LLMTuning"):
condition = f'"instance"=="{instanceid}" AND "status"=="Published"'
datas = sqlite_obj.read_data('LLMTuning',condition)
if len(datas)>0:
return True,datas[0][0]
return False,''
def maac_command(request,Existusecases,usecasedetails):
command = request.POST.get('maacsubmit')
kafkaSetting = kafka_setting()
ruuningSetting = running_setting()
computeinfrastructure = compute.readComputeConfig()
modelID = request.POST.get('modelID')
Version = request.POST.get('Version')
p = Existusecases.objects.get(id=modelID,Version=Version)
usecasename = p.ModelName.usecaseid #bugid 13339
usecaseid = p.ModelName.id
# runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename)
# installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename)
usecasedetail = usecasedetails.objects.get(id=p.ModelName.id)
usecase = usecasedetails.objects.all()
problemType = p.ProblemType
score = 0
scoreType = ''
deployedModel = ''
deployedModelVersion = p.Version
models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS')
computeinfrastructure = compute.readComputeConfig()
for model in models:
model.scoringCreteria = 'NA'
model.score = 'NA'
model.deploymodel = 'NA'
if os.path.isdir(str(model.DeployPath)):
modelPath = os.path.join(str(model.DeployPath),'etc','output.json')
try:
with open(modelPath) as file:
outputconfig = json.load(file)
file.close()
if outputconfig['status'] == 'SUCCESS':
if deployedModelVersion == model.Version:
problemType = outputconfig['data']['ModelType']
scoreType = outputconfig['data']['ScoreType']
score = outputconfig['data']['BestScore']
deployedModel = outputconfig['data']['BestModel']
model.scoringCreteria = outputconfig['data']['ScoreType']
model.score = outputconfig['data']['BestScore']
model.deploymodel = outputconfig['data']['BestModel']
model.maacsupport = 'True'
model.flserversupport = 'False'
supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"]
if model.deploymodel in supportedmodels:
model.flserversupport = 'True'
else:
model.flserversupport = 'False'
supportedmodels = ["Extreme Gradient Boosting (XGBoost)"]
if model.deploymodel in supportedmodels:
model.encryptionsupport = 'True'
else:
model.encryptionsupport = 'False'
except Exception as e:
print(e)
pass
MLaaC_output = ''
if command == 'generatemaac':
deployPath = str(p.DeployPath)
codeconfig = os.path.join(deployPath,'etc','code_config.json')
if os.path.isfile(codeconfig):
with open(codeconfig,'r') as f:
cconfig = json.load(f)
f.close()
dbserver = request.POST.get('productiondb')
db_config = {}
if dbserver.lower() == 'influxdb':
cconfig['prod_db_type'] = 'influx'
db_config['host'] = request.POST.get('influxdbhost')
db_config['port'] = request.POST.get('influxdbportno')
db_config['user'] = request.POST.get('influxdbuser')
db_config['password'] = request.POST.get('influxpassword')
db_config['database'] = 'production'
db_config['measurement'] = usecasename
tags = {}
db_config['tags']=tags
cconfig['db_config'] = db_config
else:
cconfig['prod_db_type'] = 'sqlite'
cconfig['db_config'] = db_config
dbserver = request.POST.get('mlflowserver')
mlflow_config = {}
if dbserver.lower() == 'local':
cconfig['mlflow_config'] = mlflow_config
else:
mlflow_config['tracking_uri_type'] = request.POST.get('mlflowserverurl')
mlflow_config['tracking_uri'] = request.POST.get('mlflowserverurl')
mlflow_config['registry_uri'] = request.POST.get('mlflowserverurl')
mlflow_config['artifacts_uri'] = request.POST.get('mlflowserverurl')
cconfig['mlflow_config'] = mlflow_config
with open(codeconfig,'w') as f:
json.dump(cconfig, f)
f.close()
from bin.aion_mlac import generate_mlac_code
outputStr = generate_mlac_code(codeconfig)
output = json.loads(outputStr)
from appbe.telemetry import UpdateTelemetry
UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'MLaC','Yes')
if output['Status'] == 'SUCCESS':
Status = 'SUCCESS'
MLaaC_output = output['MLaC_Location'].replace('\\', '\\\\')
Msg = 'MLaC code successfully generated'
else:
Status = 'Failure'
Msg = output['msg']
else:
Status = 'Failure'
Msg = 'Code Config Not Present'
if command == 'buildContainer':
deployPath = str(p.DeployPath)
maac_path = os.path.join(deployPath,'publish','MLaC')
if os.path.isdir(maac_path):
config={'usecase':str(usecasename),'version':str(p.Version),'mlacPath':maac_path}
config = json.dumps(config)
scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','aion.py'))
if platform.system() == 'Windows':
outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','buildMLaCContainerLocal' ,'-j',config],creationflags = subprocess.CREATE_NEW_CONSOLE)
else:
outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','buildMLaCContainerLocal' ,'-j',config])
#cmd = scriptPath+" "+str(usecasename)+" "+str(p.Version)+" "+str(maac_path)
#subprocess.Popen(cmd,shell=True)
Status = 'SUCCESS'
Msg = 'Build Container Started'
else:
Status = 'Failure'
Msg = 'Run Code Generator'
if command == 'runpipeline':
deployPath = str(p.DeployPath)
dockerlist = os.path.join(deployPath,'publish','MLaC','dockerlist.json')
if os.path.isfile(dockerlist):
persistancevolume = request.POST.get('persistancevolume')
datasetpath = request.POST.get('dataset')
filetimestamp = str(int(time.time()))
logfilepath = os.path.join(LOG_FILE_PATH,'AIONPipeline_'+str(filetimestamp)+'.log')
config={'usecase':str(usecasename),'version':str(p.Version),'persistancevolume':persistancevolume,'datasetpath':datasetpath,'dockerlist':str(dockerlist),'logfilepath':logfilepath}
config = json.dumps(config)
scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','aion.py'))
if platform.system() == 'Windows':
outputStr = subprocess.Popen([sys.executable, scriptPath,'-m','runpipelinelocal','-j',config],creationflags = subprocess.CREATE_NEW_CONSOLE)
else:
outputStr = subprocess.Popen([sys.executable, scriptPath, str(usecasename),str(p.Version),persistancevolume,datasetpath,str(dockerlist),logfilepath])
Status = 'SUCCESS'
Msg = 'Pipeline Started'
MLaaC_output = 'Check log file for pipeline execution status: ' + str(logfilepath)
else:
Status = 'Failure'
Msg = 'Not found container information'
if command == 'generateyaml':
deployPath = str(p.DeployPath)
maac_path = os.path.join(deployPath,'publish','MLaC')
if os.path.isdir(maac_path):
persistancevolume = request.POST.get('persistancevolume')
datasetpath = request.POST.get('dataset')
supported_urls_starts_with = ('gs://','https://','http://')
if datasetpath.startswith(supported_urls_starts_with):
datasetpath = request.POST.get('dataset')
else:
datasetpath = '/aion/'+request.POST.get('dataset')
serviceport = request.POST.get('serviceport')
scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','bin','run_generateyaml.py'))
outputStr = subprocess.check_output([sys.executable, scriptPath, str(usecasename),str(p.Version),persistancevolume,datasetpath,maac_path,serviceport])
outputStr = outputStr.decode('utf-8')
outputStr=outputStr.strip()
print(outputStr)
output = json.loads(outputStr)
if output['Status'] == 'SUCCESS':
Status = 'SUCCESS'
MLaaC_output = output['location']
Msg = 'MLaaC dockerfile successfully generated'
else:
Status = 'Failure'
Msg = output['msg']
else:
Status = 'Failure'
Msg = 'Execute generate code first'
if command == 'githubupload':
if shutil.which('git') is None:
Status = 'Failure'
Msg = 'Git is not installed, Please install Git first.'
else:
try:
deployPath = str(p.DeployPath)
maac_path = os.path.join(deployPath,'publish','MLaC')
if os.path.isdir(maac_path):
githuburl = request.POST.get('githuburl')
githubusername = request.POST.get('githubusername')
githubtoken = request.POST.get('githubtoken')
githubemail = request.POST.get('githubemail')
githubconfig = {"url_type":"https","url":githuburl,"username":githubusername,"email":githubemail,"token":githubtoken,"location":maac_path,"modelName":usecasename,"gitFolderLocation":GITHUB_FILE_PATH}
from mlops import git_upload
outputStr = git_upload.upload(githubconfig)
print(outputStr)
output = json.loads(outputStr)
if output['Status'] == 'SUCCESS':
Status = 'SUCCESS'
MLaaC_output = githuburl
Msg = 'Code Uploaded to GitHub Successfully'
else:
Status = 'Failure'
Msg = output['msg']
else:
Status = 'Failure'
Msg = 'GitHub Upload failed'
except Exception as e:
print(e)
Status = 'Failure'
Msg = 'GitHub Upload failed'
if command == 'unpublishmodel':
try:
models = Existusecases.objects.filter(ModelName=usecasedetail,publishStatus='Published')
if len(models) > 0:
for model in models:
try:
if problemType.lower() == "llm fine-tuning":
cloudconfig = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'compute_conf.json'))
modelid = usecasename + '_' + str(Version)
usecasename = usecasename.replace(" ", "_")
hypervisor,instanceid,region,image,status = get_instance(usecasename + '_' + str(Version))
from llm.llm_inference import kill_inference_server
kill_inference_server(cloudconfig,instanceid,hypervisor,region,image)
update_sqllite_data(modelid,'status','Success')
else:
try:
os.kill(int(model.publishPID), signal.SIGTERM)
mod.publishPID = 0
except Exception as e:
print(e)
mod = Existusecases.objects.get(id=model.id)
mod.publishStatus = ''
mod.portNo = 0
mod.save()
Status = 'SUCCESS'
Msg = 'Model Unpublished Successfully'
except Exception as e:
print(e)
Status = 'Error'
Msg = 'Model Unpublished Error'
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
print(e)
pass
if command == 'publishmodel':
try:
portNo=0
models = Existusecases.objects.filter(ModelName=usecasedetail,publishStatus='Published')
if len(models) > 0:
for model in models:
try:
portNo = model.portNo
try:
os.kill(int(model.publishPID), signal.SIGTERM)
except Exception as e:
print(e)
mod = Existusecases.objects.get(id=model.id)
mod.publishStatus = ''
mod.publishPID = 0
mod.portNo = 0
mod.save()
except Exception as e:
print(e)
pass
missingNumbers = []
if problemType.lower() == "llm fine-tuning":
model = Existusecases.objects.get(ModelName=usecasedetail,Version=Version)
try:
usecasename = usecasename.replace(" ", "_")
hypervisor,instanceid,region,image,status = get_instance(usecasename + '_' + str(Version))
if status.lower() in ['published','success'] :
if status.lower() == 'published':
from llm.llm_inference import kill_inference_server
kill_inference_server('',instanceid, hypervisor, region, image)
update_sqllite_data(usecasename + '_' + str(Version), 'status', 'Success')
already_published,published_usecase = get_published_models(instanceid)
if already_published:
Status = 'Error'
Msg = f'{published_usecase} is published at the same id, Please Unpublish mentioned model to proceed.'
else:
if not region:
region = ''
cloudconfig = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'config', 'compute_conf.json'))
usecase = usecasename + '_' + str(Version)
#modelid = usecasename + '_' + str(Version)
scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'aion.py'))
cmd = [sys.executable, scriptPath, '-m', 'llmpublish', '-cc', cloudconfig, '-i',instanceid,'-hv',hypervisor,'-md',deployedModel,'-uc',usecase,'-r',region,'-im',image ]
outputStr = subprocess.Popen(cmd)
model.publishStatus = 'Published'
model.publishPID = 0
model.portNo = 8000
model.save()
Status = 'SUCCESS'
from llm.llm_inference import get_ip
instanceip = get_ip(cloudconfig,instanceid,hypervisor,region,image)
print(instanceip)
url = 'http://' + instanceip + ':' + str(model.portNo) + '/generate'
Msg = 'Model Published Successfully, Server will take few minutes to be ready for Inferencing. URL: ' + url
update_sqllite_data(usecase,'status','Published')
else:
Status = 'Error'
Msg = 'Only Trained models are availble for Publish.'
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
Status = 'Error'
Msg = 'Model Published Error'
else:
if portNo == 0:
models = Existusecases.objects.filter(publishStatus='Published')
usedPortNo=[]
for model in models:
usedPortNo.append(model.portNo)
startPortNo = 8091
endPortNo = 8091+5
missingNumbers = [ i for i in range(startPortNo,endPortNo) if i not in usedPortNo]
if len(missingNumbers) > 0:
portNo = missingNumbers[0]
if portNo != 0:
model = Existusecases.objects.get(ModelName=usecasedetail,Version=Version)
scriptPath = os.path.join(PUBLISH_PATH,usecasename,'aion_publish_service.py')
isExist = os.path.exists(scriptPath)
if isExist:
configfile = os.path.join(PUBLISH_PATH,usecasename,'config.json')
configdata = {'version': str(Version)}
with open(configfile, "w") as outfile:
json.dump(configdata, outfile)
outfile.close()
outputStr = subprocess.Popen([sys.executable, scriptPath,'-ip','0.0.0.0','-p',str(portNo)])
model.publishStatus = 'Published'
model.publishPID = outputStr.pid
model.portNo = portNo
model.save()
Status = 'SUCCESS'
hosturl =request.get_host()
hosturl = hosturl.split(':')
url = 'http://'+hosturl[0]+':'+str(portNo)+'/AION/'+str(usecasename)+'/predict'
Msg = 'Model Published Successfully URL: '+url
else:
Status = 'Error'
Msg = 'Model Published Error'
else:
Status = 'Error'
Msg = 'All ports are utilized'
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
print(e)
pass
if command == 'generatekubeflowyaml':
try:
if problemType.lower() == 'timeseriesforecasting': #task 11997
from appbe.aionpipelinets import aionpipelinets
else:
from appbe.aionpipeline import aionpipeline
deployPath = str(p.DeployPath)
codeconfig = os.path.join(deployPath,'etc','code_config.json')
featuresmapping = {'modelBased':'mlbased','statisticalBased':'statisticalBased'}
if os.path.isfile(codeconfig):
with open(codeconfig,'r') as f:
codeconfig = json.load(f)
f.close()
modelsarray=[]
for featureselection in codeconfig['feature_selector']:
for algo in codeconfig['algorithms'].keys():
if problemType.lower() == 'timeseriesforecasting': #task 11997
modelname = 'modeltraining_'+algo.lower()
else:
modelname = 'modeltraining_'+algo.lower()+'_'+featuresmapping[featureselection]
modelx = {'modelname':modelname}
modelsarray.append(modelx)
modelsjson = {'models':modelsarray}
kubeflowhost= request.POST.get('kubeflowhost')
containerregistry= request.POST.get('containerregistry')
containerlabel= request.POST.get('containerlabel')
containersecret= request.POST.get('containersecret')
if problemType.lower() == 'timeseriesforecasting': #task 11997
ap = aionpipelinets(modelsjson,containerregistry,containerlabel,containersecret)
else:
ap = aionpipeline(modelsjson,containerregistry,containerlabel,containersecret)
ap.aion_mlops()
ap.compilepl()
ap.executepl(kubeflowhost)
Status = 'SUCCESS'
MLaaC_output = ''
Msg = 'MLOps pipeline executed successfully'
except Exception as e:
print(e)
Status = 'Failure'
Msg = 'Error in pipeline execution'
from appbe.pages import get_usecase_page
if command in ['publishmodel','unpublishmodel']:
status,context,action = get_usecase_page(request,usecasedetails,Existusecases,usecaseid)
context['Status'] = Status
context['MLaaC_output'] = MLaaC_output
context['Msg'] = Msg
return(context,'usecasedetails.html')
else:
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
context['Status'] = Status
context['MLaaC_output'] = MLaaC_output
context['Msg'] = Msg
return(context,'usecases.html')
def getusercasestatus(request):
if 'UseCaseName' in request.session:
selected_use_case = request.session['UseCaseName']
else:
selected_use_case = 'Not Defined'
if 'ModelVersion' in request.session:
ModelVersion = request.session['ModelVersion']
else:
ModelVersion = 0
if 'ModelStatus' in request.session:
ModelStatus = request.session['ModelStatus']
else:
ModelStatus = 'Not Trained'
return selected_use_case,ModelVersion,ModelStatus |
textSummarization.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import time
import os
import subprocess
import base64
import sys
import re
from appbe.dataIngestion import getcommonfields
from appbe.dataIngestion import getusercasestatus
def startSummarization(request,DEFAULT_FILE_PATH,CONFIG_PATH,DATA_FILE_PATH):
try:
if request.FILES:
Datapath = request.FILES['summarypath']
ext = str(Datapath).split('.')[-1]
filetimestamp = str(int(time.time()))
if ext.lower() in ['txt','pdf','doc','docs']:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext)
else:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp)
with open(dataFile, 'wb+') as destination:
for chunk in Datapath.chunks():
destination.write(chunk)
destination.close()
configFile = os.path.join(DEFAULT_FILE_PATH,'aion_textSummerization.json')
filetimestamp = str(int(time.time()))
config_json_filename = os.path.join(CONFIG_PATH, 'AION_' + filetimestamp + '.json')
f = open(configFile)
data = json.load(f)
f.close()
data['basic']['dataLocation'] = dataFile
type = request.POST.get('type')
model = request.POST.get('model')
slength = request.POST.get('length')
types = data['basic']['analysisAproach']['textSummarization']
for x in list(types.keys()):
data['basic']['analysisAproach']['textSummarization'][x] = 'False'
data['basic']['analysisAproach']['textSummarization'][type] = 'True'
format = request.POST.get('format')
algorithm = data['basic']['algorithms']['textSummarization']
for x in list(algorithm.keys()):
data['basic']['algorithms']['textSummarization'][x] = 'False'
data['basic']['algorithms']['textSummarization'][model]='True'
length = data['advance']['textSummarization']['summaryLength']
for x in list(types.keys()):
data['advance']['textSummarization']['summaryLength'][x] = 'False'
data['advance']['textSummarization']['summaryLength'][slength] = 'True'
with open(config_json_filename, "w") as outfile:
json.dump(data, outfile)
outfile.close()
from bin.aion_text_summarizer import aion_textsummary
outputStr = aion_textsummary(config_json_filename)
#scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','bin','aion_text_summarizer.py'))
#outputStr = subprocess.check_output([sys.executable, scriptPath, config_json_filename])
#outputStr = outputStr.decode('utf-8')
#outputStr = re.search(r'Summary:(.*)', str(outputStr), re.IGNORECASE).group(1)
predict_dict = json.loads(str(outputStr))
summary = predict_dict['summary']
except Exception as e:
print(e)
summary = str(e)
context = getcommonfields()
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
context.update({'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion})
context.update({'summary':summary})
return context |
s3bucketsDB.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import sqlite3
from pathlib import Path
import json
import os
import rsa
import boto3 #usnish
import pandas as pd
import time
class sqlite_db():
def __init__(self, location, database_file=None):
if not isinstance(location, Path):
location = Path(location)
if database_file:
self.database_name = database_file
else:
self.database_name = location.stem
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
def table_exists(self, name):
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
listOfTables = self.cursor.execute(query).fetchall()
return len(listOfTables) > 0
def read_data(self, table_name):
query = f"SELECT * FROM {table_name}"
row = self.cursor.execute(query).fetchall()
return list(row)
#return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
def create_table(self,name, columns, dtypes):
query = f'CREATE TABLE IF NOT EXISTS {name} ('
for column, data_type in zip(columns, dtypes):
query += f"'{column}' TEXT,"
query = query[:-1]
query += ');'
self.conn.execute(query)
return True
def delete_record(self,table_name,col_name, col_value):
try:
query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'"
self.conn.execute(query)
self.conn.commit()
return 'success'
except Exception as e :
print(str(e))
print("Deletion Failed")
return 'error'
def get_data(self,table_name,col_name,col_value):
query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'"
row = self.cursor.execute(query).fetchone()
if(row == None):
return []
return list(row)
def write_data(self,data, table_name):
if not self.table_exists(table_name):
self.create_table(table_name, data.columns, data.dtypes)
tuple_data = list(data.itertuples(index=False, name=None))
insert_query = f'INSERT INTO {table_name} VALUES('
for i in range(len(data.columns)):
insert_query += '?,'
insert_query = insert_query[:-1] + ')'
self.cursor.executemany(insert_query, tuple_data)
self.conn.commit()
return True
def close(self):
self.conn.close()
def add_new_s3bucket(request):
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if request.POST["aionreferencename"] =='' or request.POST["s3bucketname"] == '' or request.POST["awsaccesskey"] == '' :
return 'error'
pkeydata='''-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAxIHM1FphEMMwViUrG0b2Bqf8tOxbhUWlnmjgFt5A25qbY1AfnrMv
fVx8+7iCcZ/3TY9Jv2I584SOc1tvsgESCke/t6+o/u2esPBsnDFzV62l3Zvw0m4e
wQeKlFC8EoOblyIXRbZdelSJinzlr9lOiKuid/xPvXHou6jxF1A2W7a89A2PM4Re
n0W9YkjB7dRGW1sSrpruHdVJvgHhGZFZ7sCTue0jVOnc5sT3Tq5saLfEDqHyKxlq
i/mcThmcTfisRIYFH5pyt/Ysr4VVP924QlcoqPOyg3RMCS3G0VjstSoVwNhxWrs/
lujDuCnpxvWzNpq21OWmF66GXxwiq+6W0wIDAQAB
-----END RSA PUBLIC KEY-----'''
pubkey = rsa.PublicKey.load_pkcs1(pkeydata)
awssecretaccesskey = rsa.encrypt(request.POST["awssecretaccesskey"].encode(), pubkey)
newdata = {}
newdata['Name'] = [request.POST["aionreferencename"]]
newdata['AWSAccessKeyID'] = [request.POST["awsaccesskey"]]
newdata['AWSSecretAccessKey'] = [str(awssecretaccesskey)]
newdata['S3BucketName'] = [request.POST["s3bucketname"]]
name = request.POST["aionreferencename"]
if sqlite_obj.table_exists("s3bucket"):
if(len(sqlite_obj.get_data("s3bucket","Name",name)) > 0):
return 'error1'
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'s3bucket')
except Exception as e:
print(e)
return 'error'
def get_s3_bucket():
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
temp_data = sqlite_obj.read_data('s3bucket')
data = []
for x in temp_data:
data_dict = {}
data_dict['Name'] = x[0]
data_dict['AWSAccessKeyID'] = x[1]
data_dict['AWSSecretAccessKey'] = x[2]
data_dict['S3BucketName'] = x[3]
data.append(data_dict)
except Exception as e:
print(e)
data = []
return data
def remove_s3_bucket(name):
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
return sqlite_obj.delete_record('s3bucket','Name',name)
def read_s3_bucket(name,filename,DATA_FILE_PATH):
privkey = '''-----BEGIN RSA PRIVATE KEY-----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-----END RSA PRIVATE KEY-----'''
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
data = sqlite_obj.get_data("s3bucket",'Name',name)
except:
data = []
awssecretaccesskey = ''
found = False
if len(data)!=0:
aws_access_key_id = data[1]
awssecretaccesskey = data[2]
bucketName = data[3]
found = True
if found:
privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM')
awssecretaccesskey = eval(awssecretaccesskey)
awssecretaccesskey = rsa.decrypt(awssecretaccesskey, privkey)
awssecretaccesskey = awssecretaccesskey.decode('utf-8')
#awssecretaccesskey = 'SGcyJavYEQPwTbOg1ikqThT+Op/ZNsk7UkRCpt9g'#rsa.decrypt(awssecretaccesskey, privkey)
client_s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(awssecretaccesskey))
#print(bucketName,filename)
try:
response = client_s3.get_object(Bucket=bucketName, Key=filename)
df = pd.read_csv(response['Body'])
except Exception as e:
print(str(e))#usnish
return 'Error',str(e), pd.DataFrame()
#return 'Error', pd.DataFrame()
return 'Success','',df
return 'Error',"Please check bucket configuration", pd.DataFrame() |
sqliteUtility.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from pathlib import Path
import sqlite3
class sqlite_db():
def __init__(self, location, database_file=None):
if not isinstance(location, Path):
location = Path(location)
if database_file:
self.database_name = database_file
else:
self.database_name = location.stem
db_file = str(location / self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
def table_exists(self, name):
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
listOfTables = self.cursor.execute(query).fetchall()
return len(listOfTables) > 0
def read_data(self, table_name, condition = None):
if condition:
query = f"SELECT * FROM {table_name} WHERE "+condition
else:
query = f"SELECT * FROM {table_name}"
row = self.cursor.execute(query).fetchall()
return list(row)
def column_names(self, table_name):
query = f"SELECT * FROM {table_name}"
row = self.cursor.execute(query).fetchall()
column_names = list(map(lambda x:x[0],self.cursor.description))
return column_names
# return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
def create_table(self, name, columns, dtypes):
query = f'CREATE TABLE IF NOT EXISTS {name} ('
for column, data_type in zip(columns, dtypes):
query += f"'{column}' TEXT,"
query = query[:-1]
query += ');'
self.conn.execute(query)
return True
def delete_record(self, table_name, col_name, col_value):
try:
query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'"
self.conn.execute(query)
self.conn.commit()
return 'success'
except Exception as e:
print(str(e))
print("Deletion Failed")
return 'error'
def drop_table(self,table_name):
query = f"DROP TABLE {table_name}"
self.cursor.execute(query)
print("Table dropped... ")
# Commit your changes in the database
self.conn.commit()
def get_data(self, table_name, col_name, col_value):
query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'"
row = self.cursor.execute(query).fetchone()
if (row == None):
return []
return list(row)
def execute_query(self,query):
self.cursor.execute(query)
self.conn.commit()
def write_data(self, data, table_name):
if not self.table_exists(table_name):
self.create_table(table_name, data.columns, data.dtypes)
tuple_data = list(data.itertuples(index=False, name=None))
insert_query = f'INSERT INTO {table_name} VALUES('
for i in range(len(data.columns)):
insert_query += '?,'
insert_query = insert_query[:-1] + ')'
self.cursor.executemany(insert_query, tuple_data)
self.conn.commit()
return True
def update_dict_data(self,data:dict,condition,table_name):
if not data:
return
if not table_name:
raise ValueError('Database table name is not provided')
updates = ''
#TODO validation of keys
for i,kv in enumerate(data.items()):
if i:
updates += ','
updates += f'"{kv[0]}"="{kv[1]}"'
if condition == '':
update_query = f'UPDATE {table_name} SET {updates}'
else:
update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}'
self.cursor.execute(update_query)
self.conn.commit()
def update_data(self,updates,condition,table_name):
if condition == '':
update_query = f'UPDATE {table_name} SET {updates}'
else:
update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}'
self.cursor.execute(update_query)
self.conn.commit()
def close(self):
self.conn.close() |
prediction.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import logging
from appbe.dataIngestion import getcommonfields
from appbe.dataIngestion import getusercasestatus
from appbe import service_url
import json
from appbe.dataIngestion import delimitedsetting
import os,sys
import pandas as pd
from django.http import HttpResponse
import time
from appbe.dataPath import LOG_LOCATION
from appbe.log_ut import logg
def get_instance_id(modelID):
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if sqlite_obj.table_exists("LLMTuning"):
data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID)
print(data)
if len(data) > 0:
return (data[3]+' instance '+data[2])
else:
return 'Instance ID not available'
else:
return 'Instance ID not available'
def get_instance(modelID):
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if sqlite_obj.table_exists("LLMTuning"):
data = sqlite_obj.get_data('LLMTuning','usecaseid',modelID)
if len(data) > 0:
return (data[3],data[2],data[5],data[6])
else:
return '','','',''
else:
return '','','',''
def getprompt(promptfeature,contextFeature,responseFeature,promptFriendlyName,responseFriendlyName,data):
if contextFeature != '':
promptData = data[promptfeature].replace('\n','')
inputData = data[contextFeature].replace('\n','')
prompt = (
f"Below is an {promptFriendlyName} that describes a task, paired with an Input that provides further context. "
f"Write a {responseFriendlyName} that appropriately completes the request.\n\n"
f"### {promptFriendlyName}:\n{promptData}\n\n### Input:\n{inputData}\n\n### {responseFriendlyName}:\n")
else:
promptData = data[promptfeature].replace('\n','')
prompt=(
f"Below is an {promptFriendlyName} that describes a task. "
f"Write a {responseFriendlyName} that appropriately completes the request.\n\n"
f"### {promptFriendlyName}:\n{promptData}\n\n### {responseFriendlyName}:\n")
return prompt
def getDataInstance(problem_type,mlmodels,configSettingsJson):
log = logging.getLogger('log_ux')
delimiters,textqualifier = delimitedsetting(configSettingsJson['basic']['fileSettings']['delimiters'],configSettingsJson['basic']['fileSettings']['textqualifier'])
if problem_type == 'timeSeriesForecasting': #task 11997
inputFieldsDict = {'noofforecasts': 10}
elif problem_type == 'recommenderSystem' and mlmodels =='ItemRating':
inputFieldsDict = {"uid": 1, "iid": 31, "rating": 0}
elif problem_type == 'stateTransition':
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
inputFeaturesList = inputFeatures.split(',')
inputFieldsDict = {inputFeatures:'session',targetFeature:'Activity'}
else:
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
inputFeaturesList = inputFeatures.split(',')
if targetFeature in inputFeaturesList:
inputFeaturesList.remove(targetFeature)
if problem_type == 'survivalAnalysis':
inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature'])
dataFilePath = str(configSettingsJson['basic']['dataLocation'])
if os.path.isfile(dataFilePath):
df = pd.read_csv(dataFilePath,encoding='utf8',nrows=2,sep=delimiters,quotechar=textqualifier,encoding_errors= 'replace')
try:
singleInstanceData = df.loc[0, inputFeaturesList]
except:
singleInstanceData = pd.Series(0, index =inputFeaturesList)
inputFieldsDict = singleInstanceData.to_dict()
else:
inputFieldsDict = {"File":"EnterFileContent"}
inputFields = []
inputFields.append(inputFieldsDict)
return inputFields
def createInstanceFeatures(configSettingsJson,problem_type,mlmodels,usecaseid,version,ser_url):
delimiters,textqualifier = delimitedsetting(configSettingsJson['basic']['fileSettings']['delimiters'],configSettingsJson['basic']['fileSettings']['textqualifier'])
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
if inputFeatures != '':
inputFeaturesList = inputFeatures.split(',')
else:
inputFeaturesList = []
if targetFeature in inputFeaturesList:
inputFeaturesList.remove(targetFeature)
if configSettingsJson['basic']['contextFeature'] != '':
inputFeaturesList.append(configSettingsJson['basic']['contextFeature'])
if problem_type == 'llmFineTuning':
inputFeaturesList.append('Temperature')
inputFeaturesList.append('Max Tokens')
if problem_type in ['survivalAnalysis','anomalyDetection', 'timeSeriesAnomalyDetection']: #task 11997
if configSettingsJson['basic']['dateTimeFeature'] != '' and configSettingsJson['basic']['dateTimeFeature'] != 'na':
inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature'])
dataFilePath = str(configSettingsJson['basic']['dataLocation'])
if problem_type == 'timeSeriesForecasting': #task 11997
inputFieldsDict = {'noofforecasts': 10}
elif problem_type == 'recommenderSystem' and mlmodels=='ItemRating':
inputFieldsDict = {"uid": 1, "numberOfRecommendation":10}
elif problem_type == 'stateTransition':
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
if inputFeatures != '':
inputFeaturesList = inputFeatures.split(',')
else:
inputFeaturesList = []
inputFieldsDict = {inputFeatures:'session',targetFeature:'Activity'}
elif problem_type != 'llmFineTuning':
if os.path.isfile(dataFilePath):
df = pd.read_csv(dataFilePath,encoding='utf8',nrows=2,sep=delimiters,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace')
try:
inputFieldsDict = df.to_dict(orient='index')[0]
except:
inputFieldsDict = pd.Series(0, index =inputFeaturesList).to_dict()
else:
inputFieldsDict = {"File":"EnterFileContent"}
else:
inputFieldsDict = pd.Series('', index =inputFeaturesList).to_dict()
inputFieldsDict['Temperature'] = '0.1'
hypervisor,instanceid,region,image = get_instance(usecaseid+'_'+str(version))
if hypervisor.lower() == 'AWS':
inputFieldsDict['Max Tokens'] = '1024'
else:
inputFieldsDict['Max Tokens'] = '4096'
inputFields = []
inputFields.append(inputFieldsDict)
if problem_type == 'llmFineTuning':
ser_url = get_instance_id(usecaseid+'_'+str(version))
elif problem_type == 'stateTransition':
ser_url = ser_url+'pattern_anomaly_predict?usecaseid='+usecaseid+'&version='+str(version)
else:
ser_url = ser_url+'predict?usecaseid='+usecaseid+'&version='+str(version)
return inputFields,ser_url
def singleInstancePredict(request, Existusecases, usecasedetails):
log = logging.getLogger('log_ux')
modelType=''
context = getcommonfields()
submittype = request.POST.get('predictsubmit')
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
t1 = time.time()
try:
try:
model = Existusecases.objects.get(ModelName=request.session['ModelName'],
Version=request.session['ModelVersion'])
output_train_json_filename = str(model.TrainOuputLocation)
f = open(output_train_json_filename, "r+")
training_output = f.read()
f.close()
training_output = json.loads(training_output)
featureused = training_output['data']['featuresused']
except:
featureused = []
from appbe.telemetry import UpdateTelemetry
UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Prediction','Yes')
usecasename = request.session['usecaseid'].replace(" ", "_")
context.update({'usecasename':usecasename})
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r", encoding = "utf-8")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
if inputFeatures != '':
inputFeaturesList = inputFeatures.split(',')
else:
inputFeaturesList = []
if targetFeature in inputFeaturesList:
inputFeaturesList.remove(targetFeature)
if configSettingsJson['basic']['contextFeature'] != '':
inputFeaturesList.append(configSettingsJson['basic']['contextFeature'])
problemtypes = configSettingsJson['basic']['analysisType']
problem_type = ''
modelSize = ''
for k in problemtypes.keys():
if configSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
if problem_type == 'llmFineTuning':
inputFeaturesList.append('Temperature')
inputFeaturesList.append('Max Tokens')
mlmodels =''
algorihtms = configSettingsJson['basic']['algorithms'][problem_type]
for k in algorihtms.keys():
if configSettingsJson['basic']['algorithms'][problem_type][k] == 'True':
if mlmodels != '':
mlmodels += ', '
mlmodels += k
if problem_type == 'llmFineTuning':
ser_url = get_instance_id(usecasename+'_'+str(request.session['ModelVersion']))
if 'modelSize' in configSettingsJson['basic']:
selectedModelSize = configSettingsJson['basic']['modelSize']['llmFineTuning'][mlmodels]
for k in selectedModelSize.keys():
if configSettingsJson['basic']['modelSize']['llmFineTuning'][mlmodels][k] == 'True':
modelSize = k
break
elif problem_type == 'stateTransition':
ser_url = service_url.read_service_url_params(request)
ser_url = ser_url+'pattern_anomaly_predict?usecaseid='+usecasename+'&version='+str(request.session['ModelVersion'])
else:
ser_url = service_url.read_service_url_params(request)
ser_url = ser_url+'predict?usecaseid='+usecasename+'&version='+str(request.session['ModelVersion'])
if submittype.lower() == 'predict':
inputFieldsDict = {}
if problem_type == 'timeSeriesForecasting': #task 11997
inputFieldsDict['noofforecasts'] = int(request.POST.get('noofforecasts'))
elif problem_type == 'stateTransition':
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
sessionid = request.POST.get('SessionID')
activity = request.POST.get(targetFeature)
inputFieldsDict[inputFeatures] = request.POST.get(inputFeatures)
inputFieldsDict[targetFeature] = request.POST.get(targetFeature)
elif problem_type == 'recommenderSystem' and mlmodels == 'ItemRating':
inputFieldsDict['uid'] = request.POST.get('uid')
inputFieldsDict['numberOfRecommendation'] = int(request.POST.get('numberOfRecommendation')) #Task 11190
else:
if problem_type in ['survivalAnalysis','anomalyDetection', 'timeSeriesAnomalyDetection']: #task 11997
if configSettingsJson['basic']['dateTimeFeature'] != '' and configSettingsJson['basic']['dateTimeFeature'] != 'na':
inputFeaturesList.insert(0,configSettingsJson['basic']['dateTimeFeature'])
for feature in inputFeaturesList:
inputFieldsDict[feature] = request.POST.get(feature)
if problem_type.lower() not in ['contextualsearch','similarityidentification']:
for key, value in inputFieldsDict.items():
if value == 'nan':
inputFieldsDict[key] = ''
if value == '':
if key in featureused:
context.update({'tab': 'predict','ser_url':ser_url, 'error': ' Error : Mandatory field(s) are empty', 'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion})
return context
inputFieldsJson = json.dumps(inputFieldsDict)
if problem_type == 'llmFineTuning':
modelType = request.POST.get('modelTypeforInferencing')
x = inputFieldsDict.keys()
from appbe.dataPath import DATA_DIR
prompt = inputFieldsDict[configSettingsJson['basic']['trainingFeatures']]
promptobj = {'prompt':prompt}
if configSettingsJson['basic']['contextFeature'] != '':
inputData = inputFieldsDict[configSettingsJson['basic']['contextFeature']]
promptobj.update({'input':inputData})
filetimestamp = str(int(time.time()))
file_path = os.path.join(DATA_DIR,'logs',filetimestamp+'.json')
f= open(file_path,"w",encoding="utf-8")
#print(promptobj)
json.dump(promptobj,f)
f.close()
from llm.llm_inference import LLM_predict
cloudconfig = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config','compute_conf.json'))
hypervisor,instanceid,region,image = get_instance(usecasename+'_'+str(request.session['ModelVersion']))
if hypervisor and instanceid:
if modelSize != '':
mlmodels = mlmodels+'-'+modelSize
cachepath = os.path.join(DATA_DIR,'sqlite','cachePrompt.db')
import sqlite3
conn = sqlite3.connect(cachepath)
from llm.llm_cache import CachePrompt
cachepromptObj = CachePrompt(conn)
searchFlag,result = cachepromptObj.selectFromCache(prompt,usecasename+'_'+str(request.session['ModelVersion']),modelType,temperature=inputFieldsDict['Temperature'],max_token=inputFieldsDict['Max Tokens'])
if searchFlag:
buf = LLM_predict(cloudconfig,instanceid,file_path,hypervisor,mlmodels,usecasename+'_'+str(request.session['ModelVersion']),region,image,inputFieldsDict['Temperature'],inputFieldsDict['Max Tokens'],modelType)
import re
outputStr = buf.split('ModelOutput:')[1]
cachepromptObj.insertRecord(prompt,outputStr,usecasename+'_'+str(request.session['ModelVersion']),modelType,temperature=inputFieldsDict['Temperature'],max_token=inputFieldsDict['Max Tokens'])
else:
outputStr = result
if configSettingsJson['basic']['folderSettings']['fileType'].lower() != 'llm_document':
outputStr = outputStr.split('### '+configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response']+':')[1]
singlePredictionResults = []
singlePredictionsummary=""
Results={}
Results['Response'] = outputStr
singlePredictionResults.append(Results)
else:
context.update(
{'tab': 'tabconfigure', 'error': 'Prediction Error: Instance ID not found ', 'selected': 'prediction',
'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,
'ModelVersion': ModelVersion,'mlmodels':mlmodels})
log.info('Predict Instance :' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Prediction Error, Instance ID not found')
return context
else:
try:
import requests
#response = requests.post(ser_url,auth=(aion_service_username,aion_service_password),data=inputFieldsJson,headers={"Content-Type":"application/json",})
response = requests.post(ser_url,data=inputFieldsJson,headers={"Content-Type":"application/json",})
if response.status_code != 200:
outputStr=response.content
context.update({'tab': 'tabconfigure', 'error': outputStr.decode('utf-8'), 'selected': 'prediction'})
log.info('Predict Instance : '+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0 '+'sec'+' : '+'Error : '+str(outputStr.decode('utf-8')))
return context
except Exception as inst:
if 'Failed to establish a new connection' in str(inst):
context.update({'tab': 'tabconfigure', 'error': 'AION service need to be started', 'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion})
log.info('Predict Instance :'+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0'+' sec'+' : '+'Error : AION service need to be started, '+str(inst))
return context
else:
context.update({'tab': 'tabconfigure', 'error': 'Prediction Error '+str(inst),'selected': 'prediction', 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion})
log.info('Predict Instance :'+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0 '+'sec'+' : '+'Error : Prediction Error, '+str(inst))
return context
outputStr=response.content
outputStr = outputStr.decode('utf-8','ignore')
outputStr = outputStr.strip()
predict_dict = json.loads(str(outputStr))
#print(predict_dict)
singlePredictionsummary=""
if (predict_dict['status'] == 'SUCCESS'):
data = predict_dict['data']
singlePredictionResults = []
Results = {}
if problem_type == 'multiModalLearning':
data = data[0]
Results['prediction'] = data['predict']
singlePredictionResults.append(Results)
if problem_type == 'textSummarization':
data = data[0]
Results['msg'] = predict_dict['msg']
singlePredictionResults.append(Results)
Results['prediction'] = predict_dict['data']
singlePredictionResults.append(Results)
Results1 = {}
Results1['prediction'] = predict_dict['data']
print("prdata------------",predict_dict['data'])
singlePredictionsummary=predict_dict['data']
print("singlePredictionsummary",singlePredictionsummary)
t2 = time.time()
elif problem_type == 'multiLabelPrediction':
prediction = ''
for x in data:
for y in x:
if 'predict' in y:
if prediction != '':
prediction += ','
prediction += str(y)+':'+str(x[y])
Results['prediction'] = prediction
singlePredictionResults.append(Results)
elif problem_type == 'timeSeriesForecasting': #task 11997
Results['prediction'] = json.dumps(data)
singlePredictionResults.append(Results)
elif problem_type == 'stateTransition':
if str(data['Anomaly']) == 'False':
Results['prediction'] = 'No Anomaly'
else:
Results['prediction'] = str(data['Remarks'])
singlePredictionResults.append(Results)
elif problem_type.lower() in ['similarityidentification','contextualsearch']:
data = data[0]
prediction = data['prediction']
i = 1
for x in prediction:
te = ''
for y in x:
info = (str(x[y])[:50] + '...') if len(str(x[y])) > 50 else str(x[y])
te += y+': '+info+'\n\n'
Results[i] = te
i = i+1
singlePredictionResults.append(Results)
else:
data = data[0]
if 'prediction' in data:
Results['prediction'] = data['prediction']
if 'probability' in data:
Results['probability'] = data['probability']
if 'remarks' in data:
Results['remarks'] = json.loads(data['remarks'])
singlePredictionResults.append(Results)
t2 = time.time()
log.info('Predict Instance : '+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+str(round(t2-t1))+' sec'+' : '+'Success')
else:
context.update({'tab': 'tabconfigure', 'error': 'Prediction Error '+str(predict_dict['message']), 'selected': 'prediction','selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion})
log.info('Predict Instance : '+str(selected_use_case) + ' : ' + str(ModelVersion) + ' : '+'0 '+'sec'+' : '+'Error : Prediction Error')
return context
inputFields = []
inputFields.append(inputFieldsDict)
##Below added by sjayaram for llm langkit evaluation metrics Task:17109
prompt_response_results = ''
if problem_type == 'llmFineTuning':
try:
response_msg = outputStr
prompt_msg = prompt
except:
response_msg = ''
prompt_msg = ''
from appbe.evaluate_prompt import evaluate_prompt_response_inputs
final_output_json,prompt_response_results = evaluate_prompt_response_inputs(prompt_msg,response_msg)
#ser_url = service_url.read_service_url_params(request)
#ser_url = ser_url+'predict?usecaseid='+usecasename+'&version='+str(ModelVersion)
context.update({'tab': 'predict','mlmodels':mlmodels,'fineTunedModelType':modelType,'ser_url':ser_url, 'inputFields': inputFields,'singlePredictionResults': singlePredictionResults,'singlePredictionsummary':singlePredictionsummary,'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'prediction',
'prompt_response_results':prompt_response_results})
return context
elif submittype.lower() == 'script':
scriptdata="'''\n"
scriptdata+="* =============================================================================\n"
scriptdata+="* COPYRIGHT NOTICE\n"
scriptdata+="* =============================================================================\n"
scriptdata+="* @ Copyright HCL Technologies Ltd. 2021, 2022, 2023\n"
scriptdata+="* Proprietary and confidential. All information contained herein is, and\n"
scriptdata+="* remains the property of HCL Technologies Limited. Copying or reproducing the\n"
scriptdata+="* contents of this file, via any medium is strictly prohibited unless prior\n"
scriptdata+="* written permission is obtained from HCL Technologies Limited.\n"
scriptdata+="'''\n"
scriptdata+='import sys\n'
scriptdata+='import json\n'
scriptdata+='import requests\n'
scriptdata+='import pandas as pd\n'
scriptdata+='from pandas import json_normalize\n'
scriptdata+='ser_url ="'+ser_url+'"\n\n'
scriptdata+="def predict(data):\n"
scriptdata+=" if data.endswith('.tsv'):\n"
scriptdata+=" df=pd.read_csv(data,encoding='utf-8',encoding_errors= 'replace',sep='\\t')\n"
scriptdata+=" else:\n"
scriptdata+=" df=pd.read_csv(data,encoding='utf-8',encoding_errors= 'replace')\n"
scriptdata+=' features = "'+",".join([feature for feature in inputFeaturesList])+'"\n'
scriptdata+=" features = features.split(',')\n"
scriptdata+=" df = df[features]\n"
scriptdata+=" data = df.to_json(orient='records')\n"
scriptdata+=" try:\n"
scriptdata+=' response = requests.post(ser_url,data=data,headers={"Content-Type":"application/json",})\n'
scriptdata+=" if response.status_code == 200:\n"
scriptdata+=" outputStr=response.content\n"
scriptdata+=" outputStr = outputStr.decode('utf-8')\n"
scriptdata+=" outputStr = outputStr.strip()\n"
scriptdata+=" predict_dict = json.loads(str(outputStr))\n"
scriptdata+=" print(predict_dict)\n"
scriptdata+=" except Exception as e:\n"
scriptdata+=' print(e)\n'
scriptdata+='\nif __name__ == "__main__":\n'
scriptdata+=' predict(sys.argv[1])'
response = HttpResponse()
response['content_type'] = 'text/plain'
response['Content-Disposition'] = 'attachment; filename=prediction.py'
response.write(scriptdata)
return response
except Exception as inst:
print(inst)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno))
context.update({'tab': 'tabconfigure', 'error': 'Failed To perform prediction','selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'ModelVersion': ModelVersion, 'selected': 'prediction'})
log.info('Predict Instance :' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + ' 0 ' + 'sec' + ' : ' + 'Error : Failed To perform prediction, '+ str(inst))
return context
|
flconfig.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os.path
import time
import subprocess
import sys
from appbe.aion_config import kafka_setting
from appbe.aion_config import running_setting
from appbe import installPackage
from appbe import compute
from appbe.models import getusercasestatus
import json
import pandas as pd
from os.path import expanduser
import ntpath
import shutil
import platform
from pathlib import Path
home = expanduser("~")
if platform.system() == 'Windows':
LOG_FILE_PATH = os.path.join(home,'AppData','Local','HCLT','AION','logs')
else:
LOG_FILE_PATH = os.path.join(home,'HCLT','AION','logs')
def convert(obj):
if isinstance(obj, bool):
return str(obj).capitalize()
if isinstance(obj, (list, tuple)):
return [convert(item) for item in obj]
if isinstance(obj, dict):
return {convert(key):convert(value) for key, value in obj.items()}
return obj
def fl_command(request,Existusecases,usecasedetails):
command = request.POST.get('flsubmit')
print(command)
#kafkaSetting = kafka_setting()
ruuningSetting = running_setting()
computeinfrastructure = compute.readComputeConfig()
modelID = request.POST.get('modelID')
p = Existusecases.objects.get(id=modelID)
usecasename = p.ModelName.UsecaseName
runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename)
installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename)
usecasedetail = usecasedetails.objects.get(id=p.ModelName.id)
usecase = usecasedetails.objects.all()
models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS')
for model in models:
model.scoringCreteria = 'NA'
model.score = 'NA'
model.deploymodel = 'NA'
if os.path.isdir(str(model.DeployPath)):
modelPath = os.path.join(str(model.DeployPath),'etc','output.json')
try:
with open(modelPath) as file:
outputconfig = json.load(file)
file.close()
if outputconfig['status'] == 'SUCCESS':
model.scoringCreteria = outputconfig['data']['ScoreType']
model.score = outputconfig['data']['BestScore']
model.deploymodel = outputconfig['data']['BestModel']
model.modelType = outputconfig['data']['ModelType']
model.featuresused = eval(outputconfig['data']['featuresused'])
model.targetFeature = outputconfig['data']['targetFeature']
model.modelParams = outputconfig['data']['params']
model.dataPath = os.path.join(str(model.DeployPath),'data', 'postprocesseddata.csv.gz')
model.maacsupport = 'True'
model.flserversupport = 'False'
supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"]
if model.deploymodel in supportedmodels:
model.flserversupport = 'True'
else:
model.flserversupport = 'False'
supportedmodels = ["Logistic Regression",
"Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)","Categorical Boosting (CatBoost)"]
if model.deploymodel in supportedmodels:
model.maacsupport = 'True'
else:
model.maacsupport = 'False'
supportedmodels = ["Extreme Gradient Boosting (XGBoost)"]
if model.deploymodel in supportedmodels:
model.encryptionsupport = 'True'
else:
model.encryptionsupport = 'False'
except Exception as e:
pass
flserver = os.path.join(str(p.DeployPath),'publish','FedLearning')
if command == 'startServer':
flservicefile = os.path.join(flserver,'fedServer','aionfls.py')
confilefile = os.path.join(flserver,'fedServer','config.json')
if platform.system() == 'Windows':
outputStr = subprocess.Popen([sys.executable, flservicefile,confilefile],creationflags = subprocess.CREATE_NEW_CONSOLE)
else:
outputStr = subprocess.Popen([sys.executable, flservicefile,confilefile])
Status = 'SUCCESS'
Msg = 'Federated Learning Server Started'
if command == 'saveflconfig':
#print(command)
fedserverPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedServer'))
shutil.rmtree(flserver, ignore_errors=True)
Path(flserver).mkdir(parents=True, exist_ok=True)
flcopypath = os.path.join(flserver,'fedServer')
shutil.copytree(fedserverPath,flcopypath)
fedserverDataPath = os.path.join(flcopypath,'data')
shutil.copy2(request.POST.get('flserver_datalocation'),fedserverDataPath)
flcon = {}
AlgorithmNames={'Logistic Regression':'LogisticRegression','Neural Network':'deeplearning','Linear Regression':'LinearRegression'}
flcon['server_IP'] = request.POST.get('flserver_ipaddress')
flcon['server_port'] = request.POST.get('flserver_port')
flcon['model_name'] = AlgorithmNames[request.POST.get('flserver_model')]
flcon['version'] = request.POST.get('flserver_Version')
flcon['model_hyperparams'] = convert(eval(request.POST.get('flserver_params')))
dataLocation = request.POST.get('flserver_datalocation')
dataPath,datafile = ntpath.split(dataLocation)
flcon['data_location'] = 'data/'+datafile
flcon['selected_feature'] = ",".join([model for model in eval(request.POST.get('flserver_trainingfeatures'))])
flcon['target_feature'] = request.POST.get('flserver_targetfeature')
flcon['problem_type'] = request.POST.get('flserver_modelType')
flcon['min_available_clients'] = request.POST.get('flserver_noofclient')
flcon['min_fit_clients'] = 2
flcon['fl_round'] = request.POST.get('flserver_trainround')
flcon['evaluation_required'] = request.POST.get('flserver_evaluation')
flcon['model_store'] = ""
flconfigfile = os.path.join(flcopypath,'config.json')
flconjson = json.dumps(flcon)
f = open(flconfigfile, "w+")
f.seek(0)
f.write(flconjson)
f.truncate()
f.close()
nouc = 0
Status = 'Success'
Msg = 'Federated Learning Server Configured'
if command =='startClient':
flconfigfile = os.path.join(str(model.DeployPath),'publish','FedLearning','fedServer','config.json')
if os.path.isfile(flconfigfile):
with open(flconfigfile) as file:
flconfig = json.load(file)
file.close()
numberofclient = flconfig['min_available_clients']
for x in range(int(numberofclient)):
flclientdirectory = os.path.join(str(model.DeployPath),'publish','FedLearning','fedClient_'+str(x+1))
flclientpath = os.path.join(str(model.DeployPath),'publish','FedLearning','fedClient_'+str(x+1),'fedClient.bat')
if platform.system() == 'Windows':
outputStr = subprocess.Popen([flclientpath],creationflags = subprocess.CREATE_NEW_CONSOLE,cwd=flclientdirectory)
else:
outputStr = subprocess.Popen([flclientpath],cwd=flclientdirectory)
Status = 'SUCCESS'
Msg = 'Federated Learning Client Started'
if command == 'generateClient':
flconfigfile = os.path.join(str(model.DeployPath),'publish','FedLearning','fedServer','config.json')
if os.path.isfile(flconfigfile):
with open(flconfigfile) as file:
flconfig = json.load(file)
file.close()
numberofclient = flconfig['min_available_clients']
trainingDataLocation = os.path.join(str(p.DeployPath),'data','postprocesseddata.csv.gz')
from utils.file_ops import read_df_compressed
status,df = read_df_compressed(trainingDataLocation,encoding='utf8')
for x in range(int(numberofclient)):
flclientpath = os.path.join(str(model.DeployPath),'publish','FedLearning','fedClient_'+str(x+1))
logPath = os.path.join(flclientpath,'logs')
modelsPath = os.path.join(flclientpath,'models')
Path(flclientpath).mkdir(parents=True, exist_ok=True)
Path(logPath).mkdir(parents=True, exist_ok=True)
Path(modelsPath).mkdir(parents=True, exist_ok=True)
flclientor = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedClient','aionflc.py'))
shutil.copy2(flclientor,flclientpath)
flclientor = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedClient','utils.py'))
shutil.copy2(flclientor,flclientpath)
flclientor = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','fedClient','dl_model.py'))
shutil.copy2(flclientor,flclientpath)
subset = df.sample(frac=0.8)
dataPath = os.path.join(flclientpath,'data')
Path(dataPath).mkdir(parents=True, exist_ok=True)
datafile = os.path.join(dataPath,'data.dat')
subset.to_csv(datafile, index=False)
flclient = {}
flclient['server_IP'] = flconfig['server_IP']
flclient['server_port'] = flconfig['server_port']
flclient['model_name'] = flconfig['model_name']
flclient['problem_type'] = flconfig['problem_type']
flclient['version'] = flconfig['version']
flclient['model_hyperparams'] = flconfig['model_hyperparams']
flclient['data_location'] = 'data\data.dat'
flclient['selected_feature'] = flconfig['selected_feature']
flclient['target_feature'] = flconfig['target_feature']
flclient['train_size'] = 80
#flclient['deploy_location'] = flconfig['deploy_location']
flclient['num_records_per_round'] = request.POST.get('flserver_recordperround')
flclient['wait_time'] = request.POST.get('flserver_roundtime')
flclient['model_overwrite'] = request.POST.get('model_overwritelabel')
configPath = os.path.join(flclientpath,'config')
Path(configPath).mkdir(parents=True, exist_ok=True)
configFile = os.path.join(configPath,'config.json')
flconjson = json.dumps(flclient)
f = open(configFile, "w+")
f.seek(0)
f.write(flconjson)
f.truncate()
f.close()
locate_python = sys.exec_prefix
bathfilePath = os.path.join(flclientpath,'fedClient.bat')
batfilecontent='''
@ECHO OFF
GOTO weiter
:setenv
SET "Path={python_path}\;%Path%;"
GOTO :EOF
:weiter
IF "%1" EQU "setenv" (
ECHO.
ECHO Setting environment for AION Federated Learning Client.
CALL :setenv
python %CD%\\aionflc.py %CD%\config\config.json
) ELSE (
SETLOCAL EnableDelayedExpansion
TITLE ION Federated Learning Client
PROMPT %username%@%computername%$S$P$_#$S
IF EXIST aion.config (FOR /F "delims=" %%A IN (aion.config) DO SET "%%A")
START "" /B %COMSPEC% /K "%~f0" setenv
)
'''.format(python_path=str(locate_python))
f = open(bathfilePath, "w",encoding="utf-8")
f.write(str(batfilecontent))
f.close()
Status = 'Success'
Msg = 'Federated Learning Client Code Generated'
nouc = 0
#selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
from appbe.pages import get_usecase_page
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
context['Status'] = Status
context['Msg'] = Msg
return(context) |
evaluate_prompt.py | from langkit import textstat
from whylogs.experimental.core.udf_schema import udf_schema
import pandas as pd
import whylogs as why
from langkit import light_metrics
from whylogs.experimental.core.udf_schema import udf_schema
from whylogs.experimental.core.udf_schema import register_dataset_udf
import whylogs as why
import json
from sentence_transformers import SentenceTransformer, util
from langkit import lang_config, response_column
def evaluate_prompt_metrics(prompt_msg: any):
""" Evaluate prompt only information."""
text_schema = udf_schema()
llm_schema = light_metrics.init()
df = pd.DataFrame({
"prompt": [
prompt_msg
]})
results = why.log(df, schema=udf_schema()) # .profile()
view = results.view()
automated_readability_index_prompt = view.get_column("prompt.automated_readability_index").to_summary_dict()
automated_readability_index_prompt_mean = automated_readability_index_prompt['distribution/mean']
arip_m = lambda x:1 if x < 1 else (14 if x > 14 else x)
automated_readability_index_prompt_mean = arip_m(automated_readability_index_prompt_mean)
automated_readability_index_prompt_value = get_readability_index_range_value(automated_readability_index_prompt_mean)
flesch_reading_ease_prompt = view.get_column("prompt.flesch_reading_ease").to_summary_dict()
flesch_reading_ease_prompt_mean = flesch_reading_ease_prompt['distribution/mean']
frep_m = lambda x:1 if x < 1 else (100 if x > 100 else x)
flesch_reading_ease_prompt_mean = frep_m(flesch_reading_ease_prompt_mean)
flesch_reading_ease_prompt_value = get_flesch_reading_ease_prompt_value(flesch_reading_ease_prompt_mean)
prompt_results = {'prompt_readability_score': str(automated_readability_index_prompt_mean),
'prompt_readability_value': automated_readability_index_prompt_value,
'prompt_reading_ease': str(flesch_reading_ease_prompt_mean),
'prompt_reading_ease_value': flesch_reading_ease_prompt_value}
prompt_results_json = json.dumps(prompt_results, indent=4)
return prompt_results_json,prompt_results
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
@register_dataset_udf(["prompt", "response"], "response.relevance_to_prompt")
def similarity_MiniLM_L6_v2(text):
x = text["prompt"]
y = text["response"]
embedding_1 = model.encode(x, convert_to_tensor=True)
embedding_2 = model.encode(y, convert_to_tensor=True)
similarity = util.pytorch_cos_sim(embedding_1, embedding_2)
result = similarity.item()
return result
def get_readability_index_range_value(readability_value):
if readability_value <= 1:
## Grade level Kindergarden to fourth grade
return "Kindergarten"
elif 1 < readability_value <= 2:
## Grade level Kindergarden to fourth grade
return "First Grade"
elif 2 < readability_value <= 3:
## Grade level Fifth grade to Ninth grade
return "Second Grade"
elif 3 < readability_value <= 4:
## Grade level Fifth grade to Ninth grade
return "Third Grade"
elif 4 < readability_value <= 5:
## Grade level Fifth grade to Ninth grade
return "Fourth Grade"
elif 5 < readability_value <= 6:
## Grade level Fifth grade to Ninth grade
return "Fifth Grade"
elif 6 < readability_value <= 7:
## Grade level Fifth grade to Ninth grade
return "Sixth Grade"
elif 7 < readability_value <= 8:
## Grade level Fifth grade to Ninth grade
return "Seventh Grade"
elif 8 < readability_value <= 9:
## Grade level Fifth grade to Ninth grade
return "Eighth Grade"
elif 9 < readability_value <=10:
## Grade level Fifth grade to Ninth grade
return "Ninth Grade"
elif 10 < readability_value <=11:
## Grade level Fifth grade to Ninth grade
return "Tenth Grade"
elif 11 < readability_value <=12:
## Grade level Fifth grade to Ninth grade
return "Eleventh Grade"
elif 12 < readability_value <= 13:
## Grade level Fifth grade to Ninth grade
return "Twelfth Grade"
elif readability_value > 13:
## Grade level Fifth grade to Ninth grade
return "College Grade"
else:
return "College Grade"
def get_flesch_reading_ease_prompt_value(readability_value):
""" Get flesch readability score range approximation"""
if readability_value <= 29:
return "Very Confusing"
elif 29 < readability_value <= 49:
return "Difficult"
elif 49 < readability_value <= 59:
return "Fairly Difficult"
elif 59 < readability_value <= 69:
return "Standard"
elif 69 < readability_value <= 79:
return "Fairly Easy"
elif 79 < readability_value <= 89:
return "Easy"
elif 89 < readability_value <= 100:
return "Very Easy"
else:
return "Very Easy"
def get_relevence_to_response_value(similarity_score):
""" To findout relevence to response results based on similarity score."""
if similarity_score <=0.3:
return "Low"
elif 0.3 < similarity_score <= 0.5:
return "Average"
elif 0.5 < similarity_score <= 0.8:
return "Good"
elif similarity_score > 0.8:
return "High"
def evaluate_prompt_response_inputs (prompt_msg:any, response_msg:any)->str:
""" Predict the text quality, text relevence for both prompt and response messages."""
df = pd.DataFrame({
"prompt": [prompt_msg],
"response": [response_msg]})
results = why.log(df, schema=udf_schema())
view = results.view()
automated_readability_index_prompt = view.get_column("prompt.automated_readability_index").to_summary_dict()
automated_readability_index_prompt_mean = automated_readability_index_prompt['distribution/mean']
arip_m = lambda x:1 if x < 1 else (14 if x > 14 else x)
automated_readability_index_prompt_mean = arip_m(automated_readability_index_prompt_mean)
automated_readability_index_prompt_value = get_readability_index_range_value(automated_readability_index_prompt_mean)
flesch_reading_ease_prompt = view.get_column("prompt.flesch_reading_ease").to_summary_dict()
flesch_reading_ease_prompt_mean = flesch_reading_ease_prompt['distribution/mean']
frep_m = lambda x:1 if x < 1 else (100 if x > 100 else x)
flesch_reading_ease_prompt_mean = frep_m(flesch_reading_ease_prompt_mean)
flesch_reading_ease_prompt_value = get_flesch_reading_ease_prompt_value(flesch_reading_ease_prompt_mean)
automated_readability_index_response = view.get_column("response.automated_readability_index").to_summary_dict()
automated_readability_index_response_mean = automated_readability_index_response['distribution/mean']
arir_m = lambda x:1 if x < 1 else (14 if x > 14 else x)
automated_readability_index_response_mean = arir_m(automated_readability_index_response_mean)
automated_readability_index_response_value = get_readability_index_range_value(automated_readability_index_response_mean)
flesch_reading_ease_response = view.get_column("response.flesch_reading_ease").to_summary_dict()
flesch_reading_ease_response_mean = flesch_reading_ease_response['distribution/mean']
frer_m = lambda x:1 if x < 1 else (100 if x > 100 else x)
flesch_reading_ease_response_mean = frer_m(flesch_reading_ease_response_mean)
flesch_reading_ease_response_value = get_flesch_reading_ease_prompt_value(flesch_reading_ease_response_mean)
relevance_to_response = view.get_column("response.relevance_to_prompt").to_summary_dict()
relevance_to_response_mean = relevance_to_response['distribution/mean']
r2r_m = lambda x:0 if x < 0 else (1 if x > 1 else x)
relevance_to_response_mean = r2r_m(relevance_to_response_mean)
relevance_to_response_value = get_relevence_to_response_value(relevance_to_response_mean)
sentence_count_response = view.get_column("response.sentence_count").to_summary_dict()
sentence_count_response_mean = sentence_count_response['distribution/mean']
word_count_response = view.get_column("response.lexicon_count").to_summary_dict()
word_count_response_mean = word_count_response['distribution/mean']
prompt_response_results = {'prompt_readability_score': str(automated_readability_index_prompt_mean),
'prompt_readability_value': automated_readability_index_prompt_value,
'prompt_reading_ease': str(flesch_reading_ease_prompt_mean),
'prompt_reading_ease_value': flesch_reading_ease_prompt_value,
'response_readability': str(automated_readability_index_response_mean),
'response_readability_value': str(automated_readability_index_response_value),
'response_reading_ease': str(flesch_reading_ease_response_mean),
'response_reading_ease_value': str(flesch_reading_ease_response_value),
'response_sentence_count': str(sentence_count_response_mean),
'response_word_count_response': str(word_count_response_mean),
'relevance_to_response': str(relevance_to_response_mean),
'relevance_to_response_value': relevance_to_response_value
}
final_output_json = json.dumps(prompt_response_results, indent=4)
return final_output_json,prompt_response_results
if __name__ == "__main__":
##Test only prompt message information
option = 'predict'
if option == 'evaluate':
prompt_only_response_msg = "A large language model is an advanced artificial intelligence (AI) system designed to process, understand, and generate human-like text based on massive amounts of data. These models are typically built using deep learning techniques, such as neural networks, and are trained on extensive datasets that include text from a broad range, such as books and websites, for natural language processing.Fine-tuning a large language model involves adjusting and adapting a pre-trained model to perform specific tasks or to cater to a particular domain more effectively. The process usually entails training the model further on a smaller, targeted dataset that is relevant to the desired task or subject matter.Few-shot learning (FSL) can be considered as a meta-learning problem where the model learns how to learn to solve the given problem. In this approach, the model is provided with a very limited number of examples (i.e., “few shots”) from the new task, and it uses this information to adapt and perform well on that task. Adapter Training: Adapter training is a method that involves training lightweight modules that are plugged into the pre-trained model, allowing for fine-tuning on a specific task without affecting the original model’s performance on other tasks.Multi-task Learning: Multi-task learning is a method where the pre-trained model is fine-tuned on multiple tasks simultaneously. This approach enables the model to learn and leverage the shared representations across different tasks, leading to better generalization and performance. Task-specific Fine-tuning: Task-specific fine-tuning is a method where the pre-trained model is fine-tuned on a specific task or domain using a task-specific dataset. This method requires more data and time than transfer learning but can result in higher performance on the specific task. Sequential Fine-tuning: Sequential fine-tuning is a method where a pre-trained model is fine-tuned on multiple related tasks or domains sequentially. This allows the model to learn more nuanced and complex language patterns across different tasks, leading to better generalization and performance.A noteworthy avenue of research within LLM fine-tuning explores strategies to reduce the expenses associated with updating model parameters. This endeavor is the essence of parameter-efficient fine-tuning (PEFT), a collection of techniques aiming to curtail the number of parameters requiring adjustments.Various PEFT techniques exist, and one prominent example is a low-rank adaptation (LoRA), a technique gaining popularity among open-source language models."
prompt_res = evaluate_prompt_metrics(prompt_only_response_msg)
elif option == 'predict':
prompt_msg = "What is AION?"
response_msg = "AION (Artificial Intelligence ONline) is an open -source software platform for building, deploying and operating the entire lifecycle of AI applications. It supports various use cases such as predictive analytics , machine learning and deep learning . Key features: 1. Data Ingestion : Supports multiple data sources like text files, excel sheet, database etc."
evaluation_metrics_json = evaluate_prompt_response_inputs(prompt_msg,response_msg)
print("evaluation_metrics_json: \n",evaluation_metrics_json) |
aionpipeline.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import kfp
import kfp.dsl as dsl
import json
from pathlib import Path
class aionpipeline():
containerRegistry = str()
containerLabel = str()
containerSecret = str()
pipelineName = 'AION MLOps Pipeline {0}'
exeCmd = 'python'
codeFile = 'aionCode.py'
mntPoint = '/aion'
inputArg = '-i'
msIP = '0.0.0.0'
port = '8094'
cachingStrategy = 'P0D'
deafultVolume = '1Gi'
volName = 'aion-pvc'
volMode = 'ReadWriteMany'
fileExt = '.tar.gz'
fileName = 'aion_mlops_pipeline_{0}'
containerMM = 'modelmonitoring'
containerDI = 'dataingestion'
containerDT = 'datatransformation'
containerFE = 'featureengineering'
containerMR = 'modelregistry'
containerMS = 'modelserving'
containerImage = '{0}/{1}:{2}'
models = {}
nameSeprator = '-'
modelsLiteral = 'models'
modelNameLiteral = 'modelname'
msTemplate = '{"apiVersion": "v1", "kind": "Pod", "metadata": {"name": "{{workflow.name}}-{0}"}, "spec": {"containers": [{"name": "{0}", "image": "{1}", "command": ["python"], "args": ["aionCode.py", "-ip", "{2}", "-pn", "{3}"],"volumeMounts": [{"name": "aion-pvc", "mountPath": "{4}"}], "ports": [{"name": "http", "containerPort": {3}, "protocol": "TCP"}]}], "imagePullSecrets": [{"name": "{5}"}], "volumes": [{"name": "aion-pvc", "persistentVolumeClaim": {"claimName": "{{workflow.name}}-{6}"}}]}}'
def __init__(self, models, containerRegistry, containerLabel, containerSecret=str()):
self.models = models
self.containerRegistry = containerRegistry
self.containerLabel = containerLabel
self.containerSecret = containerSecret
@dsl.pipeline(
name=pipelineName.format(containerLabel),
description=pipelineName.format(containerLabel),
)
def aion_mlops(self, inputUri=str(), volSize=deafultVolume):
vop = dsl.VolumeOp(
name=self.volName + self.nameSeprator + self.containerLabel,
resource_name=self.volName,
modes=[self.volMode],
size=volSize
)
mm = dsl.ContainerOp(
name=self.containerMM,
image=self.containerImage.format(self.containerRegistry,self.containerMM,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
self.inputArg,
inputUri,
],
pvolumes={self.mntPoint: vop.volume}
)
mm.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
di = dsl.ContainerOp(
name=self.containerDI,
image=self.containerImage.format(self.containerRegistry,self.containerDI,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: mm.pvolume}
)
di.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
dt = dsl.ContainerOp(
name=self.containerDT,
image=self.containerImage.format(self.containerRegistry,self.containerDT,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: di.pvolume}
)
dt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
fe = dsl.ContainerOp(
name=self.containerFE,
image=self.containerImage.format(self.containerRegistry,self.containerFE,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: dt.pvolume}
)
fe.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
dictMT = {}
listMTOps = []
for model in self.models[self.modelsLiteral]:
modelName = model[self.modelNameLiteral]
mt=dsl.ContainerOp(
name=modelName,
image=self.containerImage.format(self.containerRegistry,modelName,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: fe.pvolume})
mt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
listMTOps.append(mt)
dictMT[self.mntPoint]=mt.pvolume
mr = dsl.ContainerOp(
name=self.containerMR,
image=self.containerImage.format(self.containerRegistry,self.containerMR,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes=dictMT
).after(*tuple(listMTOps))
mr.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
msJson = self.msTemplate.replace(str({0}),self.containerMS).replace(str({1}),self.containerImage.format(self.containerRegistry,self.containerMS,self.containerLabel)).replace(str({2}),self.msIP).replace(str({3}),self.port).replace(str({4}),self.mntPoint).replace(str({5}),self.containerSecret).replace(str({6}),self.volName)
ms = dsl.ResourceOp(
name=self.containerMS + self.nameSeprator + self.containerLabel,
k8s_resource=json.loads(msJson),
)
ms.after(mr)
def compilepl(self, targetPath=str()):
filePath = self.fileName.format(self.containerLabel.lower()) + self.fileExt
if targetPath != str():
filePath = Path(targetPath, filePath)
kfp.compiler.Compiler().compile(self.aion_mlops, str(filePath))
def executepl(self, kfhost=str()):
client = kfp.Client(kfhost)
client.create_run_from_pipeline_func(self.aion_mlops,arguments={})
|
telemetry.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import requests
import json
import os
from datetime import datetime
import socket
import getmac
from appbe.sqliteUtility import sqlite_db
import pandas as pd
from appbe.dataPath import DATA_DIR
def TelemetryCreateSyncState(state):
try:
newdata = {}
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'telemetry.db')
now = datetime.now()
SyncingTime = int(datetime.timestamp(now))
newdata.update({'ID':['1'],'state':[state],'syncingTime':[SyncingTime]})
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'syncState')
except Exception as e:
print(e)
pass
def TelemetryUpdateSyncState(state):
try:
newdata = {}
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'telemetry.db')
now = datetime.now()
SyncingTime = int(datetime.timestamp(now))
updated_data = '"state"="'+state+'","syncingTime"="'+str(SyncingTime)+'"'
sqlite_obj.update_data(updated_data,'ID="1"','syncState')
except Exception as e:
print(e)
pass
def checkTelemtry():
import subprocess
import sys
scriptPath = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','aion.py'))
if os.path.exists(scriptPath):
outputStr = subprocess.Popen([sys.executable,scriptPath,'-m','pushtelemetry'])
def SyncTelemetry():
try:
newdata = {}
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'telemetry.db')
if sqlite_obj.table_exists('syncState'):
data = sqlite_obj.read_data('syncState')[0]
param_keys = ['ID','state','syncingTime']
sync_data = dict((x,y) for x,y in zip(param_keys,data))
#print(sync_data['state'],sync_data['syncingTime'])
if sync_data['state'].lower() != 'syncing':
sync_time = sync_data['syncingTime']
now = datetime.now()
currTime = datetime.timestamp(now)
diffTime = int(float(currTime)) - int(float(sync_time))
#print(diffTime)
if int(diffTime) > 86400:
TelemetryUpdateSyncState('Syncing')
SendTelemetryUpdate(sync_time)
TelemetryUpdateSyncState('Done')
else:
TelemetryCreateSyncState('Initialize')
except Exception as e:
print(e)
pass
def UseCaseCreated(Usecase):
try:
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'telemetry.db')
newdata = {}
now = datetime.now()
ID = datetime.timestamp(now)
record_date = int(datetime.timestamp(now))
computername = socket.getfqdn()
macaddress = getmac.get_mac_address()
try:
user = os.getlogin()
except:
user = 'NA'
newdata.update({'ID':[str(int(ID))],'RecordDate': [record_date],'Usecase': [Usecase],'Operation':['Created'],'User':[str(user)],'HostName' :[computername],'MACAddress':[macaddress],'ProblemType':[''],'Algorithms':[''],'EDA':['No'],'Prediction':['No'],'MLaC':['No'],'Drift':['No'],'TrustedAI':['No']})
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'logs')
except Exception as e:
print(e)
pass
def UpdateTelemetry(Usecase,operation,value):
try:
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'telemetry.db')
data = sqlite_obj.read_data('logs','Usecase="'+Usecase+'"')
#print(data)
if sqlite_obj.table_exists('logs'):
updated_data = operation+'="'+value+'"'
now = datetime.now()
ID = datetime.timestamp(now)
record_date = int(datetime.timestamp(now))
updated_data += ',"RecordDate"="'+str(record_date)+'"'
sqlite_obj.update_data(updated_data,'Usecase="'+Usecase+'"','logs')
except Exception as e:
print(e)
pass
def SendTelemetryUpdate(sync_time):
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'telemetry.db')
if sqlite_obj.table_exists('logs'):
ddata = sqlite_obj.read_data("logs","RecordDate >= '"+str(sync_time)+"'")
#print(ddata)
keys = sqlite_obj.column_names('logs')
for data in ddata:
now = datetime.now()
ID = datetime.timestamp(now)
item = {}
item['ID'] = str(int(ID))
item['RecordID'] = data[ keys.index('ID')]
item['RecordDate'] = data[ keys.index('RecordDate')]
item['Usecase'] = data[ keys.index('Usecase')]
item['Operation'] = data[ keys.index('Operation')]
item['User'] = data[ keys.index('User')]
item['HostName'] = data[ keys.index('HostName')]
item['MACAddress'] = data[ keys.index('MACAddress')]
item['Algorithms'] = data[ keys.index('Algorithms')]
item['ProblemType'] = data[ keys.index('ProblemType')]
item['EDA'] = data[ keys.index('EDA')]
item['Prediction'] = data[ keys.index('Prediction')]
item['MLaC'] = data[ keys.index('MLaC')]
item['Drift'] = data[ keys.index('Drift')]
item['TrustedAI'] = data[ keys.index('TrustedAI')]
url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry'
record = {}
record['TableName'] = 'AION_LOGS'
record['Item'] = item
record = json.dumps(record)
#print(record)
try:
response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",})
except Exception as e:
print(e)
def telemetry_data(operation,Usecase,data):
now = datetime.now()
ID = datetime.timestamp(now)
record_date = now.strftime("%y-%m-%d %H:%M:%S")
computername = socket.getfqdn()
macaddress = getmac.get_mac_address()
try:
user = os.getlogin()
except:
user = 'NA'
item = {}
item['ID'] = str(int(ID))
item['record_date'] = record_date
item['UseCase'] = Usecase
item['operation'] = operation
item['remarks'] = data
item['user'] = str(user)
item['hostname'] = computername
item['macaddress'] = macaddress
url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry'
record = {}
record['TableName'] = 'AION_OPERATION'
record['Item'] = item
record = json.dumps(record)
try:
response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",})
check_telemetry_file()
except Exception as inst:
filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),'telemetry.txt')
f=open(filename, "a+")
f.write(record+'\n')
f.close()
def check_telemetry_file():
file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),'telemetry.txt')
if(os.path.isfile(file_path)):
f = open(file_path, 'r')
url = 'https://l5m119j6v9.execute-api.ap-south-1.amazonaws.com/default/aion_telemetry'
file_content = f.read()
f.close()
matched_lines = file_content.split('\n')
write_lines = []
for record in matched_lines:
try:
response = requests.post(url, data=record,headers={"x-api-key":"Obzt8ijfOT3dgBYma9JCt1tE3W6tzHaV8rVuQdMK","Content-Type":"application/json",})
except:
write_lines.append(record)
f = open(file_path, "a")
f.seek(0)
f.truncate()
for record in write_lines:
f.write(record+'\n')
f.close()
else:
return True |
xplain.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import os
import pandas as pd
import numpy as np
import subprocess
import sys
import re
import plotly.graph_objects as go
import plotly.figure_factory as ff
def global_explain(request):
try:
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
problemType = 'classification'
for key in configSettingsJson['basic']['analysisType']:
if configSettingsJson['basic']['analysisType'][key] == 'True':
problemType = key
break
if problemType.lower() != 'classification' and problemType.lower() != 'regression':
return 'Problem Type Error','Explainable AI only available for classification and regression problem','NA','NA','NA','NA',0,0,'NA','NA','NA','NA',0,'NA','NA',0,'NA','NA','NA','NA','NA','NA'
displaypath = os.path.join( request.session['deploypath'],'etc','display.json')
with open(displaypath) as file:
config = json.load(file)
file.close()
inputFeatures = configSettingsJson['basic']['trainingFeatures']
targetFeature = configSettingsJson['basic']['targetFeature']
inputFeatures = inputFeatures.split(',')
if targetFeature in inputFeatures:
inputFeatures.remove(targetFeature)
dataFilePath = str(configSettingsJson['basic']['dataLocation'])
from utils.file_ops import read_df_compressed
status,df = read_df_compressed(config['postprocessedData'],encoding='utf8',nrows=10)
#print(df)
df.rename(columns=lambda x: x.strip(), inplace=True)
df = df[inputFeatures]
#print(df)
singleInstanceData = df.loc[5, inputFeatures]
inputFieldsDict = singleInstanceData.to_dict()
inputFields = []
inputFields.append(inputFieldsDict)
if 'nrows' in config:
nrows = config['nrows']
else:
nrows = 'Not Available'
if 'ncols' in config:
ncols = config['ncols']
else:
ncols = 'Not Available'
if 'targetFeature' in config:
targetFeature = config['targetFeature']
else:
targetFeature = ''
labelMaps = config['labelMaps']
modelfeatures = config['modelFeatures']
mfcount = len(modelfeatures)
df_proprocessed = pd.read_csv(dataFilePath)
if 'targetFeature' != '':
target_classes = df_proprocessed[targetFeature].unique()
numberofclasses = len(target_classes)
else:
target_classes = []
numberofclasses = 'Not Available'
dataPoints = df_proprocessed.shape[0]
df_proprocessed = df_proprocessed.head(5)
df_proprocessed = df_proprocessed.to_json(orient="records")
df_proprocessed = json.loads(df_proprocessed)
expainableAIPath = os.path.join(request.session['deploypath'],'aion_xai.py')
outputStr = subprocess.check_output([sys.executable,expainableAIPath,'global'])
outputStr = outputStr.decode('utf-8')
outputStr = re.search(r'aion_ai_explanation:(.*)',str(outputStr), re.IGNORECASE).group(1)
outputStr = outputStr.strip()
ale_json = json.loads(str(outputStr))
ale_json = ale_json['data']
ale_view = ale_json['data']
sentences = ale_json['sentences']
scoreMessage = ''
feature_importance = ale_json['feature_importance']
dfimp = pd.DataFrame.from_dict(feature_importance)
dfimp = dfimp.sort_values(by=['values'],ascending=False).reset_index()
yaxis_data = dfimp['values'].tolist()
xaxis_data = dfimp['labels'].tolist()
cfig = go.Figure()
cfig.add_trace(go.Bar(x=xaxis_data,y=yaxis_data,name='Feature Importance'))
cfig.update_layout(barmode='stack',xaxis_title='Features')
bargraph = cfig.to_html(full_html=False, default_height=450,default_width=1000)
dftoprecords = dfimp.head(2)
topTwoFeatures = dfimp['labels'].tolist()
topFeaturesMsg = []
for i in range(0,len(dfimp)):
value = round(dfimp.loc[i, "values"],2)*100
value = round(value,2)
tvalue = str(dfimp.loc[i, "labels"])+' contributing to '+ str(value)+'%'
topFeaturesMsg.append(tvalue)
most_influencedfeature = ale_json['most_influencedfeature']
interceppoint = ale_json['interceptionpoint']
anchorjson = ale_json['anchorjson']
return 'Success','Success',ale_view,sentences,bargraph,inputFields,nrows,ncols,targetFeature,dataPoints,target_classes,df_proprocessed,numberofclasses,modelfeatures,problemType,mfcount,topTwoFeatures,topFeaturesMsg,most_influencedfeature,interceppoint,anchorjson,labelMaps
except Exception as Inst:
print(Inst)
return 'Error','Exception: '+str(Inst),'NA','NA','NA','NA',0,0,'NA','NA','NA','NA',0,'NA','NA',0,'NA','NA','NA','NA','NA','NA' |
codeclonedetection_sklearn.py | # -*- coding: utf-8 -*-
import os
# import glob
from glob import glob
import ast
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd
import json
import time
import logging
from datetime import datetime
""" Code clone detection based on user input files. """
class codeCloneDetectionSklearn:
""" Detect code clones based on sklearn text vectorizer modules.
Input params: files_dir: python files folder,
deply_dir: logs,resultant dataframe stored location.
chunk_size: max size split for the input text or code function.
return values: report_dict which contains clone type, path and clones. """
def __init__(self,files_dir,deploy_dir, chunk_size):
self.files_dir = files_dir
self.deploy_dir = deploy_dir
self.chunk_size = chunk_size
try:
self.ccdreportpath = os.path.join(self.deploy_dir, "codeCloneReport")
os.makedirs(self.ccdreportpath, exist_ok = True)
except OSError as error:
print("Directory 'codeCloneReport' cann't be created.Error msg: ",error)
try:
current_datetime = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
str_current_datetime = str(current_datetime)
log_file_name = 'codeclonelog_sklearn'+f"_{str_current_datetime}"+".log"
logpath = os.path.join(self.ccdreportpath,log_file_name)
logging.basicConfig(level=logging.INFO,filename=logpath,filemode='w',format='%(message)s')
self.log = logging.getLogger()
except Exception as e:
print("code clone log object creation error.",e)
pass
def get_function_names(self,filename):
""" Get the function names from python files """
try:
with open(filename, 'r') as file:
content = file.read()
tree = ast.parse(content)
function_names = []
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
function_names.append(node.name)
except Exception as e:
self.log.info("function name read error: "+str(e))
return function_names
def get_function_code(self,filename, function_name):
""" To get the function codes """
try:
with open(filename, 'r') as file:
content = file.read()
tree = ast.parse(content)
function_code = ""
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef) and node.name == function_name:
function_code = ast.unparse(node)
except Exception as e:
self.log.info("function name read error: "+str(e))
return function_code
def get_python_files(self,root_dir):
""" Walk thru the directory user given, get all py files. """
try:
code_files = [y for x in os.walk(root_dir) for y in glob(os.path.join(x[0], '*.py'))]
except Exception as e:
self.log.info("Python file read error: "+str(e))
return code_files
def chunk_functions(self,function_code, chunk_size):
""" Check the function size based on chunk size. """
try:
if (len(function_code) > 20):
chunks = [function_code[i:i + chunk_size] for i in range(0, len(function_code), chunk_size)]
else:
chunks = list((function_code,))
except Exception as e:
self.log.info("function chunk based on chunk_size error: "+str(e))
total_tokens = round(len(function_code)/4)
return chunks,total_tokens
def get_clone(self):
""" Main code clone detection function using sklearn tfidf_vectorizer and cosine_similarity.
return values:report_dict which contains total_clones, """
try:
start_time = time.time()
chunk_size = int(self.chunk_size)
ccdreportpath = os.path.join(self.deploy_dir, "codeCloneReport")
python_files = self.get_python_files(self.files_dir)
total_files = len(python_files)
# print('python_files: \n',python_files)
function_codes = []
function_n = []
file_name=[]
# files_info=[]
total_tokens_used = []
for file in python_files:
function_names = self.get_function_names(file)
for i,function_name in enumerate(function_names):
file_name.append(file)
function_n.append(function_name)
function_code = self.get_function_code(file, function_name)
chunks,total_tokens = self.chunk_functions(function_code, chunk_size)
total_tokens_used.append(total_tokens)
function_codes.extend(chunks)
total_functions = len(function_n)
files_info=list(zip(file_name, function_n,function_codes))
tfidf_vectorizer = TfidfVectorizer()
## we can use other vectorizer models also.
# tfidf_vectorizer = HashingVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(function_codes)
similarity_matrix = cosine_similarity(tfidf_matrix)
#Uncomment if you want to send two different code clonne blocks at a time for similarity comparison
# similarity_matrix = cosine_similarity(tfidf_matrix, tfidf_matrix)
clone_d = dict()
total_clones = 0
final_report=list()
#getting funtion and next function for comparison
for i in range(len(similarity_matrix)):
for j in range(i + 1, len(similarity_matrix)):
if(similarity_matrix[i, j] >= 0.90 and similarity_matrix[i, j] <= 0.95):
clone_d.update({f'codeclone_{total_clones+1}':{f'function{i}':{'clone_fun_name':function_n[i],'clone1_path':files_info[i][0]},f'function{j}':{'clone_fun_name':function_n[j],'clone1_path':files_info[j][0]},'cloneType':'parametricClone'}})
report_json = json.dumps(clone_d, indent = 4)
total_clones=total_clones+1
elif(similarity_matrix[i, j] > 0.95):
clone_d.update({f'codeclone_{total_clones+1}':{f'function{i}':{'clone_fun_name':function_n[i],'clone_path':files_info[i][0]},f'function{j}':{'clone_fun_name':function_n[j],'clone_path':files_info[j][0]
},'cloneType':'exactClone'}})
report_json = json.dumps(clone_d, indent = 4)
final_report.append(clone_d)
total_clones=total_clones+1
elif(similarity_matrix[i, j] > 0.80 and similarity_matrix[i, j] < 0.90):
clone_d.update({f'codeclone_{total_clones+1}':{f'function{i}':{'clone_fun_name':function_n[i],'clone_path':files_info[i][0]},f'function{j}':{'clone_fun_name':function_n[j],'clone_path':files_info[j][0]
},'cloneType':'NearMissClones'}})
report_json = json.dumps(clone_d, indent = 4)
final_report.append(clone_d)
total_clones=total_clones+1
else:
##add other conditionas in future
pass
## To get clone type
clone_type = [list(item.values())[2] for item in list(clone_d.values())]
report_str = json.dumps(final_report)
json_l=json.loads(report_str)
json_keys = list(json_l[0].keys())
json_values = list(json_l[0].values())
end_time = time.time()
total_time_taken = end_time - start_time
# self.log.info("ccd_report: \n"+str(ccd_report))
f_df=pd.DataFrame(list(zip(json_keys, json_values,clone_type)),
columns =['Clone', 'CloneDetails','CloneType'])
codeclonereport_file = os.path.join(self.ccdreportpath,'clone_detection_report_sklearn.csv')
f_df.to_csv(codeclonereport_file, index=False)
ccd_report = f_df.to_markdown(tablefmt='psql')
self.log.info("total_clones: \n"+str(total_clones))
exact_clone_count = f_df['CloneType'].str.count("exactClone").sum()
parametric_clone_count = f_df['CloneType'].str.count("parametricClone").sum()
nearmiss_clone_count = f_df['CloneType'].str.count("NearMissClones").sum()
total_tokens = sum(total_tokens_used)
# nearmiss_clone_count =0
self.log.info("exact_clone_count: \n"+str(exact_clone_count))
self.log.info("parametric_clone_count: \n"+str(parametric_clone_count))
self.log.info("nearmiss_clone_count: \n"+str(nearmiss_clone_count))
self.log.info("Total tokens used: \n"+str(total_tokens))
self.log.info("Total time taken to excute code clone detction: \t"+str(total_time_taken))
clone_info="1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces,\
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments and less similarity threshold (0.90-0.95), result in this clone,\
3. Near-miss clone: Near-miss clone are clones detected with less similarity threshold."
clone_count = {"Exact Clone":exact_clone_count,"Parametric Clone":parametric_clone_count,"Nearmiss Clone":nearmiss_clone_count}
report_str = f"""Code_directory: {self.files_dir}
Files: {total_files}
Functions: {total_functions}
Total_code_clones_detected: {total_clones}
Tokens used: {total_tokens}
Three_types_of_clone:
1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces.
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments and less similarity threshold (0.90-0.95), result in this clone.
3. Near-miss clone: Near-miss clone are clones detected with less similarity threshold.
Code_clones_count_by_clone_type:
{clone_count}
Clone_functions:
{ccd_report}
total_time_taken: {total_time_taken}
"""
codeclonereport_txt = os.path.join(self.ccdreportpath,'code_clone_report.txt')
with open(codeclonereport_txt, "w") as f:
f.write(report_str)
report_dict = {"clone_info":clone_info,"total_clones":total_clones,'total_files':total_files,"exact_clone_count":exact_clone_count,'total_functions':total_functions,"total_tokens":total_tokens, "parametric_clone_count":parametric_clone_count,"nearmiss_clone_count":nearmiss_clone_count,"result_df":f_df }
self.log.info("ccd_report: \n"+str(ccd_report))
# print("report_dict:\n\n",report_dict)
# end_time = time.time()
# total_time = (end_time - start_time)
return report_dict
except Exception as e:
self.log.info("Clone detection function error. error msg: "+str(e))
# import traceback
# print("traceback error: \n",traceback.print_exc())
if __name__ == "__main__":
print("code clone detection started....")
##Use this for standalone fn debuging. |
exploratory_Analysis.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from wordcloud import WordCloud, STOPWORDS
import pandas as pd
import numpy as np
from appbe import distribution
import io
import urllib
import os
import sys
import base64
from appbe import help_Text as ht
import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from natsort import natsorted
from sklearn.cluster import KMeans
import json
from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator
from appbe.aion_config import eda_setting
from dython.nominal import associations
def calculateNumberofCluster(featureData):
Sum_of_squared_distances = []
K = range(1, 15)
for k in K:
km = KMeans(n_clusters=k)
km = km.fit(featureData)
Sum_of_squared_distances.append(km.inertia_)
x1, y1 = 1, Sum_of_squared_distances[0]
x2, y2 = 15, Sum_of_squared_distances[len(Sum_of_squared_distances) - 1]
distances = []
for inertia in range(len(Sum_of_squared_distances)):
x0 = inertia + 2
y0 = Sum_of_squared_distances[inertia]
numerator = abs((y2 - y1) * x0 - (x2 - x1) * y0 + x2 * y1 - y2 * x1)
denominator = math.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2)
distances.append(numerator / denominator)
n_clusters = distances.index(max(distances)) + 2
#print(n_clusters)
return (n_clusters)
def get_eda(request):
hopkins_val = ''
hopkins_tip = ''
if request.session['datatype'] == 'Normal':
from appbe.eda import ux_eda
# EDA Subsampling changes
# ----------------------------
edasampleSize = request.POST.get('SubsampleSize')
edasampleSize = str(int(edasampleSize)/100)
sampleFile = str(request.session['datalocation'])
repText = sampleFile[sampleFile.find('sub_'):sampleFile.find('_sampled_') + 9]
if len(repText) == 30:
dataLocation = sampleFile.replace(repText,"")
else:
dataLocation = sampleFile
eda_obj = ux_eda(dataLocation,request.session['delimiter'],request.session['textqualifier'])
df0 = eda_obj.getdata()
if os.path.isfile(dataLocation):
if(len(edasampleSize) > 0):
df0 = df0.sample(frac = float(edasampleSize))
#EDA Performance change
# ----------------------------
dflength = len(df0)
# sample_size = int(eda_setting())
# if dflength >= sample_size:
# eda_obj.subsampleData(sample_size)
# else:
eda_obj.subsampleData(dflength)
# ----------------------------
TrainSampleSelected = request.POST.get('TrainSampleSize')
if(TrainSampleSelected == 'EDASize'):
from pathlib import Path
filePath = Path(dataLocation)
import datetime
timestamp = datetime.datetime.now().replace(microsecond=0).isoformat()
timestamp = str(timestamp.replace(":",""))
sub_sampledFile = filePath.parent/("sub_" + timestamp + "_sampled_"+filePath.name)
# sub_sampledFile = filePath.parent/(usename + "_sub_sampled_"+filePath.name)
df0.to_csv(sub_sampledFile,index=False,)
request.session['datalocation'] = str(sub_sampledFile)
records = df0.shape[0]
request.session['NoOfRecords'] = records
edaFeatures = request.POST.getlist('InputFeatures')
request.session['edaFeatures'] = edaFeatures
if(len(edaFeatures) > 0):
eda_obj.subsetFeatures(edaFeatures)
# ----------------------------
features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures()
request.session['edanumericCatFeatures'] = numericCatFeatures
request.session['edatextFeature'] = textFeature
categoricalfeatures = catfeatures
numericfeaturecount = eda_obj.getNumericFeatureCount()
cluster_details = []
dataCharts = []
# correlated_features=[]
pca_details = []
if numericfeaturecount > 1:
try:
cluster_details,hopkins_val = eda_obj.getClusterDetails()
if hopkins_val!='':
if float(hopkins_val) <0.3:
hopkins_tip = ht.hopkins_tip[0]
elif float(hopkins_val)>0.7:
hopkins_tip = ht.hopkins_tip[2]
else:
hopkins_tip = ht.hopkins_tip[1]
else:
hopkins_tip = ''
except Exception as e:
print("========================"+str(e))
pass
try:
pca_map = eda_obj.getPCATop10Features()
pca_details = pca_map
yaxis_data = pca_map.tolist()
xaxis_data = pca_map.index.values.tolist()
import plotly.graph_objects as go
cfig = go.Figure()
cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance'))
cfig.update_layout(barmode='stack', xaxis_title='Features',yaxis_title='Explained Variance Ratio')
bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000)
dataCharts.append(bargraph)
except:
pass
df = eda_obj.getdata()
# try:
# top5highcorr = eda_obj.getHighlyCorrelatedFeatures(5)
# correlated_features = getHighlyCorrelatedFeatureCharts(df,top5highcorr)
# except:
# pass
else:
df = eda_obj.getdata()
# # EDA Subsampling changes
# # ----------------------------
# if os.path.isfile(dataLocation):
# if dflength < 10000:
# if(len(edasampleSize) > 0):
# df = df.sample(frac = float(edasampleSize))
# ----------------------------
if len(textFeature) > 0:
commonfeatures = eda_obj.getTopTextFeatures(10)
# comment_words = eda_obj.word_token()
del eda_obj
wordcloudpic = ''
showtextFeature = False
if len(textFeature) > 0:
showtextFeature = True
# try:
# stopwords = set(STOPWORDS)
# wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords,
# min_font_size=10).generate(comment_words)
# try:
# plt.clf()
# except:
# pass
# plt.imshow(wordcloud, interpolation='bilinear')
# plt.axis("off")
# plt.tight_layout(pad=0)
# image = io.BytesIO()
# plt.savefig(image, format='png')
# image.seek(0)
# string = base64.b64encode(image.read())
# wordcloudpic = 'data:image/png;base64,' + urllib.parse.quote(string)
# except:
# pass
xaxis_data = commonfeatures['most_common_words'].tolist()
yaxis_data = commonfeatures['freq'].tolist()
import plotly.graph_objects as go
cfig = go.Figure()
cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance'))
cfig.update_layout(barmode='stack', xaxis_title='Features',yaxis_title='Count')
bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000)
dataCharts.append(bargraph)
df_top = df.head(10)
df_json = df_top.to_json(orient="records")
df_json = json.loads(df_json)
# if len(df) > 10000:
# df1 = df.sample(n=10000, random_state=1)
# else:
# df1 = df
df1 = df
data_deep_json = df_top.to_json(orient='records') #df1.to_json(orient='records')
try:
gfsg = GenericFeatureStatisticsGenerator()
proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': df1}])
protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8")
except Exception as e:
protostr=''
print('protostr '+str(e))
try:
correlationgraph = getCorrelationMatrix(df)
except Exception as e:
print(e)
try:
dataDrift = 'onRequest' #getDriftDistribution(numericCatFeatures, df[numericCatFeatures])
except Exception as e:
dataDrift = ''
print(e)
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
statusmsg = 'Successfully Done'
DF_list = list()
des1 = df.describe(include='all').T
des1['missing count %'] = df.isnull().mean() * 100
des1['zero count %'] = df.isin([0]).mean() * 100
data = list(df.columns.values)
des1.insert(0, 'Features', data)
des1 = des1.to_json(orient="records")
pca_df=pd.DataFrame()
#print(pca_details)
# if pca_details.empty:
if len(pca_details) > 0:
pca_df = pd.DataFrame({'Feature':pca_details.index, 'Explained Variance Ratio':pca_details.values}).round(4)
pca_df = pca_df.to_json(orient="records")
if len(df.columns) > 25:
df3 = df[df.columns[0:24]]
else:
df3 = df.copy()
#cor_mat = abs(df3.corr())
#cor_mat = cor_mat.round(2)
try:
if len(df3.columns) > 25:
df3 = df3[df3.columns[0:24]]
cor_mat= associations(df3,compute_only=True)
cor_mat=cor_mat['corr']
#cor_mat = df3.corr()
cor_mat = cor_mat.astype(float).round(2)
except Exception as e:
print("creating correlation mat issue: \n",e)
pass
data = list(cor_mat.index)
cor_mat.insert(0, 'Features', data)
cor_mat = cor_mat.to_json(orient="records")
cluster_df = pd.DataFrame.from_dict(cluster_details)
cluster_df = cluster_df.to_json(orient="records")
#textFeature = json.dumps(textFeature)
# 2.2 patch changes
#-------------------------------------------------
request.session['edaRecords'] = df.shape[0]
print(textFeature)
context = {'data_deep_json': data_deep_json, 'sampleFile':sampleFile,'protostr': protostr, 'data': df_json, 'oneda': True,
'dataCharts': dataCharts,'dataDrift': dataDrift, 'drift_tip': ht.drift_tip,'des1':des1,'cluster_df':cluster_df,'hopkins_val':hopkins_val,
'pca_df':pca_df,'cor_mat':cor_mat,'correlationgraph': correlationgraph, 'centroids':cluster_details, 'wordcloudpic': wordcloudpic, 'showtextFeature': showtextFeature, 'textFeature': textFeature,
# 'featurepairgraph': correlated_features,
'data_overview_tip': ht.data_overview_tip,'timeseries_analysis_tip':ht.timeseries_analysis_tip, 'feature_importance_tip': ht.feature_importance_tip,'hopkins_tip':hopkins_tip,
'correlation_analysis_tip': ht.correlation_analysis_tip,
'exploratory_analysis_tip': ht.exploratory_analysis_tip, 'data_deep_drive_tip': ht.data_deep_drive_tip,'status_msg': statusmsg,'selected_use_case': selected_use_case,
'pair_graph_tip':ht.pair_graph_tip, 'fair_metrics_tip':ht.fair_metrics_tip, 'categoricalfeatures':categoricalfeatures, 'numericCatFeatures':numericCatFeatures,
'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'selected': 'modeltraning',
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'exploratory':True,'NumericFeatureList':numericFeature,'dateFeature':dateFeature,'targetFeature':targetFeature}
return(context)
# EDA Visualization changes
# ----------------------------
def get_edaGraph(request):
if request.session['datatype'] == 'Normal':
from appbe.eda import ux_eda
df_temp = dict(request.GET).get('features[]')
graphType = request.GET.get('graphType')
d3_url = request.GET.get('d3_url')
mpld3_url = request.GET.get('mpld3_url')
dataLocation = request.session['datalocation']
eda_obj = ux_eda(dataLocation)
# 2.2 patch changes
#-------------------------------------------------
edaRecords = request.session['edaRecords']
#df = df.sample(n=int(edaRecords), random_state=1)
eda_obj.subsampleData(edaRecords)
eda_obj.subsetFeatures(df_temp)
features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature, catfeatures = eda_obj.getFeatures()
numericfeaturecount = eda_obj.getNumericFeatureCount()
correlated_features=[]
df = eda_obj.getdata()
if numericfeaturecount > 1:
try:
if graphType == 'Default':
top5highcorr = eda_obj.getHighlyCorrelatedFeatures(5)
correlated_features = getHighlyCorrelatedFeatureCharts(df,top5highcorr)
else:
correlated_features = getFeatureCharts(df,graphType,d3_url,mpld3_url)
except:
pass
return correlated_features
# ----------------------------
# ---------------------- 12686:Data Distribution related Changes S T A R T ----------------------
def get_DataDistribution(request):
selectedFeature = request.GET.get('selected_feature')
_featureItem = []
_featureItem.append(selectedFeature)
from appbe.eda import ux_eda
dataLocation = request.session['datalocation']
eda_obj = ux_eda(dataLocation)
df = eda_obj.getdata()
numericCatFeatures = request.session['edanumericCatFeatures']
textFeature = request.session['edatextFeature']
# features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures()
dataDrift = ''
if selectedFeature in numericCatFeatures:
dataDrift = getDriftDistribution(_featureItem, df[numericCatFeatures])
elif selectedFeature in textFeature:
try:
comment_words = eda_obj.word_token_for_feature(selectedFeature, df[_featureItem])
stopwords = set(STOPWORDS)
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords,
min_font_size=10).generate(comment_words)
try:
plt.clf()
except:
pass
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.tight_layout(pad=0)
image = io.BytesIO()
plt.savefig(image, format='png')
image.seek(0)
string = base64.b64encode(image.read())
# wordcloudpic = 'data:image/png;base64,' + urllib.parse.quote(string)
dataDrift = urllib.parse.quote(string)
except:
dataDrift = ''
del eda_obj
return dataDrift
# -------------------------------------------- E N D --------------------------------------------
def get_DeepDiveData(request):
if request.session['datatype'] == 'Normal':
from appbe.eda import ux_eda
dataLocation = request.session['datalocation']
eda_obj = ux_eda(dataLocation)
edaRecords = request.session['edaRecords']
edaFeatures = request.session['edaFeatures']
eda_obj.subsampleData(edaRecords)
eda_obj.subsetFeatures(edaFeatures)
df = eda_obj.getdata()
data_deep_json = df.to_json(orient='records')
return (data_deep_json)
# Fairness Metrics changes
# ----------------------------
def get_fairmetrics(request):
import mpld3
if request.session['datatype'] == 'Normal':
from appbe.eda import ux_eda
df_temp = dict(request.GET).get('features[]')
d3_url = request.GET.get('d3_url')
mpld3_url = request.GET.get('mpld3_url')
global metricvalue
metricvalue = request.GET.get('metricvalue')
dataLocation = request.session['datalocation']
# dataLocation = 'C:\\MyFolder\\AION\\AION Datasets\\AIF360\\database.csv'
eda_obj = ux_eda(dataLocation, optimize=1)
features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures()
# data = eda_obj.getdata()
data = pd.read_csv(dataLocation, na_values=['Unknown', ' '])
features_toEncode = features
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
data_encoded = data.copy()
categorical_names = {}
encoders = {}
# Use Label Encoder for categorical columns (including target column)
for feature in features_toEncode:
le = LabelEncoder()
le.fit(data_encoded[feature])
data_encoded[feature] = le.transform(data_encoded[feature])
categorical_names[feature] = le.classes_
encoders[feature] = le
data_perp = data_encoded
protected_feature = df_temp[0] #'Victim Race'
target_feature = df_temp[1] #'Perpetrator Sex'
# ------Theil index----- Task->13843
from aif360.sklearn.metrics import generalized_entropy_index
Ti_List = []
for items in categorical_names[protected_feature]:
df = data[data[protected_feature]==items]
le = LabelEncoder()
le.fit(df[target_feature])
df[target_feature] = le.transform(df[target_feature])
tf = generalized_entropy_index(df[target_feature], alpha = 1)
tf = round(tf, 4)
Ti_List.append(tf)
global Thi_idx
Thi_idx = Ti_List
#claas_size = categorical_names[protected_feature].size
new_list = [item for item in categorical_names[protected_feature] if not(pd.isnull(item)) == True]
claas_size = len(new_list)
if claas_size > 10:
return 'HeavyFeature'
metrics = fair_metrics(categorical_names, data_perp, protected_feature, target_feature, claas_size)
figure = plot_fair_metrics(metrics)
html_graph = mpld3.fig_to_html(figure,d3_url=d3_url,mpld3_url=mpld3_url)
return html_graph
def fair_metrics(categorical_names, data_perp, protected_feature, target_feature, claas_size):
import aif360
from aif360.datasets import StandardDataset
from aif360.metrics import BinaryLabelDatasetMetric
cols = [metricvalue]
obj_fairness = [[0]]
fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols)
for indx in range(claas_size):
priv_group = categorical_names[protected_feature][indx]
privileged_class = np.where(categorical_names[protected_feature] == priv_group)[0]
data_orig = StandardDataset(data_perp,
label_name=target_feature,
favorable_classes=[1],
protected_attribute_names=[protected_feature],
privileged_classes=[privileged_class])
dataset_pred = data_orig
attr = dataset_pred.protected_attribute_names[0]
idx = dataset_pred.protected_attribute_names.index(attr)
privileged_groups = [{attr:dataset_pred.privileged_protected_attributes[idx][0]}]
unprivileged_size = dataset_pred.unprivileged_protected_attributes[0].size
unprivileged_groups = []
for idx2 in range(unprivileged_size):
unprivileged_groups.extend([{attr:dataset_pred.unprivileged_protected_attributes[idx][idx2]}])
metric_pred = BinaryLabelDatasetMetric(dataset_pred,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
if metricvalue == "Theil Index":
row = pd.DataFrame([Thi_idx[indx]],
columns = cols ,
index = [priv_group])
elif metricvalue == "Disparate Impact":
row = pd.DataFrame([[metric_pred.disparate_impact()]],
columns = cols ,
index = [priv_group])
elif metricvalue == "Statistical Parity Difference":
row = pd.DataFrame([[metric_pred.mean_difference()]],
columns = cols ,
index = [priv_group])
#fair_metrics = fair_metrics.append(row)
fair_metrics = pd.concat([fair_metrics,row])
return fair_metrics
def plot_fair_metrics(fair_metrics):
import matplotlib.patches as patches
plt.style.use('default')
import seaborn as sns
fig, ax = plt.subplots(figsize=(10,4), ncols=1, nrows=1)
plt.subplots_adjust(
left = 0.125,
bottom = 0.1,
right = 0.9,
top = 0.9,
wspace = .5,
hspace = 1.1
)
y_title_margin = 1.2
plt.suptitle("Fairness metrics", y = 1.09, fontsize=20)
sns.set(style="dark")
cols = fair_metrics.columns.values
obj = fair_metrics.loc['objective']
if metricvalue == "Theil Index":
size_rect = [0.5]
rect = [-0.1]
bottom = [-0.1]
top = [2]
bound = [[-0.1,0.1]]
elif metricvalue == "Disparate Impact":
size_rect = [0.4]
rect = [0.8]
bottom = [0]
top = [2]
bound = [[-0.1,0.1]]
elif metricvalue == "Statistical Parity Difference":
size_rect = [0.2]
rect = [-0.1]
bottom = [-1]
top = [1]
bound = [[-0.1,0.1]]
#display(Markdown("### Check bias metrics :"))
#display(Markdown("A model can be considered bias if just one of these five metrics show that this model is biased."))
for attr in fair_metrics.index[0:len(fair_metrics)].values:
#display(Markdown("#### For the %s attribute :"%attr))
check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,1)]
#display(Markdown("With default thresholds, bias against unprivileged group detected in **%d** out of 5 metrics"%(5 - sum(check))))
for i in range(0,1):
plt.subplot(1, 1, i+1)
xx = fair_metrics.index[1:len(fair_metrics)].values.tolist()
yy = fair_metrics.iloc[1:len(fair_metrics)][cols[i]].values.tolist()
palette = sns.color_palette('husl', len(xx))
ax = sns.pointplot(x=fair_metrics.index[1:len(fair_metrics)], y=yy, palette=palette, hue=xx)
index = 0
for p in zip(ax.get_xticks(), yy):
if (p[1] > 2.0):
_color = palette.as_hex()[index]
_val = 'Outlier(' + str(round(p[1],3)) + ')'
ax.text(p[0]-0.5, 0.02, _val, color=_color)
else:
ax.text(p[0], p[1]+0.05, round(p[1],3), color='k')
index = index + 1
plt.ylim(bottom[i], top[i])
plt.setp(ax.patches, linewidth=0)
ax.get_xaxis().set_visible(False)
ax.legend(loc='right', bbox_to_anchor=(1, 0.8), ncol=1)
ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor="green", linewidth=1, linestyle='solid'))
# plt.axhline(obj[i], color='black', alpha=0.3)
plt.title(cols[i], fontname="Times New Roman", size=20,fontweight="bold")
ax.set_ylabel('')
ax.set_xlabel('')
return fig
# ----------------------------
def getDriftDistribution(feature, dataframe, newdataframe=pd.DataFrame()):
try:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy
from scipy import stats
from scipy.stats import norm
import matplotlib.gridspec as gridspec
import math
import io, base64, urllib
np.seterr(divide='ignore', invalid='ignore')
from appbe.eda import ux_eda
eda_obj = ux_eda()
try:
plt.clf()
except:
pass
plt.rcParams.update({'figure.max_open_warning': 0})
sns.set(color_codes=True)
pandasNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
if len(feature) > 4:
numneroffeatures = len(feature)
plt.figure(figsize=(10, numneroffeatures*2))
else:
plt.figure(figsize=(10,5))
for i in enumerate(feature):
dataType = dataframe[i[1]].dtypes
if dataType not in pandasNumericDtypes:
dataframe[i[1]] = pd.Categorical(dataframe[i[1]])
dataframe[i[1]] = dataframe[i[1]].cat.codes
dataframe[i[1]] = dataframe[i[1]].astype(int)
dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mode()[0])
else:
dataframe[i[1]] = dataframe[i[1]].fillna(dataframe[i[1]].mean())
plt.subplots_adjust(hspace=0.5, wspace=0.7, top=1)
plt.subplot(math.ceil((len(feature) / 2)), 2, i[0] + 1)
distname, sse = eda_obj.DistributionFinder(dataframe[i[1]])
try:
ax = sns.distplot(dataframe[i[1]], label=distname)
ax.legend(loc='best')
if newdataframe.empty == False:
dataType = newdataframe[i[1]].dtypes
if dataType not in pandasNumericDtypes:
newdataframe[i[1]] = pd.Categorical(newdataframe[i[1]])
newdataframe[i[1]] = newdataframe[i[1]].cat.codes
newdataframe[i[1]] = newdataframe[i[1]].astype(int)
newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mode()[0])
else:
newdataframe[i[1]] = newdataframe[i[1]].fillna(newdataframe[i[1]].mean())
distname, sse = distribution.DistributionFinder(newdataframe[i[1]])
ax = sns.distplot(newdataframe[i[1]], label=distname)
ax.legend(loc='best')
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno))
pass
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
string = base64.b64encode(buf.read())
uri = urllib.parse.quote(string)
return uri
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
def getCategoryWordCloud(df):
labels = df.Label.unique()
df_output = pd.DataFrame()
tcolumns=['text']
for label in labels:
df2 = df[df['Label'] == label]
df2 = df2.reset_index()
wordcloud,df_text = getWordCloud(df2,tcolumns)
newrow = {'Label':label,'wordCloud':wordcloud}
df_output = df_output.append(newrow,ignore_index=True)
return(df_output)
def getHighlyCorrelatedFeatureCharts(df, df_top):
numOfRows = df.shape[0]
cratio = 0.01
if (numOfRows < 1000):
cratio = 0.2
elif (numOfRows < 10000):
cratio = 0.1
elif (numOfRows < 100000):
cratio = 0.01
barcolor = ["red", "green", "blue", "goldenrod", "magenta"]
ffig = make_subplots(rows=2, cols=3)
height = 800
rowno = 1
colno = 1
featureCharts = []
try:
for index, row in df_top.iterrows():
feature1 = row['FEATURE_1']
feature2 = row['FEATURE_2']
df_temp = df[[feature1, feature2]]
feature1data = df_temp[feature1]
feature2data = df_temp[feature2]
nUnique = len(feature1data.unique().tolist())
if nUnique / numOfRows >= cratio:
feature1type = 'Continous'
else:
feature1type = 'Category'
nUnique = len(feature2data.unique().tolist())
if nUnique / numOfRows >= cratio:
feature2type = 'Continous'
else:
feature2type = 'Category'
charttype = 0
if feature1type == 'Continous' and feature2type == 'Continous':
df_temp[feature1] = pd.qcut(df_temp[feature1], q=8, duplicates='drop',precision=0)
df_temp[feature1] = df_temp[feature1].astype(str).str.strip('()[]')
feature1type = 'Category'
xaxis = feature1
yaxis = feature2
charttype = 1
if feature1type == 'Category' and feature2type == 'Continous':
xaxis = feature1
yaxis = feature2
charttype = 1
if feature1type == 'Continous' and feature2type == 'Category':
xaxis = feature1 #xaxis = feature2
yaxis = feature2 #yaxis = feature1
charttype = 1
if feature1type == 'Category' and feature2type == 'Category':
if (len(feature1data.unique().tolist()) < len(feature2data.unique().tolist())):
xaxis = feature1 #xaxis = feature2
yaxis = feature2 #yaxis = feature1
else:
xaxis = feature1
yaxis = feature2
if (len(df_temp[xaxis].unique().tolist()) > 5):
df_temp[xaxis] = pd.qcut(df_temp[xaxis], q=5, duplicates='drop',precision=0)
df_temp[xaxis] = df_temp[xaxis].astype(str).str.strip('()[]')
if (len(df_temp[yaxis].unique().tolist()) > 5):
df_temp[yaxis] = pd.qcut(df_temp[yaxis], q=3, duplicates='drop',precision=0)
df_temp[yaxis] = df_temp[yaxis].astype(str).str.strip('()[]')
charttype = 2
# if feature1type == 'Category' and feature2type == 'Category':
if charttype == 2:
uniqueclasses = df_temp[yaxis].unique().tolist()
cfig = go.Figure()
i = 1
for x in uniqueclasses:
df_temp3 = df_temp.loc[df_temp[yaxis] == x]
df_temp2 = df_temp3.groupby(xaxis, as_index=False)[yaxis].count()
if df_temp2[xaxis].dtypes == "object":
df_temp2 = df_temp2.set_index(xaxis).reindex(
natsorted(df_temp2[xaxis].tolist(), key=lambda y: y.lower())).reset_index()
xaxis_data = df_temp2[xaxis].tolist()
yaxis_data = df_temp2[yaxis].tolist()
cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name=x, marker_color=barcolor[i]))
i = i + 1
if i == 5:
break
cfig.update_layout(barmode='stack', xaxis_title=xaxis, yaxis_title=yaxis)
bargraph = cfig.to_html(full_html=False, default_height=450, default_width=400)
featureCharts.append(bargraph)
if charttype == 1:
df_temp2 = df_temp.groupby(xaxis, as_index=False)[yaxis].mean()
if df_temp2[xaxis].dtypes == "object":
df_temp2 = df_temp2.set_index(xaxis).reindex(
natsorted(df_temp2[xaxis].tolist(), key=lambda y: y.lower())).reset_index()
xaxis_data = df_temp2[xaxis].tolist()
yaxis_data = df_temp2[yaxis].tolist()
cfig = go.Figure()
cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Primary Product', marker_color='blue'))
cfig.update_layout(xaxis_title=xaxis, yaxis_title=yaxis)
bargraph = cfig.to_html(full_html=False, default_height=450, default_width=400)
featureCharts.append(bargraph)
colno += 1
if colno > 3:
colno = 1
rowno += 1
except Exception as e:
print(e)
return (featureCharts)
# EDA Visualization changes
# ----------------------------
def getFeatureCharts(df, graphType, d3_url,mpld3_url):
featureCharts = []
feature1 = df.columns[0]
feature2 = df.columns[1]
import seaborn as sns
import mpld3
fig, ax = plt.subplots(figsize=[10,5])
if graphType == 'marker':
df.plot(ax=ax, marker='o')
# df[['age','education-num']].plot(ax=ax, marker='o')
if graphType == 'area':
df.plot(ax=ax, kind ="area")
# df[['education-num','age']].plot(ax=ax, kind ="area") # UIprb
if graphType == 'hexbin':
df.plot.hexbin(ax=ax, x=feature1, y=feature2, gridsize=2)
if graphType == 'boxplot':
plt.boxplot(df)
if graphType == 'scatter':
ax.scatter(df[feature1], df[feature2])
if graphType == 'regplot':
ax = sns.regplot(x= feature1, y=feature2, data= df, fit_reg = False, scatter_kws={"alpha": 0.5})
if graphType == 'lineplot':
ax = sns.lineplot(x= feature1, y=feature2, data= df)
if graphType == 'barplot':
ax = sns.barplot(x= feature1, y=feature2, data= df)
# ax = sns.barplot(x= 'age', y='fnlwgt', data= df) #Start_prb
ax.legend()
ax.set_xlabel(feature1)
ax.set_ylabel(feature2)
#print(d3_url)
#print(mpld3_url)
html_graph = mpld3.fig_to_html(fig,d3_url=d3_url,mpld3_url=mpld3_url)
if graphType == 'kde':
ax = sns.pairplot(df, kind="kde", height=4, x_vars=feature1,y_vars = feature2)
# ax = sns.pairplot(df[['age','fnlwgt']], kind="kde")
html_graph = mpld3.fig_to_html(ax.fig)
if graphType == 'relplot':
sns.set(style ="darkgrid")
ax = sns.relplot(x =feature1, y =feature2, data = df)
html_graph = mpld3.fig_to_html(ax.fig)
featureCharts.append(html_graph)
return (featureCharts)
# ----------------------------
def MostCommonWords(stopwords, inputCorpus, num_of_words=10):
try:
from collections import Counter
new = inputCorpus.str.split()
new = new.values.tolist()
corpus = [word for i in new for word in i if word not in stopwords]
counter = Counter(corpus)
most = counter.most_common()
x, y = [], []
for word, count in most[: num_of_words + 1]:
x.append(word)
y.append(count)
return pd.DataFrame([x, y], index=['most_common_words', 'freq']).T
except:
print("exception", sys.exc_info())
return False
def removeFeature(df):
featuresList = df.columns.values.tolist()
modelFeatures = featuresList.copy()
datetimeFeatures = []
sequenceFeatures = []
unimportantFeatures = []
featuresRatio = {}
for i in featuresList:
check = match_date_format(df[i])
if check == True:
modelFeatures.remove(i)
continue
seq_check = check_seq_feature(df[i])
if seq_check == True:
modelFeatures.remove(i)
continue
ratio = check_category(df[i])
if ratio != 0:
featuresRatio[i] = ratio
else:
modelFeatures.remove(i)
return featuresList, modelFeatures
def check_category(data):
total_record = len(data)
nUnique = len(data.unique().tolist())
if nUnique == 1:
return 0
ratio = nUnique / total_record
return (ratio)
def check_seq_feature(data):
if data.dtypes in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']:
total_record = data.count()
count = (data - data.shift() == 1).sum()
if ((total_record - count) == 1):
return True
return False
def match_date_format(data):
data = data.astype(str)
beforecheckcount = (data.count()*80)/100
#####YYYY-MM-DD HH:MM:SS####
check1 = data[data.str.match(
r'(^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check1.count()
if (beforecheckcount <= aftercheckcount):
return True
#####MM/DD/YYYY HH:MM####
check2 = data[data.str.match(
r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount <= aftercheckcount):
return True
#####DD-MM-YYYY HH:MM####
check2 = data[data.str.match(
r'(^(0?[1-9]|[12][0-9]|3[01])-(0?[1-9]|1[0-2])-(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount <= aftercheckcount):
return True
#####YYYY/MM/DD####
check2 = data[data.str.match(r'(^\d\d\d\d/(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount <= aftercheckcount):
return True
#####MM/DD/YYYY####
check2 = data[data.str.match(r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d)$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount <= aftercheckcount):
return True
return False
def check_text_features(df, modelFeatures):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
textFeature = []
for i in enumerate(modelFeatures):
dataType = df[i[1]].dtypes
numOfRows = df.shape[0]
if dataType not in aionNumericDtypes:
if dataType != 'bool':
nUnique = len(df[i[1]].unique().tolist())
textnumbericratio = 0.01
if (numOfRows < 1000):
textnumbericratio = 0.2
elif (numOfRows < 10000):
textnumbericratio = 0.1
elif (numOfRows < 100000):
textnumbericratio = 0.01
if nUnique / numOfRows >= textnumbericratio:
textFeature.append(i[1])
return (textFeature)
def getWordCloud(df, text_columns):
df_text = pd.DataFrame()
stopwords = set(STOPWORDS)
if (len(text_columns) > 1):
df_text['combined'] = df[text_columns].apply(lambda row: ' '.join(row.values.astype(str)), axis=1)
features = ['combined']
else:
df_text[['combined']] = df[text_columns]
features = ['combined']
df_text[features[0]] = df_text[features[0]].fillna("NA")
textCorpus = df_text[features[0]]
from text import TextProcessing
tp = TextProcessing.TextProcessing()
preprocessed_text = tp.transform(textCorpus)
df_text['combined'] = preprocessed_text
df_text_list = df_text.values.tolist()
comment_words = ""
for val in df_text_list:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += " ".join(tokens) + " "
wordcloud = WordCloud(stopwords=stopwords).generate(comment_words)
try:
plt.clf()
except:
pass
try:
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.tight_layout(pad=0)
image = io.BytesIO()
plt.savefig(image, format='png')
image.seek(0)
string = base64.b64encode(image.read())
image_64 = 'data:image/png;base64,' + urllib.parse.quote(string)
except Exception as inst:
print(inst)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
image_64=''
return (image_64, df_text)
def getTopTextFeatures(df_text):
stopwords = set(STOPWORDS)
commonfeatures = MostCommonWords(stopwords, df_text['combined'])
xaxis_data = commonfeatures['most_common_words'].tolist()
yaxis_data = commonfeatures['freq'].tolist()
import plotly.graph_objects as go
cfig = go.Figure()
cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance'))
cfig.update_layout(barmode='stack', xaxis_title='Features')
bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000)
return (bargraph)
def getPCATop10Features(df, modelFeatures):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
categorial_features = []
for i in enumerate(modelFeatures):
dataType = df[i[1]].dtypes
if dataType not in aionNumericDtypes:
categorial_features.append(i[1])
df[i[1]] = pd.Categorical(df[i[1]])
df[i[1]] = df[i[1]].cat.codes
df[i[1]] = df[i[1]].astype(int)
df[i[1]] = df[i[1]].fillna(df[i[1]].mode()[0])
else:
df[i[1]] = df[i[1]].fillna(df[i[1]].mean())
from sklearn.decomposition import PCA
pca = PCA(n_components=2).fit(df)
map = pd.DataFrame(pca.components_, columns=modelFeatures)
map = map.diff(axis=0).abs()
map = map.iloc[1]
map = map.sort_values(ascending=False).head(10)
yaxis_data = map.tolist()
xaxis_data = map.index.values.tolist()
import plotly.graph_objects as go
cfig = go.Figure()
cfig.add_trace(go.Bar(x=xaxis_data, y=yaxis_data, name='Feature Importance'))
cfig.update_layout(barmode='stack', xaxis_title='Features')
bargraph = cfig.to_html(full_html=False, default_height=450, default_width=1000)
return (bargraph)
def getCorrelationMatrix(df):
try:
#from dython.nominal import associations
if len(df.columns) > 25:
df3 = df[df.columns[0:24]]
else:
df3 = df.copy()
cor_mat= associations(df3,compute_only=True)
cor_mat=cor_mat['corr']
#cor_mat = df3.corr()
cor_mat = cor_mat.astype(float).round(2)
#print(cor_mat)
z = cor_mat.values.tolist()
fig = ff.create_annotated_heatmap(z, x=cor_mat.columns.tolist(), y=cor_mat.index.tolist(), annotation_text=z,
colorscale='Blues')
fig.layout.yaxis.automargin = True
correlationgraph = fig.to_html(full_html=True, default_height=450, default_width=1000)
except Exception as e:
print(e)
correlationgraph = ''
return (correlationgraph)
|
publish.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from appbe.data_io import sqlite_db
from os.path import expanduser
import platform
import pandas as pd
import os
from appbe.dataPath import DATA_DIR
PUBLISH_PATH = os.path.join(DATA_DIR,'publish')
DEPLOY_DATABASE_PATH = os.path.join(DATA_DIR,'sqlite')
def chech_publish_info(usecasename):
version = 0
status = 'Not Published'
inputDriftStatus = 'No Drift'
MODEL_DEPLOY_DATABASE_PATH = os.path.join(PUBLISH_PATH,usecasename,'database')
sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db')
if sqlite_dbObj.table_exists('publish'):
data = sqlite_dbObj.read('publish',"usecase = '"+usecasename+"' and status = 'Published'")
if data.shape[0] > 0:
model_sqlite_dbObj = sqlite_db(MODEL_DEPLOY_DATABASE_PATH,'deploy.db')
version = data['version'].iloc[0]
status = 'Published'
if model_sqlite_dbObj.table_exists('monitoring'):
data = model_sqlite_dbObj.read('monitoring',"version = '"+str(version)+"'")
if data.shape[0] > 0:
msg = data['Msg'].iloc[-1]
if 'Affected Columns' in msg:
inputDriftStatus = 'Input Drift Found'
return version,status,inputDriftStatus
def check_input_data(usecasename):
MODEL_DEPLOY_DATABASE_PATH = os.path.join(PUBLISH_PATH,usecasename,'database')
sqlite_dbObj = sqlite_db(DEPLOY_DATABASE_PATH,'deploy.db')
data = pd.DataFrame()
if sqlite_dbObj.table_exists('publish'):
dataa = sqlite_dbObj.read('publish',"usecase = '"+usecasename+"' and status = 'Published'")
if dataa.shape[0] > 0:
modelsqlite_dbObj = sqlite_db(MODEL_DEPLOY_DATABASE_PATH,'deploy.db')
if modelsqlite_dbObj.table_exists('prodData'):
data = modelsqlite_dbObj.read('prodData')
return data
|
mlstyles.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
from os.path import expanduser
import platform
import json
import subprocess
import re
import sys
import pandas as pd
from django.http import HttpResponse
from appbe.dataPath import DATA_DIR
Usecaselocation = os.path.join(DATA_DIR,'Usecases')
def mlstyles(request):
try:
from appbe.aion_config import settings
usecasetab = settings()
selectid = request.GET['usecaseid']
configFile = os.path.join(Usecaselocation, 'usecases.json')
f = open(configFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
#usecase = configSettingsJson['usecaselist']
desciption=""
usecasename=""
found = False
for v_id in configSettingsJson['verticallist']:
for p_id in v_id['usecaselist']:
usecaseid = p_id.get('usecaseid')
if str(usecaseid) == str(selectid) :
usecasename = p_id.get('usecasename')
desciption = p_id.get('desciption')
usecaseid = p_id.get('usecaseid')
iconname = p_id.get('iconname')
prediction_input = p_id.get('prediction_input')
outputtype = p_id.get('outputtype')
smalldescription = p_id.get('smalldescription')
trainingFeatures = p_id.get('trainingFeatures','None')
if trainingFeatures != 'None':
trainingFeatures = trainingFeatures.split(',')
found = True
break
if found == True:
break
#print(usecaseid,selectid)
context ={'usecasename':usecasename,'desciption':desciption,'prediction_input':prediction_input,'usecaseid':usecaseid,'trainingFeatures':trainingFeatures,'iconname':iconname,'smalldescription':smalldescription,'outputtype':outputtype,'usecasetab':usecasetab}
return context
except Exception as inst:
print(inst)
context = { 'error3':'error3','error1': "No UseCases to show"}
return context
def getusecasedetails(selectid):
configFile = os.path.join(Usecaselocation, 'usecases.json')
f = open(configFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
#usecase = configSettingsJson['usecaselist']
desciption=""
usecasename=""
found = False
for v_id in configSettingsJson['verticallist']:
for p_id in v_id['usecaselist']:
usecaseid = p_id.get('usecaseid')
if str(usecaseid) == str(selectid) :
usecasename = p_id.get('usecasename')
desciption = p_id.get('desciption')
usecaseid = p_id.get('usecaseid')
modelConfig = p_id.get('modelConfig')
folder = p_id.get('folder')
prediction = p_id.get('prediction')
prediction_input = p_id.get('prediction_input')
ai_modeldata = p_id.get('modeldata')
outputtype = p_id.get('outputtype')
smalldescription = p_id.get('smalldescription')
prediction_template = p_id.get('prediction_template')
trainingFeatures = p_id.get('trainingFeatures','None')
if trainingFeatures != 'None':
trainingFeatures = trainingFeatures.split(',')
found = True
break
if found == True:
break
#print(usecasename)
return(usecasename,desciption,usecaseid,modelConfig,folder,prediction,prediction_input,ai_modeldata,outputtype,smalldescription,prediction_template,trainingFeatures)
def mlpredict(request):
selectid=request.POST.get('usecaseid')
mlpredict =request.POST.get('mlpredict')
usecasename,desciption,usecaseid,modelConfig,folder,prediction,prediction_input,ai_modeldata,outputtype,smalldescription,prediction_template,trainingFeatures = getusecasedetails(selectid)
from appbe.aion_config import settings
usecasetab = settings()
usecasename = usecasename
desciption = desciption
input=''
for x in prediction_input:
if input != '':
input += ','
input = request.POST.get(x['name'])
if mlpredict in ['prediction','predictsingle']:
if mlpredict == 'prediction':
dataFile = request.POST.get('predictfilePath')
if(os.path.isfile(dataFile) == False) or dataFile=="":
context = {'usecaseid':selectid ,'dataFile':dataFile,'usecasename':usecasename,'desciption':desciption , 'error1': 'Please enter a valid csv filepath','usecasetab':usecasetab}
return context, mlpredict
else:
inputFieldsDict = {}
for feature in trainingFeatures:
inputFieldsDict[feature] = request.POST.get(feature)
dataFile = json.dumps(inputFieldsDict)
try:
predictionScriptPath= os.path.join(Usecaselocation,folder,'model',prediction)
# predictionScriptPath = os.path.join(predictionscript, 'aion_prediction.py')
outputStr = subprocess.check_output([sys.executable, predictionScriptPath, dataFile,input])
outputStr = outputStr.decode('utf-8')
outputStr = re.search(r'predictions:(.*)', str(outputStr), re.IGNORECASE).group(1)
outputStr = outputStr.strip()
predict_dict = json.loads(outputStr)
#print(predict_dict)
heading =''
timetaken=''
print(predict_dict)
if (predict_dict['status'] == 'SUCCESS'):
predictionResults = predict_dict['data']
#print(predictionResults)
if 'heading' in predict_dict:
heading = predict_dict['heading']
if 'Time' in predict_dict:
timetaken = round(predict_dict['Time'],2)
if outputtype.lower() in ['similarityidentification','contextualsearch']:
data = predictionResults[0]
predictionResults= []
Results={}
prediction = data['prediction']
i = 1
for x in prediction:
te = ''
for y in x:
info = (str(x[y])[:100] + '...') if len(str(x[y])) > 100 else str(x[y])
te += y+': '+info+'\n\n'
Results[i] = te
i = i+1
predictionResults.append(Results)
else:
context = {'usecaseid':selectid ,'dataFile':dataFile,'prediction_input':prediction_input,'usecasename':usecasename,'desciption':desciption , 'error': 'Failed To perform prediction','usecasetab':usecasetab}
return context, mlpredict
print(heading)
context = {'usecasename':usecasename,'desciption':desciption,'prediction_input':prediction_input,'usecaseid':selectid ,'dataFile':dataFile,'predictionResults': predictionResults,'outputtype':outputtype,'heading':heading,'timetaken':timetaken,'usecasetab':usecasetab,'trainingFeatures':trainingFeatures}
return context, mlpredict
except Exception as inst:
print(inst)
context = { 'usecaseid':selectid ,'dataFile':dataFile,'usecasename':usecasename,'desciption':desciption ,'errorp': 'Failed To perform prediction','usecasetab':usecasetab}
return context, mlpredict
if mlpredict == 'download_predict':
# predictionResults = 'C:\\DataSets\\Classification\\bug_severity_class.csv'
try:
csvdata= os.path.join(Usecaselocation,folder,'Data',prediction_template)
if os.path.isfile(csvdata) and os.path.exists(csvdata):
df = pd.read_csv(csvdata,encoding='utf8',encoding_errors= 'replace')
downloadFileName = usecasename.replace(" ", "_") + '_predict.csv'
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename='+downloadFileName
df.to_csv(response, index=False)
return response,mlpredict
else:
context = {'usecaseid':selectid ,'dataFile':dataFile,'usecasename':usecasename,'desciption':desciption, 'error': 'File not found','usecasetab':usecasetab}
return context, mlpredict
except Exception as inst:
context = { 'usecaseid':selectid ,'usecasename':usecasename,'desciption':desciption, 'error3':'error3','error1': 'Failed To Download','usecasetab':usecasetab}
return context, mltrain
def process(data):
cleaned_data = {"verticallist":[]}
for vertical in data['verticallist']:
updated_list = []
for usecase in vertical['usecaselist']:
if usecase['prediction'] and usecase['prediction'] != "Not Implemented":
updated_list.append(usecase)
if updated_list:
cleaned_data['verticallist'].append({'id':vertical['id'],'name':vertical['name'],'usecaselist':updated_list})
return cleaned_data
def Aiusecases(request,selectedoption='Implemented'):
try:
from appbe.aion_config import settings
usecasetab = settings()
configFile = os.path.join(Usecaselocation, 'usecases.json')
f = open(configFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
if selectedoption == 'Implemented':
configSettingsJson = process(configSettingsJson)
usecasedetails = configSettingsJson['verticallist']
context ={'desciption1':usecasedetails,'selected':'AIusecases','usecasetab':usecasetab}
return context
except Exception as e:
print(e)
context ={'error':"No Usecases to Show",'selected':'AIusecases','usecasetab':usecasetab}
return context
def mltrain(request):
from appbe.aion_config import settings
usecasetab = settings()
selectid =request.POST.get('usecaseid1')
mltrain =request.POST.get('mltrain')
usecasename,desciption,usecaseid,modelConfig,folder,prediction,prediction_input,ai_modeldata,outputtype,smalldescription,prediction_template,trainingFeatures = getusecasedetails(selectid)
usecasename = usecasename
desciption = desciption
if mltrain == 'training':
dataFile = request.POST.get('trainfilePath')
if(os.path.isfile(dataFile) == False) or dataFile=="":
context = {'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption ,'error3':'error3','error1': 'Please enter a valid csv filepath'}
return context, mltrain
try:
scriptPath = os.path.join(Usecaselocation,folder,'config','aion_train.py')
print(scriptPath,dataFile)
outputStr = subprocess.check_output([sys.executable, scriptPath, dataFile])
outputStr = outputStr.decode('utf-8')
outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1)
outputStr = outputStr.strip()
train = json.loads(outputStr)
status = train['status']
DeployLocation = train['data']['deployLocation']
ModelType = train['data']['ModelType']
BestModel = train['data']['BestModel']
BestScore = train['data']['BestScore']
ScoreType = train['data']['ScoreType']
FeaturesUsed = train['data']['featuresused']
context={'result':train,'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption,'status':status,'DeployLocation':DeployLocation,'ModelType':ModelType,'BestModel':BestModel,'BestScore':BestScore,'ScoreType':ScoreType,'FeaturesUsed':FeaturesUsed,'result':'result','usecasetab':usecasetab}
return context,mltrain
except Exception as inst:
context = {'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption, 'errort': 'Failed To perform Training','usecasetab':usecasetab}
return context, mltrain
if mltrain == 'download_train':
try:
csvdata= os.path.join(Usecaselocation,folder,'data',ai_modeldata)
#print(csvdata)
if os.path.isfile(csvdata) and os.path.exists(csvdata):
df = pd.read_csv(csvdata,encoding='utf8',encoding_errors= 'replace')
downloadFileName = usecasename.replace(" ", "_") + '_training.csv'
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename='+downloadFileName
df.to_csv(response, index=False)
return response,mltrain
else:
context = {'usecaseid':selectid ,'datatrainFile':dataFile,'usecasename':usecasename,'desciption':desciption, 'error': 'File not found','usecasetab':usecasetab}
return context, mltrain
except Exception as inst:
context = { 'usecaseid':selectid ,'usecasename':usecasename,'desciption':desciption, 'error3':'error3','error1': 'Failed To Download','usecasetab':usecasetab}
return context, mltrain
|
log_ut.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import time
from pathlib import Path
import logging
from datetime import datetime as dt
class logg():
from appbe.dataPath import LOG_LOCATION
def __init__(self, LOG_LOCATION):
self.log_location = LOG_LOCATION
def create_log(self,version):
log_file_path = Path(self.log_location)
log_file_path.mkdir(parents=True, exist_ok=True)
time_stamp = dt.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H-%M-%S')
fileName='log_ux_'+time_stamp+'.log'
filehandler = logging.FileHandler(log_file_path/fileName, 'a','utf-8')
formatter = logging.Formatter('%(asctime)s %(message)s')
filehandler.setFormatter(formatter)
log = logging.getLogger('log_ux')
log.propagate = False
for hdlr in log.handlers[:]: # remove the existing file handlers
if isinstance(hdlr,logging.FileHandler):
log.removeHandler(hdlr)
log.addHandler(filehandler)
log.setLevel(logging.INFO)
log.info('********** AION_'+str(version)+' **********')
return log |
hopkinsStat.py | from typing import Union
import numpy as np
import pandas as pd
from sklearn.neighbors import BallTree
def hopkins(data_frame: Union[np.ndarray, pd.DataFrame], sampling_size: int) -> float:
if type(data_frame) == np.ndarray:
data_frame = pd.DataFrame(data_frame)
data_frame_sample = sample_observation_from_dataset(data_frame, sampling_size)
sample_distances_to_nearest_neighbours = get_distance_sample_to_nearest_neighbours(
data_frame, data_frame_sample
)
uniformly_selected_observations_df = simulate_df_with_same_variation(
data_frame, sampling_size
)
df_distances_to_nearest_neighbours = get_nearest_sample(
data_frame, uniformly_selected_observations_df
)
x = sum(sample_distances_to_nearest_neighbours)
y = sum(df_distances_to_nearest_neighbours)
if x + y == 0:
raise Exception("The denominator of the hopkins statistics is null")
return x / (x + y)[0]
def get_nearest_sample(df: pd.DataFrame, uniformly_selected_observations: pd.DataFrame):
tree = BallTree(df, leaf_size=2)
dist, _ = tree.query(uniformly_selected_observations, k=1)
uniformly_df_distances_to_nearest_neighbours = dist
return uniformly_df_distances_to_nearest_neighbours
def simulate_df_with_same_variation(
df: pd.DataFrame, sampling_size: int
) -> pd.DataFrame:
max_data_frame = df.max()
min_data_frame = df.min()
uniformly_selected_values_0 = np.random.uniform(
min_data_frame[0], max_data_frame[0], sampling_size
)
uniformly_selected_values_1 = np.random.uniform(
min_data_frame[1], max_data_frame[1], sampling_size
)
uniformly_selected_observations = np.column_stack(
(uniformly_selected_values_0, uniformly_selected_values_1)
)
if len(max_data_frame) >= 2:
for i in range(2, len(max_data_frame)):
uniformly_selected_values_i = np.random.uniform(
min_data_frame[i], max_data_frame[i], sampling_size
)
to_stack = (uniformly_selected_observations, uniformly_selected_values_i)
uniformly_selected_observations = np.column_stack(to_stack)
uniformly_selected_observations_df = pd.DataFrame(uniformly_selected_observations)
return uniformly_selected_observations_df
def get_distance_sample_to_nearest_neighbours(df: pd.DataFrame, data_frame_sample):
tree = BallTree(df, leaf_size=2)
dist, _ = tree.query(data_frame_sample, k=2)
data_frame_sample_distances_to_nearest_neighbours = dist[:, 1]
return data_frame_sample_distances_to_nearest_neighbours
def sample_observation_from_dataset(df, sampling_size: int):
if sampling_size > df.shape[0]:
raise Exception("The number of sample of sample is bigger than the shape of D")
data_frame_sample = df.sample(n=sampling_size)
return data_frame_sample
|
basic_Config.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from appbe import exploratory_Analysis as ea
import pandas as pd
from appbe.checkConfiguration import start_check
import json
import os
import ast
import time
import numpy as np
from appfe.modelTraining.models import usecasedetails
from appfe.modelTraining.models import Existusecases
# from modelTraining.models import view
from appbe.aion_config import kafka_setting
from appbe.aion_config import running_setting
from appbe.s3buckets import get_s3_bucket
from appbe.gcsbuckets import get_gcs_bucket
from appbe import help_Text as ht
def is_value_na( value):
if isinstance( value, str):
return value.strip().lower() in ['','na','none']
return not value
def set_ts_preprocessing(request,configSettingsJson): #Task 13052 Timeseries Preprocessing
interpolationType = request.POST.get('interpolationType')
ts_config = configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']
for key in ts_config['interpolation']:
configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['interpolation'][
key] = 'False'
if interpolationType != 'na':
configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['interpolation'][
interpolationType] = 'True'
ts_config['rollingWindow'] = request.POST.get('rollingWindow')
if ts_config['rollingWindow'] == 'True':
ts_config['rollingWindowSize'] = request.POST.get('rollWindowsize')
aggregation = request.POST.get('aaggregationType')
for key in ts_config['aggregation']['type']:
ts_config['aggregation']['type'][key]='False'
if is_value_na(aggregation) == False:
ts_config['aggregation']['type'][aggregation] = 'True'
granularityType = request.POST.get('unitType')
granularitySize = request.POST.get('garnularitysize')
for key in ts_config['aggregation']['granularity']['unit']:
ts_config['aggregation']['granularity']['unit'][key] = 'False'
ts_config['aggregation']['granularity']['unit'][granularityType]='True'
ts_config['aggregation']['granularity']['size'] = granularitySize
configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']= ts_config
return configSettingsJson
def update_granularity(configSettingsJson,datapath=None):
try:
from AION.appbe.utils import set_true_option
import pandas as pd
from pathlib import Path
MINUTES = 60
if not is_value_na(configSettingsJson['basic']['dateTimeFeature']):
if not datapath:
datapath = configSettingsJson['basic']['dataLocation']
if Path( datapath).exists():
df = pd.read_csv(datapath, nrows=2)
if isinstance( configSettingsJson['basic']['dateTimeFeature'], list):
datetime_feature = configSettingsJson['basic']['dateTimeFeature'][0]
else:
datetime_feature = configSettingsJson['basic']['dateTimeFeature']
datetime = pd.to_datetime(df[ datetime_feature])
if len(datetime) > 1:
time_delta = (datetime[1] - datetime[0]).total_seconds()
granularity_unit = configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['granularity']['unit']
if time_delta < (1 * MINUTES):
set_true_option(granularity_unit, key='second')
elif time_delta < (60 * MINUTES):
set_true_option(granularity_unit, key='minute')
elif time_delta < (24 * 60 * MINUTES):
set_true_option(granularity_unit, key='hour')
elif time_delta < (7 * 24 * 60 * MINUTES):
set_true_option(granularity_unit, key='day')
elif time_delta < (30 * 24 * 60 * MINUTES):
set_true_option(granularity_unit, key='week')
elif time_delta < (365 * 24 * 60 * MINUTES):
set_true_option(granularity_unit, key='month')
else:
set_true_option(granularity_unit, key='year')
return configSettingsJson
except Exception as e:
print(f'\nIgnoring error during granularity unit conversion\n:{str(e)}')
return configSettingsJson
def save(request):
try:
status = 'pass'
msg = ""
DEPLOY_LOCATION = request.session['deploylocation']
if request.method == 'POST':
submittype = request.POST.get('BasicSubmit')
if submittype != 'BasicDefault':
filterjson = 'NA'
timegroupingjson = 'NA'
groupingjson = 'NA'
if request.POST.get('filters') != '':
filterjson = str(json.loads(request.POST.get('filters')))
if request.POST.get('timegroup') != '':
timegroupingjson = str(json.loads(request.POST.get('timegroup')))
if request.POST.get('idgroup') != '':
groupingjson = str(json.loads(request.POST.get('idgroup')))
configFile = request.session['config_json']
f = open(configFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
temp = {}
# Retraing settings changes
# -------- S T A R T --------
prbType = request.POST.get('ProblemType')
if prbType is None:
prbType = request.POST.get('tempProblemType')
# temp['ProblemType'] = request.POST.get('ProblemType')
# request.session['Problem'] = request.POST.get('ProblemType')
temp['ProblemType'] = prbType
request.session['Problem'] = request.POST.get('ProblemType')
# ---------------------------
temp['ModelName'] = request.session['usecaseid']
temp['Version'] = str(request.session['ModelVersion'])
temp['InputFeatures'] = request.POST.getlist('IncInputFeatures')
temp['dataLocation'] = str(request.session['datalocation'])
onlinelearning=request.POST.get('onlineLearning',None)
if (onlinelearning is not None):
if onlinelearning.lower() == 'onlinelearning':
configSettingsJson['basic']['onlineLearning'] = 'True'
if onlinelearning.lower() == 'distributedlearning':
configSettingsJson['basic']['distributedLearning'] = 'True'
temp['InputFeatures'] = request.POST.getlist('IncInputFeatures')
temp['TargetFeatures'] = request.POST.getlist('TargetFeatures')
temp['DateTimeFeatures'] = ''
temp['IndexFeatures'] = ''
for x in configSettingsJson['advance']['profiler']['normalization'].keys():
configSettingsJson['advance']['profiler']['normalization'][x] = 'False'
configSettingsJson['advance']['profiler']['normalization']['standardScaler'] = 'True'
for x in configSettingsJson['advance']['profiler']['numericalFillMethod'].keys():
configSettingsJson['advance']['profiler']['numericalFillMethod'][x] = 'False'
configSettingsJson['advance']['profiler']['numericalFillMethod']['Mean'] = 'True'
if onlinelearning.lower() == 'distributedlearning':
for x in configSettingsJson['advance']['profiler']['categoricalFillMethod'].keys():
configSettingsJson['advance']['profiler']['categoricalFillMethod'][x] = 'False'
configSettingsJson['advance']['profiler']['categoricalFillMethod']['MostFrequent'] = 'True'
for x in configSettingsJson['advance']['profiler']['categoryEncoding'].keys():
configSettingsJson['advance']['profiler']['categoryEncoding'][x] = 'False'
configSettingsJson['advance']['profiler']['categoryEncoding']['OneHotEncoding'] = 'True'
configSettingsJson['advance']['profiler']['normalization']['standardScaler'] = 'False'
for x in configSettingsJson['advance']['selector']['featureEngineering'].keys():
if x != 'numberofComponents':
configSettingsJson['advance']['selector']['featureEngineering'][x] = 'False'
elif prbType == 'llmFineTuning':
if configSettingsJson['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'False':
temp['InputFeatures'] = request.POST.getlist('IncInputFeatures')
temp['TargetFeatures'] = request.POST.getlist('TargetFeatures')
contextFeatures = request.POST.getlist('contextFeatures')
configSettingsJson['basic']['contextFeature'] = ",".join([model for model in contextFeatures])
temp['DateTimeFeatures'] = ''
temp['IndexFeatures'] = ''
if request.POST.get('promptfriendlyname') != '':
configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['prompt'] = request.POST.get('promptfriendlyname')
else:
configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['prompt'] = 'Instruction'
if request.POST.get('responsefriendlyname') != '':
configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response'] = request.POST.get('responsefriendlyname')
else:
configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response'] = ''
else:
if request.session['datatype'] == 'LLM_Document':
for x in configSettingsJson['basic']['preprocessing']['llmFineTuning']['document'].keys():
configSettingsJson['basic']['preprocessing']['llmFineTuning']['document'][x] = 'False'
configSettingsJson['basic']['preprocessing']['llmFineTuning']['document'][request.POST.get('dataPreprocessing')] = 'True'
if request.session['datatype'] == 'LLM_Code':
for x in configSettingsJson['basic']['preprocessing']['llmFineTuning']['objective'].keys():
configSettingsJson['basic']['preprocessing']['llmFineTuning']['objective'][x] = 'False'
configSettingsJson['basic']['preprocessing']['llmFineTuning']['objective'][request.POST.get('llmObjective')] = 'True'
for x in configSettingsJson['basic']['preprocessing']['llmFineTuning']['code'].keys():
configSettingsJson['basic']['preprocessing']['llmFineTuning']['code'][x] = 'False'
configSettingsJson['basic']['preprocessing']['llmFineTuning']['code'][request.POST.get('dataPreprocessing')] = 'True'
else:
configSettingsJson['basic']['onlineLearning'] = 'False'
configSettingsJson['basic']['distributedLearning'] = 'False'
temp['InputFeatures'] = request.POST.getlist('InputFeatures')
temp['TargetFeatures'] = request.POST.getlist('TargetFeatures')
temp['DateTimeFeatures'] = request.POST.getlist('DateTimeFeatures')
temp['IndexFeatures'] = request.POST.getlist('IndexFeatures')
if (configSettingsJson['basic']['algorithms']['timeSeriesAnomalyDetection']['AutoEncoder'] == 'True'):#task 11997
if (request.POST.get('analysis') == 'MultiVariate'):
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'True' #task 11997
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'False' #task 11997
else:
#print(configSettingsJson)
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'True'
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'False' #task 11997
temp['UserID'] = ''
temp['ItemID'] = ''
temp['rating'] = ''
temp['secondDocFeature'] = ''
temp['firstDocFeature'] = ''
temp['invoiceNoFeature'] = ''
temp['itemFeature'] = ''
model = ''
if temp['ProblemType'].lower() == 'recommendersystem':
model = request.POST.get('MachineLearningModels')
if model == 'ItemRating':
temp['ProblemType'] = 'RecommenderSystem'
temp['MachineLearningModels'] = ['ItemRating']
temp['DeepLearningModels'] = ''
temp['UserID'] = request.POST.get('UserID')
temp['ItemID'] = request.POST.get('ItemID')
temp['rating'] = request.POST.get('rating')
temp['InputFeatures'] = []
temp['InputFeatures'].append(temp['UserID'])
temp['InputFeatures'].append(temp['ItemID'])
temp['InputFeatures'].append(temp['rating'])
if model == 'TextSimilarity-Siamese':
temp['ProblemType'] = 'recommenderSystem'
temp['MachineLearningModels'] = ['TextSimilarity-Siamese']
temp['secondDocFeature'] = request.POST.get('secondDocFeature')
temp['firstDocFeature'] = request.POST.get('firstDocFeature')
temp['InputFeatures'] = []
temp['InputFeatures'].append(temp['secondDocFeature'])
temp['InputFeatures'].append(temp['firstDocFeature'])
if model == 'AssociationRules-Apriori':
temp['ProblemType'] = 'recommenderSystem'
temp['DeepLearningModels'] = ''
temp['MachineLearningModels'] = ['AssociationRules-Apriori']
temp['invoiceNoFeature'] = request.POST.get('associationRuleInvoiceNo')
temp['itemFeature'] = request.POST.get('associationRuleItem')
temp['InputFeatures'] = []
temp['InputFeatures'].append(temp['invoiceNoFeature'])
temp['InputFeatures'].append(temp['itemFeature'])
temp['ScoringCriteria'] = request.POST.get('ScoringCriteria')
if temp['ProblemType'].lower() not in ['recommendersystem','textsimilarity','associationrules','llmfinetuning']:
temp['MachineLearningModels'] = request.POST.getlist('MachineLearningModels')
temp['DeepLearningModels'] = request.POST.getlist('SelectDeepLearningModels')
elif temp['ProblemType'].lower() == 'llmfinetuning':
temp['MachineLearningModels'] = request.POST.getlist('MachineLearningModels')
model = temp['MachineLearningModels'][0]
supportedModelsSize = configSettingsJson['basic']['modelSize'][temp['ProblemType']][model]
selectedModelSize = request.POST.get('modelSize')
for x in supportedModelsSize.keys():
configSettingsJson['basic']['modelSize'][temp['ProblemType']][model][x] = 'False'
configSettingsJson['basic']['modelSize'][temp['ProblemType']][model][selectedModelSize] = 'True'
temp['noofforecasts'] = request.POST.get('noofforecasts')
temp['inlierLabels'] = request.POST.get('inlierLabels')
#temp['filterExpression'] = request.POST.get('filterExpression')
if temp['ProblemType'].lower() in ['clustering','topicmodelling','similarityidentification','contextualsearch']:
temp['TargetFeatures'] = ''
configSettingsJson['basic']['modelName'] = temp['ModelName']
configSettingsJson['basic']['modelVersion'] = temp['Version']
configSettingsJson['basic']['dataLocation'] = str(temp['dataLocation'])
configSettingsJson['basic']['deployLocation'] = DEPLOY_LOCATION
if configSettingsJson['basic']['preprocessing']['llmFineTuning']['unstructuredData'] == 'False':
configSettingsJson['basic']['trainingFeatures'] = ",".join([model for model in temp['InputFeatures']])
configSettingsJson['basic']['dateTimeFeature'] = ",".join([model for model in temp['DateTimeFeatures']])
configSettingsJson['basic']['targetFeature'] = ",".join([model for model in temp['TargetFeatures']])
configSettingsJson['basic']['indexFeature'] = ",".join([model for model in temp['IndexFeatures']])
if filterjson == 'NA':
configSettingsJson['basic']['filter'] = 'NA'
else:
configSettingsJson['basic']['filter'] = eval(filterjson)
if timegroupingjson == 'NA':
configSettingsJson['basic']['timegrouper'] = 'NA'
else:
configSettingsJson['basic']['timegrouper'] = eval(timegroupingjson)
if groupingjson == 'NA':
configSettingsJson['basic']['group'] = 'NA'
else:
configSettingsJson['basic']['group'] = eval(groupingjson)
problemtyp = configSettingsJson['basic']['analysisType']
for i in list(problemtyp.keys()):
configSettingsJson['basic']['analysisType'][i]='False'
algorithm = configSettingsJson['basic']['algorithms']
for i in list(algorithm.keys()):
for x in list(configSettingsJson['basic']['algorithms'][i].keys()):
if x not in ['textSimilarityConfig','itemRatingConfig','associationRulesConfig','textSummarization']:
configSettingsJson['basic']['algorithms'][i][x] = 'False'
configSettingsJson['basic']['analysisType'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]] = 'True'
# configSettingsJson['basic']['problem_type'] = temp['ProblemType']
scoring = configSettingsJson['basic']['scoringCriteria']
for i in list(scoring.keys()):
for x in list(configSettingsJson['basic']['scoringCriteria'][i].keys()):
configSettingsJson['basic']['scoringCriteria'][i][x] = 'False'
if temp['ProblemType'].lower() in ["classification","regression","survivalanalysis","similarityidentification","timeseriesforecasting","contextualsearch"]: #task 11997
configSettingsJson['basic']['scoringCriteria'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][temp['ScoringCriteria']] = 'True'
# configSettingsJson['basic']['problem_type'] = temp['ProblemType']
# configSettingsJson['basic']['scoringCriteria'] = temp['ScoringCriteria']
configSettingsJson['basic']['noofforecasts'] = temp['noofforecasts']
configSettingsJson['basic']['inlierLabels'] = temp['inlierLabels']
#configSettingsJson['basic']['filterExpression'] = temp['filterExpression']
configSettingsJson['basic']['algorithms']['recommenderSystem']['itemRatingConfig']['userID'] = temp['UserID']
configSettingsJson['basic']['algorithms']['recommenderSystem']['itemRatingConfig']['itemID'] = temp['ItemID']
configSettingsJson['basic']['algorithms']['recommenderSystem']['itemRatingConfig']['rating'] = temp['rating']
configSettingsJson['basic']['algorithms']['recommenderSystem']['textSimilarityConfig']['baseFeature'] = temp['firstDocFeature']
configSettingsJson['basic']['algorithms']['recommenderSystem']['textSimilarityConfig']['comparisonFeature'] = temp['secondDocFeature']
configSettingsJson['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'] = temp['invoiceNoFeature']
configSettingsJson['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'] = temp['itemFeature']
for x in temp['MachineLearningModels']:
if temp['ProblemType'].lower() =='associationrules' or temp['ProblemType'].lower() == 'textsimilarity':
temp['ProblemType'] = 'recommenderSystem'
if request.POST.get('SearchType') != 'NAS' and request.POST.get('SearchType') != 'GoogleModelSearch'and request.POST.get('SearchType') != 'AutoGluon':
configSettingsJson['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][x] = 'True'
#for y in temp['DeepLearningModels']:
# configSettingsJson['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]][y] = 'True'
configSettingsJson['basic']['output']['profilerStage'] = 'True'
configSettingsJson['basic']['output']['selectorStage'] = 'True'
for key in configSettingsJson['advance']['profiler']['textConversionMethod']:
configSettingsJson['advance']['profiler']['textConversionMethod'][key] = 'False'
if temp['ProblemType'].lower() != 'topicmodelling':
configSettingsJson['advance']['profiler']['textConversionMethod']['TF_IDF'] ='True'
else:
configSettingsJson['advance']['profiler']['textConversionMethod']['CountVectors'] ='True'
#print('============================')
#print(temp['ProblemType'].lower())
#print('============================')
if temp['ProblemType'].lower() == 'textsummarization':
configSettingsJson['basic']['algorithms']['textSummarization']['Text Summarization'] = 'True'
configSettingsJson['basic']['textSummarization']['KeyWords'] = str(request.POST.get('addKeywordsForSummarization'))
configSettingsJson['basic']['textSummarization']['pathForKeywordFile'] = str(request.POST.get('DataFilePath'))
if temp['ProblemType'].lower() not in ['recommendersystem','textsummarization','llmfinetuning']:
if configSettingsJson['basic']['onlineLearning'] != 'True' and configSettingsJson['basic']['distributedLearning'] != 'True':
jsonarr =request.POST.get('jsonarr')
res = ast.literal_eval(jsonarr)
for x in res:
if x['type'].lower() == 'text':
configSettingsJson['advance']['selector']['featureSelection']['allFeatures'] = 'False'
configSettingsJson['advance']['selector']['featureSelection']['statisticalBased'] = 'True'
configSettingsJson['advance']['selector']['featureSelection']['modelBased'] = 'False'
if len(request.POST.get('traindfeatures').split(',')) > 30:
configSettingsJson['advance']['selector']['featureSelection']['allFeatures'] = 'False'
configSettingsJson['advance']['selector']['featureSelection']['statisticalBased'] = 'True'
configSettingsJson['advance']['selector']['featureSelection']['modelBased'] = 'False'
configSettingsJson['advance']['profiler']['featureDict'] = res
configSettingsJson['basic']['indexFeature'] = request.POST.get('indexfeatures')
configSettingsJson['basic']['trainingFeatures'] = request.POST.get('traindfeatures')
configSettingsJson['basic']['dateTimeFeature'] = request.POST.get('datefeatures')
if request.POST.get('SearchType') == 'GoogleModelSearch':
configSettingsJson['basic']['algorithms'][temp['ProblemType'][0].lower() + temp['ProblemType'][1:]]['GoogleModelSearch_DNN'] = 'True'
configSettingsJson['basic']['output']['profilerStage']= 'True'
#---------- Time series Changes Task 13052 -----------------
if temp['ProblemType'].lower() == 'timeseriesforecasting':
configSettingsJson = set_ts_preprocessing(request,configSettingsJson)
status,msg= start_check(configSettingsJson)
updatedConfigSettings = json.dumps(configSettingsJson)
updatedConfigFile = request.session['config_json']
with open(updatedConfigFile, "w") as fpWrite:
fpWrite.write(updatedConfigSettings)
fpWrite.close()
request.session['ModelStatus'] = 'Not Trained'
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
request.session['currentstate'] = 1
from appbe.telemetry import UpdateTelemetry
UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'ProblemType',prbType)
UpdateTelemetry(request.session['usecaseid']+'-'+str(request.session['ModelVersion']),'Operation','Configured')
context = {'tab': 'configure', 'temp': temp,'advconfig': configSettingsJson,
'basic_status_msg': 'Configuration Done',
'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,
'currentstate': request.session['currentstate'], 'selected': 'modeltraning','training':True,'basic_help':ht.basic_help}
# return render(request, 'basicconfig.html', context)
if submittype == 'BasicDefault':
temp = {}
temp['ModelName'] = request.session['UseCaseName']
temp['Version'] = request.session['ModelVersion']
dataLocation = str(request.session['datalocation'])
df = pd.read_csv(dataLocation, encoding='latin1')
featuresList = df.columns.values.tolist()
datetimeFeatures = []
sequenceFeatures = []
unimportantFeatures = []
featuresRatio = {}
for i in featuresList:
check = ea.match_date_format(df[i])
if check == True:
datetimeFeatures.append(i)
unimportantFeatures.append(i)
seq_check = ea.check_seq_feature(df[i])
if seq_check == True:
sequenceFeatures.append(i)
unimportantFeatures.append(i)
ratio = ea.check_category(df[i])
if ratio != 0:
featuresRatio[i] = ratio
else:
unimportantFeatures.append(i)
targetFeature = min(featuresRatio, key=featuresRatio.get)
unimportantFeatures.append(targetFeature)
config = {}
config['modelName'] = request.session['UseCaseName']
config['modelVersion'] = request.session['ModelVersion']
config['datetimeFeatures'] = datetimeFeatures
config['sequenceFeatures'] = sequenceFeatures
config['FeaturesList'] = featuresList
config['unimportantFeatures'] = unimportantFeatures
config['targetFeature'] = targetFeature
request.session['currentstate'] = 1
context = {'tab': 'configure', 'temp': temp, 'config': config,
'currentstate': request.session['currentstate'], 'selected': 'modeltraning'}
except Exception as e:
print(e)
import sys
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return status,msg,context
def openbasicconf(request):
# 10012:Decision Threshold related Changes
data_is_under_RAM_threshold = True
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r+")
configSettingsData = f.read()
configSettingsJson = json.loads(configSettingsData)
temp = {}
# temp['ModelName'] = request.session['UseCaseName']
# temp['Version'] = request.session['ModelVersion']
if request.session['datatype'] == 'Video' or request.session['datatype'] == 'Image' or request.session['datatype'] == 'Document':
folderLocation = str(request.session['datalocation'])
dataFile = os.path.join(folderLocation, request.session['csvfullpath'])
else:
dataFile = str(request.session['datalocation'])
# -------------------------------- 10012:Decision Threshold related Changes S T A R T -------------------------------
from appbe.dataIngestion import checkRAMThreshold
data_is_under_RAM_threshold = checkRAMThreshold(request.session['datalocation'])
# ------------------------------------------------------ E N D ------------------------------------------------------
# Retraing settings changes
# -------- S T A R T --------
IsReTrainingCase = False
if request.session['IsRetraining'] == 'Yes':
IsReTrainingCase = True
IsSameFeatures = True
# ---------------------------
featuresList = configSettingsJson['basic']['featureList']
unimportantFeatures = []
modelfeatures = configSettingsJson['basic']['trainingFeatures']
for x in featuresList:
if x not in modelfeatures:
unimportantFeatures.append(x)
config = {}
config['ModelName'] = request.session['usecaseid']
config['Version'] = request.session['ModelVersion']
config['datetimeFeatures'] = configSettingsJson['basic']['dateTimeFeature'] # .split(",")
if configSettingsJson['basic']['indexFeature']:
config['sequenceFeatures'] = configSettingsJson['basic']['indexFeature'] # .split(",")
config['FeaturesList'] = featuresList
config['unimportantFeatures'] = unimportantFeatures
config['targetFeature'] = configSettingsJson['basic']['targetFeature'].split(",")
problemtypes = configSettingsJson['basic']['analysisType']
onlineLearning = configSettingsJson['basic']['onlineLearning']
problem_type = ""
for k in problemtypes.keys():
if configSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
#print('123',problem_type)
config['ProblemType'] = problem_type
# config['ProblemType'] = configSettingsJson['basic']['problem_type']
scoring = configSettingsJson['basic']['scoringCriteria']
scoringCriteria = ""
for k in scoring.keys():
if configSettingsJson['basic']['scoringCriteria'][k] == 'True':
scoringCriteria = k
break
config['ScoringCriteria'] = scoringCriteria
# config['ProblemType'] = configSettingsJson['basic']['problem_type']
# config['ScoringCriteria'] = configSettingsJson['basic']['scoringCriteria']
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
if 'NoOfRecords' in request.session:
records = request.session['NoOfRecords']
else:
records = 'NA'
if request.session['finalstate'] <= 1:
request.session['finalstate'] = 1
request.session['currentstate'] = 1
# dataFile = str(request.session['datalocation'])
# df = pd.read_csv(dataFile,encoding='utf8')
if 'NoOfRecords' in request.session:
noofforecast = 20
else:
noofforecast = 20
config['noofforecasts'] = noofforecast
if 'numericFeature' in request.session:
numericFeature = request.session['numericFeature']
else:
numericFeature = ''
problemType = 'classification'
for key in configSettingsJson['basic']['analysisType']:
if configSettingsJson['basic']['analysisType'][key] == 'True':
problemType = key
break
scoringCreteria = 'NA'
if problemType in ['classification','regression','survivalAnalysis','timeSeriesForecasting']: #task 11997
for key in configSettingsJson['basic']['scoringCriteria'][problemType]:
if configSettingsJson['basic']['scoringCriteria'][problemType][key] == 'True':
scoringCreteria = key
break
selectAlgo = ""
if problemType in ['classification','regression','timeSeriesForecasting',
'timeSeriesAnomalyDetection',
'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition','llmFineTuning']: #task 11997
for key in configSettingsJson['basic']['algorithms'][problemType]:
if configSettingsJson['basic']['algorithms'][problemType][key] == 'True':
if selectAlgo != "":
selectAlgo += ','
selectAlgo += key
modelSize = ''
if problemType == 'llmFineTuning':
for key in configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo].keys():
if configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo][key] == 'True':
modelSize = key
break
featuresdict = [feature['feature'] for feature in configSettingsJson['advance']['profiler']['featureDict']]
context = {'tab': 'tabconfigure','modelSize':modelSize,'featuresdict':featuresdict, 'configsettings': configSettingsJson, 'temp': temp, 'config': config,'numericFeature':numericFeature,'onlineLearning':onlineLearning,
'noOfRecords': records, 'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus,'problemType':problemType,'scoringCreteria':scoringCreteria,'selectAlgo':selectAlgo,
'ModelVersion': ModelVersion, 'currentstate': request.session['currentstate'],
'finalstate': request.session['finalstate'], 'selected': 'modeltraning','IsSameFeatures':IsSameFeatures,'IsReTrainingCase':IsReTrainingCase,'basic_help':ht.basic_help
# 10012:Decision Threshold related changes
, 'DLCheckpoint':data_is_under_RAM_threshold}
return context
def gotoconf(request):
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
try:
# 10012:Decision Threshold related Changes
data_is_under_RAM_threshold = True
ModelName = usecasedetails.objects.get(id=request.session['ModelName'])
Version = request.session['ModelVersion']
import os
if request.session['datatype'] in ['Video', 'Image','Document','Object']:
folderLocation = str(request.session['datalocation'])
dataFile = os.path.join(folderLocation, request.session['csvfullpath'])
else:
dataFile = str(request.session['datalocation'])
# -------------------------------- 10012:Decision Threshold related Changes S T A R T -------------------------------
from appbe.dataIngestion import checkRAMThreshold
data_is_under_RAM_threshold = checkRAMThreshold(request.session['datalocation'])
# ------------------------------------------------------ E N D ------------------------------------------------------
if request.session['datatype'] not in ['LLM_Document','LLM_Code']:
from appbe.eda import ux_eda
if 'delimiter' not in request.session:
request.session['delimiter'] = ','
if 'textqualifier' not in request.session:
request.session['textqualifier'] = '"'
eda_obj = ux_eda(dataFile,request.session['delimiter'],request.session['textqualifier'],optimize=1)
featuresList,datetimeFeatures,sequenceFeatures,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catFeatures = eda_obj.getFeatures()
else:
featuresList = []
featuresList.append('Instruction')
datetimeFeatures=[]
sequenceFeatures=[]
constantFeature=[]
textFeature=[]
targetFeature='Response'
numericCatFeatures = []
numericFeature=[]
catFeatures=[]
featuresListJson = []
for x in featuresList:
featureOperation={}
featureOperation['feature'] = x
if x in datetimeFeatures:
featureOperation['type'] = 'date'
featureOperation['fillMethod'] = 'na'
featureOperation['categoryEncoding'] = 'na'
elif x in textFeature:
featureOperation['type'] = 'text'
featureOperation['fillMethod'] = 'na'
featureOperation['categoryEncoding'] = 'na'
elif x in sequenceFeatures:
featureOperation['type'] = 'index'
featureOperation['fillMethod'] = 'median'
featureOperation['categoryEncoding'] = 'na'
elif (x in catFeatures) or (x in constantFeature):
featureOperation['type'] = 'categorical'
featureOperation['fillMethod'] = 'mode'
featureOperation['categoryEncoding'] = 'targetEncoding'
else:
featureOperation['type'] = 'numerical'
featureOperation['fillMethod'] = 'medium'
featureOperation['categoryEncoding'] = 'na'
featureOperation['outlierDetection'] = 'disable'
featureOperation['outlierOperation'] = 'nochange'
featureOperation['normalizer'] = 'none'
featuresListJson.append(featureOperation)
request.session['numericFeature'] = numericFeature
records = 0
import os
if os.path.isfile(dataFile):
for chunk in pd.read_csv(dataFile, chunksize=20000,encoding="utf-8",encoding_errors= 'replace'):
records = records+len(chunk)
request.session['NoOfRecords'] = records
filetimestamp = str(int(time.time()))
CONFIG_FILE_PATH = request.session['configfilepath']
config_json_filename = os.path.join(CONFIG_FILE_PATH, 'AION_' + filetimestamp + '.json')
outputfile = os.path.join(CONFIG_FILE_PATH, 'AION_OUTPUT_' + filetimestamp + '.json')
request.session['outputfilepath'] = str(outputfile)
modelname = request.session['usecaseid']
modelname = modelname.replace(" ", "_")
DEPLOY_LOCATION = request.session['deploylocation']
request.session['logfilepath'] = os.path.join(DEPLOY_LOCATION, modelname,str(Version),'log','model_training_logs.log')
request.session['config_json'] = config_json_filename
#request.session['ModelVersion'] = Version
request.session['ModelStatus'] = 'Not Trained'
# p = Existusecases(DataFilePath=dataFile, DeployPath=DEPLOY_LOCATION, Status='Not Trained',
# ConfigPath=config_json_filename, Version=Version, ModelName=ModelName,
# TrainOuputLocation=outputfile)
# p.save()
# from AION_UX import telemetry
# telemetry.telemetry_data('UseCaseCreated',modelname+'_'+str(Version),'UseCaseCreated')
# request.session['modelid'] = p.id
temp = {}
temp['ModelName'] = request.session['usecaseid']
temp['Version'] = request.session['ModelVersion']
'''
featuresList = features #df.columns.values.tolist()
datetimeFeatures =
datetimeFeatures = []
sequenceFeatures = []
unimportantFeatures = []
featuresRatio = {}
for i in featuresList:
check = ea.match_date_format(df[i])
if check == True:
datetimeFeatures.append(i)
unimportantFeatures.append(i)
seq_check = ea.check_seq_feature(df[i])
if seq_check == True:
sequenceFeatures.append(i)
unimportantFeatures.append(i)
ratio = ea.check_category(df[i])
if ratio != 0:
featuresRatio[i] = ratio
else:
unimportantFeatures.append(i)
targetFeature = min(featuresRatio, key=featuresRatio.get)
unimportantFeatures.append(targetFeature)
'''
unimportantFeatures = list(datetimeFeatures)
unimportantFeatures.extend(sequenceFeatures)
#unimportantFeatures = list(set(unimportantFeatures) + set(sequenceFeatures))
unimportantFeatures.append(targetFeature)
config = {}
noofforecast = 20
config['ModelName'] = request.session['usecaseid']
config['Version'] = request.session['ModelVersion']
config['datetimeFeatures'] = datetimeFeatures
config['sequenceFeatures'] = sequenceFeatures
config['FeaturesList'] = featuresList
config['unimportantFeatures'] = unimportantFeatures
config['targetFeature'] = targetFeature
config['noofforecasts'] = noofforecast
DEFAULT_FILE_PATH = request.session['defaultfilepath']
# Retraing settings changes
# -------- S T A R T --------
IsReTrainingCase = False
if request.session['IsRetraining'] == 'Yes':
id = request.session['ModelName']
p = usecasedetails.objects.get(id=id)
model = Existusecases.objects.filter(ModelName=p)
indexVal = model.count() - 1
configFile = str(model[indexVal].ConfigPath)
# configFile = str(model[0].ConfigPath)
# request.session['IsRetraining'] = 'No'
IsReTrainingCase = True
# ---------------------------
else:
configFile = os.path.join(DEFAULT_FILE_PATH, 'aion_config.json')
f = open(configFile, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
# Retraing settings changes
# -------- S T A R T --------
pickDefaultSettings = False
IsSameFeatures = False
if 'featureList' not in configSettingsJson['basic']:
pickDefaultSettings = True
IsSameFeatures = True
else:
if configSettingsJson['basic']['featureList'] == featuresList:
pickDefaultSettings = False
IsSameFeatures = True
else:
pickDefaultSettings = True
if pickDefaultSettings:
# ---------------------------
configSettingsJson['basic']['featureList'] = featuresList
configSettingsJson['basic']['dateTimeFeature'] = ",".join([feature for feature in datetimeFeatures])
configSettingsJson['basic']['indexFeature'] = sequenceFeatures
trainingFeatures = list(set(featuresList) - set(unimportantFeatures))
configSettingsJson['basic']['trainingFeatures'] = ",".join([feature for feature in trainingFeatures])
configSettingsJson['basic']['targetFeature'] = targetFeature
if request.session['datatype'].lower() in ['video','image','object','document','llm_document','llm_code']:
for x in configSettingsJson['basic']['analysisType'].keys():
configSettingsJson['basic']['analysisType'][x] = 'False'
configSettingsJson['basic']['folderSettings']['fileType'] = request.session['datatype']
configSettingsJson['basic']['folderSettings']['labelDataFile'] = request.session['csvfullpath']
configSettingsJson['basic']['folderSettings']['fileExtension'] = request.session['fileExtension']
if request.session['datatype'] in ['LLM_Document','LLM_Code']:
configSettingsJson['basic']['analysisType']['llmFineTuning'] = 'True'
configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['prompt']='Instruction'
configSettingsJson['basic']['preprocessing']['llmFineTuning']['friendlyNames']['response']='Response'
configSettingsJson['basic']['preprocessing']['llmFineTuning']['unstructuredData'] = 'True'
elif request.session['datatype'] == 'Video':
configSettingsJson['basic']['analysisType']['videoForecasting'] = 'True'
elif request.session['datatype'] == 'Image':
configSettingsJson['basic']['analysisType']['imageClassification'] = 'True'
elif request.session['datatype'] == 'Object':
configSettingsJson['basic']['analysisType']['objectDetection'] = 'True'
elif request.session['datatype'].lower() == 'document':
df = pd.read_csv(dataFile, encoding='utf8',sep=request.session['delimiter'],quotechar=request.session['textqualifier'],nrows=100)
noOfEmotyLevels = 0
shape = df.shape
if shape[1] == 2:
noOfEmotyLevels = df['Label'].isnull().sum()
#print(noOfEmotyLevels)
if noOfEmotyLevels == 100:
configSettingsJson['basic']['analysisType']['topicModelling'] = 'True'
else:
configSettingsJson['basic']['analysisType']['classification'] = 'True'
else:
if 'uploadfiletype' in request.session:
configSettingsJson['basic']['folderSettings']['fileType'] = request.session['uploadfiletype']
configSettingsJson['basic']['folderSettings']['labelDataFile'] = request.session['uploadLocation']
try:
if isinstance(datetimeFeatures, list):
if len(datetimeFeatures) != 0:
configSettingsJson = update_granularity(configSettingsJson,datapath=dataFile)
elif isinstance(datetimeFeatures, str):
if datetimeFeatures != '':
configSettingsJson = update_granularity(configSettingsJson,datapath=dataFile)
except:
pass
# Retraing settings changes
# -------- S T A R T --------
tot_count=len(numericCatFeatures)
#task 11997
if (tot_count > 1):
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'True'
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'False'
else:
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['uniVariate'] = 'True'
configSettingsJson['basic']['analysisApproach']['timeSeriesAnomalyDetection']['AutoEncoder']['multiVariate'] = 'False'
if 'delimiter' in request.session:
configSettingsJson['basic']['fileSettings']['delimiters'] = request.session['delimiter']
else:
configSettingsJson['basic']['fileSettings']['delimiters'] = ','
if 'textqualifier' in request.session:
configSettingsJson['basic']['fileSettings']['textqualifier'] = request.session['textqualifier']
else:
request.session['textqualifier'] = '"'
configSettingsJson['advance']['profiler']['featureDict'] = featuresListJson
configSettingsJson['basic']['onlineLearning'] = 'False'
configSettingsJson['basic']['dataLocation'] = request.session['datalocation']
configSettingsJson['basic']['noOfRecords'] = request.session['NoOfRecords']
onlineLearning = configSettingsJson['basic']['onlineLearning']
updatedConfigSettings = json.dumps(configSettingsJson)
with open(config_json_filename, "w") as fpWrite:
fpWrite.write(updatedConfigSettings)
fpWrite.close()
'''
p = Existusecases(DataFilePath=dataFile, DeployPath=DEPLOY_LOCATION, Status='Not Trained',
ConfigPath=config_json_filename, Version=Version, ModelName=ModelName,
TrainOuputLocation=outputfile)
p.save()
'''
p = Existusecases.objects.get(ModelName=ModelName,Version=Version)
p.DataFilePath = dataFile
p.DeployPath = DEPLOY_LOCATION
p.ConfigPath = config_json_filename
p.TrainOuputLocation = outputfile
p.save()
#from appbe import telemetry
#telemetry.telemetry_data('UseCaseCreated',modelname+'_'+str(Version),'UseCaseCreated')
request.session['modelid'] = p.id
# ---------------------------
from appbe.compute import selectedInfratructure
infra = selectedInfratructure()
if infra.lower() in ['aws','gcp']:
problemType = 'llmFineTuning'
else:
problemType = 'classification'
#print(problemType)
for key in configSettingsJson['basic']['analysisType']:
if configSettingsJson['basic']['analysisType'][key] == 'True':
problemType = key
break
scoringCreteria = 'NA'
if problemType in ['classification','regression','survivalAnalysis','timeSeriesForecasting']: #task 11997
for key in configSettingsJson['basic']['scoringCriteria'][problemType]:
if configSettingsJson['basic']['scoringCriteria'][problemType][key] == 'True':
scoringCreteria = key
break
selectAlgo = ""
if problemType in ['classification','regression','timeSeriesForecasting','timeSeriesAnomalyDetection',
'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition','llmFineTuning']: #task 11997
for key in configSettingsJson['basic']['algorithms'][problemType]:
if configSettingsJson['basic']['algorithms'][problemType][key] == 'True':
if selectAlgo != "":
selectAlgo += ','
selectAlgo += key
modelSize = ''
if problemType == 'llmFineTuning':
for key in configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo].keys():
if configSettingsJson['basic']['modelSize']['llmFineTuning'][selectAlgo][key] == 'True':
modelSize = key
break
movenext = True
request.session['finalstate'] = 1
request.session['currentstate'] = 1
context = {'tab': 'tabconfigure','modelSize':modelSize,'tot_count':tot_count, 'temp': temp, 'configsettings': configSettingsJson, 'config': config,'numericFeature':numericFeature,'onlineLearning':onlineLearning,
'noOfRecords': records, 'selected_use_case': selected_use_case,'problemType':problemType,'scoringCreteria':scoringCreteria,'selectAlgo':selectAlgo,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'movenext': movenext,
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],
'selected': 'modeltraning','advance':True,'basic_help':ht.basic_help
# Retraing settings changes
,'IsSameFeatures':IsSameFeatures,'IsReTrainingCase':IsReTrainingCase
# 10012:Decision Threshold related
,'DLCheckpoint':data_is_under_RAM_threshold}
return context
except UnicodeDecodeError as e:
print(e)
context = {'tab': 'tabconfigure','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'ModelStatus': ModelStatus,'selected': 'modeltraning','error': 'File Reading Error: '+str(e)}
return context
except Exception as e:
print(e)
import sys,os
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context = {'tab': 'tabconfigure','selected_use_case': selected_use_case,'ModelVersion': ModelVersion,'ModelStatus': ModelStatus,'selected': 'modeltraning','error': 'Config Error: '+str(e)}
return context |
advance_Config.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import pandas as pd
import json
import os,sys
from appbe import help_Text as ht
def save(request):
from appbe.dataPath import DEFAULT_FILE_PATH
if request.method == 'POST':
submittype = request.POST.get('AdvanceSubmit')
if submittype != 'AdvanceDefault':
configFile = request.session['config_json']
f = open(configFile, "r+")
configSettingsData = f.read()
configSettings = json.loads(configSettingsData)
try:
if configSettings['basic']['analysisType']['llmFineTuning'].lower() == 'false':
numericselectedmethod = request.POST.get('numericfillmethod')
for x in list(configSettings['advance']['profiler']['numericalFillMethod'].keys()):
configSettings['advance']['profiler']['numericalFillMethod'][x] = 'False'
configSettings['advance']['profiler']['numericalFillMethod'][numericselectedmethod] = 'True'
categoricalselectedmethod = request.POST.get('categorialfillmethod')
for x in list(configSettings['advance']['profiler']['categoricalFillMethod'].keys()):
configSettings['advance']['profiler']['categoricalFillMethod'][x] = 'False'
configSettings['advance']['profiler']['categoricalFillMethod'][categoricalselectedmethod] = 'True'
categoryEncodingMethod = request.POST.get('categoryencoding')
for x in list(configSettings['advance']['profiler']['categoryEncoding'].keys()):
configSettings['advance']['profiler']['categoryEncoding'][x] = 'False'
configSettings['advance']['profiler']['categoryEncoding'][categoryEncodingMethod] = 'True'
outlierDetection = request.POST.get('outlierDetection')
for x in list(configSettings['advance']['profiler']['outlierDetection'].keys()):
configSettings['advance']['profiler']['outlierDetection'][x] = 'False'
if outlierDetection != 'Disable':
configSettings['advance']['profiler']['outlierDetection'][outlierDetection] = 'True'
#configSettings['advance']['profiler']['outlierDetectionStatus'] = request.POST.get('AnamolyDetectionStatus')
#configSettings['advance']['profiler']['outlierDetectionMethod'] = request.POST.get('AnaTreatmentMethod')
configSettings['advance']['profiler']['misValueRatio'] = request.POST.get('MisValueRatio')
#configSettings['advance']['profiler']['categoricalToNumeric'] = request.POST.get('CategoricalToNumeric')
configSettings['advance']['profiler']['numericFeatureRatio'] = request.POST.get('NumFeatureRatio')
configSettings['advance']['profiler']['categoryMaxLabel'] = request.POST.get('CatMaxLabels')
configSettings['advance']['selector']['categoryMaxLabel'] = request.POST.get('CatMaxLabels')
normalizationtypes = configSettings['advance']['profiler']['normalization']
for k in normalizationtypes.keys():
configSettings['advance']['profiler']['normalization'][k] = 'False'
if request.POST.get('NormalizationMethod').lower() != 'none':
configSettings['advance']['profiler']['normalization'][request.POST.get('NormalizationMethod')] = 'True'
#configSettings['advance']['profiler']['normalizationMethod'] = request.POST.get('NormalizationMethod')
configSettings['advance']['profiler']['removeDuplicate'] = request.POST.get('removeDuplicate')
# ---------------------------------------------- Debiasing Changes ----------------------------------------------
configSettings['advance']['profiler']['deBiasing']['FeatureName'] = request.POST.get('InputFeature')
configSettings['advance']['profiler']['deBiasing']['ClassName'] = request.POST.get('InputClass')
configSettings['advance']['profiler']['deBiasing']['Algorithm'] = request.POST.get('InputAlgorithm')
configSettings['advance']['profiler']['deBiasing']['TargetFeature'] = configSettings['basic']['targetFeature']
# ---------------------------------------------- ----------------------------------------------
problemtypes = configSettings['basic']['analysisType']
problem_type = ""
for k in problemtypes.keys():
if configSettings['basic']['analysisType'][k] == 'True':
problem_type = k
break
if configSettings['basic']['analysisType']['llmFineTuning'].lower() == 'false' and configSettings['basic']['onlineLearning'].lower() == 'false' and configSettings['basic']['distributedLearning'].lower() == 'false':
configSettings['advance']['profiler']['textCleaning']['removeNoise'] = request.POST.get('noiseStatus')
# -------------------------------- 12301:Remove Noise Config related Changes S T A R T --------------------------------
if request.POST.get('noiseStatus') == 'True':
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['decodeHTML'] = request.POST.get('DecodeHTML')
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHyperLinks'] = request.POST.get('removeHyperlinks')
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeMentions'] = request.POST.get('RemoveMentions')
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHashtags'] = request.POST.get('removeHashtags')
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeEmoji'] = request.POST.get('removeEmoji')
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['unicodeToAscii'] = request.POST.get('unicodeToAscii')
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeNonAscii'] = request.POST.get('removeNonAscii')
else:
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['decodeHTML'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHyperLinks'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeMentions'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeHashtags'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeEmoji'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['unicodeToAscii'] = "False"
configSettings['advance']['profiler']['textCleaning']['removeNoiseConfig']['removeNonAscii'] = "False"
# ---------------------------------------------------------------- E N D ----------------------------------------------------------------
configSettings['advance']['profiler']['textCleaning']['expandContractions'] = request.POST.get(
'expandContractions')
configSettings['advance']['profiler']['textCleaning']['normalize'] = request.POST.get('normalize')
if (request.POST.get('normalizeMethod') == 'Lemmatization'):
configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['lemmatization'] = "True"
configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['stemming'] = "False"
else:
configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['stemming'] = "True"
configSettings['advance']['profiler']['textCleaning']['normalizeMethod']['lemmatization'] = "False"
configSettings['advance']['profiler']['textCleaning']['replaceAcronym'] = request.POST.get('replaceAcronym')
if request.POST.get('acronymDict') != '' and request.POST.get('acronymDict') != 'None':
configSettings['advance']['profiler']['textCleaning']['acronymConfig']['acronymDict'] = eval(request.POST.get(
'acronymDict'))
configSettings['advance']['profiler']['textCleaning']['correctSpelling'] = request.POST.get(
'correctSpelling')
configSettings['advance']['profiler']['textCleaning']['removeStopwords'] = request.POST.get(
'removeStopwords')
if (request.POST.get('ExtendOrReplace') == 'NA'):
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['extend'] = "False"
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['replace'] = "False"
elif (request.POST.get('ExtendOrReplace') == 'Extend'):
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['extend'] = "True"
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['replace'] = "False"
else:
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['extend'] = "False"
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig']['replace'] = "True"
configSettings['advance']['profiler']['textCleaning']['stopWordsConfig'][
'stopwordsList'] = request.POST.get('stopwordsList')
configSettings['advance']['profiler']['textCleaning']['removePunctuation'] = request.POST.get(
'removePunctuation')
configSettings['advance']['profiler']['textCleaning']['removePunctuationConfig'][
'removePuncWithinTokens'] = request.POST.get('removePuncWithinTokens')
configSettings['advance']['profiler']['textCleaning']['removeNumericTokens'] = request.POST.get(
'removeNumericTokens')
configSettings['advance']['profiler']['textCleaning']['removeNumericConfig'][
'removeNumeric_IncludeSpecialCharacters'] = request.POST.get('removeNumeric_IncludeSpecialCharacters')
if (request.POST.get('tokenizationLib') == 'nltk'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'textblob'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'gensim'] = "False"
elif (request.POST.get('tokenizationLib') == 'textblob'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'textblob'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'gensim'] = "False"
elif (request.POST.get('tokenizationLib') == 'spacy'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'textblob'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'gensim'] = "False"
elif (request.POST.get('tokenizationLib') == 'keras'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'textblob'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'gensim'] = "False"
else:
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib'][
'textblob'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['spacy'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['keras'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['tokenizationLib']['gensim'] = "True"
if (request.POST.get('lemmatizationLib') == 'nltk'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['nltk'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][
'textblob'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][
'spacy'] = "False"
elif (request.POST.get('lemmatizationLib') == 'textblob'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][
'textblob'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][
'spacy'] = "False"
else:
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib'][
'textblob'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['lemmatizationLib']['spacy'] = "True"
if (request.POST.get('stopwordsRemovalLib') == 'nltk'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'nltk'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'gensim'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'spacy'] = "False"
elif (request.POST.get('stopwordsRemovalLib') == 'gensim'):
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'gensim'] = "True"
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'spacy'] = "False"
else:
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'nltk'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'gensim'] = "False"
configSettings['advance']['profiler']['textCleaning']['libConfig']['stopwordsRemovalLib'][
'spacy'] = "True"
configSettings['advance']['profiler']['textFeatureExtraction']['n_grams'] = request.POST.get('n_grams')
configSettings['advance']['profiler']['textFeatureExtraction']['n_grams_config'][
'min_n'] = int(request.POST.get('range_min_n'))
configSettings['advance']['profiler']['textFeatureExtraction']['n_grams_config'][
'max_n'] = int(request.POST.get('range_max_n'))
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags'] = request.POST.get('pos_tags')
if (request.POST.get('pos_tags_lib') == 'nltk'):
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['nltk'] = "True"
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['textblob'] = "False"
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['spacy'] = "False"
elif (request.POST.get('pos_tags_lib') == 'textblob'):
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['nltk'] = "False"
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['textblob'] = "True"
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['spacy'] = "False"
else:
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['nltk'] = "False"
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['textblob'] = "False"
configSettings['advance']['profiler']['textFeatureExtraction']['pos_tags_lib']['spacy'] = "True"
textconvertionmethods = configSettings['advance']['profiler']['textConversionMethod']
for k in textconvertionmethods.keys():
configSettings['advance']['profiler']['textConversionMethod'][k] = 'False'
if problem_type.lower() not in ['similarityidentification','contextualsearch']:
configSettings['advance']['profiler']['textConversionMethod'][request.POST.get('textConvertionMethod')] = 'True'
if 'embeddingSize' in configSettings['advance']['profiler']:
glove = configSettings['advance']['profiler']['embeddingSize']['Glove']
for k in glove.keys():
configSettings['advance']['profiler']['embeddingSize']['Glove'][k] = 'False'
configSettings['advance']['profiler']['embeddingSize']['Glove'][request.POST.get('txtglovedimensions')] = 'True'
fastText = configSettings['advance']['profiler']['embeddingSize']['FastText']
for k in fastText.keys():
configSettings['advance']['profiler']['embeddingSize']['FastText'][k] = 'False'
configSettings['advance']['profiler']['embeddingSize']['FastText'][request.POST.get('txtFastTextdimensions')] = 'True'
if 'LatentSemanticAnalysis' in configSettings['advance']['profiler']['embeddingSize']:
LatentSemanticAnalysis = configSettings['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis']
for k in LatentSemanticAnalysis.keys():
configSettings['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'][k] = 'False'
configSettings['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'][request.POST.get('txttfidfdimensions')] = 'True'
if 'TF_IDF' in configSettings['advance']['profiler']['embeddingSize']:
configSettings['advance']['profiler']['embeddingSize']['TF_IDF']['maxFeatures'] = request.POST.get('tfidfmaxfeatures')
if 'CountVectors' in configSettings['advance']['profiler']['embeddingSize']:
configSettings['advance']['profiler']['embeddingSize']['CountVectors']['maxFeatures'] = request.POST.get('cvmaxfeatures')
if problem_type.lower() == 'imageclassification':
configSettings['advance']['image_config']['img_width'] = int(request.POST.get('img_width'))
configSettings['advance']['image_config']['img_height'] = int(request.POST.get('img_height'))
configSettings['advance']['image_config']['img_channel'] = int(request.POST.get('img_channel'))
configSettings['advance']['image_config']['lr'] = float(request.POST.get('lr'))
configSettings['advance']['image_config']['epochs'] = int(request.POST.get('epochs'))
configSettings['advance']['image_config']['test_split_ratio'] = float(request.POST.get('test_split_ratio'))
if problem_type.lower() == "llmfinetuning":
configSettings = llmadvancesettings(configSettings,request)
if problem_type.lower() == 'objectdetection' or problem_type.lower() == 'imageclassification':
configSettings['advance']['ImageAugmentation']['Enable'] = request.POST.get('advance_ImageAugmentation_Enable')
configSettings['advance']['ImageAugmentation']['KeepAugmentedImages'] = request.POST.get('advance_ImageAugmentation_keepAugmentedImages')
configSettings['advance']['ImageAugmentation']['Noise']['Blur'] = request.POST.get('advance_ImageAugmentation_Noise_Blur')
configSettings['advance']['ImageAugmentation']['Noise']['Brightness'] = request.POST.get('advance_ImageAugmentation_Noise_Brightness')
configSettings['advance']['ImageAugmentation']['Noise']['Contrast'] = request.POST.get('advance_ImageAugmentation_Noise_Contrast')
configSettings['advance']['ImageAugmentation']['Transformation']['Flip'] = request.POST.get('advance_ImageAugmentation_Transformation_Flip')
configSettings['advance']['ImageAugmentation']['Transformation']['Rotate'] = request.POST.get('advance_ImageAugmentation_Transformation_Rotate')
configSettings['advance']['ImageAugmentation']['Transformation']['Shift'] = request.POST.get('advance_ImageAugmentation_Transformation_Shift')
configSettings['advance']['ImageAugmentation']['Transformation']['Crop'] = request.POST.get('advance_ImageAugmentation_Transformation_Crop')
configSettings['advance']['ImageAugmentation']['configuration']['Blur']['noOfImages'] = request.POST.get('noofblurimages')
configSettings['advance']['ImageAugmentation']['configuration']['Blur']['limit'] = request.POST.get('limitblurimage')
configSettings['advance']['ImageAugmentation']['configuration']['Brightness']['noOfImages'] = request.POST.get('noofbrightnessimages')
configSettings['advance']['ImageAugmentation']['configuration']['Brightness']['limit'] = request.POST.get('limitbrightnessimage')
configSettings['advance']['ImageAugmentation']['configuration']['Contrast']['noOfImages'] = request.POST.get('noofcontrastimages')
configSettings['advance']['ImageAugmentation']['configuration']['Contrast']['limit'] = request.POST.get('limitcontrastimage')
configSettings['advance']['ImageAugmentation']['configuration']['Flip']['noOfImages'] = request.POST.get('noofflipimages')
configSettings['advance']['ImageAugmentation']['configuration']['Rotate']['noOfImages'] = request.POST.get('noofrotateimages')
configSettings['advance']['ImageAugmentation']['configuration']['Shift']['noOfImages'] = request.POST.get('noofshiftimages')
configSettings['advance']['ImageAugmentation']['configuration']['Crop']['noOfImages'] = request.POST.get('noofcropimages')
configSettings['advance']['selector']['selectionMethod']['featureSelection'] = 'False'
configSettings['advance']['selector']['selectionMethod']['featureEngineering'] = 'False'
configSettings['advance']['selector']['featureSelection']['allFeatures'] = 'False'
configSettings['advance']['selector']['featureSelection']['statisticalBased'] = 'False'
configSettings['advance']['selector']['featureSelection']['modelBased'] = 'False'
if(request.POST.get('selectionMethod') == 'FeatureSelection'):
configSettings['advance']['selector']['selectionMethod']['featureSelection'] = 'True'
else:
configSettings['advance']['selector']['selectionMethod']['featureEngineering'] = 'True'
if request.POST.get('allFeatures'):
configSettings['advance']['selector']['featureSelection']['allFeatures'] = request.POST.get('allFeatures')
if request.POST.get('statisticalBased'):
configSettings['advance']['selector']['featureSelection']['statisticalBased'] = request.POST.get('statisticalBased')
if request.POST.get('modelBased'):
configSettings['advance']['selector']['featureSelection']['modelBased'] = request.POST.get('modelBased')
dimentionalityreductionmethod = request.POST.get('dimentionalityreductionmethod')
for x in list(configSettings['advance']['selector']['featureEngineering'].keys()):
if x != 'numberofComponents':
configSettings['advance']['selector']['featureEngineering'][x] = 'False'
configSettings['advance']['selector']['featureEngineering'][dimentionalityreductionmethod] = 'True'
configSettings['advance']['selector']['featureEngineering']['numberofComponents'] = request.POST.get('numberofComponents')
#configSettings['advance']['selector']['categoricalFeatureRatio'] = request.POST.get('CatFeatureRatio')
configSettings['advance']['selector']['statisticalConfig']['correlationThresholdFeatures'] = request.POST.get('correlationThresholdFeatures')
configSettings['advance']['selector']['statisticalConfig']['correlationThresholdTarget'] = request.POST.get('correlationThresholdTarget')
configSettings['advance']['selector']['statisticalConfig']['pValueThresholdFeatures'] = request.POST.get('pValueThresholdFeatures')
configSettings['advance']['selector']['statisticalConfig']['pValueThresholdTarget'] = request.POST.get('pValueThresholdTarget')
configSettings['advance']['selector']['statisticalConfig']['varianceThreshold'] = request.POST.get('VarianceThreshold')
if problem_type.lower() == 'recommendersystem':
configSettings['advance']['recommenderparam']['svd_params']= eval(request.POST.get('svd_params'))
configSettings['advance']['associationrule']['modelParams']['apriori'] = eval(request.POST.get('apriori'))
configSettings['advance']['textSimilarityConfig'] = eval(request.POST.get('textsimilarity'))
if configSettings['basic']['distributedLearning'].lower() == 'true':
configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('classDistributedXGBoost'))
configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('classDistributedLightGBM'))
configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('DistributedXGBoostreg'))
configSettings['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('DistributedLightGBMreg'))
if configSettings['basic']['onlineLearning'].lower() != 'true' and configSettings['basic']['distributedLearning'].lower() != 'true':
if (problem_type.lower() == 'classification') or (problem_type.lower() == 'regression') or (problem_type.lower() == 'clustering') or (problem_type.lower() == 'topicmodelling'):
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Logistic Regression'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Logistic Regression'] = eval(request.POST.get('classification_LogisticRegression'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Naive Bayes'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Naive Bayes'] = eval(request.POST.get('classification_GaussianNB'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Support Vector Machine'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Support Vector Machine'] = eval(request.POST.get('classification_SVC'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['K Nearest Neighbors'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['K Nearest Neighbors'] = eval(request.POST.get('classification_KNeighborsClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Decision Tree'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Decision Tree'] = eval(request.POST.get('classification_DecisionTreeClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Random Forest'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Random Forest'] = eval(request.POST.get('classification_RandomForestClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Gradient Boosting'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Gradient Boosting'] = eval(request.POST.get('classification_GradientBoostingClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Extreme Gradient Boosting (XGBoost)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('classification_ExtremeGradientBoostingClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Light Gradient Boosting (LightGBM)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('classification_LightGradientBoostingClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Categorical Boosting (CatBoost)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Categorical Boosting (CatBoost)'] = eval(request.POST.get('classification_CategoricalBoostingClassifier'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Linear Regression'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Linear Regression'] = eval(request.POST.get('regression_LinearRegression'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Lasso'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Lasso'] = eval(request.POST.get('regression_Lasso'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Ridge'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Ridge'] = eval(request.POST.get('regression_Ridge'))
if problem_type.lower() == 'topicmodelling' and configSettings['basic']['algorithms']['topicModelling']['LDA'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['topicModellingParams']['LDA']= eval(request.POST.get('topicmodeling_lda'))
if problem_type.lower() == 'clustering' and configSettings['basic']['algorithms']['clustering']['KMeans'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['clusteringModelParams']['KMeans']= eval(request.POST.get('cluster_kmeans'))
if problem_type.lower() == 'clustering' and configSettings['basic']['algorithms']['clustering']['DBSCAN'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['clusteringModelParams']['DBSCAN']= eval(request.POST.get('cluster_DBSCAN'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Decision Tree'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Decision Tree'] = eval(request.POST.get('regression_DecisionTreeRegressor'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Random Forest'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Random Forest'] = eval(request.POST.get('regression_RandomForestRegressor'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Extreme Gradient Boosting (XGBoost)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Extreme Gradient Boosting (XGBoost)'] = eval(request.POST.get('regression_XGBoostRegressor'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Light Gradient Boosting (LightGBM)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Light Gradient Boosting (LightGBM)'] = eval(request.POST.get('regression_LightGBMRegressor'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Categorical Boosting (CatBoost)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Categorical Boosting (CatBoost)'] = eval(request.POST.get('regression_CatBoostRegressor'))
configSettings['advance']['mllearner_config']['modelparamsfile'] = request.POST.get('ModelParamFile')
configSettings['advance']['mllearner_config']['optimizationMethod'] = request.POST.get('OptimizationMethod')
configSettings['advance']['mllearner_config']['optimizationHyperParameter'][
'iterations'] = request.POST.get('iterations')
configSettings['advance']['mllearner_config']['optimizationHyperParameter'][
'trainTestCVSplit'] = request.POST.get('trainTestCVSplit')
configSettings['advance']['mllearner_config']['thresholdTunning'] = request.POST.get('thresholdTunning')
configSettings['advance']['mllearner_config']['Stacking (Ensemble)'] = request.POST.get('EnsembleStacking')
configSettings['advance']['mllearner_config']['Voting (Ensemble)'] = request.POST.get('EnsembleVoting')
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['enable'] = request.POST.get('ensemple_bagging_lr_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Logistic Regression']['param'] = eval(request.POST.get('classi_ensemple_bagging_lr_param'))
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Naive Bayes']['enable'] = request.POST.get('ensemple_bagging_naivebayes_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Naive Bayes']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Naive Bayes']['param'] = eval(request.POST.get('classi_ensemple_bagging_naivebayes_param'))
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Support Vector Machine']['enable'] = request.POST.get('ensemple_bagging_svm_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Support Vector Machine']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Support Vector Machine']['param'] = eval(request.POST.get('classi_ensemple_bagging_svm_param'))
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['K Nearest Neighbors']['enable'] = request.POST.get('ensemple_bagging_knn_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['K Nearest Neighbors']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['K Nearest Neighbors']['param'] = eval(request.POST.get('classi_ensemple_bagging_knn_param'))
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] = request.POST.get('ensemple_bagging_dt_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Decision Tree']['param'] = eval(request.POST.get('classi_ensemple_bagging_dt_param'))
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Random Forest']['enable'] = request.POST.get('ensemple_bagging_rf_enable')
if configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Random Forest']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']['Random Forest']['param'] = eval(request.POST.get('classi_ensemple_bagging_rf_param'))
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Linear Regression']['enable'] = request.POST.get('ensemple_bagging_lir_enable')
if configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Linear Regression']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Linear Regression']['param'] = eval(request.POST.get('reg_ensemple_bagging_lir_param'))
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] = request.POST.get('ensemple_bagging_dit_enable')
if configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Decision Tree']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Decision Tree']['param'] = eval(request.POST.get('reg_ensemple_bagging_dit_param'))
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Ridge']['enable'] = request.POST.get('ensemple_bagging_ridge_enable')
if configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Ridge']['enable'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']['Ridge']['param'] = eval(request.POST.get('reg_ensemple_bagging_ridge_param'))
if problem_type.lower() == 'classification':
if configSettings['advance']['mllearner_config']['Stacking (Ensemble)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['classifierModelParams']['Stacking (Ensemble)'] = eval(request.POST.get('ensamblestackingClassifierparams'))
if problem_type.lower() == 'regression':
if configSettings['advance']['mllearner_config']['Stacking (Ensemble)'] == 'True':
configSettings['advance']['mllearner_config']['modelParams']['regressorModelParams']['Stacking (Ensemble)'] = eval(request.POST.get('ensamblestackingRegressorparams'))
configSettings['basic']['filterExpression'] = request.POST.get('filterExpression')
#configSettings['advance']['mllearner_config']['trainPercentage'] = request.POST.get('trainPercentage')
if (problem_type.lower() == 'classification') or (problem_type.lower() == 'regression'):
configSettings['advance']['modelEvaluation']['smcStrategy'] = request.POST.get('smcStrategy')
configSettings['advance']['modelEvaluation']['smcMaxDepth'] = request.POST.get('smcMaxDepth')
configSettings['advance']['modelEvaluation']['smcCondition'] = request.POST.get('smcCondition')
configSettings['advance']['modelEvaluation']['miCondition'] = request.POST.get('miCondition')
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Neural Network'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Network'] = eval(
request.POST.get('dl_classification_SNN'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Recurrent Neural Network'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network'] = eval(
request.POST.get('dl_classification_RNN'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Recurrent Neural Network (GRU)'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (GRU)'] = eval(
request.POST.get('dl_classification_GRURNN'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Recurrent Neural Network (LSTM)'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (LSTM)'] = eval(
request.POST.get('dl_classification_LSTMRNN'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Convolutional Neural Network (1D)'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Convolutional Neural Network (1D)'] = eval(
request.POST.get('dl_classification_CNN'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification'].get('Neural Architecture Search') == 'True':
configSettings['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Architecture Search'] = eval(
request.POST.get('dl_classification_NAS'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Neural Network'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Network'] = eval(
request.POST.get('dl_regression_SNN'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Recurrent Neural Network'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network'] = eval(
request.POST.get('dl_regression_RNN'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Recurrent Neural Network (GRU)'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (GRU)'] = eval(
request.POST.get('dl_regression_GRURNN'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Recurrent Neural Network (LSTM)'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (LSTM)'] = eval(
request.POST.get('dl_regression_LSTMRNN'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Convolutional Neural Network (1D)'] == 'True':
configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Convolutional Neural Network (1D)'] = eval(
request.POST.get('dl_regression_CNN'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression'].get('Neural Architecture Search') == 'True':
configSettings['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Architecture Search'] = eval(
request.POST.get('dl_regression_NAS'))
#configSettings['advance']['dllearner_config']['optimizationMethod'] = request.POST.get('DLOptimizationMethod')
else:
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online Logistic Regression'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Logistic Regression'] = eval(request.POST.get('OnlineLogisticRegression'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online Decision Tree Classifier'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Decision Tree Classifier'] = eval(request.POST.get('OnlineDecisionTreeClassifier'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online Softmax Regression'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Softmax Regression'] = eval(request.POST.get('OnlineSoftmaxRegression'))
if problem_type.lower() == 'classification' and configSettings['basic']['algorithms']['classification']['Online KNN Classifier'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online KNN Classifier'] = eval(request.POST.get('OnlineKNNClassifier'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Online Linear Regression'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Linear Regression'] = eval(request.POST.get('OnlineLinearRegression'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Online Decision Tree Regressor'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Decision Tree Regressor'] = eval(request.POST.get('OnlineDecisionTreeRegressor'))
if problem_type.lower() == 'regression' and configSettings['basic']['algorithms']['regression']['Online KNN Regressor'] == 'True':
configSettings['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online KNN Regressor'] = eval(request.POST.get('OnlineKNNRegressor'))
configSettings['advance']['profiler']['targetEncodingParams'] = eval(request.POST.get('targetEncodingParams'))
configSettings['advance']['profiler']['outlierDetectionParams'] = eval(request.POST.get('outlierDetectionParams'))
if problem_type.lower() == 'objectdetection':
configSettings['advance']['objectDetection']['pretrainedModel']= request.POST.get('objectdetectionpretrainedmodel')
configSettings['advance']['objectDetection']['n_epoch'] = int(request.POST.get('objectDetection_n_epoch'))
configSettings['advance']['objectDetection']['batch_size'] = int(request.POST.get('objectDetection_batch_size'))
if problem_type.lower() == 'timeseriesforecasting': #task 11997 #task 13052
configSettings['advance']['timeSeriesForecasting']['fix_seasonality'] = request.POST.get('seasionality') # task 13052
configSettings['advance']['timeSeriesForecasting']['fix_stationarity'] =request.POST.get('stationarity') # task 13052
configSettings['advance']['timeSeriesForecasting']['modelParams']['ARIMA'] = eval(request.POST.get('ARIMA')) #task 11997
configSettings['advance']['timeSeriesForecasting']['modelParams']['FBPROPHET'] = eval(request.POST.get('FBPROPHET')) #task 11997
configSettings['advance']['timeSeriesForecasting']['modelParams']['LSTM'] = eval(request.POST.get('TSLSTM')) #task 11997
configSettings['advance']['timeSeriesForecasting']['modelParams']['Encoder_Decoder_LSTM_MVI_UVO'] = eval(request.POST.get('TSLSTMencoderdecoder'))
configSettings['advance']['timeSeriesForecasting']['modelParams']['MLP'] = eval(request.POST.get('TSMLP')) #task 11997
if problem_type.lower() == 'timeseriesanomalydetection':
configSettings['advance']['timeSeriesAnomalyDetection']['modelParams']['AutoEncoder'] = eval(request.POST.get('autoEncoderAD')) #task 11997
configSettings['advance']['timeSeriesAnomalyDetection']['modelParams']['DBScan'] = eval(request.POST.get('dbscanAD')) #task 13316
if problem_type.lower() == 'anomalydetection':
configSettings['advance']['anomalyDetection']['modelParams']['IsolationForest'] = eval(request.POST.get('IsolationForest'))
configSettings['advance']['anomalyDetection']['modelParams']['oneclassSVM'] = eval(request.POST.get('oneclassSVM'))
configSettings['advance']['anomalyDetection']['modelParams']['DBScan'] = eval(request.POST.get('DBScanAD'))
updatedConfigSettingsJson = json.dumps(configSettings)
f.seek(0)
f.write(updatedConfigSettingsJson)
f.truncate()
f.close()
errormsg = 'NA'
request.session['ModelStatus'] = 'Not Trained'
except Exception as e:
import sys
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
errormsg = 'Input value error'
print(e)
if 'NoOfRecords' in request.session:
records = request.session['NoOfRecords']
else:
records = 'NA'
if request.session['datatype'] in ['Video', 'Image','Document']:
folderLocation = str(request.session['datalocation'])
dataFilePath = os.path.join(folderLocation, request.session['csvfullpath'])
else:
dataFilePath = str(request.session['datalocation'])
# dataFilePath = configSettings['basic']['dataLocation']
#df = pd.read_csv(dataFilePath, encoding='latin1')
featuresList = configSettings['basic']['featureList']
config = {}
config['modelName'] = configSettings['basic']['modelName']
config['modelVersion'] = configSettings['basic']['modelVersion']
config['datetimeFeatures'] = configSettings['basic']['dateTimeFeature']
config['sequenceFeatures'] = configSettings['basic']['indexFeature']
config['FeaturesList'] = featuresList
config['unimportantFeatures'] = list(set(featuresList) - set(configSettings['basic']['trainingFeatures']))
config['targetFeature'] = configSettings['basic']['targetFeature']
scoring = configSettings['basic']['scoringCriteria']
scoringCriteria = ""
for k in scoring.keys():
if configSettings['basic']['scoringCriteria'][k] == 'True':
scoringCriteria = k
break
config['scoringCriteria'] = scoringCriteria
temp = {}
temp['ModelName'] = configSettings['basic']['modelName']
temp['Version'] = configSettings['basic']['modelVersion']
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
context = {'tab': 'advconfig', 'config': config, 'temp': temp, 'advconfig': configSettings,
'noOfRecords': records, 'advance_status_msg': 'Configuration Done',
'selected_use_case': selected_use_case, 'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,'errormsg':errormsg,
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],
'selected': 'modeltraining'}
return context
elif submittype == 'AdvanceDefault':
try:
MachineLearningModels = []
configFile = os.path.join(DEFAULT_FILE_PATH, 'aion_config.json')
f = open(configFile, "r")
configSettings = f.read()
f.close()
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r+")
configSettingsData = f.read()
updateconfigSettingsJson = json.loads(configSettingsData)
configSettingsJson = json.loads(configSettings)
temp = {}
temp['ModelName'] = request.session['UseCaseName']
temp['Version'] = request.session['ModelVersion']
config = {}
config['modelName'] = request.session['UseCaseName']
config['modelVersion'] = request.session['ModelVersion']
config['datetimeFeatures'] = updateconfigSettingsJson['basic']['dateTimeFeature']
config['sequenceFeatures'] = updateconfigSettingsJson['basic']['indexFeature']
config['FeaturesList'] = updateconfigSettingsJson['basic']['trainingFeatures']
config['unimportantFeatures'] = ''
config['targetFeature'] = updateconfigSettingsJson['basic']['targetFeature']
problemtypes = updateconfigSettingsJson['basic']['analysisType']
problem_type = ""
for k in problemtypes.keys():
if updateconfigSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
selectAlgo = ""
if problem_type in ['classification','regression','timeSeriesForecasting',
'timeSeriesAnomalyDetection',
'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition']: #task 11997
for key in updateconfigSettingsJson['basic']['algorithms'][problem_type]:
if updateconfigSettingsJson['basic']['algorithms'][problem_type][key] == 'True':
if selectAlgo != "":
selectAlgo += ','
selectAlgo += key
if problem_type not in ['classification','regression']:
break
for key in updateconfigSettingsJson['basic']['algorithms'][problem_type]:
if updateconfigSettingsJson['basic']['algorithms'][problem_type][key] == 'True':
MachineLearningModels.append(key)
if problem_type == 'objectDetection':
from AION import pretrainedModels
ptmObj = pretrainedModels()
obModels = ptmObj.get_info(selectAlgo)
else:
obModels = {}
problemType = problem_type
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
request.session['currentstate'] = 2
if request.session['finalstate'] <= 2:
request.session['finalstate'] = 2
outlierDetection = 'False'
updateconfigSettingsJson['advance'] = configSettingsJson['advance']
for x in list(updateconfigSettingsJson['advance']['profiler']['outlierDetection'].keys()):
if updateconfigSettingsJson['advance']['profiler']['outlierDetection'][x] == 'True':
outlierDetection = 'True'
if outlierDetection == 'False':
updateconfigSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'True'
else:
updateconfigSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'False'
updateconfigSettingsJson = advanceConfigfields(updateconfigSettingsJson)
#print(configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['ExtremeGradientBoostingClassifier'])
updateconfigSettingsJson['advance']['profiler']['normalizationMethod'] = 'None'
normalizationtypes = updateconfigSettingsJson['advance']['profiler']['normalization']
for k in normalizationtypes.keys():
if updateconfigSettingsJson['advance']['profiler']['normalization'][k] == 'True':
updateconfigSettingsJson['advance']['profiler']['normalizationMethod'] = k
break
#---------------- default Hypermarameter changes--- ----------Usnish--------------
hyperparamFile = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config', 'hyperparam_config.json'))
with open(hyperparamFile) as json_file:
hyperparamConfig = json.load(json_file)
context = {'tab': 'advconfig','temp': temp,'advconfig': updateconfigSettingsJson,
'config': config, 'selected_use_case': selected_use_case,'MachineLearningModels':MachineLearningModels,
'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,"obModels":obModels,"problemType":problemType,
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],
'selected': 'modeltraning','advance_help':ht.advance_help,'hyperparamConfig':hyperparamConfig}
return context
except Exception as e:
print(e)
def llmadvancesettings(configSettings,request):
algo = ''
for x in list(configSettings['basic']['algorithms']['llmFineTuning'].keys()):
if configSettings['basic']['algorithms']['llmFineTuning'][x] == 'True':
algo = x
if algo == 'LLaMA-2':
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['fineTuningMethod'] = request.POST.get('llama2fullfinemethod')
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['epochs'] = request.POST.get('llama2epochs')
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['learning_rate'] = request.POST.get('llama2learningrate')
if request.POST.get('llama2fullfinemethod') != 'Full Fine-Tuning':
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['lora_rank'] = request.POST.get('llama2lorarank')
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2']['lora_alpha'] = request.POST.get('llama2loraalpha')
if algo == 'LLaMA-2-Chat':
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['fineTuningMethod'] = request.POST.get('llama2chatfullfinemethod')
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['epochs'] = request.POST.get('llmllama2chatepochs')
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['learning_rate'] = request.POST.get('llama2chatlearningrate')
if request.POST.get('llama2chatfullfinemethod') != 'Full Fine-Tuning':
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['lora_rank'] = request.POST.get('llama2chatlorarank')
configSettings['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']['lora_alpha'] = request.POST.get('llama2chatloraalpha')
if algo == 'CodeLLaMA-2':
configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['fineTuningMethod'] = request.POST.get('CodeLLaMA2fullfinemethod')
configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['epochs'] = request.POST.get('CodeLLaMA2epochs')
configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['learning_rate'] = request.POST.get('CodeLLaMA2learningrate')
if request.POST.get('CodeLLaMA2fullfinemethod') != 'Full Fine-Tuning':
configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['lora_rank'] = request.POST.get('CodeLLaMA2lorarank')
configSettings['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']['lora_alpha'] = request.POST.get('CodeLLaMA2loraalpha')
if algo == 'Falcon':
configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['fullFineTuning'] = request.POST.get('falconfullfinetuning')
configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['epochs'] = request.POST.get('falconepochs')
configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['learning_rate'] = request.POST.get('falconlearningrate')
configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['lora_rank'] = request.POST.get('falconlorarank')
configSettings['advance']['llmFineTuning']['modelParams']['Falcon']['lora_alpha'] = request.POST.get('falconloraalpha')
return configSettings
def advanceConfigfields(configSettingsJson):
try:
configSettingsJson['advance']['mllearner_config']['EnsembleStacking'] = \
configSettingsJson['advance']['mllearner_config']['Stacking (Ensemble)']
configSettingsJson['advance']['mllearner_config']['EnsembleVoting'] = \
configSettingsJson['advance']['mllearner_config']['Voting (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'LogisticRegression'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Logistic Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['GaussianNB'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Naive Bayes']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['SVC'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'Support Vector Machine']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'KNeighborsClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['K Nearest Neighbors']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'DecisionTreeClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Decision Tree']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'RandomForestClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Random Forest']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'GradientBoostingClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Gradient Boosting']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'ExtremeGradientBoostingClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'LightGradientBoostingClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'CategoricalBoostingClassifier'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams'][
'Categorical Boosting (CatBoost)']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SimpleRNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][
'Recurrent Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['GRURNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][
'Recurrent Neural Network (GRU)']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['LSTMRNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][
'Recurrent Neural Network (LSTM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleStacking'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Stacking (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'LogisticRegression'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'Logistic Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'NaiveBayes'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'Naive Bayes']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'SVM'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'Support Vector Machine']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'KNN'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'K Nearest Neighbors']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'DecisionTree'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'Decision Tree']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'RandomForest'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging'][
'Random Forest']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Recurrent Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Recurrent Neural Network (GRU)']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Recurrent Neural Network (LSTM)']
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DQN'] = \
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DDQN'] = \
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams'][
'Dueling Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DQN'] = \
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DDQN'] = \
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams'][
'Dueling Deep Q Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['CNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][
'Convolutional Neural Network (1D)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LinearRegression'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Linear Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][
'DecisionTreeRegressor'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Decision Tree']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][
'RandomForestRegressor'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Random Forest']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['XGBoostRegressor'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][
'Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LightGBMRegressor'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][
'Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['CatBoostRegressor'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams'][
'Categorical Boosting (CatBoost)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleStacking'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Stacking (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][
'LinearRegression'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][
'Linear Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][
'DecisionTree'] = \
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging'][
'Decision Tree']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['NAS'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Neural Architecture Search']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['NAS'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'][
'Neural Architecture Search']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Recurrent Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Recurrent Neural Network (GRU)']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Recurrent Neural Network (LSTM)']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['CNN'] = \
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'][
'Convolutional Neural Network (1D)']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'OnlineLogisticRegression'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'Online Logistic Regression']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'OnlineDecisionTreeClassifier'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'Online Decision Tree Classifier']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'OnlineSoftmaxRegression'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'Online Softmax Regression']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'OnlineKNNClassifier'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams'][
'Online KNN Classifier']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][
'OnlineLinearRegression'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][
'Online Linear Regression']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][
'OnlineDecisionTreeRegressor'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][
'Online Decision Tree Regressor']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][
'OnlineKNNRegressor'] = \
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams'][
'Online KNN Regressor']
configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis'] = \
configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis']
configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] = \
configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis']
if 'llmFineTuning' in configSettingsJson['advance']:
configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2'] = \
configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2']
configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2Chat'] = \
configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2-Chat']
configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA2'] = \
configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA-2']
configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2'] = \
configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2']
configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2Chat'] = \
configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']
configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA2'] = \
configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2'] = \
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2']
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2Chat'] = \
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2-Chat']
configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA2'] = \
configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA-2']
if 'distributedlearner_config' in configSettingsJson['advance']:
configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][
'DistributedXGBoost'] = \
configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][
'Distributed Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][
'DistributedLightGBM'] = \
configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams'][
'Distributed Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][
'DistributedXGBoost'] = \
configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][
'Distributed Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][
'DistributedLightGBM'] = \
configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams'][
'Distributed Light Gradient Boosting (LightGBM)']
problem_type = ""
problemtypes = configSettingsJson['basic']['analysisType']
for k in problemtypes.keys():
if configSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
deepLearning = 'False'
machineLearning = 'False'
reinforcementLearning = 'False'
selectAlgo = ""
if problem_type.lower() in ['classification','regression']:
for key in configSettingsJson['basic']['algorithms'][problem_type]:
if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True':
if key in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','Neural Architecture Search']:
deepLearning = 'True'
if key in ['Logistic Regression','Naive Bayes','Decision Tree','Random Forest','Support Vector Machine','K Nearest Neighbors','Gradient Boosting','Extreme Gradient Boosting (XGBoost)','Light Gradient Boosting (LightGBM)','Categorical Boosting (CatBoost)','Linear Regression','Lasso','Ridge','Decision Tree','Random Forest','Bagging (Ensemble)']:
machineLearning = 'True'
if key in ['Deep Q Network','Dueling Deep Q Network']:
reinforcementLearning = 'True'
elif problem_type.lower() in ['clustering','topicmodelling']:#clustering(Bug 12611)
machineLearning = 'True'
configSettingsJson['basic']['deepLearning'] = deepLearning
configSettingsJson['basic']['machineLearning'] = machineLearning
configSettingsJson['basic']['reinforcementLearning'] = reinforcementLearning
except Exception as e:
print(e)
return (configSettingsJson)
def basicconfignex(request):
#pemfilename = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','modelTraining','static','key','AION_GPU.pem'))
try:
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r+")
configSettingsData = f.read()
configSettingsJson = json.loads(configSettingsData)
#---------------- default Hypermarameter changes-------------Usnish--------------
hyperparamFile = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config', 'hyperparam_config.json'))
with open(hyperparamFile) as json_file:
hyperparamConfig = json.load(json_file)
#---------------- default Hypermarameter changes end-------------Usnish--------------
# ------------------ Debiasing Changes ------------------
categorical_features = []
class_list = []
MachineLearningModels = []
check_traget = configSettingsJson['basic']['targetFeature']
selectedDebiasingFeature = 'None'
selectedDebiasingClass = 'None'
selectedDebiasingAlgorithm = ''
problemtypes = configSettingsJson['basic']['analysisType']
problem_type = ""
for k in problemtypes.keys():
if configSettingsJson['basic']['analysisType'][k] == 'True':
problem_type = k
break
if request.method == 'GET':
for key in configSettingsJson['basic']['algorithms'][problem_type]:
if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True':
MachineLearningModels.append(key)
else:
MachineLearningModels = request.POST.getlist('MachineLearningModels')
if problem_type.lower() in ['classification','regression']:
if check_traget != '':
try:
if 'deBiasing' in configSettingsJson['advance']['profiler']:
deBiasing = configSettingsJson['advance']['profiler']['deBiasing']
selectedDebiasingFeature = deBiasing.get('FeatureName','None')
selectedDebiasingClass = deBiasing.get('ClassName','None')
selectedDebiasingAlgorithm = deBiasing.get('Algorithm','')
if selectedDebiasingFeature != 'None':
df = pd.read_csv(configSettingsJson['basic']['dataLocation'],encoding='utf8',encoding_errors= 'replace')
classeslist = []
classeslist = df[selectedDebiasingFeature].unique().tolist()
for item in classeslist:
class_list.append(item)
else:
class_list.append('None')
except:
pass
feature_dict = configSettingsJson['advance']['profiler']['featureDict']
for feature_config in feature_dict:
if feature_config.get('type', '') == 'categorical' and feature_config['feature'] != check_traget:
categorical_features.append(feature_config['feature'])
# ------------------ ------------------
#print(categorical_features)
temp = {}
temp['ModelName'] = request.session['UseCaseName']
temp['Version'] = request.session['ModelVersion']
config = {}
config['modelName'] = request.session['UseCaseName']
config['modelVersion'] = request.session['ModelVersion']
config['datetimeFeatures'] = configSettingsJson['basic']['dateTimeFeature']
config['sequenceFeatures'] = configSettingsJson['basic']['indexFeature']
config['FeaturesList'] = configSettingsJson['basic']['trainingFeatures']
config['unimportantFeatures'] = ''
config['targetFeature'] = configSettingsJson['basic']['targetFeature']
deepLearning = 'False'
machineLearning = 'False'
reinforcementLearning = 'False'
selectAlgo = ""
print(problem_type)
if problem_type.lower() in ['classification','regression']:
for key in configSettingsJson['basic']['algorithms'][problem_type]:
if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True':
if key in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','Neural Architecture Search']:
deepLearning = 'True'
if key in ['Logistic Regression','Naive Bayes','Decision Tree','Random Forest','Support Vector Machine','K Nearest Neighbors','Gradient Boosting','Extreme Gradient Boosting (XGBoost)','Light Gradient Boosting (LightGBM)','Categorical Boosting (CatBoost)','Linear Regression','Lasso','Ridge','Decision Tree','Random Forest','Bagging (Ensemble)']:
machineLearning = 'True'
if key in ['Deep Q Network','Dueling Deep Q Network']:
reinforcementLearning = 'True'
elif problem_type.lower() in ['clustering','topicmodelling']:#clustering(Bug 12611)
machineLearning = 'True'
configSettingsJson['basic']['deepLearning'] = deepLearning
configSettingsJson['basic']['machineLearning'] = machineLearning
configSettingsJson['basic']['reinforcementLearning'] = reinforcementLearning
if problem_type in ['classification','regression','timeSeriesForecasting',
'timeSeriesAnomalyDetection',
'recommenderSystem','clustering','anomalyDetection','topicModelling','survivalAnalysis','videoForecasting','imageClassification','objectDetection','stateTransition']: #task 11997
for key in configSettingsJson['basic']['algorithms'][problem_type]:
if configSettingsJson['basic']['algorithms'][problem_type][key] == 'True':
if selectAlgo != "":
selectAlgo += ','
selectAlgo += key
if problem_type not in ['classification','regression']:
break
if problem_type == 'objectDetection':
from AION import pretrainedModels
ptmObj = pretrainedModels()
obModels = ptmObj.get_info(selectAlgo)
else:
obModels = {}
problemType = problem_type
selected_use_case = request.session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
request.session['currentstate'] = 2
#configSettingsJson['advance']['remoteTraining']['ssh']['keyFilePath'] = pemfilename
if request.session['finalstate'] <= 2:
request.session['finalstate'] = 2
outlierDetection = 'False'
for x in list(configSettingsJson['advance']['profiler']['outlierDetection'].keys()):
if configSettingsJson['advance']['profiler']['outlierDetection'][x] == 'True':
outlierDetection = 'True'
if outlierDetection == 'False':
configSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'True'
else:
configSettingsJson['advance']['profiler']['outlierDetection']['Disable'] = 'False'
if 'distributedLearning' not in configSettingsJson['basic']:
configSettingsJson['basic']['distributedLearning'] = 'False'
configSettingsJson['advance']['mllearner_config']['EnsembleStacking']=configSettingsJson['advance']['mllearner_config']['Stacking (Ensemble)']
configSettingsJson['advance']['mllearner_config']['EnsembleVoting']=configSettingsJson['advance']['mllearner_config']['Voting (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['LogisticRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Logistic Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['GaussianNB'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Naive Bayes']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['SVC'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Support Vector Machine']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['KNeighborsClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['K Nearest Neighbors']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['DecisionTreeClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Decision Tree']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['RandomForestClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Random Forest']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['GradientBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Gradient Boosting']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['ExtremeGradientBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['LightGradientBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['CategoricalBoostingClassifier'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Categorical Boosting (CatBoost)']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['SimpleRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['GRURNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (GRU)']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['LSTMRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Recurrent Neural Network (LSTM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']=configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Bagging (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleStacking']=configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['Stacking (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['LogisticRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Logistic Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['NaiveBayes'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Naive Bayes']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['SVM'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Support Vector Machine']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['KNN'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['K Nearest Neighbors']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['DecisionTree'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Decision Tree']
configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['RandomForest'] = configSettingsJson['advance']['mllearner_config']['modelParams']['classifierModelParams']['EnsembleBagging']['Random Forest']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (GRU)']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (LSTM)']
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['DDQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['classifierModelParams']['Dueling Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['Deep Q Network']
configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['DDQN'] = configSettingsJson['advance']['rllearner_config']['modelParams']['regressorModelParams']['Dueling Deep Q Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['CNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['Convolutional Neural Network (1D)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LinearRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Linear Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['DecisionTreeRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Decision Tree']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['RandomForestRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Random Forest']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['XGBoostRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['LightGBMRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['CatBoostRegressor'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Categorical Boosting (CatBoost)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleStacking']=configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Stacking (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']=configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['Bagging (Ensemble)']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['LinearRegression'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['Linear Regression']
configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['DecisionTree'] = configSettingsJson['advance']['mllearner_config']['modelParams']['regressorModelParams']['EnsembleBagging']['Decision Tree']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['NAS'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams'].get('Neural Architecture Search')
configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams']['NAS'] = configSettingsJson['advance']['dllearner_config']['modelParams']['classifierModelParams'].get('Neural Architecture Search')
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['SimpleRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['GRURNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (GRU)']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['LSTMRNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Recurrent Neural Network (LSTM)']
configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['CNN'] = configSettingsJson['advance']['dllearner_config']['modelParams']['regressorModelParams']['Convolutional Neural Network (1D)']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineLogisticRegression'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Logistic Regression']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineDecisionTreeClassifier'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Decision Tree Classifier']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineSoftmaxRegression'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online Softmax Regression']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['OnlineKNNClassifier'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['classifierModelParams']['Online KNN Classifier']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['OnlineLinearRegression'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Linear Regression']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['OnlineDecisionTreeRegressor'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online Decision Tree Regressor']
configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['OnlineKNNRegressor'] = configSettingsJson['advance']['onlinelearner_config']['modelParams']['regressorModelParams']['Online KNN Regressor']
configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis'] = configSettingsJson['advance']['profiler']['textConversionMethod']['LatentSemanticAnalysis']
configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis'] = configSettingsJson['advance']['profiler']['embeddingSize']['LatentSemanticAnalysis']
if 'llmFineTuning' in configSettingsJson['advance']:
configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2'] = configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2']
configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA2Chat'] = configSettingsJson['basic']['algorithms']['llmFineTuning']['LLaMA-2-Chat']
configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA2'] = configSettingsJson['basic']['algorithms']['llmFineTuning']['CodeLLaMA-2']
configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2'] = configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2']
configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA2Chat'] = configSettingsJson['advance']['llmFineTuning']['modelParams']['LLaMA-2-Chat']
configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA2'] = configSettingsJson['advance']['llmFineTuning']['modelParams']['CodeLLaMA-2']
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2'] = \
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2']
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA2Chat'] = \
configSettingsJson['basic']['modelSize']['llmFineTuning']['LLaMA-2-Chat']
configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA2'] = \
configSettingsJson['basic']['modelSize']['llmFineTuning']['CodeLLaMA-2']
if 'distributedlearner_config' in configSettingsJson['advance']:
configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['DistributedXGBoost'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['DistributedLightGBM'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['classifierModelParams']['Distributed Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['DistributedXGBoost'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['Distributed Extreme Gradient Boosting (XGBoost)']
configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['DistributedLightGBM'] = configSettingsJson['advance']['distributedlearner_config']['modelParams']['regressorModelParams']['Distributed Light Gradient Boosting (LightGBM)']
configSettingsJson['advance']['profiler']['normalizationMethod'] = 'None'
normalizationtypes = configSettingsJson['advance']['profiler']['normalization']
for k in normalizationtypes.keys():
if configSettingsJson['advance']['profiler']['normalization'][k] == 'True':
configSettingsJson['advance']['profiler']['normalizationMethod'] = k
break
context = {'temp': temp, 'advconfig': configSettingsJson, 'MachineLearningModels':MachineLearningModels,'hyperparamConfig':hyperparamConfig,'config': config, 'selected_use_case': selected_use_case,
'categorical_features': categorical_features, 'selectedDebiasingFeature': selectedDebiasingFeature, 'selectedDebiasingAlgorithm': selectedDebiasingAlgorithm, 'Class_list': class_list, 'selectedDebiasingClass': selectedDebiasingClass, #Debiasing Changes
'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,"obModels":obModels,"problemType":problemType,
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],
'selected': 'modeltraning','advance_help':ht.advance_help}
return context
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context={'erroradvance':'Fail to load advance config Json file'}
return context
|
aionpipelinets.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import kfp
import kfp.dsl as dsl
import json
from pathlib import Path
class aionpipelinets():
containerRegistry = str()
containerLabel = str()
containerSecret = str()
pipelineName = 'AION MLOps Pipeline {0}'
exeCmd = 'python'
codeFile = 'aionCode.py'
mntPoint = '/aion'
inputArg = '-i'
msIP = '0.0.0.0'
port = '8094'
cachingStrategy = 'P0D'
deafultVolume = '2Gi'
volName = 'aion-pvc'
volMode = 'ReadWriteMany'
fileExt = '.tar.gz'
fileName = 'aion_mlops_pipeline_{0}'
containerMM = 'modelmonitoring'
containerDI = 'dataingestion'
containerDT = 'datatransformation'
containerFE = 'featureengineering'
containerMR = 'modelregistry'
containerMS = 'modelserving'
containerImage = '{0}/{1}:{2}'
models = {}
nameSeprator = '-'
modelsLiteral = 'models'
modelNameLiteral = 'modelname'
msTemplate = '{"apiVersion": "v1", "kind": "Pod", "metadata": {"name": "{{workflow.name}}-{0}"}, "spec": {"containers": [{"name": "{0}", "image": "{1}", "command": ["python"], "args": ["aionCode.py", "-ip", "{2}", "-pn", "{3}"],"volumeMounts": [{"name": "aion-pvc", "mountPath": "{4}"}], "ports": [{"name": "http", "containerPort": {3}, "protocol": "TCP"}]}], "imagePullSecrets": [{"name": "{5}"}], "volumes": [{"name": "aion-pvc", "persistentVolumeClaim": {"claimName": "{{workflow.name}}-{6}"}}]}}'
def __init__(self, models, containerRegistry, containerLabel, containerSecret=str()):
self.models = models
self.containerRegistry = containerRegistry
self.containerLabel = containerLabel
self.containerSecret = containerSecret
@dsl.pipeline(
name=pipelineName.format(containerLabel),
description=pipelineName.format(containerLabel),
)
def aion_mlops(self, inputUri=str(), volSize=deafultVolume):
vop = dsl.VolumeOp(
name=self.volName + self.nameSeprator + self.containerLabel,
resource_name=self.volName,
modes=[self.volMode],
size=volSize
)
mm = dsl.ContainerOp(
name=self.containerMM,
image=self.containerImage.format(self.containerRegistry,self.containerMM,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
self.inputArg,
inputUri,
],
pvolumes={self.mntPoint: vop.volume}
)
mm.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
di = dsl.ContainerOp(
name=self.containerDI,
image=self.containerImage.format(self.containerRegistry,self.containerDI,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: mm.pvolume}
)
di.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
dt = dsl.ContainerOp(
name=self.containerDT,
image=self.containerImage.format(self.containerRegistry,self.containerDT,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: di.pvolume}
)
dt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
fe = dsl.ContainerOp(
name=self.containerFE,
image=self.containerImage.format(self.containerRegistry,self.containerFE,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: dt.pvolume}
)
fe.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
dictMT = {}
listMTOps = []
for model in self.models[self.modelsLiteral]:
modelName = model[self.modelNameLiteral]
mt=dsl.ContainerOp(
name=modelName,
image=self.containerImage.format(self.containerRegistry,modelName,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes={self.mntPoint: fe.pvolume})
mt.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
listMTOps.append(mt)
dictMT[self.mntPoint]=mt.pvolume
mr = dsl.ContainerOp(
name=self.containerMR,
image=self.containerImage.format(self.containerRegistry,self.containerMR,self.containerLabel),
command=self.exeCmd,
arguments=[
self.codeFile,
],
pvolumes=dictMT
).after(*tuple(listMTOps))
mr.execution_options.caching_strategy.max_cache_staleness = self.cachingStrategy
msJson = self.msTemplate.replace(str({0}),self.containerMS).replace(str({1}),self.containerImage.format(self.containerRegistry,self.containerMS,self.containerLabel)).replace(str({2}),self.msIP).replace(str({3}),self.port).replace(str({4}),self.mntPoint).replace(str({5}),self.containerSecret).replace(str({6}),self.volName)
ms = dsl.ResourceOp(
name=self.containerMS + self.nameSeprator + self.containerLabel,
k8s_resource=json.loads(msJson),
)
ms.after(mr)
def compilepl(self, targetPath=str()):
filePath = self.fileName.format(self.containerLabel.lower()) + self.fileExt
if targetPath != str():
filePath = Path(targetPath, filePath)
kfp.compiler.Compiler().compile(self.aion_mlops, str(filePath))
def executepl(self, kfhost=str()):
client = kfp.Client(kfhost)
client.create_run_from_pipeline_func(self.aion_mlops,arguments={})
|
llm_textdatalabelling.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import pandas as pd
import requests
import re
import json
import sys
import time
from appbe.aion_config import get_llm_data
from appbe.dataPath import LOG_LOCATION
from appbe.log_ut import logg
import logging
import openai
import tiktoken
openai.api_key = ''
openai.api_base = ''
openai.api_type = ''
openai.api_version = ''
deployment_name="Text-Datvinci-03"
def generateLabelPerRecord(OrgData):
OrgData['LabelFromGPT'] = OrgData['Head_Description'].apply(lambda x: \
generate_gpt3_response\
("I am giving you the title and short description \
in the format [Title:Description], \
give me the related low level topics in one word in the \
format[Topic: your primary topic] along with top 5 important keywords in the \
format[Keywords: keywords]'{}' ".format(x)))
#Cleaning the output as it is from ChatGPT
OrgData['temp1'] = OrgData['LabelFromGPT'].apply(lambda x: (x.split('Topic:')[1]).replace(']',''))
OrgData['LabelFromGPT'] = OrgData['temp1'].apply(lambda x: (x.split('Keywords:')[0]).replace(']','').rstrip())
OrgData['Keywords'] = OrgData['temp1'].apply(lambda x: (x.split('Keywords:')[1]).replace(']',''))
OrgData = OrgData.drop(['temp1','Head_Description'], axis=1)
return OrgData
def generateLabelForChunkedRecords(OrgData):
import io
# OrgData = OrgData.head(120)
Head_Description = {"Head_Description": [] }
Head_Description2 = {"Head_Description": [] }
Head_Description['Head_Description'] = OrgData['Head_Description']
strt_ind = 0
brk_ind = 0
# encoding = tiktoken.get_encoding('p50k_base')
encoding = tiktoken.encoding_for_model("text-davinci-003")
chunks = []
_cur_token_count = 0
_chunk_token_count = 0
for ind in Head_Description['Head_Description'].index:
tokenized_text = encoding.encode(Head_Description['Head_Description'][ind])
_cur_token_count = len(tokenized_text)
if _cur_token_count >= 600:
OrgData['Head_Description'][ind] = OrgData['Head_Description'][ind][:1000]
upto_ind = ind + 1
Head_Description2['Head_Description'] = OrgData['Head_Description'][brk_ind:ind]
_chunk_token_count = encoding.encode(Head_Description2['Head_Description'].to_string())
if len(_chunk_token_count) >= 1200:
brk_ind = ind
# print(brk_ind)
chunks.append(ind-1)
_start_count = 0
if len(chunks) == 0:
output = generate_gpt3_response("I am giving you datatable of text records \
for each record give me the related low level topics in one word as a data column called Topic\
and important top five keywords as a data column called Keywords. \
Provide me record number as Record and these two data columns as datatable for each record in the given datatable and number of records should be equivalent to the number of records in the given datatable of text records. '{}' ".format(Head_Description['Head_Description']))
out = io.StringIO(output[2:])
df = pd.read_csv(out, sep='\t')
else:
chunks.append(len(Head_Description['Head_Description']))
for ind_val in chunks:
_cur_ind_val = ind_val
_recordsSent = 0
Head_Description = {"Head_Description": [] }
if _start_count == 0:
Head_Description['Head_Description'] = OrgData['Head_Description'][strt_ind:_cur_ind_val].to_string()
_recordsSent = len(OrgData['Head_Description'][strt_ind:_cur_ind_val])
else:
Head_Description['Head_Description'] = OrgData['Head_Description'][_pre_ind_val:_cur_ind_val].to_string()
_recordsSent = len(OrgData['Head_Description'][_pre_ind_val:_cur_ind_val])
_pre_ind_val = ind_val
# if _start_count <= 5:
output = generate_gpt3_response("I am giving you datatable of text records \
for each record give me the related low level topics in one word as a data column called Topic\
and important top five keywords as a data column called Keywords. \
Provide me record number as Record and these two data columns as datatable for each record in the given datatable and number of records should be equivalent to the number of records in the given datatable of text records. '{}' ".format(Head_Description['Head_Description']))
out = io.StringIO(output[2:])
if _start_count == 0:
df = pd.read_csv(out, sep='\t')
else:
df_tmp = pd.read_csv(out, sep='\t')
if len(df_tmp) > _recordsSent:
df_tmp = df_tmp.head(_recordsSent)
# df = df.append(df_tmp, ignore_index=True)
df = pd.concat([df, df_tmp], ignore_index=True)
_start_count += 1
OrgData['LabelFromGPT'] = df['Topic']
OrgData['Keywords'] = df['Keywords']
OrgData = OrgData.drop(['Head_Description'], axis=1)
return OrgData
# Text Data Labelling using LLM related changes
# --------------------------------------------------------
def generateTextLabel(request, DATA_FILE_PATH):
log = logging.getLogger('log_ux')
key,url,api_type,api_version = get_llm_data()
openai.api_key = key
openai.api_base = url
openai.api_type = api_type
openai.api_version = api_version
try:
features = request.POST.getlist('InputFeatures')
datapath = request.session['textdatapath']
OrgData = pd.read_csv(datapath)
# OrgData = OrgData.head(2000)
OrgData.fillna("", inplace = True)
OrgData['Head_Description'] = OrgData[features[0]]
if (len(features) > 1):
for indx in range(len(features)):
if (indx > 0):
OrgData['Head_Description'] = OrgData['Head_Description'] + " "+ OrgData[features[indx]]
# OrgData = generateLabelPerRecord(OrgData)
OrgData = generateLabelForChunkedRecords(OrgData)
df = OrgData
filetimestamp = str(int(time.time()))
datasetName = 'AION_TextLabelled' + filetimestamp+'.csv'
dataFile = os.path.join(DATA_FILE_PATH,datasetName)
df.to_csv(dataFile)
request.session['texttopicdatapath'] = dataFile
df_json = df.to_json(orient="records")
df_json = json.loads(df_json)
from appbe.dataPath import DATA_DIR
from appbe.sqliteUtility import sqlite_db
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
newdata = {}
newdata['datapath'] = [dataFile]
newdata['datasetname'] = [datasetName]
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata), 'dataingest')
################################################
context = {'data_topic':df_json, 'selected':'DataOperations'}
return context
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
errormsg = str(e)
if 'Invalid URL' in errormsg or 'No connection adapters' in errormsg or 'invalid subscription key' in errormsg:
errormsg = 'Access denied due to invalid subscription key or wrong API endpoint. Please go to settings and make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'
if 'Max retries exceeded with url' in errormsg:
errormsg = 'Please make sure you have good internet connection and access to API endpoint for your resource.'
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context = {'error': 'Failed to communicate LLM','LLM' : 'openAI', 'selected':'DataOperations', 'errormessage':errormsg}
log.info('generateTextLabel -- Error : Failed to generate Text-Label.. '+str(e))
log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return context
#function to return the queried response
def generate_gpt3_response(user_text, print_output=False):
"""
Query OpenAI GPT-3 for the specific key and get back a response
:type user_text: str the user's text to query for
:type print_output: boolean whether or not to print the raw output JSON
"""
time.sleep(2)
completions = openai.Completion.create(
# engine='Text-Datvinci-03', # Determines the quality, speed, and cost. engine='text-davinci-003',
engine=deployment_name, # Determines the quality, speed, and cost. engine='text-davinci-003',
temperature=0, # Level of creativity in the response
prompt=user_text, # What the user typed in
max_tokens=2000, # Maximum tokens in the prompt AND response
n=1, # The number of completions to generate
stop=None, # An optional setting to control response generation
)
# Displaying the output can be helpful if things go wrong
if print_output:
print(completions)
# Return the first choice's text
# print(completions.choices[0].text)
return completions.choices[0].text
# -------------------------------------------------------- |
gcsbuckets.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import os
import rsa
import boto3 #usnish
import pandas as pd
import time
def add_new_GCSBucket(request):
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
f.close()
if data == '':
data = []
except:
data = []
print(request.POST["aionreferencename"])
print(request.POST["serviceaccountkey"])
print(request.POST["bucketname"])
if request.POST["aionreferencename"] =='' or request.POST["serviceaccountkey"] == '' or request.POST["bucketname"] == '' :
return 'error'
newdata = {}
newdata['Name'] = request.POST["aionreferencename"]
newdata['GCSServiceAccountKey'] = request.POST["serviceaccountkey"]
newdata['GCSbucketname'] = request.POST["bucketname"]
data.append(newdata)
with open(file_path, 'w') as f:
json.dump(data, f)
f.close()
return 'success'
def get_gcs_bucket():
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
return data
def read_gcs_bucket(name,filename,DATA_FILE_PATH):
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','gcsbuckets.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
found = False
print(data)
for x in data:
if x['Name'] == name:
GCSServiceAccountKey = x['GCSServiceAccountKey']
GCSbucketname = x['GCSbucketname']
found = True
break
print(found)
print(name)
try:
if found:
import io
from google.cloud import storage
storage_client = storage.Client.from_service_account_json(GCSServiceAccountKey)
print(GCSServiceAccountKey)
print(GCSbucketname)
bucket = storage_client.get_bucket(GCSbucketname)
blob = bucket.blob(filename)
data = blob.download_as_string()
df = pd.read_csv(io.BytesIO(data), encoding = 'utf-8', sep = ',',encoding_errors= 'replace')
return 'Success',df
except Exception as e:
print(e)
return 'Error', pd.DataFrame() |
installPackage.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import platform
import shutil
import subprocess
import sys
import glob
from pathlib import Path
import json
from django.http import FileResponse
from django.http import HttpResponse
from importlib.metadata import version
COMMON_PACKAGES = "'setuptools >=62.3.0','pandas==1.5.3','numpy==1.24.2','joblib==1.2.0','Cython==0.29.33','scipy==1.10.1',' scikit-learn==1.2.1','word2number==1.1','category_encoders==2.6.0'"
DL_COMMON_PACKAGE = "'tensorflow==2.11.0'"
TEXT_PACKAGES = "'spacy==3.5.0','nltk==3.8.1','textblob==0.15.3','demoji==1.1.0','bs4==0.0.1','text-unidecode==1.3','pyspellchecker==0.6.2','contractions==0.1.73','protobuf==3.19.6','lxml'"
def createPackagePackage(request,id,version,usecasedetails,Existusecases):
from appbe.pages import get_usecase_page
#print('2')
usecasedetail = usecasedetails.objects.get(id=id)
models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS',Version=version)
modelid = models[0].id
p = Existusecases.objects.get(id=modelid)
deploymentfolder = str(p.DeployPath)
modelname = p.ModelName.usecaseid
version = p.Version
deployed_code = 'AION'
dockerimage = os.path.join(deploymentfolder,'publish','docker_image')
dockersetup = os.path.join(deploymentfolder,'publish','docker_setup')
tempPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'temp_'+modelname+'_'+str(version))
try:
shutil.rmtree(tempPath,ignore_errors=True)
except:
pass
shutil.copytree(deploymentfolder,tempPath)
shutil.rmtree(os.path.join(tempPath,'publish'), ignore_errors=True)
try:
Path(os.path.join(deploymentfolder,'publish')).mkdir(parents=True, exist_ok=True)
os.mkdir(dockersetup)
except:
shutil.rmtree(dockersetup,ignore_errors=True)
os.mkdir(dockersetup)
try:
os.mkdir(dockerimage)
except:
shutil.rmtree(dockerimage,ignore_errors=True)
os.mkdir(dockerimage)
shutil.copytree(tempPath, os.path.join(dockersetup,deployed_code))
shutil.rmtree(tempPath)
docker_setup = os.path.join(dockersetup,'AION')
try:
os.mkdir(dockerimage)
except:
pass
requirementfilename = os.path.join(dockersetup,'requirements.txt')
installfilename = os.path.join(dockersetup,'install.py')
dockerfile = os.path.join(dockersetup,'Dockerfile')
dockerdata='FROM python:3.10-slim-buster'
dockerdata+='\n'
dockerdata+='WORKDIR /app'
dockerdata+='\n'
dockerdata+='COPY AION AION'
dockerdata+='\n'
dockerdata+='''RUN apt-get update \
&& apt-get install -y build-essential manpages-dev \
&& apt-get install -y libgomp1 \
&& python -m pip install --no-cache-dir -r AION/requirements.txt
'''
f = open(dockerfile, "w")
f.write(str(dockerdata))
f.close()
try:
try:
import docker
client = docker.from_env()
client.containers.list()
except:
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
context['Status'] = 'Error'
context['Msg'] = 'Docker should be installed and running on your machine. To build the docker image manually, the setup script is available at the following location: \\n'+dockersetup.replace('\\', '/')
return context
command = 'docker pull python:3.10-slim-buster'
os.system(command);
subprocess.check_call(["docker", "build", "-t",modelname.lower()+":"+str(version),"."], cwd=dockersetup)
subprocess.check_call(["docker", "save", "-o",modelname.lower()+"_"+str(version)+".tar",modelname.lower()+":"+str(version)], cwd=dockersetup)
dockerfilepath = os.path.join(dockersetup,modelname.lower()+"_"+str(version)+".tar")
shutil.copyfile(dockerfilepath, os.path.join(dockerimage,modelname.lower()+"_"+str(version)+".tar"))
shutil.rmtree(dockersetup)
msg = 'Done'
Status = 'SUCCESS'
except Exception as e:
msg = 'Error in docker images creation. To build manually docker image setup available in following location: '+dockersetup.replace('\\', '\\\\')
Status = 'Fail'
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
context['Status'] = Status
context['Msg'] = msg
return context
def downloadPackage(request,id,version,usecasedetails,Existusecases):
try:
if 'downloadstatus' in request.session:
if request.session['downloadstatus'] == 'Downloading':
return HttpResponse(json.dumps("Error Creating Package"), content_type="application/error")
request.session['downloadstatus'] = 'Downloading'
usecasedetail = usecasedetails.objects.get(id=id)
models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS',Version=version)
modelid = models[0].id
p = Existusecases.objects.get(id=modelid)
deployPath = str(p.DeployPath)
if os.path.isdir(os.path.join(deployPath,'publish','package')):
for f in os.listdir(os.path.join(deployPath,'publish','package')):
if f.endswith('whl'):
os.remove(os.path.join(deployPath,'publish','package',f))
usecasename = p.ModelName.usecaseid
Version = p.Version
deployed_code = usecasename
targetname = usecasename+'_'+str(Version)
whl_dir_name = 'WHEEL_'+usecasename+'_'+str(Version)
deployLocation = os.path.join (deployPath,'..',whl_dir_name)
try:
os.makedirs(deployLocation)
except OSError as e:
shutil.rmtree(deployLocation)
os.makedirs(deployLocation)
shutil.copytree(deployPath,os.path.join(deployLocation,deployed_code))
initstring = 'import os'
initstring += '\n'
initstring += 'import sys'
initstring += '\n'
initstring += 'sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__))))'
filename = os.path.join(deployLocation,deployed_code,'__init__.py')
f = open(filename, "w")
f.write(str(initstring))
f.close()
textdata=0
learner_type = 'ml'
requirementfile = os.path.join(deployPath,'requirements.txt')
install_requires = ''
if os.path.exists(requirementfile):
fileobj = open(requirementfile, 'r')
requirePackages = fileobj.readlines()
fileobj.close()
for package in requirePackages:
if install_requires != '':
install_requires = install_requires+','
install_requires = install_requires+'\''+package.strip()+'\''
setup_string = 'from setuptools import setup,find_packages'
setup_string += '\n'
setup_string += 'setup(name=\''+deployed_code+'\','
setup_string += '\n'
setup_string += 'version=\'1\','
setup_string += '\n'
setup_string += 'packages = find_packages(),'
setup_string += '\n'
setup_string += 'install_requires = ['+install_requires+'],'
setup_string += '\n'
setup_string += 'package_data={"'+deployed_code+'.pytransform":["*.*"],"'+deployed_code+'":["*.sav","*.json"],"":["*","*/*","*/*/*"]}'
setup_string += '\n'
setup_string += ')'
filename = os.path.join(deployLocation,'setup.py')
f = open(filename, "w")
f.write(str(setup_string))
f.close()
subprocess.check_call([sys.executable, "setup.py", "bdist_wheel"], cwd=deployLocation)
shutil.copytree(os.path.join(deployLocation,'dist'),os.path.join(deployPath,'publish','package'),dirs_exist_ok=True)
shutil.rmtree(deployLocation)
if os.path.isdir(os.path.join(deployPath,'publish','package')):
for f in os.listdir(os.path.join(deployPath,'publish','package')):
if f.endswith('whl'):
package = f
zip_file = open(os.path.join(deployPath,'publish','package',package), 'rb')
request.session['downloadstatus'] = 'Done'
return FileResponse(zip_file)
except Exception as e:
print(e)
request.session['downloadstatus'] = 'Done'
return HttpResponse(json.dumps("Error Creating Package"), content_type="application/error")
def installPackage(model,version,deployedPath):
deployedPath = os.path.join(deployedPath,'publish','package')
whlfilename='na'
if os.path.isdir(deployedPath):
for file in os.listdir(deployedPath):
if file.endswith(".whl"):
whlfilename = os.path.join(deployedPath,file)
if whlfilename != 'na':
subprocess.check_call([sys.executable, "-m", "pip", "uninstall","-y",model])
subprocess.check_call([sys.executable, "-m", "pip", "install","--no-dependencies",whlfilename])
status,pid,ip,port = checkModelServiceRunning(model)
if status == 'Running':
stopService(pid)
startService(model,ip,port)
return('Success')
else:
return('Installation Package not Found')
def getMIDFromUseCaseVersion(id,version,usecasedetails,Existusecases):
usecasedetail = usecasedetails.objects.get(id=id)
models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS',Version=version)
return(models[0].id)
def stopService(pid):
import psutil
p = psutil.Process(int(pid))
p.terminate()
def checkModelServiceRunning(package_name):
from os.path import expanduser
home = expanduser("~")
if platform.system() == 'Windows':
modelServices = os.path.join(home,'AppData','Local','HCLT','AION','services')
else:
modelServices = os.path.join(home,'HCLT','AION','target','services')
filename = package_name+'_service.py'
modelservicefile = os.path.join(modelServices,filename)
status = 'Not Initialized'
ip = ''
port = ''
pid = ''
if os.path.exists(modelservicefile):
status = 'Not Running'
import psutil
for proc in psutil.process_iter():
pinfo = proc.as_dict(attrs=['pid', 'name', 'cmdline','connections'])
if 'python' in pinfo['name']:
if filename in pinfo['cmdline'][1]:
status = 'Running'
pid = pinfo['pid']
for x in pinfo['connections']:
ip = x.laddr.ip
port = x.laddr.port
return(status,pid,ip,port)
def startService(package_name,ip,portNo):
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','bin','model_service.py'))
from os.path import expanduser
home = expanduser("~")
if platform.system() == 'Windows':
modelServices = os.path.join(home,'AppData','Local','HCLT','AION','services')
else:
modelServices = os.path.join(home,'HCLT','AION','target','services')
if not os.path.isdir(modelServices):
os.makedirs(modelServices)
filename = package_name+'_service.py'
modelservicefile = os.path.join(modelServices,filename)
status = 'File Not Exist'
if os.path.exists(modelservicefile):
status = 'File Exist'
r = ([line.split() for line in subprocess.check_output("tasklist").splitlines()])
for i in range(len(r)):
if filename in r[i]:
status = 'Running'
if status == 'File Not Exist':
shutil.copy(file_path,modelservicefile)
with open(modelservicefile, 'r+') as file:
content = file.read()
file.seek(0, 0)
line = 'from '+package_name+' import aion_performance'
file.write(line+"\n")
line = 'from '+package_name+' import aion_drift'
file.write(line+ "\n")
line = 'from '+package_name+' import featureslist'
file.write(line+ "\n")
line = 'from '+package_name+' import aion_prediction'
file.write(line+ "\n")
file.write(content)
file.close()
status = 'File Exist'
if status == 'File Exist':
command = "python "+modelservicefile+' '+str(portNo)+' '+str(ip)
os.system('start cmd /c "'+command+'"')
def checkInstalledPackge(package_name):
import importlib.util
spec = importlib.util.find_spec(package_name)
if spec is None:
return('Not Installed','','')
else:
if len(spec.submodule_search_locations) > 0:
displaypath = os.path.join(spec.submodule_search_locations[0],'etc','display.json')
with open(displaypath) as file:
config = json.load(file)
file.close()
if 'usecasename' in config:
modelName = config['usecasename']
else:
modelName = 'NA'
if 'version' in config:
version = config['version']
else:
version = 'NA'
return('Installed',modelName,version) |
leaderboard.py | import pandas as pd
import numpy as np
def get_leaderboard(file_content):
matched_lines = [line.replace('Model:-', '') for line in file_content.split('\n') if "Model:-" in line]
df = pd.DataFrame(columns = ['Model', 'Iterations', 'Score (%)', 'Score Type', 'Best Score (%)'])
import re
try:
for line in matched_lines:
if 'Model Name::' in line:
MODEL = line.split('::')
model = MODEL[1]
if 'ScoringType::' in line:
S = line.split('::')
#SC = ScorTyp[1]
if 'make_scorer'in line:
ST = line.split('make_scorer')
ScorTyp = ST[1]
df['Score Type'] = np.where(df['Model'] == model, ScorTyp,df['Score Type'])
if 'Validation Score::' in line:
BS = line.split('::')
BestSc = round(float(BS[1]), 4)*100
BestSc = abs(BestSc)
df['Best Score (%)'] = np.where(df['Model'] == model, BestSc, df['Best Score (%)'])
if 'Iteration::' in line:
l = line.split('::')
word = re.findall(r'\[(.*?)\]', l[1])
if ';, score=' in line:
sc = line.split('score=')
SCR = sc[1].split(' ')
Score = round(float(SCR[0]), 4)*100
Score = abs(Score)
# df = df.concat({'Model': model, 'Iterations': word,'Score (%)': Scor,'Score Type': '', 'Best Score (%)': 0}, ignore_index=True)
newdf = pd.DataFrame([{'Model': model, 'Iterations': word,'Score (%)': Score,'Score Type': '', 'Best Score (%)': 0}])
df = pd.concat([df,newdf],axis=0, ignore_index=True)
LIST = []
for i in range(int(len(df['Score (%)'])/5)):
l = (sum(df['Score (%)'][5*i:5*(i+1)])/5)
#LIST.concat(l)
LIST.append(l)
for i in range(len(LIST)):
df['Score (%)'][5*i:5*(i+1)]=LIST[i]
CL = [line.replace('------->Type of Model :classification', 'Model :classification') for line in file_content.split('\n') if "------->Type of Model :classification" in line]
for l in CL:
if 'Model :classification' in l:
df = df.sort_values(by = ['Best Score (%)'], ascending=False)
RE = [line.replace('------->Type of Model :regression', 'Model :regression') for line in file_content.split('\n') if "------->Type of Model :regression" in line]
for l in RE:
if 'Model :regression' in l:
df = df.sort_values(by = ['Best Score (%)'])
except Exception as e:
print(e)
return df
if __name__ == "__main__":
file_path = r"C:\Users\richard.mochahari\AppData\Local\Programs\HCLTech\AION\data\target\AI0335\1\log\model_training_logs.log"
my_file = open(file_path, 'r')
file_content = my_file.read()
my_file.close()
print(get_leaderboard(file_content)) |
publishDataBase.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
encryptedPackage.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os.path
import time
import subprocess
import sys
from appbe.aion_config import kafka_setting
from appbe.aion_config import running_setting
from appbe import installPackage
from appbe import compute
from appbe.models import getusercasestatus
import json
import pandas as pd
import ntpath
import shutil
import platform
from pathlib import Path
from appbe.dataPath import DATA_DIR
LOG_FILE_PATH = os.path.join(DATA_DIR,'logs')
def encrptpackage_command(request,Existusecases,usecasedetails):
command = request.POST.get('encryptedsubmit')
kafkaSetting = kafka_setting()
ruuningSetting = running_setting()
computeinfrastructure = compute.readComputeConfig()
modelID = request.POST.get('modelID')
p = Existusecases.objects.get(id=modelID)
usecasename = p.ModelName.UsecaseName
usecaseid = p.ModelName.usecaseid
runningStatus,pid,ip,port = installPackage.checkModelServiceRunning(usecasename)
installationStatus,modelName,modelVersion=installPackage.checkInstalledPackge(usecasename)
try:
tacking_url =request.get_host()
except Exception as e:
tacking_url = '127.0.0.1'
usecasedetail = usecasedetails.objects.get(id=p.ModelName.id)
usecase = usecasedetails.objects.all()
models = Existusecases.objects.filter(ModelName=usecasedetail,Status='SUCCESS')
for model in models:
model.scoringCreteria = 'NA'
model.score = 'NA'
model.deploymodel = 'NA'
if os.path.isdir(str(model.DeployPath)):
modelPath = os.path.join(str(model.DeployPath),'etc','output.json')
try:
with open(modelPath) as file:
outputconfig = json.load(file)
file.close()
if outputconfig['status'] == 'SUCCESS':
model.scoringCreteria = outputconfig['data']['ScoreType']
model.score = outputconfig['data']['BestScore']
model.deploymodel = outputconfig['data']['BestModel']
model.modelType = outputconfig['data']['ModelType']
model.maacsupport = 'True'
model.flserversupport = 'False'
supportedmodels = ["Logistic Regression","Neural Network","Linear Regression"]
if model.deploymodel in supportedmodels:
model.flserversupport = 'True'
else:
model.flserversupport = 'False'
supportedmodels = ["Logistic Regression",
"Naive Bayes","Decision Tree","Support Vector Machine","K Nearest Neighbors","Gradient Boosting","Random Forest","Linear Regression","Lasso","Ridge","Extreme Gradient Boosting (XGBoost)","Light Gradient Boosting (LightGBM)","Categorical Boosting (CatBoost)"]
if model.deploymodel in supportedmodels:
model.maacsupport = 'True'
else:
model.maacsupport = 'False'
supportedmodels = ["Extreme Gradient Boosting (XGBoost)"]
if model.deploymodel in supportedmodels:
model.encryptionsupport = 'True'
else:
model.encryptionsupport = 'False'
except Exception as e:
pass
if command.lower() == 'secureclient':
try:
encryptedclient = os.path.join(str(p.DeployPath),'publish','SecureClient')
shutil.rmtree(encryptedclient, ignore_errors=True)
logPath = os.path.join(encryptedclient,'logs')
scriptPath = os.path.join(encryptedclient,'script')
modelPath = os.path.join(encryptedclient,'model')
Path(modelPath).mkdir(parents=True, exist_ok=True)
Path(encryptedclient).mkdir(parents=True, exist_ok=True)
Path(logPath).mkdir(parents=True, exist_ok=True)
Path(scriptPath).mkdir(parents=True, exist_ok=True)
encryptedclientOrg = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','encryptedPackage'))
modelProfiler = os.path.normpath(os.path.join(str(p.DeployPath),'script','inputprofiler.py'))
modelselector = os.path.normpath(os.path.join(str(p.DeployPath),'aion_predict.py'))
preprocessmodel = os.path.normpath(os.path.join(str(p.DeployPath),'model','preprocess_pipe.pkl'))
# shutil.copy2(modelProfiler,scriptPath)
# shutil.copy2(modelselector,scriptPath)
## For bug 15975
if os.path.exists(modelProfiler):
shutil.copy2(modelProfiler,scriptPath)
if os.path.exists(modelselector):
shutil.copy2(modelselector,scriptPath)
if os.path.exists(preprocessmodel):
shutil.copy2(preprocessmodel,modelPath)
if model.modelType.lower() == 'classification':
try:
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'Readme.txt'))
shutil.copy2(opfile,encryptedclient)
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'requirements.txt'))
shutil.copy2(opfile,encryptedclient)
except:
#failed to copy readme,requirements.txt files
pass
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','heMulticlass.py'))
shutil.copy2(opfile,scriptPath)
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','aion_hemulticlient.py'))
shutil.copy2(opfile,encryptedclient)
os.rename(os.path.join(encryptedclient,'aion_hemulticlient.py'),os.path.join(encryptedclient,'aion_sclient.py'))
elif model.modelType.lower() == 'regression':
try:
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'Readme.txt'))
shutil.copy2(opfile,encryptedclient)
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'requirements.txt'))
shutil.copy2(opfile,encryptedclient)
except Exception as e:
print(e)
#failed to copy readme,requirements.txt files
pass
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','heRegression.py'))
shutil.copy2(opfile,scriptPath)
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'client','aion_heregressionclient.py'))
shutil.copy2(opfile,encryptedclient)
os.rename(os.path.join(encryptedclient,'aion_hemulticlient.py'),os.path.join(encryptedclient,'aion_sclient.py'))
except Exception as e:
Status = 'Error'
Msg = 'Secure client error: Check log file for more details'
Status = 'SUCCESS'
Msg = 'Secure Client Code Generated at '+encryptedclient
path= encryptedclient #Task 9981
elif command.lower() == 'secureserver':
try:
configPath = os.path.join(str(p.DeployPath),'etc','secure_config.json')
modelpath = usecasename+'_'+str(p.Version)+'.sav'
config = {'model_name':modelpath}
with open(configPath, "w") as outfile:
json.dump(config, outfile)
encryptedclientOrg = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','utilities','encryptedPackage'))
if model.modelType.lower() == 'classification':
opfile = os.path.normpath(os.path.join(encryptedclientOrg,'server','heMulticlass.py'))
shutil.copy2(opfile,str(p.DeployPath))
try:
os.remove(os.path.join(str(p.DeployPath),'aion_spredict.py'))
except OSError:
pass
os.rename(os.path.join(str(p.DeployPath),'heMulticlass.py'),os.path.join(str(p.DeployPath),'aion_spredict.py'))
Status = 'SUCCESS'
Msg = 'Secure rest end point enabled http://'+str(tacking_url)+'/api/spredict?usecaseid='+usecaseid+'&version='+str(p.Version)
except Exception as e:
Status = 'Error'
Msg = 'Secure rest end point error: Check log file for more details'
nouc = 0
from appbe.pages import get_usecase_page
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
context['Status'] = Status
context['Msg'] = Msg
if command.lower() == 'secureclient': #Task 9981
context['path'] = path
'''
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
context = {'tab': 'upload','nouc':nouc,'usecasedetail': usecase, 'models': models, 'selected_use_case': selected_use_case,
'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion, 'selected': 'usecase','computeinfrastructure':computeinfrastructure,'installationStatus':installationStatus,'modelName':modelName,'modelVersion':modelVersion,'usecasename':usecasename,'pid':pid,'ip':ip,'port':port,'usecaseid':p.ModelName.id,'Status':Status,'Msg':Msg}
'''
return(context)
def download_sclient(request,context): #Task 9981
import os
from django.http import HttpResponse, Http404
try:
file_name = 'SecureClient_'+request.POST.get('modelsignature')
path = context['path']
file_path = shutil.make_archive(file_name, 'zip', path)
if os.path.exists(file_path):
with open(file_path, 'rb') as fh:
response = HttpResponse(fh.read(),content_type='application/x-zip-compressed')
response['Content-Disposition'] = 'inline; filename=' + os.path.basename(file_path)
os.remove(file_path)
return response
except:
raise Http404 |
labels.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
from pathlib import Path
def label_filename(request):
filename = 'LabeledData.csv'
labelPath = os.path.join(request.session['datalocation'],'AION','Labels')
Path(labelPath).mkdir(parents=True, exist_ok=True)
filePath = os.path.join(labelPath,filename)
return filePath
|
checkConfiguration.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import pandas as pd
def get_true_option(d, default_value=None):
if isinstance(d, dict):
for k, v in d.items():
if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True):
return k
return default_value
def get_true_options(d):
options = []
if isinstance(d, dict):
for k, v in d.items():
if (isinstance(v, str) and v.lower() == 'true') or (isinstance(v, bool) and v == True):
options.append(k)
return options
def check_datetime(config):
dateTime = config['basic']['dateTimeFeature']
if dateTime == '' or dateTime.lower()=='na':
return False
return True
def check_dtype(d):
flag= 1
for item in d:
if item["type"].lower() != "text" and item["type"].lower() != "index":
flag = 0
break
return flag
def check_text(d): #task 12627
flag= 0
for item in d:
if item["type"].lower() == "text":
flag = 1
break
return flag
def check_labelencoding(ftr_dict_list, target_ftr):
for ftr_dict in ftr_dict_list:
if ftr_dict['feature']!=target_ftr and ftr_dict['type'].lower()=='categorical' and ftr_dict['categoryEncoding'].lower()!='labelencoding':
return False
return True
class timeseries():
def __init__(self,config):
self.config=config
if self.config['basic']['analysisType']['timeSeriesForecasting'].lower()=='true': #task 11997
self.problemType = 'timeSeriesForecasting'
elif self.config['basic']['analysisType']['timeSeriesAnomalyDetection'].lower()=='true':
self.problemType = 'timeSeriesAnomalyDetection' #task 11997
def validate_basic_config(self,status='pass',msg=None):
#task 12627
date_time_status = check_datetime(self.config)
text_status = check_text(self.config['advance']['profiler']['featureDict'])
if not date_time_status and text_status:
msg = 'For time series problem,\\n* One feature should be in datetime format\\n* Text feature not supported '
return 'error', msg
elif not date_time_status:
msg = 'For time series problem, one feature should be in datetime format'
return 'error', msg
elif text_status:
msg = 'For time series problem, text feature not supported '
return 'error', msg
selected_algos = get_true_options(self.config['basic']['algorithms'][self.problemType]) #task 11997
if isinstance(self.config['basic']['targetFeature'],str):
targetFeature = list(self.config['basic']['targetFeature'].split(','))
if self.problemType=='timeSeriesForecasting': #task 11997
if len(targetFeature) > 1:
if 'ARIMA' in selected_algos:
status = 'error'
msg = "ARIMA is not supported for multilabel (target) feature"
return status, msg
if "FBPROPHET" in selected_algos:
status = 'error'
msg = "FBPROPHET is not supported for multiLabel (target) feature"
return status, msg
if 'MLP' in selected_algos:
status = 'error'
msg = "MLP is not supported for multiLabel (target) feature"
return status, msg
if len(targetFeature) == 1 and 'VAR' in selected_algos:
status = 'error'
msg = "VAR is not supported for singleLabel (target) feature"
return status, msg
elif self.problemType=='timeSeriesAnomalyDetection': #task 11997
anomChecker = anomaly(self.config)
status, msg = anomChecker.validate_basic_config()
return status, msg
class anomaly():
def __init__(self,config):
self.config = config
if self.config['basic']['analysisType']['anomalyDetection'].lower()=='true': #task 11997
self.problemType = 'anomalyDetection'
elif self.config['basic']['analysisType']['timeSeriesAnomalyDetection'].lower()=='true': #task 11997
self.problemType = 'timeSeriesAnomalyDetection'
def validate_basic_config(self,status='pass',msg=None):
#task 12627
date_time_status = check_datetime(self.config)
targetFeature = self.config['basic']['targetFeature']
if self.problemType=='anomalyDetection' and date_time_status:
status = 'error'
msg = 'Date feature detected. For anomaly detection on time series change problem type to Time Series Anomaly Detection or drop Date feature'
return status, msg
if targetFeature.lower()!= 'na' and targetFeature!= "" and self.config['basic']['inlierLabels'] == '':
status = 'error'
msg = 'Please provide inlier label in case of supervised anomaly detection'
return status, msg
class survival():
def __init__(self,config):
self.config = config
self.problemType= 'survivalAnalysis'
def validate_basic_config(self):
dateTimeStatus = check_datetime(self.config)
labelencoding_status = check_labelencoding(self.config['advance']['profiler']['featureDict'], self.config['basic']['targetFeature'])
if not dateTimeStatus and not labelencoding_status:
msg = 'For survival analysis problem,\\n* One feature should be in datetime format\\n* Encoding of categorical features should be of label encoding '
return 'error', msg
elif not dateTimeStatus:
msg = 'One feature should be in datetime format for survival analysis problem. Please select it from model feature'
return 'error', msg
elif not labelencoding_status:
msg = 'Categorical features are expected to be label encoded for survival analysis problem. Please select it from feature encoding'
return 'error', msg
else:
return 'pass', " "
class associationrule():
def __init__(self,config):
self.config=config
def validate_basic_config(self,status='pass', msg=None):
if self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'].lower() == '' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'].lower() == 'na' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'].lower() == '' or self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature'].lower() == 'na':
return "error","Make sure to configure invoice feature and item feature"
elif self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['invoiceNoFeature'] == self.config['basic']['algorithms']['recommenderSystem']['associationRulesConfig']['itemFeature']:
return "error","Make sure to invoice feature and item feature is configure correctly"
else:
return "pass", " "
class itemrating(): #task 6081
def __init__(self,config):
self.config = config
def validate_basic_config(self):
data_loc = self.config['basic']['dataLocation']
data_length = len(pd.read_csv(data_loc))
if data_length >= 1000000:
return 'error', "Recommender System can handle data up to 1 million records. Please try with a smaller dataset."
else:
return "pass"," "
class documentsimilarity():
def __init__(self,config):
self.config=config
def validate_basic_config(self,status='pass', msg=None):
flag = check_dtype(self.config['advance']['profiler']['featureDict'])
if flag == 1:
return "pass", " "
else:
msg="Make sure to change the feature type from Categorical to Text and drop Numerical features for document similarity"
return "error", msg
def validate(config):
try:
problem_type = get_true_option(config['basic']['analysisType'])
status = 'pass'
msg = ''
if 'timeseries' in problem_type.lower(): #task 11997
obj = timeseries(config)
elif problem_type.lower() == 'survivalanalysis':
obj = survival(config)
elif problem_type.lower() == 'anomalydetection':
obj = anomaly(config)
elif problem_type.lower() in ['similarityidentification','contextualsearch']:
obj = documentsimilarity(config)
elif problem_type.lower() == 'recommendersystem':
if config['basic']['algorithms']['recommenderSystem']['AssociationRules-Apriori'].lower() == 'true':
obj = associationrule(config)
elif config['basic']['algorithms']['recommenderSystem']['ItemRating'].lower() == 'true': #task 6081
obj = itemrating(config)
else:
return 'pass',""
else:
return 'pass',""
status,msg= obj.validate_basic_config()
print(status, msg, 'io')
return(status,msg)
except Exception as e:
print(e)
def start_check(config):
return validate(config)
|
service_url.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os,sys
def read_service_url_params(request):
hosturl =request.get_host()
url='http://'+hosturl+'/api/'
return url
def read_monitoring_service_url_params(request):
hosturl =request.get_host()
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config'))
file = open(file_path, "r")
data = file.read()
file.close()
service_url = '127.0.0.1'
service_port='60050'
for line in data.splitlines():
if 'aion_service_url=' in line:
service_url= line.split('=',1)[1]
if 'aion_service_port=' in line:
service_port= line.split('=',1)[1]
url='http://'+hosturl+'/api/'
return url
def read_performance_service_url_params(request):
hosturl =request.get_host()
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config'))
file = open(file_path, "r")
data = file.read()
file.close()
service_url = '127.0.0.1'
service_port='60050'
for line in data.splitlines():
if 'aion_service_url=' in line:
service_url= line.split('=',1)[1]
if 'aion_service_port=' in line:
service_port= line.split('=',1)[1]
url='http://'+hosturl+'/api/'
return url
def read_pattern_anomaly_url_params(request):
hosturl =request.get_host()
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config'))
file = open(file_path, "r")
data = file.read()
file.close()
service_url = '127.0.0.1'
service_port='60050'
for line in data.splitlines():
if 'aion_service_url=' in line:
service_url= line.split('=',1)[1]
if 'aion_service_port=' in line:
service_port= line.split('=',1)[1]
url='http://'+hosturl+'/api/pattern_anomaly_predict/'
return url
def read_pattern_anomaly_setting_url_params(request):
hosturl =request.get_host()
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','aion.config'))
file = open(file_path, "r")
data = file.read()
file.close()
service_url = '127.0.0.1'
service_port='60050'
for line in data.splitlines():
if 'aion_service_url=' in line:
service_url= line.split('=',1)[1]
if 'aion_service_port=' in line:
service_port= line.split('=',1)[1]
url='http://'+hosturl+'/api/pattern_anomaly_settings/'
return url |
data_io.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
#Standard Library modules
import sqlite3
import pandas as pd
from pathlib import Path
class sqlite_writer():
def __init__(self, target_path):
self.target_path = Path(target_path)
database_file = self.target_path.stem + '.db'
self.db = sqlite_db(self.target_path, database_file)
def file_exists(self, file):
if file:
return self.db.table_exists(file)
else:
return False
def read(self, file):
return self.db.read(file)
def write(self, data, file):
self.db.write(data, file)
def close(self):
self.db.close()
class sqlite_db():
def __init__(self, location, database_file=None):
if not isinstance(location, Path):
location = Path(location)
if database_file:
self.database_name = database_file
else:
self.database_name = location.stem + '.db'
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
self.tables = []
def table_exists(self, name):
if name in self.tables:
return True
elif name:
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
listOfTables = self.cursor.execute(query).fetchall()
if len(listOfTables) > 0 :
self.tables.append(name)
return True
return False
def read(self, table_name,condition=''):
if condition == '':
return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
else:
return pd.read_sql_query(f"SELECT * FROM {table_name} WHERE {condition}", self.conn)
def create_table(self,name, columns, dtypes):
query = f'CREATE TABLE IF NOT EXISTS {name} ('
for column, data_type in zip(columns, dtypes):
query += f"'{column}' TEXT,"
query = query[:-1]
query += ');'
self.conn.execute(query)
return True
def update(self,table_name,updates,condition):
update_query = f'UPDATE {table_name} SET {updates} WHERE {condition}'
self.cursor.execute(update_query)
self.conn.commit()
return True
def write(self,data, table_name):
if not self.table_exists(table_name):
self.create_table(table_name, data.columns, data.dtypes)
tuple_data = list(data.itertuples(index=False, name=None))
insert_query = f'INSERT INTO {table_name} VALUES('
for i in range(len(data.columns)):
insert_query += '?,'
insert_query = insert_query[:-1] + ')'
self.cursor.executemany(insert_query, tuple_data)
self.conn.commit()
return True
def delete(self, name):
pass
def close(self):
self.conn.close()
|
help_Text.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
# def exploratorory_help():
#
#
#
# return (data_overview_tip, feature_importance_tip, correlation_analysis_tip, exploratory_analysis_tip, data_deep_drive_tip, drift_tip)
drift_tip = 'A data distribution represents a list of all of the possible values of each of the variables as provided in the data. Based on how the data values are distributed, it can be mapped to some well-known distribution curves so that the nature of the distribution can be shown.'
data_overview_tip = 'Data Overview give users a quick understanding of the distribution of values across the features and provides summary statistics of the features. It helps to uncover several uncommon and common issues such as unexpected feature values, missing feature values and data skew.'
timeseries_analysis_tip = "Time Series Analysis provides information about the stationarity and seasonality of each of the features in the ingested data."
feature_importance_tip = 'Feature Importance provides a features and grades the features on a scale of relative importance'
correlation_analysis_tip = 'Correlation Analysis provides the strength of relationships among various features. Values range from 0 (least correlation) to 1 (highest correlation). A high correlation means that two or more variables have a strong relationship with each other, while a weak correlation means that the variables are hardly related.'
exploratory_analysis_tip = 'This provides an unsupervised clustering view of the data and provides insights on how the data is distributed. It helps profile the attributes of different clusters and gives insight into underlying patterns of different clusters and find similarities in the data points.'
data_deep_drive_tip = 'Data Deep Dive provides an interactive interface for exploring the relationship between data points across all the different features of a dataset. Each individual item in the visualization represents a data point. Data can be grouped and binned in multiple dimensions based on their feature values.'
pair_graph_tip = 'It is used to present the correlations between two selected features.'
fair_metrics_tip = 'It provides interface to detect the bias in data associated with a sensitive or protected attribute and used for training.'
hopkins_tip =['Since the value is in between (0.0, 0.3), it indicates that the data has a high tendency to cluster.','Since the value is around 0.5, it indicates that the data distriution is random.','Since the value is in between (0.7, 0.99), it indicates that the data is regularly spaced.']
basic_help={'RowFiltering':'You can easily filter rows based on whether the column match a condition or not'}
advance_help = {'NumericFillMethod':'This is used to handle the null values present in the numerical dataset.','NumericFillMethod_Median':'Replace with middle value of the data set. Efficient and not affected by outliers.','NumericFillMethod_Mean':'Replace with average value of the columns. Affected by outliers.','NumericFillMethod_Max':'Replace all nulls with maximum value in the column.','NumericFillMethod_KNN':'This implements KNN algorithm to replace the null','NumericFillMethod_Zero':'Replace the null with 0 value','NumericFillMethod_Drop':'To remove all the null values in the dataset','NumericFillMethod_Min':'Replace all null with minimum value present in the column','CategoricalFillMethod':'This is used to handle the null values present in the categorical dataset.','CategoricalFillMethod_Mode':'Replace with most common values in the dataset. Suggested for categorical columns.','CategoricalFillMethod_Zero':'Replace the null with 0 value.','CategoricalFillMethod_KNN':'This implements KNN algorithm to replace the null','CategoricalFillMethod_Drop':'To remove all the null values in the dataset.','OutlierDetection':'An unusual data point that differs significantly from other data points.','OutlierDetection_IQR':'Identifying the outliers with interquatile range by dividing the data into quartiles.','OutlierDetection_Zscore':'If the z score of a data point is more than 3, it indicates that the data point is an outlier.','OutlierDetection_Isolation':'Randomly sub-sampled data is processed in a tree structure based on randomly selected features.','MissValueRatio':'Permitted Missing Value Ratio i.e., Number of missing values by total number of obervation. If the number of missing value in a columns is more than ratio than the columns will be assumped as empty column','NumericFeatureRatio':'In case column is mix of number and text value. If the number of numeric columns to number of rows ratio is greator than the value mentioned it is assumed as numeric columns and remaining rows which have text values will be removed','NormalStandard':'Standardize features by removing the mean and scaling to unit variance.','NormalMinMax':'This scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.','NormalLogNormal':'When a feature does not follow a linear distributio, that helps minimize skewness and map any distribution to a normal one as close as possible.','RemoveNoise':'Used to remove the noise present in the text data. Noise like special characters, unicode, emojis, hyperlinks,hashtags, html parameters etc.','ExpandContractions':'Contractions are words or combinations of words that are shortened by dropping letters and replacing them by an apostrophe.','Normalize':'Normalization is the process of converting a token into its base form. In the normalization process, the inflectional form of a word is removed so that the base form can be obtained.','Lemmatization':'It is a more effective option than stemming because it converts the word into its root word, rather than just stripping the suffices.','Stemming':'It refers to the removal of suffices, like ing,ly,s etc. by a simple rule-based approach.','NGrams':'The combination of multiple words used together.','PosTags':'The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, or simply POS-tagging.','FeatureSelection':'Feature selection is for filtering irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.','FeatureEngineering':'Feature extraction is for creating a new, smaller set of features that stills captures most of the useful information. Again, feature selection keeps a subset of the original features while feature extraction creates new ones.','PCA':'Principle Component Analysis (PCA) is a common feature extraction method in data science. Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or less dimensions.','StatisticalBased':'Features are selected on the basis of statistics measures. This method does not depend on the learning algorithm and chooses the features as a pre-processing step. The filter method filters out the irrelevant feature and redundant columns from the model by using different metrics through ranking.','ModelBased':'Different tree-based methods of feature selection help us with feature importance to provide a way of selecting features. Here, feature importance specifies which feature has more importance in model building or has a great impact on the target variable.','CorrelationThreshold':'Correlation Threshold for Statistican Based Feature Selection. Correlation relation analysis done on input features vs target feature and features having correlation value grather then threshold picks for training','PValue':'P Value again for Statistical Based Feature Selection','Variance':'For Feature Selection, features should have higher variance from threshold.','Normalization':'The goal of normalization is to change the values of numeric columns in the dataset to use a common scale , without distoring differences in the ranges of values or losing information.','SVD':'The singular value decomposition (SVD) provides another way to factorize a matrix, into singular vectors and singular values. The SVD allows us to discover some of the same kind of information as the eigendecomposition.','ReplaceAcro':'Replace any abrivations into its full form Eg:{"DM":"DirectMessage"}',
'Factoranalysis':' This algorithm creates factors from the observed variables to represent the common variance i.e. variance due to correlation among the observed variables.','ICA':'ICA stands for Independent Components Analysis and it is a linear dimension reduction method, which transforms the dataset into columns of independent components.','optimizationmethod':'Optimization is the process where we train the model iteratively that results in a maximum and minimum function evaluation.','Random':'Random search is a method in which random combinations of hyperparameters are selected and used to train a model. The best random hyperparameter combinations are used. Random search bears some similarity to grid search.','Grid':'Grid search is essentially an optimization algorithm which lets to select the best parameters for your optimization problemfrom a list of parameter options that provided, hence automating the trial-and-error method.','Bays':'Bayesian optimisation in turn takes into account past evaluations when choosing the hyperparameter set to evaluate next. This approach typically requires less iterations to get to the optimal set of hyperparameter values.','Stopwords':'Stop words are commonly eliminated which are commonly used that they carry very little useful information. They are passed in a list ["Stopword1","Stopword2"]','Tokenization':'It is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Choose the library for tokenization','Lemma':'In lemmatization, the transformation uses a dictionary to map different variants of a word back to its root format.','Stopwords1':'Stop words are commonly eliminated which are commonly used that they carry very little useful information.Select from the below library to remove them',
'Genetic':'The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population evolves toward an optimal solution.','CV':'Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into.','Ensemble':'Ensemble learning is a general meta approach to machine learning that seeks better predictive performance by combining the predictions from multiple models.','EnsembleStatus':'Enable or disable according to the preference','TargetEncoding':'Target encoding is the process of replacing a categorical value with the mean of the target variable','OneHotEndoding':'Encode categorical features as a one-hot numeric array.','LabelEncoding':'Encode target labels with value between 0 and n_classes-1.','SMCStrategy':'A most_frequent model - The default. In regression the prediction is equal to the mean value, in classification the prediction is equal to the most common value.\n A uniform model - In regression, selects a random value from the y range. In classification, selects one of the labels by random.\n A stratified model - Draws the prediction from the distribution of the labels in the train.\n A tree model - Trains a simple decision tree with a given depth. The depth can be customized using the max_depth parameter.','SMCGain':'The gain is calculated as:\ngain = (model score - simple score)/(perfect score - simple score)','SMCTreeDepth':'the max depth of the tree (used only if simple model type is tree).','MIcondition':'Measure model average inference time (in seconds) per sample'} |
create_dummy_dataset.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import random
import string
from sklearn import datasets
import pandas as pd
import names # pip install names
import time
import numpy as np
import argparse
import json
import os
import platform
import time
import sys
from appbe.dataPath import CONFIG_FILE_PATH
def randStr(chars = 'XYZABCDE', N=2):
return ''.join(random.choice(chars) for _ in range(N))
def load_json_config(file):
with open(file, 'r') as openfile:
json_object = json.load(openfile)
for key, value in json_object.items():
print(key, value)
return json_object
def gen_data_classification(number_samples=10000, number_numerical_features=25,
file_name='file_class.csv', number_categorical_features=2,
number_text_features=2,
missing_proportion=0.1,
number_informative=20, number_class=2,
weights=[0.5,0.5], shift=0.0,
value_range_dict={0:(1, 2)}):
# TO-DO: need to add min max vlinear/non-linear
try:
features, output = datasets.make_classification(
n_samples=number_samples,
n_features=number_numerical_features,
n_informative=number_informative,
n_classes=number_class,
weights = weights, # 20% of the targets will be 0, 80% will be 1. default is 50/50
shift=shift,
)
columns = []
# Numerical Features
for i in range(number_numerical_features):
columns.append('Feature_' + str(i))
features = pd.DataFrame(features, columns=columns)
# Setting min max value for features
for col_name in features.columns:
for key, value in value_range_dict.items():
if (str(features.columns.get_loc(col_name)) == key):
for item in features[col_name].values:
if item < value[0]:
features.loc[features[col_name] == item,
col_name] = random.uniform(value[0],value[1])
if item > value[1]:
features.loc[features[col_name] == item,
col_name] = random.uniform(value[0],value[1])
df_list = []
df_list.append(features)
# Add Categorical Features
for j in range(number_categorical_features):
categorical_feature1_list = []
number_categories_per_feature = random.randint(2, 5)
for i in range(number_categories_per_feature):
categorical_feature1_list.append(randStr(N=3))
print("Categories of Categorical Feature " + str(j) + ": ", categorical_feature1_list)
categorical_feature1 = []
for k in range(number_samples):
categorical_feature1.append(random.choice(categorical_feature1_list))
categorical_feature1 = pd.DataFrame(categorical_feature1, columns=['Categorical'+str(j)])
df_list.append(categorical_feature1)
# Add Text Features
for l in range(number_text_features):
text_feature = []
for k in range(number_samples):
text_feature.append(names.get_full_name())
# text_feature.append(r.get_random_word())
text_feature = pd.DataFrame(text_feature, columns=['Name'+str(l)])
# text_feature = pd.DataFrame(text_feature, columns=['Word' + str(l)])
df_list.append(text_feature)
output = pd.DataFrame(output, columns=['Target'])
df_list.append(output)
df_final = pd.concat(df_list, axis=1)
for col in df_final.columns:
# df_final.loc[df_final.sample(frac=0.1).index, col] = np.NaN
df_final.loc[df_final[col].sample(frac=missing_proportion).index, col] = np.NaN
# Check to see proportion of NaN values:
# df.isnull().sum() / len(df)
df_final.to_csv(file_name)
return True
except Exception as e:
print(e)
return False
def gen_data_regression(
number_samples=10000, number_numerical_features=25,
file_name='file_regress.csv', number_categorical_features=2,
number_text_features=2,
missing_proportion=0.1,
number_informative=10,
number_target=1, bias=0.0, noise=0.0,
value_range_dict={1:(5, 10)}
):
try:
features, output = datasets.make_regression(
n_samples=number_samples,
n_features=number_numerical_features,
n_informative=number_informative,
n_targets=number_target,
bias=bias,
noise=noise,
)
columns = []
for i in range(number_numerical_features):
columns.append('Feature_' + str(i))
features = pd.DataFrame(features, columns=columns)
for col_name in features.columns:
for key, value in value_range_dict.items():
if (str(features.columns.get_loc(col_name)) == key):
for item in features[col_name].values:
if item < value[0]:
features.loc[features[col_name] == item,
col_name] = random.uniform(value[0],value[1])
if item > value[1]:
features.loc[features[col_name] == item,
col_name] = random.uniform(value[0],value[1])
df_list = []
df_list.append(features)
for j in range(number_categorical_features):
categorical_feature1_list = []
number_categories_per_feature = random.randint(2, 5)
for i in range(number_categories_per_feature):
categorical_feature1_list.append(randStr(N=3))
print("Categories of Categorical Feature " + str(j) + ": ", categorical_feature1_list)
categorical_feature1 = []
for k in range(number_samples):
categorical_feature1.append(random.choice(categorical_feature1_list))
categorical_feature1 = pd.DataFrame(categorical_feature1, columns=['Categorical' + str(j)])
df_list.append(categorical_feature1)
for l in range(number_text_features):
text_feature = []
for k in range(number_samples):
text_feature.append(names.get_full_name())
text_feature = pd.DataFrame(text_feature, columns=['Name'+str(l)])
df_list.append(text_feature)
output = pd.DataFrame(output, columns=['Target'])
df_list.append(output)
df_final = pd.concat(df_list, axis=1)
for col in df_final.columns:
# df_final.loc[df_final.sample(frac=0.1).index, col] = np.NaN
df_final.loc[df_final[col].sample(frac=missing_proportion).index, col] = np.NaN
# Check to see proportion of NaN values:
# df.isnull().sum() / len(df)
df_final.to_csv(file_name)
return True
except Exception as e:
print(e)
return False
def gen_data_series(univariate="True",
start_time='2000-01-01 00:00',
end_time='2022-12-31 00:00',
number_samples=10000, number_numerical_features=25,
file_name='file_regress.csv', number_categorical_features=2,
# number_text_features=2,
missing_proportion=0.1,
number_informative=10,
number_target=1, bias=0.0, noise=0.0,
value_range_dict={1:(5, 10)}
):
try:
if univariate == "True":
number_numerical_features = 1
number_categorical_features = 0
features, output = datasets.make_regression(
n_samples=number_samples,
n_features=number_numerical_features,
n_informative=number_informative,
n_targets=number_target,
bias=bias,
noise=noise,
)
columns = []
# Numerical Features
for i in range(number_numerical_features):
columns.append('Feature_' + str(i))
features = pd.DataFrame(features, columns=columns)
# Setting min max value for features
for col_name in features.columns:
for key, value in value_range_dict.items():
if (str(features.columns.get_loc(col_name)) == key):
for item in features[col_name].values:
if item < value[0]:
features.loc[features[col_name] == item,
col_name] = random.uniform(value[0],value[1])
if item > value[1]:
features.loc[features[col_name] == item,
col_name] = random.uniform(value[0],value[1])
df_list = []
df_list.append(features)
# Add Categorical Features
for j in range(number_categorical_features):
categorical_feature1_list = []
number_categories_per_feature = random.randint(2, 5)
for i in range(number_categories_per_feature):
categorical_feature1_list.append(randStr(N=3))
print("Categories of Categorical Feature " + str(j) + ": ", categorical_feature1_list)
categorical_feature1 = []
for k in range(number_samples):
categorical_feature1.append(random.choice(categorical_feature1_list))
categorical_feature1 = pd.DataFrame(categorical_feature1, columns=['Categorical'+str(j)])
df_list.append(categorical_feature1)
# df2['date'] = pd.date_range(start='1890-01-01', freq="sec",periods=len(df2))
time_feature = pd.date_range(start=start_time, end=end_time, periods=number_samples) #freq="1sec"
time_feature = pd.DataFrame(time_feature, columns=['Date'])
# df_list.append(time_feature)
df_list.insert(0, time_feature)
output = pd.DataFrame(output, columns=['Feature_' + str(number_numerical_features)])
if univariate != "True":
df_list.append(output)
df_final = pd.concat(df_list, axis=1)
for col in df_final.columns:
# df_final.loc[df_final.sample(frac=0.1).index, col] = np.NaN
df_final.loc[df_final[col].sample(frac=missing_proportion).index, col] = np.NaN
# Check to see proportion of NaN values:
# df.isnull().sum() / len(df)
df_final.to_csv(file_name)
return True
except Exception as e:
print(e)
return False
def data_generated_csv():
datajson = os.path.join(CONFIG_FILE_PATH, 'data_generated.json')
with open(datajson, 'r+') as f:
dictionary = json.load(f)
# f.close()
if dictionary.get('problemType') == 'classification':
number_samples = dictionary.get("number_samples")
number_numerical_features = dictionary.get("number_numerical_features")
number_categorical_features = dictionary.get("number_categorical_features")
number_text_features = dictionary.get("number_text_features")
missing_proportion = dictionary.get("missing_proportion")
number_informative = dictionary.get("number_informative")
number_class = dictionary.get("number_class")
weights = dictionary.get("weights")
shift = dictionary.get("shift")
data_path = dictionary.get("data_path")
value_range_dict = dictionary.get("value_range_dict")
gen_data_classification(number_samples=number_samples,
number_numerical_features=number_numerical_features,
file_name=data_path,
number_categorical_features=number_categorical_features,
number_text_features=number_text_features,
missing_proportion=missing_proportion,
number_informative=number_informative,
number_class=number_class, weights=weights,
shift=shift, value_range_dict=value_range_dict)
elif dictionary.get('problemType') == 'regression':
number_samples = dictionary.get("number_samples")
number_numerical_features = dictionary.get("number_numerical_features")
number_categorical_features = dictionary.get("number_categorical_features")
number_text_features = dictionary.get("number_text_features")
missing_proportion = dictionary.get("missing_proportion")
number_informative = dictionary.get("number_informative")
number_target = dictionary.get("number_target")
bias = dictionary.get("bias")
noise = dictionary.get("noise")
data_path = dictionary.get("data_path")
value_range_dict = dictionary.get("value_range_dict")
gen_data_regression(number_samples=number_samples,
number_numerical_features=number_numerical_features,
file_name=data_path,
number_categorical_features=number_categorical_features,
number_text_features=number_text_features,
missing_proportion=missing_proportion,
number_informative=number_informative,
number_target=number_target, bias=bias,
noise=noise, value_range_dict=value_range_dict)
elif dictionary.get('problemType') == 'timeseriesforecasting': #task 11997
data_path = dictionary.get("data_path")
is_univariate = dictionary.get("univariate")
number_samples = dictionary.get("number_samples")
number_numerical_features = dictionary.get("number_numerical_features")
number_categorical_features = dictionary.get("number_categorical_features")
missing_proportion = dictionary.get("missing_proportion")
number_informative = dictionary.get("number_informative")
number_target = dictionary.get("number_target")
bias = dictionary.get("bias")
noise = dictionary.get("noise")
value_range_dict = dictionary.get("value_range_dict")
gen_data_series(univariate=is_univariate,
number_samples=number_samples,
number_numerical_features=number_numerical_features,
file_name=data_path,
number_categorical_features=number_categorical_features,
# number_text_features=2,
missing_proportion=missing_proportion,
number_informative=number_informative,
number_target=number_target, bias=bias,
noise=noise,
value_range_dict=value_range_dict)
if __name__ == "__main__":
data_generated_csv()
|
gcsbucketsDB.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import sqlite3
from pathlib import Path
import json
import os
import rsa
import boto3 #usnish
import pandas as pd
import time
import sqlite3
class sqlite_db():
def __init__(self, location, database_file=None):
if not isinstance(location, Path):
location = Path(location)
if database_file:
self.database_name = database_file
else:
self.database_name = location.stem
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
def table_exists(self, name):
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
listOfTables = self.cursor.execute(query).fetchall()
return len(listOfTables) > 0
def read_data(self, table_name):
query = f"SELECT * FROM {table_name}"
row = self.cursor.execute(query).fetchall()
return list(row)
#return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
def create_table(self,name, columns, dtypes):
query = f'CREATE TABLE IF NOT EXISTS {name} ('
for column, data_type in zip(columns, dtypes):
query += f"'{column}' TEXT,"
query = query[:-1]
query += ');'
self.conn.execute(query)
return True
def delete_record(self,table_name,col_name, col_value):
try:
query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'"
self.conn.execute(query)
self.conn.commit()
return 'success'
except Exception as e :
print(str(e))
print("Deletion Failed")
return 'error'
def get_data(self,table_name,col_name,col_value):
query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'"
row = self.cursor.execute(query).fetchone()
if(row == None):
return []
return list(row)
def write_data(self,data, table_name):
if not self.table_exists(table_name):
self.create_table(table_name, data.columns, data.dtypes)
tuple_data = list(data.itertuples(index=False, name=None))
insert_query = f'INSERT INTO {table_name} VALUES('
for i in range(len(data.columns)):
insert_query += '?,'
insert_query = insert_query[:-1] + ')'
self.cursor.executemany(insert_query, tuple_data)
self.conn.commit()
return True
def close(self):
self.conn.close()
def add_new_GCSBucket(request):
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
print(request.POST["aionreferencename"])
print(request.POST["serviceaccountkey"])
print(request.POST["bucketname"])
if request.POST["aionreferencename"] =='' or request.POST["serviceaccountkey"] == '' or request.POST["bucketname"] == '' :
return 'error'
newdata = {}
newdata['Name'] = [request.POST["aionreferencename"]]
newdata['GCSServiceAccountKey'] = [request.POST["serviceaccountkey"]]
newdata['GCSbucketname'] = [request.POST["bucketname"]]
name = request.POST["aionreferencename"]
if sqlite_obj.table_exists("gcsbucket"):
if(len(sqlite_obj.get_data("gcsbucket",'Name',name))>0):
return 'error1'
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'gcsbucket')
except:
return 'error'
def get_gcs_bucket():
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
temp_data = sqlite_obj.read_data('gcsbucket')
data = []
for x in temp_data:
data_dict = {}
data_dict['Name'] = x[0]
data_dict['GCSServiceAccountKey'] = x[1]
data_dict['GCSbucketname'] = x[2]
data.append(data_dict)
except Exception as e:
print(e)
data = []
return data
def read_gcs_bucket(name,filename,DATA_FILE_PATH):
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
data = sqlite_obj.get_data("gcsbucket",'Name',name)
except:
data = []
found = False
if len(data)!=0:
GCSServiceAccountKey = data[1]
GCSbucketname = data[2]
found = True
#print(found)
#print(name)
try:
if found:
import io
from google.cloud import storage
#print(GCSServiceAccountKey)
#print(GCSbucketname)
try:
storage_client = storage.Client.from_service_account_json(GCSServiceAccountKey)
bucket = storage_client.get_bucket(GCSbucketname)
blob = bucket.blob(filename)
data = blob.download_as_string()
df = pd.read_csv(io.BytesIO(data), encoding = 'utf-8', sep = ',',encoding_errors= 'replace')
except Exception as e:
return "Error",str(e), pd.DataFrame()
return 'Success',"",df
except Exception as e:
print(e)
return 'Error',"Please check bucket configuration",pd.DataFrame()
def remove_gcs_bucket(name):
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
return sqlite_obj.delete_record('gcsbucket','Name',name)
|
dataIngestion.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import pandas as pd
import requests
from io import StringIO
import json
import time
import shutil
import sys
from appbe import compute
from appbe.aion_config import kafka_setting
from appbe.aion_config import running_setting
from appbe.s3bucketsDB import get_s3_bucket
from appbe.gcsbucketsDB import get_gcs_bucket
from appbe.azureStorageDB import get_azureStorage
from appbe.aion_config import eda_setting
from appbe.s3bucketsDB import read_s3_bucket
from appbe.gcsbucketsDB import read_gcs_bucket
from appbe.azureStorageDB import read_azureStorage
from appbe.validatecsv import csv_validator
import time
from appbe.dataPath import LOG_LOCATION
from appbe.dataPath import DATA_FILE_PATH
from appbe.log_ut import logg
import logging
def langchain_splittext(filename):
try:
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
loader = PyPDFLoader(filename)
pages = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=50)
texts = text_splitter.split_documents(pages)
return(texts)
except Exception as e:
print(e)
def pd_lanfchain_textsplitter(datalocation,data):
try:
document=[]
for i in range(len(data)):
filename = os.path.join(datalocation,data.loc[i,"File"])
out = langchain_splittext(filename)
for doc in out:
print(doc.page_content)
document.append(doc.page_content)
my_data = pd.DataFrame({'instruction': document})
n = 1
my_data["response"] = my_data["instruction"].tolist()[n:] + my_data["instruction"].tolist()[:n]
filetimestamp = str(int(time.time()))
filename = os.path.join(DATA_FILE_PATH, 'LLMTuning_' + filetimestamp+'.csv')
my_data.to_csv(filename,index=False)
return(filename)
except Exception as e:
print(e)
def getimpfeatures(dataFile, numberoffeatures,delimiter,textqualifier):
imp_features = []
if numberoffeatures > 20:
try:
from appbe.eda import ux_eda
eda_obj = ux_eda(dataFile,delimiter,textqualifier,optimize=1)
if eda_obj.getNumericFeatureCount() >= 2:
pca_map = eda_obj.getPCATop10Features()
imp_features = pca_map.index.values.tolist()
except Exception as e:
print(e)
pass
return imp_features
def pdf2text(inpFileName):
try:
from pypdf import PdfReader
reader = PdfReader(inpFileName)
number_of_pages = len(reader.pages)
text=""
OrgTextOutputForFile=""
for i in range(number_of_pages) :
page = reader.pages[i]
text1 = page.extract_text()
text=text+text1
import nltk
tokens = nltk.sent_tokenize(text)
for sentence in tokens:
sentence=sentence.replace("\n", " ")
if len(sentence.split()) < 4 :
continue
if len(str(sentence.split(',')).split()) < 8 :
continue
if any(chr.isdigit() for chr in sentence) :
continue
OrgTextOutputForFile= OrgTextOutputForFile+str(sentence.strip())
#print("\n\n\n\nOrgTextOutputForFile------------->\n\n\n",OrgTextOutputForFile)
return (OrgTextOutputForFile)
except Exception as e:
print("Encountered exception. {0}".format(e))
def getcommonfields():
computeinfrastructure = compute.readComputeConfig()
from appbe.aion_config import settings
usecasetab = settings()
kafkaSetting = kafka_setting()
ruuningSetting = running_setting()
context = {'s3buckets':get_s3_bucket(),'gcsbuckets':get_gcs_bucket(),'computeinfrastructure':computeinfrastructure,'kafkaSetting':kafkaSetting,'ruuningSetting':ruuningSetting,'usecasetab':usecasetab,'azurestorage':get_azureStorage()}
return context
def getusercasestatus(request):
if 'UseCaseName' in request.session:
selected_use_case = request.session['UseCaseName']
else:
selected_use_case = 'Not Defined'
if 'ModelVersion' in request.session:
ModelVersion = request.session['ModelVersion']
else:
ModelVersion = 0
if 'ModelStatus' in request.session:
ModelStatus = request.session['ModelStatus']
else:
ModelStatus = 'Not Trained'
return selected_use_case,ModelVersion,ModelStatus
def delimitedsetting(delimiter='',textqualifier='',other=''):
if delimiter != '':
if delimiter.lower() == 'tab' or delimiter.lower() == '\t':
delimiter = '\t'
elif delimiter.lower() == 'semicolon' or delimiter.lower() == ';':
delimiter = ';'
elif delimiter.lower() == 'comma' or delimiter.lower() == ',':
delimiter = ','
elif delimiter.lower() == 'space' or delimiter.lower() == ' ':
delimiter = ' '
elif delimiter.lower() == 'other' or other.lower() != '':
if other != '':
delimiter = other
else:
delimiter = ','
elif delimiter != '':
delimiter = delimiter
else:
delimiter = ','
else:
delimiter = ','
if textqualifier == '':
textqualifier = '"'
return delimiter,textqualifier
def multipleZipExtraction(data,DATA_FILE_PATH):
from zipfile import ZipFile
try:
import glob
filetimestamp = str(int(time.time()))
extracted_data = os.path.join(DATA_FILE_PATH, 'extracted_' + filetimestamp)
os.mkdir(extracted_data)
with ZipFile(data, 'r') as zObject:
zObject.extractall(extracted_data)
csv_files = glob.glob(r'{}\*.{}'.format(extracted_data,'csv'))
df_csv_append = pd.DataFrame()
for file in csv_files:
df = pd.read_csv(file)
df_csv_append = df_csv_append.append(df, ignore_index=True)
for f in os.listdir(extracted_data):
os.remove(os.path.join(extracted_data, f))
#os.mkdir(extracted_data)
combined_data = os.path.join(extracted_data,filetimestamp+'.csv')
df_csv_append.to_csv(combined_data)
return combined_data
except Exception as e:
if os.path.exists(extracted_data):
shutil.rmtree(extracted_data)
#print (e)
return ''
def tarFileExtraction(data,DATA_FILE_PATH):
try:
import tarfile
filetimestamp = str(int(time.time()))
extracted_data = os.path.join(DATA_FILE_PATH, 'extracted_' + filetimestamp)
os.mkdir(extracted_data)
if data.endswith('tar'):
file = tarfile.open(data)
file.extractall(extracted_data)
file.close()
for f in os.listdir(extracted_data):
if f.endswith('csv') or f.endswith('tsv'):
dataFile = os.path.join(extracted_data,f)
return dataFile
except Exception as e:
if os.path.exists(extracted_data):
shutil.rmtree(extracted_data)
print (e)
return ''
# ------ changes for the bug 10379 starts---------------- By Usnish ------
def checkRamAfterLoading(dataPath):
import psutil
availableRam = psutil.virtual_memory()[1]/1e9
filesize = os.path.getsize(dataPath)/1e9
return availableRam < 2*filesize
def checkRamBeforeLoading(dataPath):
import psutil
filesize = os.path.getsize(dataPath)/1e9
totalRam = psutil.virtual_memory()[0] / 1e9
if( filesize > 0.8 * totalRam):
return "File size is larger than the 80% of Total RAM."
return ""
# ------ changes for the bug 10379 ends---------------- By Usnish ------
# ---------- 10012:Decision Threshold related Changes S T A R T ----------
# This method is used to check If ->
# 80% of available RAM size is greater than ingested data (or not).
def checkRAMThreshold(dataPath):
import psutil
availableRam = psutil.virtual_memory()[1]/1e9
filesize = os.path.getsize(dataPath)/1e9
return (0.8 * availableRam) > filesize
# ---------------------- E N D ----------------------
# Text Data Labelling using LLM related changes
# --------------------------------------------------------
def ingestTextData(request, DATA_FILE_PATH):
log = logging.getLogger('log_ux')
try:
Datapath = request.FILES['DataFilePath']
from appbe.eda import ux_eda
ext = str(Datapath).split('.')[-1]
request.session['uploadfiletype'] = 'Local'
request.session['datatype'] = 'Normal'
filetimestamp = str(int(time.time()))
if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext)
else:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp)
with open(dataFile, 'wb+') as destination:
for chunk in Datapath.chunks():
destination.write(chunk)
destination.close()
dataPath = dataFile
request.session['textdatapath'] = dataPath
# import pdb
# pdb.set_trace()
# check_df = pd.read_csv(dataPath)
eda_obj = ux_eda(dataPath)
check_df = eda_obj.getdata()
df_top = check_df.head(10)
df_json = df_top.to_json(orient="records")
df_json = json.loads(df_json)
# featuresList = check_df.columns.tolist()
features,dateFeature,seqFeature,constantFeature,textFeature,targetFeature,numericCatFeatures,numericFeature,catfeatures = eda_obj.getFeatures()
noTextFeature = False
if len(textFeature) == 0:
noTextFeature = True
context = {'raw_data':df_json, 'featuresList':textFeature, 'selected':'DataOperations', 'noTextFeature':noTextFeature}
return context
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context = {'error': 'Failed to read data','emptycsv' : 'emptycsv'}
log.info('Text Data Ingestion -- Error : Failed to read data, '+str(e))
log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return context
# ---------------------- E N D ---------------------------
def ingestDataFromFile(request,DATA_FILE_PATH):
log = logging.getLogger('log_ux')
delimiter,textqualifier = delimitedsetting(request.POST.get('delimiters'),request.POST.get('qualifier'),request.POST.get('delimiters_custom_value'))
request.session['delimiter'] = delimiter
request.session['textqualifier'] = textqualifier
context = getcommonfields()
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
context.update({'selected_use_case': selected_use_case,'ModelStatus': ModelStatus, 'ModelVersion': ModelVersion,})
try:
t1 = time.time()
request.session['uploadfiletype'] = ''
request.session['uploadLocation'] = ''
data_is_large = False
check_df = pd.DataFrame()
if request.method == 'POST':
if 'ModelVersion' in request.session:
ModelVersion = request.session['ModelVersion']
else:
ModelVersion = 0
if 'ModelName' not in request.session:
movenext = False
request.session['currentstate'] = 0
context.update({'tab': 'tabconfigure', 'error': 'Please Create/Select the Use Case First', 'movenext': movenext,'currentstate': request.session['currentstate']})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please Create/Select the Use Case First')
return context
else:
type = request.POST.get("optradio")
if type == "s3Bucket":
try:
request.session['uploadfiletype'] = 'S3Bucket'
bucketname = request.POST.get('s3bucketname')
fileName = request.POST.get('s3file')
if fileName != '':
status,msg,check_df = read_s3_bucket(bucketname,fileName,DATA_FILE_PATH)
if status == 'Success':
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile, index=False)
request.session['datalocation'] = dataFile
else :
request.session['currentstate'] = 0 #usnish
context.update({'error': str(msg),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0' + 'sec' + ' : ' + 'Error : ' + str(msg))
return context
else: #usnish
request.session['currentstate'] = 0
context.update({'error': 'Please provide a file name','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please provide a file name')
return context
except Exception as e:
request.session['currentstate'] = 0
context.update({'error': str(e),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+ str(e))
return context
'''request.session['datalocation'] = "S3"'''
# -------------------------------- Graviton-Integration Changes S T A R T --------------------------------
elif type == "graviton":
try:
dataServiceId = request.POST.get('dataservice')
metadataId = request.POST.get('metadata')
data = []
from appbe.aion_config import get_graviton_data
graviton_url,graviton_userid = get_graviton_data()
gravitonURL = graviton_url
gravitonUserId = graviton_userid
# url = 'https://xenius.azurewebsites.net/api/getdata?userid=1&dataserviceid='+str(dataserviceId) +'&metadataid=' +str(metadataId)
url = gravitonURL + 'getdata?userid=' + gravitonUserId +'&dataserviceid='+str(dataServiceId) +'&metadataid=' +str(metadataId)
print(url)
response = requests.get(url)
statuscode = response.status_code
if statuscode == 200:
json_dictionary = json.loads(response.content)
data = json_dictionary['result']
firstElement = next(iter(data[0].keys()))
check_df = pd.DataFrame.from_dict(data[0][firstElement])
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile, index=False)
request.session['uploadfiletype'] = 'Graviton'
request.session['datalocation'] = str(dataFile)
except Exception as e:
print(e)
request.session['currentstate'] = 0
context.update({'error':'Check log file for more details','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error :'+str(e))
return context
# ------------------------------------------------ E N D -------------------------------------------------
elif type == "azurestorage":
try:
request.session['uploadfiletype'] = 'AzureStorage'
azurename = request.POST.get('azurename')
directoryname = request.POST.get('azuredirectory')
if directoryname != '':
status,msg,check_df = read_azureStorage(azurename,directoryname,DATA_FILE_PATH)
if status == 'Success':
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile, index=False)
'''request.session['datalocation'] = "S3"'''
request.session['datalocation'] = dataFile
else :
request.session['currentstate'] = 0 #usnish
context.update({'error': str(msg),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' +str(msg))
return context
else: #usnish
request.session['currentstate'] = 0
context.update({'error': 'Please provide a file name','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please provide a file name')
return context
except Exception as e:
print(e)
request.session['currentstate'] = 0
context.update({'error': 'File does not exist','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : File does not exist, '+str(e))
return context
elif type == "googleBucket":
try:
request.session['uploadfiletype'] = 'GCPBucket'
bucketname = request.POST.get('gcpbucketname')
fileName = request.POST.get('file1')
if fileName != '':
status,msg,check_df = read_gcs_bucket(bucketname,fileName,DATA_FILE_PATH)
if status == 'Success':
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile, index=False)
'''request.session['datalocation'] = "S3"'''
request.session['datalocation'] = dataFile
else :
request.session['currentstate'] = 0 #usnish
context.update({'error': str(msg),'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+str(msg))
return context
else: #usnish
request.session['currentstate'] = 0
context.update({'error': 'Please provide a file name','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : Please provide a file name')
return context
except Exception as e:
request.session['currentstate'] = 0
context.update({'error': 'File does not exist','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : File does not exist, ' + str(e))
return context
elif type == "url":
try:
request.session['uploadfiletype'] = 'URL'
url_text = request.POST.get('urlpathinput')
log.info('Data ingesttion from URL..')
request.session['uploadLocation'] = url_text
url = url_text
check_df = pd.read_csv(url)
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile,index=False)
request.session['datalocation'] = dataFile
except Exception as e:
request.session['currentstate'] = 0
e = str(e)
print(e)
if e.find("tokenizing")!=-1:
error = "This is not an open source URL to access data"
context.update({'error': error, 'ModelVersion': ModelVersion, 'emptycsv': 'emptycsv'})
elif e.find("connection")!=-1:
error = "Can not access the URL through HCL network, please try with other network"
context.update({'error': error, 'ModelVersion': ModelVersion, 'emptycsv': 'emptycsv'})
else:
error = 'Please provide a correct URL'
context.update({'error': error,'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : '+error + ', '+str(e))
return context
elif type == "nifi":
try:
request.session['uploadfiletype'] = 'Nifi'
log.info('Data ingesttion from Nifi..')
url_text = request.POST.get('nifiurlpathinput')
request.session['uploadLocation'] = url_text
response = requests.get(url_text)
csv_str = response.content.decode('utf-8')
check_df = pd.read_csv(StringIO(csv_str))
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile,index=False)
request.session['datalocation'] = dataFile
except requests.exceptions.ConnectionError:
request.session['currentstate'] = 0
context.update({'error': 'Connection Error','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error:Connection Error')
return context
except Exception as e:
print(e)
request.session['currentstate'] = 0
e = str(e)
context.update({'error': e,'ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : '+str(e))
return context
elif type == "tblaiondata":
try:
dataset = request.POST.get('datasetname')
print('dataset',dataset)
from appbe.dataPath import DATA_DIR
from appbe.sqliteUtility import sqlite_db
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
temp_data = sqlite_obj.read_data('dataingest')
dataFile = ''
for x in temp_data:
if x[1] == dataset:
dataFile = x[0]
check_df = pd.read_csv(dataFile)
request.session['datalocation'] = dataFile
except Exception as e:
request.session['currentstate'] = 0
context.update({'error': 'Failed to read data','ModelVersion': ModelVersion,'emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : File does not exist, ' + str(e))
return context
else:
if request.FILES:
Datapath = request.FILES['DataFilePath']
if Datapath.size > 31457280:
context.update({'tab': 'tabconfigure','error': 'Upload limit is 30 MB only, use local file option for larger file','currentstate': request.session['currentstate'], 'ModelVersion': ModelVersion})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : Upload limit is 30 MB only, use local file option for larger file')
return context
ext = str(Datapath).split('.')[-1]
request.session['uploadfiletype'] = 'Local'
request.session['datatype'] = 'Normal'
filetimestamp = str(int(time.time()))
if ext.lower() in ['csv','tsv','tar','zip','avro','parquet']:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext)
else:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp)
with open(dataFile, 'wb+') as destination:
for chunk in Datapath.chunks():
destination.write(chunk)
destination.close()
dataPath = dataFile
else:
dataPath = request.POST.get('localfilePath')
#print(os.path.getsize(dataPath))
# 10012:Decision Threshold related Changes - S T A R T
#removed few lines related to the check to not allow data to be ingested
# E N D
if request.POST.get('optfiletype') == 'avro':
try:
import pandavro as pdx
if os.path.isdir(dataPath):
for f in os.listdir(dataPath):
if f.endswith('avro'):
processed_df = pdx.read_avro(f)
if not df.empty:
df = df.append(processed_df, ignore_index=True)
else:
df = pd.DataFrame(processed_df)
elif os.path.isfile(dataPath):
import pandavro as pdx
df = pdx.read_avro(dataPath)
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
df.to_csv(dataFile, index=False)
request.session['datalocation'] = str(dataFile)
except Exception as e:
print(e)
elif request.POST.get('optfiletype') == 'parquet':
if os.path.isdir(dataPath):
for f in os.listdir(dataPath):
if f.endswith('parquet'):
processed_df = pd.read_parquet(f, engine='pyarrow')
if not df.empty:
df = df.append(processed_df, ignore_index=True)
else:
df = pd.DataFrame(processed_df)
elif os.path.isfile(dataPath):
df = pd.read_parquet(dataPath, engine='pyarrow')
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
df.to_csv(dataFile, index=False)
request.session['datalocation'] = str(dataFile)
elif request.POST.get('optfiletype') == 'dilimeted':
if os.path.isdir(dataPath):
for f in os.listdir(dataPath):
if f.endswith('csv') or f.endswith('tsv'):
processed_df = pd.read_csv(dataFile, encoding='utf8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace')
if not df.empty:
df = df.append(processed_df, ignore_index=True)
else:
df = pd.DataFrame(processed_df)
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
df.to_csv(dataFile, index=False,sep=delimiter,quotechar=textqualifier)
request.session['datalocation'] = str(dataFile)
else:
dataFile = dataPath
request.session['uploadfiletype'] = 'Local'
request.session['datatype'] = 'Normal'
FileReadingstatus = True
request.session['currentstate'] = 0
if dataPath.endswith('tar'):
dataFile = tarFileExtraction(dataPath,DATA_FILE_PATH)
if dataPath.endswith('zip'):
dataFile = multipleZipExtraction(dataPath,DATA_FILE_PATH)
if dataFile == '':
FileReadingstatus = False
msg = 'Please provide a file name'
elif dataFile.endswith(".xls") or dataFile.endswith(".xlsx"):
FileReadingstatus = False
msg = 'Please provide a dilimited file'
elif not os.path.isfile(dataFile):
FileReadingstatus = False
msg = 'File does not exist'
else:
check_df = pd.DataFrame();
try:
try:
cvobj = csv_validator()
valid_header, validrows, rownumbers = cvobj.validate_header(dataFile,delimiter,textqualifier)
request.session['datalocation'] = str(dataFile)
if not validrows:
FileReadingstatus = False
msg = 'Data Format issue'
else:
if valid_header:
check_df = pd.read_csv(dataFile, encoding='utf8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,nrows=100,encoding_errors= 'replace')
request.session['datalocation'] = str(dataFile)
else:
check_df = pd.read_csv(dataFile, header=None, encoding='utf8', prefix='X',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,encoding_errors= 'replace')
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH, 'AION_' + filetimestamp+'.csv')
check_df.to_csv(dataFile, index=False)
request.session['datalocation'] = str(dataFile)
except Exception as e:
print(e)
check_df = pd.read_csv(dataFile, encoding='utf8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,nrows=100)
request.session['datalocation'] = str(dataFile)
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+str(e))
except UnicodeDecodeError:
FileReadingstatus = False
msg = 'Only utf8 file encoding supported'
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error:'+msg)
except pd.errors.EmptyDataError:
FileReadingstatus = False
msg = 'File is empty'
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error:'+msg)
except pd.errors.ParserError:
FileReadingstatus = False
msg = 'File Parsng Error'
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+msg)
except FileNotFoundError:
FileReadingstatus = False
msg = 'File does not exist'
request.session['currentstate'] = 0
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : '+msg)
except Exception as e:
msg = 'File Read Error'
FileReadingstatus = False
print(e)
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(
ModelVersion) + ' : ' + '0 ' + 'sec' + ' : ' + 'Error : ' + msg+', '+str(e))
if check_df.empty and FileReadingstatus:
FileReadingstatus = False
msg = 'Date file is empty'
if not FileReadingstatus:
context.update({'tab': 'tabconfigure','error': msg,'currentstate': request.session['currentstate'], 'ModelVersion': ModelVersion})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : '+msg)
return context
# -------------------------------- 10012:Decision Threshold related Changes S T A R T -------------------------------
data_is_under_RAM_threshold = checkRAMThreshold(request.session['datalocation'])
msg = ""
if data_is_under_RAM_threshold == False:
msg = "AION will not be able to train on data set provided as it is bigger than available RAM, Please choose distributed learning for further processing."
# ------------------------------------------------------ E N D ------------------------------------------------------
check_df.rename(columns=lambda x: x.strip(), inplace=True)
featuresList = check_df.columns.tolist()
numberoffeatures = len(featuresList)
imp_features = getimpfeatures(dataFile,numberoffeatures,delimiter,textqualifier)
samplePercentage = 100
samplePercentval = 0
showRecommended = False
sample_size = int(eda_setting())
dflength = len(check_df)
if dflength > sample_size:
samplePercentage = round(float((sample_size/dflength) * 100),2)
samplePercentval = samplePercentage / 100
showRecommended = True
df_top = check_df.head(10)
df_json = df_top.to_json(orient="records")
df_json = json.loads(df_json)
statusmsg = 'Data File Uploaded Successfully '
request.session['currentstate'] = 0
request.session['finalstate'] = 0
request.session['datatype'] = 'Normal'
records = check_df.shape[0]
request.session['NoOfRecords'] = records
statusmsg = 'Data File Uploaded Successfully'
t2 = time.time()
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + str(
round(t2 - t1)) + ' sec' + ' : ' + 'Success')
# EDA Subsampling changes
context.update({'range':range(1,101),'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList': featuresList,'tab': 'tabconfigure', 'data': df_json, 'status_msg': statusmsg,
'selected': 'modeltraning','imp_features':imp_features,'numberoffeatures':numberoffeatures,
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],
'exploratory': False})
if msg!="":
context.update({'data_size_alert': msg})
return context
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
request.session['currentstate'] = 0
context.update({'error': 'Failed to read data','emptycsv' : 'emptycsv'})
log.info('Data Ingestion : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + ' sec' + ' : ' + 'Error : Failed to read data, '+str(e))
log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return context
|
llm_generateQnA.py | import os
import re
import json
import time
import sys
import tiktoken
import openai
import requests
from appbe.aion_config import get_llm_data
import logging
import pdfplumber
from docx import Document
openai.api_key = ''
openai.api_base = ''
openai.api_type = ''
openai.api_version = ''
deployment_name="GPT-35-Turbo"
model_name='gpt-3.5-turbo'
set_tokens_limit = 500
set_tokens_limit_offline = 400
set_prompt="You are an expert user generating questions and answers. You will be passed a page extracted from a documentation. Generate a numbered list of questions as Q. and equivelant answer as A. for every question based *solely* on the given text."
# QnA Generator using LLM related changes
# --------------------------------------------------------
def ingestDataForQA(request, DATA_FILE_PATH):
log = logging.getLogger('log_ux')
try:
Datapath = request.FILES['DataFileQnA']
from appbe.eda import ux_eda
ext = str(Datapath).split('.')[-1]
request.session['uploadfiletype'] = 'Local'
request.session['datatype'] = 'Normal'
filetimestamp = str(int(time.time()))
if ext.lower() in ['txt','pdf','docx']:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.'+ext)
else:
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp)
with open(dataFile, 'wb+') as destination:
for chunk in Datapath.chunks():
destination.write(chunk)
destination.close()
dataPath = dataFile
request.session['textdatapathQA'] = dataPath
llm_choice = request.POST.get("llm_choice")
_result = ''
# if llm_choice == 'Haystack':
# _result = generateQA_Haystack(request, DATA_FILE_PATH)
if llm_choice == 'Offline':
_result = generateQA_Offline(request, DATA_FILE_PATH)
else:
_result = generateQA_OpenAI(request, DATA_FILE_PATH)
return _result
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context = {'error': 'Failed to read data','emptytxt' : 'emptytxt'}
log.info('Text Data Ingestion -- Error : Failed to read data, '+str(e))
log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return context
# ---------------------- E N D ---------------------------
def generateQA_OpenAI(request, DATA_FILE_PATH):
log = logging.getLogger('log_ux')
try:
file_path = request.session['textdatapathQA']
# Read the file content
if file_path.endswith('.pdf'):
pdf_file=pdfplumber.open(file_path)
file_content = " ".join([x.extract_text() for x in pdf_file.pages])
elif file_path.endswith('.docx'):
doc_file=Document(file_path)
file_content = " \n".join([x.text for x in doc_file.paragraphs])
else:
with open(file_path, "r", encoding="utf-8",errors = "ignore") as file:
file_content = file.read()
text = file_content.strip()
#text = text.strip()
extracted_QnA = []
chunk_counter = 0
num_tokens_text = count_tokens_text(text)
if num_tokens_text > set_tokens_limit:
for sub_text in split_text(text):
chunk_counter = chunk_counter + 1
_result = extract_questions_from_splittedtext(sub_text)
print(f"Currently executed chunk no is - {chunk_counter}.")
extracted_QnA.extend(_result)
else:
_prompt = set_prompt
msg = [ {"role": "system", "content": _prompt},
{"role": "user", "content": text} ]
extracted_QnA = run_model(msg)
quesCount = len(extracted_QnA)
context = {'extracted_QnA':extracted_QnA, 'quesCount':quesCount}
filetimestamp = str(int(time.time()))
output_filepath = os.path.join(DATA_FILE_PATH,'AION_QnA' + filetimestamp+'.txt')
# Save the extracted questions as a JSON file
with open(output_filepath, 'w') as output_file:
json.dump(extracted_QnA, output_file, indent=4)
print(f"QnAs have been saved to {output_filepath}.")
request.session['QnAfilepath'] = output_filepath
return context
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
errormsg = str(e)
if 'Invalid URL' in errormsg or 'No connection adapters' in errormsg or 'invalid subscription key' in errormsg:
errormsg = 'Access denied due to invalid subscription key or wrong API endpoint. Please go to settings and make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'
if 'The API type provided in invalid' in errormsg:
errormsg = "The API type provided is invalid. Please select one of the supported API types:'azure', 'azure_ad' or 'open_ai'"
if 'Max retries exceeded with url' in errormsg:
errormsg = 'Please make sure you have good internet connection and access to API endpoint for your resource.'
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context = {'error': 'Failed to generate QnA List using openAI','LLM' : 'openAI', 'selected':'DataOperations', 'errormessage':errormsg}
log.info('generateQA_OpenAI -- Error : Failed to generate QnA List using openAI.. '+str(e))
log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return context
def run_model(msg):
key,url,api_type,api_version = get_llm_data()
openai.api_key = key
openai.api_base = url
openai.api_type = api_type
openai.api_version = api_version
completions = openai.ChatCompletion.create(engine=deployment_name, temperature=0.0, max_tokens=2000, n=1, stop=None, messages=msg)
# return completions.choices[0].message.content
_questionList = completions.choices[0].message.content
question_pattern = re.compile(r"^Q\s*\d+\.\s*(.+)$", re.MULTILINE)
questions = question_pattern.findall(_questionList)
answer_pattern = re.compile(r"^A\s*\d+\.\s*(.+)$", re.MULTILINE)
answers = answer_pattern.findall(_questionList)
if (len(questions) > 0) and (not re.search(r"[.!?)]$", questions[-1].strip())):
print(f"WARNING: Popping incomplete question: '{questions[-1]}'")
questions.pop()
extracted_QnA = []
for question, answer in zip(questions, answers):
extracted_QnA.append({'question': question, 'answer': answer})
return extracted_QnA
def count_tokens_text(text):
import tiktoken
model_type = model_name
encoding = tiktoken.encoding_for_model(model_type)
encoded_text = encoding.encode(text)
return len(encoded_text)
def extract_questions_from_splittedtext(text):
_prompt = set_prompt
msg = [ {"role": "system", "content": _prompt},
{"role": "user", "content": text} ]
_ques_ans_List = run_model(msg)
return _ques_ans_List
def split_text(text):
lines = text.split('\n')
current_section = ''
sections = []
_lastsection = 0
for line in lines:
num_tokens_text = count_tokens_text(''.join([current_section,line]))
if num_tokens_text < set_tokens_limit:
current_section = ''.join([current_section,line])
else:
sections.append(current_section)
current_section = line
_lastsection = 1
if _lastsection == 1:
sections.append(current_section)
return sections
# --------------------------------------------------------------------------------- #
def generateQA_Haystack(request, DATA_FILE_PATH):
file_path = request.session['textdatapathQA']
# Read the file content
with open(file_path, "r", encoding="utf-8") as file:
file_content = file.read()
text = file_content.strip()
text = text.strip()
docs = []
num_tokens_text = count_tokens_text(text)
if num_tokens_text > set_tokens_limit:
for sub_text in split_text(text):
docs.append({"content": sub_text})
else:
docs = [{"content": text}]
from pprint import pprint
from tqdm.auto import tqdm
from haystack.nodes import QuestionGenerator, BM25Retriever, FARMReader
# from haystack.document_stores import ElasticsearchDocumentStore
from haystack.document_stores import InMemoryDocumentStore
# from haystack.document_stores import PineconeDocumentStore
from haystack.pipelines import (
QuestionGenerationPipeline,
RetrieverQuestionGenerationPipeline,
QuestionAnswerGenerationPipeline,
)
from haystack.utils import print_questions
document_store = InMemoryDocumentStore(use_bm25=True)
document_store.write_documents(docs)
question_generator = QuestionGenerator()
# reader = FARMReader("deepset/roberta-base-squad2")
# reader.save("my_local_roberta_model")
reader_local = FARMReader(model_name_or_path="my_local_roberta_model_1")
qag_pipeline = QuestionAnswerGenerationPipeline(question_generator, reader_local)
extracted_QnA = []
for idx, document in enumerate(tqdm(document_store)):
print(f"\n * Generating questions and answers for document {idx}: {document.content[:100]}...\n")
result = qag_pipeline.run(documents=[document])
print_questions(result)
answers = []
questions = result['queries']
answerList = result["answers"]
for _answers in answerList:
for answer in _answers:
ans = answer.answer
answers.append(ans)
for question, answer in zip(questions, answers):
extracted_QnA.append({'question': question, 'answer': answer})
quesCount = len(extracted_QnA)
context = {'extracted_QnA':extracted_QnA, 'quesCount':quesCount}
filetimestamp = str(int(time.time()))
output_filepath = os.path.join(DATA_FILE_PATH,'AION_QnA' + filetimestamp+'.txt')
# Save the extracted questions as a JSON file
with open(output_filepath, 'w') as output_file:
json.dump(extracted_QnA, output_file, indent=4)
print(f"QnAs have been saved to {output_filepath}.")
request.session['QnAfilepath'] = output_filepath
return context
# --------------------------------------------------------------------------------- #
def generateQA_Offline(request, DATA_FILE_PATH):
log = logging.getLogger('log_ux')
try:
file_path = request.session['textdatapathQA']
if file_path.endswith('.pdf'):
pdf_file=pdfplumber.open(file_path)
file_content = " ".join([x.extract_text() for x in pdf_file.pages])
elif file_path.endswith('.docx'):
doc_file=Document(file_path)
file_content = " \n".join([x.text for x in doc_file.paragraphs])
else:
with open(file_path, "r", encoding="utf-8",errors = "ignore") as file:
file_content = file.read()
# # Read the file content
# with open(file_path, "r", encoding="utf-8") as file:
# file_content = file.read()
text = file_content.strip()
# text = text.strip()
docs = []
# num_tokens_text = count_tokens_text(text)
# if num_tokens_text > set_tokens_limit:
# for sub_text in split_text(text):
# docs.append(sub_text)
# else:
# docs.append(text)
model_name = "valhalla/t5-base-qg-hl"
num_tokens_text = count_tokens_text_offline(text, model_name)
if num_tokens_text > set_tokens_limit_offline:
for sub_text in split_text_for_Offline(text, model_name):
docs.append(sub_text)
else:
docs.append(text)
from question_generation.pipelines import pipeline
extracted_QnA = []
extracted_QnAList = []
nlp = pipeline("question-generation", model = model_name)
# nlp = pipeline("question-generation", model="valhalla/t5-base-e2e-qg")
# nlp = pipeline("e2e-qg", model="valhalla/t5-base-qg-hl")
# nlp = pipeline("multitask-qa-qg", model="valhalla/t5-base-qa-qg-hl")
for _text in docs:
res = nlp(_text)
print(res)
extracted_QnAList.extend(res)
for _record in extracted_QnAList:
extracted_QnA.append({'question': _record['question'], 'answer': _record['answer'].replace('<pad>', '')})
quesCount = len(extracted_QnA)
context = {'extracted_QnA':extracted_QnA, 'quesCount':quesCount}
filetimestamp = str(int(time.time()))
output_filepath = os.path.join(DATA_FILE_PATH,'AION_QnA' + filetimestamp+'.txt')
# Save the extracted questions as a JSON file
with open(output_filepath, 'w') as output_file:
json.dump(extracted_QnA, output_file, indent=4)
print(f"T5 based QnAs have been saved to {output_filepath}.")
request.session['QnAfilepath'] = output_filepath
return context
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
errormsg = str(e)
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
context = {'error': 'Failed to generate QnA List using T5','LLM' : 'T5', 'selected':'DataOperations', 'errormessage':errormsg}
log.info('generateQA_Offline -- Error : Failed to generate QnA List using T5.. '+str(e))
log.info('Details : '+ str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
return context
def split_text_for_Offline(text, model_name):
lines = text.split('\n')
current_section = ''
sections = []
_lastsection = 0
for line in lines:
num_tokens = count_tokens_text_offline(''.join([current_section,line]), model_name)
if num_tokens < set_tokens_limit_offline:
current_section = ''.join([current_section,line])
else:
sections.append(current_section)
current_section = line
_lastsection = 1
if _lastsection == 1:
sections.append(current_section)
return sections
def count_tokens_text_offline(text, model_name):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer(text, return_tensors="pt")
input_ids = inputs["input_ids"]
_token_count = len(input_ids[0])
return _token_count
|
onlineLearning.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import platform
import shutil
import subprocess
import sys
import time
import glob
import re
from appbe.pages import get_usecase_page
import json
from django.http import FileResponse
def startIncrementallearning(request,usecasedetails,Existusecases,DATA_FILE_PATH):
try:
modelid = request.POST.get('modelid')
#incfilepath = request.POST.get('incfilepath')
Datapath = request.FILES['incfilepath']
filetimestamp = str(int(time.time()))
dataFile = os.path.join(DATA_FILE_PATH,'AION_' + filetimestamp+'.csv')
with open(dataFile, 'wb+') as destination:
for chunk in Datapath.chunks():
destination.write(chunk)
# destination.close()#bugfix 11656
incfilepath = dataFile
p = Existusecases.objects.get(id=modelid)
deployPath = str(p.DeployPath)
scriptPath = os.path.abspath(os.path.join(deployPath,'aion_inclearning.py'))
request.session['IsRetraining'] = 'No'
if not os.path.exists(scriptPath):
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
context['Msg'] = 'Incremental/Online learning not supported for this model.For online training select Online Training in basic configuration page and provide with training'
else:
outputStr = subprocess.check_output([sys.executable, scriptPath, incfilepath])
outputStr = outputStr.decode('utf-8')
outputStr = re.search(r'aion_learner_status:(.*)', str(outputStr), re.IGNORECASE).group(1)
outputStr = outputStr.strip()
decoded_data = json.loads(outputStr)
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
if decoded_data['status'] == 'SUCCESS':
msg = decoded_data['Msg']
context['Status'] = 'SUCCESS'
context['Msg'] = msg
else:
msg = decoded_data['Msg']
context['Status'] = 'SUCCESS'
context['Msg'] = msg
except Exception as e:
print(e)
try:
status,context,action = get_usecase_page(request,usecasedetails,Existusecases)
except Exception as msg:
context['errorMsg'] = msg
return action,context
|
llmTesting.py | import os
import openai
from langchain.llms import AzureOpenAI
from sentence_transformers.SentenceTransformer import SentenceTransformer
import time
import datetime
import pandas as pd
import sys
import subprocess
import importlib
from appbe.aion_config import get_llm_data
from appbe.dataPath import DATA_FILE_PATH
remote_data_dir = "/home/aion/data/storage/llm_testing_data"
remote_data_processeddata_dir = '/home/aion/data/storage/processed_data'
remote_config_dir = '/home/aion/data/config'
sh_file_path = '/home/aion/llm/sbin/llm_testing.sh'
prompt_command = '/home/aion/llm/sbin/llm_testing.sh'
PRE_CONTEXT = "Answer the following question in a concise manner.\n"
DEFAULT_PARAMS = {
'OPENAI_API_TYPE' : "azure",
'OPENAI_API_BASE' : "",
'OPENAI_API_KEY' : "",
'OPENAI_API_VERSION' : "2023-03-15-preview"
}
faq=""
def getAMIDetails(config,selectedAMI):
y = {}
for x in config:
print(x)
if x['id'] == selectedAMI:
return x
return y
class test_LLM():
def __init__(self,
deployment_name='Text-Datvinci-03', params=DEFAULT_PARAMS, transformer=None,
sentence_txfr_model='sentence-transformers/paraphrase-mpnet-base-v2'):
self.deployment_name=deployment_name
self.set_params( params)
self.transformer = transformer
self.sentence_txfr_model = sentence_txfr_model
def fiddlerAuditorCheck(self):
status = importlib.util.find_spec('auditor')
if not status:
subprocess.check_call([sys.executable, "-m", "pip","uninstall", "-q","-y","notebook"])
subprocess.check_call([sys.executable, "-m", "pip", "install","-q", "notebook==6.4.5" ])
subprocess.check_call([sys.executable, "-m", "pip", "install","-q","fiddler-auditor==0.0.2"])
subprocess.check_call([sys.executable, "-m", "pip", "install","-q","notebook==7.0.2"])
status = importlib.util.find_spec('auditor')
return status
def set_params(self, params={}):
valid_params = ['OPENAI_API_TYPE','OPENAI_API_KEY','OPENAI_API_BASE','OPENAI_API_VERSION']
for key, value in params.items():
if 'OPENAI_API_TYPE' == key:
openai.api_type = value
os.environ['OPENAI_API_TYPE'] = openai.api_type
elif 'OPENAI_API_KEY' == key:
openai.api_key = value
os.environ['OPENAI_API_KEY'] = openai.api_key
elif 'OPENAI_API_BASE' == key:
openai.api_base = value
os.environ['OPENAI_API_BASE'] = openai.api_base
elif key in valid_params:
os.environ[key] = value
def run(self,modelName, temperature, similarity_threshold, perturbations_per_sample, prompts, reference_generation,pre_context=PRE_CONTEXT):
if not self.fiddlerAuditorCheck():
raise ValueError('Fiddler-auditor is not instlled "python -m pip install fiddler-auditor==0.0.2"')
openai_llm = AzureOpenAI(deployment_name=self.deployment_name, temperature=temperature, openai_api_key=openai.api_key)
from auditor.perturbations import Paraphrase
from auditor.evaluation.expected_behavior import SimilarGeneration
from auditor.evaluation.evaluate import LLMEval
# For Azure OpenAI, it might be the case the api_version for chat completion
# is different from the base model so we need to set that parameter as well.
if self.transformer:
azure_perturber = self.transformer
else:
azure_perturber = Paraphrase(
model="GPT-35-Turbo",
api_version="2023-03-15-preview",
num_perturbations=perturbations_per_sample,
)
sent_xfmer = SentenceTransformer(self.sentence_txfr_model)
similar_generation = SimilarGeneration(
similarity_model=sent_xfmer,
similarity_threshold=similarity_threshold,)
llm_eval = LLMEval(
llm=openai_llm,
expected_behavior=similar_generation,
transformation=azure_perturber,)
test_result = llm_eval.evaluate_prompt_correctness(
prompt=prompts,
pre_context=pre_context,
reference_generation=reference_generation,
perturbations_per_sample=perturbations_per_sample
)
return test_result
def runmultiple(self,modelName, temperature, similarity_threshold, perturbations_per_sample, prompts, reference_generation,pre_context=PRE_CONTEXT,faq=faq):
if not self.fiddlerAuditorCheck():
raise ValueError('Fiddler-auditor is not instlled "python -m pip install fiddler-auditor==0.0.2"')
from auditor.evaluation.expected_behavior import SimilarGeneration
from auditor.evaluation.evaluate import LLMEval
openai_llm = AzureOpenAI(deployment_name=self.deployment_name, temperature=temperature, openai_api_key=openai.api_key)
from auditor.perturbations import Paraphrase
# For Azure OpenAI, it might be the case the api_version for chat completion
# is different from the base model so we need to set that parameter as well.
if self.transformer:
azure_perturber = self.transformer
else:
azure_perturber = Paraphrase(
model="GPT-35-Turbo",
api_version="2023-03-15-preview",
num_perturbations=perturbations_per_sample,
)
sent_xfmer = SentenceTransformer(self.sentence_txfr_model)
similar_generation = SimilarGeneration(
similarity_model=sent_xfmer,
similarity_threshold=similarity_threshold,)
llm_eval = LLMEval(
llm=openai_llm,
expected_behavior=similar_generation,
transformation=azure_perturber,)
rows = faq.shape[0]
prompts = list(faq['Question'])
listofDf = []
for i in range(rows):
test_result = llm_eval.evaluate_prompt_robustness(
prompt=prompts[i],
pre_context=pre_context,
)
try:
now = datetime.datetime.now().strftime("%H%M%S")
name = str(i)+str(now)+'.html'
test_result.save(name)
df_iter=pd.read_html(name)
df_actual = df_iter[0]
listofDf.append(df_actual)
except:
pass
perturbatedDF = pd.concat(listofDf)
return perturbatedDF
def run_offline_model(self, usecasename,modelName, temperature, similarity_threshold, perturbations_per_sample, reference_generation, prompts,isfinetuned):
from appbe.compute import readComputeConfig
from appbe.prediction import get_instance
cloud_infra = readComputeConfig()
dataFile = os.path.join(DATA_FILE_PATH, 'prompt.csv')
remoteFile = os.path.join(remote_data_dir, 'prompt.csv')
if not reference_generation:
reference_generation = ''
prompt = pd.DataFrame([{'prompts':prompts, 'reference_generation':reference_generation}])
prompt.to_csv(dataFile, index=False)
hypervisor, instanceid, region, image = get_instance(usecasename)
key, url, api_type, api_version = get_llm_data()
if hypervisor == 'AWS':
aws_access_key_id = cloud_infra['awsCredentials']['accessKey']
aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey']
currentDirectory = os.path.dirname(os.path.abspath(__file__))
LLM_DIR = os.path.normpath(os.path.join(currentDirectory, '..', 'llm'))
if image != '' and image != 'NA':
amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image)
else:
amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid)
if region == '' or region == 'NA':
region = amiDetails['regionName']
from llm.aws_instance_api import start_instance
# print(aws_access_key_id, aws_secret_key, instanceid, region)
status, msg, ip = start_instance(aws_access_key_id, aws_secret_key, instanceid, region)
if status.lower() == 'success':
pem_file = os.path.join(LLM_DIR, amiDetails['ssh']['keyFilePath'])
username = amiDetails['ssh']['userName']
# cope file to server for sinfle prompt
from AION.llm.ssh_command import copy_files_to_server
copy_files_to_server(ip,pem_file,dataFile,'',username,'',remote_data_dir,remote_config_dir)
if isfinetuned:
command = prompt_command + ' ' + usecasename + ' ' + str(modelName) \
+ ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \
+ str(perturbations_per_sample) + \
' '+ str(key) + \
' '+ str(url) + \
' '+ str(api_type) + \
' '+ str(api_version)+ \
' '+ str("single")
else:
command = prompt_command + ' ' + 'BaseModel' + ' ' + str(modelName) \
+ ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \
+ str(perturbations_per_sample) + \
' '+ str(key) + \
' '+ str(url) + \
' '+ str(api_type) + \
' '+ str(api_version)+ \
' '+ str("single")
from llm.ssh_command import run_ssh_cmd
buf = run_ssh_cmd(ip, pem_file, username, '', '', command)
print(buf)
return buf
def run_multiple_offline_model(self, usecasename,modelName, temperature, similarity_threshold, perturbations_per_sample, faq,isfinetuned):
dataFile = os.path.join(DATA_FILE_PATH, 'prompt.csv')
remoteFile = os.path.join(remote_data_dir, 'prompt.csv')
faq.to_csv(dataFile, index=False)
print("This is done")
from appbe.compute import readComputeConfig
from appbe.prediction import get_instance
cloud_infra = readComputeConfig()
hypervisor, instanceid, region, image = get_instance(usecasename)
key, url, api_type, api_version = get_llm_data()
if hypervisor == 'AWS':
aws_access_key_id = cloud_infra['awsCredentials']['accessKey']
aws_secret_key = cloud_infra['awsCredentials']['secretAccessKey']
currentDirectory = os.path.dirname(os.path.abspath(__file__))
LLM_DIR = os.path.normpath(os.path.join(currentDirectory, '..', 'llm'))
if image != '' and image != 'NA':
amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['amis'], image)
else:
amiDetails = getAMIDetails(cloud_infra['AWS_EC2']['instances'], instanceid)
if region == '' or region == 'NA':
region = amiDetails['regionName']
from llm.aws_instance_api import start_instance
# print(aws_access_key_id, aws_secret_key, instanceid, region)
status, msg, ip = start_instance(aws_access_key_id, aws_secret_key, instanceid, region)
if status.lower() == 'success':
pem_file = os.path.join(LLM_DIR, amiDetails['ssh']['keyFilePath'])
username = amiDetails['ssh']['userName']
#print(ip,pem_file,promptfile,'',username,'',remote_data_dir,remote_config_dir)
from AION.llm.ssh_command import copy_files_to_server
copy_files_to_server(ip,pem_file,dataFile,'',username,'',remote_data_dir,remote_config_dir)
if isfinetuned:
command = prompt_command + ' ' + usecasename + ' ' + str(modelName) \
+ ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \
+ str(perturbations_per_sample) + \
' '+ str(key) + \
' '+ str(url) + \
' '+ str(api_type) + \
' '+ str(api_version)+ \
' '+ str("multiple")
else:
command = prompt_command + ' ' + 'BaseModel' + ' ' + str(modelName) \
+ ' ' + str(temperature) + ' ' + str(similarity_threshold) + ' ' \
+ str(perturbations_per_sample) + \
' '+ str(key) + \
' '+ str(url) + \
' '+ str(api_type) + \
' '+ str(api_version)+ \
' '+ str("multiple")
from llm.ssh_command import run_ssh_cmd
buf = run_ssh_cmd(ip, pem_file, username, '', '', command)
print(buf)
return buf
|
train_output.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os
import time
import subprocess
import sys
import json
import pandas as pd
def getDataSetRecordsCount(datalocation):
try:
records = 0
if os.path.isfile(datalocation):
for chunk in pd.read_csv(datalocation, chunksize=20000):
records = records+len(chunk)
if records == 0:
records = 'NA'
except Exception as e:
print(e)
records = 'NA'
return records
def get_train_model_details(deploy_location,request):
updatedConfigFile = request.session['config_json']
f = open(updatedConfigFile, "r")
configSettings = f.read()
f.close()
usename = request.session['usecaseid'].replace(" ", "_")
outputfile = os.path.join(deploy_location,usename,str(request.session['ModelVersion']),'etc','output.json')
if os.path.isfile(outputfile):
f1 = open(outputfile, "r+", encoding="utf-8")
outputStr = f1.read()
f1.close()
resultJsonObj = json.loads(outputStr)
trainingStatus = resultJsonObj['status']
if trainingStatus.lower() == 'success':
details = resultJsonObj['data']
modelType = details['ModelType']
bestModel = details['BestModel']
return trainingStatus,modelType,bestModel
else:
return trainingStatus,'NA','NA'
else:
return 'Not Trained','NA','NA' |
generate_json_config.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import os
import platform
import time
import sys
from os.path import expanduser
from pathlib import Path
import ast
import pandas as pd
from appbe.dataPath import CONFIG_FILE_PATH
def generate_json_config(request):
from appbe.create_dummy_dataset import gen_data_classification
from appbe.create_dummy_dataset import gen_data_regression
from appbe.create_dummy_dataset import gen_data_series
try:
problem_type = request.POST.get('ProblemType')
datadict1 = request.POST.get('rangedict')
datadict = eval(datadict1)
if request.POST.get('univariate') == "True":
features = request.POST.get('features')
features = '1'
catfeatures = request.POST.get('catfeatures')
catfeatures = '0'
informative = request.POST.get('informative')
informative = '1'
elif request.POST.get('univariate') == "False":
features = request.POST.get('features')
catfeatures = request.POST.get('catfeatures')
informative = request.POST.get('informative')
data_path = request.POST.get('dataypath')
number_informative = int(request.POST.get('informative'))
number_numerical_features = int(request.POST.get('features'))
if os.path.isdir(data_path):
raise Exception('Incorrect path. Please include filename. Eg: C:/AION/data.csv')
if os.path.isfile(data_path):
raise ValueError('The file ({}) exists.'.format(os.path.basename(data_path)))
if number_informative > number_numerical_features:
raise ValueError('The No. numerical features ({}) must larger than No. informative features ({}).'.format(number_numerical_features, number_informative))
if problem_type == 'classification':
status = gen_data_classification(int(request.POST.get('samples')),int(request.POST.get('features')),request.POST.get('dataypath'),int(request.POST.get('catfeatures')),int(request.POST.get('txtfeatures')),float(request.POST.get('proportion')),int(request.POST.get('informative')),int(request.POST.get('class')),[float(val) for val in request.POST.get('weights').split(",")],float(request.POST.get('shift')),datadict)
elif problem_type == 'regression':
status = gen_data_regression(int(request.POST.get('samples')),int(request.POST.get('features')),request.POST.get('dataypath'),int(request.POST.get('catfeatures')),int(request.POST.get('txtfeatures')),float(request.POST.get('proportion')),int(request.POST.get('informative')),int(request.POST.get('target')),float(request.POST.get('bias')),float(request.POST.get('noise')),datadict)
elif problem_type == 'timeseriesforecasting': #task 11997
status = gen_data_series(request.POST.get('univariate'),request.POST.get('starttime'),request.POST.get('endtime'),int(request.POST.get('samples')),int(features),request.POST.get('dataypath'),int(catfeatures),float(request.POST.get('proportion')),int(informative),int(request.POST.get('target')),float(request.POST.get('bias')),float(request.POST.get('noise')),datadict)
else:
raise Exception("Unsupperted Problem Type.")
if status:
from appbe.dataPath import DATA_DIR
from appbe.sqliteUtility import sqlite_db
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if request.POST["dataypath"] =='' or request.POST["dataset"] == '':
return 'error'
newdata = {}
newdata['datapath'] = [request.POST.get('dataypath')]
newdata['datasetname'] = [request.POST.get('dataset')]
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'dataingest')
else:
raise Exception("Data Genration failed.")
except Exception as e:
print(e)
raise Exception(str(e))
if __name__ == "__main__":
generate_json_config('classification')
generate_json_config('regression')
generate_json_config('timeseriesforecasting') #task 11997 |
training.py | import json
import os
import sys
import re
import numpy as np
def check_unsupported_col(config): #bugId14444
unsupported_chars = '[]<>#{}@&'
try:
featureList = config['basic']['featureList']
return any([x in y for x in unsupported_chars for y in featureList])
except Exception as e:
print(str(e))
return False
def check_granularity(configSettingsJson,datapath=None):
try:
from AION.appbe.utils import get_true_option
import pandas as pd
from pathlib import Path
seconds_per_unit = {'second':1,'minute':60,'hour':60 * 60,'day':24 * 60 * 60,'week':7 * 24 * 60 * 60,'month':30 * 24 * 60 * 60,'year':365 * 24 * 60 * 60}
if not get_true_option(configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['type']):
return ''
if isinstance( configSettingsJson['basic']['dateTimeFeature'], list):
datetime_feature = configSettingsJson['basic']['dateTimeFeature'][0]
else:
datetime_feature = configSettingsJson['basic']['dateTimeFeature']
if get_true_option(configSettingsJson['basic']['analysisType']) == 'timeSeriesForecasting' and datetime_feature:
if not datapath:
datapath = configSettingsJson['basic']['dataLocation']
if Path( datapath).exists():
df = pd.read_csv(datapath, nrows=2)
datetime = pd.to_datetime(df[ datetime_feature])
if len(datetime) > 1:
source_time_delta = (datetime[1] - datetime[0]).total_seconds()
granularity_unit = get_true_option(configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['granularity']['unit'])
size = int(configSettingsJson['basic']['preprocessing']['timeSeriesForecasting']['aggregation']['granularity']['size'])
target_time_delta = size * seconds_per_unit[granularity_unit]
amplify = int(source_time_delta / target_time_delta)
if amplify > 20:
return f'Current Granularity setting will amplify the data approx {amplify} times. Depending on your system configuration, this may cause Memory error'
return ''
except Exception as e:
return ''
def getStatusCount(matched_lines,total_steps):
stepsdone = 0
leaner = True
#print(matched_lines)
for line in matched_lines:
if 'AION feature transformation completed' in line:
stepsdone = stepsdone + 1
elif 'AION feature engineering completed' in line:
stepsdone = stepsdone + 1
elif 'AION Association Rule completed' in line:
stepsdone = stepsdone + 1
elif 'AION Image Classification completed' in line:
stepsdone = stepsdone + 1
elif 'AION Association Rule completed' in line:
stepsdone = stepsdone + 1
elif 'AION State Transition completed' in line:
stepsdone = stepsdone + 1
elif 'AION SurvivalAnalysis completed' in line:
stepsdone = stepsdone + 1
elif 'AION Recommender completed' in line:
stepsdone = stepsdone + 1
elif 'AION Gluon Stop' in line:
stepsdone = stepsdone + 1
elif 'AION Evaluation Stop' in line:
stepsdone = stepsdone + 1
elif 'AION Object Detection completed' in line:
stepsdone = stepsdone + 1
elif ('training completed' in line) and leaner:
stepsdone = stepsdone + 1
leaner = False
elif 'Prediction Service completed' in line:
stepsdone = stepsdone + 1
elif 'AION TimeSeries Forecasting started' in line: #task 11997
stepsdone = stepsdone + 1
elif 'Distributed Learning Completed' in line:
stepsdone = stepsdone + 4
elif 'AION Batch Deployment completed' in line:
stepsdone = stepsdone + 2
match_lines = []
for line in matched_lines:
count = len(line)-len(line.lstrip())
uline = line.split('...')
uline = uline[1]
if count == 0:
uline = '|... <span style="border: 1px solid black; line-height:2; padding: 2px">'+uline+'</span>'
elif count == 8 or count == 1:
uline = ' |... <span style="border: 1px dashed darkblue; line-height:2; padding: 2px">'+uline+'</span>'
elif count == 16 or count == 2:
uline = ' |... <span style="border: 1px dotted darkgray; line-height:2; padding: 2px">'+uline+'</span>'
elif count == 32 or count == 3:
uline = ' |... <span style="border: 1px dotted lightgray ; line-height:2; padding: 2px">'+uline+'</span>'
else:
uline = line
match_lines.append(uline)
stepline = '<b>Stage: ' + str(stepsdone) + '/' + str(total_steps) + ' Complete</b>'
match_lines.insert(0, stepline)
#print(match_lines)
output = "\n".join([status_text for status_text in match_lines])
output = "<pre>{}</pre>".format(output)
#print(output)
return(output)
def calculate_total_interations(config):
try:
noOfIterations = 0
problemtypes = config['basic']['analysisType']
problem_type = ""
for key in problemtypes:
if config['basic']['analysisType'][key] == 'True':
problem_type = key
break
if problem_type.lower() in ['classification','regression']:
algorithms = config['basic']['algorithms'][problem_type]
for key in algorithms:
if config['basic']['algorithms'][problem_type][key] == 'True':
if key not in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)','Deep Q Network','Dueling Deep Q Network']:
if problem_type.lower() == 'classification':
configparam = config['advance']['mllearner_config']['modelParams']['classifierModelParams'][key]
else:
configparam = config['advance']['mllearner_config']['modelParams']['regressorModelParams'][key]
param = paramDefine(configparam,config['advance']['mllearner_config']['optimizationMethod'])
interationsum = 1
for x in param.values():
interationsum = interationsum*len(x)
if config['advance']['mllearner_config']['optimizationMethod'].lower() == 'random':
if interationsum > int(config['advance']['mllearner_config']['optimizationHyperParameter']['iterations']):
interationsum = int(config['advance']['mllearner_config']['optimizationHyperParameter']['iterations'])
noOfIterations = noOfIterations+interationsum
else:
if key in ['Neural Network','Convolutional Neural Network (1D)','Recurrent Neural Network','Recurrent Neural Network (GRU)','Recurrent Neural Network (LSTM)']:
if problem_type.lower() == 'classification':
configparam = config['advance']['dllearner_config']['modelParams']['classifierModelParams'][key]
else:
configparam = config['advance']['dllearner_config']['modelParams']['regressorModelParams'][key]
interationsum = 1
for j in list(configparam.keys()):
if isinstance(configparam[j],(list,dict,tuple,str)):
x = configparam[j].split(',')
interationsum = interationsum*len(x)
noOfIterations = noOfIterations+interationsum
elif key in ['Deep Q Network','Dueling Deep Q Network']:
if problem_type.lower() == 'classification':
configparam = config['advance']['rllearner_config']['modelParams']['classifierModelParams'][key]
interationsum = 1
for j in list(configparam.keys()):
if isinstance(configparam[j],(list,dict,tuple,str)):
x = configparam[j].split(',')
interationsum = interationsum*len(x)
noOfIterations = noOfIterations+interationsum
elif problem_type.lower() in ['llmfinetuning']:
algorithms = config['basic']['algorithms'][problem_type]
for key in algorithms:
if config['basic']['algorithms'][problem_type][key] == 'True':
noOfIterations = configparam = config['advance']['llmFineTuning']['modelParams'][key]['epochs']
break
else:
noOfIterations= 'NA'
except Exception as e:
print(e)
noOfIterations = 'NA'
pass
return(noOfIterations)
def paramDefine(paramSpace, method):
paramDict = {}
for j in list(paramSpace.keys()):
inp = paramSpace[j]
try:
isLog = False
isLin = False
isRan = False
isList = False
isString = False
try:
# check if functions are given as input and reassign paramspace
v = paramSpace[j]
if 'logspace' in paramSpace[j]:
paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "")
isLog = True
elif 'linspace' in paramSpace[j]:
paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "")
isLin = True
elif 'range' in paramSpace[j]:
paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "")
isRan = True
elif 'list' in paramSpace[j]:
paramSpace[j] = v[v.find("(") + 1:v.find(")")].replace(" ", "")
isList = True
elif '[' and ']' in paramSpace[j]:
paramSpace[j] = v.split('[')[1].split(']')[0].replace(" ", "")
isList = True
x = paramSpace[j].split(',')
except:
x = paramSpace[j]
str_arg = paramSpace[j]
# check if arguments are string
try:
test = eval(x[0])
except:
isString = True
if isString:
paramDict.update({j: hp.choice(j, x)} if method == 'bayesopt' else {j: x})
else:
res = eval(str_arg)
if isLin:
y = eval('np.linspace' + str(res))
paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))} if method == 'bayesopt' else {j: y})
elif isLog:
y = eval('np.logspace' + str(res))
paramDict.update(
{j: hp.uniform(j, 10 ** eval(x[0]), 10 ** eval(x[1]))} if method == 'bayesopt' else {j: y})
elif isRan:
y = eval('np.arange' + str(res))
paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y})
# check datatype of argument
elif isinstance(eval(x[0]), bool):
y = list(map(lambda i: eval(i), x))
paramDict.update({j: hp.choice(j, eval(str(y)))} if method == 'bayesopt' else {j: y})
elif isinstance(eval(x[0]), float):
res = eval(str_arg)
if len(str_arg.split(',')) == 3 and not isList:
y = eval('np.linspace' + str(res))
#print(y)
paramDict.update({j: hp.uniform(j, eval(x[0]), eval(x[1]))} if method == 'bayesopt' else {j: y})
else:
y = list(res) if isinstance(res, tuple) else [res]
paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y})
else:
res = eval(str_arg)
if len(str_arg.split(',')) == 3 and not isList:
y = eval('np.linspace' +str(res)) if eval(x[2]) >= eval(x[1]) else eval('np.arange'+str(res))
else:
y = list(res) if isinstance(res, tuple) else [res]
paramDict.update({j: hp.choice(j, y)} if method == 'bayesopt' else {j: y})
except Exception as inst:
print(inst)
return paramDict
def calculate_total_activities(config):
req_step = 0
problemtypes = config['basic']['analysisType']
problem_type = ""
for key in problemtypes:
if config['basic']['analysisType'][key] == 'True':
problem_type = key
break
Modelproblem = problem_type
if Modelproblem.lower() in ['classification','regression','clustering','anomalydetection','topicmodelling']:
req_step = req_step+4
if Modelproblem.lower() in ['timeseriesforecasting','imageclassification','objectdetection','multilabelprediction','similarityidentification','contextualsearch']: #task 11997
req_step = req_step+2
if Modelproblem.lower() in ['survivalanalysis']:
req_step = req_step+3
if Modelproblem.lower() in ['recommendersystem']:
if config['basic']['algorithms']['recommenderSystem']['ItemRating'] == 'True':
req_step = req_step+3
if config['basic']['algorithms']['recommenderSystem']['AssociationRules-Apriori'] == 'True':
req_step = req_step+1
if Modelproblem.lower() in ['statetransition']:
req_step = req_step+1
return (req_step)
def getModelStatus(Existusecases,modelid):
model = Existusecases.objects.get(id=modelid)
return(model.Status)
def changeModelStatus(Existusecases,modelid,status,problemType,deployPath):
model = Existusecases.objects.get(id=modelid)
model.Status = status
model.ProblemType = problemType
model.DeployPath = deployPath
model.save()
def checkversionrunningstatus(modelid,usecasedetails,Existusecases):
modelx = Existusecases.objects.get(id=modelid)
ConfigPath = str(modelx.ConfigPath)
status = 'Running'
try:
if os.path.exists(ConfigPath):
with open(ConfigPath, 'r') as json_file:
data = json.load(json_file)
json_file.close()
deployPath = str(data['basic']['deployLocation'])
modelName = data['basic']['modelName']
modelVersion = data['basic']['modelVersion']
modelName = modelName.replace(" ", "_")
logfile = os.path.join(deployPath,modelName,str(modelVersion),'log','model_training_logs.log')
print(logfile)
if os.path.exists(logfile):
with open(logfile) as f:
contents = f.read()
f.close()
contents = re.search(r'aion_learner_status:(.*)', str(contents), re.IGNORECASE).group(1)
contents = contents.strip()
print(contents)
if contents != '':
resultJsonObj = json.loads(contents)
odataFile = str(modelx.TrainOuputLocation)
with open(odataFile, 'w') as json_file:
json.dump(resultJsonObj, json_file)
json_file.close()
modelx.Status = resultJsonObj['status']
status = modelx.Status
if resultJsonObj['status'] == 'SUCCESS':
modelx.DeployPath = str(resultJsonObj['data']['deployLocation'])
if resultJsonObj['data']['ModelType'] in ['clustering','anomalydetection']:
modelx.ProblemType = 'unsupervised'
else:
modelx.ProblemType = 'supervised'
modelx.save()
except Exception as e:
pass
return status
def updateLLM_Model_training_logs(deployPath,modelName,modelVersion,model,configPath):
from appbe.prediction import get_instance
hypervisor,instanceid,region,image = get_instance(modelName+'_'+str(modelVersion))
from llm.llm_tuning import llm_logs
cloudconfig = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..','config','compute_conf.json'))
llm_logs(configPath,cloudconfig,instanceid,hypervisor,model)
def checkModelUnderTraining(request,usecasedetails,Existusecases):
try:
models = Existusecases.objects.filter(Status='Running')
for model in models:
ConfigPath = str(model.ConfigPath)
try:
if os.path.exists(ConfigPath):
with open(ConfigPath, 'r') as json_file:
data = json.load(json_file)
json_file.close()
deployPath = str(data['basic']['deployLocation'])
modelName = data['basic']['modelName']
modelVersion = data['basic']['modelVersion']
modelName = modelName.replace(" ", "_")
if data['basic']['analysisType']['llmFineTuning'] == 'True':
mlmodels =''
algorihtms = data['basic']['algorithms']['llmFineTuning']
for k in algorihtms.keys():
if data['basic']['algorithms']['llmFineTuning'][k] == 'True':
if mlmodels != '':
mlmodels += ', '
mlmodels += k
updateLLM_Model_training_logs(deployPath,modelName,modelVersion,mlmodels,ConfigPath)
logfile = os.path.join(deployPath,modelName,str(modelVersion),'log','model_training_logs.log')
if os.path.exists(logfile):
with open(logfile,encoding="utf-8") as f:
contents = f.read()
f.close()
contents = re.search(r'aion_learner_status:(.*)', str(contents), re.IGNORECASE).group(1)
contents = contents.strip()
if contents != '':
resultJsonObj = json.loads(contents)
odataFile = str(model.TrainOuputLocation)
with open(odataFile, 'w') as json_file:
json.dump(resultJsonObj, json_file)
json_file.close()
modelx = Existusecases.objects.get(id=model.id)
modelx.Status = resultJsonObj['status']
if resultJsonObj['status'] == 'SUCCESS':
modelx.DeployPath = str(resultJsonObj['data']['deployLocation'])
if resultJsonObj['data']['ModelType'] in ['clustering','anomalydetection']:
modelx.ProblemType = 'unsupervised'
else:
modelx.ProblemType = 'supervised'
modelx.save()
except Exception as e:
print(ConfigPath)
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
pass
except Exception as e:
print(e)
|
codeclonedetection.py | # -*- coding: utf-8 -*-
import os
import glob, os
import pandas as pd
from openai.embeddings_utils import cosine_similarity
import numpy as np
from openai.embeddings_utils import get_embedding
import tiktoken
import openai
import importlib.util
from sklearn.preprocessing import StandardScaler
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
import time
from tqdm import tqdm
import concurrent.futures
from openai.error import RateLimitError, Timeout
try:
import chromadb
from chromadb.api.types import Documents, Embeddings
except:
#Looks no chromadb installed,just proceed to use csv embedd
pass
from openai.embeddings_utils import get_embedding
import json
from openai.embeddings_utils import cosine_similarity
from langchain.schema import Document
from langchain.vectorstores import Chroma
import warnings
import logging
warnings.simplefilter(action='ignore', category=FutureWarning)
"""Code clone detection parent class, based on user input data,the class will detect similar code snippets in the python file """
class CodeCloneDetection:
#Constructor for base inputs
def __init__(self,rootdir,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId):
self.rootdir=rootdir
self.embedd_storage_path=embedd_storage_path
self.openai_baseurl=openai_baseurl
self.openai_key=openai_key
self.openai_api_type=openai_api_type
self.openai_api_version=openai_api_version
self.ccdreportpath = os.path.join(self.embedd_storage_path, "codeCloneReport")
self.generativeai_chat_model=generativeai_chat_model
self.generativeai_embedding_engine = generativeai_embedding_engine
self.generativeai_embedding_model = generativeai_embedding_model
self.generativeai_deploymentId = generativeai_deploymentId
try:
os.makedirs(self.ccdreportpath, exist_ok = True)
except OSError as error:
print("Directory 'codeclonedetection' can not be created",self.ccdreportpath)
try:
self.logpath = os.path.join(self.ccdreportpath,'codeclonelog.log')
logging.basicConfig(level=logging.INFO,filename=self.logpath,filemode='w',format='%(message)s')
self.log = logging.getLogger()
except Exception as e:
print("code clone log object creation error.",e)
def get_function_name(self,code):
"""
Extract function name from a line beginning with "def "
"""
assert code.startswith("def ")
return code[len("def "): code.index("(")]
def get_until_no_space(self,all_lines, i) -> str:
"""
Get all lines until a line outside the function definition is found.
"""
ret = [all_lines[i]]
for j in range(i + 1, i + 10000):
if j < len(all_lines):
if len(all_lines[j]) == 0 or all_lines[j][0] in [" ", "\t", ")"]:
ret.append(all_lines[j])
else:
break
return "\n".join(ret)
def chunk_functions(self,function_code, chunk_size):
""" To chunk input for gpt models because max token per model is 4090 """
try:
# chunk_size = 1900
chunks = [function_code[i:i + chunk_size] for i in range(0, len(function_code), chunk_size)]
except Exception as e:
self.log.info('Error in chunking input prompt data.')
return chunks
def get_functions(self,filepath):
"""
Get all functions in a Python file.
"""
try:
whole_code = open(filepath).read().replace("\r", "\n")
all_lines = whole_code.split("\n")
for i, l in enumerate(all_lines):
if l.startswith("def "):
code = self.get_until_no_space(all_lines, i)
function_name = self.get_function_name(code)
yield {"code": code, "function_name": function_name, "filepath": filepath}
except Exception as e:
self.log.info("Error in getting function from file. Error message: \n"+str(e))
def get_clone_function_details(self):
""" To get available functions from python files """
try:
code_root=self.rootdir
from glob import glob
code_files = [y for x in os.walk(code_root) for y in glob(os.path.join(x[0], '*.py'))]
if code_files:
all_funcs = []
total_locs = 0
for code_file in code_files:
with open(code_file) as f:
total_locs += len(f.readlines())
funcs = list(self.get_functions(code_file))
for func in funcs:
all_funcs.append(func)
return all_funcs,code_root,code_files,total_locs
else:
self.log.info("no python files available in the dir:"+str(code_root))
return {"pythondiles_error":"No python files are found."}
except Exception as e:
print("Error in reading the functions from the given directory. Error message: \n",e)
self.log.info("Error in reading the functions from the given directory. Error message: \n"+str(e))
def getOpenAICredentials(self):
""" To set openai credential using user input """
#Currently only support openai
try:
package_name = 'openai'
lib_name = importlib.util.find_spec(package_name)
if lib_name is None:
return "openai_pkg_check_failed"
else:
embedding_model_lib ='openai'
#
if isinstance(self.openai_baseurl,str) and isinstance(self.openai_key,str) and isinstance(self.openai_api_type,str):
os.environ['OPENAI_API_TYPE'] = self.openai_api_type
os.environ['OPENAI_API_BASE'] = self.openai_baseurl
# os.environ['OPENAI_API_VERSION'] = '2023-05-15'
# os.environ['OPENAI_API_VERSION'] = "2022-12-01"
os.environ['OPENAI_API_VERSION'] = self.openai_api_version
os.environ['OPENAI_API_KEY'] = self.openai_key
if (embedding_model_lib.lower()=='openai'):
try:
openai.api_type=os.getenv('OPENAI_API_TYPE')
openai.api_base = os.getenv('OPENAI_API_BASE')
openai.api_key = os.getenv('OPENAI_API_KEY')
openai.api_version = os.getenv('OPENAI_API_VERSION')
except Exception as e:
self.log.info("Unable to get openai credentials,please provide proper credentials."+str(e))
return {"error_msg":"openai_environment_error"}
except Exception as e:
print("Openai credential set and get function error. Error message: \n",e)
return openai.api_type,openai.api_base,openai.api_key,openai.api_version
def get_embedding_local(self,model: str, text: str) -> list[float]:
""" To get embedding data for single user given prompt text"""
try:
response = openai.Embedding.create(
input=text,
engine=self.generativeai_embedding_engine)
except Exception as e:
self.log.info("openai embedding creation error."+str(e))
return result['data'][0]['embedding']
def get_embeddings_pyfiles(self,all_funcs):
""" To get embedding for python functions """
try:
import tiktoken
openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials()
encoding = tiktoken.encoding_for_model("text-embedding-ada-002")
df = pd.DataFrame(all_funcs)
df["tokens"] = df["code"].apply(lambda c: len(encoding.encode(c)))
embedding_cost = df["tokens"].sum() * (0.0004/1000)
EMBEDDING_FILEPATH=self.ccdreportpath+'\code_embeddings.csv'
self.log.info("embedding storage location: "+str(EMBEDDING_FILEPATH))
vdb_status = self.get_vdb_status('chromadb')
##Currently chromadb not integrated
vdb_status = False
if not vdb_status:
df['code_embedding'] = df['code'].apply(lambda x: get_embedding(x, engine=self.generativeai_embedding_engine))
df['filepath'] = df['filepath'].apply(lambda x: x.replace(self.rootdir, ""))
df.to_csv(EMBEDDING_FILEPATH, index=False)
else:
df = self.chromadb_embedding(df)
""" Please uncomment below, currently assumption is each run we create embedd based on python files dir """
import numpy as np
df = pd.read_csv(EMBEDDING_FILEPATH)
df["code_embedding"] = df["code_embedding"].apply(eval).apply(np.array)
except Exception as e:
self.log.info("Error in get_embeddings_pyfiles for embedding conversion process. Error Message: "+str(e))
raise Exception("Error in get_embeddings_pyfiles for embedding conversion process.")
return df,embedding_cost
def search_functions_vectordb(df,db, code_query, n=3, pprint=True, n_lines=7):
""" Search function for user query (prompt content), used for vector database embedding query option. """
try:
docs = db.similarity_search_with_score(code_query )[:n]
docs = [{"similarities":score, "code": d.page_content, **d.metadata} for d,score in docs]
res = pd.DataFrame(docs).drop("_additional", axis=1)
##Uncomment for debug
# if pprint:
# for r in res.iterrows():
# print(r[1].filepath+" : "+r[1].function_name + " score=" + str(round(r[1].similarities, 3)))
# print("\n".join(r[1].code.split("\n")[:n_lines]))
# print('-'*70)
except Exception as e:
self.log.info("Error in search_functions_vectordb to get similarity information based on user query. Error Message: "+str(e))
raise Exception("Error in search_functions_csv to get similarity information based on user query.")
return res
def search_functions_csv(self,df, code_query, n=3, pprint=True, n_lines=7):
""" Search function for user query (prompt content), used for csv embedding query option. """
try:
embedding = get_embedding(code_query, engine=self.generativeai_embedding_engine)
df['similarities'] = df.code_embedding.apply(lambda x: cosine_similarity(x, embedding))
res = df.sort_values('similarities', ascending=False)
## uncomment for debug purpose
# if pprint:
# for r in res.iterrows():
# print(r[1].filepath+" : "+r[1].function_name + " score=" + str(round(r[1].similarities, 3)))
# print("\n".join(r[1].code.split("\n")[:n_lines]))
# print('-'*70)
except Exception as e:
self.log.info("Error in search_functions_functions_csv to get similarity information based on user query. Error Message: "+str(e))
raise Exception("Error in search_functions_csv to get similarity information based on user query.")
return res
def get_prediction(self,prompt_data):
""" To get prediction for given user data """
try:
all_funcs,code_root,code_files,total_locs=self.get_clone_function_details()
if not isinstance(all_funcs,type(None)):
df,embedding_cost=self.get_embeddings_pyfiles(all_funcs)
res = self.search_functions_csv(df, prompt_data, n=3)
return res
else:
return dict({"error":"Empty_root_directory"})
except Exception as e:
self.log.info("Error in get prediction for user prompt information. Error Message: "+str(e))
raise Exception("Error in get prediction for user prompt information. .")
def get_vdb_status(self,vdb_name):
""" To check chromadb python package installed or not"""
try:
vdb_name = 'chromadb'
vdb_status=False
lib_name = importlib.util.find_spec(vdb_name)
if lib_name is None:
vdb_status=False
else:
vdb_status=True
## Processing the files and create a embedding and save it using csv.
except Exception as e:
self.log.info("Error in checking chromadb installed or not. Error Message: "+str(e))
raise Exception("Error in checking chromadb installed or not. .")
## Currently vector db (chromadb) not implemented, so vdb_status is set as False
vdb_status = False
return vdb_status
def create_chroma_db(self,documents, name):
""" Craete chromadb instance (persistant) """
#get openai status
openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials()
# openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials()
try:
from langchain.embeddings.openai import OpenAIEmbeddings
embed_function = OpenAIEmbeddings(deployment=self.generativeai_embedding_engine, chunk_size=1)
except:
from chromadb.utils import embedding_functions
embed_function = embedding_functions.OpenAIEmbeddingFunction(
api_key=openai.api_key,
api_base=openai.api_base,
api_type = openai.api_type,
model_name=self.generativeai_embedding_model
)
try:
# chroma_client = chromadb.Client()
persist_directory = self.embedd_storage_path
chroma_client = chromadb.Client(
Settings(
persist_directory=persist_directory,
chroma_db_impl="duckdb+parquet",
)
)
# Start from scratch
chroma_client.reset()
chroma_client.persist()
try:
embed_function = OpenAIEmbeddings(deployment=self.generativeai_embedding_engine, chunk_size=1)
except:
embed_function = OpenAIEmbeddings()
db = Chroma.from_documents(documents, embed_function, persist_directory=persist_directory)
db.persist()
except Exception as e:
self.log.info("Error in chromadb based embeding creation. Error Message: "+str(e))
raise Exception("Error in chromadb based embeding creation.")
return db,chroma_client
def chromadb_embedding(self,df):
""" Base chromadb embedding creation and storage function, it calls above create_chroma_db() to create db.
"""
try:
documents = df.apply(lambda x: Document(page_content= x["code"], metadata= {"function_name": x["function_name"], "filepath": x["filepath"]}), axis=1)
#setup the chromadb
db,chroma_client = self.create_chroma_db(documents,collection_name)
try:
chromadb_df=pd.DataFrame(db)
except:
db_json = db.get(include=['embeddings', 'documents', 'metadatas'])
chromadb_df = pd.read_json(db_json)
self.log.info("chromadb_df records (top ~5 records): "+str(chromadb_df.head(5)))
except Exception as e:
self.log.info("chromadb embedding error. Error message: "+str(e))
return chromadb_df
def num_tokens_from_string(self, string: str) -> int:
""" Get number of tokens of text using tiktokens lib."""
encoding = tiktoken.encoding_for_model("text-embedding-ada-002")
num_tokens = len(encoding.encode(string))
return num_tokens
def validate_code_clone_with_explanation(self,code1, code2, verbose=False):
""" Validate clone detection code snippet and get explanation from openai chat model (gpt-3.5-turbo) """
## Openai using 4 chars as 1 token, same method here followed. Here,we dont need to call tiktoken lib to save cost.
if (len(code1)/4 >1900):
chunk = self.chunk_functions(code1, 1900)
code1 = chunk[0]
print("In side , len of code1\n",len(code1))
if (len(code2)/4 >1900):
chunk = self.chunk_functions(code2, 1900)
code2 = chunk[0]
print("In side , len of code2\n",len(code2))
try:
SYS_ROLE = "You are a Senior Code Reviewer, who helps in Code review and integration using code clone detection approach."
openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials()
# openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials()
prompt = f"""Given two code snippets, find if they are clones or not with suitable explaination.
Four types of clone:
1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces.
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.
3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones.
4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone.
Use JSON object format with following keys:
IsClone: (True, False) wheather two code snippets are clone or not.
CloneType: (Exact clone, Parameterized clone, Never-miss clone, Semantic clone) Choose appropriate clone type or "None".
Explanation: A short explanation for the above answer.
### Code Snippets:
## Code 1:
{code1}
## Code 2:
{code2}
### Answer(Valid JSON object):
"""
response = openai.ChatCompletion.create(deployment_id=self.generativeai_deploymentId,
messages=[{"role": "system", "content": SYS_ROLE},
{"role": "user", "content": prompt},],
temperature = 0,max_tokens = 3900,request_timeout=90)
text = response['choices'][0]['message']['content']
if verbose:
self.log.info("validate_code_clone_with_explanation, text: "+str(text))
except Exception as e:
print(" validate_code_clone_with_explanation: \n",e)
response = "OpenAI Model Connection"
if e.code == "invalid_request" and "token limit" in e.message.lower():
# Implement your logic to reduce the length of messages or split them into smaller parts
# Modify messages or take appropriate action
self.log.info("Given function is too large and exceeds openai chat model token limit,please review the source file function length. "+str(e))
return response
def validate_code_clone_with_explanation_davinci(self,code1, code2, verbose=False):
""" Validate clone detection code snippet and get explanation from openai chat model (davinci) """
if (len(code1)/4 >1900):
chunk = self.chunk_functions(code1, 1900)
code1 = chunk[0]
if (len(code2)/4 >1900):
chunk = self.chunk_functions(code2, 1900)
code2 = chunk[0]
try:
SYS_ROLE = "You are a Senior Code Reviewer, who helps in Code review and integration. Detecting code clone in the repository."
openai_api_type,openai_api_base,openai_api_key,openai_api_version = self.getOpenAICredentials()
# openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials()
prompt = f"""Given two code snippets, find if they are clones or not with suitable explaination.
Four types of clone:
1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces.
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.
3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones.
4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone.
Use JSON object format with following keys:
IsClone: (True, False) wheather two code snippets are clone or not.
CloneType: (Exact clone, Parameterized clone, Never-miss clone, Semantic clone) Choose appropriate clone type or "None".
Explanation: A short explanation for the above answer.
### Code Snippets:
## Code 1:
{code1}
## Code 2:
{code2}
### Answer(Valid JSON object):
"""
# response = openai.Completion.create(engine='Text-Datvinci-03', prompt=prompt, temperature=0, max_tokens=1166)
response = openai.Completion.create(engine=self.generativeai_chat_model, prompt=prompt, temperature=0, max_tokens=3900)
text = response.choices[0]["text"]
if verbose:
self.log.info("validate_code_clone_with_explanation, text (chatmodel response) "+str(text))
except Exception as e:
response = "OpenAI Model Connection Error"
if e.code == "invalid_request" and "token limit" in e.message.lower():
# Implement your logic to reduce the length of messages or split them into smaller parts
# Modify messages or take appropriate action
self.log.info("Given function is too large and exceeds openai chat model token limit,please review the source file function length. Error msg: "+str(e))
return response
## For dbscan based clone detction from python files, we use CodeCloneDetection parent class. (Using inheritance)
class CodeCloneDetectionFiles(CodeCloneDetection):
"""For dbscan based clone detction from python files, we use CodeCloneDetection
parent class. (Using inheritance)
"""
def __init__(self,root_dir,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId):
super().__init__(root_dir,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId)
def get_embedd_fns(self):
""" To get embedd vector, using parent class methods"""
try:
## Processing the files and create a embedding and save it using csv.
vdb_status = super().get_vdb_status('chromadb')
self.log.info("<------- AION Code Clone Detection started ... ------>\n ")
if not vdb_status:
openai_api_type,openai_api_base,openai_api_key,openai_api_version = super().getOpenAICredentials()
# openai_api_type,openai_api_base,openai_api_key = self.getOpenAICredentials()
all_funcs,code_root,code_files,total_locs = super().get_clone_function_details()
if (openai.api_key or openai_api_key):
if not isinstance(all_funcs,type(None)):
embedded_df,embedding_cost=super().get_embeddings_pyfiles(all_funcs)
else:
return status
except Exception as e:
# print("Error in getting embedding vector using openai. Error message: ",e)
self.log.info("Error in getting embedding vector using openai. Error message: "+str(e))
raise Exception("Error in getting embedding vector using openai.")
return embedded_df,embedding_cost
def dbscan_clone_detection(self,df):
""" DBScan based code clone similarity detection (for functions in given dir """
try:
vdb_status = super().get_vdb_status('chromadb')
if not vdb_status:
X = np.array(list(df.code_embedding.values))
else:
X = np.array(list(df.embeddings.values))
#X = StandardScaler().fit_transform(X)
db = DBSCAN(eps=0.2, min_samples=2).fit(X)
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
df["cluster"] = labels
cluster_result = []
for i in range(n_clusters_):
cluster_df = df.loc[df['cluster'] == i]
# with open("{}/cluster_{}.txt".format(self.ccdreportpath,i), "w") as f:
for index, row in cluster_df.iterrows():
cluster_result.append({"cluster_id": i,"filepath": row.filepath,"function_name": row.function_name,"code": row.code })
# f.write(f"Source File: {row.filepath}, Function Name: {row.function_name}")
#f.write(f"\n{row.code}\n\n{'-'*80}\n\n")
cluster_result_df = pd.DataFrame(cluster_result)
codeclonereport_df = os.path.join(self.ccdreportpath,'cluster_result.csv')
cluster_result_df.to_csv(codeclonereport_df, index=False)
return cluster_result_df
except Exception as e:
self.log.info("Error in dbscan based similar code clone clustering. Error Message: "+str(e))
raise Exception("Error in dbscan based similar code clone clustering.")
def make_pairs(self,data_list:list):
try:
if len(data_list) <=1:
return []
return [(data_list[0], d) for d in data_list[1:]] + self.make_pairs(data_list[1:])
except Exception as e:
self.log.info("Error in make pairs function, error message: "+str(e))
raise Exception("Error in clone code mapping.")
def code_clone_check_with_retry(self,code1,code2, retry_interval=1):
""" Call chat models for code clone detection with retry mechanism. """
try:
# res = super().validate_code_clone_with_explanation(code1,code2)
##sj
if (self.generativeai_embedding_model.lower() =='text-embedding-ada-002' and self.generativeai_chat_model.lower() == 'text-datvinci-03'):
res = super().validate_code_clone_with_explanation_davinci(code1,code2)
return res
elif (self.generativeai_embedding_model.lower() =='text-embedding-ada-002' and self.generativeai_chat_model.lower() == 'gpt-3.5-turbo'):
res = super().validate_code_clone_with_explanation(code1,code2)
return res
except (RateLimitError, Timeout) as e:
self.log.info("Calling chat model issue in code clone check function, error message: "+str(e))
time.sleep(retry_interval)
return self.code_clone_check_with_retry(code1, code2)
def res_formater(self,inp):
""" Function to format gpt-3.5 or text-davinci-003 response body. """
try:
line = inp.replace('{','')
line = line.replace('}','')
line = line.replace('"','')
end=line.split(',')
d1={}
l2=[]
for l in end:
l=l.split(',')
for i in l:
l1=i.split(":")
l2.append(l1)
import pandas as pd
df=pd.DataFrame(l2)
df=df.T
df.columns = df.iloc[0]
df = df[1:]
df.columns = df.columns.str.replace('[#,@,&,\']', '')
# df.to_csv('test1.csv', index=False)
response=df.iloc[0]
fl=response.to_list()
clone_status=fl[0]
clone_type=fl[1]
result=fl[2]
except Exception as e:
self.log.info("chat model response formatter error. Error message: "+str(e))
return clone_status,clone_type,result
def getcloneresult_modelspecific(self,code_clone_check_tasks,embedding_cost):
""" get the clone type and associated information from chat model response data. """
try:
max_workers = min(len(code_clone_check_tasks), 100)
all_funcs,code_root,code_files,total_locs = super().get_clone_function_details()
if (self.generativeai_chat_model.lower() == 'text-datvinci-03'):
self.log.info("<--- Text-Datvinci-03 chat model based code clone detection. --->")
code_clone_result = []
for task in code_clone_check_tasks:
response=self.code_clone_check_with_retry(task[0]["code"], task[1]["code"])
with concurrent.futures.ThreadPoolExecutor(max_workers= max_workers) as executor:
llm_requests = {
executor.submit(self.code_clone_check_with_retry, task[0]["code"], task[1]["code"]): task for task in code_clone_check_tasks
}
with tqdm(total= len(llm_requests)) as progress:
for future in concurrent.futures.as_completed(llm_requests):
task = llm_requests[future]
try:
res = future.result()
try:
my_openai_obj1 = res["choices"][0]["text"]
clone_status,clone_type,result = self.res_formater(my_openai_obj1)
model_value=res['model']
total_tokens_value=res['usage']['total_tokens']
code_clone_result.append({"task": task,
"result":result,
"IsClone": clone_status,
"CloneType": clone_type,
"model":model_value,
"total_tokens":total_tokens_value})
except Exception as e:
self.log.info("getCloneReport, code_clone_result.append error: "+str(e))
except Exception as exc:
self.log.info("getCloneReport error (text davinci chat model): "+str(exc))
progress.update()
## Please uncomment below part if you need to check chat model response body.
#codeclonecheckresult_json = os.path.join(self.ccdreportpath,'code_clone_chatmodel_responsebody.json')
#with open(codeclonecheckresult_json, "w+") as fp:
#json.dump(code_clone_result, fp, indent=2)
code_clone_result_json=json.dumps(code_clone_result)
clone_report=pd.read_json(code_clone_result_json)
cr_totaltokens = clone_report['total_tokens']
total_amt = (cr_totaltokens).sum() * (0.002/1000)
clone_report["function1"] = clone_report["task"].apply(lambda x: x[0]["filepath"] + " -> " + x[0]["function_name"])
clone_report["function2"] = clone_report["task"].apply(lambda x: x[1]["filepath"] + " -> " + x[1]["function_name"])
# clone_report["clone_type"] = clone_report["result"].apply(lambda x: x["CloneType"])
clone_report["clone_type"] = clone_report["CloneType"]
code_dir = code_root
total_files = len(code_files)
total_locs = total_locs
total_functions = len(all_funcs)
total_tokens = clone_report['total_tokens'].sum()
total_cost= embedding_cost + clone_report['total_tokens'].sum() * (0.002/1000)
total_clones = len(clone_report[clone_report.clone_type != "None"])
code_clone_count_by_df = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')).to_markdown(tablefmt='psql')
clone_functions = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1").to_markdown(tablefmt='psql', index=False)
code_clone_count_dict = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count'))
clone_function_dict = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1")
##Final report on code clone detection
report_str = f"""Code_directory: {code_dir}
Files: {total_files}
LOCs: {total_locs}
Functions: {total_functions}
Total_code_clones_detected: {total_clones}
Tokens used: {total_tokens}
Total cost(embedding + clone check): {total_cost}
Four_types_of_clone:
1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces.
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.
3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones.
4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone.
Code_clones_count_by_clone_type:
{code_clone_count_by_df}
Clone_functions:
{clone_functions}
"""
codeclonereport_txt = os.path.join(self.ccdreportpath,'code_clone_report.txt')
with open(codeclonereport_txt, "w") as f:
f.write(report_str)
report_dict=dict({"Code_directory":code_dir,"total_files":total_files,
"total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones,
"total_tokens":total_tokens,"total_cost":total_cost,
"Code_clones_count_by_clone_type":code_clone_count_dict,"clone_functions":clone_function_dict})
## report for chat model is gpt 3.5 turbo
elif (self.generativeai_chat_model.lower() == 'gpt-3.5-turbo'):
try:
self.log.info("<--- gpt-3.5-turbo chat model based code clone detection. --->")
code_clone_result = []
for task in code_clone_check_tasks:
response=self.code_clone_check_with_retry(task[0]["code"], task[1]["code"])
with concurrent.futures.ThreadPoolExecutor(max_workers= max_workers) as executor:
llm_requests = {
executor.submit(self.code_clone_check_with_retry, task[0]["code"], task[1]["code"]): task for task in code_clone_check_tasks
}
with tqdm(total= len(llm_requests)) as progress:
for future in concurrent.futures.as_completed(llm_requests):
task = llm_requests[future]
try:
res = future.result()
my_openai_obj1 = res["choices"][0]["message"]['content']
clone_status,clone_type,result = self.res_formater(my_openai_obj1)
# result = json.loads(res['choices'][0]['message']['content'])
total_tokens = res["usage"]["total_tokens"]
code_clone_result.append({"task": task,
"result":result ,
"CloneType": clone_type,
"total_tokens": total_tokens})
except Exception as exc:
self.log.info("gpt 3.5 chat model error: "+str(exc))
progress.update()
except Exception as e:
print("In gpt3.5,getcloneresult_modelspecific fn exception : \n",e)
import traceback
print("traceback, In gpt3.5,getcloneresult_modelspecific fn exception \n",traceback.print_exc())
## Please uncomment below part if you need to check chat model response body.
#codeclonecheckresult_json = os.path.join(self.ccdreportpath,'code_clone_chatmodel_responsebody.json')
#with open(codeclonecheckresult_json, "w+") as fp:
#json.dump(code_clone_result, fp, indent=2)
try:
code_clone_result_json=json.dumps(code_clone_result)
clone_report = pd.read_json(code_clone_result_json)
codeclone_total_amt = clone_report["total_tokens"].sum() * (0.002/1000)
clone_report["function1"] = clone_report["task"].apply(lambda x: x[0]["filepath"] + " -> " + x[0]["function_name"])
clone_report["function2"] = clone_report["task"].apply(lambda x: x[1]["filepath"] + " -> " + x[1]["function_name"])
# clone_report["clone_type"] = clone_report["result"].apply(lambda x: x["CloneType"])
clone_report["clone_type"] = clone_report["CloneType"]
code_dir = code_root
total_files = len(code_files)
total_locs = total_locs
total_functions = len(all_funcs)
total_tokens = clone_report["total_tokens"].sum()
except Exception as e:
self.log.info("Error in getting clone report: "+str(e))
total_cost= embedding_cost + clone_report["total_tokens"].sum() * (0.002/1000)
total_clones = len(clone_report[clone_report.clone_type != "None"])
code_clone_count_by_df = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')).to_markdown(tablefmt='psql')
clone_functions = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1").to_markdown(tablefmt='psql', index=False)
code_clone_count_dict = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count'))
clone_function_dict = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1")
report_str = f"""Code_directory: {code_dir}
Files: {total_files}
LOCs: {total_locs}
Functions: {total_functions}
Total code clones detected: {total_clones}
Tokens used: {total_tokens}
Total cost(embedding + clone check): {total_cost}
Four types of clone:
1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces.
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.
3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones.
4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone.
5. None: Not a clone, discard this one.
Code_clones_count_by_clone_type:
{code_clone_count_by_df}
Clone_functions:
{clone_functions}
"""
codeclonereport_txt = os.path.join(self.ccdreportpath,'code_clone_report.txt')
with open(codeclonereport_txt, "w") as f:
f.write(report_str)
report_dict=dict({"Code_directory":code_dir,"total_files":total_files,
"total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones,
"total_tokens":total_tokens,"total_cost":total_cost,
"Code_clones_count_by_clone_type":code_clone_count_dict,"clone_functions":clone_function_dict})
except Exception as e:
self.log.info("Error in clone type and information retrival process .Error message: "+str(e))
return code_clone_result,report_str,report_dict
def getCloneReport(self):
""" To get the clone report from the given python directory """
try:
self.log.info("To get clone report, we are calling embedding and chat model.")
import time
vdb_status = super().get_vdb_status('chromadb')
start_time = time.time()
# self.log.info("code clone detection start time."+str(start_time))
if not vdb_status:
embedded_df,embedding_cost = self.get_embedd_fns()
cluster_df = self.dbscan_clone_detection(embedded_df)
cluster_df_group = cluster_df.groupby("cluster_id")
len_cluster_df_group = len(cluster_df_group)
code_clone_check_tasks = []
for name, group in cluster_df_group:
res = self.make_pairs(group.to_dict(orient="records"))
code_clone_check_tasks += res
#For text-embedding-ada-002 and gpt 3.5 chat model
code_clone_result,report_str,report_dict = self.getcloneresult_modelspecific(code_clone_check_tasks,embedding_cost)
end_time = time.time()
total_time_taken = end_time - start_time
self.log.info("Total time taken for code clone detction: "+str(total_time_taken))
self.log.info("<------------- Final code clone report: -------------------> \n"+str(report_str))
report_df = pd.DataFrame.from_dict(report_dict, orient="index").reset_index()
report_df.columns = ['ccd_properties', 'Values']
report_df=report_df.T
codecloneresult_df = os.path.join(self.ccdreportpath,'code_clone_report_df.csv')
report_df.to_csv(codecloneresult_df)
return report_str,report_dict,report_df,json.dumps(report_str)
else:
#Below code indended for vector db.
all_funcs,code_root,code_files,total_locs = super().get_clone_function_details()
df = pd.DataFrame(all_funcs)
df['filepath'] = df['filepath'].apply(lambda x: x.replace(code_root, ""))
chromadb_df=super().chromadb_embedding(df)
df = self.dbscan_clone_detection(chromadb_df)
cluster_df_group = cluster_df.groupby("cluster_id")
len_cluster_df_group = len(cluster_df_group)
code_clone_check_tasks = []
for name, group in cluster_df_group:
res = self.make_pairs(group.to_dict(orient="records"))
code_clone_check_tasks += res
code_clone_result = []
max_workers = min(len(code_clone_check_tasks), 100)
with concurrent.futures.ThreadPoolExecutor(max_workers= max_workers) as executor:
llm_requests = {
executor.submit(self.code_clone_check_with_retry, task[0]["code"], task[1]["code"]): task for task in code_clone_check_tasks
}
with tqdm(total= len(llm_requests)) as progress:
for future in concurrent.futures.as_completed(llm_requests):
task = llm_requests[future]
try:
res = future.result()
code_clone_result.append({"task": task,
"result": json.loads(res['choices'][0]['message']['content']),
"total_tokens": res["usage"]["total_tokens"]})
except Exception as exc:
print('%r generated an exception: %s' % (task, exc))
progress.update()
with open("code_clone_check_result.json", "w+") as fp:
json.dump(code_clone_result, fp, indent=2)
code_clone_result_json=json.dumps(code_clone_result)
clone_report=pd.read_json(code_clone_result_json)
total_amt = clone_report["total_tokens"].sum() * (0.002/1000)
clone_report["function1"] = clone_report["task"].apply(lambda x: x[0]["filepath"] + " -> " + x[0]["function_name"])
clone_report["function2"] = clone_report["task"].apply(lambda x: x[1]["filepath"] + " -> " + x[1]["function_name"])
clone_report["clone_type"] = clone_report["result"].apply(lambda x: x["CloneType"])
all_funcs,code_root,code_files,total_locs = super().get_clone_function_details()
code_dir = code_root
total_files = len(code_files)
total_locs = total_locs
total_functions = len(all_funcs)
total_tokens = clone_report["total_tokens"].sum()
# total_cost= embedding_cost + clone_report["total_tokens"].sum() * (0.002/1000)
total_clones = len(clone_report[clone_report.clone_type != "None"])
code_clone_count_by_df = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count')).to_markdown(tablefmt='psql')
clone_functions = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1").to_markdown(tablefmt='psql', index=False)
code_clone_count_dict = clone_report[clone_report.clone_type != "None"].groupby("clone_type").agg(Count=('clone_type', 'count'))
clone_function_dict = clone_report[["function1", "function2", "clone_type"]][clone_report.clone_type != "None"].sort_values("function1")
##Final report on code clone detection
report_str = f"""Code_directory: {code_dir}
Files: {total_files}
LOCs: {total_locs}
Functions: {total_functions}
Total code clones detected: {total_clones}
Tokens used: {total_tokens}
Four types of clone:
1. Exact clone: Two code fragments similar to each other with little transformation in comments, layout, or whitespaces.
2. Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.
3. Near-miss clone: Near-miss clone occurs by adding, deleting statements in code fragments of type 2 clones.
4. Semantic clone: The code snippets have different syntax but with alike functionality results in this clone.
Code_clones_count_by_clone_type:
{code_clone_count_by_df}
Clone_functions:
{clone_functions}
"""
with open("code_clone_report.txt", "w") as f:
f.write(report_str)
# print(report_str)
self.log.info("<------------- Final code clone report: -------------------> \n"+str(report_str))
self.log.info("<------------- clone_functions code clone report: -------------------> \n"+str(clone_functions))
report_dict=dict({"Code_directory":code_dir,"total_files":total_files,
"total_locs":total_locs,"total_functions":total_functions,"total_clones":total_clones,
"total_tokens":total_tokens,
"Code_clones_count_by_clone_type":code_clone_count_dict,"clone_functions": clone_function_dict})
report_df= pd.DataFrame([report_dict.keys(), report_dict.values()]).T
report_df.columns = ["Code_directory", "total_files","total_locs","total_functions","total_clones","total_tokens","Code_clones_count_by_clone_type","clone_functions"]
report_df.to_csv("code_clone_report_df.csv")
return report_str,report_dict,report_df,json.dumps(report_str)
except Exception as e:
self.log.info("Error in clone detection function call. Error Message: \n"+str(e))
raise Exception("Error in clone detection function.")
#For testing and code instance privacy
if __name__=='__main__':
## For testing purpose.Uncomment n use.
root_directory = r"C:\AION_Works\Anomaly_Detection\anomalydetectionpackage\code_clone_testing_pyfiles\code_clone_testing_pyfiles_large"
embedd_storage_path = r"C:\AION_Works\ccddir"
generativeai_credentials={'openai_baseurl':"",
'openai_key':"",
'openai_api_type':"",
'openai_api_version':"",
'generativeai_embedding_engine':"",
'generativeai_embedding_model':"",
'generativeai_chat_model':"",
'generativeai_deploymentId':""}
openai_baseurl = generativeai_credentials['openai_baseurl']
openai_key = generativeai_credentials['openai_key']
openai_api_type = generativeai_credentials['openai_api_type']
openai_api_version = generativeai_credentials['openai_api_version']
generativeai_embedding_engine = generativeai_credentials['generativeai_embedding_engine']
generativeai_embedding_model = generativeai_credentials['generativeai_embedding_model']
generativeai_chat_model = generativeai_credentials['generativeai_chat_model']
generativeai_deploymentId = generativeai_credentials['generativeai_deploymentId']
codeclonedetection_obj = CodeCloneDetectionFiles(root_directory,openai_baseurl, openai_key,openai_api_type,openai_api_version,embedd_storage_path,generativeai_embedding_engine,generativeai_embedding_model,generativeai_chat_model,generativeai_deploymentId)
report_str,report_dict,report_json = codeclonedetection_obj.getCloneReport()
print("End of code clone detection....\n")
|
azureStorageDB.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import sqlite3
from pathlib import Path
import json
import os
import rsa
import boto3 #usnish
import pandas as pd
import time
import sqlite3
class sqlite_db():
def __init__(self, location, database_file=None):
if not isinstance(location, Path):
location = Path(location)
if database_file:
self.database_name = database_file
else:
self.database_name = location.stem
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
def table_exists(self, name):
query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{name}';"
listOfTables = self.cursor.execute(query).fetchall()
return len(listOfTables) > 0
def read_data(self, table_name):
query = f"SELECT * FROM {table_name}"
row = self.cursor.execute(query).fetchall()
return list(row)
#return pd.read_sql_query(f"SELECT * FROM {table_name}", self.conn)
def create_table(self,name, columns, dtypes):
query = f'CREATE TABLE IF NOT EXISTS {name} ('
for column, data_type in zip(columns, dtypes):
query += f"'{column}' TEXT,"
query = query[:-1]
query += ');'
self.conn.execute(query)
return True
def delete_record(self,table_name,col_name, col_value):
try:
query = f"DELETE FROM {table_name} WHERE {col_name}='{col_value}'"
self.conn.execute(query)
self.conn.commit()
return 'success'
except Exception as e :
print(str(e))
print("Deletion Failed")
return 'error'
def get_data(self,table_name,col_name,col_value):
query = f"SELECT * FROM {table_name} WHERE {col_name}='{col_value}'"
row = self.cursor.execute(query).fetchone()
if(row == None):
return []
return list(row)
def write_data(self,data, table_name):
if not self.table_exists(table_name):
self.create_table(table_name, data.columns, data.dtypes)
tuple_data = list(data.itertuples(index=False, name=None))
insert_query = f'INSERT INTO {table_name} VALUES('
for i in range(len(data.columns)):
insert_query += '?,'
insert_query = insert_query[:-1] + ')'
self.cursor.executemany(insert_query, tuple_data)
self.conn.commit()
return True
def close(self):
self.conn.close()
def add_new_azureStorage(request):
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
if request.POST["azurename"] =='' or request.POST["azureaccountkey"] == '' or request.POST["containername"] == '' :
return 'error'
newdata = {}
newdata['azurename'] = [request.POST["azurename"]]
newdata['azureaccountkey'] = [request.POST["azureaccountkey"]]
newdata['containername'] = [request.POST["containername"]]
name = request.POST["azurename"]
if sqlite_obj.table_exists("azurebucket"):
if(len(sqlite_obj.get_data('azurebucket','azurename',name))>0):
return 'error1'
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'azurebucket')
except:
return 'error'
def get_azureStorage():
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
temp_data = sqlite_obj.read_data('azurebucket')
data = []
for x in temp_data:
data_dict = {}
data_dict['azurename'] = x[0]
data_dict['azureaccountkey'] = x[1]
data_dict['containername'] = x[2]
data.append(data_dict)
except Exception as e:
print(e)
data = []
return data
def read_azureStorage(name,directoryname,DATA_FILE_PATH):
try:
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
data = sqlite_obj.get_data('azurebucket','azurename',name)
except:
data = []
found = False
if len(data)!=0:
storage_account_name = str(data[0])
storage_account_key = str(data[1])
azure_container_name = data[2]
found = True
try:
if found:
root_dir = str(directoryname)
from azure.storage.filedatalake import DataLakeServiceClient
import io
import pandavro as pdx
from detect_delimiter import detect
try:
service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format("https", storage_account_name), credential=storage_account_key)
print(azure_container_name)
file_system_client = service_client.get_file_system_client(azure_container_name)
print(root_dir)
file_paths = file_system_client.get_paths(path=root_dir)
main_df = pd.DataFrame()
for path in file_paths:
if not path.is_directory:
file_client = file_system_client.get_file_client(path.name)
file_ext = os.path.basename(path.name).split('.', 1)[1]
if file_ext in ["csv", "tsv"]:
with open(csv_local, "wb") as my_file:
download = file_client.download_file()
download.readinto(my_file)
with open(csv_local, 'r') as file:
data = file.read()
row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t'])
processed_df = pd.read_csv(csv_local, sep=row_delimiter)
if file_ext == "parquet":
download = file_client.download_file()
stream = io.BytesIO()
download.readinto(stream)
processed_df = pd.read_parquet(stream, engine='pyarrow')
if file_ext == "avro":
with open(avro_local, "wb") as my_file:
download = file_client.download_file()
download.readinto(my_file)
processed_df = pdx.read_avro(avro_local)
if not main_df.empty:
main_df = main_df.append(processed_df, ignore_index=True)
else:
main_df = pd.DataFrame(processed_df)
except Exception as e:
msg = str(e).split(".")[0]
print(msg)
return 'Error',str(msg), pd.DataFrame()
return "Success","",main_df
except:
return 'Error',"Please check bucket configuration", pd.DataFrame()
def remove_azure_bucket(name):
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR,'sqlite')
sqlite_obj = sqlite_db(file_path,'config.db')
return sqlite_obj.delete_record('azurebucket','azurename',name) |
labelling_utils.py | import json
import os
import random
import time
from avro.datafile import DataFileReader
from avro.io import DatumReader
from pyarrow.parquet import ParquetFile
from snorkel.labeling.model import LabelModel
from snorkel.labeling import PandasLFApplier, LFAnalysis
import pandas as pd
import pandavro as pdx
import pyarrow as pa
import numpy as np
import platform
from os.path import expanduser
home = expanduser("~")
if platform.system() == 'Windows':
DATA_FILE_PATH = os.path.join(home,'AppData','Local','Programs','HCLTech','AION','data','storage')
else:
DATA_FILE_PATH = os.path.join(home,'HCLT','AION','data')
def get_join(condition):
if condition["join"] == 'and':
return "&"
elif condition["join"] == 'or':
return "|"
else:
return ""
def create_labelling_function(rule_list, label_list):
lfs_main_func = 'def lfs_list_create():\n'
lfs_main_func += '\tfrom snorkel.labeling import labeling_function\n'
lfs_main_func += '\timport numpy as np\n'
lfs_main_func += '\timport json\n'
lfs_main_func += '\tABSTAIN = -1\n'
lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n'
lfs_list = '\tlfs_list=['
for rule in rule_list:
lfs_list += 'lf_' + rule["rule_name"] + ','
lfs = '\t@labeling_function()\n'
lfs += '\tdef lf_' + rule["rule_name"] + '(data):\n'
lfs += '\t\treturn np.where('
for condition in rule["conditions"]:
if "string" in condition["sel_datatype"]:
if condition["sel_condition"] in ["==", "!="]:
cond_statement = '(data["' + condition["sel_column"] + '"]' + condition[
"sel_condition"] + '("' + str(condition["input_value"]) + '"))' + get_join(condition)
else:
cond_statement = '(data["' + condition["sel_column"] + '"].' + condition[
"sel_condition"] + '("' + str(condition["input_value"]) + '"))' + get_join(condition)
else:
cond_statement = '(data["' + condition["sel_column"] + '"]' + condition["sel_condition"] + \
str(condition["input_value"]) + ')' + get_join(condition)
lfs += cond_statement
lfs += ', labels.index("' + rule["label"] + '"), ABSTAIN)\n'
lfs_main_func += lfs
if lfs_list.endswith(","):
lfs_list = lfs_list.rstrip(lfs_list[-1])
lfs_list += ']\n'
else:
lfs_list += ']\n'
lfs_main_func += lfs_list
lfs_main_func += '\treturn lfs_list\n'
lfs_main_func += 'lfs_list_create()'
f = open(os.path.join(DATA_FILE_PATH, 'lfs_list.txt'), 'w')
f.write(lfs_main_func)
f.close()
return lfs_main_func
def label_dataset(rule_list, file_ext, label_list, not_satisfy_label):
file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext)
if file_ext in ["csv", "tsv"]:
df = pd.read_csv(file_path)
elif file_ext == "json":
df = pd.json_normalize(pd.read_json(file_path).to_dict("records"))
elif file_ext == "avro":
reader = DataFileReader(open(file_path, "rb"), DatumReader())
schema = json.loads(reader.meta.get('avro.schema').decode('utf-8'))
df = pdx.read_avro(file_path, schema=schema, na_dtypes=True)
elif file_ext == "parquet":
df = pd.read_parquet(file_path, engine="pyarrow")
labelling_functions = create_labelling_function(rule_list, label_list)
exec(labelling_functions)
lfs = eval('lfs_list_create()')
applier = PandasLFApplier(lfs)
l_data = applier.apply(df)
label_model = LabelModel(cardinality=len(label_list) + 1, verbose=True)
label_model.fit(l_data, n_epochs=500, log_freq=50, seed=123)
df["label"] = label_model.predict(L=l_data, tie_break_policy="abstain")
df.loc[df["label"] == -1, "label"] = not_satisfy_label
for item in label_list:
df.loc[df["label"] == label_list.index(item), "label"] = item
if file_ext in ["csv", "tsv"]:
df.to_csv(os.path.join(DATA_FILE_PATH, "result_file." + file_ext), index=False)
elif file_ext == "parquet":
df.to_parquet(os.path.join(DATA_FILE_PATH, "result_file." + file_ext),
engine="pyarrow", index=False)
elif file_ext == "avro":
pdx.to_avro(os.path.join(DATA_FILE_PATH, "result_file." + file_ext), df)
else:
raise ValueError("Invalid file format")
num_records = len(df.index)
size_take = 100
if num_records <= size_take:
size_take = num_records
display_df = df.sample(n=size_take)
return display_df.to_html(classes='table table-striped text-left', justify='left', index=False)
def create_sample_function(rule, label_list, not_satisfy_label):
lfs_main_func = 'def lf_rule_apply(data):\n'
lfs_main_func += '\timport numpy as np\n'
lfs_main_func += '\tABSTAIN = -1\n'
lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n'
lfs = '\treturn np.where('
for condition in rule["conditions"]:
if "string" in condition["sel_datatype"]:
if condition["sel_condition"] in ["==", "!="]:
cond_statement = '(data["' + condition["sel_column"] + '"]' + condition["sel_condition"] + '("' + str(
condition["input_value"]) + '"))' + get_join(condition)
else:
cond_statement = '(data["' + condition["sel_column"] + '"].str.' + condition[
"sel_condition"] + '("' + str(condition["input_value"]) + '"))' + get_join(condition)
print(cond_statement)
else:
cond_statement = '(data["' + condition["sel_column"] + '"]' + condition["sel_condition"] + \
str(condition["input_value"]) + ')' + get_join(condition)
lfs += cond_statement
lfs += ', "' + rule["label"] + '", "' + not_satisfy_label + '")\n'
lfs_main_func += lfs
return lfs_main_func
def get_sample_result_of_individual_rule(rule_json, file_ext, label_list, not_satisfy_label):
file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext)
size_take = 100
if file_ext in ["csv", "tsv"]:
num_records = sum(1 for line in open(file_path)) - 1
if num_records > size_take:
skip = sorted(random.sample(range(1, num_records + 1), num_records - size_take))
else:
skip = 0
df = pd.read_csv(file_path, skiprows=skip)
elif file_path.endswith(".json"):
df = pd.read_json(file_path)
df = pd.json_normalize(df.to_dict("records"))
elif file_path.endswith(".avro"):
reader = DataFileReader(open(file_path, "rb"), DatumReader())
schema = json.loads(reader.meta.get('avro.schema').decode('utf-8'))
df = pdx.read_avro(file_path, schema=schema, na_dtypes=True)
elif file_path.endswith(".parquet"):
pf = ParquetFile(file_path)
take_rows = next(pf.iter_batches(batch_size=size_take))
df = pa.Table.from_batches([take_rows]).to_pandas()
# file_content = pd.read_parquet(file_path, engine="pyarrow")
else:
raise ValueError("Invalid file format")
rule_applier_func = create_sample_function(rule_json, label_list, not_satisfy_label)
exec(rule_applier_func)
df[rule_json["rule_name"]] = eval('lf_rule_apply')(df)
return df.to_html(classes='table table-striped text-left', justify='left', index=False)
def create_sample_function_ver2(rule_json, label_list, not_satisfy_label):
lfs_main_func = 'def lf_rule_apply(data):\n'
lfs_main_func += '\timport numpy as np\n'
lfs_main_func += '\tABSTAIN = -1\n'
lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n'
counter = 0
for condition in rule_json["conditions"]:
lfs_return = condition["sel_label"]
if counter > 0:
lfs_return_condition = '\telif'
else:
lfs_return_condition = '\tif'
for label_condition in condition["label_condition"]:
if label_condition["sel_datatype"] == "string":
if label_condition["sel_condition"] == "contains":
lfs_return_condition += '((' + str(label_condition["input_value"]) + ') in data["' + \
label_condition["sel_column"] + '"])' + get_join(label_condition)
elif label_condition["sel_condition"] in ["==", "!="]:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[
"sel_condition"] + '("' + str(
label_condition["input_value"]) + '"))' + get_join(label_condition)
else:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"].' + label_condition[
"sel_condition"] + '("' + str(label_condition["input_value"]) + '"))' + get_join(
label_condition)
else:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[
"sel_condition"] + str(label_condition["input_value"]) + ')' + get_join(label_condition)
if get_join(label_condition) == "":
lfs_return_condition += ":\n"
lfs_return_condition += '\t\treturn "' + lfs_return + '"\n'
lfs_main_func += lfs_return_condition
counter += 1
lfs_return_condition = '\n\telse:\n'
lfs_return_condition += '\t\treturn "' + not_satisfy_label + '"'
lfs_main_func += lfs_return_condition
return lfs_main_func
def get_sample_result_of_individual_rule_ver2(rule_json, file_ext, label_list, not_satisfy_label):
file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext)
size_take = 100
if file_ext in ["csv", "tsv"]:
num_records = sum(1 for line in open(file_path)) - 1
if num_records > size_take:
skip = sorted(random.sample(range(1, num_records + 1), num_records - size_take))
else:
skip = 0
df = pd.read_csv(file_path, skiprows=skip)
elif file_path.endswith(".json"):
df = pd.read_json(file_path)
df = pd.json_normalize(df.to_dict("records"))
elif file_path.endswith(".avro"):
reader = DataFileReader(open(file_path, "rb"), DatumReader())
schema = json.loads(reader.meta.get('avro.schema').decode('utf-8'))
df = pdx.read_avro(file_path, schema=schema, na_dtypes=True)
elif file_path.endswith(".parquet"):
pf = ParquetFile(file_path)
take_rows = next(pf.iter_batches(batch_size=size_take))
df = pa.Table.from_batches([take_rows]).to_pandas()
# file_content = pd.read_parquet(file_path, engine="pyarrow")
else:
raise ValueError("Invalid file format")
rule_applier_func = create_sample_function_ver2(rule_json, label_list, not_satisfy_label)
exec(rule_applier_func)
df[rule_json["rule_name"]] = df.apply(eval('lf_rule_apply'), axis=1)
return df.to_html(classes='table table-striped text-left', justify='left', index=False)
def create_labelling_function_ver2(rule_list, label_list):
lfs_main_func = 'def lfs_list_create():\n'
lfs_main_func += '\tfrom snorkel.labeling import labeling_function\n'
lfs_main_func += '\timport numpy as np\n'
lfs_main_func += '\timport json\n'
lfs_main_func += '\tABSTAIN = -1\n'
lfs_main_func += '\tlabels = json.loads(json.dumps(' + json.dumps(label_list) + '))\n'
lfs_list = '\tlfs_list=['
for rule in rule_list:
lfs_list += 'lf_' + rule["rule_name"] + ','
lfs = '\t@labeling_function()\n'
lfs += '\tdef lf_' + rule["rule_name"] + '(data):\n'
counter = 0
for condition in rule["conditions"]:
lfs_return = 'labels.index("' + condition["sel_label"] + '")'
if counter > 0:
lfs_return_condition = '\t\telif'
else:
lfs_return_condition = '\t\tif'
for label_condition in condition["label_condition"]:
if label_condition["sel_datatype"] == "string":
if label_condition["sel_condition"] == "contains":
lfs_return_condition += '((' + str(label_condition["input_value"]) + ') in data["' + \
label_condition["sel_column"] + '"])' + get_join(label_condition)
elif label_condition["sel_condition"] in ["==", "!="]:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[
"sel_condition"] + '("' + str(
label_condition["input_value"]) + '"))' + get_join(label_condition)
else:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"].' + label_condition[
"sel_condition"] + '("' + str(label_condition["input_value"]) + '"))' + get_join(
label_condition)
else:
lfs_return_condition += '(data["' + label_condition["sel_column"] + '"]' + label_condition[
"sel_condition"] + str(label_condition["input_value"]) + ')' + get_join(label_condition)
if get_join(label_condition) == "":
lfs_return_condition += ":\n"
lfs_return_condition += '\t\t\treturn ' + lfs_return + '\n'
lfs += lfs_return_condition
counter += 1
lfs_return_condition = '\n\t\telse:\n'
lfs_return_condition += '\t\t\treturn ABSTAIN\n'
lfs += lfs_return_condition
lfs_main_func += lfs
if lfs_list.endswith(","):
lfs_list = lfs_list.rstrip(lfs_list[-1])
lfs_list += ']\n'
else:
lfs_list += ']\n'
lfs_main_func += lfs_list
lfs_main_func += '\treturn lfs_list\n'
lfs_main_func += 'lfs_list_create()'
# f = open(os.path.join(DATA_FILE_PATH, 'lfs_list.txt'), 'w')
# f.write(lfs_main_func)
# f.close()
return lfs_main_func
def get_rule_name_list(rule_list):
rule_name_list = []
for rule in rule_list:
rule_name_list.append(rule["rule_name"])
return rule_name_list
def label_dataset_ver2(request,rule_list, file_ext, label_list, not_satisfy_label, label_weightage, include_proba):
file_path = os.path.join(DATA_FILE_PATH, "uploaded_file." + file_ext)
if file_ext in ["csv", "tsv"]:
df = pd.read_csv(file_path)
elif file_ext == "json":
df = pd.json_normalize(pd.read_json(file_path).to_dict("records"))
elif file_ext == "avro":
reader = DataFileReader(open(file_path, "rb"), DatumReader())
schema = json.loads(reader.meta.get('avro.schema').decode('utf-8'))
df = pdx.read_avro(file_path, schema=schema, na_dtypes=True)
elif file_ext == "parquet":
df = pd.read_parquet(file_path, engine="pyarrow")
labelling_functions = create_labelling_function_ver2(rule_list, label_list)
exec(labelling_functions)
lfs = eval('lfs_list_create()')
applier = PandasLFApplier(lfs)
l_data = applier.apply(df)
label_model = LabelModel(cardinality=len(label_list), verbose=True)
label_model.fit(l_data, n_epochs=500, log_freq=50, seed=123, class_balance=label_weightage)
df["label"] = label_model.predict(L=l_data, tie_break_policy="abstain")
if include_proba:
prediction_of_prob = label_model.predict_proba(L=l_data)
for label in label_list:
df[label + "_prob"] = np.around(prediction_of_prob[:, label_list.index(label)], 2) * 100
df.loc[df["label"] == -1, "label"] = not_satisfy_label
filetimestamp = str(int(time.time()))
datasetName = "AION_labelled_"+filetimestamp + '.' + file_ext
request.session['AION_labelled_Dataset'] = datasetName
for item in label_list:
df.loc[df["label"] == label_list.index(item), "label"] = item
if file_ext in ["csv", "tsv"]:
df.to_csv(os.path.join(DATA_FILE_PATH, datasetName), index=False)
elif file_ext == "parquet":
df.to_parquet(os.path.join(DATA_FILE_PATH, datasetName),
engine="pyarrow", index=False)
elif file_ext == "avro":
pdx.to_avro(os.path.join(DATA_FILE_PATH, datasetName), df)
else:
raise ValueError("Invalid file format")
#### saving file to database
from appbe.dataPath import DATA_DIR
from appbe.sqliteUtility import sqlite_db
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
newdata = {}
newdata['datapath'] = [os.path.join(DATA_FILE_PATH, datasetName)]
newdata['datasetname'] = [datasetName]
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata), 'dataingest')
num_records = len(df.index)
size_take = 100
if num_records <= size_take:
size_take = num_records
display_df = df.sample(n=size_take)
weightage = np.around(label_model.get_weights(), 2)
rule_name_list = get_rule_name_list(rule_list)
analysis_df = LFAnalysis(l_data, lfs).lf_summary()
analysis_df["Rule"] = analysis_df.index
analysis_df["Rule"] = analysis_df["Rule"].str.replace("lf_", "")
analysis_df = analysis_df[["Rule", "Polarity", "Coverage", "Overlaps", "Conflicts"]]
weightage_dict = dict(zip(rule_name_list, weightage))
analysis_json = analysis_df.to_dict(orient="records")
for item in analysis_json:
item["Weightage"] = weightage_dict[item["Rule"]]
analysis_df = pd.json_normalize(analysis_json)
# rules_weightage = []
# for key in weightage_dict:
# rules_weightage.append({
# "label": key,
# "y": weightage_dict[key],
# "legendText": key
# })
response = {
# "rule_name_list": rule_name_list,
# "weightage_list": list(weightage),
"analysis_df": analysis_df.to_html(classes='table table-striped text-left', justify='left', index=False),
"result_html": display_df.to_html(classes='table table-striped text-left', justify='left', index=False)
}
return response
def get_label_and_weightage(test_file_ext, marked_label_column,file_delim_test, custom_test_delim ):
file_path = os.path.join(DATA_FILE_PATH, "test_data_file." + test_file_ext)
if test_file_ext in ["csv", "tsv"]:
df = pd.read_csv(file_path)
elif test_file_ext == "json":
df = pd.json_normalize(pd.read_json(file_path).to_dict("records"))
elif test_file_ext == "avro":
reader = DataFileReader(open(file_path, "rb"), DatumReader())
schema = json.loads(reader.meta.get('avro.schema').decode('utf-8'))
df = pdx.read_avro(file_path, schema=schema, na_dtypes=True)
elif test_file_ext == "parquet":
df = pd.read_parquet(file_path, engine="pyarrow")
json_df = pd.DataFrame(df[marked_label_column].value_counts(normalize=True) * 100)
json_dict = json.loads(json_df.to_json())
label_with_weightage = []
for k in json_dict[marked_label_column]:
label_with_weightage.append(
{"label_name": k, "label_weightage": np.around(json_dict[marked_label_column][k], 2)})
return label_with_weightage
|
s3buckets.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import os
import rsa
import boto3 #usnish
import pandas as pd
import time
def add_new_bucket(request):
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','s3bucket.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
if request.POST["aionreferencename"] =='' or request.POST["s3bucketname"] == '' or request.POST["awsaccesskey"] == '' :
return 'error'
pkeydata='''-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAxIHM1FphEMMwViUrG0b2Bqf8tOxbhUWlnmjgFt5A25qbY1AfnrMv
fVx8+7iCcZ/3TY9Jv2I584SOc1tvsgESCke/t6+o/u2esPBsnDFzV62l3Zvw0m4e
wQeKlFC8EoOblyIXRbZdelSJinzlr9lOiKuid/xPvXHou6jxF1A2W7a89A2PM4Re
n0W9YkjB7dRGW1sSrpruHdVJvgHhGZFZ7sCTue0jVOnc5sT3Tq5saLfEDqHyKxlq
i/mcThmcTfisRIYFH5pyt/Ysr4VVP924QlcoqPOyg3RMCS3G0VjstSoVwNhxWrs/
lujDuCnpxvWzNpq21OWmF66GXxwiq+6W0wIDAQAB
-----END RSA PUBLIC KEY-----'''
pubkey = rsa.PublicKey.load_pkcs1(pkeydata)
awssecretaccesskey = rsa.encrypt(request.POST["awssecretaccesskey"].encode(), pubkey)
print(awssecretaccesskey)
newdata = {}
newdata['Name'] = request.POST["aionreferencename"]
newdata['AWSAccessKeyID'] = request.POST["awsaccesskey"]
newdata['AWSSecretAccessKey'] = str(awssecretaccesskey)
newdata['S3BucketName'] = request.POST["s3bucketname"]
data.append(newdata)
with open(file_path, 'w') as f:
json.dump(data, f)
f.close()
def get_s3_bucket():
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','s3bucket.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
return data
def read_s3_bucket(name,filename,DATA_FILE_PATH):
privkey = '''-----BEGIN RSA PRIVATE KEY-----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-----END RSA PRIVATE KEY-----'''
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','s3bucket.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
awssecretaccesskey = ''
found = False
for x in data:
if x['Name'] == name:
awssecretaccesskey = x['AWSSecretAccessKey']
aws_access_key_id = x['AWSAccessKeyID']
bucketName = x['S3BucketName']
found = True
break
if found:
privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM')
awssecretaccesskey = eval(awssecretaccesskey)
awssecretaccesskey = rsa.decrypt(awssecretaccesskey, privkey)
awssecretaccesskey = awssecretaccesskey.decode('utf-8')
#awssecretaccesskey = 'SGcyJavYEQPwTbOg1ikqThT+Op/ZNsk7UkRCpt9g'#rsa.decrypt(awssecretaccesskey, privkey)
client_s3 = boto3.client('s3', aws_access_key_id=aws_access_key_id, aws_secret_access_key=str(awssecretaccesskey))
#print(bucketName,filename)
try:
response = client_s3.get_object(Bucket=bucketName, Key=filename)
df = pd.read_csv(response['Body'])
except Exception as e:
print(e)#usnish
return 'Error', pd.DataFrame()
#return 'Error', pd.DataFrame()
return 'Success',df
return 'Error', pd.DataFrame() |
alchemy.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import pyodbc as pyodbc
import pandas as pd
import json
import sqlalchemy as db
import pandas as pd
import urllib
def get_connection(request):
dbType = request.session['dbType']
connection_string = ""
if dbType.lower()=="sqlite":
filepath = request.session['filepath']
#table = request.session["tablenamesql"]
connection_string = "sqlite:///"+str(filepath)
elif dbType.lower() in ["postgresql","mysql","mssql"]:
db_name = request.session['dbname']
password = request.session['password']
user = request.session['username']
port = request.session['port']
host = request.session['host']
password=urllib.parse.quote_plus(password)
if dbType.lower()=="postgresql":
connection_string = "postgresql+psycopg2://" + user + ":" + password + "@" + host + ":" + port + "/" + db_name
if dbType.lower()=="mysql":
connection_string = "mysql+pyodbc://" + user + ":" + password + "@" + host + ":" + port + "/" + db_name
if dbType.lower()=="mssql":
driver=request.session['driver']
params = urllib.parse.quote_plus(
'Driver=%s;' % driver +
'Server=tcp:%s,' % host +
'%s;' % port +
'Database=%s;' % db_name +
'Uid=%s;' % user +
'Pwd={%s};' % password +
'Encrypt=yes;' +
'TrustServerCertificate=no;' +
'Connection Timeout=30;')
connection_string = 'mssql+pyodbc:///?odbc_connect=' + params
return connection_string
def list_tables(request):
connection_string = get_connection(request)
engine = db.create_engine(connection_string)
connection = engine.connect()
metadata = db.MetaData()
metadata.reflect(engine)
dt_list = []
try:
dt_list= list(metadata.tables.keys())
print(dt_list)
return dt_list
except:
print("Something went wrong")
return dt_list
def list_tables_fields(request,table_list):
connection_string = get_connection(request)
engine = db.create_engine(connection_string)
connection = engine.connect()
metadata = db.MetaData()
metadata.reflect(engine)
table_field_obj = {}
table_field_obj['data'] = []
try:
# filepath = request.session['filepath']
#table = request.session["tablenamesql"]
table_list = json.loads(table_list)
for table in table_list:
tf_obj = {}
tf_obj['TableName'] = str(table).strip()
tf_obj['Fields']= []
table = db.Table(table, metadata, autoload=True, autoload_with=engine)
col = table.columns.keys()
tempdata = []
for x in col:
my_list = {"column_name": x,"is_select":"false"}
tempdata.append(my_list)
tf_obj['Fields'] = tempdata
table_field_obj['data'].append(tf_obj)
return json.dumps(table_field_obj)
except Exception as e:
print("Something went wrong "+str(e))
return table_field_obj
def get_data(connection_string,table):
engine = db.create_engine(connection_string)
connection = engine.connect()
metadata = db.MetaData()
metadata.reflect(engine)
table = db.Table(table,metadata, autoload=True, autoload_with=engine)
query = db.select([table])
ResultProxy = connection.execute(query)
ResultSet = ResultProxy.fetchall()
col = table.columns.keys()
return pd.DataFrame(ResultSet, columns=col)
def getDataFromSingleTable(request):
dbType = request.session['dbType']
if dbType.lower() == "sqlite":
table = request.session["tablenamesql"]
else:
table = request.session["tablename"]
connection_string = get_connection(request)
df = get_data(connection_string,table)
return df
def validatequery(request,table_details,join_details,where_details):
resultdata = []
try:
table_details = json.loads(table_details)
join_details = json.loads(join_details)
where_details = json.loads(where_details)
connection_string = get_connection(request)
engine = db.create_engine(connection_string)
connection = engine.connect()
metadata = db.MetaData()
metadata.reflect(engine)
sel_col = []
for item in table_details:
table = item["TableName"]
table = db.Table(table, metadata, autoload=True, autoload_with=engine)
for ele in item["Fields"]:
if str(ele["is_select"]).lower() == 'true':
sel_col.append(table.columns[ele["column_name"]])
join_condition = []
where_clause = ""
for item in join_details:
table1 = item["Table1Name"]
table1 = db.Table(table1, metadata, autoload=True, autoload_with=engine)
left_join = table1.columns[item["Table1Field"]]
table2 = item["Table2Name"]
table2 = db.Table(table2, metadata, autoload=True, autoload_with=engine)
right_join = table2.columns[item["Table2Field"]]
join_condition = "{left_join} {Condition}= {right_join}".format(left_join=left_join,
Condition=item["Condition"],right_join= right_join)
'''dbType = request.session['dbType']
if dbType.lower()=="sqlite":
for item in where_details:
where_clause = "{table}.'{column}'{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"])
if dbType.lower()=="postgresql":
for item in where_details:
where_clause = "{table}.{column}{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"])
'''
if len(join_details)!=0:
try:
for item in where_details:
where_clause = "{table}.'{column}'{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"])
query =db.select(sel_col).\
select_from(table1.join(table2,db.text(join_condition))). \
where(db.and_(db.text(where_clause)))
ResultProxy = connection.execute(query)
ResultSet = ResultProxy.fetchall()
except:
for item in where_details:
where_clause = "{table}.{column}{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"])
query =db.select(sel_col).\
select_from(table1.join(table2,db.text(join_condition))). \
where(db.and_(db.text(where_clause)))
ResultProxy = connection.execute(query)
ResultSet = ResultProxy.fetchall()
else:
table = table_details[0]["TableName"]
table = db.Table(table, metadata, autoload=True, autoload_with=engine)
try:
for item in where_details:
where_clause = "{table}.'{column}'{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"])
query = db.select(sel_col). \
select_from(table). \
where(db.and_(db.text(where_clause)))
ResultProxy = connection.execute(query)
ResultSet = ResultProxy.fetchall()
except:
for item in where_details:
where_clause = "{table}.{column}{condition}{value}".format(table=item["TableName"],column=str(item["FieldName"]),condition=item["Condition"],value=item["CompareValue"])
query = db.select(sel_col). \
select_from(table). \
where(db.and_(db.text(where_clause)))
ResultProxy = connection.execute(query)
ResultSet = ResultProxy.fetchall()
if len(ResultSet) > 0:
data = pd.DataFrame(ResultSet)
data.columns = ResultSet[0].keys()
print(data)
return data,"query exectuted successfully"
else:
return pd.DataFrame(),"No rows returned"
# conn = get_connection(server_url,username_actian,password_actian,database_actian)
# sql_text = query
# cur = conn.cursor()
# resultdata = simple_select(cur, query)
# cur.close()
#df = pd.DataFrame(resultdata)
#print(df)
except Exception as e:
print(e)
return pd.DataFrame(), str(e) |
trainresult.py | import json
import os
import pandas as pd
import urllib, base64
def check_deepCheckPlots(deployedLocation):
deepCheck = 'False'
boostOverfit = 'False'
boostOverfitCond = 'False'
mi='False'
miCond='False'
smc = 'False'
smsCond = 'False'
boostOverfitFile= os.path.join(deployedLocation,'log','boosting_overfit.html')
boostOverfitCondFile= os.path.join(deployedLocation,'log','boosting_overfit_condition.html')
smcFile= os.path.join(deployedLocation,'log','smc.html')
smcCondFile= os.path.join(deployedLocation,'log','smc_condition.html')
miFile= os.path.join(deployedLocation,'log','mi.html')
miConFile= os.path.join(deployedLocation,'log','mi_con.html')
file_exists = os.path.exists(boostOverfitFile)
if file_exists:
deepCheck = 'True'
boostOverfit = 'True'
file_exists = os.path.exists(boostOverfitCondFile)
if file_exists:
deepCheck = 'True'
boostOverfitCond = 'True'
file_exists = os.path.exists(miFile)
if file_exists:
deepCheck = 'True'
mi = 'True'
file_exists = os.path.exists(miConFile)
if file_exists:
deepCheck = 'True'
miCond = 'True'
file_exists = os.path.exists(smcFile)
if file_exists:
deepCheck = 'True'
smc = 'True'
file_exists = os.path.exists(smcCondFile)
if file_exists:
deepCheck = 'True'
smsCond = 'True'
output = {'deepCheck':deepCheck,'boostOverfit':boostOverfit,'boostOverfitCond':boostOverfitCond,'mi':mi,'miCond':miCond,'smc':smc,'smsCond':smsCond}
return output
def FeaturesUsedForTraining(output_json):
resultJsonObj = json.loads(output_json)
result = {}
result['Status'] = resultJsonObj['status']
result['ModelType'] = resultJsonObj['data']['ModelType']
result['ScoreType'] = resultJsonObj['data']['ScoreType']
result['FeaturesUsed'] = resultJsonObj['data']['featuresused']
result['BestModel'] = resultJsonObj['data']['BestModel']
return result
def ParseResults(output_json):
msg1 = 'Results...'
resultJsonObj = json.loads(output_json)
result = {}
survical_images = []
result['Status'] = resultJsonObj['status']
result['ModelType'] = resultJsonObj['data']['ModelType']
if 'vmDetails' in resultJsonObj['data']:
result['DeployLocation'] = resultJsonObj['data']['vmDetails']
else:
result['DeployLocation'] = resultJsonObj['data']['deployLocation']
result['BestModel'] = resultJsonObj['data']['BestModel']
if str(resultJsonObj['data']['BestScore']) == "NA":
result['BestScore'] = 'NA'
else:
result['BestScore'] = round(float(resultJsonObj['data']['BestScore']), 2)
result['ScoreType'] = resultJsonObj['data']['ScoreType']
result['FeaturesUsed'] = resultJsonObj['data']['featuresused']
##### Training Confusion Matrix
result['problem_type'] = result['ModelType']
if result['ModelType'].lower() == 'timeseriesanomalydetection':
result['problem_type'] = 'TimeSeriesAnomalydetection'
if result['ModelType'] == 'classification' or result['ModelType'].lower() == 'distributed classification' or (result['ModelType'] == 'anomalydetection' and (result['BestScore']) != 0) or result['ModelType'] == 'ImageClassification':
bestmodel = resultJsonObj['data']['BestModel']
if bestmodel.lower() == 'nas':
modelSummary= os.path.join(result['DeployLocation'],'summary.txt')
f = open(modelSummary, 'r')
file_content = f.read()
f.close()
#print(file_content)
result['modelSummary'] = file_content
#task 11997
if result['ModelType'].lower() == 'classification':
result['problem_type'] = 'Classification'
elif result['ModelType'].lower() == 'anomalydetection':
result['problem_type'] = 'AnomalyDetection'
elif result['ModelType'].lower() == 'imageclassification':
result['problem_type'] = 'ImageClassification'
elif result['ModelType'].lower() == 'distributed classification':
result['problem_type'] = 'Distributed Classification'
try:
result['deepCheck'] = check_deepCheckPlots(result['DeployLocation'])
except Exception as e:
print(e)
if 'ConfusionMatrix' in resultJsonObj['data']['trainmatrix']:
TrainConfusionMatrix = resultJsonObj['data']['trainmatrix']['ConfusionMatrix']
numLabels = len(TrainConfusionMatrix)
TrainConfusionMatrixList = []
for act_key, value in TrainConfusionMatrix.items():
temp = {}
temp['Label'] = act_key
for pred_key, pred_value in value.items():
temp[pred_key] = pred_value
TrainConfusionMatrixList.append(temp)
result['TrainConfusionMatrix'] = TrainConfusionMatrixList
TrainClassificationReport = resultJsonObj['data']['trainmatrix']['ClassificationReport']
numRows = len(TrainClassificationReport)
TrainClassificationReportList = []
metrics_keys_list = []
for key, value in TrainClassificationReport.items():
temp = {}
temp['Label'] = key
if isinstance( value, dict):
for metricsKey, metricsValue in value.items():
temp[metricsKey] = round(metricsValue, 4)
if metricsKey not in metrics_keys_list:
metrics_keys_list.append( metricsKey)
else:
if metrics_keys_list:
for key in metrics_keys_list:
temp[key] = round(value, 4)
TrainClassificationReportList.append(temp)
result['TrainClassificationReport'] = TrainClassificationReportList
result['Train_ROC_AUC_SCORE'] = round(float(resultJsonObj['data']['trainmatrix']['ROC_AUC_SCORE']), 4)
else:
result['TrainClassificationReport'] = ''
result['Train_ROC_AUC_SCORE']=''
##### Testing Confusion Matix
if 'ConfusionMatrix' in resultJsonObj['data']['matrix']:
ConfusionMatrix = resultJsonObj['data']['matrix']['ConfusionMatrix']
numLabels = len(ConfusionMatrix)
ConfusionMatrixList = []
for act_key, value in ConfusionMatrix.items():
temp = {}
temp['Label'] = act_key
for pred_key, pred_value in value.items():
temp[pred_key] = pred_value
ConfusionMatrixList.append(temp)
result['ConfusionMatrix'] = ConfusionMatrixList
ClassificationReport = resultJsonObj['data']['matrix']['ClassificationReport']
numRows = len(ClassificationReport)
ClassificationReportList = []
metrics_keys_list = []
for key, value in ClassificationReport.items():
temp = {}
temp['Label'] = key
if isinstance( value, dict):
for metricsKey, metricsValue in value.items():
temp[metricsKey] = round(metricsValue, 4)
if metricsKey not in metrics_keys_list:
metrics_keys_list.append( metricsKey)
else:
if metrics_keys_list:
for key in metrics_keys_list:
temp[key] = round(value, 4)
ClassificationReportList.append(temp)
result['ClassificationReport'] = ClassificationReportList
result['ROC_AUC_SCORE'] = round(float(resultJsonObj['data']['matrix']['ROC_AUC_SCORE']), 4)
elif result['ModelType'] == 'similarityIdentification':
result['problem_type'] = 'similarityIdentification'
elif result['ModelType'] == 'contextualSearch':
result['problem_type'] = 'contextualSearch'
elif result['ModelType'] == 'MultiLabelPrediction':
result['problem_type'] = 'MultiLabelPrediction'
matrix = resultJsonObj['data']['matrix']
training_matrix = []
for x in matrix:
fmatrix = {}
fmatrix['feature'] = x
performance = {}
for y in matrix[x]:
performance[y] = matrix[x][y]
fmatrix['performance'] = performance
training_matrix.append(fmatrix)
testmatrix = resultJsonObj['data']['testmatrix']
testing_matrix = []
for x in testmatrix:
fmatrix = {}
fmatrix['feature'] = x
performance = {}
for y in testmatrix[x]:
performance[y] = testmatrix[x][y]
fmatrix['performance'] = performance
testing_matrix.append(fmatrix)
result['testing_matrix'] = testing_matrix
result['training_matrix'] = training_matrix
elif result['ModelType'] == 'regression' or result['ModelType'].lower() == 'distributed regression':
try:
result['deepCheck'] = check_deepCheckPlots(result['DeployLocation'])
except Exception as e:
print(e)
try:
result['problem_type'] = 'Regression'
testing_matrix = {}
if 'MAE' in resultJsonObj['data']['matrix']:
testing_matrix['MAE'] = float(resultJsonObj['data']['matrix'].get('MAE','0'))
testing_matrix['R2Score'] = float(resultJsonObj['data']['matrix'].get('R2Score','0'))
testing_matrix['MSE'] = float(resultJsonObj['data']['matrix'].get('MSE','0'))
testing_matrix['MAPE'] = float(resultJsonObj['data']['matrix'].get('MAPE','0'))
testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix'].get('RMSE','0'))
testing_matrix['NormalisedRMSEPercentage'] = float(resultJsonObj['data']['matrix'].get('Normalised RMSE(%)','0'))
result['testing_matrix'] = testing_matrix
training_matrix = {}
training_matrix['MAE'] = float(resultJsonObj['data']['trainmatrix'].get('MAE','0'))
training_matrix['R2Score'] = float(resultJsonObj['data']['trainmatrix'].get('R2Score','0'))
training_matrix['MSE'] = float(resultJsonObj['data']['trainmatrix'].get('MSE','0'))
training_matrix['MAPE'] = float(resultJsonObj['data']['trainmatrix'].get('MAPE','0'))
training_matrix['RMSE'] = float(resultJsonObj['data']['trainmatrix'].get('RMSE','0'))
training_matrix['NormalisedRMSEPercentage'] = float(resultJsonObj['data']['trainmatrix'].get('Normalised RMSE(%)','0'))
result['training_matrix'] = training_matrix
except Exception as e:
print(e)
elif result['ModelType'] == 'Text Similarity':
result['problem_type'] = 'textsimilarity'
testing_matrix = {}
testing_matrix['Accuracy'] = float(resultJsonObj['data']['matrix']['Accuracy'])
testing_matrix['ROC_AUC'] = float(resultJsonObj['data']['matrix']['ROC AUC'])
result['testing_matrix'] = testing_matrix
training_matrix = {}
training_matrix['Accuracy'] = float(resultJsonObj['data']['trainmatrix']['Accuracy'])
training_matrix['ROC_AUC'] = float(resultJsonObj['data']['trainmatrix']['ROC AUC'])
result['training_matrix'] = training_matrix
elif result['ModelType'] == 'RecommenderSystem': #taskid 11190
result['problem_type'] = 'Recommender'
testing_matrix = {}
testing_matrix['RMSE'] = 'NA'
result['testing_matrix'] = testing_matrix
training_matrix = {}
training_matrix['RMSE'] = 'NA'
result['training_matrix'] = training_matrix
elif result['ModelType'] == 'SurvivalAnalysis':
result['problem_type'] = 'SurvivalAnalysis'
survivalProbabilityjson = resultJsonObj['data']['survivalProbability']
performanceimages = resultJsonObj['data']['imageLocation']
start = '['
end = ']'
performanceimages = performanceimages[performanceimages.find(start) + len(start):performanceimages.rfind(end)]
performanceimages = performanceimages.split(',')
for imagefile in performanceimages:
imagefile = imagefile.replace("'", "")
string = base64.b64encode(open(imagefile, "rb").read())
image_64 = 'data:image/png;base64,' + urllib.parse.quote(string)
survical_images.append(image_64)
result['survivalProbability'] = survivalProbabilityjson
elif result['ModelType'] == 'StateTransition':
result['problem_type'] = 'StateTransition'
stateprobabilityfile = os.path.join(result['DeployLocation'],'stateTransitionProbability.csv')
clusterfile = os.path.join(result['DeployLocation'],'stateClustering.csv')
if(os.path.isfile(stateprobabilityfile)):
df_prob = pd.read_csv(stateprobabilityfile)
df_prob = df_prob[['State','NextState','Probability']]
result['probability'] = df_prob
if(os.path.isfile(clusterfile)):
df_clus = pd.read_csv(clusterfile)
df_clus = df_clus[['clusterid','clusterlist']]
result['cluster'] = df_clus
elif result['ModelType'].lower() == 'timeseriesforecasting': #task 11997
result['problem_type'] = 'TimeSeriesForecasting'
if result['BestModel'] == 'FBPROPHET':
imagefile = os.path.join(result['DeployLocation'],'log','img','prophet_fig.png')
string = base64.b64encode(open(imagefile, "rb").read())
image_64 = 'data:image/png;base64,' + urllib.parse.quote(string)
survical_images.append(image_64)
testing_matrix = {}
testing_matrix['MAE'] = float(resultJsonObj['data']['matrix']['MAE'])
testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE'])
testing_matrix['R2'] = float(resultJsonObj['data']['matrix']['R2'])
testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE'])
result['testing_matrix'] = testing_matrix
forecastjson = resultJsonObj['data']['forecasts']
result['forecast'] = forecastjson
if result['BestModel'] == 'VAR':
'''
FeaturesMatrix = resultJsonObj['data']['matrix']['FeaturesMatrix']
mae = ''
mse = ''
mape = ''
rmse = ''
for x in FeaturesMatrix:
if mae != '':
mae += ','
if mse != '':
mse += ','
if R2 != '':
R2 += ','
if rmse != '':
rmse += ','
featurename = x['Features']
mae = mae + featurename + '=' + x['MAE']
mse = mse + featurename + '=' + x['MSE']
R2 = R2 + featurename + '=' + x['R2']
rmse = rmse + featurename + '=' + x['RMSE']
testing_matrix = {}
testing_matrix['MAE'] = mae
testing_matrix['MSE'] = mse
testing_matrix['R2'] = R2
testing_matrix['RMSE'] = rmse
result['testing_matrix'] = testing_matrix
forecastjson = resultJsonObj['data']['forecasts']
result['forecast'] = forecastjson
'''
testing_matrix = {}
testing_matrix['MAE'] = float(resultJsonObj['data']['matrix']['MAE'])
testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE'])
testing_matrix['R2'] = float(resultJsonObj['data']['matrix']['R2'])
testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE'])
result['testing_matrix'] = testing_matrix
forecastjson = resultJsonObj['data']['forecasts']
result['forecast'] = forecastjson
elif result['BestModel'] == 'LSTM' or result['BestModel'] == 'MLP':
testing_matrix = {}
testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE'])
testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE'])
result['testing_matrix'] = testing_matrix
forecastjson = resultJsonObj['data']['forecasts']
result['forecast'] = forecastjson
else:
testing_matrix = {}
testing_matrix['MAE'] = float(resultJsonObj['data']['matrix']['MAE'])
testing_matrix['MSE'] = float(resultJsonObj['data']['matrix']['MSE'])
testing_matrix['R2'] = float(resultJsonObj['data']['matrix']['R2'])
testing_matrix['RMSE'] = float(resultJsonObj['data']['matrix']['RMSE'])
result['testing_matrix'] = testing_matrix
forecastjson = resultJsonObj['data']['forecasts']
result['forecast'] = forecastjson
elif result['ModelType'] == 'topicmodelling':
result['problem_type'] = 'TopicModelling'
topics = resultJsonObj['topics']
df_topic = []
dataDict = {}
for x in topics:
dataDict = {}
words = topics[x]
print(words)
word = ''
for key in words:
print(key)
if word != '':
word = word+', '
word = word+key+'('+str(round(words[key],2))+')'
dataDict["ID"] = x
dataDict["Words"] = word
df_topic.append(dataDict)
result['topics'] = df_topic
elif result['ModelType'].lower() == 'association rule':
result['problem_type'] = 'AssociationRules'
deploy_location = result['DeployLocation']
freq_item_file = os.path.join(result['DeployLocation'],'frequentItems.csv')
if(os.path.isfile(freq_item_file)):
rules_file = os.path.join(result['DeployLocation'],'associationRules.csv')
if(os.path.isfile(rules_file)):
df_rules = pd.read_csv(rules_file)
df_rules = df_rules[['antecedents','consequents','support','confidence','lift']]
#df_rules['antecedents'] = df_rules['antecedents']
result['rules'] = df_rules
else:
result['error'] = 'There are no association found in frequent items above that threshold (minThreshold)'
else:
result['error'] = 'There are no frequent items above that threshold (minSupport), try by reducing the minSupport value'
elif result['ModelType'] == 'clustering':
result['problem_type'] = 'Clustering'
testing_matrix = {}
if 'SilHouette_Avg' in resultJsonObj['data']['matrix']:
testing_matrix['SilHouette_Avg'] = round(float(resultJsonObj['data']['matrix']['SilHouette_Avg']),2)
else:
testing_matrix['SilHouette_Avg'] = 'NA'
if 'DaviesBouldinScore' in resultJsonObj['data']['matrix']:
testing_matrix['DaviesBouldinScore'] = round(float(resultJsonObj['data']['matrix']['DaviesBouldinScore']),2)
else:
testing_matrix['DaviesBouldinScore'] = 'NA'
if 'CalinskiHarabazScore' in resultJsonObj['data']['matrix']:
testing_matrix['CalinskiHarabazScore'] = round(float(resultJsonObj['data']['matrix']['CalinskiHarabazScore']),2)
else:
testing_matrix['CalinskiHarabazScore'] = 'NA'
centroidpath = os.path.join(result['DeployLocation'],'centers.csv')
if(os.path.isfile(centroidpath)):
df_center = pd.read_csv(centroidpath)
df_center = df_center.rename(columns={"Unnamed: 0": "Cluster"})
result['centerpoints'] = round(df_center,2)
result['testing_matrix'] = testing_matrix
training_matrix = {}
if 'SilHouette_Avg' in resultJsonObj['data']['matrix']:
training_matrix['SilHouette_Avg'] = round(float(resultJsonObj['data']['matrix']['SilHouette_Avg']),2)
training_matrix['DaviesBouldinScore'] = round(float(resultJsonObj['data']['matrix']['DaviesBouldinScore']),2)
training_matrix['CalinskiHarabazScore'] = round(float(resultJsonObj['data']['matrix']['CalinskiHarabazScore']),2)
else:
training_matrix['SilHouette_Avg'] = 'NA'
training_matrix['DaviesBouldinScore'] = 'NA'
training_matrix['CalinskiHarabazScore'] = 'NA'
result['training_matrix'] = training_matrix
#print(result)
evaluatedModelsList = resultJsonObj['data']['EvaluatedModels']
#print(evaluatedModelsList)
for index in range(len(evaluatedModelsList)):
if evaluatedModelsList[index]['Score'] == 'NA':
evaluatedModelsList[index]['Score'] = 'NA'
else:
evaluatedModelsList[index]['Score'] = round(float(evaluatedModelsList[index]['Score']), 4)
if result['ModelType'] == 'classification':
evaluatedModelsList = sorted(evaluatedModelsList, key=lambda k: k['Score'],reverse=True)
else:
evaluatedModelsList = sorted(evaluatedModelsList, key=lambda k: k['Score'])
result['EvaluatedModels'] = evaluatedModelsList
result['LogFile'] = resultJsonObj['data']['LogFile']
return result, survical_images |
compute.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import os,sys
import json
def getInstanceonGCP(image,instances):
try:
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('LLMTuning'):
data = sqlite_obj.read_data('LLMTuning','image="'+image['id']+'"')
for values in data:
instance = {}
instance['type'] = 'instance'
instance['id'] = values[2]
instance['workLoad'] = image['workLoad']
instance['machineImageProjectID'] = image['machineImageProjectID']
instance['ssh'] = image['ssh']
instance['machineConfiguration'] = image['machineConfiguration']
instance['instanceType'] = image['instanceType']
instances.append(instance)
except Exception as e:
print(e)
return instances
def getInstanceonAWS(amiid,instances):
try:
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('LLMTuning'):
data = sqlite_obj.read_data('LLMTuning','image="'+amiid['id']+'"')
for values in data:
instance = {}
instance['type'] = 'instance'
instance['id'] = values[2]
instance['workLoad'] = amiid['workLoad']
instance['regionName'] = amiid['regionName']
instance['ssh'] = amiid['ssh']
instance['machineConfiguration'] = amiid['machineConfiguration']
instance['instanceType'] = amiid['instanceType']
instances.append(instance)
except Exception as e:
print(e)
return instances
def updatelocalsetings(request):
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('computeInfrastructure'):
updated_data = 'selectedInfrastructure="Local"'
sqlite_obj.update_data(updated_data,'','computeInfrastructure')
def updateToComputeSettings(infratructure):
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('computeInfrastructure'):
updated_data = 'selectedInfrastructure="'+infratructure+'"'
sqlite_obj.update_data(updated_data,'','computeInfrastructure')
def updateGCPConfig(request):
try:
credentialsJson = request.POST.get('credentialsJson')
projectID = request.POST.get('gcpProjectid')
machineType = request.POST.get('gcpmachineType')
selectedID = request.POST.get('gcpInstance')
gcpZone = request.POST.get('gcpZone')
workload = request.POST.get('gcpworkload')
noOfInstance = request.POST.get('GCPnoofinstance')
#print(credentialsJson,projectID,machineType,selectedID,gcpZone,workload,noOfInstance)
if credentialsJson != '' and projectID != '':
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('gcpCredentials'):
updated_data = 'credentialsJson="'+credentialsJson+'",projectID="'+projectID+'",machineType="'+machineType+'",selectedID="'+selectedID+'",regionName="'+gcpZone+'",noOfInstance="'+str(noOfInstance)+'",workload="'+workload+'"'
sqlite_obj.update_data(updated_data,'','gcpCredentials')
else:
newdata = {}
newdata.update({'id':['1'],'credentialsJson': [credentialsJson],'projectID': [projectID],'machineType':[machineType],'selectedID':[selectedID],'regionName':[gcpZone],'noOfInstance':[noOfInstance],'workload':[workload]})
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'gcpCredentials')
return('success')
else:
return('error')
except Exception as e:
print(e)
return('error')
def updateComputeConfig(request):
try:
AWSAccessKeyID = request.POST.get('AWSAccessKeyID')
AWSSecretAccessKey = request.POST.get('AWSSecretAccessKey')
workload = request.POST.get('workload')
machineType = request.POST.get('machineType')
selectedID = request.POST.get('amiInstance')
regionName = request.POST.get('regionName')
noOfInstance = request.POST.get('NoOfInstance')
securitygroupid = request.POST.get('AWSSecuritygroupID')
if AWSAccessKeyID != '' and AWSSecretAccessKey != '':
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
if sqlite_obj.table_exists('awsCredentials'):
column_names = sqlite_obj.column_names('awsCredentials')
if 'securitygroupid' not in column_names:
query = 'Alter Table awsCredentials ADD securitygroupid TEXT'
sqlite_obj.execute_query(query)
updated_data = 'AWSAccessKeyID="'+AWSAccessKeyID+'",AWSSecretAccessKey="'+AWSSecretAccessKey+'",machineType="'+machineType+'",selectedID="'+selectedID+'",regionName="'+regionName+'",noOfInstance="'+noOfInstance+'",workload="'+workload+'",securitygroupid="'+securitygroupid+'"'
sqlite_obj.update_data(updated_data,'','awsCredentials')
else:
newdata = {}
newdata.update({'id':['1'],'AWSAccessKeyID': [AWSAccessKeyID],'AWSSecretAccessKey': [AWSSecretAccessKey],'machineType':[machineType],'selectedID':[selectedID],'regionName':[regionName],'noOfInstance':[noOfInstance],'workload':[workload],'securitygroupid':[securitygroupid]})
sqlite_obj.write_data(pd.DataFrame.from_dict(newdata),'awsCredentials')
return('success')
else:
return('error')
except Exception as e:
print(e)
return('error')
def selectedInfratructure():
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
selcInfra = 'Local'
if sqlite_obj.table_exists('computeInfrastructure'):
data = sqlite_obj.read_data('computeInfrastructure')
for values in data:
selcInfra = values[1]
return selcInfra
def readComputeConfig():
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','config','compute_conf.json'))
f = open(file_path, "r")
configSettings = f.read()
f.close()
configSettingsJson = json.loads(configSettings)
from appbe.sqliteUtility import sqlite_db
from appbe.dataPath import DATA_DIR
import pandas as pd
file_path = os.path.join(DATA_DIR, 'sqlite')
sqlite_obj = sqlite_db(file_path, 'config.db')
selcInfra = 'Local'
if sqlite_obj.table_exists('computeInfrastructure'):
data = sqlite_obj.read_data('computeInfrastructure')
for values in data:
selcInfra = values[1]
else:
data = {}
data.update({'id':['1'],'selectedInfrastructure': ['Local']})
sqlite_obj.write_data(pd.DataFrame.from_dict(data),'computeInfrastructure')
configSettingsJson['computeInfrastructure'] = selcInfra
for ami in configSettingsJson['AWS_EC2']['amis']:
configSettingsJson['AWS_EC2']['instances'] = getInstanceonAWS(ami,configSettingsJson['AWS_EC2']['instances'])
for image in configSettingsJson['GCP']['machineImage']:
configSettingsJson['GCP']['instances'] = getInstanceonGCP(image,configSettingsJson['GCP']['instances'])
AWSAccessKeyID = ''
AWSSecretAccessKey = ''
securitygroupid = ''
machineType = 'AMI'
selectedID = ''
regionName = ''
noofInfra = 1
workLoad = 'LLM'
if sqlite_obj.table_exists('awsCredentials'):
column_names = sqlite_obj.column_names('awsCredentials')
#print(column_names)
if 'workload' not in column_names:
query = 'Alter Table awsCredentials ADD workload TEXT'
sqlite_obj.execute_query(query)
if 'securitygroupid' not in column_names:
query = 'Alter Table awsCredentials ADD securitygroupid TEXT'
sqlite_obj.execute_query(query)
data = sqlite_obj.read_data('awsCredentials')
for values in data:
AWSAccessKeyID = values[1]
AWSSecretAccessKey = values[2]
machineType = values[3]
selectedID = values[4]
regionName = values[5]
noofInfra = values[6]
workLoad = values[7]
securitygroupid = values[8]
selectedAWS = {}
selectedAWS['accessKey'] = AWSAccessKeyID
selectedAWS['secretAccessKey'] = AWSSecretAccessKey
selectedAWS['machineType']=machineType
selectedAWS['selectedID'] = selectedID
selectedAWS['regionName'] = regionName
selectedAWS['noOfInstance']=noofInfra
selectedAWS['workLoad'] = workLoad
selectedAWS['securitygroupid'] = securitygroupid
configSettingsJson['awsCredentials'] = selectedAWS
gcpCredentials=''
projectID = ''
selectedID = ''
machineType = ''
regionName = ''
noOfInstance = 1
workLoad = 'LLM'
if sqlite_obj.table_exists('gcpCredentials'):
column_names = sqlite_obj.column_names('gcpCredentials')
if 'workload' not in column_names:
query = 'Alter Table gcpCredentials ADD workload TEXT'
sqlite_obj.execute_query(query)
data = sqlite_obj.read_data('gcpCredentials')
for values in data:
gcpCredentials = values[1]
projectID = values[2]
machineType = values[3]
selectedID = values[4]
regionName = values[5]
noOfInstance = values[6]
workLoad = values[7]
selectedGCP = {}
selectedGCP['gcpCredentials'] = gcpCredentials
selectedGCP['selectedID'] = selectedID
selectedGCP['projectID'] = projectID
selectedGCP['machineType'] = machineType
selectedGCP['regionName'] = regionName
selectedGCP['noOfInstance'] = noOfInstance
selectedAWS['workLoad'] = workLoad
configSettingsJson['gcpCredentials'] = selectedGCP
#print(configSettingsJson)
return(configSettingsJson)
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno)) |
validatecsv.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import csv
import logging
import pandas as pd
class csv_validator:
def __init__(self):
self.log = logging.getLogger('eion')
def __text_header(self, filename, threshold=0.75):
df = pd.read_csv(filename, header=None,nrows=1000)
numeric_columns = df.dtypes[df.dtypes != object]
if not len(numeric_columns):
first_row_len = df.iloc[0].str.len()
index = 0
for c in df:
if (df[c].map(len).mean() * threshold <= first_row_len[index]):
return False
index += 1
return True
return False
def validate_header(self, filename,delimiter,textqualifier,threshold=0.75):
with open(filename, 'rt',encoding='utf-8') as csvfile:
has_header = csv.Sniffer().has_header(csvfile.read(8192))
csvfile.seek(0)
if not has_header:
has_header = self.__text_header(filename, threshold)
reader = csv.reader(csvfile, delimiter=delimiter,quotechar=textqualifier)
good_csv = True
col_len = len(next(reader))
bad_lines = []
offset = 2 # +1 for first read and +1 for python index start at 0
for index, row in enumerate(reader):
if len(row) != col_len:
good_csv = False
if(index == 1 and has_header):
offset += 1
bad_lines.append(index + offset)
return has_header, good_csv, bad_lines
if __name__ == '__main__':
import sys
val = csv_validator()
print(val.validate_header(sys.argv[1]))
|
eda.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import math
import sys,os
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import numpy as np
import scipy.stats as st
from sklearn.preprocessing import StandardScaler
from dython.nominal import associations
class ux_eda ():
def __init__(self, dataPath=pd.DataFrame(),delimiter=',',textqualifier='"',optimize=None,):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
self.dataFrame = pd.DataFrame()
if isinstance(dataPath, pd.DataFrame):
self.dataFrame = dataPath
if optimize == 1:
self.dataFrame = self.dataFrame.sample(n=1000, random_state=1)
else:
if optimize == 1:
self.dataFrame = pd.read_csv(dataPath,nrows=1000,encoding='utf-8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace')
else:
self.dataFrame = pd.read_csv(dataPath, encoding='utf-8',sep=delimiter,quotechar=textqualifier,skipinitialspace = True,na_values=['-','?'],encoding_errors= 'replace')
self.dataFrame.rename(columns=lambda x: x.strip(), inplace=True)
self.features = self.dataFrame.columns.tolist()
self.indexFeature = []
self.dateFeature = []
self.categoricalFeature = []
self.constantFeature = []
self.textFeature = []
self.numericFeature = []
self.numericAndCatFeature = []
for feature, featureType in zip(self.features, self.dataFrame.dtypes):
if self.__check_seq_feature(self.dataFrame[feature]):
self.indexFeature.append(feature)
elif self.__match_date_format(self.dataFrame[feature]):
self.dateFeature.append(feature)
elif self.__check_constant_features(self.dataFrame[feature]):
self.constantFeature.append(feature)
elif self.__check_category_features(self.dataFrame[feature]):
self.categoricalFeature.append(feature)
elif featureType == 'object':
'''
numOfRows = self.dataFrame.shape[0]
distinctCount = len(self.dataFrame[feature].unique())
tempDff = self.dataFrame[feature]
self.dataFrame[feature]=self.dataFrame[feature].apply(lambda x: self.testNum(x))
tempDf = self.dataFrame[feature]
tempDf = tempDf.dropna()
numberOfNonNullVals = tempDf.count()
numericRatio = 0.8
if(numberOfNonNullVals > int(numOfRows * numericRatio)):
self.numericFeature.append(feature)
else:
self.dataFrame[feature] = tempDff
'''
self.textFeature.append(feature)
elif featureType in aionNumericDtypes:
self.numericFeature.append(feature)
# self.dataFrame[self.categoricalFeature] = self.dataFrame[self.categoricalFeature].apply(lambda x: x.cat.codes)
self.numericAndCatFeature = self.numericFeature + self.categoricalFeature
# EDA Performance change
# ----------------------------
def subsampleData(self, subsampleData):
self.dataFrame = self.dataFrame.sample(n=subsampleData, random_state=1)
def get_features_datatype(self,v,num_list,cat_list,text_list):
""" To get exact datatype of the feature in Data Overview."""
if v in cat_list:
return 'Categorical'
elif v in num_list:
return 'Numerical'
elif v in text_list:
return 'Text'
def getCorrelationMatrix(self):
try:
if len(self.dataFrame.columns) > 25:
df3 = df[self.dataFrame.columns[0:24]]
else:
df3 = self.dataFrame.copy()
cor_mat= associations(self.dataFrame,compute_only=True)
cor_mat=cor_mat['corr']
cor_mat = cor_mat.astype(float).round(2)
cor_mat.replace(np.nan, 0, inplace=True)
cor_mat.fillna('None',inplace=True)
return cor_mat
except Exception as e:
print(e)
correlationgraph = pd.DataFrame()
return (correlationgraph)
def dataDistribution(self):
df_eda_actual = self.dataFrame.copy()
des1 = df_eda_actual.describe(include='all').T
des1['missing count %'] = df_eda_actual.isnull().mean() * 100
des1['zero count %'] = df_eda_actual.isin([0]).mean() * 100
dataColumns = list(self.dataFrame.columns.values)
des1.insert(0, 'Features', dataColumns)
actual_df_numerical_features = df_eda_actual.select_dtypes(exclude='object')
actual_df_categorical_features = df_eda_actual.select_dtypes(include='object')
#For text features
textFeature_df = df_eda_actual.filter(self.textFeature)
actual_df_categorical_features = actual_df_categorical_features.drop(self.textFeature, axis=1)
for i in des1['Features']:
num_cols = actual_df_numerical_features.columns.to_list()
cat_cols = actual_df_categorical_features.columns.to_list()
text_cols = self.textFeature
des1['Features Type'] = des1['Features'].apply(lambda x: self.get_features_datatype(x, num_cols,cat_cols,text_cols))
curr_columns = des1.columns.to_list()
curr_columns.remove('Features Type')
insert_i = curr_columns.index('Features')+1
curr_columns.insert(insert_i,'Features Type')
des1 = des1[curr_columns]
return des1
# ----------------------------
def subsetFeatures(self, edaFeatures):
print(self.dataFrame.columns)
self.dataFrame = self.dataFrame[edaFeatures]
self.features = edaFeatures
self.indexFeature = []
self.dateFeature = []
self.categoricalFeature = []
self.constantFeature = []
self.textFeature = []
self.numericFeature = []
self.numericAndCatFeature = []
print('abc')
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
for feature, featureType in zip(self.features, self.dataFrame.dtypes):
if self.__check_seq_feature(self.dataFrame[feature]):
self.indexFeature.append(feature)
elif self.__match_date_format(self.dataFrame[feature]):
self.dateFeature.append(feature)
elif self.__check_constant_features(self.dataFrame[feature]):
self.constantFeature.append(feature)
elif self.__check_category_features(self.dataFrame[feature]):
self.categoricalFeature.append(feature)
elif featureType == 'object':
'''
numOfRows = self.dataFrame.shape[0]
distinctCount = len(self.dataFrame[feature].unique())
tempDff = self.dataFrame[feature]
self.dataFrame[feature]=self.dataFrame[feature].apply(lambda x: self.testNum(x))
tempDf = self.dataFrame[feature]
tempDf = tempDf.dropna()
numberOfNonNullVals = tempDf.count()
numericRatio = 0.8
if(numberOfNonNullVals > int(numOfRows * numericRatio)):
self.numericFeature.append(feature)
else:
self.dataFrame[feature] = tempDff
'''
self.textFeature.append(feature)
elif featureType in aionNumericDtypes:
self.numericFeature.append(feature)
print('def')
self.numericAndCatFeature = self.numericFeature + self.categoricalFeature
# ----------------------------
def testNum(self,value):
try:
x=eval(value)
return x
except:
return np.nan
def getFeatures(self):
leastRatioFeature = self.__LeastfeatureRatio()
return (self.features, self.dateFeature, self.indexFeature, self.constantFeature, self.textFeature, leastRatioFeature,self.numericAndCatFeature,self.numericFeature,self.categoricalFeature)
def getNumericFeatureCount(self):
return(len(self.numericAndCatFeature))
def calculateNumberofCluster(self):
df = self.dataFrame[self.numericFeature]
return self.__NumberofCluster(df)
def getTopTextFeatures(self,topn):
df_text = pd.DataFrame()
if (len(self.textFeature) > 1):
df_text['combined'] = self.dataFrame[self.textFeature].apply(lambda row: ' '.join(row.values.astype(str)), axis=1)
features = ['combined']
else:
df_text[['combined']] = self.dataFrame[self.textFeature]
features = ['combined']
df_text[features[0]] = df_text[features[0]].fillna("NA")
textCorpus = df_text[features[0]]
from text import eda
texteda_obj = eda.ExploreTextData()
df = texteda_obj.MostCommonWords(textCorpus,topn)
return df
def __NumberofCluster(self, featureData):
Sum_of_squared_distances = []
K = range(1, 15)
for k in K:
km = KMeans(n_clusters=k)
km = km.fit(featureData)
Sum_of_squared_distances.append(km.inertia_)
x1, y1 = 1, Sum_of_squared_distances[0]
x2, y2 = 15, Sum_of_squared_distances[len(Sum_of_squared_distances) - 1]
distances = []
for inertia in range(len(Sum_of_squared_distances)):
x0 = inertia + 2
y0 = Sum_of_squared_distances[inertia]
numerator = abs((y2 - y1) * x0 - (x2 - x1) * y0 + x2 * y1 - y2 * x1)
denominator = math.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2)
distances.append(numerator / denominator)
n_clusters = distances.index(max(distances)) + 2
return (n_clusters)
#13841 : TrustedAI: hopkins stat
def getHopkinsVal(self,df):
try:
from appbe.hopkinsStat import hopkins
from sklearn.preprocessing import StandardScaler,OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
numeric_transformer = Pipeline(
steps=[("imputer", SimpleImputer(missing_values=np.nan,strategy="mean")),
("standard_scaler", StandardScaler())]
)
categorical_transformer = Pipeline(
steps=[
("imputer", SimpleImputer(missing_values=np.nan,strategy="most_frequent")),
("encoder", OneHotEncoder(handle_unknown="ignore"))
]
)
preprocessor = ColumnTransformer(
transformers=[
("num", numeric_transformer, self.numericFeature),
("cat", categorical_transformer, self.categoricalFeature)
]
)
pipe = Pipeline([('scaler',preprocessor)])
scaled_df = pipe.fit_transform(df)
if type(scaled_df) != np.ndarray:
scaled_df = scaled_df.toarray()
score = round(hopkins(scaled_df,scaled_df.shape[0]),2)
return str(score)
except Exception as e:
print(e)
return ''
def getClusterDetails(self):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
df_clus = pd.get_dummies(self.dataFrame[self.numericAndCatFeature], prefix_sep='####')
for i in df_clus.columns:
dataType = df_clus[i].dtypes
if dataType not in aionNumericDtypes:
df_clus[i] = df_clus[i].fillna(df_clus[i].mode()[0])
else:
df_clus[i] = df_clus[i].fillna(df_clus[i].mean())
n = self.__NumberofCluster(df_clus)
n = n - 1
kmeans = KMeans(n_clusters=n, init='k-means++', max_iter=10, n_init=10, random_state=0)
# Fit and predict
y_means = kmeans.fit_predict(df_clus)
centroids = kmeans.cluster_centers_.squeeze()
labels = kmeans.labels_
features = df_clus.columns
cluster_details = []
for j in range(len(features)):
cluster = {}
feature = features[j]
perflag = 0
if '####' in feature:
x = features[j].split('####')
feature = x[0] + ' ' + x[1] + '(%)'
perflag = 1
else:
feature = feature + '(AVG)'
cluster['label'] = feature
total_sum = 0
if perflag == 1:
for i in range(n):
centroid = centroids[i]
value = round(centroid[j], 2)
total_sum = total_sum + value
for i in range(n):
centroid = centroids[i]
value = round(centroid[j], 2)
if perflag == 1:
value = (value / total_sum) * 100
value = round(value, 2)
cluster['Cluster ' + str(i + 1)] = value
cluster_details.append(cluster)
hopkins_val = self.getHopkinsVal(self.dataFrame,)
return cluster_details,hopkins_val
def getHighlyCorrelatedFeatures(self,noOfTop):
df_corr = abs(self.dataFrame[self.numericAndCatFeature].corr()).stack().reset_index()
df_corr.columns = ['FEATURE_1', 'FEATURE_2', 'CORRELATION']
mask_dups = (df_corr[['FEATURE_1', 'FEATURE_2']].apply(frozenset, axis=1).duplicated()) | (
df_corr['FEATURE_1'] == df_corr['FEATURE_2'])
df_corr = df_corr[~mask_dups]
df_corr = df_corr.sort_values(by='CORRELATION', ascending=False)
df_top = df_corr.head(n=noOfTop)
return(df_top)
# ---------------------- 12686:Data Distribution related Changes S T A R T ----------------------
def word_token_for_feature(self, selectedFeature, dataframe):
comment_words = ""
try:
df_text = pd.DataFrame()
df_text[[selectedFeature]] = dataframe
features = [selectedFeature]
df_text[features[0]] = df_text[features[0]].fillna("NA")
textCorpus = df_text[features[0]]
from text import TextProcessing
tp = TextProcessing.TextProcessing()
preprocessed_text = tp.transform(textCorpus)
df_text[selectedFeature] = preprocessed_text
df_text_list = df_text.values.tolist()
for val in df_text_list:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += " ".join(tokens) + " "
except:
comment_words = ""
return comment_words
# -------------------------------------------- E N D --------------------------------------------
def word_token(self):
df_text = pd.DataFrame()
if (len(self.textFeature) > 1):
df_text['combined'] = self.dataFrame[self.textFeature].apply(lambda row: ' '.join(row.values.astype(str)), axis=1)
features = ['combined']
else:
df_text[['combined']] = self.dataFrame[self.textFeature]
features = ['combined']
df_text[features[0]] = df_text[features[0]].fillna("NA")
textCorpus = df_text[features[0]]
from text import TextProcessing
tp = TextProcessing.TextProcessing()
preprocessed_text = tp.transform(textCorpus)
df_text['combined'] = preprocessed_text
df_text_list = df_text.values.tolist()
comment_words = ""
for val in df_text_list:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += " ".join(tokens) + " "
if comment_words == "":
comment_words = 'Not found any token'
return comment_words
def getdata(self):
return self.dataFrame
def getPCATop10Features(self):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
df = self.dataFrame[self.numericAndCatFeature]
for feature in self.numericAndCatFeature:
if feature in self.categoricalFeature:
df[feature] = pd.Categorical(df[feature])
df[feature] = df[feature].cat.codes
df[feature] = df[feature].fillna(df[feature].mode()[0])
else:
df[feature] = df[feature].fillna(df[feature].mean())
pca = PCA(n_components=2).fit(StandardScaler().fit_transform(df))
mapping = pd.DataFrame(pca.components_, columns=self.numericAndCatFeature)
mapping = mapping.diff(axis=0).abs()
mapping = mapping.iloc[1]
mapping = mapping.sort_values(ascending=False).head(10)
return mapping
def getTopRows(self, rows=5):
return self.dataFrame.head(rows)
def __check_seq_feature(self, data):
if data.dtypes in ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']:
total_record = data.count()
count = (data - data.shift() == 1).sum()
if ((total_record - count) == 1):
return True
return False
def __match_date_format(self, data):
try:
## Using regex lib, we are check if any col contains datetime format like yyyy-mm-dd or yyyy/mm/dd format. if it finds return true.
import re
u_data = data.to_string()
date_find = (re.findall(r"[0-9]{1,4}[\_|\-|\/|\|][0-9]{1,2}[\_|\-|\/|\|][0-9]{1,4}", u_data) or re.findall(r'\d{,2}\-[A-Za-z]{,9}\-\d{,4}', u_data) or re.findall(r"[0-9]{1,4}[\_|\-|\/|\|][0-9]{1,2}[\_|\-|\/|\|][0-9]{1,4}.\d" , u_data) or re.findall(r"[0-9]{1,4}[\_|\-|\/|\|][A-Za-z]{,9}[\_|\-|\/|\|][0-9]{1,4}", u_data))
if (date_find):
try:
data = pd.to_datetime(data, utc=True)
return True
except Exception as e:
##If not a datetime col, just pass to return false statement.
pass
except Exception as e:
data = data.astype(str)
beforecheckcount = data.count()
#####YYYY-MM-DD HH:MM:SS####
check1 = data[data.str.match(
r'(^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check1.count()
if (beforecheckcount == aftercheckcount):
return True
#####MM/DD/YYYY HH:MM####
check2 = data[data.str.match(
r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount == aftercheckcount):
return True
#####DD-MM-YYYY HH:MM####
check2 = data[data.str.match(
r'(^(0?[1-9]|[12][0-9]|3[01])-(0?[1-9]|1[0-2])-(\d\d\d\d) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9])$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount == aftercheckcount):
return True
#####YYYY/MM/DD####
check2 = data[data.str.match(r'(^\d\d\d\d/(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount == aftercheckcount):
return True
#####MM/DD/YYYY####
check2 = data[data.str.match(r'(^(0?[1-9]|1[0-2])/(0?[1-9]|[12][0-9]|3[01])/(\d\d\d\d)$)') == True]
aftercheckcount = check2.count()
if (beforecheckcount == aftercheckcount):
return True
#####YYYY-MM-DD HH:MM:SS.fff####
check11 = data[data.str.match(
r'(^\d\d\d\d-(0?[1-9]|1[0-2])-(0?[1-9]|[12][0-9]|3[01]) (00|0?[0-9]|1[0-9]|2[0-4]):([0-9]|[0-5][0-9]):([0-9]|[0-5][0-9])\.(\d{3})$)') == True]
aftercheckcount = check11.count()
if (beforecheckcount == aftercheckcount):
return True
return False
def __check_category_features(self, modelFeatures):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
dataType = modelFeatures.dtypes
numOfRows = len(modelFeatures)
if dataType not in aionNumericDtypes:
if dataType != 'bool':
nUnique = len(modelFeatures.unique().tolist())
if nUnique <= 30:
return True
return False
def __check_constant_features(self, modelFeatures):
return len(modelFeatures.unique().tolist()) == 1
def __featureRatio(self, modelFeatures):
if len(modelFeatures):
return len(modelFeatures.unique().tolist()) / len(modelFeatures)
return 0
def __LeastfeatureRatio(self):
ratio = 1
feat = ""
for feature in (self.numericAndCatFeature + self.textFeature):
r = self.__featureRatio(self.dataFrame[feature])
if r < ratio:
ratio = r
feat = feature
return feat
def getDistribution(self):
aionNumericDtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
df = self.dataFrame[self.numericAndCatFeature]
dist={}
for feature in self.numericAndCatFeature:
if feature in self.categoricalFeature:
df[feature] = pd.Categorical(df[feature])
df[feature] = df[feature].cat.codes
df[feature] = df[feature].fillna(df[feature].mode()[0])
else:
df[feature] = df[feature].fillna(df[feature].mean())
distributionname,sse = self.DistributionFinder(df[feature])
if distributionname == '':
dist[feature] = 'Unknown'
else:
dist[feature] = distributionname
return dist
def DistributionFinder(self,data):
try:
distributionName = ""
sse = 0.0
KStestStatic = 0.0
dataType = ""
if (data.dtype == "float64"):
dataType = "Continuous"
elif (data.dtype == "int"):
dataType = "Discrete"
elif (data.dtype == "int64"):
dataType = "Discrete"
if (dataType == "Discrete"):
distributions = [st.bernoulli, st.binom, st.geom, st.nbinom, st.poisson]
index, counts = np.unique(data.astype(int), return_counts=True)
if (len(index) >= 2):
best_sse = np.inf
y1 = []
total = sum(counts)
mean = float(sum(index * counts)) / total
variance = float((sum(index ** 2 * counts) - total * mean ** 2)) / (total - 1)
dispersion = mean / float(variance)
theta = 1 / float(dispersion)
r = mean * (float(theta) / 1 - theta)
datamin = data.min()
datamax = data.max()
for j in counts:
y1.append(float(j) / total)
pmf1 = st.bernoulli.pmf(index, mean)
pmf2 = st.binom.pmf(index, len(index), p=mean / len(index))
pmf3 = st.geom.pmf(index, 1 / float(1 + mean))
pmf4 = st.nbinom.pmf(index, mean, r)
pmf5 = st.poisson.pmf(index, mean)
sse1 = np.sum(np.power(y1 - pmf1, 2.0))
sse2 = np.sum(np.power(y1 - pmf2, 2.0))
sse3 = np.sum(np.power(y1 - pmf3, 2.0))
sse4 = np.sum(np.power(y1 - pmf4, 2.0))
sse5 = np.sum(np.power(y1 - pmf5, 2.0))
sselist = [sse1, sse2, sse3, sse4, sse5]
best_distribution = 'NA'
for i in range(0, len(sselist)):
if best_sse > sselist[i] > 0:
best_distribution = distributions[i].name
best_sse = sselist[i]
elif (len(index) == 1):
best_distribution = "Constant Data-No Distribution"
best_sse = 0.0
distributionName = best_distribution
sse = best_sse
elif (dataType == "Continuous"):
distributions = [st.uniform, st.expon, st.weibull_max, st.weibull_min, st.chi, st.norm, st.lognorm, st.t,
st.gamma, st.beta]
best_distribution = st.norm.name
best_sse = np.inf
datamin = data.min()
datamax = data.max()
nrange = datamax - datamin
y, x = np.histogram(data.astype(float), bins='auto', density=True)
x = (x + np.roll(x, -1))[:-1] / 2.0
for distribution in distributions:
params = distribution.fit(data.astype(float))
arg = params[:-2]
loc = params[-2]
scale = params[-1]
pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)
sse = np.sum(np.power(y - pdf, 2.0))
if (best_sse > sse > 0):
best_distribution = distribution.name
best_sse = sse
distributionName = best_distribution
sse = best_sse
except:
response = str(sys.exc_info()[0])
message = 'Job has Failed' + response
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno))
return distributionName, sse
|
stationarity_seasonality_check.py | import pandas as pd
import numpy as np
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import kpss
from statsmodels.tsa.seasonal import seasonal_decompose
import logging
import os
import warnings
warnings.filterwarnings('ignore')
## Main class to find out seassonality and stationary in timeseries data.
class StationarySeasonalityTest:
def __init__(self,df,featurename,datetimefeature):
self.df=df
self.targetFeature=featurename
self.datetimefeature=datetimefeature
## to get the timeseries data stationary information
def stationary_model(self,df,target_feature,stationary_check_method):
stationary_status=None
if (stationary_check_method.lower()=='adfuller'):
stats_model=adfuller(df[target_feature])
statistic, p_value, n_lags, num_bservations,critical_values,info_criterion_best=stats_model[0],stats_model[1],stats_model[2],stats_model[3],stats_model[4],stats_model[5]
if (p_value>0.05):
stationary_status=str("Non-Stationary")
elif(p_value<0.05):
stationary_status=str("Stationary")
##kpss is opposite to ADF in considering null hypothesis. In KPSS, if null hypothesis,then it is stationary as oppose to ADF.
elif (stationary_check_method.lower()=='kpss'):
from statsmodels.tsa.stattools import kpss
stats_model = kpss(df[target_feature])
statistic, p_value, n_lags, critical_values=stats_model[0],stats_model[1],stats_model[2],stats_model[3]
##In kpss, the stationary condition is opposite to Adafuller.
if (p_value>0.05):
stationary_status=str("Stationary")
else:
stationary_status=str("Non-Stationary")
return stats_model,n_lags,p_value,stationary_status
## Get stationary details
def stationary_check(self,target_feature,time_col,method):
df=self.df
df[time_col]=pd.to_datetime(df[time_col])
df=df.set_index(time_col)
try:
stationary_check_method=method
except:
stationary_check_method='adfuller'
if (len(target_feature) == 1):
try:
if isinstance(target_feature,list):
target_feature=''.join(target_feature)
elif isinstance(target_feature,int):
target_feature=str(target_feature)
elif isinstance(target_feature,str):
pass
except Exception as e:
pass
stationary_result={}
stats_model,n_lags,p_value,stationary_status=self.stationary_model(df,target_feature,stationary_check_method)
# stationary_result[target_feature]=stationary_status
stationary_result[target_feature]=stationary_status
elif(len(target_feature) > 1):
stationary_result={}
for col in df.columns:
stats_model,n_lags,p_value,stationary_status=self.stationary_model(df,col,stationary_check_method)
stationary_result[col]=stationary_status
else:
pass
stationary_val=None
for v in stationary_result.values():
stationary_val=v
stationary_combined_res=dict()
c_dict=[k for k,v in stationary_result.items() if 'non-stationary' in v]
if (len(c_dict)>=1):
stationary_combined_res['dataframe_stationarity']='Non-Stationary'
else:
stationary_combined_res['dataframe_stationarity']='Stationary'
return stats_model,n_lags,p_value,stationary_val,stationary_combined_res
#Get seasonality by using seasonal_decompose lib.
def seasonality_model(self,target_feature,df):
seasonality_status=None
try:
try:
stats_model = kpss(df[target_feature])
statistic, p_value, n_lags, critical_values=stats_model[0],stats_model[1],stats_model[2],stats_model[3]
except:
n_lags=1
pass
try:
df_target=self.df[target_feature]
decompose_result_mult = seasonal_decompose(df_target,model='additive', extrapolate_trend='freq', period=n_lags)
except Exception as e:
##If additive model (type of seasonal component) failed, use multiplicative
decompose_result_mult = seasonal_decompose(df_target,model='multiplicative', extrapolate_trend='freq', period=1)
trend = decompose_result_mult.trend
observed=decompose_result_mult.observed
seasonal = decompose_result_mult.seasonal
residual = decompose_result_mult.resid
try:
if isinstance(df_target, pd.Series):
auto_correlation = df_target.autocorr(lag=n_lags)
elif isinstance(df_target, pd.DataFrame):
df_target = df_target.squeeze()
auto_correlation = df_target.autocorr(lag=n_lags)
except:
pass
if (seasonal.sum()==0):
seasonality_status="Non-Seasonal"
else:
seasonality_status="Seasonal"
# #Please use the below plot for GUI show (seasonality components)
# decompose_result_mult.plot().savefig('seasonality_plot.png')
df['observed'] = decompose_result_mult.observed
df['residual'] = decompose_result_mult.resid
df['seasonal'] = decompose_result_mult.seasonal
df['trend'] = decompose_result_mult.trend
except Exception as e:
print("Seasonality function exception: \t",e)
return df,decompose_result_mult,seasonality_status
##Main function to check seasonlity in data
def seasonal_check(self,target_feature,time_col,seasonal_model):
df=self.df
try:
df[time_col]=pd.to_datetime(df[time_col])
except Exception as e:
pass
df=df.set_index(time_col)
if (len(target_feature)==1):
try:
if isinstance(target_feature,list):
target_feature=''.join(target_feature)
elif isinstance(target_feature,int):
target_feature=str(target_feature)
elif isinstance(target_feature,str):
pass
except Exception as e:
## Because of EDA, all log messages removed. (self.log.info )
pass
## Seasonal component for individual feature based.
seasonality_result=dict()
df,decompose_result_mult,seasonality_status = self.seasonality_model(target_feature,df)
# seasonality_result[target_feature]=seasonality_status
seasonality_result['Feature: '+str(target_feature)]=seasonality_status
elif(len(target_feature) > 1):
seasonality_result=dict()
for col in df.columns:
df,decompose_result_mult,seasonality_status = self.seasonality_model(col,df)
seasonality_result[col]=seasonality_status
else:
pass
# ## Seasonal component for whole dataset
seasonality_val=None
for v in seasonality_result.values():
seasonality_val=v
seasonality_combined_res=dict()
c_dict=[k for k,v in seasonality_result.items() if 'non-seasonality' in v]
if (len(c_dict)>=1):
seasonality_combined_res['dataframe_seasonality']='No Seasonal elements'
else:
seasonality_combined_res['dataframe_seasonality']='contains seasonal elements.'
return df,decompose_result_mult,seasonality_val,seasonality_combined_res
#Main user defined caller for stationary and seasonality (SS)
def analysis(self,seasonality_status,stationarity_status):
seasonal_model="additive"
time_col=self.datetimefeature
stationary_method='adfuller'
if (isinstance(self.targetFeature,list)):
target=self.targetFeature
pass
elif (isinstance(self.targetFeature,str)):
target=list(self.targetFeature.split(','))
if (stationarity_status.lower()=="true"):
stats_model,n_lags,p_value,stationary_result,stationary_combined_res=self.stationary_check(target,time_col,stationary_method)
return stationary_result
if (seasonality_status.lower()=="true"):
df,decompose_result_mult,seasonality_result,seasonality_combined_res=self.seasonal_check(target,time_col,seasonal_model)
return seasonality_result
#Main fn for standalone test purpose
if __name__=='__main__':
print("Inside seasonality-stationary test main function...")
print("Below code used for standalone test purpose.")
|
azureStorage.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import json
import os
import rsa
import boto3 #usnish
import pandas as pd
import time
def add_new_azureStorage(request):
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','azurestorage.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
f.close()
if data == '':
data = []
except:
data = []
if request.POST["azurename"] =='' or request.POST["azureaccountkey"] == '' or request.POST["containername"] == '' :
return 'error'
newdata = {}
newdata['azurename'] = request.POST["azurename"]
newdata['azureaccountkey'] = request.POST["azureaccountkey"]
newdata['containername'] = request.POST["containername"]
data.append(newdata)
with open(file_path, 'w') as f:
json.dump(data, f)
f.close()
return 'success'
def get_azureStorage():
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','azurestorage.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
return data
def read_azureStorage(name,directoryname,DATA_FILE_PATH):
try:
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','config','azurestorage.conf'))
with open(file_path, 'r') as f:
data = json.load(f)
except:
data = []
found = False
for x in data:
if x['azurename'] == name:
storage_account_name = str(x['azurename'])
storage_account_key = str(x['azureaccountkey'])
azure_container_name = x['containername']
found = True
break
try:
if found:
root_dir = str(directoryname)
from azure.storage.filedatalake import DataLakeServiceClient
import io
import pandavro as pdx
from detect_delimiter import detect
try:
service_client = DataLakeServiceClient(account_url="{}://{}.dfs.core.windows.net".format("https", storage_account_name), credential=storage_account_key)
print(azure_container_name)
file_system_client = service_client.get_file_system_client(azure_container_name)
print(root_dir)
file_paths = file_system_client.get_paths(path=root_dir)
main_df = pd.DataFrame()
for path in file_paths:
if not path.is_directory:
file_client = file_system_client.get_file_client(path.name)
file_ext = os.path.basename(path.name).split('.', 1)[1]
if file_ext in ["csv", "tsv"]:
with open(csv_local, "wb") as my_file:
download = file_client.download_file()
download.readinto(my_file)
with open(csv_local, 'r') as file:
data = file.read()
row_delimiter = detect(text=data, default=None, whitelist=[',', ';', ':', '|', '\t'])
processed_df = pd.read_csv(csv_local, sep=row_delimiter)
if file_ext == "parquet":
download = file_client.download_file()
stream = io.BytesIO()
download.readinto(stream)
processed_df = pd.read_parquet(stream, engine='pyarrow')
if file_ext == "avro":
with open(avro_local, "wb") as my_file:
download = file_client.download_file()
download.readinto(my_file)
processed_df = pdx.read_avro(avro_local)
if not main_df.empty:
main_df = main_df.append(processed_df, ignore_index=True)
else:
main_df = pd.DataFrame(processed_df)
except Exception as e:
print(e)
return 'Success',main_df
except Exception as e:
print(e)
return 'Error', pd.DataFrame() |
utils.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
from typing import Tuple, Union, List
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import SGDClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from flwr.common.logger import log
from logging import INFO
TRUE_FALSE_MAPPING = {'True':'False','true':'false',True:False,'y':'n','Y':'N','Yes':'No','yes':'no','YES':'NO'}
XY = Tuple[np.ndarray, np.ndarray]
Dataset = Tuple[XY, XY]
LogRegParams = Union[XY, Tuple[np.ndarray]]
XYList = List[XY]
modelUsed=None
modelname=None
def setmodelName(modelselected):
try:
modelname=str(modelselected)
print("setmodelName ,given modelname: \n",modelname)
if (modelname.lower() == 'logisticregression'):
modelUsed=LogisticRegression()
return True
elif (modelname.lower() == "naivebayes"):
modelUsed = GaussianNB()
return True
elif (modelname.lower() == "sgdclassifier"):
#from sklearn.linear_model import SGDClassifier
modelUsed=SGDClassifier()
return True
elif (modelname.lower() == "knn"):
modelUsed = KNeighborsClassifier()
return True
elif (modelname.lower() == "decisiontreeclassifier"):
modelUsed = DecisionTreeClassifier()
return True
else:
return False
except Exception as e:
log(INFO, "set fl model name fn issue: ",e)
def get_model_parameters(model:modelUsed) -> LogRegParams:
"""Returns the paramters of a sklearn LogisticRegression model."""
model_name=model.__class__.__name__
if model.fit_intercept:
params = (model.coef_, model.intercept_)
else:
params = (model.coef_,)
return params
def set_model_params(
model:modelUsed, params: LogRegParams
) -> modelUsed:
"""Sets the parameters of a sklean LogisticRegression model."""
model.coef_ = params[0]
model_name=model.__class__.__name__
try:
if model.fit_intercept:
model.intercept_ = params[1]
except Exception as e:
log(INFO, "set_model_params fn issue: ",e)
pass
return model
def set_initial_params(model,no_classes,no_features):
"""Sets initial parameters as zeros Required since model params are
uninitialized until model.fit is called.
But server asks for initial parameters from clients at launch. Refer
to sklearn.linear_model.LogisticRegression documentation for more
information.
"""
n_classes = no_classes
n_features = no_features
model.classes_ = np.array([i for i in range(n_classes)])
model.coef_ = np.zeros((n_classes, n_features))
model_name=model.__class__.__name__
try:
if model.fit_intercept:
model.intercept_ = np.zeros((n_classes,))
except Exception as e:
log(INFO, "set_initial_params fn issue: ",e)
pass
def shuffle(X: np.ndarray, y: np.ndarray) -> XY:
"""Shuffle X and y."""
rng = np.random.default_rng()
idx = rng.permutation(len(X))
return X[idx], y[idx]
def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList:
"""Split X and y into a number of partitions."""
return list(
zip(np.array_split(X, num_partitions), np.array_split(y, num_partitions))
)
def get_true_option(d, default_value=None):
if isinstance(d, dict):
for k,v in d.items():
if v in TRUE_FALSE_MAPPING.keys():
return k
return default_value
def get_true_options( d):
options = []
if isinstance(d, dict):
for k,v in d.items():
if v in TRUE_FALSE_MAPPING.keys():
options.append(k)
return options
def set_true_option(d, key=None, value='True'):
if key in d.keys():
if value in TRUE_FALSE_MAPPING.keys():
for k in d.keys():
d[ k] = TRUE_FALSE_MAPPING[ value]
d[key] = value
return d
|
distribution.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import numpy as np
import os
import sys
import scipy.stats as st
def DistributionFinder(data):
try:
distributionName = ""
sse = 0.0
KStestStatic = 0.0
dataType = ""
if (data.dtype == "float64"):
dataType = "Continuous"
elif (data.dtype == "int"):
dataType = "Discrete"
elif (data.dtype == "int64"):
dataType = "Discrete"
if (dataType == "Discrete"):
distributions = [st.bernoulli, st.binom, st.geom, st.nbinom, st.poisson]
index, counts = np.unique(data.astype(int), return_counts=True)
if (len(index) >= 2):
best_sse = np.inf
y1 = []
total = sum(counts)
mean = float(sum(index * counts)) / total
variance = float((sum(index ** 2 * counts) - total * mean ** 2)) / (total - 1)
dispersion = mean / float(variance)
theta = 1 / float(dispersion)
r = mean * (float(theta) / 1 - theta)
for j in counts:
y1.append(float(j) / total)
pmf1 = st.bernoulli.pmf(index, mean)
pmf2 = st.binom.pmf(index, len(index), p=mean / len(index))
pmf3 = st.geom.pmf(index, 1 / float(1 + mean))
pmf4 = st.nbinom.pmf(index, mean, r)
pmf5 = st.poisson.pmf(index, mean)
sse1 = np.sum(np.power(y1 - pmf1, 2.0))
sse2 = np.sum(np.power(y1 - pmf2, 2.0))
sse3 = np.sum(np.power(y1 - pmf3, 2.0))
sse4 = np.sum(np.power(y1 - pmf4, 2.0))
sse5 = np.sum(np.power(y1 - pmf5, 2.0))
sselist = [sse1, sse2, sse3, sse4, sse5]
best_distribution = 'NA'
for i in range(0, len(sselist)):
if best_sse > sselist[i] > 0:
best_distribution = distributions[i].name
best_sse = sselist[i]
elif (len(index) == 1):
best_distribution = "Constant Data-No Distribution"
best_sse = 0.0
distributionName = best_distribution
sse = best_sse
elif (dataType == "Continuous"):
distributions = [st.uniform, st.expon, st.weibull_max, st.weibull_min, st.chi, st.norm, st.lognorm, st.t,
st.gamma, st.beta]
best_distribution = st.norm.name
best_sse = np.inf
datamin = data.min()
datamax = data.max()
nrange = datamax - datamin
y, x = np.histogram(data.astype(float), bins='auto', density=True)
x = (x + np.roll(x, -1))[:-1] / 2.0
for distribution in distributions:
params = distribution.fit(data.astype(float))
arg = params[:-2]
loc = params[-2]
scale = params[-1]
pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)
sse = np.sum(np.power(y - pdf, 2.0))
if (best_sse > sse > 0):
best_distribution = distribution.name
best_sse = sse
distributionName = best_distribution
sse = best_sse
except:
response = str(sys.exc_info()[0])
message = 'Job has Failed' + response
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
print(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno))
print(message)
return distributionName, sse |
pushrecords.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import socket
import os
import rsa
from os.path import expanduser
from pathlib import Path
import requests
import platform
from appbe.dataPath import DATA_DIR
import socket
import getmac
import subprocess
import sys
import json
from datetime import datetime
import binascii
computername = socket.getfqdn()
global_key = '''
-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAzJcxqRiUpp7CzViyqNlYaeyceDh5y6Ib4SoxoyNkN3+k0q+cr1lb
k0KdWTtHIVqH1wsLYofYjpB7X2RN0KYTv8VfwmfQNrpFEbiRz4gcAeuxGCPgGaue
N1ttujQMWHWCcY+UH5Voh8YUfkW8P+T3zxvr1d30D+kVBJC59y/31JvTzr3Bw/T+
NYv6xiienYiEYtm9d5ATioEwZOXaQBrtVvRmqcod5A1h4kn1ZauLX2Ph8H4TAuit
NLtw6xUCJNumphP7xdU+ca6P6a6eaLprgKhvky+nz16u9/AC2AazRQHKWf8orS6b
fw16JDCRs0zU4mTQLCjkUUt0edOaRhUtcQIDAQAB
-----END RSA PUBLIC KEY-----
'''
quarter_key = '''
-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAmKzOJxVEV9ulA+cjfxguAduLMD47OWjLcEAEmEuK8vR4O5f6e2h1
08NniGC+nkwqmM00U7JTVBkqnt9S/JgE3pAH2xwfWda2OvXNWisWmOQdqB0+XRHh
NXsIG3yRk/sMlDpe7MJIyM5ADSu01PLn9FZTfmMq7lEp32tAf71cuUE/dwuWSvEQ
WK2hn1L4D97O43XCd7FHtMSHfgtjdcCFgX9IRgWLKC8Bm3q5qcqF4v3cHuYTj3V9
njxPtRqPg6HJFiJrm9AX5bUEHAvbTcw4wAmsNTRQHPvVB+Lc+yGh5x8crhKjNB01
gdB5I3a4mPO7dKvadR6Mr28trr0Ff5t2HQIDAQAB
-----END RSA PUBLIC KEY-----
'''
halfYear_key='''
-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAgrGNwl8CNYQmVxi8/GEgPjfL5aEmyPkDyaJb9h4hZDSZCeeKd7Rv
wwhuRTdBBfOp0bQ7QS7NYMg38Xlc3x85I9RnxdQdDKn2nRuvG0hG3wMBFy/DCSXF
tXbDjJkLijAhqcBNu8m+a2Gtn14ShC7TbcfY4iVXho3WFUrn0xq6S5ducqWCsLJh
R+TNImCaMICqfoAzEDGC3ojO5Hi3vJmmyK5CVp6bt4wLRATQjcp1ujGW4Uv4kEgp
7TR077c226v1KOdKdyZPHJzT1MKwZrG2Gdluk3/Y1apbwyGzYqFdTCOAB+mE73Dn
wFXURgDJQmaU2oxxaA13WRcELpnirm+aIwIDAQAB
-----END RSA PUBLIC KEY-----
'''
oneYear_key='''
-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEA3GLqn+vkKn3fTNH3Bbb3Lq60pCoe+mn0KPz74Bp7p5OkZAUe14pP
Tcf/UqdPwiENhSCseWtfZmfKDK8qYRHJ5xW02+AhHPPdiacS45X504/lGG3q/4SG
ZgaFhMDvX+IH/ZH+qqbU3dRQhXJCCrAVAa7MonzM6yPiVeS2SdpMkNg1VDR1oTLB
Pn+qSV6CnkK1cYtWCRQ23GH2Ru7fc09r7m8hVcifKJze84orpHC5FX0WScQuR8h/
fs1IbGkxTOxP8vplUj/cd4JjUxgd+w+8R4kcoPhdGZF5UGeZA8xMERzQLvh+4Ui0
KIvz5/iyKB/ozaeSG0OMwDAk3WDEnb1WqQIDAQAB
-----END RSA PUBLIC KEY-----
'''
full_key='''
-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAhqfNMuYYLdVrePhkO9rU/qT6FgolzI0YyzIJ2OeJE+++JioYm6nn
ohQU32iiE0DZlCCLrHJXOOIAz2Op80goX0lxtngyxVUPsiB5CI77sAC7x6K3anJ0
elpnQCC0+xV2ZL5eIMNQHLe+X6wJl/HGWqkUlxKpWr4/kBEB4EisW60OePfhntIN
4OUJ7iEq+sDdOM5WazJIXeNV1cig4i6057GE3k5ITcQUmw17DZu2+dqkIscckaG+
t5SF7Qnvt4IY8IeQp2htx3yD+CJCV0u2uKwoSFMGJn3OWdaixC3+eojyMXmfAWtQ
Ee9NLNNaTCMIvQ8BeItJLQs2Htw3bZNMvwIDAQAB
-----END RSA PUBLIC KEY-----
'''
def validate_key_Pair(privatepath,publickey):
with open(privatepath, 'rb') as privatefile:
keydata = privatefile.read()
privatefile.close()
try:
privkey = rsa.PrivateKey.load_pkcs1(keydata,'PEM')
data = 'Validate Global License'
signature = rsa.sign(data.encode('utf-8'), privkey, 'SHA-1')
pubkey = rsa.PublicKey.load_pkcs1(publickey)
except:
return False
try:
rsa.verify(data.encode('utf-8'), signature, pubkey)
return True
except Exception as e:
return False
def updateDRecord(licensepath):
domain_license_path = os.path.join(DATA_DIR,'License','license_domain.lic')
if(os.path.isfile(licensepath)):
with open(licensepath, 'rb') as f:
licensekey = f.read()
f.close()
with open(domain_license_path, 'wb') as f:
f.write(licensekey)
f.close()
if(validate_key_Pair(domain_license_path,global_key)):
return True,'Valid Domain License'
else:
return False,'Invalid Domain License'
else:
return False,'File Not Exists'
def generateLicenseKey(userKey):
record = {'UserKey':userKey}
record = json.dumps(record)
status = 'Error'
url = 'https://qw7e33htlk.execute-api.ap-south-1.amazonaws.com/default/aion_license'
try:
response = requests.post(url, data=record,headers={"x-api-key":"3cQKRkKA4S57pYrkFp1Dd9jRXt4xnFoB9iqhAQRM","Content-Type":"application/json",})
if response.status_code == 200:
outputStr=response.content
outputStr = outputStr.decode('utf-8','ignore')
outputStr = outputStr.strip()
license_dict = json.loads(str(outputStr))
if license_dict['status'] == 'success':
status = 'Success'
licenseKey = license_dict['msg']
else:
status = 'Error'
licenseKey = ''
else:
status = 'Error'
licenseKey = ''
except Exception as inst:
print(inst)
status = 'Error'
licenseKey = ''
msg = {'status':status,'key':userKey,'licenseKey':licenseKey,'link':''}
return msg
def updateRecord(licensepath):
currentDirectory = os.path.dirname(os.path.abspath(__file__))
license_path = os.path.join(currentDirectory,'..','lic','license.lic')
if(os.path.isfile(licensepath)):
with open(licensepath, 'rb') as f:
licensekey = f.read()
f.close()
with open(license_path, 'wb') as f:
f.write(licensekey)
f.close()
status,msg = check_domain_license()
if status:
status,msg = getdaysfromstartdate()
if status:
status,msg = check_days_license(int(msg))
return status,msg
else:
return False,'File Not Exists'
def check_domain_license():
if 'CORP.HCL.IN' in computername:
return True,'HCL Domain'
else:
return True,'HCL Domain'
def diff_month(d1, d2):
return (d1.year - d2.year) * 12 + d1.month - d2.month
def getdaysfromstartdate():
currentDirectory = os.path.dirname(os.path.abspath(__file__))
startdatePath = os.path.join(currentDirectory,'..','lic','startdate.txt')
if(os.path.isfile(startdatePath)):
with open(startdatePath, "rb") as fl:
encrypted_message = fl.read()
fl.close()
privkey = '''-----BEGIN RSA PRIVATE KEY-----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=
-----END RSA PRIVATE KEY-----
'''
privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM')
decrypted_message = rsa.decrypt(encrypted_message, privkey)
decrypted_message = decrypted_message.decode()
import datetime
start_time = datetime.datetime.strptime(decrypted_message, '%Y-%m-%d')
current_date = datetime.datetime.today().strftime('%Y-%m-%d')
current_date = datetime.datetime.strptime(current_date, '%Y-%m-%d')
Months = diff_month(current_date,start_time)
return True,Months
else:
return False,'Start Date Not Exists'
def check_days_license(months):
currentDirectory = os.path.dirname(os.path.abspath(__file__))
license_path = os.path.join(currentDirectory,'..','lic','license.lic')
if(os.path.isfile(license_path)):
if(validate_key_Pair(license_path,full_key)):
return True,'Valid License'
elif(validate_key_Pair(license_path,oneYear_key)):
if months <= 12:
return True,'Valid License'
else:
return False,'License for AI.ON has expired. Please contact ERS Research for renewal.'
elif(validate_key_Pair(license_path,halfYear_key)):
if months <= 6:
return True,'Valid License'
else:
return False,'License for AI.ON has expired. Please contact ERS Research for renewal.'
elif(validate_key_Pair(license_path,quarter_key)):
if months <= 3:
return True,'Valid License'
else:
return False,'License for AI.ON has expired. Please contact ERS Research for renewal.'
else:
return False,'Invalid License'
else:
return False,'License Not exists.Please contact ERS Research for renewal.'
def checklicense():
import binascii
license_path = os.path.join(DATA_DIR,'License','license.lic')
if(os.path.isfile(license_path)):
try:
with open(license_path, 'r') as privatefile:
license_key = privatefile.read()
privatefile.close()
encrypted_message = binascii.unhexlify(license_key.encode())
privkey = '''-----BEGIN RSA PRIVATE KEY-----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-----END RSA PRIVATE KEY-----
'''
privkey = rsa.PrivateKey.load_pkcs1(privkey,'PEM')
decrypted_message = rsa.decrypt(encrypted_message, privkey)
msg = decrypted_message.decode().split('####')
product = msg[0]
computernameLicense = msg[1]
computername = socket.getfqdn()
licenseValid = False
if product.lower() == 'aion':
if computernameLicense == computername:
uuidlicense = msg[3]
uuid = guid()
if uuidlicense == uuid:
current_date = datetime.now()
license_expiry_date = msg[5]
license_expiry_date = datetime.strptime(license_expiry_date,'%Y-%m-%d %H:%M:%S')
if current_date > license_expiry_date:
return False,'License Expire'
else:
return True,''
return False,'License Error'
except Exception as e:
print(e)
return False,'License Error'
else:
return False,'Generate License'
def generate_record_key(product,version):
computername = socket.getfqdn()
macaddress = getmac.get_mac_address()
license_date = datetime.today().strftime('%Y-%m-%d %H:%M:%S')
try:
user = os.getlogin()
except:
user = 'NA'
uuid = guid()
msg = product+'###'+version+'###'+computername+'###'+macaddress+'###'+user+'###'+sys.platform+'###'+uuid+'###'+license_date
pkeydata='''-----BEGIN RSA PUBLIC KEY-----
MIIBCgKCAQEAm75ZwaepuxGJjU1Slk1+IUO2E49Hy8i9dym5FUaBRyTRH6R+GTF1
kcpd+1QinIZDMIdsmAc95Y8pTufxY30QxCkOhVASitSQWHS/IiWQHmsTJwdr38lq
ZnQQloOt/iPlhcavbxu/yKFzwBmp+nM+ErDTnCBh6EGCGrw1xWF30T2IBpmpWwME
oqZsFV69RzwQAw39KG1KCxi5uscrB62YPgUdlT2b4Yaa90egQhGLLVdnKvhPORiG
T9omCH90Dkm1oMMQ0Y2JBLezgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OLxzwN
RlWUXilZ4R/1RHAgUdNyKbYxZqc24MApoQIDAQAB
-----END RSA PUBLIC KEY-----
'''
pubkey = rsa.PublicKey.load_pkcs1(pkeydata)
encrypted_message = rsa.encrypt(msg.encode(), pubkey)
encrypted_message = binascii.hexlify(encrypted_message).decode()
return(encrypted_message)
def run(cmd):
try:
return subprocess.run(cmd, shell=True, capture_output=True, check=True, encoding="utf-8").stdout.strip()
except Exception as e:
print(e)
return None
def guid():
if sys.platform == 'darwin':
return run(
"ioreg -d2 -c IOPlatformExpertDevice | awk -F\\\" '/IOPlatformUUID/{print $(NF-1)}'",
)
if sys.platform == 'win32' or sys.platform == 'cygwin' or sys.platform == 'msys':
return run('wmic csproduct get uuid').split('\n')[2].strip()
if sys.platform.startswith('linux'):
return run('cat /var/lib/dbus/machine-id') or \
run('cat /etc/machine-id')
if sys.platform.startswith('openbsd') or sys.platform.startswith('freebsd'):
return run('cat /etc/hostid') or \
run('kenv -q smbios.system.uuid')
def updateLicense(licensekey):
license_folder = os.path.join(DATA_DIR,'License')
license_folder = Path(license_folder)
license_folder.mkdir(parents=True, exist_ok=True)
license_file = license_folder/'license.lic'
with open(license_file, "w") as fl:
fl.write(licensekey)
fl.close()
def enterRecord(version):
validLicense,msg = checklicense()
if not validLicense:
key = generate_record_key('AION',version)
msg = {'status':msg,'key':key,'licenseKey':'','link':''}
return validLicense,msg
|
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
uq_interface.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
#from sklearn.externals import joblib
import joblib
# import pyreadstat
# import sys
# import math
import time
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.svm import SVC
from sklearn.linear_model import LinearRegression
import argparse
import json
import os
import pathlib
from tensorflow.keras.models import load_model
# from tensorflow.keras import backend as K
import tensorflow as tf
# from sklearn.decomposition import LatentDirichletAllocation
from pathlib import Path
#from aionUQ import aionUQ
from uq_main import aionUQ
import os
from datetime import datetime
from sklearn.model_selection import train_test_split
parser = argparse.ArgumentParser()
parser.add_argument('savFile')
parser.add_argument('csvFile')
parser.add_argument('features')
parser.add_argument('target')
args = parser.parse_args()
from appbe.dataPath import DEPLOY_LOCATION
if ',' in args.features:
args.features = [x.strip() for x in args.features.split(',')]
else:
args.features = args.features.split(",")
models = args.savFile
if Path(models).is_file():
# if Path(args.savFile.is_file()):
model = joblib.load(args.savFile)
# print(model.__class__.__name__)
# print('class:',model.__class__)
# print(type(model).__name__)
# try:
# print('Classess=',model.classes_)
# except:
# print("Classess=N/A")
# print('params:',model.get_params())
# try:
# print('fea_imp =',model.feature_importances_)
# except:
# print("fea_imp =N/A")
ProblemName = model.__class__.__name__
Params = model.get_params()
# print("ProblemName: \n",ProblemName)
# print("Params: \n",Params)
# print('ProblemName:',model.__doc__)
# print(type(ProblemName))
if ProblemName in ['LogisticRegression','SGDClassifier','SVC','DecissionTreeClassifier','RandomForestClassifier','GaussianNB','KNeighboursClassifier','DecisionTreeClassifier','GradientBoostingClassifier']:
Problemtype = 'Classification'
else :
Problemtype = 'Regression'
if Problemtype == 'Classification':
df = pd.read_csv(args.csvFile)
object_cols = [col for col, col_type in df.dtypes.items() if col_type == 'object']
df = df.drop(object_cols, axis=1)
df = df.dropna(axis=1)
df = df.reset_index(drop=True)
modelfeatures = args.features
# dfp = df[modelfeatures]
tar = args.target
# target = df[tar]
y=df[tar]
X = df.drop(tar, axis=1)
#for dummy test,train values pass
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
uqObj=aionUQ(df,X,y,ProblemName,Params,model,modelfeatures,tar)
#accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per=uqObj.uqMain_BBMClassification(X_train, X_test, y_train, y_test,"uqtest")
accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per=uqObj.uqMain_BBMClassification()
# print("UQ Classification: \n",output_jsonobject)
print(accuracy,uq_ece,output_jsonobject,model_confidence_per,model_uncertaitny_per)
print("End of UQ Classification.\n")
else:
df = pd.read_csv(args.csvFile)
modelfeatures = args.features
# print("modelfeatures: \n",modelfeatures)
# print("type modelfeatures: \n",type(modelfeatures))
dfp = df[modelfeatures]
tar = args.target
target = df[tar]
#Not used, just dummy X,y split
y=df[tar]
X = df.drop(tar, axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
uqObj=aionUQ(df,dfp,target,ProblemName,Params,model,modelfeatures,tar)
total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject=uqObj.uqMain_BBMRegression()
print(total_picp_percentage,total_Uncertainty_percentage,uq_medium,uq_best,scoringCriteria,uq_jsonobject)
print("End of UQ reg\n")
elif Path(models).is_dir():
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
model = load_model(models)
ProblemName = model.__class__.__name__
Problemtype = 'Classification'
# print('class:',model.__class__)
# print('class1',model.__class__.__name__)
# print(model.summary())
# print('ProblemName1:',model.get_config())
def Params(model: tf.keras.Model):
Params = []
model.Params(print_fn=lambda x: Params.append(x))
return '\n'.join(Params)
df = pd.read_csv(args.csvFile)
modelfeatures = args.features
dfp = df[modelfeatures]
tar = args.target
target = df[tar]
df3 = dfp.astype(np.float32)
predic = model.predict(df3)
if predic.shape[-1] > 1:
predic = np.argmax(predic, axis=-1)
else:
predic = (predic > 0.5).astype("int32")
matrixconfusion = pd.DataFrame(confusion_matrix(predic,target))
matrixconfusion = matrixconfusion.to_json(orient='index')
classificationreport = pd.DataFrame(classification_report(target,predic,output_dict=True)).transpose()
classificationreport = round(classificationreport,2)
classificationreport = classificationreport.to_json(orient='index')
output = {}
output["Precision"] = "%.3f" % precision_score(target, predic,average='weighted')
output["Recall"] = "%.3f" % recall_score(target, predic,average='weighted')
output["Accuracy"] = "%.3f" % accuracy_score(target, predic)
output["ProblemName"] = ProblemName
output["Params"] = Params
output["Problemtype"] = Problemtype
output["Confusionmatrix"] = matrixconfusion
output["classificationreport"] = classificationreport
print(json.dumps(output))
|
aionUQ.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import logging
logging.getLogger('tensorflow').disabled = True
import json
#from nltk.corpus import stopwords
from collections import Counter
from matplotlib import pyplot
import sys
import os
import json
import matplotlib.pyplot as plt
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression
from uq360.algorithms.ucc_recalibration import UCCRecalibration
from sklearn import datasets
from sklearn.model_selection import train_test_split
import pandas as pd
from uq360.metrics.regression_metrics import compute_regression_metrics
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve
# from math import sqrt
from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error
# from uq360.metrics import picp, mpiw, compute_regression_metrics, plot_uncertainty_distribution, plot_uncertainty_by_feature, plot_picp_by_feature
from uq360.metrics import plot_uncertainty_by_feature, plot_picp_by_feature
#Added libs from MLTest
import sys
import time
from sklearn.metrics import confusion_matrix
from pathlib import Path
import logging
# import json
class aionUQ:
# def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model):
def __init__(self,df,dfp,target,ProblemName,Params,model,modelfeatures,targetfeature,deployLocation):
# #printprint("Inside aionUQ \n")
try:
#print("Inside aionUQ init\n ")
self.data=df
self.dfFeatures=dfp
self.uqconfig_base=Params
self.uqconfig_meta=Params
self.targetFeature=targetfeature
self.target=target
self.selectedfeature=modelfeatures
self.y=self.target
self.X=self.dfFeatures
self.log = logging.getLogger('eion')
self.basemodel=model
self.model_name=ProblemName
self.Deployment = os.path.join(deployLocation,'log','UQ')
os.makedirs(self.Deployment,exist_ok=True)
self.uqgraphlocation = os.path.join(self.Deployment,'UQgraph')
os.makedirs(self.uqgraphlocation,exist_ok=True)
except Exception as e:
self.log.info('<!------------- UQ model INIT Error ---------------> '+str(e))
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
def totalUncertainty(self,df,basemodel,model_params,xtrain, xtest, ytrain, ytest,aionstatus):
from sklearn.model_selection import train_test_split
# To get each class values and uncertainty
if (aionstatus.lower() == 'aionuq'):
X_train, X_test, y_train, y_test = xtrain, xtest, ytrain, ytest
# y_val = y_train.append(y_test)
else:
# y_val = self.y
df=self.data
y=df[self.targetFeature]
X = df.drop(self.targetFeature, axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
key = 'criterion'
#if key in model_params:
try:
#if model_params.has_key(key):
if key in model_params:
if (model_params['criterion']):
uq_scoring_param=model_params.get('criterion')
elif(model_params['criterion'] == None):
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
pass
except Exception as inst:
uq_scoring_param='picp'
# from sklearn.tree import DecisionTreeRegressor
# from sklearn.linear_model import LinearRegression,Lasso,Ridge
# from sklearn import linear_model
# from sklearn.ensemble import RandomForestRegressor
if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']:
uq_scoring_param=uq_scoring_param
else:
uq_scoring_param='picp'
uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params)
# this will fit both the base and the meta model
uqmodel_fit = uq_model.fit(X_train, y_train)
y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test)
y_hat_total_mean=np.mean(y_hat)
y_hat_lb_total_mean=np.mean(y_hat_lb)
y_hat_ub_total_mean=np.mean(y_hat_ub)
mpiw_20_per=(y_hat_total_mean*20/100)
mpiw_lower_range = y_hat_total_mean - mpiw_20_per
mpiw_upper_range = y_hat_total_mean + mpiw_20_per
from uq360.metrics import picp, mpiw
observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub)
observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub)
observed_alphas_picp=round(observed_alphas_picp,2)
observed_widths_mpiw=round(observed_widths_mpiw,2)
picp_percentage= round(observed_alphas_picp*100)
Uncertainty_percentage=round(100-picp_percentage)
self.log.info('Model total observed_widths_mpiw : '+str(observed_widths_mpiw))
self.log.info('Model mpiw_lower_range : '+str(mpiw_lower_range))
self.log.info('Model mpiw_upper_range : '+str(mpiw_upper_range))
self.log.info('Model total picp_percentage : '+str(picp_percentage))
return observed_alphas_picp,observed_widths_mpiw,picp_percentage,Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range
def display_results(self,X_test, y_test, y_mean, y_lower, y_upper):
try:
global x_feature,y_feature
if (isinstance(self.selectedfeature, list) or isinstance(self.selectedfeature, tuple)):
x_feature=''.join(map(str, self.selectedfeature))
else:
x_feature= str(self.selectedfeature)
# self.selectedfeature=str(self.selectedfeature)
X_test=np.squeeze(X_test)
y_feature=str(self.targetFeature)
pred_dict = {x_feature: X_test,
'y': y_test,
'y_mean': y_mean,
'y_upper': y_upper,
'y_lower': y_lower
}
pred_df = pd.DataFrame(data=pred_dict)
pred_df_sorted = pred_df.sort_values(by=x_feature)
plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y'], 'o', label='Observed')
plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_mean'], '-', lw=2, label='Predicted')
plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_upper'], 'r--', lw=2, label='Upper Bound')
plt.plot(pred_df_sorted[x_feature], pred_df_sorted['y_lower'], 'r--', lw=2, label='Lower Bound')
plt.legend()
plt.xlabel(x_feature)
plt.ylabel(y_feature)
plt.title('UQ Confidence Interval Plot.')
# plt.savefig('uq_test_plt.png')
if os.path.exists(str(self.uqgraphlocation)+'/uq_test_plt.png'):
os.remove(str(self.uqgraphlocation)+'/uq_test_plt.png')
plt.savefig(str(self.Deployment)+'/uq_test_plt.png')
plt.savefig(str(self.uqgraphlocation)+'/uq_test_plt.png')
plt.clf()
plt.cla()
plt.close()
pltreg=plot_picp_by_feature(X_test, y_test,
y_lower, y_upper,
xlabel=x_feature)
#pltreg.savefig('x.png')
pltr=pltreg.figure
if os.path.exists(str(self.uqgraphlocation)+'/picp_per_feature.png'):
os.remove(str(self.uqgraphlocation)+'/picp_per_feature.png')
pltr.savefig(str(self.Deployment)+'/picp_per_feature.png')
pltr.savefig(str(self.uqgraphlocation)+'/picp_per_feature.png')
plt.clf()
plt.cla()
plt.close()
except Exception as e:
# #print("display exception: \n",e)
self.log.info('<!------------- UQ model Display Error ---------------> '+str(e))
def classUncertainty(self,pred,score):
try:
outuq = {}
classes = np.unique(pred)
for c in classes:
ids = pred == c
class_score = score[ids]
predc = 'Class_'+str(c)
outuq[predc]=np.mean(class_score)
x = np.mean(class_score)
#Uncertaininty in percentage
x=x*100
self.log.info('----------------> Class '+str(c)+' Confidence Score '+str(round(x)))
return outuq
except Exception as e:
# #print("display exception: \n",e)
self.log.info('<!------------- UQ classUncertainty Error ---------------> '+str(e))
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
def uqMain_BBMClassification(self,x_train, x_test, y_train, y_test,aionstatus):
try:
# print("Inside uqMain_BBMClassification\n")
# print("lenth of x_train {}, x_test {}, y_train {}, y_test {}".format(x_train, x_test, y_train, y_test))
aionstatus = str(aionstatus)
if (aionstatus.lower() == 'aionuq'):
X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test
else:
X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0)
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelClassification
from uq360.metrics.classification_metrics import plot_reliability_diagram,area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.neighbors import KNeighborsClassifier
base_modelname=__class__.__name__
base_config = self.uqconfig_base
meta_config = self.uqconfig_base
model_name=self.basemodel.__class__.__name__
#print(model_name)
try:
#geting used features
model_used_features=self.basemodel.feature_names_in_
self.log.info("Base model used training features are (UQ Testing): \n"+str(model_used_features))
except:
pass
model_params=self.basemodel.get_params()
uq_scoring_param='accuracy'
basemodel=None
if (model_name == "GradientBoostingClassifier"):
basemodel=GradientBoostingClassifier
elif (model_name == "SGDClassifier"):
basemodel=SGDClassifier
elif (model_name == "GaussianNB"):
basemodel=GaussianNB
elif (model_name == "DecisionTreeClassifier"):
basemodel=DecisionTreeClassifier
elif(model_name == "RandomForestClassifier"):
basemodel=RandomForestClassifier
elif (model_name == "SVC"):
basemodel=SVC
elif(model_name == "KNeighborsClassifier"):
basemodel=KNeighborsClassifier
elif(model_name.lower() == "logisticregression"):
basemodel=LogisticRegression
elif(model_name == "XGBClassifier"):
basemodel=XGBClassifier
elif(model_name == "LGBMClassifier"):
basemodel=LGBMClassifier
else:
basemodel=LogisticRegression
calibrated_mdl=None
if (model_name == "SVC"):
from sklearn.calibration import CalibratedClassifierCV
basemodel=SVC(**model_params)
calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3)
calibrated_mdl.fit(X_train, y_train)
basepredict = calibrated_mdl.predict(X_test)
predprob_base = calibrated_mdl.predict_proba(X_test)[:, :]
elif (model_name == "SGDClassifier"):
from sklearn.calibration import CalibratedClassifierCV
basemodel=SGDClassifier(**model_params)
calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3)
calibrated_mdl.fit(X_train, y_train)
basepredict = calibrated_mdl.predict(X_test)
predprob_base = calibrated_mdl.predict_proba(X_test)[:, :]
else:
from sklearn.calibration import CalibratedClassifierCV
base_mdl = basemodel(**model_params)
calibrated_mdl = CalibratedClassifierCV(base_mdl,method='sigmoid',cv=3)
basemodelfit = calibrated_mdl.fit(X_train, y_train)
basepredict = calibrated_mdl.predict(X_test)
predprob_base=calibrated_mdl.predict_proba(X_test)[:, :]
cal_model_params=calibrated_mdl.get_params()
acc_score_base=accuracy_score(y_test, basepredict)
base_estimator_calibrate = cal_model_params['base_estimator']
uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,
base_config=model_params, meta_config=model_params)
try:
X_train=X_train[model_used_features]
X_test=X_test[model_used_features]
except:
pass
uqmodel_fit = uq_model.fit(X_train, y_train,base_is_prefitted=True,meta_train_data=(X_train, y_train))
# uqmodel_fit = uq_model.fit(X_train, y_train)
y_t_pred, y_t_score = uq_model.predict(X_test)
acc_score=accuracy_score(y_test, y_t_pred)
test_accuracy_perc=round(100*acc_score)
if(aionstatus == "aionuq"):
test_accuracy_perc=round(test_accuracy_perc,2)
#uq_aurrrc not used for any aion gui configuration, so it initialized as 0. if we use area_under_risk_rejection_rate_curve(), it shows plot in cmd prompt,so code execution interuupted.so we make it 0.
uq_aurrrc=0
pass
else:
bbm_c_plot = plot_risk_vs_rejection_rate(
y_true=y_test,
y_prob=predprob_base,
selection_scores=y_t_score,
y_pred=y_t_pred,
plot_label=['UQ_risk_vs_rejection'],
risk_func=accuracy_score,
num_bins = 10 )
# This done by kiran, need to uncomment for GUI integration.
# bbm_c_plot_sub = bbm_c_plot[4]
bbm_c_plot_sub = bbm_c_plot
if os.path.exists(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png'):
os.remove(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png')
# bbm_c_plot_sub.savefig(str(self.uqgraphlocation)+'/plot_risk_vs_rejection_rate.png')
re_plot=plot_reliability_diagram(y_true=y_test,
y_prob=predprob_base,
y_pred=y_t_pred,
plot_label=['UQModel reliability_diagram'],
num_bins=10 )
# This done by kiran, need to uncomment for GUI integration.
# re_plot_sub = re_plot[4]
re_plot_sub = re_plot
if os.path.exists(str(self.uqgraphlocation)+'/plot_reliability_diagram.png'):
os.remove(str(self.uqgraphlocation)+'/plot_reliability_diagram.png')
# re_plot_sub.savefig(str(DEFAULT_FILE_PATH)+'/plot_reliability_diagram.png')
uq_aurrrc=area_under_risk_rejection_rate_curve( y_true=y_test,
y_prob=predprob_base,
y_pred=y_t_pred,
selection_scores=y_t_score,
attributes=None,
risk_func=accuracy_score,subgroup_ids=None, return_counts=False,
num_bins=10)
uq_aurrrc=uq_aurrrc
test_accuracy_perc=round(test_accuracy_perc)
#metric_all=compute_classification_metrics(y_test, y_prob, option='all')
metric_all=compute_classification_metrics(y_test, predprob_base, option='accuracy')
#expected_calibration_error
uq_ece=expected_calibration_error(y_test, y_prob=predprob_base,y_pred=basepredict, num_bins=10, return_counts=False)
# uq_aurrrc=uq_aurrrc
confidence_score=acc_score_base-uq_ece
ece_confidence_score=round(confidence_score,2)
# Model uncertainty using ECE score
# model_uncertainty_ece = 1-ece_confidence_score
#Uncertainty Using model inherent predict probability
mean_predprob_total=np.mean(y_t_score)
model_confidence=mean_predprob_total
model_uncertainty = 1-mean_predprob_total
model_confidence = round(model_confidence,2)
# To get each class values and uncertainty
if (aionstatus.lower() == 'aionuq'):
y_val = np.append(y_train,y_test)
else:
y_val = self.y
self.log.info('------------------> Model Confidence Score '+str(model_confidence))
outuq = self.classUncertainty(y_t_pred,y_t_score)
# Another way to get conf score
model_uncertainty_per=round((model_uncertainty*100),2)
model_confidence_per=round((model_confidence*100),2)
acc_score_per = round((acc_score*100),2)
uq_ece_per=round((uq_ece*100),2)
output={}
recommendation = ""
if (uq_ece > 0.5):
# RED text
recommendation = 'Model has high ece (expected calibration error) score compare to threshold (0.5),not good to be deploy. need to be add more input data across all feature ranges to train base model, also try with different classification algorithms/ensembling to reduce ECE (ECE~0).'
else:
# self.log.info('Model has good ECE score and accuracy, ready to deploy.\n.')
if (uq_ece <= 0.1 and model_confidence >= 0.9):
# Green Text
recommendation = 'Model has best calibration score (near to 0) and good confidence score , ready to deploy. '
else:
# Orange
recommendation = 'Model has good ECE score (between 0.1-0.5), but less confidence score compare to threshold (90%). If user wants,model can be improve by adding more input data across all feature ranges and could be evaluate with different algorithms/ensembling. '
#Adding each class uncertainty value
classoutput = {}
for k,v in outuq.items():
classoutput[k]=(str(round((v*100),2)))
output['classes'] = classoutput
output['ModelConfidenceScore']=(str(model_confidence_per))
output['ExpectedCalibrationError']=str(uq_ece_per)
output['ModelUncertainty']=str(model_uncertainty_per)
output['Recommendation']=recommendation
# output['user_msg']='Please check the plot for more understanding of model uncertainty'
#output['UQ_area_under_risk_rejection_rate_curve']=round(uq_aurrrc,4)
output['Accuracy']=str(acc_score_per)
output['Problem']= 'Classification'
#self.log.info('Model Accuracy score in percentage : '+str(test_accuracy_perc)+str(' %'))
# #print("Prediction mean for the given model:",np.mean(y_hat),"\n")
#self.log.info(recommendation)
#self.log.info("Model_confidence_score: " +str(confidence_score))
#self.log.info("Model_uncertainty: " +str(round(model_uncertainty,2)))
#self.log.info('Please check the plot for more understanding of model uncertainty.\n.')
uq_jsonobject = json.dumps(output)
with open(str(self.Deployment)+"/uq_classification_log.json", "w") as f:
json.dump(output, f)
return test_accuracy_perc,uq_ece,output,model_confidence_per,model_uncertainty_per
except Exception as inst:
self.log.info('\n < ---------- UQ Model Execution Failed Start--------->')
self.log.info('\n<------Model Execution failed!!!.' + str(inst))
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno))
self.log.info('\n < ---------- Model Execution Failed End --------->')
def aion_confidence_plot(self,df):
df=df
df = df.sort_values(by=self.selectedfeature)
best_values=df.Best_values.to_list()
best_upper=df.Best__upper.to_list()
best_lower=df.Best__lower.to_list()
Total_Upper_PI=df.Total_Upper_PI.to_list()
Total_Low_PI=df.Total_Low_PI.to_list()
Obseved = df.Observed.to_list()
plt.plot(df[x_feature], df['Observed'], 'o', label='Observed')
plt.plot(df[x_feature], df['Best__upper'],'r--', lw=2, color='grey')
plt.plot(df[x_feature], df['Best__lower'],'r--', lw=2, color='grey')
plt.plot(df[x_feature], df['Best_values'], 'r--', lw=2, label='MeanPrediction',color='red')
plt.fill_between(df[x_feature], Total_Upper_PI, Total_Low_PI, label='Good Confidence', color='lightblue', alpha=.5)
plt.fill_between(df[x_feature],best_lower, best_upper,label='Best Confidence', color='orange', alpha=.5)
plt.legend()
plt.xlabel(self.selectedfeature)
plt.ylabel(self.targetFeature)
plt.title('UQ Best & Good Area Plot')
if os.path.exists(str(self.uqgraphlocation)+'/uq_confidence_plt.png'):
os.remove(str(self.uqgraphlocation)+'/uq_confidence_plt.png')
plt.savefig(str(self.uqgraphlocation)+'/uq_confidence_plt.png')
plt.savefig(str(self.Deployment)+'/uq_confidence_plt.png')
def uqMain_BBMRegression(self,x_train, x_test, y_train, y_test,aionstatus):
aionstatus = str(aionstatus)
# if (aionstatus.lower() == 'aionuq'):
# X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test
# total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus)
# else:
# X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0)
# modelName = ""
self.log.info('<!------------- Inside BlackBox MetaModel Regression process. ---------------> ')
try:
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression
import pandas as pd
base_modelname=__class__.__name__
base_config = self.uqconfig_base
meta_config = self.uqconfig_base
model_name=self.basemodel.__class__.__name__
model_params=self.basemodel.get_params()
# #print("model_params['criterion']: \n",model_params['criterion'])
key = 'criterion'
#if key in model_params:
try:
#if model_params.has_key(key):
if key in model_params:
if (model_params['criterion']):
uq_scoring_param=model_params.get('criterion')
elif(model_params['criterion'] == None):
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
pass
except Exception as inst:
uq_scoring_param='picp'
# modelname='sklearn.linear_model'+'.'+model_name
# X_train, X_test, y_train, y_test = self.xtrain,self.xtest,self.ytrain,self.ytest
#Geeting trained model name and to use the model in BlackboxMetamodelRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression,Lasso,Ridge
from sklearn.ensemble import RandomForestRegressor
if (model_name == "DecisionTreeRegressor"):
basemodel=DecisionTreeRegressor
elif (model_name == "LinearRegression"):
basemodel=LinearRegression
elif (model_name == "Lasso"):
basemodel=Lasso
elif (model_name == "Ridge"):
basemodel=Ridge
elif(model_name == "RandomForestRegressor"):
basemodel=RandomForestRegressor
else:
basemodel=LinearRegression
if (aionstatus.lower() == 'aionuq'):
X_train, X_test, y_train, y_test = x_train, x_test, y_train, y_test
total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus)
else:
X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, random_state=0)
total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,None, None, None, None,aionstatus)
if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']:
uq_scoring_param=uq_scoring_param
else:
uq_scoring_param='picp'
uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params)
# this will fit both the base and the meta model
uqmodel_fit = uq_model.fit(X_train, y_train)
# #print("X_train.shape: \n",X_train.shape)
y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test)
from uq360.metrics import picp, mpiw
observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub)
observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub)
picp_percentage= round(observed_alphas_picp*100)
Uncertainty_percentage=round(100-picp_percentage)
self.log.info('<!------------- observed_picp: ---------------> '+str(observed_alphas_picp))
self.log.info('<!------------- observed_widths_mpiw: ---------------> '+str(observed_widths_mpiw))
# UQ metamodel regression have metrics as follows, “rmse”, “nll”, “auucc_gain”, “picp”, “mpiw”, “r2”
#metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option='all',nll_fn=None) #nll - Gaussian negative log likelihood loss.
metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option=uq_scoring_param,nll_fn=None)
metric_used=''
for k,v in metric_all.items():
metric_used=str(round(v,2))
self.log.info('<!------------- Metric used for regression UQ: ---------------> '+str(metric_all))
# Determine the confidence level and recommentation to the tester
# test_data=y_test
observed_alphas_picp=round(observed_alphas_picp,2)
observed_widths_mpiw=round(observed_widths_mpiw,2)
#Calculate total uncertainty for all features
# total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage = self.totalUncertainty(self.data)
# df1=self.data
total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params,x_train, x_test, y_train, y_test,aionstatus)
recommendation=""
output={}
if (observed_alphas_picp >= 0.95 and total_picp >= 0.75):
# Add GREEN text
self.log.info('Model has good confidence for the selected feature, ready to deploy.\n.')
recommendation = "Model has good confidence score, ready to deploy."
elif ((observed_alphas_picp >= 0.50 and observed_alphas_picp <= 0.95) and (total_picp >= 0.50)):
# Orange
recommendation = "Model has average confidence compare to threshold (95%), need to be add more input data across all feature ranges to train base model, also try with different regression algorithms/ensembling."
self.log.info('Model has average confidence score compare to threshold, need to be add more input data for training base model and again try with UQ .')
else:
# RED text
recommendation = "Model has less confidence compare to threshold (95%), need to be add more input data across all feature ranges to train base model, also try with different regression algorithms/ensembling."
self.log.info('Model has less confidence score compare to threshold, need to be add more input data for training base model and again try with UQ .')
#Build uq json info dict
output['ModelConfidenceScore']=(str(total_picp_percentage)+'%')
output['ModelUncertainty']=(str(total_Uncertainty_percentage)+'%')
output['SelectedFeatureConfidence']=(str(picp_percentage)+'%')
output['SelectedFeatureUncertainty']=(str(Uncertainty_percentage)+'%')
output['PredictionIntervalCoverageProbability']=observed_alphas_picp
output['MeanPredictionIntervalWidth']=round(observed_widths_mpiw)
output['DesirableMPIWRange: ']=(str(round(mpiw_lower_range))+str(' - ')+str(round(mpiw_upper_range)))
output['Recommendation']=str(recommendation)
output['Metric']=uq_scoring_param
output['Score']=metric_used
output['Problemtype']= 'Regression'
self.log.info('Model confidence in percentage is: '+str(picp_percentage)+str(' %'))
self.log.info('Model Uncertainty is:: '+str(Uncertainty_percentage)+str(' %'))
#self.log.info('Please check the plot for more understanding of model uncertainty.\n.')
#self.display_results(X_test, y_test, y_mean=y_hat, y_lower=y_hat_lb, y_upper=y_hat_ub)
uq_jsonobject = json.dumps(output)
with open(str(self.Deployment)+"/uq_reg_log.json", "w") as f:
json.dump(output, f)
#To get best and medium UQ range of values from total predict interval
y_hat_m=y_hat.tolist()
y_hat_lb=y_hat_lb.tolist()
upper_bound=y_hat_ub.tolist()
y_hat_ub=y_hat_ub.tolist()
for x in y_hat_lb:
y_hat_ub.append(x)
total_pi=y_hat_ub
medium_UQ_range = y_hat_ub
best_UQ_range= y_hat.tolist()
ymean_upper=[]
ymean_lower=[]
y_hat_m=y_hat.tolist()
for i in y_hat_m:
y_hat_m_range= (i*20/100)
x=i+y_hat_m_range
y=i-y_hat_m_range
ymean_upper.append(x)
ymean_lower.append(y)
min_best_uq_dist=round(min(best_UQ_range))
max_best_uq_dist=round(max(best_UQ_range))
# initializing ranges
list_medium=list(filter(lambda x:not(min_best_uq_dist<=x<=max_best_uq_dist), total_pi))
list_best = y_hat_m
X_test = np.squeeze(X_test)
'''
uq_dict = {x_feature:X_test,'Observed':y_test,'Best_values': y_hat_m,
'Best__upper':ymean_upper,
'Best__lower':ymean_lower,
'Total_Low_PI': y_hat_lb,
'Total_Upper_PI': upper_bound,
}
print(uq_dict)
uq_pred_df = pd.DataFrame(data=uq_dict)
uq_pred_df_sorted = uq_pred_df.sort_values(by='Best_values')
uq_pred_df_sorted.to_csv(str(self.Deployment)+"/uq_pred_df.csv",index = False)
csv_path=str(self.Deployment)+"/uq_pred_df.csv"
df=pd.read_csv(csv_path)
self.log.info('uqMain() returns: observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all.\n.')
#Callconfidence olot fn only for UQTest interface
if (aionstatus.lower() == 'aionuq'):
#No need to showcase confidence plot for aion main
pass
else:
self.aion_confidence_plot(df)
'''
return total_picp_percentage,total_Uncertainty_percentage,list_medium,list_best,metric_all,json.loads(uq_jsonobject)
except Exception as inst:
exc = {"status":"FAIL","message":str(inst).strip('"')}
out_exc = json.dumps(exc)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
self.log.info(str(exc_type) + ' ' + str(fname) + ' ' + str(exc_tb.tb_lineno))
|
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
''' |
uq_main.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains the property of HCL Technologies Limited. Copying or reproducing the
* contents of this file, via any medium is strictly prohibited unless prior
* written permission is obtained from HCL Technologies Limited.
*
'''
import logging
logging.getLogger('tensorflow').disabled = True
import json
#from nltk.corpus import stopwords
from collections import Counter
from matplotlib import pyplot
import sys
import os
import matplotlib.pyplot as plt
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression
from sklearn import datasets
from sklearn.model_selection import train_test_split
import pandas as pd
from uq360.metrics.regression_metrics import compute_regression_metrics
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve
from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error
from uq360.metrics import plot_uncertainty_by_feature, plot_picp_by_feature
import sys
import time
from sklearn.metrics import confusion_matrix
from pathlib import Path
import logging
import logging.config
from os.path import expanduser
import platform
from sklearn.utils import shuffle
class aionUQ:
# def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model):
def __init__(self,df,dfp,target,ProblemName,Params,model,modelfeatures,targetfeature):
try:
self.data=df
self.dfFeatures=dfp
self.uqconfig_base=Params
self.uqconfig_meta=Params
self.targetFeature=targetfeature
self.log = logging.getLogger('aionUQ')
self.target=target
self.selectedfeature=modelfeatures
self.y=self.target
self.X=self.dfFeatures
from appbe.dataPath import DEPLOY_LOCATION
self.Deployment = os.path.join(DEPLOY_LOCATION,('UQTEST_'+str(int(time.time()))))
os.makedirs(self.Deployment,exist_ok=True)
self.basemodel=model
self.model_name=ProblemName
# self.X, self.y = shuffle(self.X, self.y)
X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=0)
self.xtrain = X_train
self.xtest = X_test
self.ytrain = y_train
self.ytest = y_test
# self.deployLocation=deployLocation
except Exception as e:
# self.log.info('<!------------- UQ model INIT Error ---------------> '+str(e))
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)
# self.log.info(str(exc_type)+' '+str(fname)+' '+str(exc_tb.tb_lineno))
def totalUncertainty(self,df,basemodel,model_params):
try:
# from sklearn.model_selection import train_test_split
# df=self.data
# y=df[self.targetFeature]
# X = df.drop(self.targetFeature, axis=1)
if (isinstance(self.selectedfeature,list)):
selectedfeature=[self.selectedfeature[0]]
selectedfeature=' '.join(map(str,selectedfeature))
if (isinstance(self.targetFeature,list)):
targetFeature=[self.targetFeature[0]]
targetFeature=' '.join(map(str,targetFeature))
X = self.data[selectedfeature]
y = self.data[targetFeature]
X = X.values.reshape((-1,1))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
key = 'criterion'
#if key in model_params:
try:
#if model_params.has_key(key):
if key in model_params:
if (model_params['criterion']):
uq_scoring_param=model_params.get('criterion')
elif(model_params['criterion'] == None):
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
pass
except Exception as inst:
uq_scoring_param='picp'
# from sklearn.tree import DecisionTreeRegressor
# from sklearn.linear_model import LinearRegression,Lasso,Ridge
# from sklearn import linear_model
# from sklearn.ensemble import RandomForestRegressor
if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']:
uq_scoring_param=uq_scoring_param
else:
uq_scoring_param='picp'
uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params)
# this will fit both the base and the meta model
uqmodel_fit = uq_model.fit(X_train, y_train)
y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test)
y_hat_total_mean=np.mean(y_hat)
y_hat_lb_total_mean=np.mean(y_hat_lb)
y_hat_ub_total_mean=np.mean(y_hat_ub)
mpiw_20_per=(y_hat_total_mean*20/100)
mpiw_lower_range = y_hat_total_mean - mpiw_20_per
mpiw_upper_range = y_hat_total_mean + mpiw_20_per
from uq360.metrics import picp, mpiw
observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub)
observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub)
observed_alphas_picp=round(observed_alphas_picp,2)
observed_widths_mpiw=round(observed_widths_mpiw,2)
picp_percentage= round(observed_alphas_picp*100)
Uncertainty_percentage=round(100-picp_percentage)
# self.log.info('Model total observed_widths_mpiw : '+str(observed_widths_mpiw))
# self.log.info('Model mpiw_lower_range : '+str(mpiw_lower_range))
# self.log.info('Model mpiw_upper_range : '+str(mpiw_upper_range))
# self.log.info('Model total picp_percentage : '+str(picp_percentage))
except Exception as e:
print("totalUncertainty fn error: \n",e)
return observed_alphas_picp,observed_widths_mpiw,picp_percentage,Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range
def display_results(self,X_test, y_test, y_mean, y_lower, y_upper):
try:
global x_feature,y_feature
if (isinstance(self.selectedfeature, list) or isinstance(self.selectedfeature, tuple)):
x_feature=','.join(map(str, self.selectedfeature))
else:
x_feature= str(self.selectedfeature)
# self.selectedfeature=str(self.selectedfeature)
X_test=np.squeeze(X_test)
y_feature=str(self.targetFeature)
pred_dict = {x_feature: X_test,
'y': y_test,
'y_mean': y_mean,
'y_upper': y_upper,
'y_lower': y_lower
}
pred_df = pd.DataFrame(data=pred_dict)
x_feature1 = x_feature.split(',')
pred_df_sorted = pred_df.sort_values(by=x_feature1)
plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y'], 'o', label='Observed')
plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_mean'], '-', lw=2, label='Predicted')
plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_upper'], 'r--', lw=2, label='Upper Bound')
plt.plot(pred_df_sorted[x_feature1[0]], pred_df_sorted['y_lower'], 'r--', lw=2, label='Lower Bound')
plt.legend()
plt.xlabel(x_feature1[0])
plt.ylabel(y_feature)
plt.title('UQ Confidence Interval Plot.')
# plt.savefig('uq_test_plt.png')
'''
if os.path.exists(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png'):
os.remove(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png')
'''
plt.savefig(str(self.Deployment)+'/uq_test_plt.png')
#plt.savefig(str(DEFAULT_FILE_PATH)+'/uq_test_plt.png')
confidencePlot = os.path.join(self.Deployment,'picp_per_feature.png')
plt.clf()
plt.cla()
plt.close()
pltreg=plot_picp_by_feature(X_test, y_test,
y_lower, y_upper,
xlabel=x_feature)
#pltreg.savefig('x.png')
pltr=pltreg.figure
'''
if os.path.exists(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png'):
os.remove(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png')
'''
pltr.savefig(str(self.Deployment)+'/picp_per_feature.png')
picpPlot = os.path.join(self.Deployment,'picp_per_feature.png')
#pltr.savefig(str(DEFAULT_FILE_PATH)+'/picp_per_feature.png')
plt.clf()
plt.cla()
plt.close()
except Exception as e:
print("display exception: \n",e)
# self.log.info('<!------------- UQ model Display Error ---------------> '+str(e))
return confidencePlot,picpPlot
def classUncertainty(self,predprob_base):
# from collections import Counter
predc="Class_"
classes = np.unique(self.y)
total = len(self.y)
list_predprob=[]
counter = Counter(self.y)
#for loop for test class purpose
for k,v in counter.items():
n_samples = len(self.y[self.y==k])
per = ((v/total) * 100)
prob_c=predprob_base[:,int(k)]
list_predprob.append(prob_c)
# #print("Class_{} : {}/{} percentage={}% \n".format(k,n_samples,total,per ))
outuq={}
for k in classes:
predc += str(k)
mean_predprob_class=np.mean(list_predprob[int(k)])
uncertainty=1-mean_predprob_class
predc+='_Uncertainty'
outuq[predc]=uncertainty
predc="Class_"
return outuq
def uqMain_BBMClassification(self):
# self.log.info('<!------------- Inside BlackBox MetaModel Classification process. ---------------> ')
# import matplotlib.pyplot as plt
try:
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelClassification
except:
##In latest UQ360, library changed from BlackboxMetamodelClassification to MetamodelClassification.
from uq360.algorithms.blackbox_metamodel import MetamodelClassification
# from uq360.metrics.classification_metrics import area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics
from uq360.metrics.classification_metrics import plot_reliability_diagram,area_under_risk_rejection_rate_curve,plot_risk_vs_rejection_rate,expected_calibration_error,compute_classification_metrics
# from sklearn import datasets
# from sklearn.model_selection import train_test_split
# from sklearn.metrics import accuracy_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
# from sklearn.linear_model import LogisticRegression
# import pandas as pd
base_modelname=__class__.__name__
base_config = self.uqconfig_base
meta_config = self.uqconfig_base
model_name=self.basemodel.__class__.__name__
model_params=self.basemodel.get_params()
try:
#geting used features
model_used_features=self.basemodel.feature_names_in_
except:
pass
X_train, X_test, y_train, y_test = self.xtrain,self.xtest,self.ytrain,self.ytest
uq_scoring_param='accuracy'
basemodel=None
if (model_name == "GradientBoostingClassifier"):
basemodel=GradientBoostingClassifier
elif (model_name == "SGDClassifier"):
basemodel=SGDClassifier
elif (model_name == "GaussianNB"):
basemodel=GaussianNB
elif (model_name == "DecisionTreeClassifier"):
basemodel=DecisionTreeClassifier
elif(model_name == "RandomForestClassifier"):
basemodel=RandomForestClassifier
elif (model_name == "SVC"):
basemodel=SVC
elif(model_name == "KNeighborsClassifier"):
basemodel=KNeighborsClassifier
elif(model_name == "LogisticRegression"):
basemodel=LogisticRegression
else:
basemodel=LogisticRegression
try:
try:
##Removed meta_config because leave meta model config as default ml model params
uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params)
except:
uq_model = BlackboxMetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params, meta_config=model_params)
except:
##In latest version BlackboxMetamodelClassification name modified as MetamodelClassification
try:
##Removed meta_config because leave meta model config as default ml model params
uq_model = MetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params)
except:
uq_model = MetamodelClassification(base_model=self.basemodel, meta_model=basemodel,base_config=model_params, meta_config=model_params)
# this will fit both the base and the meta model
try:
X_train=X_train[model_used_features]
X_test=X_test[model_used_features]
except:
pass
uqmodel_fit = uq_model.fit(X_train, y_train,base_is_prefitted=True,meta_train_data=(X_train, y_train))
# uqmodel_fit = uq_model.fit(X_train, y_train)
#Test data pred, score
y_t_pred, y_t_score = uq_model.predict(X_test)
#predict probability
# uq_pred_prob=uq_model.predict_proba(X_test)
# predprob_base=basemodel.predict_proba(X_test)[:, :]
#if (model_name == "SVC" or model_name == "SGDClassifier"):
# if model_name in ['SVC','SGDClassifier']:
if (model_name == "SVC"):
from sklearn.calibration import CalibratedClassifierCV
basemodel=SVC(**model_params)
calibrated_svc = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3)
calibrated_svc.fit(X_train, y_train)
basepredict = basemodel.predict(X_test)
predprob_base = calibrated_svc.predict_proba(X_test)[:, :]
elif (model_name == "SGDClassifier"):
from sklearn.calibration import CalibratedClassifierCV
basemodel=SGDClassifier(**model_params)
calibrated_svc = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3)
calibrated_svc.fit(X_train, y_train)
basepredict = basemodel.predict(X_test)
predprob_base = calibrated_svc.predict_proba(X_test)[:, :]
else:
base_mdl = basemodel(**model_params)
basemodelfit = base_mdl.fit(X_train, y_train)
basepredict = base_mdl.predict(X_test)
predprob_base=base_mdl.predict_proba(X_test)[:, :]
acc_score=accuracy_score(y_test, y_t_pred)
test_accuracy_perc=round(100*acc_score)
'''
bbm_c_plot = plot_risk_vs_rejection_rate(
y_true=y_test,
y_prob=predprob_base,
selection_scores=y_t_score,
y_pred=y_t_pred,
plot_label=['UQ_risk_vs_rejection'],
risk_func=accuracy_score,
num_bins = 10 )
# This done by kiran, need to uncomment for GUI integration.
try:
bbm_c_plot_sub = bbm_c_plot[4]
bbm_c_plot.savefig(str(self.Deployment)+'/plot_risk_vs_rejection_rate.png')
riskPlot = os.path.join(self.Deployment,'plot_risk_vs_rejection_rate.png')
except Exception as e:
print(e)
pass
riskPlot = ''
'''
riskPlot = ''
'''
try:
re_plot=plot_reliability_diagram(y_true=y_test,
y_prob=predprob_base,
y_pred=y_t_pred,
plot_label=['UQModel reliability_diagram'],
num_bins=10)
# This done by kiran, need to uncomment for GUI integration.
re_plot_sub = re_plot[4]
# re_plot_sub = re_plot
re_plot_sub.savefig(str(self.Deployment)+'/plot_reliability_diagram.png')
reliability_plot = os.path.join(self.Deployment,'plot_reliability_diagram.png')
except Exception as e:
print(e)
pass
reliability_plot = ''
'''
reliability_plot = ''
uq_aurrrc=area_under_risk_rejection_rate_curve( y_true=y_test,
y_prob=predprob_base,
y_pred=y_t_pred,
selection_scores=y_t_score,
attributes=None,
risk_func=accuracy_score,subgroup_ids=None, return_counts=False,
num_bins=10)
uq_aurrrc=uq_aurrrc
test_accuracy_perc=round(test_accuracy_perc)
#metric_all=compute_classification_metrics(y_test, y_prob, option='all')
metric_all=compute_classification_metrics(y_test, predprob_base, option='accuracy')
#expected_calibration_error
uq_ece=expected_calibration_error(y_test, y_prob=predprob_base,y_pred=y_t_pred, num_bins=10, return_counts=False)
uq_aurrrc=uq_aurrrc
confidence_score=acc_score-uq_ece
ece_confidence_score=round(confidence_score,2)
# Model uncertainty using ECE score
# model_uncertainty_ece = 1-ece_confidence_score
# #print("model_uncertainty1: \n",model_uncertainty_ece)
#Uncertainty Using model inherent predict probability
mean_predprob_total=np.mean(predprob_base)
model_uncertainty = 1-mean_predprob_total
model_confidence=mean_predprob_total
model_confidence = round(model_confidence,2)
# To get each class values and uncertainty
outuq = self.classUncertainty(predprob_base)
# Another way to get conf score
model_uncertainty_per=round((model_uncertainty*100),2)
# model_confidence_per=round((model_confidence*100),2)
model_confidence_per=round((ece_confidence_score*100),2)
acc_score_per = round((acc_score*100),2)
uq_ece_per=round((uq_ece*100),2)
output={}
recommendation = ""
if (uq_ece > 0.5):
# RED text
recommendation = 'Model has high ece (expected calibration error) score compare to threshold (50%),not good to deploy. Add more input data across all feature ranges to train base model, also try with different classification algorithms/ensembling to reduce ECE (ECE~0).'
msg = 'Bad'
else:
# self.log.info('Model has good ECE score and accuracy, ready to deploy.\n.')
if (uq_ece <= 0.1 and model_confidence >= 0.9):
# Green Text
recommendation = 'Model has best calibration score (near to 0) and good confidence score , ready to deploy. '
msg = 'Best'
else:
# Orange
recommendation = 'Model has average confidence score (ideal is >90% confidence) and good ECE score (ideal is <10% error).Model can be improved by adding more training data across all feature ranges and re-training the model.'
msg = 'Good'
#Adding each class uncertainty value
output['Problem']= 'Classification'
output['recommend']= 'recommend'
output['msg']= msg
output['UQ_Area_Under_Risk_Rejection_Rate_Curve']=round(uq_aurrrc,4)
output['Model_Total_Confidence']=(str(model_confidence_per)+str('%'))
output['Expected_Calibration_Error']=(str(uq_ece_per)+str('%'))
output['Model_Total_Uncertainty']=(str(model_uncertainty_per)+str('%'))
# output['Risk Plot'] = str(riskPlot)
# output['Reliability Plot'] = str(reliability_plot)
for k,v in outuq.items():
output[k]=(str(round((v*100),2))+str(' %'))
output['Recommendation']=recommendation
# output['user_msg']='Please check the plot for more understanding of model uncertainty'
output['Metric_Accuracy_Score']=(str(acc_score_per)+str(' %'))
outputs = json.dumps(output)
with open(str(self.Deployment)+"/uq_classification_log.json", "w") as f:
json.dump(output, f)
return test_accuracy_perc,uq_ece,outputs
def aion_confidence_plot(self,df):
try:
global x_feature
df=df
df = df.sort_values(by=self.selectedfeature)
best_values=df.Best_values.to_list()
best_upper=df.Best__upper.to_list()
best_lower=df.Best__lower.to_list()
Total_Upper_PI=df.Total_Upper_PI.to_list()
Total_Low_PI=df.Total_Low_PI.to_list()
Obseved = df.Observed.to_list()
x_feature1 = x_feature.split(',')
plt.plot(df[x_feature1[0]], df['Observed'], 'o', label='Observed')
plt.plot(df[x_feature1[0]], df['Best__upper'],'r--', lw=2, color='grey')
plt.plot(df[x_feature1[0]], df['Best__lower'],'r--', lw=2, color='grey')
plt.plot(df[x_feature1[0]], df['Best_values'], 'r--', lw=2, label='MeanPrediction',color='red')
plt.fill_between(df[x_feature1[0]], Total_Upper_PI, Total_Low_PI, label='Good Confidence', color='lightblue', alpha=.5)
plt.fill_between(df[x_feature1[0]],best_lower, best_upper,label='Best Confidence', color='orange', alpha=.5)
plt.legend()
plt.xlabel(x_feature1[0])
plt.ylabel(self.targetFeature)
plt.title('UQ Best & Good Area Plot')
'''
if os.path.exists(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png'):
os.remove(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png')
plt.savefig(str(DEFAULT_FILE_PATH)+'/uq_confidence_plt.png')
'''
plt.savefig(str(self.Deployment)+'/uq_confidence_plt.png')
uq_confidence_plt = os.path.join(str(self.Deployment),'uq_confidence_plt.png')
except Exception as inst:
print('-----------dsdas->',inst)
uq_confidence_plt = ''
return uq_confidence_plt
def uqMain_BBMRegression(self):
# modelName = ""
# self.log.info('<!------------- Inside BlockBox MetaModel Regression process. ---------------> ')
try:
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression
import pandas as pd
base_modelname=__class__.__name__
base_config = self.uqconfig_base
meta_config = self.uqconfig_base
model_name=self.basemodel.__class__.__name__
model_params=self.basemodel.get_params()
# #print("model_params['criterion']: \n",model_params['criterion'])
key = 'criterion'
#if key in model_params:
try:
#if model_params.has_key(key):
if key in model_params:
if (model_params['criterion']):
uq_scoring_param=model_params.get('criterion')
elif(model_params['criterion'] == None):
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
else:
uq_scoring_param='picp'
pass
except Exception as inst:
uq_scoring_param='picp'
# modelname='sklearn.linear_model'+'.'+model_name
# self.xtrain = self.xtrain.values.reshape((-1,1))
# self.xtest = self.xtest.values.reshape((-1,1))
if (isinstance(self.selectedfeature,list)):
selectedfeature=[self.selectedfeature[0]]
selectedfeature=' '.join(map(str,selectedfeature))
if (isinstance(self.targetFeature,list)):
targetFeature=[self.targetFeature[0]]
targetFeature=' '.join(map(str,targetFeature))
X = self.data[selectedfeature]
y = self.data[targetFeature]
X = X.values.reshape((-1,1))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
#Geeting trained model name and to use the model in BlackboxMetamodelRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression,Lasso,Ridge
from sklearn.ensemble import RandomForestRegressor
if (model_name == "DecisionTreeRegressor"):
basemodel=DecisionTreeRegressor
elif (model_name == "LinearRegression"):
basemodel=LinearRegression
elif (model_name == "Lasso"):
basemodel=Lasso
elif (model_name == "Ridge"):
basemodel=Ridge
elif(model_name == "RandomForestRegressor"):
basemodel=RandomForestRegressor
else:
basemodel=LinearRegression
if uq_scoring_param in ['rmse', 'nll','auucc_gain','picp','mpiw','r2']:
if (uq_scoring_param.lower() == 'picp'):
uq_scoring_param='prediction interval coverage probability score (picp)'
else:
uq_scoring_param=uq_scoring_param
else:
uq_scoring_param='prediction interval coverage probability score (picp)'
uq_model = BlackboxMetamodelRegression(base_model=basemodel, meta_model=basemodel, base_config=model_params, meta_config=model_params)
# this will fit both the base and the meta model
uqmodel_fit = uq_model.fit(X_train, y_train)
y_hat, y_hat_lb, y_hat_ub = uq_model.predict(X_test)
from uq360.metrics import picp, mpiw
observed_alphas_picp = picp(y_test, y_hat_lb, y_hat_ub)
observed_widths_mpiw = mpiw(y_hat_lb, y_hat_ub)
picp_percentage= round(observed_alphas_picp*100)
Uncertainty_percentage=round(100-picp_percentage)
# UQ metamodel regression have metrics as follows, “rmse”, “nll”, “auucc_gain”, “picp”, “mpiw”, “r2”
metric_all=compute_regression_metrics(y_test, y_hat,y_hat_lb, y_hat_ub,option=uq_scoring_param,nll_fn=None)
metric_used=''
for k,v in metric_all.items():
metric_used=str(round(v,2))
# Determine the confidence level and recommentation to the tester
# test_data=y_test
observed_alphas_picp=round(observed_alphas_picp,2)
observed_widths_mpiw=round(observed_widths_mpiw,2)
#Calculate total uncertainty for all features
# total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage = self.totalUncertainty(self.data)
# df1=self.data
total_picp,total_mpiw,total_picp_percentage,total_Uncertainty_percentage,mpiw_lower_range,mpiw_upper_range = self.totalUncertainty(self.data,basemodel,model_params)
recommendation=""
observed_widths_mpiw = round((observed_widths_mpiw/1000000)*100)
if observed_widths_mpiw > 100:
observed_widths_mpiw = 100
output={}
if (observed_alphas_picp >= 0.90 and total_picp >= 0.75):
# GREEN text
recommendation = "Model has good confidence and MPIW score, ready to deploy."
msg='Good'
elif ((observed_alphas_picp >= 0.50 and observed_alphas_picp <= 0.90) and (total_picp >= 0.50)):
# Orange
recommendation = " Model has average confidence compare to threshold (ideal is both model confidence and MPIW should be >90%) .Model can be improved by adding more training data across all feature ranges and re-training the model."
msg = 'Average'
else:
# RED text
recommendation = "Model has less confidence compare to threshold (ideal is both model confidence and MPIW should be >90%), need to be add more input data across all feature ranges and retrain base model, also try with different regression algorithms/ensembling."
msg = 'Bad'
#Build uq json info dict
output['Model_total_confidence']=(str(total_picp_percentage)+'%')
output['Model_total_Uncertainty']=(str(total_Uncertainty_percentage)+'%')
output['Selected_feature_confidence']=(str(picp_percentage)+'%')
output['Selected_feature_Uncertainty']=(str(Uncertainty_percentage)+'%')
output['Prediction_Interval_Coverage_Probability']=observed_alphas_picp
output['Mean_Prediction_Interval_Width']=str(observed_widths_mpiw)+'%'
output['Desirable_MPIW_range']=(str(round(mpiw_lower_range))+str(' - ')+str(round(mpiw_upper_range)))
output['Recommendation']=str(recommendation)
output['Metric_used']=uq_scoring_param
output['Metric_value']=metric_used
output['Problem']= 'Regression'
output['recommend']= 'recommend'
output['msg'] = msg
with open(str(self.Deployment)+"/uq_reg_log.json", "w") as f:
json.dump(output, f)
#To get best and medium UQ range of values from total predict interval
y_hat_m=y_hat.tolist()
y_hat_lb=y_hat_lb.tolist()
upper_bound=y_hat_ub.tolist()
y_hat_ub=y_hat_ub.tolist()
for x in y_hat_lb:
y_hat_ub.append(x)
total_pi=y_hat_ub
medium_UQ_range = y_hat_ub
best_UQ_range= y_hat.tolist()
ymean_upper=[]
ymean_lower=[]
y_hat_m=y_hat.tolist()
for i in y_hat_m:
y_hat_m_range= (i*20/100)
x=i+y_hat_m_range
y=i-y_hat_m_range
ymean_upper.append(x)
ymean_lower.append(y)
min_best_uq_dist=round(min(best_UQ_range))
max_best_uq_dist=round(max(best_UQ_range))
# initializing ranges
list_medium=list(filter(lambda x:not(min_best_uq_dist<=x<=max_best_uq_dist), total_pi))
list_best = y_hat_m
'''
print(X_test)
print(X_test)
X_test = np.squeeze(X_test)
print(x_feature)
'''
uq_dict = pd.DataFrame(X_test)
#print(uq_dict)
uq_dict['Observed'] = y_test
uq_dict['Best_values'] = y_hat_m
uq_dict['Best__upper'] = ymean_upper
uq_dict['Best__lower'] = ymean_lower
uq_dict['Total_Low_PI'] = y_hat_lb
uq_dict['Total_Upper_PI'] = upper_bound
'''
uq_dict = {x_feature:X_test,'Observed':y_test,'Best_values': y_hat_m,
'Best__upper':ymean_upper,
'Best__lower':ymean_lower,
'Total_Low_PI': y_hat_lb,
'Total_Upper_PI': upper_bound,
}'''
uq_pred_df = pd.DataFrame(data=uq_dict)
uq_pred_df_sorted = uq_pred_df.sort_values(by='Best_values')
uq_pred_df_sorted.to_csv(str(self.Deployment)+"/uq_pred_df.csv",index = False)
csv_path=str(self.Deployment)+"/uq_pred_df.csv"
df=pd.read_csv(csv_path)
# self.log.info('uqMain() returns: observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all.\n.')
# confidenceplot = self.aion_confidence_plot(df)
# output['Confidence Plot']= confidenceplot
uq_jsonobject = json.dumps(output)
print("UQ regression problem training completed...\n")
return observed_alphas_picp,observed_widths_mpiw,list_medium,list_best,metric_all,uq_jsonobject
except Exception as inst:
print('-------',inst)
exc = {"status":"FAIL","message":str(inst).strip('"')}
out_exc = json.dumps(exc)
|
regression_metrics.py | import numpy as np
from scipy.stats import norm
from sklearn.metrics import mean_squared_error, r2_score
from ..utils.misc import fitted_ucc_w_nullref
def picp(y_true, y_lower, y_upper):
"""
Prediction Interval Coverage Probability (PICP). Computes the fraction of samples for which the grounds truth lies
within predicted interval. Measures the prediction interval calibration for regression.
Args:
y_true: Ground truth
y_lower: predicted lower bound
y_upper: predicted upper bound
Returns:
float: the fraction of samples for which the grounds truth lies within predicted interval.
"""
satisfies_upper_bound = y_true <= y_upper
satisfies_lower_bound = y_true >= y_lower
return np.mean(satisfies_upper_bound * satisfies_lower_bound)
def mpiw(y_lower, y_upper):
"""
Mean Prediction Interval Width (MPIW). Computes the average width of the the prediction intervals. Measures the
sharpness of intervals.
Args:
y_lower: predicted lower bound
y_upper: predicted upper bound
Returns:
float: the average width the prediction interval across samples.
"""
return np.mean(np.abs(y_lower - y_upper))
def auucc_gain(y_true, y_mean, y_lower, y_upper):
""" Computes the Area Under the Uncertainty Characteristics Curve (AUUCC) gain wrt to a null reference
with constant band.
Args:
y_true: Ground truth
y_mean: predicted mean
y_lower: predicted lower bound
y_upper: predicted upper bound
Returns:
float: AUUCC gain
"""
u = fitted_ucc_w_nullref(y_true, y_mean, y_lower, y_upper)
auucc = u.get_AUUCC()
assert(isinstance(auucc, list) and len(auucc) == 2), "Failed to calculate auucc gain"
assert (not np.isclose(auucc[1], 0.)), "Failed to calculate auucc gain"
auucc_gain = (auucc[1]-auucc[0])/auucc[0]
return auucc_gain
def negative_log_likelihood_Gaussian(y_true, y_mean, y_lower, y_upper):
""" Computes Gaussian negative_log_likelihood assuming symmetric band around the mean.
Args:
y_true: Ground truth
y_mean: predicted mean
y_lower: predicted lower bound
y_upper: predicted upper bound
Returns:
float: nll
"""
y_std = (y_upper - y_lower) / 4.0
nll = np.mean(-norm.logpdf(y_true.squeeze(), loc=y_mean.squeeze(), scale=y_std.squeeze()))
return nll
def compute_regression_metrics(y_true, y_mean, y_lower, y_upper, option="all", nll_fn=None):
"""
Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes
the ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"] metrics.
Args:
y_true: Ground truth
y_mean: predicted mean
y_lower: predicted lower bound
y_upper: predicted upper bound
option: string or list of string contained the name of the metrics to be computed.
nll_fn: function that evaluates NLL, if None, then computes Gaussian NLL using y_mean and y_lower.
Returns:
dict: dictionary containing the computed metrics.
"""
assert y_true.shape == y_mean.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_mean.shape)
assert y_true.shape == y_lower.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_lower.shape)
assert y_true.shape == y_upper.shape, "y_true shape: {}, y_mean shape: {}".format(y_true.shape, y_upper.shape)
results = {}
if not isinstance(option, list):
if option == "all":
option_list = ["rmse", "nll", "auucc_gain", "picp", "mpiw", "r2"]
else:
option_list = [option]
if "rmse" in option_list:
results["rmse"] = mean_squared_error(y_true, y_mean, squared=False)
if "nll" in option_list:
if nll_fn is None:
nll = negative_log_likelihood_Gaussian(y_true, y_mean, y_lower, y_upper)
results["nll"] = nll
else:
results["nll"] = np.mean(nll_fn(y_true))
if "auucc_gain" in option_list:
gain = auucc_gain(y_true, y_mean, y_lower, y_upper)
results["auucc_gain"] = gain
if "picp" in option_list:
results["picp"] = picp(y_true, y_lower, y_upper)
if "mpiw" in option_list:
results["mpiw"] = mpiw(y_lower, y_upper)
if "r2" in option_list:
results["r2"] = r2_score(y_true, y_mean)
return results
def _check_not_tuple_of_2_elements(obj, obj_name='obj'):
"""Check object is not tuple or does not have 2 elements."""
if not isinstance(obj, tuple) or len(obj) != 2:
raise TypeError('%s must be a tuple of 2 elements.' % obj_name)
def plot_uncertainty_distribution(dist, show_quantile_dots=False, qd_sample=20, qd_bins=7,
ax=None, figsize=None, dpi=None,
title='Predicted Distribution', xlims=None, xlabel='Prediction', ylabel='Density', **kwargs):
"""
Plot the uncertainty distribution for a single distribution.
Args:
dist: scipy.stats._continuous_distns.
A scipy distribution object.
show_quantile_dots: boolean.
Whether to show quantil dots on top of the density plot.
qd_sample: int.
Number of dots for the quantile dot plot.
qd_bins: int.
Number of bins for the quantile dot plot.
ax: matplotlib.axes.Axes or None, optional (default=None).
Target axes instance. If None, new figure and axes will be created.
figsize: tuple of 2 elements or None, optional (default=None).
Figure size.
dpi : int or None, optional (default=None).
Resolution of the figure.
title : string or None, optional (default=Prediction Distribution)
Axes title.
If None, title is disabled.
xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``.
xlabel : string or None, optional (default=Prediction)
X-axis title label.
If None, title is disabled.
ylabel : string or None, optional (default=Density)
Y-axis title label.
If None, title is disabled.
Returns:
matplotlib.axes.Axes: ax : The plot with prediction distribution.
"""
import matplotlib.pyplot as plt
if ax is None:
if figsize is not None:
_check_not_tuple_of_2_elements(figsize, 'figsize')
_, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)
x = np.linspace(dist.ppf(0.01), dist.ppf(0.99), 100)
ax.plot(x, dist.pdf(x), **kwargs)
if show_quantile_dots:
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
import matplotlib.ticker as ticker
data = dist.rvs(size=10000)
p_less_than_x = np.linspace(1 / qd_sample / 2, 1 - (1 / qd_sample / 2), qd_sample)
x_ = np.percentile(data, p_less_than_x * 100) # Inverce CDF (ppf)
# Create bins
hist = np.histogram(x_, bins=qd_bins)
bins, edges = hist
radius = (edges[1] - edges[0]) / 2
ax2 = ax.twinx()
patches = []
max_y = 0
for i in range(qd_bins):
x_bin = (edges[i + 1] + edges[i]) / 2
y_bins = [(i + 1) * (radius * 2) for i in range(bins[i])]
max_y = max(y_bins) if max(y_bins) > max_y else max_y
for _, y_bin in enumerate(y_bins):
circle = Circle((x_bin, y_bin), radius)
patches.append(circle)
p = PatchCollection(patches, alpha=0.4)
ax2.add_collection(p)
# Axis tweek
y_scale = (max_y + radius) / max(dist.pdf(x))
ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x_ / y_scale))
ax2.yaxis.set_major_formatter(ticks_y)
ax2.set_yticklabels([])
if xlims is not None:
ax2.set_xlim(left=xlims[0], right=xlims[1])
else:
ax2.set_xlim([min(x_) - radius, max(x) + radius])
ax2.set_ylim([0, max_y + radius])
ax2.set_aspect(1)
if title is not None:
ax.set_title(title)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
return ax
def plot_picp_by_feature(x_test, y_test, y_test_pred_lower_total, y_test_pred_upper_total, num_bins=10,
ax=None, figsize=None, dpi=None, xlims=None, ylims=None, xscale="linear",
title=None, xlabel=None, ylabel=None):
"""
Plot how prediction uncertainty varies across the entire range of a feature.
Args:
x_test: One dimensional ndarray.
Feature column of the test dataset.
y_test: One dimensional ndarray.
Ground truth label of the test dataset.
y_test_pred_lower_total: One dimensional ndarray.
Lower bound of the total uncertainty range.
y_test_pred_upper_total: One dimensional ndarray.
Upper bound of the total uncertainty range.
num_bins: int.
Number of bins used to discritize x_test into equal-sample-sized bins.
ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created.
figsize: tuple of 2 elements or None, optional (default=None). Figure size.
dpi : int or None, optional (default=None). Resolution of the figure.
xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``.
ylims: tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.ylim()``.
xscale: Passed to ``ax.set_xscale()``.
title : string or None, optional
Axes title.
If None, title is disabled.
xlabel : string or None, optional
X-axis title label.
If None, title is disabled.
ylabel : string or None, optional
Y-axis title label.
If None, title is disabled.
Returns:
matplotlib.axes.Axes: ax : The plot with PICP scores binned by a feature.
"""
from scipy.stats.mstats import mquantiles
import matplotlib.pyplot as plt
if ax is None:
if figsize is not None:
_check_not_tuple_of_2_elements(figsize, 'figsize')
_, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)
x_uniques_sorted = np.sort(np.unique(x_test))
num_unique = len(x_uniques_sorted)
sample_bin_ids = np.searchsorted(x_uniques_sorted, x_test)
if len(x_uniques_sorted) > 10: # bin the values
q_bins = mquantiles(x_test, np.histogram_bin_edges([], bins=num_bins-1, range=(0.0, 1.0))[1:])
q_sample_bin_ids = np.digitize(x_test, q_bins)
picps = np.array([picp(y_test[q_sample_bin_ids==bin], y_test_pred_lower_total[q_sample_bin_ids==bin],
y_test_pred_upper_total[q_sample_bin_ids==bin]) for bin in range(num_bins)])
unique_sample_bin_ids = np.digitize(x_uniques_sorted, q_bins)
picp_replicated = [len(x_uniques_sorted[unique_sample_bin_ids == bin]) * [picps[bin]] for bin in range(num_bins)]
picp_replicated = np.array([item for sublist in picp_replicated for item in sublist])
else:
picps = np.array([picp(y_test[sample_bin_ids == bin], y_test_pred_lower_total[sample_bin_ids == bin],
y_test_pred_upper_total[sample_bin_ids == bin]) for bin in range(num_unique)])
picp_replicated = picps
ax.plot(x_uniques_sorted, picp_replicated, label='PICP')
ax.axhline(0.95, linestyle='--', label='95%')
ax.set_ylabel('PICP')
ax.legend(loc='best')
if title is None:
title = 'Test data overall PICP: {:.2f} MPIW: {:.2f}'.format(
picp(y_test,
y_test_pred_lower_total,
y_test_pred_upper_total),
mpiw(y_test_pred_lower_total,
y_test_pred_upper_total))
if xlims is not None:
ax.set_xlim(left=xlims[0], right=xlims[1])
if ylims is not None:
ax.set_ylim(bottom=ylims[0], top=ylims[1])
ax.set_title(title)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if xscale is not None:
ax.set_xscale(xscale)
return ax
def plot_uncertainty_by_feature(x_test, y_test_pred_mean, y_test_pred_lower_total, y_test_pred_upper_total,
y_test_pred_lower_epistemic=None, y_test_pred_upper_epistemic=None,
ax=None, figsize=None, dpi=None, xlims=None, xscale="linear",
title=None, xlabel=None, ylabel=None):
"""
Plot how prediction uncertainty varies across the entire range of a feature.
Args:
x_test: one dimensional ndarray.
Feature column of the test dataset.
y_test_pred_mean: One dimensional ndarray.
Model prediction for the test dataset.
y_test_pred_lower_total: One dimensional ndarray.
Lower bound of the total uncertainty range.
y_test_pred_upper_total: One dimensional ndarray.
Upper bound of the total uncertainty range.
y_test_pred_lower_epistemic: One dimensional ndarray.
Lower bound of the epistemic uncertainty range.
y_test_pred_upper_epistemic: One dimensional ndarray.
Upper bound of the epistemic uncertainty range.
ax: matplotlib.axes.Axes or None, optional (default=None). Target axes instance. If None, new figure and axes will be created.
figsize: tuple of 2 elements or None, optional (default=None). Figure size.
dpi : int or None, optional (default=None). Resolution of the figure.
xlims : tuple of 2 elements or None, optional (default=None). Tuple passed to ``ax.xlim()``.
xscale: Passed to ``ax.set_xscale()``.
title : string or None, optional
Axes title.
If None, title is disabled.
xlabel : string or None, optional
X-axis title label.
If None, title is disabled.
ylabel : string or None, optional
Y-axis title label.
If None, title is disabled.
Returns:
matplotlib.axes.Axes: ax : The plot with model's uncertainty binned by a feature.
"""
import matplotlib.pyplot as plt
if ax is None:
if figsize is not None:
_check_not_tuple_of_2_elements(figsize, 'figsize')
_, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)
x_uniques_sorted = np.sort(np.unique(x_test))
y_pred_var = ((y_test_pred_upper_total - y_test_pred_lower_total) / 4.0)**2
agg_y_std = np.array([np.sqrt(np.mean(y_pred_var[x_test==x])) for x in x_uniques_sorted])
agg_y_mean = np.array([np.mean(y_test_pred_mean[x_test==x]) for x in x_uniques_sorted])
ax.plot(x_uniques_sorted, agg_y_mean, '-b', lw=2, label='mean prediction')
ax.fill_between(x_uniques_sorted,
agg_y_mean - 2.0 * agg_y_std,
agg_y_mean + 2.0 * agg_y_std,
alpha=0.3, label='total uncertainty')
if y_test_pred_lower_epistemic is not None:
y_pred_var_epistemic = ((y_test_pred_upper_epistemic - y_test_pred_lower_epistemic) / 4.0)**2
agg_y_std_epistemic = np.array([np.sqrt(np.mean(y_pred_var_epistemic[x_test==x])) for x in x_uniques_sorted])
ax.fill_between(x_uniques_sorted,
agg_y_mean - 2.0 * agg_y_std_epistemic,
agg_y_mean + 2.0 * agg_y_std_epistemic,
alpha=0.3, label='model uncertainty')
ax.legend(loc='best')
if xlims is not None:
ax.set_xlim(left=xlims[0], right=xlims[1])
if title is not None:
ax.set_title(title)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if xscale is not None:
ax.set_xscale(xscale)
return ax
|
classification_metrics.py | import numpy as np
import pandas as pd
from scipy.stats import entropy
from sklearn.metrics import roc_auc_score, log_loss, accuracy_score
def entropy_based_uncertainty_decomposition(y_prob_samples):
""" Entropy based decomposition [2]_ of predictive uncertainty into aleatoric and epistemic components.
References:
.. [2] Depeweg, S., Hernandez-Lobato, J. M., Doshi-Velez, F., & Udluft, S. (2018, July). Decomposition of
uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In International Conference
on Machine Learning (pp. 1184-1193). PMLR.
Args:
y_prob_samples: list of array-like of shape (n_samples, n_classes) containing class prediction probabilities
corresponding to samples from the model posterior.
Returns:
tuple:
- total_uncertainty: entropy of the predictive distribution.
- aleatoric_uncertainty: aleatoric component of the total_uncertainty.
- epistemic_uncertainty: epistemic component of the total_uncertainty.
"""
y_preds_samples_stacked = np.stack(y_prob_samples)
preds_mean = np.mean(y_preds_samples_stacked, 0)
total_uncertainty = entropy(preds_mean, axis=1)
aleatoric_uncertainty = np.mean(
np.concatenate([entropy(y_pred, axis=1).reshape(-1, 1) for y_pred in y_prob_samples], axis=1),
axis=1)
epistemic_uncertainty = total_uncertainty - aleatoric_uncertainty
return total_uncertainty, aleatoric_uncertainty, epistemic_uncertainty
def multiclass_brier_score(y_true, y_prob):
"""Brier score for multi-class.
Args:
y_true: array-like of shape (n_samples,)
ground truth labels.
y_prob: array-like of shape (n_samples, n_classes).
Probability scores from the base model.
Returns:
float: Brier score.
"""
assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)"
y_target = np.zeros_like(y_prob)
y_target[:, y_true] = 1.0
return np.mean(np.sum((y_target - y_prob) ** 2, axis=1))
def area_under_risk_rejection_rate_curve(y_true, y_prob, y_pred=None, selection_scores=None, risk_func=accuracy_score,
attributes=None, num_bins=10, subgroup_ids=None,
return_counts=False):
""" Computes risk vs rejection rate curve and the area under this curve. Similar to risk-coverage curves [3]_ where
coverage instead of rejection rate is used.
References:
.. [3] Franc, Vojtech, and Daniel Prusa. "On discriminative learning of prediction uncertainty."
In International Conference on Machine Learning, pp. 1963-1971. 2019.
Args:
y_true: array-like of shape (n_samples,)
ground truth labels.
y_prob: array-like of shape (n_samples, n_classes).
Probability scores from the base model.
y_pred: array-like of shape (n_samples,)
predicted labels.
selection_scores: scores corresponding to certainty in the predicted labels.
risk_func: risk function under consideration.
attributes: (optional) if risk function is a fairness metric also pass the protected attribute name.
num_bins: number of bins.
subgroup_ids: (optional) selectively compute risk on a subgroup of the samples specified by subgroup_ids.
return_counts: set to True to return counts also.
Returns:
float or tuple:
- aurrrc (float): area under risk rejection rate curve.
- rejection_rates (list): rejection rates for each bin (returned only if return_counts is True).
- selection_thresholds (list): selection threshold for each bin (returned only if return_counts is True).
- risks (list): risk in each bin (returned only if return_counts is True).
"""
if selection_scores is None:
assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)"
selection_scores = y_prob[np.arange(y_prob.shape[0]), np.argmax(y_prob, axis=1)]
if y_pred is None:
assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)"
y_pred = np.argmax(y_prob, axis=1)
order = np.argsort(selection_scores)[::-1]
rejection_rates = []
selection_thresholds = []
risks = []
for bin_id in range(num_bins):
samples_in_bin = len(y_true) // num_bins
selection_threshold = selection_scores[order[samples_in_bin * (bin_id+1)-1]]
selection_thresholds.append(selection_threshold)
ids = selection_scores >= selection_threshold
if sum(ids) > 0:
if attributes is None:
if isinstance(y_true, pd.Series):
y_true_numpy = y_true.values
else:
y_true_numpy = y_true
if subgroup_ids is None:
risk_value = 1.0 - risk_func(y_true_numpy[ids], y_pred[ids])
else:
if sum(subgroup_ids & ids) > 0:
risk_value = 1.0 - risk_func(y_true_numpy[subgroup_ids & ids], y_pred[subgroup_ids & ids])
else:
risk_value = 0.0
else:
risk_value = risk_func(y_true.iloc[ids], y_pred[ids], prot_attr=attributes)
else:
risk_value = 0.0
risks.append(risk_value)
rejection_rates.append(1.0 - 1.0 * sum(ids) / len(y_true))
aurrrc = np.nanmean(risks)
if not return_counts:
return aurrrc
else:
return aurrrc, rejection_rates, selection_thresholds, risks
def expected_calibration_error(y_true, y_prob, y_pred=None, num_bins=10, return_counts=False):
""" Computes the reliability curve and the expected calibration error [1]_ .
References:
.. [1] Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger; Proceedings of the 34th International Conference
on Machine Learning, PMLR 70:1321-1330, 2017.
Args:
y_true: array-like of shape (n_samples,)
ground truth labels.
y_prob: array-like of shape (n_samples, n_classes).
Probability scores from the base model.
y_pred: array-like of shape (n_samples,)
predicted labels.
num_bins: number of bins.
return_counts: set to True to return counts also.
Returns:
float or tuple:
- ece (float): expected calibration error.
- confidences_in_bins: average confidence in each bin (returned only if return_counts is True).
- accuracies_in_bins: accuracy in each bin (returned only if return_counts is True).
- frac_samples_in_bins: fraction of samples in each bin (returned only if return_counts is True).
"""
assert len(y_prob.shape) > 1, "y_prob should be array-like of shape (n_samples, n_classes)"
num_samples, num_classes = y_prob.shape
top_scores = np.max(y_prob, axis=1)
if y_pred is None:
y_pred = np.argmax(y_prob, axis=1)
if num_classes == 2:
bins_edges = np.histogram_bin_edges([], bins=num_bins, range=(0.5, 1.0))
else:
bins_edges = np.histogram_bin_edges([], bins=num_bins, range=(0.0, 1.0))
non_boundary_bin_edges = bins_edges[1:-1]
bin_centers = (bins_edges[1:] + bins_edges[:-1])/2
sample_bin_ids = np.digitize(top_scores, non_boundary_bin_edges)
num_samples_in_bins = np.zeros(num_bins)
accuracies_in_bins = np.zeros(num_bins)
confidences_in_bins = np.zeros(num_bins)
for bin in range(num_bins):
num_samples_in_bins[bin] = len(y_pred[sample_bin_ids == bin])
if num_samples_in_bins[bin] > 0:
accuracies_in_bins[bin] = np.sum(y_true[sample_bin_ids == bin] == y_pred[sample_bin_ids == bin]) / num_samples_in_bins[bin]
confidences_in_bins[bin] = np.sum(top_scores[sample_bin_ids == bin]) / num_samples_in_bins[bin]
ece = np.sum(
num_samples_in_bins * np.abs(accuracies_in_bins - confidences_in_bins) / num_samples
)
frac_samples_in_bins = num_samples_in_bins / num_samples
if not return_counts:
return ece
else:
return ece, confidences_in_bins, accuracies_in_bins, frac_samples_in_bins, bin_centers
def compute_classification_metrics(y_true, y_prob, option='all'):
"""
Computes the metrics specified in the option which can be string or a list of strings. Default option `all` computes
the [aurrrc, ece, auroc, nll, brier, accuracy] metrics.
Args:
y_true: array-like of shape (n_samples,)
ground truth labels.
y_prob: array-like of shape (n_samples, n_classes).
Probability scores from the base model.
option: string or list of string contained the name of the metrics to be computed.
Returns:
dict: a dictionary containing the computed metrics.
"""
results = {}
if not isinstance(option, list):
if option == "all":
option_list = ["aurrrc", "ece", "auroc", "nll", "brier", "accuracy"]
else:
option_list = [option]
if "aurrrc" in option_list:
results["aurrrc"] = area_under_risk_rejection_rate_curve(y_true=y_true, y_prob=y_prob)
if "ece" in option_list:
results["ece"] = expected_calibration_error(y_true=y_true, y_prob=y_prob)
if "auroc" in option_list:
results["auroc"], _ = roc_auc_score(y_true=y_true, y_score=y_prob)
if "nll" in option_list:
results["nll"] = log_loss(y_true=y_true, y_pred=np.argmax(y_prob, axis=1))
if "brier" in option_list:
results["brier"] = multiclass_brier_score(y_true=y_true, y_prob=y_prob)
if "accuracy" in option_list:
results["accuracy"] = accuracy_score(y_true=y_true, y_pred=np.argmax(y_prob, axis=1))
return results
def plot_reliability_diagram(y_true, y_prob, y_pred, plot_label=[""], num_bins=10):
"""
Plots the reliability diagram showing the calibration error for different confidence scores. Multiple curves
can be plot by passing data as lists.
Args:
y_true: array-like or or a list of array-like of shape (n_samples,)
ground truth labels.
y_prob: array-like or or a list of array-like of shape (n_samples, n_classes).
Probability scores from the base model.
y_pred: array-like or or a list of array-like of shape (n_samples,)
predicted labels.
plot_label: (optional) list of names identifying each curve.
num_bins: number of bins.
Returns:
tuple:
- ece_list: ece: list containing expected calibration error for each curve.
- accuracies_in_bins_list: list containing binned average accuracies for each curve.
- frac_samples_in_bins_list: list containing binned sample frequencies for each curve.
- confidences_in_bins_list: list containing binned average confidence for each curve.
"""
import matplotlib.pyplot as plt
if not isinstance(y_true, list):
y_true, y_prob, y_pred = [y_true], [y_prob], [y_pred]
if len(plot_label) != len(y_true):
raise ValueError('y_true and plot_label should be of same length.')
ece_list = []
accuracies_in_bins_list = []
frac_samples_in_bins_list = []
confidences_in_bins_list = []
for idx in range(len(plot_label)):
ece, confidences_in_bins, accuracies_in_bins, frac_samples_in_bins, bins = expected_calibration_error(y_true[idx],
y_prob[idx],
y_pred[idx],
num_bins=num_bins,
return_counts=True)
ece_list.append(ece)
accuracies_in_bins_list.append(accuracies_in_bins)
frac_samples_in_bins_list.append(frac_samples_in_bins)
confidences_in_bins_list.append(confidences_in_bins)
fig = plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
for idx in range(len(plot_label)):
plt.plot(bins, frac_samples_in_bins_list[idx], 'o-', label=plot_label[idx])
plt.title("Confidence Histogram")
plt.xlabel("Confidence")
plt.ylabel("Fraction of Samples")
plt.grid()
plt.ylim([0.0, 1.0])
plt.legend()
plt.subplot(1, 2, 2)
for idx in range(len(plot_label)):
plt.plot(bins, accuracies_in_bins_list[idx], 'o-',
label="{} ECE = {:.2f}".format(plot_label[idx], ece_list[idx]))
plt.plot(np.linspace(0, 1, 50), np.linspace(0, 1, 50), 'b.', label="Perfect Calibration")
plt.title("Reliability Plot")
plt.xlabel("Confidence")
plt.ylabel("Accuracy")
plt.grid()
plt.legend()
plt.show()
return ece_list, accuracies_in_bins_list, frac_samples_in_bins_list, confidences_in_bins_list
def plot_risk_vs_rejection_rate(y_true, y_prob, y_pred, selection_scores=None, plot_label=[""], risk_func=None,
attributes=None, num_bins=10, subgroup_ids=None):
"""
Plots the risk vs rejection rate curve showing the risk for different rejection rates. Multiple curves
can be plot by passing data as lists.
Args:
y_true: array-like or or a list of array-like of shape (n_samples,)
ground truth labels.
y_prob: array-like or or a list of array-like of shape (n_samples, n_classes).
Probability scores from the base model.
y_pred: array-like or or a list of array-like of shape (n_samples,)
predicted labels.
selection_scores: ndarray or a list of ndarray containing scores corresponding to certainty in the predicted labels.
risk_func: risk function under consideration.
attributes: (optional) if risk function is a fairness metric also pass the protected attribute name.
num_bins: number of bins.
subgroup_ids: (optional) ndarray or a list of ndarray containing subgroup_ids to selectively compute risk on a
subgroup of the samples specified by subgroup_ids.
Returns:
tuple:
- aurrrc_list: list containing the area under risk rejection rate curves.
- rejection_rate_list: list containing the binned rejection rates.
- selection_thresholds_list: list containing the binned selection thresholds.
- risk_list: list containing the binned risks.
"""
import matplotlib.pyplot as plt
if not isinstance(y_true, list):
y_true, y_prob, y_pred, selection_scores, subgroup_ids = [y_true], [y_prob], [y_pred], [selection_scores], [subgroup_ids]
if len(plot_label) != len(y_true):
raise ValueError('y_true and plot_label should be of same length.')
aurrrc_list = []
rejection_rate_list = []
risk_list = []
selection_thresholds_list = []
for idx in range(len(plot_label)):
aursrc, rejection_rates, selection_thresholds, risks = area_under_risk_rejection_rate_curve(
y_true[idx],
y_prob[idx],
y_pred[idx],
selection_scores=selection_scores[idx],
risk_func=risk_func,
attributes=attributes,
num_bins=num_bins,
subgroup_ids=subgroup_ids[idx],
return_counts=True
)
aurrrc_list.append(aursrc)
rejection_rate_list.append(rejection_rates)
risk_list.append(risks)
selection_thresholds_list.append(selection_thresholds)
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
for idx in range(len(plot_label)):
plt.plot(rejection_rate_list[idx], risk_list[idx], label="{} AURRRC={:.5f}".format(plot_label[idx], aurrrc_list[idx]))
plt.legend(loc="best")
plt.xlabel("Rejection Rate")
if risk_func is None:
ylabel = "Prediction Error Rate"
else:
if 'accuracy' in risk_func.__name__:
ylabel = "1.0 - " + risk_func.__name__
else:
ylabel = risk_func.__name__
plt.ylabel(ylabel)
plt.title("Risk vs Rejection Rate Plot")
plt.grid()
plt.subplot(1, 2, 2)
for idx in range(len(plot_label)):
plt.plot(selection_thresholds_list[idx], risk_list[idx], label="{}".format(plot_label[idx]))
plt.legend(loc="best")
plt.xlabel("Selection Threshold")
if risk_func is None:
ylabel = "Prediction Error Rate"
else:
if 'accuracy' in risk_func.__name__:
ylabel = "1.0 - " + risk_func.__name__
else:
ylabel = risk_func.__name__
plt.ylabel(ylabel)
plt.title("Risk vs Selection Threshold Plot")
plt.grid()
plt.show()
return aurrrc_list, rejection_rate_list, selection_thresholds_list, risk_list
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