def KNN():
# Fitting Simple Linear Regression to the Training set
classifier = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test).astype(int)
return evaluate_Comparison("KNN", y_test, y_pred)
def decision_tree():
# Fitting Simple Linear Regression to the Training set
regressor = DecisionTreeRegressor()
regressor.fit(X_train, y_train)
# Predicting the Test set results
y_predict = regressor.predict(X_test)
y_pred = np.where(y_predict > 0.5, 1, 0)
return evaluate_Comparison("Decision Tree", y_test, y_pred)
def logistic_training():
# Fitting Simple Linear Regression to the Training set
regressor = LogisticRegression()
regressor.fit(X_train, y_train)
# Predicting the Test set results
y_predict = regressor.predict(X_test)
y_pred = np.where(y_predict > 0.5, 1, 0)
return evaluate_Comparison("Logistic Regression", y_test, y_pred)
def gradient_boosting():
# Fitting Gradient boosting
gbc = GradientBoostingClassifier(n_estimators=100,
learning_rate=0.01,
max_depth=3)
gbc.fit(X_train, y_train)
# Predicting the Test set results
y_predict = gbc.predict(X_test)
y_pred = np.where(y_predict.astype(int) > 0.5, 1, 0)
return evaluate_Comparison("Gradient Boosting", y_test, y_pred)
def GaussianMixture_model():
from sklearn.mixture import GaussianMixture
gmm = GaussianMixture(n_components=1)
gmm.fit(X_train[y_train == 0])
OKscore = gmm.score_samples(X_train[y_train == 0])
threshold = OKscore.mean() - 1 * OKscore.std()
score = gmm.score_samples(X_test)
# majority_correct = len(score[(y_test == 1) & (score > thred)])
y_pred = np.where(score < threshold, 1, 0)
return evaluate_Comparison("GaussianMixture_model", y_test, y_pred)