def pima_models(x_train_p, y_train_p):
"""
3 ML models for PIMA (scaled) data
:param x_train_p:
:param Y_train:
:return:
"""
def create_mlp():
mlp = Sequential()
mlp.add(Dense(60, input_dim=len(x_train_p.columns), activation='relu'))
mlp.add(Dropout(0.2))
mlp.add(Dense(30, input_dim=60, activation='relu'))
mlp.add(Dropout(0.2))
mlp.add(Dense(1, activation='sigmoid'))
mlp.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return mlp
mlp = KerasClassifier(build_fn=create_mlp,
epochs=100,
batch_size=64,
verbose=0)
mlp._estimator_type = "classifier"
mlp.fit(x_train_p, y_train_p)
rf = RandomForestClassifier(n_estimators=1000)
rf.fit(x_train_p, y_train_p)
sigmoidRF = CalibratedClassifierCV(
RandomForestClassifier(n_estimators=1000), cv=5, method='sigmoid')
sigmoidRF.fit(x_train_p, y_train_p)
# lr = LogisticRegression(C=1.)
# lr.fit(x_train_p, y_train_p)
svm = SVC(kernel='linear', probability=True)
svm.fit(x_train_p, y_train_p)
# create a dictionary of our models
estimators = [('rf', sigmoidRF), ('mlp', mlp), ('svm', svm)]
# create our voting classifier, inputting our models
ensemble = VotingClassifier(estimators, voting='soft')
ensemble._estimator_type = "classifier"
ensemble.fit(x_train_p, y_train_p)
return {'mlp': mlp, 'rf': sigmoidRF, 'svm': svm, 'ensemble': ensemble}
