393 lines
13 KiB
Python
393 lines
13 KiB
Python
"""Restricted Boltzmann Machine
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"""
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# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
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# Vlad Niculae
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# Gabriel Synnaeve
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# Lars Buitinck
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# License: BSD 3 clause
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import time
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import numpy as np
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import scipy.sparse as sp
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from scipy.special import expit # logistic function
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from ..base import BaseEstimator
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from ..base import TransformerMixin
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from ..utils import check_array
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from ..utils import check_random_state
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from ..utils import gen_even_slices
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from ..utils.extmath import safe_sparse_dot
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from ..utils.extmath import log_logistic
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from ..utils.validation import check_is_fitted, _deprecate_positional_args
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class BernoulliRBM(TransformerMixin, BaseEstimator):
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"""Bernoulli Restricted Boltzmann Machine (RBM).
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A Restricted Boltzmann Machine with binary visible units and
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binary hidden units. Parameters are estimated using Stochastic Maximum
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Likelihood (SML), also known as Persistent Contrastive Divergence (PCD)
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[2].
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The time complexity of this implementation is ``O(d ** 2)`` assuming
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d ~ n_features ~ n_components.
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Read more in the :ref:`User Guide <rbm>`.
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Parameters
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----------
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n_components : int, default=256
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Number of binary hidden units.
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learning_rate : float, default=0.1
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The learning rate for weight updates. It is *highly* recommended
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to tune this hyper-parameter. Reasonable values are in the
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10**[0., -3.] range.
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batch_size : int, default=10
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Number of examples per minibatch.
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n_iter : int, default=10
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Number of iterations/sweeps over the training dataset to perform
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during training.
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verbose : int, default=0
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The verbosity level. The default, zero, means silent mode.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for:
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- Gibbs sampling from visible and hidden layers.
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- Initializing components, sampling from layers during fit.
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- Corrupting the data when scoring samples.
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Pass an int for reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
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intercept_hidden_ : array-like of shape (n_components,)
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Biases of the hidden units.
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intercept_visible_ : array-like of shape (n_features,)
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Biases of the visible units.
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components_ : array-like of shape (n_components, n_features)
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Weight matrix, where n_features in the number of
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visible units and n_components is the number of hidden units.
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h_samples_ : array-like of shape (batch_size, n_components)
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Hidden Activation sampled from the model distribution,
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where batch_size in the number of examples per minibatch and
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n_components is the number of hidden units.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.neural_network import BernoulliRBM
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>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
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>>> model = BernoulliRBM(n_components=2)
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>>> model.fit(X)
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BernoulliRBM(n_components=2)
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References
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----------
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[1] Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for
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deep belief nets. Neural Computation 18, pp 1527-1554.
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https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf
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[2] Tieleman, T. Training Restricted Boltzmann Machines using
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Approximations to the Likelihood Gradient. International Conference
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on Machine Learning (ICML) 2008
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"""
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@_deprecate_positional_args
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def __init__(self, n_components=256, *, learning_rate=0.1, batch_size=10,
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n_iter=10, verbose=0, random_state=None):
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self.n_components = n_components
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self.learning_rate = learning_rate
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self.batch_size = batch_size
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self.n_iter = n_iter
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self.verbose = verbose
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self.random_state = random_state
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def transform(self, X):
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"""Compute the hidden layer activation probabilities, P(h=1|v=X).
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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The data to be transformed.
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Returns
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-------
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h : ndarray of shape (n_samples, n_components)
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Latent representations of the data.
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"""
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check_is_fitted(self)
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X = self._validate_data(X, accept_sparse='csr', reset=False,
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dtype=(np.float64, np.float32))
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return self._mean_hiddens(X)
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def _mean_hiddens(self, v):
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"""Computes the probabilities P(h=1|v).
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Parameters
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----------
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v : ndarray of shape (n_samples, n_features)
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Values of the visible layer.
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Returns
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-------
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h : ndarray of shape (n_samples, n_components)
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Corresponding mean field values for the hidden layer.
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"""
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p = safe_sparse_dot(v, self.components_.T)
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p += self.intercept_hidden_
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return expit(p, out=p)
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def _sample_hiddens(self, v, rng):
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"""Sample from the distribution P(h|v).
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Parameters
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----------
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v : ndarray of shape (n_samples, n_features)
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Values of the visible layer to sample from.
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rng : RandomState instance
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Random number generator to use.
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Returns
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-------
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h : ndarray of shape (n_samples, n_components)
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Values of the hidden layer.
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"""
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p = self._mean_hiddens(v)
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return (rng.random_sample(size=p.shape) < p)
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def _sample_visibles(self, h, rng):
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"""Sample from the distribution P(v|h).
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Parameters
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----------
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h : ndarray of shape (n_samples, n_components)
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Values of the hidden layer to sample from.
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rng : RandomState instance
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Random number generator to use.
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Returns
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-------
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v : ndarray of shape (n_samples, n_features)
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Values of the visible layer.
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"""
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p = np.dot(h, self.components_)
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p += self.intercept_visible_
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expit(p, out=p)
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return (rng.random_sample(size=p.shape) < p)
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def _free_energy(self, v):
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"""Computes the free energy F(v) = - log sum_h exp(-E(v,h)).
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Parameters
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----------
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v : ndarray of shape (n_samples, n_features)
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Values of the visible layer.
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Returns
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-------
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free_energy : ndarray of shape (n_samples,)
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The value of the free energy.
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"""
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return (- safe_sparse_dot(v, self.intercept_visible_)
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- np.logaddexp(0, safe_sparse_dot(v, self.components_.T)
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+ self.intercept_hidden_).sum(axis=1))
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def gibbs(self, v):
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"""Perform one Gibbs sampling step.
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Parameters
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----------
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v : ndarray of shape (n_samples, n_features)
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Values of the visible layer to start from.
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Returns
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-------
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v_new : ndarray of shape (n_samples, n_features)
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Values of the visible layer after one Gibbs step.
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"""
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check_is_fitted(self)
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if not hasattr(self, "random_state_"):
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self.random_state_ = check_random_state(self.random_state)
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h_ = self._sample_hiddens(v, self.random_state_)
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v_ = self._sample_visibles(h_, self.random_state_)
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return v_
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def partial_fit(self, X, y=None):
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"""Fit the model to the data X which should contain a partial
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segment of the data.
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Parameters
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----------
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X : ndarray of shape (n_samples, n_features)
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Training data.
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Returns
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-------
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self : BernoulliRBM
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The fitted model.
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"""
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first_pass = not hasattr(self, 'components_')
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X = self._validate_data(X, accept_sparse='csr', dtype=np.float64,
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reset=first_pass)
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if not hasattr(self, 'random_state_'):
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self.random_state_ = check_random_state(self.random_state)
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if not hasattr(self, 'components_'):
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self.components_ = np.asarray(
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self.random_state_.normal(
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0,
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0.01,
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(self.n_components, X.shape[1])
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),
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order='F')
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if not hasattr(self, 'intercept_hidden_'):
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self.intercept_hidden_ = np.zeros(self.n_components, )
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if not hasattr(self, 'intercept_visible_'):
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self.intercept_visible_ = np.zeros(X.shape[1], )
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if not hasattr(self, 'h_samples_'):
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self.h_samples_ = np.zeros((self.batch_size, self.n_components))
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self._fit(X, self.random_state_)
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def _fit(self, v_pos, rng):
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"""Inner fit for one mini-batch.
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Adjust the parameters to maximize the likelihood of v using
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Stochastic Maximum Likelihood (SML).
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Parameters
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----------
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v_pos : ndarray of shape (n_samples, n_features)
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The data to use for training.
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rng : RandomState instance
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Random number generator to use for sampling.
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"""
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h_pos = self._mean_hiddens(v_pos)
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v_neg = self._sample_visibles(self.h_samples_, rng)
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h_neg = self._mean_hiddens(v_neg)
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lr = float(self.learning_rate) / v_pos.shape[0]
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update = safe_sparse_dot(v_pos.T, h_pos, dense_output=True).T
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update -= np.dot(h_neg.T, v_neg)
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self.components_ += lr * update
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self.intercept_hidden_ += lr * (h_pos.sum(axis=0) - h_neg.sum(axis=0))
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self.intercept_visible_ += lr * (np.asarray(
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v_pos.sum(axis=0)).squeeze() -
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v_neg.sum(axis=0))
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h_neg[rng.uniform(size=h_neg.shape) < h_neg] = 1.0 # sample binomial
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self.h_samples_ = np.floor(h_neg, h_neg)
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def score_samples(self, X):
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"""Compute the pseudo-likelihood of X.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Values of the visible layer. Must be all-boolean (not checked).
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Returns
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-------
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pseudo_likelihood : ndarray of shape (n_samples,)
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Value of the pseudo-likelihood (proxy for likelihood).
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Notes
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-----
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This method is not deterministic: it computes a quantity called the
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free energy on X, then on a randomly corrupted version of X, and
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returns the log of the logistic function of the difference.
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"""
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check_is_fitted(self)
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v = check_array(X, accept_sparse='csr')
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rng = check_random_state(self.random_state)
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# Randomly corrupt one feature in each sample in v.
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ind = (np.arange(v.shape[0]),
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rng.randint(0, v.shape[1], v.shape[0]))
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if sp.issparse(v):
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data = -2 * v[ind] + 1
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v_ = v + sp.csr_matrix((data.A.ravel(), ind), shape=v.shape)
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else:
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v_ = v.copy()
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v_[ind] = 1 - v_[ind]
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fe = self._free_energy(v)
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fe_ = self._free_energy(v_)
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return v.shape[1] * log_logistic(fe_ - fe)
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def fit(self, X, y=None):
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"""Fit the model to the data X.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training data.
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Returns
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-------
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self : BernoulliRBM
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The fitted model.
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"""
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X = self._validate_data(
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X, accept_sparse='csr', dtype=(np.float64, np.float32)
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)
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n_samples = X.shape[0]
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rng = check_random_state(self.random_state)
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self.components_ = np.asarray(
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rng.normal(0, 0.01, (self.n_components, X.shape[1])),
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order='F',
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dtype=X.dtype)
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self.intercept_hidden_ = np.zeros(self.n_components, dtype=X.dtype)
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self.intercept_visible_ = np.zeros(X.shape[1], dtype=X.dtype)
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self.h_samples_ = np.zeros((self.batch_size, self.n_components),
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dtype=X.dtype)
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n_batches = int(np.ceil(float(n_samples) / self.batch_size))
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batch_slices = list(gen_even_slices(n_batches * self.batch_size,
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n_batches, n_samples=n_samples))
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verbose = self.verbose
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begin = time.time()
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for iteration in range(1, self.n_iter + 1):
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for batch_slice in batch_slices:
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self._fit(X[batch_slice], rng)
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if verbose:
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end = time.time()
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print("[%s] Iteration %d, pseudo-likelihood = %.2f,"
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" time = %.2fs"
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% (type(self).__name__, iteration,
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self.score_samples(X).mean(), end - begin))
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begin = end
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return self
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def _more_tags(self):
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return {
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'_xfail_checks': {
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'check_methods_subset_invariance':
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'fails for the decision_function method',
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'check_methods_sample_order_invariance':
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'fails for the score_samples method',
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}
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}
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