# Copyright 2019 IBM Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import lale.operators import lale.docstrings import sklearn.neural_network class MLPClassifierImpl(): def __init__(self, **hyperparams): self._hyperparams = hyperparams self._wrapped_model = sklearn.neural_network.MLPClassifier(**self._hyperparams) def fit(self, X, y=None): self._wrapped_model.fit(X, y) return self def predict(self, X): return self._wrapped_model.predict(X) def predict_proba(self, X): return self._wrapped_model.predict_proba(X) def partial_fit(self, X, y=None, classes = None): if not hasattr(self, "_wrapped_model"): self._wrapped_model = sklearn.neural_network.MLPClassifier( **self._hyperparams) self._wrapped_model.partial_fit(X, y, classes = classes) return self _hyperparams_schema = { 'description': 'Hyperparameter schema for the MLPClassifier model from scikit-learn.', 'allOf': [{ 'description': 'This first sub-object lists all constructor arguments with their ' 'types, one at a time, omitting cross-argument constraints.', 'type': 'object', 'additionalProperties': False, 'required': [ 'hidden_layer_sizes', 'activation', 'solver', 'alpha', 'batch_size', 'learning_rate', 'learning_rate_init', 'power_t', 'max_iter', 'shuffle', 'random_state', 'tol', 'verbose', 'warm_start', 'momentum', 'nesterovs_momentum', 'early_stopping', 'validation_fraction', 'beta_1', 'beta_2', 'epsilon', 'n_iter_no_change'], 'relevantToOptimizer': [ 'hidden_layer_sizes', 'activation', 'solver', 'alpha', 'batch_size', 'learning_rate', 'tol', 'momentum', 'nesterovs_momentum', 'early_stopping', 'validation_fraction', 'beta_1', 'beta_2', 'epsilon'], 'properties': { 'hidden_layer_sizes': { 'description': 'The ith element represents the number of neurons in ' 'the ith hidden layer.', 'type': 'array', 'laleType': 'tuple', 'minItemsForOptimizer': 1, 'maxItemsForOptimizer': 20, 'items': { 'type': 'integer', 'minimumForOptimizer': 1, 'maximumForOptimizer': 500}, 'default': [100]}, 'activation': { 'description': 'Activation function for the hidden layer.', 'enum': ['identity', 'logistic', 'tanh', 'relu'], 'default': 'relu'}, 'solver': { 'description': 'The solver for weight optimization.', 'enum': ['lbfgs', 'sgd', 'adam'], 'default': 'adam'}, 'alpha': { 'description': 'L2 penalty (regularization term) parameter.', 'type': 'number', 'distribution': 'loguniform', 'minimumForOptimizer': 1e-10, 'maximumForOptimizer': 1, 'default': 0.0001}, 'batch_size': { 'description': 'Size of minibatches for stochastic optimizers.', 'anyOf': [{ 'description': 'Size of minibatches', 'type': 'integer', 'distribution': 'uniform', 'minimumForOptimizer': 3, 'maximumForOptimizer': 128}, { 'description': 'Automatic selection, batch_size=min(200, n_samples)', 'enum': ['auto']}], 'default': 'auto'}, 'learning_rate': { 'description': 'Learning rate schedule for weight updates.', 'enum': ['constant', 'invscaling', 'adaptive'], 'default': 'constant'}, 'learning_rate_init': { 'description': 'The initial learning rate used. It controls the ' 'step-size in updating the weights.', 'type': 'number', 'default': 0.001}, 'power_t': { 'description': 'The exponent for inverse scaling learning rate.', 'type': 'number', 'default': 0.5}, 'max_iter': { 'description': 'Maximum number of iterations. The solver iterates until ' 'convergence (determined by "tol") or this number of ' 'iterations.', 'type': 'integer', 'distribution': 'uniform', 'minimum': 1, 'minimumForOptimizer': 10, 'maximumForOptimizer': 1000, 'default': 200}, 'shuffle': { 'description': 'Whether to shuffle samples in each iteration.', 'type': 'boolean', 'default': True}, 'random_state': { 'description': 'Random generator selection', 'anyOf': [{ 'description': 'seed used by the random number generators', 'type': 'integer'}, { 'description': 'Random number generator', 'laleType': 'numpy.random.RandomState'}, { 'description': 'RandomState instance used by np.random', 'enum': [None]}], 'default': None}, 'tol': { 'description': 'Tolerance for the optimization. When the loss or score ' 'is not improving by at least tol for n_iter_no_change ' 'consecutive iterations, unless learning_rate is set to ' '"adaptive", convergence is considered to be reached and ' 'training stops.', 'type': 'number', 'distribution': 'loguniform', 'minimumForOptimizer': 1e-08, 'maximumForOptimizer': 0.01, 'default': 0.0001}, 'verbose': { 'description': 'Whether to print progress messages to stdout.', 'type': 'boolean', 'default': False}, 'warm_start': { 'description': 'When set to True, reuse the solution of the previous ' 'call to fit as initialization, otherwise, just erase ' 'the previous solution.', 'type': 'boolean', 'default': False}, 'momentum': { 'description': 'Momentum for gradient descent update.', 'type': 'number', 'minimum': 0, 'maximum': 1, 'default': 0.9}, 'nesterovs_momentum': { 'description': "Whether to use Nesterov's momentum.", 'type': 'boolean', 'default': True}, 'early_stopping': { 'description': 'Whether to use early stopping to terminate training when ' 'validation score is not improving. If set to true, it ' 'will automatically set aside 10% of training data as ' 'validation and terminate training when validation score ' 'is not improving by at least tol for n_iter_no_change ' 'consecutive epochs.', 'type': 'boolean', 'default': False}, 'validation_fraction': { 'description': 'The proportion of training data to set aside as ' 'validation set for early stopping.', 'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.1}, 'beta_1': { 'description': 'Exponential decay rate for estimates of first moment ' 'vector in adam.', 'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'exclusiveMaximum': True, 'default': 0.9}, 'beta_2': { 'description': 'Exponential decay rate for estimates of second moment ' 'vector in adam.', 'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'exclusiveMaximum': True, 'default': 0.999}, 'epsilon': { 'description': 'Value for numerical stability in adam.', 'type': 'number', 'distribution': 'loguniform', 'minimumForOptimizer': 1e-08, 'maximumForOptimizer': 1.35, 'default': 1e-08}, 'n_iter_no_change': { 'description': 'Maximum number of epochs to not meet tol improvement.', 'type': 'integer', 'default': 10, 'minimum': 1}, }}]} _input_fit_schema = { 'description': 'Fit the model to data matrix X and target(s) y.', 'type': 'object', 'required': ['X', 'y'], 'additionalProperties': False, 'properties': { 'X': { 'description': 'Features; the outer array is over samples.', 'type': 'array', 'items': { 'type': 'array', 'items': { 'type': 'number'}}}, 'y': { 'description': 'Target class labels; the array is over samples.', 'anyOf': [ { 'type': 'array', 'items': { 'type': 'array', 'items': { 'type': 'number'}}}, { 'type': 'array', 'items': {'type': 'number'}}, { 'type': 'array', 'items': {'type': 'string'}}, { 'type': 'array', 'items': {'type': 'boolean'}}]}}} _input_predict_schema = { 'description': 'Predict using the multi-layer perceptron classifier', 'type': 'object', 'required': ['X'], 'additionalProperties': False, 'properties': { 'X': { 'description': 'Features; the outer array is over samples.', 'type': 'array', 'items': { 'type': 'array', 'items': {'type': 'number'}}}}} _output_predict_schema = { 'description': 'Predict using the multi-layer perceptron classifier', 'anyOf': [ { 'type': 'array', 'items': {'type': 'number'}}, { 'type': 'array', 'items': {'type': 'string'}}, { 'type': 'array', 'items': {'type': 'boolean'}}]} _input_predict_proba_schema = { 'type': 'object', 'required': ['X'], 'additionalProperties': False, 'properties': { 'X': { 'description': 'Features; the outer array is over samples.', 'type': 'array', 'items': { 'type': 'array', 'items': {'type': 'number'}}}}} _output_predict_proba_schema = { 'type': 'array', 'items': { 'type': 'array', 'items': {'type': 'number'}}} _combined_schemas = { '$schema': 'http://json-schema.org/draft-04/schema#', 'description': """`Multi-layer perceptron`_ dense deep neural network from scikit-learn for classification. .. _`Multi-layer perceptron`: https://scikit-learn.org/0.20/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn-neural-network-mlpclassifier """, 'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.mlp_classifier.html', 'import_from': 'sklearn.neural_network', 'type': 'object', 'tags': { 'pre': ['~categoricals'], 'op': ['estimator', 'classifier', '~interpretable'], 'post': ['probabilities']}, 'properties': { 'hyperparams': _hyperparams_schema, 'input_fit': _input_fit_schema, 'input_predict': _input_predict_schema, 'output_predict': _output_predict_schema, 'input_predict_proba': _input_predict_proba_schema, 'output_predict_proba': _output_predict_proba_schema}} MLPClassifier : lale.operators.IndividualOp MLPClassifier = lale.operators.make_operator(MLPClassifierImpl, _combined_schemas) if sklearn.__version__ >= '0.22': # old: https://scikit-learn.org/0.20/modules/generated/sklearn.neural_network.MLPClassifier.html # new: https://scikit-learn.org/0.23/modules/generated/sklearn.neural_network.MLPClassifier.html from lale.schemas import Int MLPClassifier = MLPClassifier.customize_schema( max_fun=Int( desc='Maximum number of loss function calls.', default=15000, forOptimizer=False, min=0)) lale.docstrings.set_docstrings(MLPClassifierImpl, MLPClassifier._schemas)