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)