Python neural_network.MLPClassifier方法代碼示例

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
def test_lbfgs_classification():
    # Test lbfgs on classification.
    # It should achieve a score higher than 0.95 for the binary and multi-class
    # versions of the digits dataset.
    for X, y in classification_datasets:
        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_greater(mlp.score(X_train, y_train), 0.95)
            assert_equal((y_predict.shape[0], y_predict.dtype.kind),
                         expected_shape_dtype) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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_equal(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_equal(roc_auc_score(y, y_proba[:, 1]), 1.0) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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() 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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_equal(prev_eta, post_eta)
        elif learning_rate == 'invscaling':
            assert_equal(mlp.learning_rate_init / pow(8 + 1, mlp.power_t),
                         post_eta) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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_greater(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_greater(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) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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_greater(mlp.score(X, y), 0.95) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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_equal(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_greater((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)) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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_) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
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) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
def test_38_mlp_classifier(self):
        print("\ntest 38 (mlp classifier without preprocessing)[multi-class]\n")
        X, X_test, y, features, target, test_file = self.data_utility.get_data_for_multi_class_classification()

        model = MLPClassifier()
        pipeline_obj = Pipeline([
            ("model", model)
        ])
        pipeline_obj.fit(X,y)
        file_name = 'test38sklearn.pmml'
        
        skl_to_pmml(pipeline_obj, features, target, file_name)
        model_name  = self.adapa_utility.upload_to_zserver(file_name)
        predictions, probabilities = self.adapa_utility.score_in_zserver(model_name, test_file)
        model_pred = pipeline_obj.predict(X_test)
        model_prob = pipeline_obj.predict_proba(X_test)
        self.assertEqual(self.adapa_utility.compare_predictions(predictions, model_pred), True)
        self.assertEqual(self.adapa_utility.compare_probability(probabilities, model_prob), True) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
def test_39_mlp_classifier(self):
        print("\ntest 39 (mlp classifier without preprocessing)[binary-class]\n")
        X, X_test, y, features, target, test_file = self.data_utility.get_data_for_binary_classification()

        model = MLPClassifier()
        pipeline_obj = Pipeline([
            ("model", model)
        ])
        pipeline_obj.fit(X,y)
        file_name = 'test39sklearn.pmml'
        
        skl_to_pmml(pipeline_obj, features, target, file_name)
        model_name  = self.adapa_utility.upload_to_zserver(file_name)
        predictions, probabilities = self.adapa_utility.score_in_zserver(model_name, test_file)
        model_pred = pipeline_obj.predict(X_test)
        model_prob = pipeline_obj.predict_proba(X_test)
        self.assertEqual(self.adapa_utility.compare_predictions(predictions, model_pred), True)
        self.assertEqual(self.adapa_utility.compare_probability(probabilities, model_prob), True) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
def __init__(self, classifier=FaceClassifierModels.DEFAULT):
        self._clf = None
        if classifier == FaceClassifierModels.LINEAR_SVM:
            self._clf = SVC(C=1.0, kernel="linear", probability=True)
        elif classifier == FaceClassifierModels.NAIVE_BAYES:
            self._clf = GaussianNB()
        elif classifier == FaceClassifierModels.RBF_SVM:
            self._clf = SVC(C=1, kernel='rbf', probability=True, gamma=2)
        elif classifier == FaceClassifierModels.NEAREST_NEIGHBORS:
            self._clf = KNeighborsClassifier(1)
        elif classifier == FaceClassifierModels.DECISION_TREE:
            self._clf = DecisionTreeClassifier(max_depth=5)
        elif classifier == FaceClassifierModels.RANDOM_FOREST:
            self._clf = RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)
        elif classifier == FaceClassifierModels.NEURAL_NET:
            self._clf = MLPClassifier(alpha=1)
        elif classifier == FaceClassifierModels.ADABOOST:
            self._clf = AdaBoostClassifier()
        elif classifier == FaceClassifierModels.QDA:
            self._clf = QuadraticDiscriminantAnalysis()
        print("classifier={}".format(FaceClassifierModels(classifier))) 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
def getModels():
    result = []
    result.append("LinearRegression")
    result.append("BayesianRidge")
    result.append("ARDRegression")
    result.append("ElasticNet")
    result.append("HuberRegressor")
    result.append("Lasso")
    result.append("LassoLars")
    result.append("Rigid")
    result.append("SGDRegressor")
    result.append("SVR")
    result.append("MLPClassifier")
    result.append("KNeighborsClassifier")
    result.append("SVC")
    result.append("GaussianProcessClassifier")
    result.append("DecisionTreeClassifier")
    result.append("RandomForestClassifier")
    result.append("AdaBoostClassifier")
    result.append("GaussianNB")
    result.append("LogisticRegression")
    result.append("QuadraticDiscriminantAnalysis")
    return result 

# 需要導入模塊: from sklearn import neural_network [as 別名]
# 或者: from sklearn.neural_network import MLPClassifier [as 別名]
def learn():
    print('Loading previous dataset to learn')
    n_files = 0
    training_set = list()
    training_labels = list()
    for file in os.listdir(data_dir):
        if file.endswith(".jpg"):
            img_file = os.path.join(data_dir, file)
            label_name = str(file).split('_')
            training_set.append(cv2.imread(img_file, 1).reshape(6912))
            training_labels.append(label_name[0])
            n_files += 1
    
    x = training_set
    y = tools.integerize(training_labels)

    net = MLPClassifier()

    print('\nLearning...\n')
    net.fit(x, y)

    print('MLP has already learned previous instances')

    return net