664 lines
24 KiB
Python
664 lines
24 KiB
Python
"""
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Testing for Multi-layer Perceptron module (sklearn.neural_network)
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"""
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# Author: Issam H. Laradji
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# License: BSD 3 clause
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import sys
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import warnings
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_array_equal
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from sklearn.datasets import load_digits, load_boston, load_iris
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from sklearn.datasets import make_regression, make_multilabel_classification
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from sklearn.exceptions import ConvergenceWarning
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from io import StringIO
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from sklearn.metrics import roc_auc_score
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from sklearn.neural_network import MLPClassifier
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from sklearn.neural_network import MLPRegressor
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from sklearn.preprocessing import LabelBinarizer
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from scipy.sparse import csr_matrix
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from sklearn.utils.testing import (assert_raises, assert_greater,
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assert_equal, ignore_warnings)
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from sklearn.utils.testing import assert_raise_message
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np.seterr(all='warn')
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ACTIVATION_TYPES = ["identity", "logistic", "tanh", "relu"]
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digits_dataset_multi = load_digits(n_class=3)
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X_digits_multi = MinMaxScaler().fit_transform(digits_dataset_multi.data[:200])
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y_digits_multi = digits_dataset_multi.target[:200]
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digits_dataset_binary = load_digits(n_class=2)
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X_digits_binary = MinMaxScaler().fit_transform(
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digits_dataset_binary.data[:200])
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y_digits_binary = digits_dataset_binary.target[:200]
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classification_datasets = [(X_digits_multi, y_digits_multi),
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(X_digits_binary, y_digits_binary)]
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boston = load_boston()
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Xboston = StandardScaler().fit_transform(boston.data)[: 200]
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yboston = boston.target[:200]
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iris = load_iris()
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X_iris = iris.data
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y_iris = iris.target
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def test_alpha():
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# Test that larger alpha yields weights closer to zero
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X = X_digits_binary[:100]
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y = y_digits_binary[:100]
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alpha_vectors = []
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alpha_values = np.arange(2)
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absolute_sum = lambda x: np.sum(np.abs(x))
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for alpha in alpha_values:
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mlp = MLPClassifier(hidden_layer_sizes=10, alpha=alpha, random_state=1)
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with ignore_warnings(category=ConvergenceWarning):
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mlp.fit(X, y)
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alpha_vectors.append(np.array([absolute_sum(mlp.coefs_[0]),
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absolute_sum(mlp.coefs_[1])]))
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for i in range(len(alpha_values) - 1):
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assert (alpha_vectors[i] > alpha_vectors[i + 1]).all()
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def test_fit():
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# Test that the algorithm solution is equal to a worked out example.
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X = np.array([[0.6, 0.8, 0.7]])
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y = np.array([0])
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mlp = MLPClassifier(solver='sgd', learning_rate_init=0.1, alpha=0.1,
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activation='logistic', random_state=1, max_iter=1,
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hidden_layer_sizes=2, momentum=0)
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# set weights
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mlp.coefs_ = [0] * 2
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mlp.intercepts_ = [0] * 2
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mlp.n_outputs_ = 1
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mlp.coefs_[0] = np.array([[0.1, 0.2], [0.3, 0.1], [0.5, 0]])
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mlp.coefs_[1] = np.array([[0.1], [0.2]])
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mlp.intercepts_[0] = np.array([0.1, 0.1])
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mlp.intercepts_[1] = np.array([1.0])
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mlp._coef_grads = [] * 2
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mlp._intercept_grads = [] * 2
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# Initialize parameters
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mlp.n_iter_ = 0
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mlp.learning_rate_ = 0.1
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# Compute the number of layers
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mlp.n_layers_ = 3
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# Pre-allocate gradient matrices
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mlp._coef_grads = [0] * (mlp.n_layers_ - 1)
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mlp._intercept_grads = [0] * (mlp.n_layers_ - 1)
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mlp.out_activation_ = 'logistic'
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mlp.t_ = 0
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mlp.best_loss_ = np.inf
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mlp.loss_curve_ = []
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mlp._no_improvement_count = 0
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mlp._intercept_velocity = [np.zeros_like(intercepts) for
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intercepts in
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mlp.intercepts_]
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mlp._coef_velocity = [np.zeros_like(coefs) for coefs in
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mlp.coefs_]
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mlp.partial_fit(X, y, classes=[0, 1])
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# Manually worked out example
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# h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.1 + 0.8 * 0.3 + 0.7 * 0.5 + 0.1)
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# = 0.679178699175393
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# h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.2 + 0.8 * 0.1 + 0.7 * 0 + 0.1)
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# = 0.574442516811659
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# o1 = g(h * W2 + b21) = g(0.679 * 0.1 + 0.574 * 0.2 + 1)
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# = 0.7654329236196236
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# d21 = -(0 - 0.765) = 0.765
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# d11 = (1 - 0.679) * 0.679 * 0.765 * 0.1 = 0.01667
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# d12 = (1 - 0.574) * 0.574 * 0.765 * 0.2 = 0.0374
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# W1grad11 = X1 * d11 + alpha * W11 = 0.6 * 0.01667 + 0.1 * 0.1 = 0.0200
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# W1grad11 = X1 * d12 + alpha * W12 = 0.6 * 0.0374 + 0.1 * 0.2 = 0.04244
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# W1grad21 = X2 * d11 + alpha * W13 = 0.8 * 0.01667 + 0.1 * 0.3 = 0.043336
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# W1grad22 = X2 * d12 + alpha * W14 = 0.8 * 0.0374 + 0.1 * 0.1 = 0.03992
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# W1grad31 = X3 * d11 + alpha * W15 = 0.6 * 0.01667 + 0.1 * 0.5 = 0.060002
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# W1grad32 = X3 * d12 + alpha * W16 = 0.6 * 0.0374 + 0.1 * 0 = 0.02244
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# W2grad1 = h1 * d21 + alpha * W21 = 0.679 * 0.765 + 0.1 * 0.1 = 0.5294
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# W2grad2 = h2 * d21 + alpha * W22 = 0.574 * 0.765 + 0.1 * 0.2 = 0.45911
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# b1grad1 = d11 = 0.01667
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# b1grad2 = d12 = 0.0374
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# b2grad = d21 = 0.765
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# W1 = W1 - eta * [W1grad11, .., W1grad32] = [[0.1, 0.2], [0.3, 0.1],
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# [0.5, 0]] - 0.1 * [[0.0200, 0.04244], [0.043336, 0.03992],
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# [0.060002, 0.02244]] = [[0.098, 0.195756], [0.2956664,
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# 0.096008], [0.4939998, -0.002244]]
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# W2 = W2 - eta * [W2grad1, W2grad2] = [[0.1], [0.2]] - 0.1 *
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# [[0.5294], [0.45911]] = [[0.04706], [0.154089]]
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# b1 = b1 - eta * [b1grad1, b1grad2] = 0.1 - 0.1 * [0.01667, 0.0374]
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# = [0.098333, 0.09626]
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# b2 = b2 - eta * b2grad = 1.0 - 0.1 * 0.765 = 0.9235
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assert_almost_equal(mlp.coefs_[0], np.array([[0.098, 0.195756],
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[0.2956664, 0.096008],
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[0.4939998, -0.002244]]),
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decimal=3)
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assert_almost_equal(mlp.coefs_[1], np.array([[0.04706], [0.154089]]),
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decimal=3)
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assert_almost_equal(mlp.intercepts_[0],
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np.array([0.098333, 0.09626]), decimal=3)
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assert_almost_equal(mlp.intercepts_[1], np.array(0.9235), decimal=3)
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# Testing output
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# h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.098 + 0.8 * 0.2956664 +
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# 0.7 * 0.4939998 + 0.098333) = 0.677
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# h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.195756 + 0.8 * 0.096008 +
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# 0.7 * -0.002244 + 0.09626) = 0.572
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# o1 = h * W2 + b21 = 0.677 * 0.04706 +
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# 0.572 * 0.154089 + 0.9235 = 1.043
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# prob = sigmoid(o1) = 0.739
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assert_almost_equal(mlp.predict_proba(X)[0, 1], 0.739, decimal=3)
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def test_gradient():
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# Test gradient.
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# This makes sure that the activation functions and their derivatives
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# are correct. The numerical and analytical computation of the gradient
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# should be close.
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for n_labels in [2, 3]:
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n_samples = 5
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n_features = 10
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X = np.random.random((n_samples, n_features))
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y = 1 + np.mod(np.arange(n_samples) + 1, n_labels)
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Y = LabelBinarizer().fit_transform(y)
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for activation in ACTIVATION_TYPES:
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mlp = MLPClassifier(activation=activation, hidden_layer_sizes=10,
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solver='lbfgs', alpha=1e-5,
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learning_rate_init=0.2, max_iter=1,
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random_state=1)
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mlp.fit(X, y)
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theta = np.hstack([l.ravel() for l in mlp.coefs_ +
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mlp.intercepts_])
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layer_units = ([X.shape[1]] + [mlp.hidden_layer_sizes] +
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[mlp.n_outputs_])
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activations = []
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deltas = []
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coef_grads = []
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intercept_grads = []
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activations.append(X)
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for i in range(mlp.n_layers_ - 1):
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activations.append(np.empty((X.shape[0],
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layer_units[i + 1])))
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deltas.append(np.empty((X.shape[0],
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layer_units[i + 1])))
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fan_in = layer_units[i]
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fan_out = layer_units[i + 1]
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coef_grads.append(np.empty((fan_in, fan_out)))
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intercept_grads.append(np.empty(fan_out))
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# analytically compute the gradients
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def loss_grad_fun(t):
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return mlp._loss_grad_lbfgs(t, X, Y, activations, deltas,
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coef_grads, intercept_grads)
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[value, grad] = loss_grad_fun(theta)
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numgrad = np.zeros(np.size(theta))
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n = np.size(theta, 0)
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E = np.eye(n)
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epsilon = 1e-5
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# numerically compute the gradients
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for i in range(n):
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dtheta = E[:, i] * epsilon
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numgrad[i] = ((loss_grad_fun(theta + dtheta)[0] -
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loss_grad_fun(theta - dtheta)[0]) /
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(epsilon * 2.0))
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assert_almost_equal(numgrad, grad)
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def test_lbfgs_classification():
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# Test lbfgs on classification.
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# It should achieve a score higher than 0.95 for the binary and multi-class
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# versions of the digits dataset.
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for X, y in classification_datasets:
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X_train = X[:150]
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y_train = y[:150]
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X_test = X[150:]
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expected_shape_dtype = (X_test.shape[0], y_train.dtype.kind)
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for activation in ACTIVATION_TYPES:
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mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes=50,
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max_iter=150, shuffle=True, random_state=1,
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activation=activation)
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mlp.fit(X_train, y_train)
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y_predict = mlp.predict(X_test)
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assert_greater(mlp.score(X_train, y_train), 0.95)
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assert_equal((y_predict.shape[0], y_predict.dtype.kind),
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expected_shape_dtype)
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def test_lbfgs_regression():
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# Test lbfgs on the boston dataset, a regression problems.
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X = Xboston
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y = yboston
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for activation in ACTIVATION_TYPES:
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mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50,
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max_iter=150, shuffle=True, random_state=1,
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activation=activation)
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mlp.fit(X, y)
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if activation == 'identity':
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assert_greater(mlp.score(X, y), 0.84)
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else:
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# Non linear models perform much better than linear bottleneck:
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assert_greater(mlp.score(X, y), 0.95)
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def test_learning_rate_warmstart():
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# Tests that warm_start reuse past solutions.
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X = [[3, 2], [1, 6], [5, 6], [-2, -4]]
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y = [1, 1, 1, 0]
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for learning_rate in ["invscaling", "constant"]:
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mlp = MLPClassifier(solver='sgd', hidden_layer_sizes=4,
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learning_rate=learning_rate, max_iter=1,
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power_t=0.25, warm_start=True)
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with ignore_warnings(category=ConvergenceWarning):
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mlp.fit(X, y)
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prev_eta = mlp._optimizer.learning_rate
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mlp.fit(X, y)
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post_eta = mlp._optimizer.learning_rate
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if learning_rate == 'constant':
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assert_equal(prev_eta, post_eta)
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elif learning_rate == 'invscaling':
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assert_equal(mlp.learning_rate_init / pow(8 + 1, mlp.power_t),
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post_eta)
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def test_multilabel_classification():
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# Test that multi-label classification works as expected.
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# test fit method
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X, y = make_multilabel_classification(n_samples=50, random_state=0,
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return_indicator=True)
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mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes=50, alpha=1e-5,
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max_iter=150, random_state=0, activation='logistic',
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learning_rate_init=0.2)
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mlp.fit(X, y)
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assert_greater(mlp.score(X, y), 0.97)
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# test partial fit method
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mlp = MLPClassifier(solver='sgd', hidden_layer_sizes=50, max_iter=150,
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random_state=0, activation='logistic', alpha=1e-5,
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learning_rate_init=0.2)
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for i in range(100):
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mlp.partial_fit(X, y, classes=[0, 1, 2, 3, 4])
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assert_greater(mlp.score(X, y), 0.9)
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def test_multioutput_regression():
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# Test that multi-output regression works as expected
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X, y = make_regression(n_samples=200, n_targets=5)
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mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50, max_iter=200,
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random_state=1)
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mlp.fit(X, y)
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assert_greater(mlp.score(X, y), 0.9)
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def test_partial_fit_classes_error():
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# Tests that passing different classes to partial_fit raises an error
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X = [[3, 2]]
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y = [0]
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clf = MLPClassifier(solver='sgd')
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clf.partial_fit(X, y, classes=[0, 1])
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assert_raises(ValueError, clf.partial_fit, X, y, classes=[1, 2])
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def test_partial_fit_classification():
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# Test partial_fit on classification.
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# `partial_fit` should yield the same results as 'fit' for binary and
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# multi-class classification.
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for X, y in classification_datasets:
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X = X
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y = y
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mlp = MLPClassifier(solver='sgd', max_iter=100, random_state=1,
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tol=0, alpha=1e-5, learning_rate_init=0.2)
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with ignore_warnings(category=ConvergenceWarning):
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mlp.fit(X, y)
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pred1 = mlp.predict(X)
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mlp = MLPClassifier(solver='sgd', random_state=1, alpha=1e-5,
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learning_rate_init=0.2)
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for i in range(100):
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mlp.partial_fit(X, y, classes=np.unique(y))
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pred2 = mlp.predict(X)
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assert_array_equal(pred1, pred2)
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assert_greater(mlp.score(X, y), 0.95)
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def test_partial_fit_unseen_classes():
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# Non regression test for bug 6994
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# Tests for labeling errors in partial fit
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clf = MLPClassifier(random_state=0)
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clf.partial_fit([[1], [2], [3]], ["a", "b", "c"],
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classes=["a", "b", "c", "d"])
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clf.partial_fit([[4]], ["d"])
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assert_greater(clf.score([[1], [2], [3], [4]], ["a", "b", "c", "d"]), 0)
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def test_partial_fit_regression():
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# Test partial_fit on regression.
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# `partial_fit` should yield the same results as 'fit' for regression.
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X = Xboston
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y = yboston
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for momentum in [0, .9]:
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mlp = MLPRegressor(solver='sgd', max_iter=100, activation='relu',
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random_state=1, learning_rate_init=0.01,
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batch_size=X.shape[0], momentum=momentum)
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with warnings.catch_warnings(record=True):
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# catch convergence warning
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mlp.fit(X, y)
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pred1 = mlp.predict(X)
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mlp = MLPRegressor(solver='sgd', activation='relu',
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learning_rate_init=0.01, random_state=1,
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batch_size=X.shape[0], momentum=momentum)
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for i in range(100):
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mlp.partial_fit(X, y)
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pred2 = mlp.predict(X)
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assert_almost_equal(pred1, pred2, decimal=2)
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score = mlp.score(X, y)
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assert_greater(score, 0.75)
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def test_partial_fit_errors():
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# Test partial_fit error handling.
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X = [[3, 2], [1, 6]]
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y = [1, 0]
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# no classes passed
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assert_raises(ValueError,
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MLPClassifier(solver='sgd').partial_fit, X, y, classes=[2])
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# lbfgs doesn't support partial_fit
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assert not hasattr(MLPClassifier(solver='lbfgs'), 'partial_fit')
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def test_params_errors():
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# Test that invalid parameters raise value error
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X = [[3, 2], [1, 6]]
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y = [1, 0]
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clf = MLPClassifier
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assert_raises(ValueError, clf(hidden_layer_sizes=-1).fit, X, y)
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assert_raises(ValueError, clf(max_iter=-1).fit, X, y)
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assert_raises(ValueError, clf(shuffle='true').fit, X, y)
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assert_raises(ValueError, clf(alpha=-1).fit, X, y)
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assert_raises(ValueError, clf(learning_rate_init=-1).fit, X, y)
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assert_raises(ValueError, clf(momentum=2).fit, X, y)
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assert_raises(ValueError, clf(momentum=-0.5).fit, X, y)
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assert_raises(ValueError, clf(nesterovs_momentum='invalid').fit, X, y)
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assert_raises(ValueError, clf(early_stopping='invalid').fit, X, y)
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assert_raises(ValueError, clf(validation_fraction=1).fit, X, y)
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assert_raises(ValueError, clf(validation_fraction=-0.5).fit, X, y)
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assert_raises(ValueError, clf(beta_1=1).fit, X, y)
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assert_raises(ValueError, clf(beta_1=-0.5).fit, X, y)
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assert_raises(ValueError, clf(beta_2=1).fit, X, y)
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assert_raises(ValueError, clf(beta_2=-0.5).fit, X, y)
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assert_raises(ValueError, clf(epsilon=-0.5).fit, X, y)
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assert_raises(ValueError, clf(n_iter_no_change=-1).fit, X, y)
|
|
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assert_raises(ValueError, clf(solver='hadoken').fit, X, y)
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assert_raises(ValueError, clf(learning_rate='converge').fit, X, y)
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assert_raises(ValueError, clf(activation='cloak').fit, X, y)
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|
|
|
|
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def test_predict_proba_binary():
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# Test that predict_proba works as expected for binary class.
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X = X_digits_binary[:50]
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y = y_digits_binary[:50]
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|
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clf = MLPClassifier(hidden_layer_sizes=5, activation='logistic',
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random_state=1)
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with ignore_warnings(category=ConvergenceWarning):
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clf.fit(X, y)
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y_proba = clf.predict_proba(X)
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y_log_proba = clf.predict_log_proba(X)
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|
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(n_samples, n_classes) = y.shape[0], 2
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|
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proba_max = y_proba.argmax(axis=1)
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proba_log_max = y_log_proba.argmax(axis=1)
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|
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assert_equal(y_proba.shape, (n_samples, n_classes))
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assert_array_equal(proba_max, proba_log_max)
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assert_array_equal(y_log_proba, np.log(y_proba))
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|
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assert_equal(roc_auc_score(y, y_proba[:, 1]), 1.0)
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|
|
|
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def test_predict_proba_multiclass():
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# Test that predict_proba works as expected for multi class.
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X = X_digits_multi[:10]
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|
y = y_digits_multi[:10]
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|
|
|
clf = MLPClassifier(hidden_layer_sizes=5)
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|
with ignore_warnings(category=ConvergenceWarning):
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|
clf.fit(X, y)
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y_proba = clf.predict_proba(X)
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|
y_log_proba = clf.predict_log_proba(X)
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|
|
|
(n_samples, n_classes) = y.shape[0], np.unique(y).size
|
|
|
|
proba_max = y_proba.argmax(axis=1)
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|
proba_log_max = y_log_proba.argmax(axis=1)
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|
|
|
assert_equal(y_proba.shape, (n_samples, n_classes))
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|
assert_array_equal(proba_max, proba_log_max)
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|
assert_array_equal(y_log_proba, np.log(y_proba))
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|
|
|
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|
def test_predict_proba_multilabel():
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|
# Test that predict_proba works as expected for multilabel.
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|
# Multilabel should not use softmax which makes probabilities sum to 1
|
|
X, Y = make_multilabel_classification(n_samples=50, random_state=0,
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|
return_indicator=True)
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|
n_samples, n_classes = Y.shape
|
|
|
|
clf = MLPClassifier(solver='lbfgs', hidden_layer_sizes=30,
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|
random_state=0)
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|
clf.fit(X, Y)
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|
y_proba = clf.predict_proba(X)
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|
|
|
assert_equal(y_proba.shape, (n_samples, n_classes))
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|
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_greater((y_proba.sum(1) - 1).dot(y_proba.sum(1) - 1), 1e-10)
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|
assert_array_equal(proba_max, proba_log_max)
|
|
assert_array_equal(y_log_proba, np.log(y_proba))
|
|
|
|
|
|
def test_shuffle():
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|
# Test that the shuffle parameter affects the training process (it should)
|
|
X, y = make_regression(n_samples=50, n_features=5, n_targets=1,
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|
random_state=0)
|
|
|
|
# The coefficients will be identical if both do or do not shuffle
|
|
for shuffle in [True, False]:
|
|
mlp1 = MLPRegressor(hidden_layer_sizes=1, max_iter=1, batch_size=1,
|
|
random_state=0, shuffle=shuffle)
|
|
mlp2 = MLPRegressor(hidden_layer_sizes=1, max_iter=1, batch_size=1,
|
|
random_state=0, shuffle=shuffle)
|
|
mlp1.fit(X, y)
|
|
mlp2.fit(X, y)
|
|
|
|
assert np.array_equal(mlp1.coefs_[0], mlp2.coefs_[0])
|
|
|
|
# The coefficients will be slightly different if shuffle=True
|
|
mlp1 = MLPRegressor(hidden_layer_sizes=1, max_iter=1, batch_size=1,
|
|
random_state=0, shuffle=True)
|
|
mlp2 = MLPRegressor(hidden_layer_sizes=1, max_iter=1, batch_size=1,
|
|
random_state=0, shuffle=False)
|
|
mlp1.fit(X, y)
|
|
mlp2.fit(X, y)
|
|
|
|
assert not np.array_equal(mlp1.coefs_[0], mlp2.coefs_[0])
|
|
|
|
|
|
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_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_greater(clf.max_iter, clf.n_iter_)
|
|
|
|
|
|
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_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_greater(clf.max_iter, clf.n_iter_)
|
|
|
|
valid_scores = clf.validation_scores_
|
|
best_valid_score = clf.best_validation_score_
|
|
assert_equal(max(valid_scores), best_valid_score)
|
|
assert_greater(best_valid_score + tol, valid_scores[-2])
|
|
assert_greater(best_valid_score + tol, valid_scores[-1])
|
|
|
|
|
|
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_greater(clf.max_iter, clf.n_iter_)
|
|
assert_greater(1e-6, clf._optimizer.learning_rate)
|
|
|
|
|
|
@ignore_warnings(category=RuntimeWarning)
|
|
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))
|
|
assert_raise_message(ValueError, message, clf.fit, X, y_i)
|
|
|
|
|
|
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_equal(clf._no_improvement_count, n_iter_no_change + 1)
|
|
assert_greater(max_iter, clf.n_iter_)
|
|
|
|
|
|
@ignore_warnings(category=ConvergenceWarning)
|
|
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_equal(clf.n_iter_, max_iter)
|
|
|
|
# validate _update_no_improvement_count() was always triggered
|
|
assert_equal(clf._no_improvement_count, clf.n_iter_ - 1)
|