missing stuff

Author: dengemann

stlearn/tests/__init__.py

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+from . import test_stacking

stlearn/tests/test_stacking.py

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+from nose.tools import assert_equal
+from nose.tools import assert_true
+from nose.tools import assert_raises
+import numpy as np
+from numpy.testing import assert_array_equal
+
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import ShuffleSplit
+from stlearn import StackingClassifier
+
+n_samples = 200
+n_estimators = 2
+X0, y = make_classification(n_samples=200, random_state=42)
+X1 = X0 ** 2
+X = np.array([X0, X1])
+ss = ShuffleSplit(n_splits=10, test_size=0.2, random_state=42)
+
+
+def test_stacking_essentials():
+    """Test initializaing and essential basic function"""
+    stacking = StackingClassifier(
+        estimators=n_estimators * [LogisticRegression()],
+        stacking_estimator=LogisticRegression())
+    # assert_equal(getattr(stacking, 'predictions_', None), None)
+    assert_equal(stacking.stacking_estimator.__class__,
+                 LogisticRegression)
+    assert_equal([ee.__class__ for ee in stacking.estimators],
+                 n_estimators * [LogisticRegression])
+    assert_raises(ValueError, stacking.fit, X[0], y)
+    assert_raises(ValueError, stacking.fit, X[:1], y)
+    assert_raises(ValueError, stacking.fit, X[:, :1], y)
+
+    stacking.fit(X, y)
+
+    predictions = stacking.predict(X)
+    assert_array_equal(np.unique(predictions), np.array([0, 1]))
+
+    score = stacking.score(X, y)
+    assert_true(np.isscalar(score))
+
+    predictions_estimators = stacking.predict_estimators(X)
+    assert_array_equal(
+        predictions_estimators.shape, (n_samples, n_estimators))
+    scores_estimators = stacking.score_estimators(X, y)
+    assert_equal(len(scores_estimators), n_estimators)