def __init__(self, log_database, log_collection, execution_id):
self.log_database = log_database
self.log_collection = log_collection
self.execution_id = execution_id
self.mongoDBObject = MongodbOperation()
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, execution_id):
self.execution_id = execution_id
#self.goodDataPath = "Prediction_Raw_Files_Validated/Good_Raw"
self.goodDataPath = "good-raw-file-prediction-validated"
#self.logger = App_Logger()
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.log_database = "wafer_prediction_log"
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, path, execution_id):
self.execution_id = execution_id
#self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
#self.log_writer = logger.App_Logger()
self.log_database = "wafer_prediction_log"
self.log_collection = "prediction_log"
self.log_db_writer = App_LoggerDB(execution_id)
self.az_blob_mgt = AzureBlobManagement()
if path is not None:
self.pred_data_val = Prediction_Data_validation(path, execution_id)
def __init__(self, log_database, log_collection, execution_id):
#self.prediction_file='Prediction_FileFromDB/InputFile.csv'
#self.file_object=file_object
#self.logger_object=logger_object
self.log_database = log_database
self.log_collection = log_collection
self.prediction_directory = "prediction-file-from-db"
self.filename = "prediction-inputfile.csv"
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, log_database, log_collection, execution_id):
#self.file_object = file_object
#self.logger_object = logger_object
#self.model_directory='models/'
self.log_database = log_database
self.log_collection = log_collection
self.execution_id = execution_id
self.log_db_writer = App_LoggerDB(execution_id=self.execution_id)
self.model_directory = 'model'
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, path, execution_id):
self.Batch_Directory = path
self.execution_id = execution_id
self.collection_name = "strength_schema_prediction" #code added by Avnish yadav
self.database_name = "Wafer-sys" #code added by Avnish yadav
self.logger_db_writer = App_LoggerDB(
execution_id=execution_id) #code added by Avnish yadav
self.mongdb = MongodbOperation()
self.az_blob_mgt = AzureBlobManagement()
self.good_directory_path = "good-raw-file-prediction-validated"
self.bad_directory_path = "bad-raw-file-prediction-validated"
class App_LoggerDB:
def __init__(self, execution_id):
self.mongoDBObject = MongodbOperation()
self.azureBlobObject = AzureBlobManagement()
self.execution_id = execution_id
pass
def log(self, database_name, collection_name, log_message):
try:
self.now = datetime.now()
self.date = self.now.date()
self.current_time = self.now.strftime("%H:%M:%S")
log = {
'Log_updated_date': self.now,
'Log_update_time': self.current_time,
'Log_message': log_message,
'execution_id': self.execution_id
}
res = self.mongoDBObject.insertRecordInCollection(
database_name, collection_name, log)
if res > 0:
return True
else:
log = {
'Log_updated_date': [self.now],
'Log_update_time': [self.current_time],
'Log_message': [log_message],
'execution_id': self.execution_id
}
self.azureBlobObject.saveDataFrametoCSV("db-fail-log",
"log_" + self.execution_id,
pd.DataFrame(log),
mode="a+",
header=True)
return True
except Exception as e:
log = {
'Log_updated_date': [self.now],
'Log_update_time': [self.current_time],
'Log_message': [log_message],
'execution_id': self.execution_id
}
log["Log_message"][0] = log["Log_message"][0] + str(e)
self.azureBlobObject.saveDataFrametoCSV("db-fail-log",
"log_" + self.execution_id,
pd.DataFrame(log),
mode="a+",
header=True)
def predictionTest(path=None):
try:
az_blb_mgt = AzureBlobManagement()
execution_id = str(uuid.uuid4())
if path is None:
path = 'prediction-batch-files'
else:
path = path
pred_val = pred_validation(path, execution_id) # object initialization
pred_val.prediction_validation(
) # calling the prediction_validation function
pred = prediction(path, execution_id) # object initialization
# predicting for dataset present in database
path, json_predictions = pred.predictionFromModel()
prediction_location = "prediction-output-file"
file_list = "prediction-output-file"
#selecting all failed file name
return Response("Prediction File created at !!!" + str(path) +
'and few of the predictions are ' +
str(json.loads(json_predictions)))
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
def __init__(self, execution_id):
#self.file_object = file_object
#self.logger_object = logger_object
self.log_database="strength_training_log"
self.log_collection="stg-training_main_log"
self.execution_id=execution_id
self.log_db_writer=App_LoggerDB(execution_id=execution_id)
self.mongoDBObject = MongodbOperation()
self.az_blob_mgt=AzureBlobManagement()
class Data_Getter_Pred:
"""
This class shall be used for obtaining the data from the source for prediction.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, log_database, log_collection, execution_id):
#self.prediction_file='Prediction_FileFromDB/InputFile.csv'
#self.file_object=file_object
#self.logger_object=logger_object
self.log_database = log_database
self.log_collection = log_collection
self.prediction_directory = "prediction-file-from-db"
self.filename = "prediction-inputfile.csv"
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def get_data(self):
"""
Method Name: get_data
Description: This method reads the data from source.
Output: A pandas DataFrame.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the get_data method of the Data_Getter class')
print("Entered the get_data method of the Data_Getter class")
try:
#self.data= pd.read_csv(self.filename) # reading the data file
self.data = self.az_blob_mgt.readCSVFilefromDir(
self.prediction_directory, self.filename)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Data Load Successful.Exited the get_data method of the Data_Getter class'
)
return self.data
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in get_data method of the Data_Getter class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Data Load Unsuccessful.Exited the get_data method of the Data_Getter class'
)
raise Exception()
def __init__(self, path, execution_id):
self.raw_data = Prediction_Data_validation(path, execution_id)
self.dataTransform = dataTransformPredict(execution_id)
self.dBOperationMongoDB = DbOperationMongoDB(execution_id)
#self.dBOperation = dBOperation(execution_id)
self.log_database = "wafer_prediction_log"
self.log_collection = "prediction_main_log"
self.execution_id = execution_id
#self.log_writer = logger.App_Logger()
self.logDB_write = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, path, execution_id):
self.raw_data = Raw_Data_validation(path, execution_id)
self.dataTransform = dataTransform(execution_id)
self.dBOperationMongoDB = DbOperationMongoDB(execution_id)
#self.file_object = open("Training_Logs/Training_Main_Log.txt", 'a+')
self.log_database = "wafer_training_log"
self.log_collection = "training_main_log"
self.execution_id = execution_id
#self.log_writer = logger.App_Logger()
self.logDB_write = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, path, execution_id):
self.raw_data = Prediction_Data_validation(path, execution_id)
self.dataTransform = dataTransformPredict(execution_id)
#self.dBOperation = dBOperation()
#self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
#self.log_writer = logger.App_Logger()
self.dBOperationMongoDB = DbOperationMongoDB(execution_id)
self.log_database = "strength_prediction_log"
self.log_collection = "stg-prediction_main_log"
self.execution_id = execution_id
self.logDB_write = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def __init__(self, log_database, log_collection, execution_id):
#self.file_object = file_object
#self.logger_object = logger_object
self.execution_id = execution_id
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.log_database = log_database
self.log_collection = log_collection
self.az_blob_mgt = AzureBlobManagement()
self.mongoDBObject = MongodbOperation()
self.clf = RandomForestClassifier()
self.xgb = XGBClassifier(objective='binary:logistic')
def __init__(self, log_database, log_collection, execution_id):
#self.file_object = file_object
#self.logger_object = logger_object
self.execution_id = execution_id
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.log_database = log_database
self.log_collection = log_collection
self.az_blob_mgt = AzureBlobManagement()
self.mongoDBObject = MongodbOperation()
self.linearReg = LinearRegression()
self.RandomForestReg = RandomForestRegressor()
self.DecisionTreeReg = DecisionTreeRegressor()
self.XGBoostReg = XGBRegressor()
self.AdaboostReg = AdaBoostRegressor()
self.svm = SVC()
def __init__(self, execution_id):
self.mongodb=MongodbOperation()
self.az_blob_mgt=AzureBlobManagement()
self.logger_db_writer=App_LoggerDB(execution_id=execution_id)
self.good_file_path="good-raw-file-prediction-validated"
self.bad_file_path="bad-raw-file-prediction-validated"
def __init__(self, execution_id):
self.mongoDBObject = MongodbOperation()
self.azureBlobObject = AzureBlobManagement()
self.execution_id = execution_id
pass
class Prediction_Data_validation:
"""
This class shall be used for handling all the validation done on the Raw Prediction Data!!.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, path, execution_id):
self.Batch_Directory = path
self.execution_id = execution_id
self.collection_name = "strength_schema_prediction" #code added by Avnish yadav
self.database_name = "Wafer-sys" #code added by Avnish yadav
self.logger_db_writer = App_LoggerDB(
execution_id=execution_id) #code added by Avnish yadav
self.mongdb = MongodbOperation()
self.az_blob_mgt = AzureBlobManagement()
self.good_directory_path = "good-raw-file-prediction-validated"
self.bad_directory_path = "bad-raw-file-prediction-validated"
def valuesFromSchema(self):
"""
Method Name: valuesFromSchema
Description: This method extracts all the relevant information from the pre-defined "Schema" file.
Output: LengthOfDateStampInFile, LengthOfTimeStampInFile, column_names, Number of Columns
On Failure: Raise ValueError,KeyError,Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_database = "strength_prediction_log"
log_collection = "values_from_schema_validation"
try:
log_database = "wafer_prediction_log"
log_collection = "values_from_schema_validation"
df_schema_training = self.mongdb.getDataFrameofCollection(
self.database_name, self.collection_name)
dic = {}
[
dic.update({i: df_schema_training.loc[0, i]})
for i in df_schema_training.columns
]
del df_schema_training
#with open(self.schema_path, 'r') as f:
# dic = json.load(f)
# f.close()
pattern = dic['SampleFileName']
LengthOfDateStampInFile = dic['LengthOfDateStampInFile']
LengthOfTimeStampInFile = dic['LengthOfTimeStampInFile']
column_names = dic['ColName']
NumberofColumns = dic['NumberofColumns']
#file = open("Training_Logs/valuesfromSchemaValidationLog.txt", 'a+')
message = "LengthOfDateStampInFile:: %s" % LengthOfDateStampInFile + "\t" + "LengthOfTimeStampInFile:: %s" % LengthOfTimeStampInFile + "\t " + "NumberofColumns:: %s" % NumberofColumns + "\n"
self.logger_db_writer.log(log_database, log_collection, message)
#file.close()
except ValueError:
self.logger_db_writer.log(
log_database, log_collection,
"KeyError:Key value error incorrect key passed")
raise ValueError
except KeyError:
self.logger_db_writer.log(
log_database, log_collection,
"KeyError:Key value error incorrect key passed")
raise KeyError
except Exception as e:
self.logger_db_writer.log(log_database, log_collection, str(e))
raise e
return LengthOfDateStampInFile, LengthOfTimeStampInFile, column_names, NumberofColumns
def manualRegexCreation(self):
"""
Method Name: manualRegexCreation
Description: This method contains a manually defined regex based on the "FileName" given in "Schema" file.
This Regex is used to validate the filename of the prediction data.
Output: Regex pattern
On Failure: None
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
regex = "['cement_strength']+['\_'']+[\d_]+[\d]+\.csv"
return regex
def createDirectoryForGoodBadRawData(self):
"""
Method Name: createDirectoryForGoodBadRawData
Description: This method creates directories to store the Good Data and Bad Data
after validating the prediction data.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_database = "strength_prediction_log"
log_collection = "general_log"
try:
log_database = "strength_prediction_log"
log_collection = "general_log"
self.az_blob_mgt.createDir(self.good_directory_path,
is_replace=True)
self.az_blob_mgt.createDir(self.bad_directory_path,
is_replace=True)
msg = self.good_directory_path + " and " + self.bad_directory_path + " created successfully."
self.logger_db_writer.log(log_database, log_collection, msg)
except Exception as e:
msg = "Error Occured in class Prediction_Data_validation method:createDirectoryForGoodBadRawData error: Failed to create directory " + self.good_directory_path + " and " + self.bad_directory_path
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def deleteExistingGoodDataTrainingFolder(self):
"""
Method Name: deleteExistingGoodDataTrainingFolder
Description: This method deletes the directory made to store the Good Data
after loading the data in the table. Once the good files are
loaded in the DB,deleting the directory ensures space optimization.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_database = "strength_prediction_log"
log_collection = "general_log"
try:
log_database = "strength_prediction_log"
log_collection = "general_log"
self.az_blob_mgt.deleteDir(self.good_directory_path)
self.logger_db_writer.log(
log_database, log_collection,
self.good_directory_path + " deleted successfully!!")
except Exception as e:
msg = "Error Occured in class Raw_Data_validation method:deleteExistingGoodDataTrainingFolder Error occured while deleting :" + self.good_directory_path
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def deleteExistingBadDataTrainingFolder(self):
"""
Method Name: deleteExistingBadDataTrainingFolder
Description: This method deletes the directory made to store the bad Data.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_database = "strength_prediction_log"
log_collection = "general_log"
try:
log_database = "strength_prediction_log"
log_collection = "general_log"
self.az_blob_mgt.deleteDir(self.bad_directory_path)
self.logger_db_writer.log(
log_database, log_collection,
self.bad_directory_path + " deleted successfully!!")
except Exception as e:
msg = "Error Occured in class Raw_Data_validation method:deleteExistingGoodDataTrainingFolder Error occured while deleting :" + self.good_directory_path
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def moveBadFilesToArchiveBad(self):
"""
Method Name: moveBadFilesToArchiveBad
Description: This method deletes the directory made to store the Bad Data
after moving the data in an archive folder. We archive the bad
files to send them back to the client for invalid data issue.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
now = datetime.now()
date = now.date()
time = now.strftime("%H%M%S")
log_database = "strength_prediction_log"
log_collection = "general_log"
try:
log_database = "strength_prediction_log"
log_collection = "general_log"
# source = 'Training_Raw_files_validated/Bad_Raw/'
source = self.bad_directory_path
destination = "lap-" + self.execution_id
self.logger_db_writer.log(log_database, log_collection,
"Started moving bad raw data..")
for file in self.az_blob_mgt.getAllFileNameFromDirectory(source):
self.az_blob_mgt.moveFileinDir(source, destination, file)
self.logger_db_writer.log(
log_database, log_collection, "File:" + file +
" moved to directory:" + destination + " successfully.")
self.logger_db_writer.log(
log_database, log_collection,
"All bad raw file moved to directory:" + destination)
self.az_blob_mgt.deleteDir(source)
self.logger_db_writer.log(log_database, log_collection,
"Deleting bad raw directory:" + source)
except Exception as e:
self.logger_db_writer.log(
log_database, log_collection,
"class Raw_Data_validation method:moveBadFilesToArchiveBad Error while moving bad files to archive:"
+ str(e))
raise e
def validationFileNameRaw(self, regex, LengthOfDateStampInFile,
LengthOfTimeStampInFile):
"""
Method Name: validationFileNameRaw
Description: This function validates the name of the prediction csv file as per given name in the schema!
Regex pattern is used to do the validation.If name format do not match the file is moved
to Bad Raw Data folder else in Good raw data.
Output: None
On Failure: Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
# delete the directories for good and bad data in case last run was unsuccessful and folders were not deleted.
self.createDirectoryForGoodBadRawData()
onlyfiles = self.az_blob_mgt.getAllFileNameFromDirectory(
self.Batch_Directory)
try:
log_database = "strength_prediction_log"
log_collection = "name_validation_log"
for filename in onlyfiles:
if (re.match(regex, filename)):
splitAtDot = re.split('.csv', filename)
splitAtDot = (re.split('_', splitAtDot[0]))
if len(splitAtDot[2]) == LengthOfDateStampInFile:
if len(splitAtDot[3]) == LengthOfTimeStampInFile:
self.az_blob_mgt.CopyFileinDir(
self.Batch_Directory, self.good_directory_path,
filename)
self.logger_db_writer.log(
log_database, log_collection,
"Valid File name!! File moved to " +
self.good_directory_path + filename)
else:
self.az_blob_mgt.CopyFileinDir(
self.Batch_Directory, self.bad_directory_path,
filename)
msg = "Invalid File Name !! File moved to " + self.bad_directory_path + filename
self.logger_db_writer.log(log_database,
log_collection, msg)
else:
self.az_blob_mgt.CopyFileinDir(self.Batch_Directory,
self.bad_directory_path,
filename)
msg = "Invalid File Name !! File moved to " + self.bad_directory_path + filename
self.logger_db_writer.log(log_database, log_collection,
msg)
else:
self.az_blob_mgt.CopyFileinDir(self.Batch_Directory,
self.bad_directory_path,
filename)
msg = "Invalid File Name !! File moved to " + self.bad_directory_path + filename
self.logger_db_writer.log(log_database, log_collection,
msg)
except Exception as e:
msg = "Error occured while validating FileName " + str(e)
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def validateColumnLength(self, NumberofColumns):
"""
Method Name: validateColumnLength
Description: This function validates the number of columns in the csv files.
It is should be same as given in the schema file.
If not same file is not suitable for processing and thus is moved to Bad Raw Data folder.
If the column number matches, file is kept in Good Raw Data for processing.
The csv file is missing the first column name, this function changes the missing name to "Wafer".
Output: None
On Failure: Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
try:
log_database = "strength_prediction_log"
log_collection = "column_validation_log"
self.logger_db_writer.log(log_database, log_collection,
"Column length validation Started!!")
#for file in listdir('Prediction_Raw_Files_Validated/Good_Raw/'):
for file in self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_directory_path):
#csv = pd.read_csv("Prediction_Raw_Files_Validated/Good_Raw/" + file)
csv = self.az_blob_mgt.readCSVFilefromDir(
self.good_directory_path, file)
print(csv.shape)
if csv.shape[1] == NumberofColumns:
#csv.rename(columns={"Unnamed: 0": "Wafer"}, inplace=True)
print(csv)
#csv.to_csv("Prediction_Raw_Files_Validated/Good_Raw/" + file, index=None, header=True)
self.az_blob_mgt.saveDataFrametoCSV(
self.good_directory_path,
file,
csv,
index=None,
header=True)
else:
self.az_blob_mgt.moveFileinDir(self.good_directory_path,
self.bad_directory_path,
file)
self.logger_db_writer.log(
log_database, log_collection,
"Invalid Column Length for the file!! "
"File moved to Bad Raw Folder :: %s" % file)
self.logger_db_writer.log(log_database, log_collection,
"Column Length Validation Completed!!")
except Exception as e:
self.logger_db_writer.log(log_database, log_collection,
'Error Occured::' + str(e))
raise e
def deletePredictionFile(self):
try:
log_database = "strenth_prediction_log"
log_collection = "general_log"
directory = "prediction-file"
filename = "Prediction.csv"
if directory in self.az_blob_mgt.dir_list:
filenames = self.az_blob_mgt.getAllFileNameFromDirectory(
directory_name=directory)
if filename in filenames:
self.az_blob_mgt.deleteFilefromDir(
directory_name=directory, filename=filename)
self.logger_db_writer.log(
log_database, log_collection, filename +
" is deleted from dir:" + directory + " successfully")
except Exception as e:
self.logger_db_writer.log(
log_database, log_collection,
"Error occure while deleting prediction file from prediction-file directory"
+ str(e))
raise e
def validateMissingValuesInWholeColumn(self):
"""
Method Name: validateMissingValuesInWholeColumn
Description: This function validates if any column in the csv file has all values missing.
If all the values are missing, the file is not suitable for processing.
SUch files are moved to bad raw data.
Output: None
On Failure: Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
try:
log_database = "strength_prediction_log"
log_collection = "missing_values_in_column"
#f = open("Prediction_Logs/missingValuesInColumn.txt", 'a+')
#self.logger.log(f, "Missing Values Validation Started!!")
self.logger_db_writer.log(log_database, log_collection,
"Missing Values Validation Started!!")
#for file in listdir('Prediction_Raw_Files_Validated/Good_Raw/'):
for file in self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_directory_path):
#csv = pd.read_csv("Prediction_Raw_Files_Validated/Good_Raw/" + file)
csv = self.az_blob_mgt.readCSVFilefromDir(
self.good_directory_path, file)
print(csv)
count = 0
for columns in csv:
if (len(csv[columns]) - csv[columns].count()) == len(
csv[columns]):
count += 1
#shutil.move("Prediction_Raw_Files_Validated/Good_Raw/" + file,
# "Prediction_Raw_Files_Validated/Bad_Raw")
self.az_blob_mgt.moveFileinDir(
self.good_directory_path, self.bad_directory_path,
file)
#self.logger.log(f,"Invalid Column Length for the file!! File moved to Bad Raw Folder :: %s" % file)
self.logger_db_writer.log(
log_database, log_collection,
"Invalid Column Length for the file!! File moved to Bad Raw Folder :: %s"
% file)
break
if count == 0:
csv.rename(columns={"Unnamed: 0": "Wafer"}, inplace=True)
print("column unnamed may not be present")
self.az_blob_mgt.saveDataFrametoCSV(
self.good_directory_path,
file,
csv,
index=None,
header=True)
#csv.to_csv("Prediction_Raw_Files_Validated/Good_Raw/" + file, index=None, header=True)
except Exception as e:
self.logger_db_writer.log(log_database, log_collection,
"Error occured:" + str(e))
raise e
class dataTransform:
"""
This class shall be used for transforming the Good Raw Training Data before loading it in Database!!.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, execution_id):
self.goodDataPath = "good-raw-file-train-validated"
self.execution_id = execution_id
self.logger_db_writer = App_LoggerDB(execution_id)
self.az_blob_mgt = AzureBlobManagement()
#self.goodDataPath = "Training_Raw_files_validated/Good_Raw"
#self.logger = App_Logger()
def replaceMissingWithNull(self):
"""
Method Name: replaceMissingWithNull
Description: This method replaces the missing values in columns with "NULL" to
store in the table. We are using substring in the first column to
keep only "Integer" data for ease up the loading.
This column is anyways going to be removed during training.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_collection = "data_transform_log"
log_database = "wafer_training_log"
#log_file = open("Training_Logs/dataTransformLog.txt", 'a+')
try:
#onlyfiles = [f for f in listdir(self.goodDataPath)]
onlyfiles = self.az_blob_mgt.getAllFileNameFromDirectory(
self.goodDataPath)
for file in onlyfiles:
csv = self.az_blob_mgt.readCSVFilefromDir(self.goodDataPath,
filename=file)
csv.fillna('NULL', inplace=True)
csv["Wafer"] = csv["Wafer"].str[6:]
self.az_blob_mgt.saveDataFrametoCSV(self.goodDataPath,
file,
csv,
index=None,
header=True)
self.logger_db_writer.log(
log_database, log_collection,
"File {0} Transformed successfully!!".format(file))
# csv = pandas.read_csv(self.goodDataPath+"/" + file)
# csv.fillna('NULL',inplace=True)
# # #csv.update("'"+ csv['Wafer'] +"'")
# # csv.update(csv['Wafer'].astype(str))
# csv['Wafer'] = csv['Wafer'].str[6:]
# csv.to_csv(self.goodDataPath+ "/" + file, index=None, header=True)
# self.logger.log(log_file," %s: File Transformed successfully!!" % file)
#log_file.write("Current Date :: %s" %date +"\t" + "Current time:: %s" % current_time + "\t \t" + + "\n")
except Exception as e:
msg = "Error occured in class:dataTransform method:replaceMissingWithNull error:Data Transformation failed because:" + str(
e)
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
class File_Operation:
"""
This class shall be used to save the model after training
and load the saved model for prediction.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, log_database, log_collection, execution_id):
#self.file_object = file_object
#self.logger_object = logger_object
#self.model_directory='models/'
self.log_database = log_database
self.log_collection = log_collection
self.execution_id = execution_id
self.log_db_writer = App_LoggerDB(execution_id=self.execution_id)
self.model_directory = 'model'
self.az_blob_mgt = AzureBlobManagement()
def save_model(self, model, filename):
"""`
Method Name: save_model
Description: Save the model file to directory
Outcome: File gets saved
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the save_model method of the File_Operation class')
directory_name = self.model_directory + '-' + filename
try:
self.az_blob_mgt.createDir(
directory_name, is_replace=True) # create or replace directory
# path = os.path.join(self.model_directory,filename) #create seperate directory for each cluster
# if os.path.isdir(path): #remove previously existing models for each clusters
# shutil.rmtree(self.model_directory)
# os.makedirs(path)
#else:
# os.makedirs(path) #
# with open(path +'/' + filename+'.sav',
# 'wb') as f:
# pickle.dump(model, f) # save the model to file
self.az_blob_mgt.saveObject(directory_name=directory_name,
filename=filename + '.sav',
object_name=model)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Model File ' + filename +
' saved. Exited the save_model method of the Model_Finder class'
)
return 'success'
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in save_model method of the'
' Model_Finder class. Exception message: ' + str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Model File ' + filename +
' could not be saved. Exited the save_model method of the Model_Finder class'
)
raise Exception()
def load_model(self, filename):
"""
Method Name: load_model
Description: load the model file to memory
Output: The Model file loaded in memory
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the load_model method of the File_Operation class')
try:
directory = self.model_directory + '-' + filename
filename = filename + '.sav'
object_model = self.az_blob_mgt.loadObject(directory, filename)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Model File ' + filename + ' loaded. Exited '
'the load_model method of the Model_Finder class')
#with open(self.model_directory + filename + '/' + filename + '.sav',
# 'rb') as f:
# self.log_db_writer.log(self.log_database,self.log_collection,
# 'Model File ' + filename + ' loaded. Exited the load_model method of the Model_Finder class')
return object_model
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in load_model method of the Model_Finder class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Model File ' + filename +
' could not be saved. Exited the load_model method of the Model_Finder class'
)
raise Exception()
def find_correct_model_file(self, cluster_number):
"""
Method Name: find_correct_model_file
Description: Select the correct model based on cluster number
Output: The Model file
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the find_correct_model_file method of the File_Operation class'
)
try:
self.cluster_number = cluster_number
self.folder_name = self.model_directory
self.list_of_model_files = []
self.required_files = self.az_blob_mgt.dir_list
self.list_of_files = []
# selecting model directory only
#self.list_of_files = os.listdir(self.folder_name)
for dir in self.required_files:
if re.search("^model[-][a-zA-z]{2,17}[0-9]", dir):
self.list_of_files.append(dir)
for self.file in self.list_of_files:
try:
#selecting model file name in models
models = self.az_blob_mgt.getAllFileNameFromDirectory(
self.file)
for model_name_ in models:
if (model_name_.index(str(self.cluster_number)) != -1):
self.model_name = model_name_
except:
continue
self.model_name = self.model_name.split('.')[0]
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exited the find_correct_model_file method of the Model_Finder class.'
)
return self.model_name
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in find_correct_model_file method of the Model_Finder class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exited the find_correct_model_file method of the Model_Finder class with Failure'
)
raise Exception()
class Raw_Data_validation:
"""
This class shall be used for handling all the validation done on the Raw Training Data!!.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, path, execution_id):
self.Batch_Directory = path
self.execution_id = execution_id
#self.schema_path = 'schema_training.json'
self.collection_name = "schema-training" #code added by Avnish yadav
self.database_name = "Wafer-sys" #code added by Avnish yadav
self.logger_db_writer = App_LoggerDB(execution_id=execution_id)
self.mongdb = MongodbOperation()
self.az_blob_mgt = AzureBlobManagement()
self.good_directory_path = "good-raw-file-train-validated"
self.bad_directory_path = "bad-raw-file-train-validated"
def valuesFromSchema(self):
"""
Method Name: valuesFromSchema
Description: This method extracts all the relevant information from the pre-defined "Schema" file.
Output: LengthOfDateStampInFile, LengthOfTimeStampInFile, column_names, Number of Columns
On Failure: Raise ValueError,KeyError,Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_database = "wafer_training_log"
log_collection = "values_from_schema_validation"
try:
#with open(self.schema_path, 'r') as f:
# dic = json.load(f)
# f.close()
log_database = "wafer_training_log"
log_collection = "values_from_schema_validation"
df_schema_training = self.mongdb.getDataFrameofCollection(
self.database_name, self.collection_name)
dic = {}
for i in df_schema_training.columns:
dic.update({i: df_schema_training.loc[0, i]})
#[dic.update({i: df_schema_training.loc[0, i]}) for i in df_schema_training.columns]
print(dic)
del df_schema_training
pattern = dic['SampleFileName']
LengthOfDateStampInFile = dic['LengthOfDateStampInFile']
LengthOfTimeStampInFile = dic['LengthOfTimeStampInFile']
column_names = dic['ColName']
NumberofColumns = dic['NumberofColumns']
#file = open("Training_Logs/valuesfromSchemaValidationLog.txt", 'a+')
message = "LengthOfDateStampInFile:: %s" % LengthOfDateStampInFile + "\t" + "LengthOfTimeStampInFile:: %s" % LengthOfTimeStampInFile + "\t " + "NumberofColumns:: %s" % NumberofColumns + "\n"
self.logger_db_writer.log(log_database, log_collection, message)
#file.close()
except ValueError:
file = open("Training_Logs/valuesfromSchemaValidationLog.txt",
'a+')
self.logger_db_writer.log(
log_database, log_collection,
"ValueError:Value not found inside schema_training.json")
file.close()
raise ValueError
except KeyError:
file = open("Training_Logs/valuesfromSchemaValidationLog.txt",
'a+')
self.logger_db_writer.log(
log_database, log_collection,
"KeyError:Key value error incorrect key passed")
file.close()
raise KeyError
except Exception as e:
file = open("Training_Logs/valuesfromSchemaValidationLog.txt",
'a+')
self.logger_db_writer.log(log_database, log_collection, str(e))
file.close()
raise e
return LengthOfDateStampInFile, LengthOfTimeStampInFile, column_names, NumberofColumns
def manualRegexCreation(self):
"""
Method Name: manualRegexCreation
Description: This method contains a manually defined regex based on the "FileName" given in "Schema" file.
This Regex is used to validate the filename of the training data.
Output: Regex pattern
On Failure: None
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
regex = "['wafer']+['\_'']+[\d_]+[\d]+\.csv"
return regex
def createDirectoryForGoodBadRawData(self):
"""
Method Name: createDirectoryForGoodBadRawData
Description: This method creates directories to store the Good Data and Bad Data
after validating the training data.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
"""try:
path = os.path.join("Training_Raw_files_validated/", "Good_Raw/")
if not os.path.isdir(path):
os.makedirs(path)
path = os.path.join("Training_Raw_files_validated/", "Bad_Raw/")
if not os.path.isdir(path):
os.makedirs(path)"""
log_database = "wafer_training_log"
log_collection = "general_log"
try:
log_database = "wafer_training_log"
log_collection = "general_log"
self.az_blob_mgt.createDir(self.good_directory_path,
is_replace=True)
self.az_blob_mgt.createDir(self.bad_directory_path,
is_replace=True)
msg = self.good_directory_path + " and " + self.bad_directory_path + " created successfully."
print(msg)
self.logger_db_writer.log(log_database, log_collection, msg)
except Exception as e:
msg = "Error Occured in class Raw_Data_validation method:createDirectoryForGoodBadRawData error: Failed to create directory " + self.good_directory_path + " and " + self.bad_directory_path
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def deleteExistingGoodDataTrainingFolder(self):
"""
Method Name: deleteExistingGoodDataTrainingFolder
Description: This method deletes the directory made to store the Good Data
after loading the data in the table. Once the good files are
loaded in the DB,deleting the directory ensures space optimization.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
#try:
#path = 'Training_Raw_files_validated/'
### if os.path.isdir("ids/" + userName):
## if os.path.isdir(path + 'Bad_Raw/'):
## shutil.rmtree(path + 'Bad_Raw/')
#if os.path.isdir(path + 'Good_Raw/'):
# shutil.rmtree(path + 'Good_Raw/')
# file = open("Training_Logs/GeneralLog.txt", 'a+')
# self.logger.log(file,"GoodRaw directory deleted successfully!!!")
# file.close()
log_database = "wafer_training_log"
log_collection = "general_log"
try:
log_database = "wafer_training_log"
log_collection = "general_log"
self.az_blob_mgt.deleteDir(self.good_directory_path)
self.logger_db_writer.log(
log_database, log_collection,
self.good_directory_path + " deleted successfully!!")
except Exception as e:
msg = "Error Occured in class Raw_Data_validation method:deleteExistingGoodDataTrainingFolder Error occured while deleting :" + self.good_directory_path
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def deleteExistingBadDataTrainingFolder(self):
"""
Method Name: deleteExistingBadDataTrainingFolder
Description: This method deletes the directory made to store the bad Data.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None """
#try:
# # path = 'Training_Raw_files_validated/'
# if os.path.isdir(path + 'Bad_Raw/'):
# shutil.rmtree(path + 'Bad_Raw/')
# file = open("Training_Logs/GeneralLog.txt", 'a+')
# self.logger.log(file,"BadRaw directory deleted before starting validation!!!")
# file.close()
#except OSError as s:
# file = open("Training_Logs/GeneralLog.txt", 'a+')
# self.logger.log(file,"Error while Deleting Directory : %s" %s)
# file.close()
# raise OSError
log_database = "wafer_training_log"
log_collection = "general_log"
try:
log_database = "wafer_training_log"
log_collection = "general_log"
self.az_blob_mgt.deleteDir(self.bad_directory_path)
self.logger_db_writer.log(
log_database, log_collection,
self.bad_directory_path + " deleted successfully!!")
except Exception as e:
msg = "Error Occured in class Raw_Data_validation method:deleteExistingGoodDataTrainingFolder Error occured while deleting :" + self.good_directory_path
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
def moveBadFilesToArchiveBad(self):
"""
Method Name: moveBadFilesToArchiveBad
Description: This method deletes the directory made to store the Bad Data
after moving the data in an archive folder. We archive the bad
files to send them back to the client for invalid data issue.
Output: None
On Failure: OSError
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
now = datetime.now()
date = now.date()
time = now.strftime("%H%M%S")
#try:
#source = 'Training_Raw_files_validated/Bad_Raw/'
#if os.path.isdir(source):
# path = "TrainingArchiveBadData"
# if not os.path.isdir(path):
# os.makedirs(path)
# dest = 'TrainingArchiveBadData/BadData_' + str(date)+"_"+str(time)
# if not os.path.isdir(dest):
# os.makedirs(dest)
# files = os.listdir(source)
# for f in files:
# if f not in os.listdir(dest):
# shutil.move(source + f, dest)
# file = open("Training_Logs/GeneralLog.txt", 'a+')
# self.logger.log(file,"Bad files moved to archive")
# path = 'Training_Raw_files_validated/'
# if os.path.isdir(path + 'Bad_Raw/'):
# shutil.rmtree(path + 'Bad_Raw/')
# self.logger.log(file,"Bad Raw Data Folder Deleted successfully!!")
# file.close() #
log_database = "wafer_training_log"
log_collection = "general_log"
try:
log_database = "wafer_training_log"
log_collection = "general_log"
# source = 'Training_Raw_files_validated/Bad_Raw/'
source = self.bad_directory_path
destination = "lat-" + self.execution_id
self.logger_db_writer.log(log_database, log_collection,
"Started moving bad raw data..")
for file in self.az_blob_mgt.getAllFileNameFromDirectory(source):
self.az_blob_mgt.moveFileinDir(source, destination, file)
self.logger_db_writer.log(
log_database, log_collection, "File:" + file +
" moved to directory:" + destination + " successfully.")
self.logger_db_writer.log(
log_database, log_collection,
"All bad raw file moved to directory:" + destination)
self.az_blob_mgt.deleteDir(source)
self.logger_db_writer.log(log_database, log_collection,
"Deleting bad raw directory:" + source)
except Exception as e:
self.logger_db_writer.log(
log_database, log_collection,
"class Raw_Data_validation method:moveBadFilesToArchiveBad "
"Error while moving bad files to archive:" + str(e))
raise e
def validationFileNameRaw(self, regex, LengthOfDateStampInFile,
LengthOfTimeStampInFile):
"""
Method Name: validationFileNameRaw
Description: This function validates the name of the training csv files as per given name in the schema!
Regex pattern is used to do the validation.If name format do not match the file is moved
to Bad Raw Data folder else in Good raw data.
Output: None
On Failure: Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
#pattern = "['Wafer']+['\_'']+[\d_]+[\d]+\.csv"
# delete the directories for good and bad data in case last run was unsuccessful and folders were not deleted.
#self.deleteExistingBadDataTrainingFolder()
#self.deleteExistingGoodDataTrainingFolder()
#create new directories
self.createDirectoryForGoodBadRawData()
onlyfiles = self.az_blob_mgt.getAllFileNameFromDirectory(
self.Batch_Directory)
#onlyfiles = [f for f in listdir(self.Batch_Directory)]
log_database = "wafer_training_log"
log_collection = "name_validation_log"
try:
log_database = "wafer_training_log"
log_collection = "name_validation_log"
#f = open("Training_Logs/nameValidationLog.txt", 'a+')
for filename in onlyfiles:
if (re.match(regex, filename)):
splitAtDot = re.split('.csv', filename)
splitAtDot = (re.split('_', splitAtDot[0]))
if len(splitAtDot[1]) == LengthOfDateStampInFile:
if len(splitAtDot[2]) == LengthOfTimeStampInFile:
#shutil.copy("Training_Batch_Files/" + filename, "Training_Raw_files_validated/Good_Raw")
#self.logger.log(f,"Valid File name!! File moved to GoodRaw Folder :: %s" % filename)
self.az_blob_mgt.CopyFileinDir(
self.Batch_Directory, self.good_directory_path,
filename)
self.logger_db_writer.log(
log_database, log_collection,
"Valid File name!! File moved to " +
self.good_directory_path + filename)
else:
#shutil.copy("Training_Batch_Files/" + filename, "Training_Raw_files_validated/Bad_Raw")
#self.logger.log(f,"Invalid File Name!! File moved to Bad Raw Folder :: %s" % filename)
self.az_blob_mgt.CopyFileinDir(
self.Batch_Directory, self.bad_directory_path,
filename)
msg = "Invalid File Name !! File moved to " + self.bad_directory_path + filename
self.logger_db_writer.log(log_database,
log_collection, msg)
else:
#shutil.copy("Training_Batch_Files/" + filename, "Training_Raw_files_validated/Bad_Raw")
#self.logger.log(f,"Invalid File Name!! File moved to Bad Raw Folder :: %s" % filename)
self.az_blob_mgt.CopyFileinDir(self.Batch_Directory,
self.bad_directory_path,
filename)
msg = "Invalid File Name !! File moved to " + self.bad_directory_path + filename
self.logger_db_writer.log(log_database, log_collection,
msg)
else:
#shutil.copy("Training_Batch_Files/" + filename, "Training_Raw_files_validated/Bad_Raw")
#self.logger.log(f, "Invalid File Name!! File moved to Bad Raw Folder :: %s" % filename)
self.az_blob_mgt.CopyFileinDir(self.Batch_Directory,
self.bad_directory_path,
filename)
msg = "Invalid File Name !! File moved to " + self.bad_directory_path + filename
self.logger_db_writer.log(log_database, log_collection,
msg)
# f.close()
except Exception as e:
msg = "Error occured while validating FileName " + str(e)
self.logger_db_writer.log(log_database, log_collection, msg)
raise e
#f = open("Training_Logs/nameValidationLog.txt", 'a+')
#self.logger.log(f, "Error occured while validating FileName %s" % e)
#f.close()
#raise e
def validateColumnLength(self, NumberofColumns):
"""
Method Name: validateColumnLength
Description: This function validates the number of columns in the csv files.
It is should be same as given in the schema file.
If not same file is not suitable for processing and thus is moved to Bad Raw Data folder.
If the column number matches, file is kept in Good Raw Data for processing.
The csv file is missing the first column name, this function changes the missing name to "Wafer".
Output: None
On Failure: Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_collection = "column_validation_log"
log_database = "wafer_training_log"
try:
log_collection = "column_validation_log"
log_database = "wafer_training_log"
#f = open("Training_Logs/columnValidationLog.txt", 'a+')
#self.logger.log(f,"Column Length Validation Started!!")
self.logger_db_writer.log(log_database, log_collection,
"Column Length Validation Started!!")
print("column lenght validation started")
for file in self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_directory_path):
#csv = pd.read_csv("Training_Raw_files_validated/Good_Raw/" + file)
print(file)
csv = self.az_blob_mgt.readCSVFilefromDir(
self.good_directory_path, file)
print(csv.shape)
if csv.shape[1] == NumberofColumns:
csv.rename(columns={"Unnamed: 0": "Wafer"}, inplace=True)
print("pass statement ")
pass
#for file in listdir('Training_Raw_files_validated/Good_Raw/'):
# csv = pd.read_csv("Training_Raw_files_validated/Good_Raw/" + file)
# if csv.shape[1] == NumberofColumns:
# # pass
else:
self.az_blob_mgt.moveFileinDir(self.good_directory_path,
self.bad_directory_path,
file)
msg = "Invalid Column Length for the file!! File moved to " + self.bad_directory_path + "file:" + file
self.logger_db_writer.log(log_database, log_collection,
msg)
self.logger_db_writer.log(
log_database, log_collection,
"Column Length Validation Completed!!")
except Exception as e:
self.logger_db_writer.log(
log_database, log_collection,
"Error Occured in class Raw_Data_validation method: validateColumnLength error:"
+ str(e))
raise e
# shutil.move("Training_Raw_files_validated/Good_Raw/" + file, "Training_Raw_files_validated/Bad_Raw")
# # self.logger.log(f, "Invalid Column Length for the file!! File moved to Bad Raw Folder :: %s" % file)
#self.logger.log(f, "Column Length Validation Completed!!")
#except OSError:
# f = open("Training_Logs/columnValidationLog.txt", 'a+')
# self.logger.log(f, "Error Occured while moving the file :: %s" % OSError)
# f.close()
# raise OSError
#except Exception as e:
# f = open("Training_Logs/columnValidationLog.txt", 'a+')
# self.logger.log(f, "Error Occured:: %s" % e)
# f.close()
# # raise e
#f.close()
def validateMissingValuesInWholeColumn(self):
"""
Method Name: validateMissingValuesInWholeColumn
Description: This function validates if any column in the csv file has all values missing.
If all the values are missing, the file is not suitable for processing.
SUch files are moved to bad raw data.
Output: None
On Failure: Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_database = "wafer_training_log"
log_collection = "missing_values_in_column"
try:
log_database = "wafer_training_log"
log_collection = "missing_values_in_column"
self.logger_db_writer.log(log_database, log_collection,
"Missing Values Validation Started!!")
#f = open("Training_Logs/missingValuesInColumn.txt", 'a+')
#self.logger.log(f,"Missing Values Validation Started!!")
#for file in listdir('Training_Raw_files_validated/Good_Raw/'):
# csv = pd.read_csv("Training_Raw_files_validated/Good_Raw/" + file)
for file in self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_directory_path):
csv = self.az_blob_mgt.readCSVFilefromDir(
self.good_directory_path, file)
print(csv)
count = 0
for columns in csv:
print(columns)
if (len(csv[columns]) - csv[columns].count()) == len(
csv[columns]):
count += 1
#shutil.move("Training_Raw_files_validated/Good_Raw/" + file,
# "Training_Raw_files_validated/Bad_Raw")
#self.logger.log(f,"Invalid Column Length for the file!! File moved to Bad Raw Folder :: %s" % file)
self.az_blob_mgt.moveFileinDir(
self.good_directory_path, self.bad_directory_path,
file)
msg = "Invalid Column Length for the file!! File moved to " + self.bad_directory_path + ":: %s" % file
self.logger_db_writer.log(log_database, log_collection,
msg)
break
if count == 0:
print("entering rename")
csv.rename(columns={"Unnamed: 0": "Wafer"}, inplace=True)
self.az_blob_mgt.saveDataFrametoCSV(
self.good_directory_path,
file,
csv,
index=None,
header=True)
#csv.to_csv("Training_Raw_files_validated/Good_Raw/" + file, index=None, header=True)
#except OSError:
# f = open("Training_Logs/missingValuesInColumn.txt", 'a+')
# self.logger.log(f, "Error Occured while moving the file :: %s" % OSError)
# f.close()
# raise OSError
except Exception as e:
# f = open("Training_Logs/missingValuesInColumn.txt", 'a+')
# # self.logger.log(f, "Error Occured:: %s" % e)
# f.close()
# raise e
#f.close()
self.logger_db_writer.log(
log_database, log_collection,
"Error Occured class:Raw_Data_validation method:validateMissingValuesInWholeColumn error:"
+ str(e))
raise e
#path = 'training-batch-files'
#noofcolumns=592
#a=Raw_Data_validation
#res=a.validateMissingValuesInWholeColumn(a(path,333))
#print(res)
def __init__(self, execution_id):
self.goodDataPath = "good-raw-file-train-validated"
self.execution_id = execution_id
self.logger_db_writer = App_LoggerDB(execution_id)
self.az_blob_mgt = AzureBlobManagement()
class prediction:
def __init__(self, path, execution_id):
#self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
#self.log_writer = logger.App_Logger()
#self.pred_data_val = Prediction_Data_validation(path)
self.execution_id = execution_id
self.log_database = "strength_prediction_log"
self.log_collection = "prediction_log"
self.log_db_writer = App_LoggerDB(execution_id)
self.az_blob_mgt = AzureBlobManagement()
if path is not None:
self.pred_data_val = Prediction_Data_validation(path, execution_id)
def predictionFromModel(self):
try:
self.pred_data_val.deletePredictionFile(
) #deletes the existing prediction file from last run!
self.log_db_writer.log(self.log_database, self.log_collection,
'Start of Prediction')
print("start of prediction")
data_getter = data_loader_prediction.Data_Getter_Pred(
self.log_database, self.log_collection, self.execution_id)
data = data_getter.get_data()
path = ""
if data.__len__() == 0:
self.log_db_writer.log(
self.log_database, self.log_collection,
"No data was present to perform prediction existing prediction method"
)
return path, "No data was present to perform prediction"
#code change
# wafer_names=data['Wafer']
# data=data.drop(labels=['Wafer'],axis=1)
preprocessor = preprocessing.Preprocessor(self.log_database,
self.log_collection,
self.execution_id)
is_null_present, cols_with_missing_values = preprocessor.is_null_present(
data)
if (is_null_present):
data = preprocessor.impute_missing_values(data)
data = preprocessor.logTransformation(data)
print("after log Transformation")
print(data)
#scale the prediction data
data_scaled = pandas.DataFrame(
preprocessor.standardScalingData(data), columns=data.columns)
print("standard scaling for data completed")
print(data_scaled)
#data=data.to_numpy()
file_loader = file_methods.File_Operation(self.log_database,
self.log_collection,
self.execution_id)
kmeans = file_loader.load_model('kkmeans')
##Code changed
#pred_data = data.drop(['Wafer'],axis=1)
clusters = kmeans.predict(
data_scaled) #drops the first column for cluster prediction
data_scaled['clusters'] = clusters
clusters = data_scaled['clusters'].unique()
result = [] # initialize blank list for storing predicitons
# with open('EncoderPickle/enc.pickle', 'rb') as file: #let's load the encoder pickle file to decode the values
# encoder = pickle.load(file)
for i in clusters:
cluster_data = data_scaled[data_scaled['clusters'] == i]
cluster_data = cluster_data.drop(['clusters'], axis=1)
model_name = file_loader.find_correct_model_file(i)
print(model_name)
model = file_loader.load_model(model_name)
for val in (model.predict(cluster_data.values)):
result.append(val)
result = pandas.DataFrame(result, columns=['strength-Predictions'])
#result = list(model.predict(cluster_data))
#self.result = pandas.DataFrame(list(zip(result)), columns=['Prediction'])
#for val in (model.predict(cluster_data.values)):
# result.append(val)
#print(self.result.shape)
print("results after prediction with prediction columns")
print(result)
path = "Prediction-Output-File"
#result.to_csv("Prediction_Output_File/Predictions.csv",header=True) #appends result to prediction file
self.az_blob_mgt.saveDataFrametoCSV(
path,
"cement-strength-prediction.csv",
result,
header=True,
mode="a+")
self.log_db_writer.log(self.log_database, self.log_collection,
'End of Prediction')
except Exception as ex:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Error occured while running the prediction!! Error:: %s' % ex)
raise ex
return path, result.head().to_json(orient="records")
class Preprocessor:
"""
This class shall be used to clean and transform the data before training.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, log_database, log_collection, execution_id):
self.log_database = log_database
self.log_collection = log_collection
self.execution_id = execution_id
self.mongoDBObject = MongodbOperation()
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def remove_columns(self, data, columns):
"""
Method Name: remove_columns
Description: This method removes the given columns from a pandas dataframe.
Output: A pandas DataFrame after removing the specified columns.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
"Entered the remove_columns method of the Preprocessor class")
self.data = data
self.columns = columns
try:
self.useful_data = self.data.drop(
labels=self.columns,
axis=1) # drop the labels specified in the columns
print("Useful data in dataframe")
print(self.useful_data)
self.log_db_writer.log(
self.log_database, self.log_collection,
"Column removal Successful.Exited the "
"remove_columns method of the Preprocessor class")
return self.useful_data
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
"Exception occured in remove_columns method"
" of the Preprocessor class. Exception message: " + str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column removal Unsuccessful. Exited the '
'remove_columns method of the Preprocessor class')
raise Exception()
def separate_label_feature(self, data, label_column_name):
"""
Method Name: separate_label_feature
Description: This method separates the features and a Label Coulmns.
Output: Returns two separate Dataframes, one containing features and the other containing Labels .
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the separate_label_feature method of the Preprocessor class'
)
try:
self.X = data.drop(
labels=label_column_name, axis=1
) # drop the columns specified ,i,e output column and separate the feature columns
self.Y = data[label_column_name] # Filter the Label columns
self.log_db_writer.log(
self.log_database, self.log_collection,
'Label Separation Successful. Exited the separate_label_feature method of the Preprocessor class'
)
print(self.X, self.Y)
return self.X, self.Y
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in separate_label_feature method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Label Separation Unsuccessful. Exited the separate_label_feature'
' method of the Preprocessor class')
raise Exception()
def is_null_present(self, data):
"""
Method Name: is_null_present
Description: This method checks whether there are null values present in the pandas Dataframe or not.
Output: Returns a Boolean Value. True if null values are present in the DataFrame, False if they are not present.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the is_null_present method of the Preprocessor class')
self.null_present = False
try:
self.null_counts = data.isna().sum(
) # check for the count of null values per column
for i in self.null_counts:
if i > 0:
self.null_present = True
break
if (self.null_present
): # write the logs to see which columns have null values
dataframe_with_null = pd.DataFrame()
dataframe_with_null['columns'] = data.columns
dataframe_with_null['missing values count'] = np.asarray(
data.isna().sum())
print(dataframe_with_null)
#dataframe_with_null.to_csv('preprocessing_data/null_values.csv') # storing the null column information to file
self.az_blob_mgt.saveDataFrametoCSV(
"preprocessing-data",
"null_values.csv",
data_frame=dataframe_with_null)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Finding missing values is a success.Data written'
'[preprocessing-data] to the null values file. '
'Exited the is_null_present method of the Preprocessor class')
return self.null_present
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in is_null_present method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Finding missing values failed. Exited the is_null_present method of the Preprocessor class'
)
raise Exception()
def impute_missing_values(self, data):
"""
Method Name: impute_missing_values
Description: This method replaces all the missing values in the Dataframe using KNN Imputer.
Output: A Dataframe which has all the missing values imputed.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the impute_missing_values method of the Preprocessor class'
)
self.data = data
try:
imputer = KNNImputer(n_neighbors=3,
weights='uniform',
missing_values=np.nan)
self.new_array = imputer.fit_transform(
self.data) # impute the missing values
# convert the nd-array returned in the step above to a Dataframe
self.new_data = pd.DataFrame(data=self.new_array,
columns=self.data.columns)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Imputing missing values Successful. Exited the impute_missing_values method of the Preprocessor class'
)
return self.new_data
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in impute_missing_values method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Imputing missing values failed. Exited the impute_missing_values method of the Preprocessor class'
)
raise Exception()
def get_columns_with_zero_std_deviation(self, data):
"""
Method Name: get_columns_with_zero_std_deviation
Description: This method finds out the columns which have a standard deviation of zero.
Output: List of the columns with standard deviation of zero
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the get_columns_with_zero_std_deviation method of the Preprocessor class'
)
self.columns = data.columns
self.data_n = data.describe()
self.col_to_drop = []
try:
for x in self.columns:
if (self.data_n[x]['std'] == 0
): # check if standard deviation is zero
self.col_to_drop.append(
x
) # prepare the list of columns with standard deviation zero
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column search for Standard Deviation of Zero Successful. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class'
)
print(self.col_to_drop)
return self.col_to_drop
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in get_columns_with_zero_std_deviation method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column search for Standard Deviation of Zero Failed. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class'
)
raise Exception()
class dataTransformPredict:
"""
This class shall be used for transforming the Good Raw Training Data before loading it in Database!!.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, execution_id):
self.execution_id = execution_id
#self.goodDataPath = "Prediction_Raw_Files_Validated/Good_Raw"
self.goodDataPath = "good-raw-file-prediction-validated"
#self.logger = App_Logger()
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.log_database = "wafer_prediction_log"
self.az_blob_mgt = AzureBlobManagement()
def replaceMissingWithNull(self):
"""
Method Name: replaceMissingWithNull
Description: This method replaces the missing values in columns with "NULL" to
store in the table. We are using substring in the first column to
keep only "Integer" data for ease up the loading.
This column is anyways going to be removed during prediction.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
log_collection = "data_transform_log"
try:
log_collection = "data_transform_log"
onlyfiles = self.az_blob_mgt.getAllFileNameFromDirectory(
self.goodDataPath)
print(onlyfiles)
for file in onlyfiles:
csv = self.az_blob_mgt.readCSVFilefromDir(
self.goodDataPath, file)
csv.fillna('NULL', inplace=True)
# #csv.update("'"+ csv['Wafer'] +"'")
# csv.update(csv['Wafer'].astype(str))
csv['Wafer'] = csv['Wafer'].str[6:]
#print(csv)
print("before updating index")
#csv.to_csv(self.goodDataPath+ "/" + file, index=None, header=True)
self.az_blob_mgt.saveDataFrametoCSV(self.goodDataPath,
file,
csv,
index=None,
header=True)
print("after updating index")
#self.logger.log(log_file," %s: File Transformed successfully!!" % file)
self.log_db_writer.log(
self.log_database, log_collection,
"File {0} transformed successfully".format(file))
print('File transformed replace missing with null succccc')
#log_file.write("Current Date :: %s" %date +"\t" + "Current time:: %s" % current_time + "\t \t" + + "\n")
except Exception as e:
self.log_db_writer.log(
self.log_database, log_collection,
'Data Transformation failed because:' + str(e))
raise e
class DbOperationMongoDB:
"""
This class shall be used for handling all the SQL operations.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, execution_id):
self.mongodb = MongodbOperation()
self.az_blob_mgt = AzureBlobManagement()
self.logger_db_writer = App_LoggerDB(execution_id=execution_id)
self.good_file_path = "good-raw-file-train-validated"
self.bad_file_path = "bad-raw-file-train-validated"
def insertIntoTableGoodData(self, column_name):
"""
Description: Load all csv file into mongo db database "training_database" ,collection:"Good_Raw_Data"
:return:
"""
database_name = "wafer_training_log"
collection_name = "db_insert_log"
try:
database_name = "wafer_training_log" # logger name
collection_name = "db_insert_log" # logger name
self.logger_db_writer.log(
database_name, collection_name,
"Droping existing collection if present in database training_database"
)
self.mongodb.dropCollection("training_database", "Good_Raw_Data")
self.logger_db_writer.log(
database_name, collection_name,
"Starting loading of good files in database:training_database and collection: Good_Raw_Data"
)
files = self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_file_path)
self.logger_db_writer.log(
database_name, collection_name,
"No of file found in good-raw-file-train-validated " +
str(len(files)))
for file in files:
try:
self.logger_db_writer.log(
database_name, collection_name,
"Insertion of file " + file + " started...")
df = self.az_blob_mgt.readCSVFilefromDir(
self.good_file_path, file)
df.columns = column_name
self.mongodb.insertDataFrame("training_database",
"Good_Raw_Data", df)
self.logger_db_writer.log(
database_name, collection_name,
"File: {0} loaded successfully".format(file))
except Exception as e:
self.logger_db_writer.log(database_name, collection_name,
str(e))
self.az_blob_mgt.moveFileinDir(self.good_file_path,
self.bad_file_path, file)
self.logger_db_writer.log(
database_name, collection_name, "File " + file +
" was not loaded successfully hence moved tp dir:" +
self.bad_file_path)
except Exception as e:
error_message = "Error occured in class:DbOperationMongoDB method:insertIntoTableGoodData error:" + str(
e)
self.logger_db_writer.log(database_name, collection_name,
error_message)
def selectingDatafromtableintocsv(self, ):
"""
:return:
"""
database_name = "wafer_training_log"
collection_name = "export_to_csv"
try:
directory_name = "training-file-from-db"
file_name = "InputFile"
training_database = "training_database"
training_collection = "Good_Raw_Data"
msg = "starting of loading of database:training_database,collection:Good_Raw_Data records into InputFile.csv"
print(msg)
self.logger_db_writer.log(database_name, collection_name, msg)
df = self.mongodb.getDataFrameofCollection(training_database,
training_collection)
print(df)
msg = "Good_Raw_data has been loaded into pandas dataframe"
self.logger_db_writer.log(database_name, collection_name, msg)
self.az_blob_mgt.saveDataFrametoCSV(directory_name, file_name, df)
msg = "InputFile.csv created successfully in directory " + directory_name
self.logger_db_writer.log(database_name, collection_name, msg)
except Exception as e:
msg = "Error occured in class:DbOperationMongoDB method:insertIntoTableGoodData error:" + str(
e)
self.logger_db_writer.log(database_name, collection_name, msg)
class DbOperationMongoDB:
"""
This class shall be used for handling all the mongodb operations.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, execution_id):
self.mongodb=MongodbOperation()
self.az_blob_mgt=AzureBlobManagement()
self.logger_db_writer=App_LoggerDB(execution_id=execution_id)
self.good_file_path="good-raw-file-prediction-validated"
self.bad_file_path="bad-raw-file-prediction-validated"
def insertIntoTableGoodData(self,column_names):
"""
Description: Load all csv file into mongo db database "prediction_database" ,collection:"Good_Raw_Data"
from azure storage -good data and clear the storage space.
:return:
"""
try:
prediction_database="prediction_database" # mongodb name
prediction_collection="Good_Raw_Data" # mongodb name
database_name = "wafer_prediction_log" ## logger name
collection_name = "db_insert_log" ## logger name
self.mongodb.dropCollection(prediction_database,prediction_collection)
self.logger_db_writer.log(database_name,collection_name,"Droping collection:"+prediction_collection+" from database:"+prediction_database)
self.logger_db_writer.log(database_name, collection_name,"Starting loading of good files in database:training_database and collection: Good_Raw_Data")
files = self.az_blob_mgt.getAllFileNameFromDirectory(self.good_file_path)
self.logger_db_writer.log(database_name, collection_name,"No of file found in good-raw-file-train-validated " + str(len(files)))
for file in files:
try:
self.logger_db_writer.log(database_name, collection_name,
"Insertion of file +" + file + " started...")
df = self.az_blob_mgt.readCSVFilefromDir(self.good_file_path, file)
df.columns=column_names
print("dataframe before insertion")
print(df)
self.mongodb.insertDataFrame(prediction_database, prediction_collection, df)
self.logger_db_writer.log(database_name, collection_name,
"File: {0} loaded successfully".format(file))
except Exception as e:
self.logger_db_writer.log(database_name, collection_name, str(e))
self.az_blob_mgt.moveFileinDir(self.good_file_path, self.bad_file_path, file)
self.logger_db_writer.log(database_name, collection_name,
"File: " + file + " was not loaded successfully hence moved to dir:" + self.bad_file_path)
except Exception as e:
error_message = "Error occured in class:DbOperationMongoDB method:insertIntoTableGoodData error:" + str(e)
self.logger_db_writer.log(database_name, collection_name, error_message)
def selectingDatafromtableintocsv(self,):
"""
:return:
"""
try:
directory_name="prediction-file-from-db" # azure storage name
file_name="InputFile.csv" # azure storage name
database_name = "wafer_prediction_log" # logger name
collection_name = "export_to_csv" # logger name
prediction_database="prediction_database" # mongodb name
prediction_collection="Good_Raw_Data" # mongodb name
msg="starting of loading of database:"+prediction_database+",collection:"+prediction_collection+" records into file:"+file_name
self.logger_db_writer.log(database_name,collection_name,msg)
df=self.mongodb.getDataFrameofCollection(prediction_database,prediction_collection)
print("after dataframe from db extraction")
print(df)
msg="Good_Raw_data has been loaded into pandas dataframe"
print(msg)
self.logger_db_writer.log(database_name,collection_name,msg)
self.az_blob_mgt.saveDataFrametoCSV(directory_name,file_name,df,index=None,header=True)
# since the inputFile.csv has unammed column, added index =0 later removed as unnamed 0.1 is present in predictionDatavalidation.py ln 410.
msg = "InputFile.csv created successfully in directory"+directory_name
print(msg)
self.logger_db_writer.log(database_name, collection_name, msg)
except Exception as e:
msg="Error occured in class:DbOperationMongoDB method:insertIntoTableGoodData error:"+str(e)
self.logger_db_writer.log(database_name,collection_name,msg)
def getEventAndSubject(data):
event_type = None
container = None
if 'eventType' in data.keys():
event_type = data['eventType']
if 'subject' in data.keys():
start_index = data['subject'].index('containers') + len(
'containers') + 1
stop_index = data['subject'].index(
'/blobs/',
start_index,
)
container = data['subject'][start_index:stop_index]
if container == 'avnish-yadav':
if event_type == 'Microsoft.Storage.BlobCreated':
azm = AzureBlobManagement(connection_string)
azm_processing_dir = AzureBlobManagement()
file_names = azm.getAllFileNameFromDirectory(
directory_name=container)
file_names = list(
filter(lambda filename: filename.split(".")[-1] == 'csv',
file_names))
if len(file_names) > 0:
is_created = azm_processing_dir.createDirectory(
"received-prediction", is_replace=True)
if is_created == True:
for file in file_names:
df = azm.readCsvFileFromDirectory(container, file)
azm_processing_dir.saveDataFrameTocsv(
"received-prediction", file, df)
for file in file_names:
azm.moveFileInDirectory(container, "recycle-bin", file)
testing.predictionTest("received-prediction")
print(event_type, container)
class Preprocessor:
"""
This class shall be used to clean and transform the data before training.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
def __init__(self, log_database, log_collection, execution_id):
#self.file_object = file_object
#self.logger_object = logger_object
self.log_database = log_database
self.log_collection = log_collection
#self.execution_id=execution_id
self.mongoDBObject = MongodbOperation()
self.log_db_writer = App_LoggerDB(execution_id=execution_id)
self.az_blob_mgt = AzureBlobManagement()
def remove_columns(self, data, columns):
"""
Method Name: remove_columns
Description: This method removes the given columns from a pandas dataframe.
Output: A pandas DataFrame after removing the specified columns.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the remove_columns method of the Preprocessor class')
self.data = data
self.columns = columns
try:
self.useful_data = self.data.drop(
labels=self.columns,
axis=1) # drop the labels specified in the columns
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column removal Successful.Exited the remove_columns method of the Preprocessor class'
)
return self.useful_data
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in remove_columns method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column removal Unsuccessful. Exited the remove_columns method of the Preprocessor class'
)
raise Exception()
def separate_label_feature(self, data, label_column_name):
"""
Method Name: separate_label_feature
Description: This method separates the features and a Label Coulmns.
Output: Returns two separate Dataframes, one containing features and the other containing Labels .
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the separate_label_feature method of the Preprocessor class'
)
try:
self.X = data.drop(
labels=label_column_name, axis=1
) # drop the columns specified and separate the feature columns
self.Y = data[label_column_name] # Filter the Label columns
self.log_db_writer.log(
self.log_database, self.log_collection,
'Label Separation Successful. Exited the separate_label_feature method of the Preprocessor class'
)
return self.X, self.Y
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in separate_label_feature method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Label Separation Unsuccessful. Exited the separate_label_feature method of the Preprocessor class'
)
raise Exception()
def dropUnnecessaryColumns(self, data, columnNameList):
"""
Method Name: is_null_present
Description: This method drops the unwanted columns as discussed in EDA section.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
data = data.drop(columnNameList, axis=1)
return data
def replaceInvalidValuesWithNull(self, data):
"""
Method Name: is_null_present
Description: This method replaces invalid values i.e. '?' with null, as discussed in EDA.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
for column in data.columns:
count = data[column][data[column] == '?'].count()
if count != 0:
data[column] = data[column].replace('?', np.nan)
return data
def is_null_present(self, data):
"""
Method Name: is_null_present
Description: This method checks whether there are null values present in the pandas Dataframe or not.
Output: Returns True if null values are present in the DataFrame, False if they are not present and
returns the list of columns for which null values are present.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the is_null_present method of the Preprocessor class')
self.null_present = False
self.cols_with_missing_values = []
self.cols = data.columns
try:
self.null_counts = data.isna().sum(
) # check for the count of null values per column
for i in range(len(self.null_counts)):
if self.null_counts[i] > 0:
self.null_present = True
self.cols_with_missing_values.append(self.cols[i])
if (self.null_present
): # write the logs to see which columns have null values
dataframe_with_null = pd.DataFrame()
dataframe_with_null['columns'] = data.columns
dataframe_with_null['missing values count'] = np.asarray(
data.isna().sum())
print(dataframe_with_null)
#dataframe_with_null.to_csv('preprocessing_data/null_values.csv') # storing the null column information to file
self.az_blob_mgt.saveDataFrametoCSV(
"preprocessing-data",
"null_values.csv",
data_frame=dataframe_with_null)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Finding missing values is a success.Data written to the null values file. Exited the is_null_present method of the Preprocessor class'
)
return self.null_present, self.cols_with_missing_values
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in is_null_present method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Finding missing values failed. Exited the is_null_present method of the Preprocessor class'
)
raise Exception()
def encodeCategoricalValues(self, data):
"""
Method Name: encodeCategoricalValues
Description: This method encodes all the categorical values in the training set.
Output: A Dataframe which has all the categorical values encoded.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
data["class"] = data["class"].map({'p': 1, 'e': 2})
for column in data.drop(['class'], axis=1).columns:
data = pd.get_dummies(data, columns=[column])
return data
def encodeCategoricalValuesPrediction(self, data):
"""
Method Name: encodeCategoricalValuesPrediction
Description: This method encodes all the categorical values in the prediction set.
Output: A Dataframe which has all the categorical values encoded.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
for column in data.columns:
data = pd.get_dummies(data, columns=[column])
return data
# def handleImbalanceDataset(self,X,Y):
# """
# Method Name: handleImbalanceDataset
# Description: This method handles the imbalance in the dataset by oversampling.
# Output: A Dataframe which is balanced now.
# On Failure: Raise Exception
#
# Written By: iNeuron Intelligence
# Version: 1.0
# Revisions: None
# """
#
#
#
# rdsmple = RandomOverSampler()
# x_sampled, y_sampled = rdsmple.fit_sample(X, Y)
#
# return x_sampled,y_sampled
def standardScalingData(self, X):
scalar = StandardScaler()
X_scaled = scalar.fit_transform(X)
return X_scaled
def logTransformation(self, X):
for column in X.columns:
X[column] += 1
X[column] = np.log(X[column])
return X
def impute_missing_values(self, data):
"""
Method Name: impute_missing_values
Description: This method replaces all the missing values in the Dataframe using KNN Imputer.
Output: A Dataframe which has all the missing values imputed.
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the impute_missing_values method of the Preprocessor class'
)
self.data = data
try:
imputer = KNNImputer(n_neighbors=3,
weights='uniform',
missing_values=np.nan)
self.new_array = imputer.fit_transform(
self.data) # impute the missing values
# convert the nd-array returned in the step above to a Dataframe
self.new_data = pd.DataFrame(data=(self.new_array),
columns=self.data.columns)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Imputing missing values Successful. Exited the impute_missing_values method of the Preprocessor class'
)
print(self.new_data)
return self.new_data
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in impute_missing_values method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Imputing missing values failed. Exited the impute_missing_values method of the Preprocessor class'
)
raise Exception()
def get_columns_with_zero_std_deviation(self, data):
"""
Method Name: get_columns_with_zero_std_deviation
Description: This method finds out the columns which have a standard deviation of zero.
Output: List of the columns with standard deviation of zero
On Failure: Raise Exception
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
self.log_db_writer.log(
self.log_database, self.log_collection,
'Entered the get_columns_with_zero_std_deviation method of the Preprocessor class'
)
self.columns = data.columns
self.data_n = data.describe()
self.col_to_drop = []
try:
for x in self.columns:
if (self.data_n[x]['std'] == 0
): # check if standard deviation is zero
self.col_to_drop.append(
x
) # prepare the list of columns with standard deviation zero
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column search for Standard Deviation of Zero Successful. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class'
)
print(self.col_to_drop)
return self.col_to_drop
except Exception as e:
self.log_db_writer.log(
self.log_database, self.log_collection,
'Exception occured in get_columns_with_zero_std_deviation method of the Preprocessor class. Exception message: '
+ str(e))
self.log_db_writer.log(
self.log_database, self.log_collection,
'Column search for Standard Deviation of Zero Failed. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class'
)
raise Exception()
class prediction:
def __init__(self, path, execution_id):
self.execution_id = execution_id
#self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
#self.log_writer = logger.App_Logger()
self.log_database = "wafer_prediction_log"
self.log_collection = "prediction_log"
self.log_db_writer = App_LoggerDB(execution_id)
self.az_blob_mgt = AzureBlobManagement()
if path is not None:
self.pred_data_val = Prediction_Data_validation(path, execution_id)
def predictionFromModel(self):
try:
self.pred_data_val.deletePredictionFile(
) #deletes the existing prediction file from last run!
self.log_db_writer.log(self.log_database, self.log_collection,
'Start of Prediction')
data_getter = data_loader_prediction.Data_Getter_Pred(
self.log_database, self.log_collection, self.execution_id)
data = data_getter.get_data()
path = ""
if data.__len__() == 0:
self.log_db_writer.log(
self.log_database, self.log_collection,
"No data was present to perform prediction existing prediction method"
)
return path, "No data was present to perform prediction"
#code change
# wafer_names=data['Wafer']
# data=data.drop(labels=['Wafer'],axis=1)
preprocessor = preprocessing.Preprocessor(self.log_database,
self.log_collection,
self.execution_id)
is_null_present = preprocessor.is_null_present(data)
if (is_null_present):
data = preprocessor.impute_missing_values(data)
cols_to_drop = preprocessor.get_columns_with_zero_std_deviation(
data)
data = preprocessor.remove_columns(data, cols_to_drop)
#data=data.to_numpy()
file_loader = file_methods.File_Operation(self.log_database,
self.log_collection,
self.execution_id)
kmeans = file_loader.load_model('KMeans')
#print(kmeans)
##Code changed
pred_data = data.drop(['Wafer'], axis=1)
clusters = kmeans.predict(
pred_data) #drops the first column for cluster prediction
data['clusters'] = clusters
clust = data['clusters'].unique()
for i in clust:
cluster_data = data[
data['clusters'] ==
i] # filteing of data with clusters no 0,1,2,etc.
wafer_names = list(cluster_data['Wafer'])
cluster_data = data.drop(labels=['Wafer'], axis=1)
cluster_data = cluster_data.drop(['clusters'], axis=1)
model_name = file_loader.find_correct_model_file(i)
model = file_loader.load_model(model_name)
result = list(model.predict(cluster_data))
self.result = pandas.DataFrame(list(zip(wafer_names, result)),
columns=['Wafer', 'Prediction'])
print(self.result.shape)
print(self.result)
#path="Prediction_Output_File/Predictions.csv"
path = "prediction-output-file"
self.az_blob_mgt.saveDataFrametoCSV(path,
"prediction.csv",
self.result,
header=True,
mode="a+")
#result.to_csv("Prediction_Output_File/Predictions.csv",header=True,mode='a+') #appends result to prediction file
#self.log_writer.log(self.file_object,'End of Prediction')
self.log_db_writer.log(self.log_database, self.log_collection,
'End of prediction')
except Exception as ex:
#self.log_writer.log(self.file_object, 'Error occured while running the prediction!! Error:: %s' % ex)
self.log_db_writer.log(
self.log_database, self.log_collection,
'Error occured while running the prediction!! Error:: %s' % ex)
raise ex
return path, self.result.head().to_json(orient="records")