def test_warm_start():
X = X_iris
y = y_iris
y_2classes = np.array([0] * 75 + [1] * 75)
y_3classes = np.array([0] * 40 + [1] * 40 + [2] * 70)
y_3classes_alt = np.array([0] * 50 + [1] * 50 + [3] * 50)
y_4classes = np.array([0] * 37 + [1] * 37 + [2] * 38 + [3] * 38)
y_5classes = np.array([0] * 30 + [1] * 30 + [2] * 30 + [3] * 30 + [4] * 30)
# No error raised
clf = MLPClassifier(hidden_layer_sizes=2, solver='lbfgs',
warm_start=True).fit(X, y)
clf.fit(X, y)
clf.fit(X, y_3classes)
for y_i in (y_2classes, y_3classes_alt, y_4classes, y_5classes):
clf = MLPClassifier(hidden_layer_sizes=2,
solver='lbfgs',
warm_start=True).fit(X, y)
message = ('warm_start can only be used where `y` has the same '
'classes as in the previous call to fit.'
' Previously got [0 1 2], `y` has %s' % np.unique(y_i))
with pytest.raises(ValueError, match=re.escape(message)):
clf.fit(X, y_i)
def test_tolerance():
# Test tolerance.
# It should force the solver to exit the loop when it converges.
X = [[3, 2], [1, 6]]
y = [1, 0]
clf = MLPClassifier(tol=0.5, max_iter=3000, solver='sgd')
clf.fit(X, y)
assert clf.max_iter > clf.n_iter_
def test_adaptive_learning_rate():
X = [[3, 2], [1, 6]]
y = [1, 0]
clf = MLPClassifier(tol=0.5,
max_iter=3000,
solver='sgd',
learning_rate='adaptive')
clf.fit(X, y)
assert clf.max_iter > clf.n_iter_
assert 1e-6 > clf._optimizer.learning_rate
def test_early_stopping_stratified():
# Make sure data splitting for early stopping is stratified
X = [[1, 2], [2, 3], [3, 4], [4, 5]]
y = [0, 0, 0, 1]
mlp = MLPClassifier(early_stopping=True)
with pytest.raises(
ValueError,
match='The least populated class in y has only 1 member'):
mlp.fit(X, y)
def test_partial_fit_errors():
# Test partial_fit error handling.
X = [[3, 2], [1, 6]]
y = [1, 0]
# no classes passed
with pytest.raises(ValueError):
MLPClassifier(solver='sgd').partial_fit(X, y, classes=[2])
# lbfgs doesn't support partial_fit
assert not hasattr(MLPClassifier(solver='lbfgs'), 'partial_fit')
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_early_stopping():
X = X_digits_binary[:100]
y = y_digits_binary[:100]
tol = 0.2
clf = MLPClassifier(tol=tol,
max_iter=3000,
solver='sgd',
early_stopping=True)
clf.fit(X, y)
assert clf.max_iter > clf.n_iter_
valid_scores = clf.validation_scores_
best_valid_score = clf.best_validation_score_
assert max(valid_scores) == best_valid_score
assert best_valid_score + tol > valid_scores[-2]
assert best_valid_score + tol > valid_scores[-1]
def test_lbfgs_classification_maxfun(X, y):
# Test lbfgs parameter max_fun.
# It should independently limit the number of iterations for lbfgs.
max_fun = 10
# classification tests
for activation in ACTIVATION_TYPES:
mlp = MLPClassifier(solver='lbfgs',
hidden_layer_sizes=50,
max_iter=150,
max_fun=max_fun,
shuffle=True,
random_state=1,
activation=activation)
with pytest.warns(ConvergenceWarning):
mlp.fit(X, y)
assert max_fun >= mlp.n_iter_
def test_n_iter_no_change():
# test n_iter_no_change using binary data set
# the classifying fitting process is not prone to loss curve fluctuations
X = X_digits_binary[:100]
y = y_digits_binary[:100]
tol = 0.01
max_iter = 3000
# test multiple n_iter_no_change
for n_iter_no_change in [2, 5, 10, 50, 100]:
clf = MLPClassifier(tol=tol,
max_iter=max_iter,
solver='sgd',
n_iter_no_change=n_iter_no_change)
clf.fit(X, y)
# validate n_iter_no_change
assert clf._no_improvement_count == n_iter_no_change + 1
assert max_iter > clf.n_iter_
def test_partial_fit_unseen_classes():
# Non regression test for bug 6994
# Tests for labeling errors in partial fit
clf = MLPClassifier(random_state=0)
clf.partial_fit([[1], [2], [3]], ["a", "b", "c"],
classes=["a", "b", "c", "d"])
clf.partial_fit([[4]], ["d"])
assert clf.score([[1], [2], [3], [4]], ["a", "b", "c", "d"]) > 0
def test_alpha():
# Test that larger alpha yields weights closer to zero
X = X_digits_binary[:100]
y = y_digits_binary[:100]
alpha_vectors = []
alpha_values = np.arange(2)
absolute_sum = lambda x: np.sum(np.abs(x))
for alpha in alpha_values:
mlp = MLPClassifier(hidden_layer_sizes=10, alpha=alpha, random_state=1)
with ignore_warnings(category=ConvergenceWarning):
mlp.fit(X, y)
alpha_vectors.append(
np.array(
[absolute_sum(mlp.coefs_[0]),
absolute_sum(mlp.coefs_[1])]))
for i in range(len(alpha_values) - 1):
assert (alpha_vectors[i] > alpha_vectors[i + 1]).all()
def test_partial_fit_classes_error():
# Tests that passing different classes to partial_fit raises an error
X = [[3, 2]]
y = [0]
clf = MLPClassifier(solver='sgd')
clf.partial_fit(X, y, classes=[0, 1])
with pytest.raises(ValueError):
clf.partial_fit(X, y, classes=[1, 2])
def test_n_iter_no_change_inf():
# test n_iter_no_change using binary data set
# the fitting process should go to max_iter iterations
X = X_digits_binary[:100]
y = y_digits_binary[:100]
# set a ridiculous tolerance
# this should always trigger _update_no_improvement_count()
tol = 1e9
# fit
n_iter_no_change = np.inf
max_iter = 3000
clf = MLPClassifier(tol=tol,
max_iter=max_iter,
solver='sgd',
n_iter_no_change=n_iter_no_change)
clf.fit(X, y)
# validate n_iter_no_change doesn't cause early stopping
assert clf.n_iter_ == max_iter
# validate _update_no_improvement_count() was always triggered
assert clf._no_improvement_count == clf.n_iter_ - 1
def test_verbose_sgd():
# Test verbose.
X = [[3, 2], [1, 6]]
y = [1, 0]
clf = MLPClassifier(solver='sgd',
max_iter=2,
verbose=10,
hidden_layer_sizes=2)
old_stdout = sys.stdout
sys.stdout = output = StringIO()
with ignore_warnings(category=ConvergenceWarning):
clf.fit(X, y)
clf.partial_fit(X, y)
sys.stdout = old_stdout
assert 'Iteration' in output.getvalue()
def test_predict_proba_multiclass():
# Test that predict_proba works as expected for multi class.
X = X_digits_multi[:10]
y = y_digits_multi[:10]
clf = MLPClassifier(hidden_layer_sizes=5)
with ignore_warnings(category=ConvergenceWarning):
clf.fit(X, y)
y_proba = clf.predict_proba(X)
y_log_proba = clf.predict_log_proba(X)
(n_samples, n_classes) = y.shape[0], np.unique(y).size
proba_max = y_proba.argmax(axis=1)
proba_log_max = y_log_proba.argmax(axis=1)
assert y_proba.shape == (n_samples, n_classes)
assert_array_equal(proba_max, proba_log_max)
assert_array_equal(y_log_proba, np.log(y_proba))
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)
def test_learning_rate_warmstart():
# Tests that warm_start reuse past solutions.
X = [[3, 2], [1, 6], [5, 6], [-2, -4]]
y = [1, 1, 1, 0]
for learning_rate in ["invscaling", "constant"]:
mlp = MLPClassifier(solver='sgd',
hidden_layer_sizes=4,
learning_rate=learning_rate,
max_iter=1,
power_t=0.25,
warm_start=True)
with ignore_warnings(category=ConvergenceWarning):
mlp.fit(X, y)
prev_eta = mlp._optimizer.learning_rate
mlp.fit(X, y)
post_eta = mlp._optimizer.learning_rate
if learning_rate == 'constant':
assert prev_eta == post_eta
elif learning_rate == 'invscaling':
assert (mlp.learning_rate_init /
pow(8 + 1, mlp.power_t) == post_eta)
def test_predict_proba_multilabel():
# Test that predict_proba works as expected for multilabel.
# Multilabel should not use softmax which makes probabilities sum to 1
X, Y = make_multilabel_classification(n_samples=50,
random_state=0,
return_indicator=True)
n_samples, n_classes = Y.shape
clf = MLPClassifier(solver='lbfgs', hidden_layer_sizes=30, random_state=0)
clf.fit(X, Y)
y_proba = clf.predict_proba(X)
assert y_proba.shape == (n_samples, n_classes)
assert_array_equal(y_proba > 0.5, Y)
y_log_proba = clf.predict_log_proba(X)
proba_max = y_proba.argmax(axis=1)
proba_log_max = y_log_proba.argmax(axis=1)
assert (y_proba.sum(1) - 1).dot(y_proba.sum(1) - 1) > 1e-10
assert_array_equal(proba_max, proba_log_max)
assert_array_equal(y_log_proba, np.log(y_proba))
def test_predict_proba_binary():
# Test that predict_proba works as expected for binary class.
X = X_digits_binary[:50]
y = y_digits_binary[:50]
clf = MLPClassifier(hidden_layer_sizes=5,
activation='logistic',
random_state=1)
with ignore_warnings(category=ConvergenceWarning):
clf.fit(X, y)
y_proba = clf.predict_proba(X)
y_log_proba = clf.predict_log_proba(X)
(n_samples, n_classes) = y.shape[0], 2
proba_max = y_proba.argmax(axis=1)
proba_log_max = y_log_proba.argmax(axis=1)
assert y_proba.shape == (n_samples, n_classes)
assert_array_equal(proba_max, proba_log_max)
assert_array_equal(y_log_proba, np.log(y_proba))
assert roc_auc_score(y, y_proba[:, 1]) == 1.0
def test_gradient():
# Test gradient.
# This makes sure that the activation functions and their derivatives
# are correct. The numerical and analytical computation of the gradient
# should be close.
for n_labels in [2, 3]:
n_samples = 5
n_features = 10
random_state = np.random.RandomState(seed=42)
X = random_state.rand(n_samples, n_features)
y = 1 + np.mod(np.arange(n_samples) + 1, n_labels)
Y = LabelBinarizer().fit_transform(y)
for activation in ACTIVATION_TYPES:
mlp = MLPClassifier(activation=activation,
hidden_layer_sizes=10,
solver='lbfgs',
alpha=1e-5,
learning_rate_init=0.2,
max_iter=1,
random_state=1)
mlp.fit(X, y)
theta = np.hstack(
[l.ravel() for l in mlp.coefs_ + mlp.intercepts_])
layer_units = ([X.shape[1]] + [mlp.hidden_layer_sizes] +
[mlp.n_outputs_])
activations = []
deltas = []
coef_grads = []
intercept_grads = []
activations.append(X)
for i in range(mlp.n_layers_ - 1):
activations.append(np.empty((X.shape[0], layer_units[i + 1])))
deltas.append(np.empty((X.shape[0], layer_units[i + 1])))
fan_in = layer_units[i]
fan_out = layer_units[i + 1]
coef_grads.append(np.empty((fan_in, fan_out)))
intercept_grads.append(np.empty(fan_out))
# analytically compute the gradients
def loss_grad_fun(t):
return mlp._loss_grad_lbfgs(t, X, Y, activations, deltas,
coef_grads, intercept_grads)
[value, grad] = loss_grad_fun(theta)
numgrad = np.zeros(np.size(theta))
n = np.size(theta, 0)
E = np.eye(n)
epsilon = 1e-5
# numerically compute the gradients
for i in range(n):
dtheta = E[:, i] * epsilon
numgrad[i] = ((loss_grad_fun(theta + dtheta)[0] -
loss_grad_fun(theta - dtheta)[0]) /
(epsilon * 2.0))
assert_almost_equal(numgrad, grad)
def test_fit():
# Test that the algorithm solution is equal to a worked out example.
X = np.array([[0.6, 0.8, 0.7]])
y = np.array([0])
mlp = MLPClassifier(solver='sgd',
learning_rate_init=0.1,
alpha=0.1,
activation='logistic',
random_state=1,
max_iter=1,
hidden_layer_sizes=2,
momentum=0)
# set weights
mlp.coefs_ = [0] * 2
mlp.intercepts_ = [0] * 2
mlp.n_outputs_ = 1
mlp.coefs_[0] = np.array([[0.1, 0.2], [0.3, 0.1], [0.5, 0]])
mlp.coefs_[1] = np.array([[0.1], [0.2]])
mlp.intercepts_[0] = np.array([0.1, 0.1])
mlp.intercepts_[1] = np.array([1.0])
mlp._coef_grads = [] * 2
mlp._intercept_grads = [] * 2
# Initialize parameters
mlp.n_iter_ = 0
mlp.learning_rate_ = 0.1
# Compute the number of layers
mlp.n_layers_ = 3
# Pre-allocate gradient matrices
mlp._coef_grads = [0] * (mlp.n_layers_ - 1)
mlp._intercept_grads = [0] * (mlp.n_layers_ - 1)
mlp.out_activation_ = 'logistic'
mlp.t_ = 0
mlp.best_loss_ = np.inf
mlp.loss_curve_ = []
mlp._no_improvement_count = 0
mlp._intercept_velocity = [
np.zeros_like(intercepts) for intercepts in mlp.intercepts_
]
mlp._coef_velocity = [np.zeros_like(coefs) for coefs in mlp.coefs_]
mlp.partial_fit(X, y, classes=[0, 1])
# Manually worked out example
# h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.1 + 0.8 * 0.3 + 0.7 * 0.5 + 0.1)
# = 0.679178699175393
# h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.2 + 0.8 * 0.1 + 0.7 * 0 + 0.1)
# = 0.574442516811659
# o1 = g(h * W2 + b21) = g(0.679 * 0.1 + 0.574 * 0.2 + 1)
# = 0.7654329236196236
# d21 = -(0 - 0.765) = 0.765
# d11 = (1 - 0.679) * 0.679 * 0.765 * 0.1 = 0.01667
# d12 = (1 - 0.574) * 0.574 * 0.765 * 0.2 = 0.0374
# W1grad11 = X1 * d11 + alpha * W11 = 0.6 * 0.01667 + 0.1 * 0.1 = 0.0200
# W1grad11 = X1 * d12 + alpha * W12 = 0.6 * 0.0374 + 0.1 * 0.2 = 0.04244
# W1grad21 = X2 * d11 + alpha * W13 = 0.8 * 0.01667 + 0.1 * 0.3 = 0.043336
# W1grad22 = X2 * d12 + alpha * W14 = 0.8 * 0.0374 + 0.1 * 0.1 = 0.03992
# W1grad31 = X3 * d11 + alpha * W15 = 0.6 * 0.01667 + 0.1 * 0.5 = 0.060002
# W1grad32 = X3 * d12 + alpha * W16 = 0.6 * 0.0374 + 0.1 * 0 = 0.02244
# W2grad1 = h1 * d21 + alpha * W21 = 0.679 * 0.765 + 0.1 * 0.1 = 0.5294
# W2grad2 = h2 * d21 + alpha * W22 = 0.574 * 0.765 + 0.1 * 0.2 = 0.45911
# b1grad1 = d11 = 0.01667
# b1grad2 = d12 = 0.0374
# b2grad = d21 = 0.765
# W1 = W1 - eta * [W1grad11, .., W1grad32] = [[0.1, 0.2], [0.3, 0.1],
# [0.5, 0]] - 0.1 * [[0.0200, 0.04244], [0.043336, 0.03992],
# [0.060002, 0.02244]] = [[0.098, 0.195756], [0.2956664,
# 0.096008], [0.4939998, -0.002244]]
# W2 = W2 - eta * [W2grad1, W2grad2] = [[0.1], [0.2]] - 0.1 *
# [[0.5294], [0.45911]] = [[0.04706], [0.154089]]
# b1 = b1 - eta * [b1grad1, b1grad2] = 0.1 - 0.1 * [0.01667, 0.0374]
# = [0.098333, 0.09626]
# b2 = b2 - eta * b2grad = 1.0 - 0.1 * 0.765 = 0.9235
assert_almost_equal(mlp.coefs_[0],
np.array([[0.098, 0.195756], [0.2956664, 0.096008],
[0.4939998, -0.002244]]),
decimal=3)
assert_almost_equal(mlp.coefs_[1],
np.array([[0.04706], [0.154089]]),
decimal=3)
assert_almost_equal(mlp.intercepts_[0],
np.array([0.098333, 0.09626]),
decimal=3)
assert_almost_equal(mlp.intercepts_[1], np.array(0.9235), decimal=3)
# Testing output
# h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.098 + 0.8 * 0.2956664 +
# 0.7 * 0.4939998 + 0.098333) = 0.677
# h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.195756 + 0.8 * 0.096008 +
# 0.7 * -0.002244 + 0.09626) = 0.572
# o1 = h * W2 + b21 = 0.677 * 0.04706 +
# 0.572 * 0.154089 + 0.9235 = 1.043
# prob = sigmoid(o1) = 0.739
assert_almost_equal(mlp.predict_proba(X)[0, 1], 0.739, decimal=3)
def test_multilabel_classification():
# Test that multi-label classification works as expected.
# test fit method
X, y = make_multilabel_classification(n_samples=50,
random_state=0,
return_indicator=True)
mlp = MLPClassifier(solver='lbfgs',
hidden_layer_sizes=50,
alpha=1e-5,
max_iter=150,
random_state=0,
activation='logistic',
learning_rate_init=0.2)
mlp.fit(X, y)
assert mlp.score(X, y) > 0.97
# test partial fit method
mlp = MLPClassifier(solver='sgd',
hidden_layer_sizes=50,
max_iter=150,
random_state=0,
activation='logistic',
alpha=1e-5,
learning_rate_init=0.2)
for i in range(100):
mlp.partial_fit(X, y, classes=[0, 1, 2, 3, 4])
assert mlp.score(X, y) > 0.9
# Make sure early stopping still work now that spliting is stratified by
# default (it is disabled for multilabel classification)
mlp = MLPClassifier(early_stopping=True)
mlp.fit(X, y).predict(X)
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