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 Data_Getter:
"""
This class shall be used for obtaining the data from the source for training.
Written By: iNeuron Intelligence
Version: 1.0
Revisions: None
"""
"""
def __init__(self, file_object, logger_object):
self.training_file='Training_FileFromDB/InputFile.csv'
self.file_object=file_object
self.logger_object=logger_object
"""
def __init__(self, log_database, log_collection, execution_id):
self.log_database = log_database
self.log_collection = log_collection
self.training_directory = "training-file-from-db"
self.filename = "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.logger_object.log(self.file_object,'Entered the get_data method of the Data_Getter class')
self.log_db_writer.log(
self.log_database, self.log_collection,
"Entered the get_data method of the Data_Getter class")
try:
#self.data= pd.read_csv(self.training_file) # reading the data file
self.data = self.az_blob_mgt.readCsvFileFromDirectory(
self.training_directory, self.filename)
#self.logger_object.log(self.file_object,'Data Load Successful.Exited the get_data method of the Data_Getter class')
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.logger_object.log(self.file_object,'Exception occured in get_data method of the Data_Getter class. Exception message: '+str(e))
#self.logger_object.log(self.file_object,
# 'Data Load Unsuccessful.Exited the get_data method of the Data_Getter class')
self.log_db_writer.log(
self.log_database, self.log_collection,
"Exception occured in get_data method of the Data_Getter class. Exception message:Data Load Unsuccessful.Exited the get_data method of the Data_Getter class "
)
raise Exception()
def initiateTransfer(your_connection_string):
"""
:param your_connection_string: pass azure storage account connection string
:return:
"""
try:
azm_source = AzureBlobManagement()
azm_destination = AzureBlobManagement(your_connection_string)
prediction_dir = "prediction-batch-files"
training_dir = "training-batch-files"
training_files = azm_source.getAllFileNameFromDirectory(training_dir)
prediction_files = azm_source.getAllFileNameFromDirectory(
prediction_dir)
for training_file in training_files:
df = azm_source.readCsvFileFromDirectory(training_dir,
training_file)
df.rename(columns={"Unnamed: 1": ""}, inplace=True)
azm_destination.saveDataFrameTocsv(training_dir, training_file, df)
print(
"File:{} transfered successfully to dir {} of account name {}".
format(training_file, training_dir,
azm_destination.blob_service_client.account_name))
for prediction_file in prediction_files:
df = azm_source.readCsvFileFromDirectory(prediction_dir,
prediction_file)
df.rename(columns={"Unnamed: 0.1": ""}, inplace=True)
azm_destination.saveDataFrameTocsv(prediction_dir, prediction_file,
df)
print(
"File:{} transfered successfully to dir {} of account name {}".
format(prediction_file, prediction_dir,
azm_destination.blob_service_client.account_name))
except Exception as e:
print(str(e))
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.exexcution_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 = App_Logger()
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-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
"""
try:
"""code commented by Avnish Yadav
with open(self.schema_path, 'r') as f:
dic = json.load(f)
f.close()
"""
#code started by Avnish Yadav
log_database = "wafer_training_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
#code ended by Avnish Yadav
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.log(file,message) code commented by Avnish Yadav
self.logger_db_writer.log(log_database, log_collection, message)
#file.close()
except ValueError:
#file = open("Training_Logs/valuesfromSchemaValidationLog.txt", 'a+')
#self.logger.log(file,"ValueError:Value not found inside schema_training.json")
#file.close()
self.logger_db_writer.log(
log_database, log_collection,
"Error occured in class:Raw_Data_validation method: valuesFromSchema ValueError:Value not found inside collection schema_training"
)
raise ValueError
except KeyError:
#file = open("Training_Logs/valuesfromSchemaValidationLog.txt", 'a+')
#self.logger.log(file, "KeyError:Key value error incorrect key passed")
#file.close()
self.logger_db_writer.log(
log_database, log_collection,
"Error occured in class:Raw_Data_validation method: valuesFromSchema KeyError:Key value error incorrect key passed"
)
raise KeyError
except Exception as e:
#file = open("Training_Logs/valuesfromSchemaValidationLog.txt", 'a+')
#self.logger.log(file, str(e))
#file.close()
self.logger_db_writer.log(
log_database, log_collection,
"Error occured in class:Raw_Data_validation method: valuesFromSchema error"
+ 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 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)
except OSError as ex:
file = open("Training_Logs/GeneralLog.txt", 'a+')
self.logger.log(file,"Error while creating Directory %s:" % ex)
file.close()
raise OSError
"""
try:
log_database = "wafer_training_log"
log_collection = "general_log"
self.az_blob_mgt.createDirectory(self.good_directory_path,
is_replace=True)
self.az_blob_mgt.createDirectory(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 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"
self.az_blob_mgt.deleteDirectory(self.good_directory_path)
self.logger_db_writer.log(
log_database, log_collection,
self.good_directory_path + " deleted successfully!!")
"""
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
"""
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"
self.az_blob_mgt.deleteDirectory(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:
log_database = "wafer_training_log"
log_collection = "general_log"
#source = 'Training_Raw_files_validated/Bad_Raw/'
source = self.bad_directory_path
destination = "lat-" + self.exexcution_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.moveFileInDirectory(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.deleteDirectory(source)
self.logger_db_writer.log(log_database, log_collection,
"Deleting bad raw directory:" + source)
"""
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()
"""
except Exception as e:
"""
file = open("Training_Logs/GeneralLog.txt", 'a+')
self.logger.log(file, "Error while moving bad files to archive:: %s" % e)
file.close()
"""
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 = [f for f in listdir(self.Batch_Directory)]
onlyfiles = self.az_blob_mgt.getAllFileNameFromDirectory(
self.Batch_Directory)
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.copyFileInDirectory(
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.copyFileInDirectory(
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.copyFileInDirectory(
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.copyFileInDirectory(
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:
"""
f = open("Training_Logs/nameValidationLog.txt", 'a+')
self.logger.log(f, "Error occured while validating FileName %s" % e)
f.close()
"""
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_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!!")
#for file in listdir('Training_Raw_files_validated/Good_Raw/'):
for file in self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_directory_path):
#csv = pd.read_csv("Training_Raw_files_validated/Good_Raw/" + file)
csv = self.az_blob_mgt.readCsvFileFromDirectory(
self.good_directory_path, file)
if csv.shape[1] == NumberofColumns:
pass
else:
"""
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.moveFileInDirectory(
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.log(f, "Column Length Validation Completed!!")
self.logger_db_writer.log(log_database, log_collection,
"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()
"""
self.logger_db_writer.log(
log_database, log_collection,
"Error Occured in class Raw_Data_validation method: validateColumnLength error:"
+ str(e))
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
"""
try:
log_database = "wafer_training_log"
log_collection = "missing_values_in_column"
#f = open("Training_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('Training_Raw_files_validated/Good_Raw/'):
for file in self.az_blob_mgt.getAllFileNameFromDirectory(
self.good_directory_path):
"""
csv = pd.read_csv("Training_Raw_files_validated/Good_Raw/" + file)
count = 0
"""
csv = self.az_blob_mgt.readCsvFileFromDirectory(
self.good_directory_path,
file,
)
count = 0
for columns in csv:
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.moveFileInDirectory(
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:
csv.rename(columns={"Unnamed: 1": "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()
self.logger_db_writer.log(
log_database, log_collection,
"Error Occured class:Raw_Data_validation method:validateMissingValuesInWholeColumn error:"
+ str(e))
raise e
class DbOperationMongoDB:
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 "training_database" ,collection:"Good_Raw_Data"
:return:
"""
try:
prediction_database = "prediction_database"
prediction_collection = "Good_Raw_Data"
database_name = "wafer_prediction_log"
collection_name = "db_insert_log"
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.readCsvFileFromDirectory(
self.good_file_path, file)
df.columns = column_names
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.moveFileInDirectory(
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:
"""
try:
directory_name = "prediction-file-from-db"
file_name = "InputFile.csv"
database_name = "wafer_prediction_log"
collection_name = "export_to_csv"
prediction_database = "prediction_database"
prediction_collection = "Good_Raw_Data"
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)
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 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
"""
try:
log_collection = "data_transform_log"
#log_file = open("Prediction_Logs/dataTransformLog.txt", 'a+')
#onlyfiles = [f for f in listdir(self.goodDataPath)]
onlyfiles = self.az_blob_mgt.getAllFileNameFromDirectory(
self.goodDataPath)
for file in onlyfiles:
#csv = pandas.read_csv(self.goodDataPath+"/" + file)
csv = self.az_blob_mgt.readCsvFileFromDirectory(
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.az_blob_mgt.saveDataFrameTocsv(self.goodDataPath,
file,
csv,
idex=None,
header=True)
#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))
#log_file.write("Current Date :: %s" %date +"\t" + "Current time:: %s" % current_time + "\t \t" + + "\n")
except Exception as e:
#self.logger.log(log_file, "Data Transformation failed because:: %s" % e)
#log_file.write("Current Date :: %s" %date +"\t" +"Current time:: %s" % current_time + "\t \t" + "Data Transformation failed because:: %s" % e + "\n")
#log_file.close()
self.log_db_writer.log(
self.log_database, log_collection,
'Data Transformation failed because:' + str(e))
raise e