def test_stacking_regressor_diabetes(cv, final_estimator, predict_params,
passthrough):
# prescale the data to avoid convergence warning without using a pipeline
# for later assert
X_train, X_test, y_train, _ = train_test_split(scale(X_diabetes),
y_diabetes,
random_state=42)
estimators = [('lr', LinearRegression()), ('svr', LinearSVR())]
reg = StackingRegressor(estimators=estimators,
final_estimator=final_estimator,
cv=cv,
passthrough=passthrough)
reg.fit(X_train, y_train)
result = reg.predict(X_test, **predict_params)
expected_result_length = 2 if predict_params else 1
if predict_params:
assert len(result) == expected_result_length
X_trans = reg.transform(X_test)
expected_column_count = 12 if passthrough else 2
assert X_trans.shape[1] == expected_column_count
if passthrough:
assert_allclose(X_test, X_trans[:, -10:])
reg.set_params(lr='drop')
reg.fit(X_train, y_train)
reg.predict(X_test)
X_trans = reg.transform(X_test)
expected_column_count_drop = 11 if passthrough else 1
assert X_trans.shape[1] == expected_column_count_drop
if passthrough:
assert_allclose(X_test, X_trans[:, -10:])
def lvl2_generate_prediction(rawdf, x_test, results_dir, lvl1_results_dir, type_, pp_choice,
passthrough=False, final_pp_choice=None):
x_train = rawdf.iloc[:, :-1]
y_train = rawdf.iloc[:, -1]
model_names = ['rf', 'et', 'xgb']
model_object = {
'xgb': XGBRegressor(),
'rf': RandomForestRegressor(),
'et': ExtraTreesRegressor()
}
with open(f'{lvl1_results_dir}/results_store.pkl', 'rb') as f:
model_results = pickle.load(f)
model_results = {k: pd.DataFrame(v).sort_values('mean_test_score', ascending=False) for k, v in
model_results.items()}
lvl1_pipeline = [
(model_name, Pipeline([
('preprocess', pp_selector(pp_choice)),
(model_name, model_object[model_name])
]).set_params(**model_results[model_name].loc[0, 'params']))
for model_name in model_names]
if type_ == 'lvl2_ridgecv':
est = StackingRegressor(estimators=lvl1_pipeline, final_estimator=RidgeCV(), passthrough=False)
elif type_ == 'lvl2_xgb':
if passthrough:
final_est = Pipeline([
('final_preprocess', final_est_pipeline(feature_names=x_train.columns.tolist(),
preprocess_pipeline=pp_selector(final_pp_choice),
no_of_lvl1=len(lvl1_pipeline))),
('debugger', DebuggerTransformer(info='final')),
('final_est', XGBRegressor())
])
else:
final_est = XGBRegressor()
est = StackingRegressor(estimators=lvl1_pipeline, final_estimator=final_est, passthrough=passthrough)
with open(f'{results_dir}/results_store.pkl', 'rb') as f:
model_results = pickle.load(f)
model_results = {k: pd.DataFrame(v).sort_values('mean_test_score', ascending=False) for k, v in
model_results.items()}
#est.set_params(
# **{f'final_estimator__{k}': v for k, v in model_results['lvl2ptvs_xgb'].loc[0, 'params'].items()})
est.set_params(**model_results['lvl2ptvs_xgb'].loc[0, 'params'])
prediction = est.fit(x_train, y_train).predict(x_test)
sub = pd.DataFrame()
sub['Id'] = x_test['Id']
sub['SalePrice'] = prediction
sub.to_csv(f'{results_dir}/{type_}_pp{pp_choice}_predictions.csv', index=False)
