class dataTransformPredict:
"""
This class shall be used for transforming the Good Raw Training Data before loading it in Database!!.
"""
def __init__(self):
self.goodDataPath = "Prediction_Good_Raw_Files_Validated"
self.logger = App_Logger()
self.awsObj = AwsStorageManagement()
def addQuotesToStringValuesInColumn(self):
"""
Method Name: addQuotesToStringValuesInColumn
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.
"""
try:
log_file = 'dataTransformLog'
onlyfiles = self.awsObj.listDirFiles(self.goodDataPath)
for file in onlyfiles:
data = self.awsObj.csvToDataframe(self.goodDataPath, file)
data['stalk-root'] = data['stalk-root'].replace('?', "'?'")
self.awsObj.saveDataframeToCsv(self.goodDataPath, file, data)
self.logger.log(log_file,
" %s: Quotes added successfully!!" % file)
except Exception as e:
log_file = 'dataTransformLog'
self.logger.log(log_file,
"Data Transformation failed because:: %s" % e)
raise e
class dataTransform:
"""
This class shall be used for transforming the Good Raw Training Data before loading it in Database!!.
"""
def __init__(self):
self.goodDataPath = "Training_Good_Raw_Files_Validated"
self.logger = App_Logger()
self.awsObj = AwsStorageManagement()
def addQuotesToStringValuesInColumn(self):
"""
Method Name: addQuotesToStringValuesInColumn
Description: This method converts all the columns with string datatype such that
each value for that column is enclosed in quotes. This is done
to avoid the error while inserting string values in table as varchar.
"""
log_file = 'addQuotesToStringValuesInColumn'
try:
onlyfiles = self.awsObj.listDirFiles(self.goodDataPath)
for file in onlyfiles:
data = self.awsObj.csvToDataframe(self.goodDataPath, file)
for column in data.columns:
count = data[column][data[column] == '?'].count()
if count != 0:
data[column] = data[column].replace('?', "'?'")
self.awsObj.saveDataframeToCsv(self.goodDataPath, file, data)
self.logger.log(log_file," %s: Quotes added successfully!!" % file)
except Exception as e:
self.logger.log(log_file, "Data Transformation failed because:: %s" % e)
class Prediction_Data_validation:
"""
This class shall be used for handling all the validation done on the Raw Prediction Data!!.
"""
def __init__(self, path):
self.Batch_Directory = path
self.schema_path = 'schema_prediction.json'
self.logger = App_Logger()
self.awsObj = AwsStorageManagement()
self.dbObj = mongoDBOperation()
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
"""
try:
if not self.dbObj.isCollectionPresent('mushroomClassifierDB',
'predict_schema'):
with open(self.schema_path, 'r') as f:
dic = json.load(f)
f.close()
self.dbObj.insertOneRecord('mushroomClassifierDB',
'predict_schema', dic)
dic = self.dbObj.getRecords('mushroomClassifierDB',
'predict_schema')
pattern = dic['SampleFileName']
LengthOfDateStampInFile = dic['LengthOfDateStampInFile']
LengthOfTimeStampInFile = dic['LengthOfTimeStampInFile']
column_names = dic['ColName']
NumberofColumns = dic['NumberofColumns']
file = 'valuesfromSchemaValidationLog'
message = "LengthOfDateStampInFile:: %s" % LengthOfDateStampInFile + "\t" + "LengthOfTimeStampInFile:: %s" % LengthOfTimeStampInFile + "\t " + "NumberofColumns:: %s" % NumberofColumns + "\n"
self.logger.log(file, message)
except ValueError:
file = 'valuesfromSchemaValidationLog'
self.logger.log(
file, "ValueError:Value not found inside schema_training.json")
raise ValueError
except KeyError:
file = 'valuesfromSchemaValidationLog'
self.logger.log(file,
"KeyError:Key value error incorrect key passed")
raise KeyError
except Exception as e:
file = 'valuesfromSchemaValidationLog'
self.logger.log(file, 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
"""
regex = "['mushroom']+['\_'']+[\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: Exception
"""
try:
self.awsObj.createS3Directory(
'Prediction_Good_Raw_Files_Validated')
self.awsObj.createS3Directory('Prediction_Bad_Raw_Files_Validated')
except Exception as ex:
file = 'GeneralLog'
self.logger.log(file, "Error while creating Directory %s:" % ex)
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: Exception
"""
try:
file = 'GeneralLog'
self.logger.log(file, "GoodRaw directory deleted successfully!!!")
self.awsObj.deleteDirectory('Prediction_Good_Raw_Files_Validated')
except Exception as s:
file = 'GeneralLog'
self.logger.log(file, "Error while Deleting Directory : %s" % s)
raise s
def deleteExistingBadDataTrainingFolder(self):
"""
Method Name: deleteExistingBadDataTrainingFolder
Description: This method deletes the directory made to store the bad Data.
Output: None
On Failure: Exception
"""
try:
file = 'GeneralLog'
self.logger.log(
file, "BadRaw directory deleted before starting validation!!!")
self.awsObj.deleteDirectory('Prediction_Bad_Raw_Files_Validated')
except Exception as s:
file = 'GeneralLog'
self.logger.log(file, "Error while Deleting Directory : %s" % s)
raise s
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: Exception
"""
now = datetime.now()
date = now.date()
time = now.strftime("%H%M%S")
try:
target_folder = 'PredictionArchivedBadData/BadData_' + str(
date) + "_" + str(time)
self.awsObj.copyFileToFolder('Prediction_Bad_Raw_Files_Validated',
target_folder)
file = 'GeneralLog'
self.logger.log(file, "Bad files moved to archive")
self.logger.log(file, "Bad Raw Data Folder Deleted successfully!!")
except Exception as e:
file = 'GeneralLog'
self.logger.log(file,
"Error while moving bad files to archive:: %s" % 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
"""
# delete the directories for good and bad data in case last run was unsuccessful and folders were not deleted.
self.deleteExistingBadDataTrainingFolder()
self.deleteExistingGoodDataTrainingFolder()
self.createDirectoryForGoodBadRawData()
batch_dir = self.Batch_Directory.strip('/').strip('\\')
print('Prediction File Path: ', batch_dir)
self.awsObj.uploadFiles(batch_dir, batch_dir)
onlyfiles = self.awsObj.listDirFiles(batch_dir)
try:
f = 'nameValidationLog'
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:
self.awsObj.copyFileToFolder(
batch_dir,
'Prediction_Good_Raw_Files_Validated',
filename)
self.logger.log(
f,
"Valid File name!! File moved to GoodRaw Folder :: %s"
% filename)
else:
self.awsObj.copyFileToFolder(
self.Batch_Directory,
'Prediction_Bad_Raw_Files_Validated', filename)
self.logger.log(
f,
"Invalid File Name!! File moved to Bad Raw Folder :: %s"
% filename)
else:
self.awsObj.copyFileToFolder(
self.Batch_Directory,
'Prediction_Bad_Raw_Files_Validated', filename)
self.logger.log(
f,
"Invalid File Name!! File moved to Bad Raw Folder :: %s"
% filename)
else:
self.awsObj.copyFileToFolder(
self.Batch_Directory,
'Prediction_Bad_Raw_Files_Validated', filename)
self.logger.log(
f,
"Invalid File Name!! File moved to Bad Raw Folder :: %s"
% filename)
except Exception as e:
f = 'nameValidationLog'
self.logger.log(f,
"Error occured while validating FileName %s" % e)
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
"""
try:
f = 'columnValidationLog'
self.logger.log(f, "Column Length Validation Started!!")
file_list = self.awsObj.listDirFiles(
'Prediction_Good_Raw_Files_Validated')
for file in file_list:
csv = self.awsObj.csvToDataframe(
'Prediction_Good_Raw_Files_Validated', file)
if csv.shape[1] == NumberofColumns:
self.awsObj.saveDataframeToCsv(
'Prediction_Good_Raw_Files_Validated', file, csv)
else:
self.awsObj.moveFileToFolder(
'Prediction_Good_Raw_Files_Validated',
'Prediction_Bad_Raw_Files_Validated', file)
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 = 'columnValidationLog'
self.logger.log(
f, "Error Occurred while moving the file :: %s" % OSError)
raise OSError
except Exception as e:
f = 'columnValidationLog'
self.logger.log(f, "Error Occurred:: %s" % e)
raise e
def deletePredictionFile(self):
self.awsObj.deleteFile('Prediction_Output_File', 'Predictions.csv')
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
"""
try:
f = 'missingValuesInColumn'
self.logger.log(f, "Missing Values Validation Started!!")
file_list = self.awsObj.listDirFiles(
'Prediction_Good_Raw_Files_Validated')
for file in file_list:
csv = self.awsObj.csvToDataframe(
'Prediction_Good_Raw_Files_Validated', file)
count = 0
for columns in csv:
if (len(csv[columns]) - csv[columns].count()) == len(
csv[columns]):
count += 1
self.awsObj.moveFileToFolder(
'Prediction_Good_Raw_Files_Validated',
'Prediction_Bad_Raw_Files_Validated', file)
self.logger.log(
f,
"Invalid Column Length for the file!! File moved to Bad Raw Folder :: %s"
% file)
break
if count == 0:
self.awsObj.saveDataframeToCsv(
'Prediction_Good_Raw_Files_Validated', file, csv)
except OSError:
f = 'missingValuesInColumn'
self.logger.log(
f, "Error Occurred while moving the file :: %s" % OSError)
raise OSError
except Exception as e:
f = 'missingValuesInColumn'
self.logger.log(f, "Error Occurred:: %s" % e)
raise e
class Preprocessor:
"""
This class shall be used to clean and transform the data before training.
"""
def __init__(self, file_object, logger_object):
self.file_object = file_object
self.logger_object = logger_object
self.awsObj = AwsStorageManagement()
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
"""
self.logger_object.log(self.file_object, '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.logger_object.log(self.file_object,
'Column removal Successful.Exited the remove_columns method of the Preprocessor class')
return self.useful_data
except Exception as e:
self.logger_object.log(self.file_object,'Exception occured in remove_columns method of the Preprocessor class. Exception message: '+str(e))
self.logger_object.log(self.file_object,
'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
"""
self.logger_object.log(self.file_object, 'Entered the separate_label_feature method of the Preprocessor class')
try:
self.X=data.drop(label_column_name,axis=1) # drop the columns specified and separate the feature columns
self.Y=data[label_column_name] # Filter the Label columns
print(self.Y)
self.logger_object.log(self.file_object,
'Label Separation Successful. Exited the separate_label_feature method of the Preprocessor class')
return self.X,self.Y
except Exception as e:
self.logger_object.log(self.file_object,'Exception occured in separate_label_feature method of the Preprocessor class. Exception message: ' + str(e))
self.logger_object.log(self.file_object, '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.
"""
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.
"""
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
"""
self.logger_object.log(self.file_object, '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
self.dataframe_with_null = pd.DataFrame()
self.dataframe_with_null['columns'] = data.columns
self.dataframe_with_null['missing values count'] = np.asarray(data.isna().sum())
self.awsObj.saveDataframeToCsv('preprocessing_data', 'null_values.csv', self.dataframe_with_null)
#self.dataframe_with_null.to_csv('preprocessing_data/null_values.csv') # storing the null column information to file
self.logger_object.log(self.file_object,'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.logger_object.log(self.file_object,'Exception occured in is_null_present method of the Preprocessor class. Exception message: ' + str(e))
self.logger_object.log(self.file_object,'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
"""
print('Encode Categorical Values')
print(data['class'].value_counts())
data['class'] = data['class'].map({"'p'": 1, "'e'": 2})
print(data['class'].value_counts())
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
"""
for column in data.columns:
data = pd.get_dummies(data, columns=[column],drop_first=True)
return data
def impute_missing_values(self, data, cols_with_missing_values):
"""
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
"""
self.logger_object.log(self.file_object, 'Entered the impute_missing_values method of the Preprocessor class')
self.data= data
self.cols_with_missing_values=cols_with_missing_values
try:
self.imputer = CategoricalImputer()
for col in self.cols_with_missing_values:
self.data[col] = self.imputer.fit_transform(self.data[col])
self.logger_object.log(self.file_object, 'Imputing missing values Successful. Exited the impute_missing_values method of the Preprocessor class')
return self.data
except Exception as e:
self.logger_object.log(self.file_object,'Exception occured in impute_missing_values method of the Preprocessor class. Exception message: ' + str(e))
self.logger_object.log(self.file_object,'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
"""
self.logger_object.log(self.file_object, '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.logger_object.log(self.file_object, 'Column search for Standard Deviation of Zero Successful. Exited the get_columns_with_zero_std_deviation method of the Preprocessor class')
return self.col_to_drop
except Exception as e:
self.logger_object.log(self.file_object,'Exception occured in get_columns_with_zero_std_deviation method of the Preprocessor class. Exception message: ' + str(e))
self.logger_object.log(self.file_object, '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):
self.file_object = 'Prediction_Log'
self.log_writer = logger.App_Logger()
self.awsObj = AwsStorageManagement()
self.emailObj = email()
if path is not None:
self.pred_data_val = Prediction_Data_validation(path)
def predictionFromModel(self):
try:
self.pred_data_val.deletePredictionFile(
) #deletes the existing prediction file from last run!
self.log_writer.log(self.file_object, 'Start of Prediction')
data_getter = data_loader_prediction.Data_Getter_Pred(
self.file_object, self.log_writer)
data = data_getter.get_data()
preprocessor = preprocessing.Preprocessor(self.file_object,
self.log_writer)
data = preprocessor.dropUnnecessaryColumns(data, ['veil-type'])
# replacing '?' values with np.nan as discussed in the EDA part
data = preprocessor.replaceInvalidValuesWithNull(data)
is_null_present, cols_with_missing_values = preprocessor.is_null_present(
data)
if (is_null_present):
data = preprocessor.impute_missing_values(
data, cols_with_missing_values)
# get encoded values for categorical data
data = preprocessor.encodeCategoricalValuesPrediction(data)
#data=data.to_numpy()
file_loader = file_methods.File_Operation(self.file_object,
self.log_writer)
kmeans = file_loader.load_model('KMeans')
print(kmeans.labels_)
##Code changed
#pred_data = data.drop(['Wafer'],axis=1)
clusters = kmeans.predict(data)
data['clusters'] = clusters
clusters = data['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[data['clusters'] == i]
cluster_data = cluster_data.drop(['clusters'], axis=1)
model_name = file_loader.find_correct_model_file(i)
model = file_loader.load_model(model_name)
for val in (model.predict(cluster_data)):
result.append(val)
result = pandas.DataFrame(result, columns=['Predictions'])
path = "Prediction_Output_File/Predictions.csv"
self.awsObj.saveDataframeToCsv('Prediction_Output_File',
'Predictions.csv', result)
self.log_writer.log(self.file_object, 'End of Prediction')
msg = MIMEMultipart()
msg['Subject'] = 'MushroomTypeClassifier - Prediction Done | ' + str(
datetime.now())
body = 'Model Prediction Done Successfully... <br><br> Thanks and Regards, <br> Rahul Garg'
msg.attach(MIMEText(body, 'html'))
to_addr = ['*****@*****.**']
self.emailObj.trigger_mail(to_addr, [], msg)
except Exception as ex:
self.log_writer.log(
self.file_object,
'Error occured while running the prediction!! Error:: %s' % ex)
raise ex
return path
class dBOperation:
"""
This class shall be used for handling all the SQL operations.
"""
def __init__(self):
self.path = 'Training_Database'
self.badFilePath = "Training_Bad_Raw_Files_Validated"
self.goodFilePath = "Training_Good_Raw_Files_Validated"
self.logger = App_Logger()
self.awsObj = AwsStorageManagement()
self.dbObj = mongoDBOperation()
def createTableDb(self, DatabaseName, column_names):
"""
Method Name: createTableDb
Description: This method creates a table in the given database which will be used to insert the Good data after raw data validation.
Output: None
On Failure: Raise Exception
"""
try:
self.dbObj.createOrGetCollection(DatabaseName, 'Good_Raw_Data')
file = 'DbTableCreateLog'
self.logger.log(file, "Tables created successfully!!")
file = 'DataBaseConnectionLog'
self.logger.log(file,
"Closed %s database successfully" % DatabaseName)
except Exception as e:
file = 'DbTableCreateLog'
self.logger.log(file, "Error while creating table: %s " % e)
file = 'DataBaseConnectionLog'
self.logger.log(file,
"Closed %s database successfully" % DatabaseName)
raise e
def insertIntoTableGoodData(self, Database):
"""
Method Name: insertIntoTableGoodData
Description: This method inserts the Good data files from the Good_Raw folder into the
above created table.
Output: None
On Failure: Raise Exception
"""
goodFilePath = self.goodFilePath
badFilePath = self.badFilePath
onlyfiles = self.awsObj.listDirFiles(goodFilePath)
log_file = 'DbInsertLog'
self.dbObj.dropCollection(Database, 'Good_Raw_Data')
print(onlyfiles)
for file in onlyfiles:
try:
df_csv = self.awsObj.csvToDataframe(self.goodFilePath, file)
print('df_csv: ', df_csv.shape)
self.dbObj.dataframeToRecords(Database, 'Good_Raw_Data',
df_csv)
except Exception as e:
self.logger.log(log_file,
"Error while creating table: %s " % e)
self.awsObj.moveFileToFolder(goodFilePath, badFilePath, file)
self.logger.log(log_file, "File Moved Successfully %s" % file)
print('Data pushed to mongodb...')
def selectingDatafromtableintocsv(self, Database):
"""
Method Name: selectingDatafromtableintocsv
Description: This method exports the data in GoodData table as a CSV file. in a given location.
above created .
Output: None
On Failure: Raise Exception
"""
self.fileFromDb = 'Training_FileFromDB'
self.fileName = 'InputFile.csv'
self.awsObj.createS3Directory(self.fileFromDb)
log_file = 'ExportToCsv'
try:
tmp_csv = self.dbObj.recordsToDataFrame(Database, 'Good_Raw_Data')
self.awsObj.saveDataframeToCsv('Training_FileFromDB',
self.fileName, tmp_csv)
self.logger.log(log_file, "File exported successfully!!!")
print('Saving data to final csv')
except Exception as e:
self.logger.log(log_file, "File exporting failed. Error : %s" % e)
