def trainingModel(self):
self.log_writer.log(self.file_object, 'Start of Training')
try:
data_getter = Data_Getter(self.file_object, self.log_writer)
data = data_getter.get_data()
preprocessor = Preprocessor(self.file_object, self.log_writer)
X, Y = preprocessor.separate_label_feature(
data, label_column_name='Calories')
is_null_present = preprocessor.is_null_present(X)
if (is_null_present):
X = preprocessor.impute_missing_values(X)
cols_to_drop = preprocessor.get_columns_with_zero_std_deviation(X)
X = preprocessor.remove_columns(X, cols_to_drop)
x_train, x_test, y_train, y_test = train_test_split(
X, Y, test_size=1 / 3, random_state=355)
model_finder = Model_Finder(self.file_object, self.log_writer)
best_model_name, best_model = model_finder.get_best_model(
x_train, y_train, x_test, y_test)
file_op = File_operation(self.file_object, self.log_writer)
save_model = file_op.save_model(best_model, best_model_name)
self.log_writer.log(self.file_object, 'Successful End of Training')
self.file_object.close()
except Exception:
self.log_writer.log(self.file_object,
'Unsuccessful End of Training')
self.file_object.close()
raise Exception
def predictFromModel(self):
try:
self.pred_data_val.deletePredictionFile()
self.log_writer.log(self.file_object, 'Start of Prediction')
data_getter = Data_Getter_Prediction(self.file_object,
self.log_writer)
data = data_getter.get_data()
preprocessor = Preprocessor(self.file_object, self.log_writer)
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)
file_loader = File_operation(self.file_object, self.log_writer)
model = file_loader.load_model('my_model')
X, y = preprocessor.separate_label_feature(data, 'Calories')
result = list(model.predict(X.values))
result = pd.Series(result, name='Predictions')
path = "Prediction_Output_File/Predictions.csv"
result.to_csv("Prediction_Output_File/Predictions.csv",
header=True,
mode='a+')
self.log_writer.log(self.file_object, 'End of Prediction')
except Exception as ex:
self.log_writer.log(
self.file_object,
'Error occured while running the prediction!! Error:: %s' % ex)
raise ex
return path, result.head().to_json(orient="records")
def predictionFromModel(self):
try:
self.pred_data_val.deletePredictionFile()
self.log_writer.log(self.file_object, "Prediction_Log",
'Start of Prediction')
data_getter = data_loader_prediction.Data_Getter_Pred(
self.file_object, self.log_writer)
data = data_getter.get_data()
"""doing the data preprocessing"""
preprocessor = Preprocessor(self.file_object, self.log_writer)
data = preprocessor.remove_columns(
data, ["Unnamed: 0"]
) # remove the unnamed column as it doesn't contribute to prediction.
is_null_present = preprocessor.is_null_present(data)
# if missing values are there, replace them appropriately.
if (is_null_present):
data = preprocessor.impute_missing_values(
data) # missing value imputation
# check further which columns do not contribute to predictions
# if the standard deviation for a column is zero, it means that the column has constant values
# and they are giving the same output both for good and bad sensors
# prepare the list of such columns to drop
cols_to_drop = preprocessor.get_columns_with_zero_std_deviation(
data)
# drop the columns obtained above
data = preprocessor.remove_columns(data, cols_to_drop)
""" Applying the clustering approach"""
file_loader = file_methods.File_Operation(self.file_object,
self.log_writer)
kmeans = file_loader.load_model('KMeans')
##Code changed
# pred_data = data.drop(['Wafer'],axis=1)
clusters = kmeans.predict(data.drop(
['Wafer'],
axis=1)) # drops the first column for cluster prediction
data['clusters'] = clusters
clusters = data['clusters'].unique()
for i in clusters:
cluster_data = data[data['clusters'] == i]
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))
result = pandas.DataFrame(list(zip(wafer_names, result)),
columns=['Wafer', 'Prediction'])
#path = "Predictions.csv"
pred_result = result.to_csv(header=True)
self.AzureFunc.uploadBlob("predictionoutputfile",
"predictions.csv", pred_result)
output = self.AzureFunc.readingcsvfile("predictionoutputfile",
"predictions.csv")
#result.to_csv("Predictions.csv", header=True,mode='a+') # appends result to prediction file
self.log_writer.log(self.file_object, "Prediction_Log",
'End of Prediction')
except Exception as ex:
self.log_writer.log(
self.file_object, "Prediction_Log",
'Error occured while running the prediction!! Error:: %s' % ex)
raise ex
return output.head().to_json(orient="records")
def trainingModel(self):
# Logging the start of Training
self.log_writer.log(self.file_object, "ModelTrainingLog",
'Start of Training')
# Getting the data from the source
data_getter = data_loader.Data_Getter(self.file_object,
self.log_writer)
data = data_getter.get_data()
"""doing the data preprocessing"""
preprocessor = Preprocessor(self.file_object, self.log_writer)
data = preprocessor.remove_columns(
data, ['Wafer', "Unnamed: 0"]
) # remove the unnamed column as it doesn't contribute to prediction.
# create separate features and labels
X, Y = preprocessor.separate_label_feature(
data, label_column_name='Good/Bad')
is_null_present = preprocessor.is_null_present(X)
# if missing values are there, replace them appropriately.
if (is_null_present):
X = preprocessor.impute_missing_values(
X) # missing value imputation
# check further which columns do not contribute to predictions
# if the standard deviation for a column is zero, it means that the column has constant values
# and they are giving the same output both for good and bad sensors
# prepare the list of such columns to drop
cols_to_drop = preprocessor.get_columns_with_zero_std_deviation(X)
# drop the columns obtained above
X = preprocessor.remove_columns(X, cols_to_drop)
""" Applying the clustering approach"""
kmeans = clustering.KMeansClustering(
self.file_object, self.log_writer) # object initialization.
number_of_clusters = kmeans.elbow_plot(
X
) # using the elbow plot to find the number of optimum clusters
# Divide the data into clusters
X = kmeans.create_clusters(X, number_of_clusters)
# create a new column in the dataset consisting of the corresponding cluster assignments.
X['Labels'] = Y
# getting the unique clusters from our dataset
list_of_clusters = X['Cluster'].unique()
"""parsing all the clusters and looking for the best ML algorithm to fit on individual cluster"""
for i in list_of_clusters:
cluster_data = X[X['Cluster'] ==
i] # filter the data for one cluster
# Prepare the feature and Label columns
cluster_features = cluster_data.drop(['Labels', 'Cluster'],
axis=1)
cluster_label = cluster_data['Labels']
# splitting the data into training and test set for each cluster one by one
x_train, x_test, y_train, y_test = train_test_split(
cluster_features,
cluster_label,
test_size=1 / 3,
random_state=355)
model_finder = tuner.Model_Finder(
self.file_object, self.log_writer) # object initialization
# getting the best model for each of the clusters
best_model_name, best_model = model_finder.get_best_model(
x_train, y_train, x_test, y_test)
# saving the best model to the directory.
file_op = file_methods.File_Operation(self.file_object,
self.log_writer)
save_model = file_op.save_model(best_model,
best_model_name + str(i))
# logging the successful Training
self.log_writer.log(self.file_object, "ModelTrainingLog",
'Successful End of Training')
