chore: import upstream snapshot with attribution

mla/tests/test_reduction.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+import pytest
+from sklearn.datasets import make_classification
+from sklearn.metrics import roc_auc_score
+
+try:
+    from sklearn.model_selection import train_test_split
+except ImportError:
+    from sklearn.cross_validation import train_test_split
+
+from mla.ensemble import RandomForestClassifier
+from mla.pca import PCA
+
+
+@pytest.fixture
+def dataset():
+    # Generate a random binary classification problem.
+    return make_classification(
+        n_samples=1000,
+        n_features=100,
+        n_informative=75,
+        random_state=1111,
+        n_classes=2,
+        class_sep=2.5,
+    )
+
+
+# TODO: fix
+@pytest.mark.skip()
+def test_PCA(dataset):
+    X, y = dataset
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.25, random_state=1111
+    )
+    p = PCA(50, solver="eigen")
+
+    # fit PCA with training set, not the entire dataset
+    p.fit(X_train)
+    X_train_reduced = p.transform(X_train)
+    X_test_reduced = p.transform(X_test)
+
+    model = RandomForestClassifier(n_estimators=25, max_depth=5)
+    model.fit(X_train_reduced, y_train)
+    predictions = model.predict(X_test_reduced)[:, 1]
+    score = roc_auc_score(y_test, predictions)
+    assert score >= 0.75