def test_partial_fit_classification(): # Test partial_fit on classification. # `partial_fit` should yield the same results as 'fit' for binary and # multi-class classification. for X, y in classification_datasets: X = X y = y mlp = MLPClassifier(solver='sgd', max_iter=100, random_state=1, tol=0, alpha=1e-5, learning_rate_init=0.2) with ignore_warnings(category=ConvergenceWarning): mlp.fit(X, y) pred1 = mlp.predict(X) mlp = MLPClassifier(solver='sgd', random_state=1, alpha=1e-5, learning_rate_init=0.2) for i in range(100): mlp.partial_fit(X, y, classes=np.unique(y)) pred2 = mlp.predict(X) assert_array_equal(pred1, pred2) assert mlp.score(X, y) > 0.95
def test_sparse_matrices(): # Test that sparse and dense input matrices output the same results. X = X_digits_binary[:50] y = y_digits_binary[:50] X_sparse = csr_matrix(X) mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes=15, random_state=1) mlp.fit(X, y) pred1 = mlp.predict(X) mlp.fit(X_sparse, y) pred2 = mlp.predict(X_sparse) assert_almost_equal(pred1, pred2) pred1 = mlp.predict(X) pred2 = mlp.predict(X_sparse) assert_array_equal(pred1, pred2)
def test_lbfgs_classification(X, y): # Test lbfgs on classification. # It should achieve a score higher than 0.95 for the binary and multi-class # versions of the digits dataset. X_train = X[:150] y_train = y[:150] X_test = X[150:] expected_shape_dtype = (X_test.shape[0], y_train.dtype.kind) for activation in ACTIVATION_TYPES: mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes=50, max_iter=150, shuffle=True, random_state=1, activation=activation) mlp.fit(X_train, y_train) y_predict = mlp.predict(X_test) assert mlp.score(X_train, y_train) > 0.95 assert ((y_predict.shape[0], y_predict.dtype.kind) == expected_shape_dtype)