def test_few_compiled(self): num_features = 20 num_examples = 1000 X1 = np.random.normal(size=(num_examples, num_features)) X1 = X1.astype(np.float32) y1 = np.random.normal(size=num_examples) X2 = np.random.normal(size=(num_examples, num_features)) X2 = X2.astype(np.float32) y2 = np.random.normal(size=num_examples) rf1 = ensemble.RandomForestRegressor() rf1.fit(X1, y1) rf2 = ensemble.RandomForestRegressor() rf2.fit(X2, y2) rf1_compiled = CompiledRegressionPredictor(rf1) rf2_compiled = CompiledRegressionPredictor(rf2) assert_array_almost_equal(rf1.predict(X1), rf1_compiled.predict(X1), decimal=10) assert_array_almost_equal(rf2.predict(X2), rf2_compiled.predict(X2), decimal=10)
def test_predictions_with_non_contiguous_input(self): num_features = 100 num_examples = 100 X_non_contiguous = np.random.normal(size=(num_features, num_examples)).T X_non_contiguous = X_non_contiguous.astype(np.float32) self.assertFalse(X_non_contiguous.flags['C_CONTIGUOUS']) y = np.random.normal(size=num_examples) rf = ensemble.RandomForestRegressor() rf.fit(X_non_contiguous, y) rf_compiled = CompiledRegressionPredictor(rf) try: rf_compiled.predict(X_non_contiguous) except ValueError as e: self.fail("predict(X) raised ValueError") X_contiguous = np.ascontiguousarray(X_non_contiguous) self.assertTrue(X_contiguous.flags['C_CONTIGUOUS']) assert_array_equal(rf_compiled.predict(X_non_contiguous), rf_compiled.predict(X_contiguous))
def test_float32_and_float_64_predictions_are_equal(self): num_features = 100 num_examples = 100 X = np.random.normal(size=(num_features, num_examples)) X_32 = X.astype(np.float32) X_64 = X.astype(np.float64) y = np.random.normal(size=num_examples) # fit on X_32 rf = ensemble.RandomForestRegressor() rf.fit(X_32, y) rf = CompiledRegressionPredictor(rf) assert_array_equal(rf.predict(X_32), rf.predict(X_64)) # fit on X_64 rf = ensemble.RandomForestRegressor() rf.fit(X_64, y) rf = CompiledRegressionPredictor(rf) assert_array_equal(rf.predict(X_32), rf.predict(X_64))
def test_many_trees(self): num_features = 20 num_examples = 1000 X1 = np.random.normal(size=(num_examples, num_features)) X1 = X1.astype(np.float32) y1 = np.random.normal(size=num_examples) rf1 = ensemble.RandomForestRegressor(n_estimators=500, max_depth=2) rf1.fit(X1,y1) rf1_compiled = CompiledRegressionPredictor(rf1) assert_array_almost_equal(rf1.predict(X1), rf1_compiled.predict(X1), decimal=10)
def test_float32_and_float_64_predictions_are_equal(self): num_features = 100 num_examples = 100 X = np.random.normal(size=(num_features, num_examples)) X_32 = X.astype(np.float32) X_64 = X.astype(np.float64) y = np.random.normal(size=num_examples) # fit on X_32 rf = ensemble.RandomForestRegressor() rf.fit(X_32, y) rf = CompiledRegressionPredictor(rf) assert_array_equal(rf.predict(X_32), rf.predict(X_64)) # fit on X_64 rf = ensemble.RandomForestRegressor() rf.fit(X_64, y) rf = CompiledRegressionPredictor(rf) assert_array_equal(rf.predict(X_32), rf.predict(X_64))
def test_few_compiled(self): num_features = 20 num_examples = 1000 X1 = np.random.normal(size=(num_examples, num_features)) X1 = X1.astype(np.float32) y1 = np.random.normal(size=num_examples) X2 = np.random.normal(size=(num_examples, num_features)) X2 = X2.astype(np.float32) y2 = np.random.normal(size=num_examples) rf1 = ensemble.RandomForestRegressor() rf1.fit(X1,y1) rf2 = ensemble.RandomForestRegressor() rf2.fit(X2,y2) rf1_compiled = CompiledRegressionPredictor(rf1) rf2_compiled = CompiledRegressionPredictor(rf2) assert_array_almost_equal(rf1.predict(X1), rf1_compiled.predict(X1), decimal=10) assert_array_almost_equal(rf2.predict(X2), rf2_compiled.predict(X2), decimal=10)
def test_predictions_with_non_contiguous_input(self): num_features = 100 num_examples = 100 X_non_contiguous = np.random.normal(size=(num_features, num_examples)).T X_non_contiguous = X_non_contiguous.astype(np.float32) self.assertFalse(X_non_contiguous.flags['C_CONTIGUOUS']) y = np.random.normal(size=num_examples) rf = ensemble.RandomForestRegressor() rf.fit(X_non_contiguous, y) rf_compiled = CompiledRegressionPredictor(rf) try: rf_compiled.predict(X_non_contiguous) except ValueError as e: self.fail("predict(X) raised ValueError") X_contiguous = np.ascontiguousarray(X_non_contiguous) self.assertTrue(X_contiguous.flags['C_CONTIGUOUS']) assert_array_equal(rf_compiled.predict(X_non_contiguous), rf_compiled.predict(X_contiguous))
def test_many_trees(self): num_features = 20 num_examples = 1000 X1 = np.random.normal(size=(num_examples, num_features)) X1 = X1.astype(np.float32) y1 = np.random.normal(size=num_examples) rf1 = ensemble.RandomForestRegressor(n_estimators=500, max_depth=2) rf1.fit(X1, y1) rf1_compiled = CompiledRegressionPredictor(rf1) assert_array_almost_equal(rf1.predict(X1), rf1_compiled.predict(X1), decimal=10)
def assert_equal_predictions(cls, X, y): clf = cls() clf.fit(X, y) compiled = CompiledRegressionPredictor(clf) assert_array_almost_equal(clf.predict(X), compiled.predict(X))