class TrainModel:
def __init__(self,run_id,data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'TrainModel', 'training')
self.loadValidate = LoadValidate(self.run_id, self.data_path,'training')
self.preProcess = Preprocessor(self.run_id, self.data_path,'training')
self.modelTuner = ModelTuner(self.run_id, self.data_path, 'training')
self.fileOperation = FileOperation(self.run_id, self.data_path, 'training')
self.cluster = KMeansCluster(self.run_id, self.data_path)
def training_model(self):
try:
self.logger.info('Start of Training')
self.logger.info('Run_id:' + str(self.run_id))
#Load, validations and transformation
self.loadValidate.validate_trainset()
#preprocessing activities
self.X, self.y = self.preProcess.preprocess_trainset()
columns = {"data_columns":[col for col in self.X.columns]}
with open('apps/database/columns.json','w') as f:
f.write(json.dumps(columns))
#create clusters
number_of_clusters = self.cluster.elbow_plot(self.X)
# Divide the data into clusters
self.X= self.cluster.create_clusters(self.X, number_of_clusters)
# create a new column in the dataset consisting of the corresponding cluster assignments.
self.X['Labels'] = self.y
# getting the unique clusters from our data set
list_of_clusters = self.X['Cluster'].unique()
# parsing all the clusters and look for the best ML algorithm to fit on individual cluster
for i in list_of_clusters:
cluster_data=self.X[self.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=0.2, random_state=0)
#getting the best model for each of the clusters
best_model_name, best_model = self.modelTuner.get_best_model(x_train, y_train, x_test, y_test)
#saving the best model to the directory.
save_model=self.fileOperation.save_model(best_model,best_model_name+str(i))
self.logger.info('End of Training')
except Exception:
self.logger.exception('Unsuccessful End of Training')
raise Exception
class TrainModel:
"""
*****************************************************************************
*
* filename: TrainModel.py
* version: 1.0
* author:
* creation date:
*
*
*
*
* description: Class to training the models
*
****************************************************************************
"""
def __init__(self, run_id, data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'TrainModel', 'training')
self.loadValidate = LoadValidate(self.run_id, self.data_path,
'training')
self.preProcess = Preprocessor(self.run_id, self.data_path, 'training')
self.modelTuner = ModelTuner(self.run_id, self.data_path, 'training')
self.fileOperation = FileOperation(self.run_id, self.data_path,
'training')
# self.cluster = KMeansCluster(self.run_id, self.data_path)
def training_model(self):
"""
* method: trainingModel
* description: method to training the model
* return: none
*
*
* Parameters
* none:
"""
try:
self.logger.info('Start of Training')
self.logger.info('Run_id:' + str(self.run_id))
# Load, validations and transformation
self.loadValidate.validate_trainset()
# preprocessing activities
self.X, self.y = self.preProcess.preprocess_trainset()
columns = {"data_columns": [col for col in self.X.columns]}
with open('apps/database/columns.json', 'w') as f:
f.write(json.dumps(columns))
# 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(
self.X, self.y, test_size=0.2, random_state=0)
# getting the best model for each of the clusters
best_model_name, best_model = self.modelTuner.get_best_model(
x_train, y_train, x_test, y_test)
# saving the best model to the directory.
save_model = self.fileOperation.save_model(
best_model, best_model_name)
self.logger.info('End of Training')
except Exception:
self.logger.exception('Unsuccessful End of Training')
raise Exception
class KMeansCluster:
def __init__(self, run_id, data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'KMeansCluster', 'training')
self.fileOperation = FileOperation(self.run_id, self.data_path,
'training')
def elbow_plot(self, data):
wcss = [] # initializing an empty list --within cluster sum of errors
try:
self.logger.info('Start of elbow plotting...')
for i in range(1, 11):
kmeans = KMeans(
n_clusters=i, init='k-means++',
random_state=0) # initializing the KMeans object
kmeans.fit(data) # fitting the data to the KMeans Algorithm
wcss.append(kmeans.inertia_)
plt.plot(
range(1, 11), wcss
) # creating the graph between WCSS and the number of clusters
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
#plt.show()
plt.savefig('apps/models/kmeans_elbow.png'
) # saving the elbow plot locally
# finding the value of the optimum cluster programmatically
self.kn = KneeLocator(range(1, 11),
wcss,
curve='convex',
direction='decreasing')
self.logger.info('The optimum number of clusters is: ' +
str(self.kn.knee))
self.logger.info('End of elbow plotting...')
return self.kn.knee
except Exception as e:
self.logger.exception('Exception raised while elbow plotting:' +
str(e))
raise Exception()
def create_clusters(self, data, number_of_clusters):
self.data = data
try:
self.logger.info('Start of Create clusters...')
self.kmeans = KMeans(n_clusters=number_of_clusters,
init='k-means++',
random_state=0)
self.y_kmeans = self.kmeans.fit_predict(
data) # divide data into clusters
self.saveModel = self.fileOperation.save_model(
self.kmeans, 'KMeans')
# saving the KMeans model to directory
# passing 'Model' as the functions need three parameters
self.data[
'Cluster'] = self.y_kmeans # create a new column in dataset for storing the cluster information
self.logger.info('succesfully created ' + str(self.kn.knee) +
'clusters.')
self.logger.info('End of Create clusters...')
return self.data
except Exception as e:
self.logger.exception('Exception raised while Creating clusters:' +
str(e))
raise Exception()
class KMeansCluster:
"""
*****************************************************************************
*
* filename: KMeansCluster.py
* version: 1.0
* author: CODESTUDIO
* creation date: 05-MAY-2020
*
* change history:
*
* who when version change (include bug# if apply)
* ---------- ----------- ------- ------------------------------
* bcheekati 05-MAY-2020 1.0 initial creation
*
*
* description: Class to cluster the dataset
*
****************************************************************************
"""
def __init__(self, run_id, data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'KMeansCluster', 'training')
self.fileOperation = FileOperation(self.run_id, self.data_path,
'training')
def elbow_plot(self, data):
"""
* method: log
* description: method to saves the plot to decide the optimum number of clusters to the file.
* return: A picture saved to the directory
*
* who when version change (include bug# if apply)
* ---------- ----------- ------- ------------------------------
* bcheekati 05-MAY-2020 1.0 initial creation
*
* Parameters
* data:
"""
wcss = [] # initializing an empty list --within cluster sum of errors
try:
self.logger.info('Start of elbow plotting...')
for i in range(1, 11):
kmeans = KMeans(
n_clusters=i, init='k-means++',
random_state=0) # initializing the KMeans object
kmeans.fit(data) # fitting the data to the KMeans Algorithm
wcss.append(kmeans.inertia_)
plt.plot(
range(1, 11), wcss
) # creating the graph between WCSS and the number of clusters
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
#plt.show()
plt.savefig('apps/models/kmeans_elbow.png'
) # saving the elbow plot locally
# finding the value of the optimum cluster programmatically
self.kn = KneeLocator(range(1, 11),
wcss,
curve='convex',
direction='decreasing')
self.logger.info('The optimum number of clusters is: ' +
str(self.kn.knee))
self.logger.info('End of elbow plotting...')
return self.kn.knee
except Exception as e:
self.logger.exception('Exception raised while elbow plotting:' +
str(e))
raise Exception()
def create_clusters(self, data, number_of_clusters):
"""
* method: create_clusters
* description: method to create clusters
* return: A date frame with cluster column
*
* who when version change (include bug# if apply)
* ---------- ----------- ------- ------------------------------
* bcheekati 05-MAY-2020 1.0 initial creation
*
* Parameters
* data:
* number_of_clusters:
"""
self.data = data
try:
self.logger.info('Start of Create clusters...')
self.kmeans = KMeans(n_clusters=number_of_clusters,
init='k-means++',
random_state=0)
self.y_kmeans = self.kmeans.fit_predict(
data) # divide data into clusters
self.saveModel = self.fileOperation.save_model(
self.kmeans, 'KMeans')
# saving the KMeans model to directory
# passing 'Model' as the functions need three parameters
self.data[
'Cluster'] = self.y_kmeans # create a new column in dataset for storing the cluster information
self.logger.info('succesfully created ' + str(self.kn.knee) +
'clusters.')
self.logger.info('End of Create clusters...')
return self.data
except Exception as e:
self.logger.exception('Exception raised while Creating clusters:' +
str(e))
raise Exception()