def regression(self):
self.nn = self.get_pure_model()
self.nn.fit(self.data.Xtrain, self.data.Ytrain.to_numpy().flatten())
nn_Ypred = self.nn.predict(self.data.Xtest)
self.nn_mean_squared_error = mean_squared_error(self.data.Ytest, nn_Ypred)
self.nn_r2_score = r2_score(self.data.Ytest, nn_Ypred)
print_training_result_summary('NeuralNetwork', self.nn_mean_squared_error, self.nn_r2_score)
def regression(self):
self.lasso = linear_model.Lasso(alpha=0.1)
self.lasso.fit(self.data.Xtrain, self.data.Ytrain)
lasso_Ypred = self.lasso.predict(self.data.Xtest)
self.lasso_mean_squared_error = mean_squared_error(
self.data.Ytest, lasso_Ypred)
self.lasso_r2_score = r2_score(self.data.Ytest, lasso_Ypred)
print_training_result_summary('Lasso', self.lasso_mean_squared_error,
self.lasso_r2_score)
def regression(self):
self.eNet = ElasticNetModel(alpha=1.0)
self.eNet.fit(self.data.Xtrain, self.data.Ytrain)
eNet_Ypred = self.eNet.predict(self.data.Xtest)
self.eNet_mean_squared_error = mean_squared_error(
self.data.Ytest, eNet_Ypred)
self.eNet_r2_score = r2_score(self.data.Ytest, eNet_Ypred)
print_training_result_summary('Elastic Net',
self.eNet_mean_squared_error,
self.eNet_r2_score)
def regression(self):
self.DecisionTreeM = tree.DecisionTreeRegressor()
self.DecisionTreeM.fit(self.data.Xtrain, self.data.Ytrain)
DecisionTree_Ypred = self.DecisionTreeM.predict(self.data.Xtest)
self.DecisionTree_mean_squared_error = mean_squared_error(
self.data.Ytest, DecisionTree_Ypred)
self.DecisionTree_r2_score = r2_score(self.data.Ytest,
DecisionTree_Ypred)
print_training_result_summary('Decision Tree',
self.DecisionTree_mean_squared_error,
self.DecisionTree_r2_score)
def regression(self):
self.lasso = linear_model.Lasso(alpha=1.0)
self.lasso.fit(self.data.Xtrain, self.data.Ytrain)
lasso_Ypred = self.lasso.predict(self.data.Xtest)
self.lasso_mean_squared_error = mean_squared_error(
self.data.Ytest, lasso_Ypred)
self.lasso_r2_score = r2_score(self.data.Ytest, lasso_Ypred)
print_training_result_summary('Lasso', self.lasso_mean_squared_error,
self.lasso_r2_score)
self.save_the_trained_model()
self.save_the_class_included_the_trained_model()
return training_result_summary('Lasso', self.lasso_mean_squared_error,
self.lasso_r2_score)
def regression(self):
self.ridge = RidgeModel(alpha=1.0)
self.ridge.fit(self.data.Xtrain, self.data.Ytrain)
ridge_Ypred = self.ridge.predict(self.data.Xtest)
self.ridge_mean_squared_error = mean_squared_error(
self.data.Ytest, ridge_Ypred)
self.ridge_r2_score = r2_score(self.data.Ytest, ridge_Ypred)
print_training_result_summary('Ridge', self.ridge_mean_squared_error,
self.ridge_r2_score)
self.save_the_trained_model()
self.save_the_class_included_the_trained_model()
return training_result_summary('Ridge', self.ridge_mean_squared_error,
self.ridge_r2_score)
def regression(self):
"""
Traing the machine learning model on the internal data.
"""
self.model = linear_model.LinearRegression()
self.model.fit(self.data.Xtrain, self.data.Ytrain)
model_Ypred = self.model.predict(self.data.Xtest)
self.model_mean_squared_error = mean_squared_error(self.data.Ytest, model_Ypred)
self.model_r2_score = r2_score(self.data.Ytest, model_Ypred)
print_training_result_summary(
'LinearRegression', self.model_mean_squared_error, self.model_r2_score)
self.save_the_trained_model()
self.save_the_class_included_the_trained_model()
return training_result_summary(
'LinearRegression', self.model_mean_squared_error, self.model_r2_score)
def regression(self):
warnings.filterwarnings("ignore")
self.RandomForestM = RandomForestRegressor(max_depth=2, random_state=0)
self.RandomForestM.fit(self.data.Xtrain, self.data.Ytrain)
RandomForest_Ypred = self.RandomForestM.predict(self.data.Xtest)
self.RandomForest_mean_squared_error = mean_squared_error(
self.data.Ytest, RandomForest_Ypred)
self.RandomForest_r2_score = r2_score(self.data.Ytest,
RandomForest_Ypred)
print_training_result_summary('Random Forest',
self.RandomForest_mean_squared_error,
self.RandomForest_r2_score)
self.save_the_trained_model()
self.save_the_class_included_the_trained_model()
return training_result_summary('Random Forest',
self.RandomForest_mean_squared_error,
self.RandomForest_r2_score)
def regression(self):
self.nn = self.get_pure_model()
self.data.Y = self.data.Y.to_numpy()
self.data.X = self.data.X.to_numpy()
# shape the data ex. (5000,)
self.data.Y = self.data.Y[:, 0]
# Partition the data into training and test sets.
self.data.Xtrain, self.data.Xtest, self.data.Ytrain, self.data.Ytest = train_test_split(self.data.X, self.data.Y, test_size=0.33,
random_state=42)
self.nn.fit(self.data.Xtrain, self.data.Ytrain)
nn_Ypred = self.nn.predict(self.data.Xtest)
self.nn_mean_squared_error = mean_squared_error(self.data.Ytest, nn_Ypred)
self.nn_r2_score = r2_score(self.data.Ytest, nn_Ypred)
print_training_result_summary('NeuralNetwork', self.nn_mean_squared_error, self.nn_r2_score)
self.save_the_trained_model()
self.save_the_class_included_the_trained_model()
return training_result_summary('NeuralNetwork', self.nn_mean_squared_error, self.nn_r2_score)