class VotingClassifierImpl():
def __init__(self,
estimators=None,
voting='hard',
weights=None,
n_jobs=None,
flatten_transform=True):
self._hyperparams = {
'estimators': estimators,
'voting': voting,
'weights': weights,
'n_jobs': n_jobs,
'flatten_transform': flatten_transform
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def predict(self, X):
return self._wrapped_model.predict(X)
def predict_proba(self, X):
return self._wrapped_model.predict_proba(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
def voting_process(df_list, label_list, scale=False):
random_state = np.random.RandomState(20180213)
vt_results = {
'prediction': [],
'probaility': [],
'y_test': [],
'y_score': []
}
try:
if scale:
df_list = [scale_df(df) for df in df_list]
print('DF Scaling successful.')
except:
raise ValueError('Failed to execute DF Scaling.')
for x, y in zip(df_list, label_list):
try:
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=.2, random_state=random_state)
except:
raise ValueError('Train/Test split failed.')
vt = VotingClassifier(estimators=[
('basic_log', LogisticRegression()),
('et', ExtraTreesClassifier()), ('ada', AdaBoostClassifier()),
('rf', RandomForestClassifier()),
('gbm',
GradientBoostingClassifier(n_estimators=100,
max_depth=5,
learning_rate=0.1))
],
voting='soft')
weighting = lambda x: 1 if x else 50
vt.fit(x_train, y_train, sample_weight=[weighting(i) for i in y_train])
vt_results['y_test'].append(y_test)
vt_results['prediction'].append(vt.predict(x_test))
vt_results['probaility'].append(vt.predict_proba(x_test)[::, 1])
try:
vt_results['y_score'].append(vt.decision_function(x_test))
except:
vt_results['y_score'].append(vt.predict_proba(x_test)[::, 1])
return vt_results