def pipeline(self):
"""
:return: the roc auc score and the accuracy
"""
self.train()
y_pred = self.predict()
roc_results(y_pred, self.y_test, 'XGBoost')
return roc_auc_score(self.y_test, y_pred), results(y_pred, self.y_test)
def pipeline(self):
"""
:return: the roc auc score and the accuracy
"""
self.train()
y_pred_class = self.predict(threshold=0.436)
y_pred = self.predict_probability()
roc_results(y_pred[:, 1], self.y_test, 'Logistic Regression')
return roc_auc_score(self.y_test,
y_pred[:, 1]), results(y_pred_class, self.y_test)
def pipeline(self):
"""
:return: the roc auc score and the accuracy
"""
self.train()
y_pred_class = self.predict()
y_pred = self.predict_probability()
roc_results(y_pred[:, 1], self.y_test, 'Gaussian Naive Bayes')
return roc_auc_score(self.y_test,
y_pred[:, 1]), results(y_pred_class, self.y_test)
def rf_pipeline(x_train, y_train, x_test, y_test):
"""
:param x_train: the x-values we want to train on (2D numpy array)
:param x_test: the y-values that correspond to x_train (1D numpy array)
:param y_train: the x-values we want to test on (2D numpy array)
:param y_test: the y-values that correspond to x_test (1D numpy array)
:return: the roc auc score
"""
clf = rf_training(x_train, y_train)
y_pred = rf_classification(clf, x_test)
roc_results(y_pred, y_test, 'Random Forest')
return roc_auc_score(y_test, y_pred), results(y_pred, y_test)
def lr_pipeline(x_train, y_train, x_test, y_test):
"""
:param x_train: the x-values we want to train on (2D numpy array)
:param y_train: the y-values that correspond to x_train (1D numpy array)
:param x_test: the x-values we want to test on (2D numpy array)
:param y_test: the y-values that correspond to x_test (1D numpy array)
:return: the roc auc score
"""
prob = lr_training(x_train, y_train)
y_pred = lr_probability(prob, x_test)
y_pred_class = lr_classification(np.copy(y_pred[:, 1]), threshold=0.436)
roc_results(y_pred[:, 1], y_test, 'Logistic Regression')
return roc_auc_score(y_test, y_pred[:, 1]), results(y_pred_class, y_test)
def nb_pipeline(x_train, y_train, x_test, y_test):
"""
:param x_train: the x-values we want to train on (2D numpy array)
:param y_train: the y-values that correspond to x_train (1D numpy array)
:param x_test: the x-values we want to test on (2D numpy array)
:param y_test: the y-values that correspond to x_test (1D numpy array)
:return: the roc auc score
"""
prob = nb_training(x_train, y_train)
y_pred = nb_probability(prob, x_test)
y_pred_class = nb_classification(np.copy(y_pred[:, 1]))
roc_results(y_pred[:, 1], y_test, 'Gaussian Naive Bayes')
return roc_auc_score(y_test, y_pred[:, 1]), results(y_pred_class, y_test)