def train_model(X_df, y_array, skf_is):
fe = FeatureExtractor()
fe.fit(X_df, y_array)
X_array = fe.transform(X_df)
# Regression
train_is, _ = skf_is
X_train_array = np.array([X_array[i] for i in train_is])
y_train_array = np.array([y_array[i] for i in train_is])
reg = Regressor()
reg.fit(X_train_array, y_train_array)
return fe, reg
def train_model(X_df, y_array, skf_is):
fe = FeatureExtractor()
fe.fit(X_df, y_array)
X_array = fe.transform(X_df)
# Regression
train_is, _ = skf_is
X_train_array = np.array([X_array[i] for i in train_is])
y_train_array = np.array([y_array[i] for i in train_is])
reg = Regressor()
reg.fit(X_train_array, y_train_array)
return fe, reg
def test_model(trained_model, X_df, skf_is):
fe, reg = trained_model
# Feature extraction
X_array = fe.transform(X_df)
# Regression
_, test_is = skf_is
X_test_array = np.array([X_array[i] for i in test_is])
y_pred_array = reg.predict(X_test_array)
return y_pred_array
def test_model(trained_model, X_df, skf_is):
fe, reg = trained_model
# Feature extraction
X_array = fe.transform(X_df)
# Regression
_, test_is = skf_is
X_test_array = np.array([X_array[i] for i in test_is])
y_pred_array = reg.predict(X_test_array)
return y_pred_array