def test_cross_val_meta_stack(self):
x, y = DataGenerator.get_digits_data()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
xgb_initparam = ParamsGenerator.get_xgb_init_param()
rf_initparam = ParamsGenerator.get_rf_init_param()
ext_initparam = ParamsGenerator.get_ext_init_param()
xgb_bestparam = CrossValStack.get_best_xgbopt(x_train, y_train, xgb_initparam)
rf_bestparam = CrossValStack.get_best_sklopt(x_train, y_train, rf_initparam)
ext_bestparam = CrossValStack.get_best_etopt(x_train, y_train, ext_initparam)
res = CrossValStack.cross_val_meta_stack(x_train, y_train, x_test, xgb_bestparam, rf_bestparam, ext_bestparam,
csvstack_cv=3)
dfres = pd.DataFrame([res[0][:, 1], res[1][:, 1], res[2][:, 1]]).transpose()
dfres.columns = ['p1', 'p2', 'p3']
y_test_xgb = CrossValStack.predict_opt_clf(XGBOpt.XGBOpt(x_train, y_train), xgb_bestparam, x_test, x_test)[0]
y_test_skl = CrossValStack.predict_opt_clf(SklearnOpt.SklearnOpt(x_train, y_train), rf_bestparam, x_test, x_test)[0]
y_test_ext = CrossValStack.predict_opt_clf(SklearnOpt.SklearnOpt(x_train, y_train), ext_bestparam, x_test, x_test)[0]
print metrics.roc_auc_score(y_test, y_test_xgb)
print metrics.roc_auc_score(y_test, y_test_skl)
print metrics.roc_auc_score(y_test, y_test_ext)
print metrics.roc_auc_score(y_test, (dfres.p1+dfres.p2+dfres.p3).values/3)
self.assertEqual(len(res), 3)
def test_cross_val_stack(self):
x, y = DataGenerator.get_digits_data()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
xgb_initparam = ParamsGenerator.get_xgb_init_param()
rf_initparam = ParamsGenerator.get_rf_init_param()
ext_initparam = ParamsGenerator.get_ext_init_param()
xgb_bestparam = CrossValStack.get_best_xgbopt(x_train, y_train, xgb_initparam)
rf_bestparam = CrossValStack.get_best_sklopt(x_train, y_train, rf_initparam)
ext_bestparam = CrossValStack.get_best_etopt(x_train, y_train, ext_initparam)
res = CrossValStack.cross_val_stack(x_train, y_train, x_test, xgb_bestparam, rf_bestparam, ext_bestparam)
dfres = pd.DataFrame([res[0][:, 1], res[1][:, 1], res[2][:, 1]]).transpose()
dfres.columns = ['p1', 'p2', 'p3']
y_test_xgb = CrossValStack.predict_opt_clf(XGBOpt.XGBOpt(x_train, y_train), xgb_bestparam, x_test, x_test)[0]
y_test_skl = CrossValStack.predict_opt_clf(SklearnOpt.SklearnOpt(x_train, y_train), rf_bestparam, x_test, x_test)[0]
y_test_ext = CrossValStack.predict_opt_clf(SklearnOpt.SklearnOpt(x_train, y_train), ext_bestparam, x_test, x_test)[0]
print metrics.roc_auc_score(y_test, y_test_xgb)
print metrics.roc_auc_score(y_test, y_test_skl)
print metrics.roc_auc_score(y_test, y_test_ext)
print metrics.roc_auc_score(y_test, dfres.p1.values)
print metrics.roc_auc_score(y_test, dfres.p2.values)
print metrics.roc_auc_score(y_test, dfres.p3.values)
print metrics.roc_auc_score(y_test, (dfres.p1+dfres.p2+dfres.p3).values/3)
print metrics.roc_auc_score(y_test, dfres.p1.values)
print metrics.roc_auc_score(y_test, (dfres.p1+dfres.p2+dfres.p3).values/3)
self.assertEqual(len(res), 5)
def test_random_forest(self):
# loading
x, y = DataGenerator.get_adult_data()
# cleaning
MissingValues.add_miss_val_indicator(x)
x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size=0.2, random_state=42)
x_train_1, x_valid_1 = Automaton.numerize(x_train, x_valid)
sklparam = Cvs.get_best_sklopt(x_train_1, y_train, ParamsGenerator.get_rf_init_param())
skopt = SklearnOpt.SklearnOpt(x_train_1, y_train)
y_pred_valid, _ = Cvs.predict_opt_clf(skopt, sklparam, x_valid_1, x_valid_1)
print 'Random Forest'
print metrics.roc_auc_score(y_valid, y_pred_valid)
print metrics.log_loss(y_valid, y_pred_valid)
