from __future__ import absolute_import, print_function, division, unicode_literals
import unittest
import numpy as np
from xcessiv import stacker
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
class TestStacker(unittest.TestCase):
def setUp(self):
bl1 = RandomForestClassifier(random_state=8)
bl2 = LogisticRegression()
bl3 = RandomForestClassifier(max_depth=10, random_state=10)
meta_est = LogisticRegression()
skf = StratifiedKFold(random_state=8).split
self.stacked_ensemble = stacker.XcessivStackedEnsemble(
[bl1, bl2, bl3],
['predict', 'predict_proba', 'predict_proba'],
meta_est,
skf
)
def test_fit_and_process_using_meta_feature_generator(self):
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=8)
self.stacked_ensemble.fit(X_train, y_train)
preds = self.stacked_ensemble._process_using_meta_feature_generator(X_test, 'predict')
assert np.round(accuracy_score(y_test, preds), 3) == 0.868
probas = self.stacked_ensemble._process_using_meta_feature_generator(X_test, 'predict_proba')
preds = np.argmax(probas, axis=1)
assert np.round(accuracy_score(y_test, preds), 3) == 0.868
def test_get_params(self):
self.stacked_ensemble.get_params()
def test_set_params(self):
self.stacked_ensemble.set_params(bl0__random_state=20)
assert self.stacked_ensemble.get_params()['bl0__random_state'] == 20
assert self.stacked_ensemble.get_params()['bl0'].get_params()['random_state'] == 20
self.stacked_ensemble.set_params(**{'secondary-learner__C': 1.5})
assert self.stacked_ensemble.get_params()['secondary-learner__C'] == 1.5
assert self.stacked_ensemble.get_params()['secondary-learner'].get_params()['C'] == 1.5
