def test_orthogonal_features(self):
"""
Test orthogonal features: PCA features, importance vs PCA importance analysis
"""
pca_features = get_orthogonal_features(self.X)
# PCA features should have mean of 0
self.assertAlmostEqual(np.mean(pca_features[:, 2]), 0, delta=1e-7)
self.assertAlmostEqual(np.mean(pca_features[:, 5]), 0, delta=1e-7)
self.assertAlmostEqual(np.mean(pca_features[:, 6]), 0, delta=1e-7)
# Check particular PCA values std
self.assertAlmostEqual(np.std(pca_features[:, 1]), 1.3813, delta=0.2)
self.assertAlmostEqual(np.std(pca_features[:, 3]), 1.0255, delta=0.2)
self.assertAlmostEqual(np.std(pca_features[:, 4]), 1.0011, delta=0.2)
mdi_feat_imp = mean_decrease_impurity(self.fit_clf, self.X.columns)
pca_corr_res = feature_pca_analysis(self.X, mdi_feat_imp)
# Check correlation metrics results
self.assertAlmostEqual(pca_corr_res['Weighted_Kendall_Rank'][0],
0.7424,
delta=1e-1)
# Check particular number of PCA features
pca_ten_features = get_orthogonal_features(self.X, num_features=10)
self.assertEqual(pca_ten_features.shape[1], 10)
pca_five_features = get_orthogonal_features(self.X, num_features=5)
self.assertEqual(pca_five_features.shape[1], 5)
def test_orthogonal_features(self):
"""
Test orthogonal features: PCA features, importance vs PCA importance analysis
"""
# Init classifiers
clf_base = RandomForestClassifier(n_estimators=1,
criterion='entropy',
bootstrap=False,
class_weight='balanced_subsample')
sb_clf = SequentiallyBootstrappedBaggingClassifier(
base_estimator=clf_base,
max_features=1.0,
n_estimators=100,
samples_info_sets=self.samples_info_sets,
price_bars=self.price_bars_trim,
oob_score=True,
random_state=1)
pca_features = get_orthogonal_features(self.X_train)
# PCA features should have mean of 0
self.assertAlmostEqual(np.mean(pca_features[:, 2]), 0, delta=1e-7)
self.assertAlmostEqual(np.mean(pca_features[:, 5]), 0, delta=1e-7)
self.assertAlmostEqual(np.mean(pca_features[:, 6]), 0, delta=1e-7)
# Check particular PCA values std
self.assertAlmostEqual(np.std(pca_features[:, 1]), 1.499, delta=0.2)
self.assertAlmostEqual(np.std(pca_features[:, 3]), 1.047, delta=0.2)
self.assertAlmostEqual(np.std(pca_features[:, 4]), 0.948, delta=0.2)
sb_clf.fit(self.X_train, self.y_train_clf)
mdi_feat_imp = feature_importance_mean_decrease_impurity(
sb_clf, self.X_train.columns)
pca_corr_res = feature_pca_analysis(self.X_train, mdi_feat_imp)
# Check correlation metrics results
self.assertAlmostEqual(pca_corr_res['Weighted_Kendall_Rank'][0],
0.26,
delta=1e-1)
def test_orthogonal_features(self):
"""
Test orthogonal features: PCA features, importance vs PCA importance analysis
"""
pca_features = get_orthogonal_features(self.X)
# PCA features should have mean of 0
self.assertAlmostEqual(np.mean(pca_features[:, 2]), 0, delta=1e-7)
self.assertAlmostEqual(np.mean(pca_features[:, 5]), 0, delta=1e-7)
self.assertAlmostEqual(np.mean(pca_features[:, 6]), 0, delta=1e-7)
# Check particular PCA values std
self.assertAlmostEqual(np.std(pca_features[:, 1]), 1.2503, delta=0.2)
self.assertAlmostEqual(np.std(pca_features[:, 3]), 1.0292, delta=0.2)
self.assertAlmostEqual(np.std(pca_features[:, 4]), 1.0134, delta=0.2)
mdi_feat_imp = mean_decrease_impurity(self.fit_clf, self.X.columns)
pca_corr_res = feature_pca_analysis(self.X, mdi_feat_imp)
# Check correlation metrics results
self.assertAlmostEqual(pca_corr_res['Weighted_Kendall_Rank'][0],
-0.0724,
delta=1e-1)
