elm.py

# -*- coding: utf8
# Author: David C. Lambert [dcl -at- panix -dot- com]
# Copyright(c) 2013
# License: Simple BSD

"""
The :mod:`elm` module implements the
Extreme Learning Machine Classifiers and Regressors (ELMClassifier,
ELMRegressor, SimpleELMRegressor, SimpleELMClassifier).

An Extreme Learning Machine (ELM) is a single layer feedforward
network with a random hidden layer components and ordinary linear
least squares fitting of the hidden->output weights by default.
[1][2]

References
----------
.. [1] http://www.extreme-learning-machines.org
.. [2] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme Learning Machine:
          Theory and Applications", Neurocomputing, vol. 70, pp. 489-501,
          2006.
"""

from abc import ABCMeta, abstractmethod

import numpy as np
from scipy.linalg import pinv2

from sklearn.utils import as_float_array
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.preprocessing import LabelBinarizer

from random_layer import RandomLayer, MLPRandomLayer

__all__ = ["ELMRegressor",
           "ELMClassifier",
           "GenELMRegressor",
           "GenELMClassifier"]


# BaseELM class, regressor and hidden_layer attributes
# and provides defaults for docstrings
class BaseELM(BaseEstimator):
    """
    Base class for ELMs.

    Warning: This class should not be used directly.
    Use derived classes instead.
    """
    __metaclass__ = ABCMeta

    def __init__(self, hidden_layer, regressor):
        self.regressor = regressor
        self.hidden_layer = hidden_layer

    @abstractmethod
    def fit(self, X, y):
        """
        Fit the model using X, y as training data.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.

        y : array-like of shape [n_samples, n_outputs]
            Target values (class labels in classification, real numbers in
            regression)

        Returns
        -------
        self : object

            Returns an instance of self.
        """

    @abstractmethod
    def predict(self, X):
        """
        Predict values using the model

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]

        Returns
        -------
        C : numpy array of shape [n_samples, n_outputs]
            Predicted values.
        """


class GenELMRegressor(BaseELM, RegressorMixin):
    """
    ELMRegressor is a regressor based on the Extreme Learning Machine.

    An Extreme Learning Machine (ELM) is a single layer feedforward
    network with a random hidden layer components and ordinary linear
    least squares fitting of the hidden->output weights by default.
    [1][2]

    Parameters
    ----------
    `hidden_layer` : random_layer instance, optional
        (default=MLPRandomLayer(random_state=0))

    `regressor`    : regressor instance, optional (default=None)
        If provided, this object is used to perform the regression from hidden
        unit activations to the outputs and subsequent predictions.  If not
        present, an ordinary linear least squares fit is performed

    Attributes
    ----------
    `coefs_` : numpy array
        Fitted regression coefficients if no regressor supplied.

    `fitted_` : bool
        Flag set when fit has been called already.

    `hidden_activations_` : numpy array of shape [n_samples, n_hidden]
        Hidden layer activations for last input.

    See Also
    --------
    RBFRandomLayer, MLPRandomLayer, ELMRegressor, ELMClassifier

    References
    ----------
    .. [1] http://www.extreme-learning-machines.org
    .. [2] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme Learning Machine:
          Theory and Applications", Neurocomputing, vol. 70, pp. 489-501,
              2006.
    """

    def __init__(self,
                 hidden_layer=MLPRandomLayer(random_state=0),
                 regressor=None):

        super(GenELMRegressor, self).__init__(hidden_layer, regressor)

        self.coefs_ = None
        self.fitted_ = False
        self.hidden_activations_ = None

    def _fit_regression(self, y):
        """
        fit regression using pseudo-inverse
        or supplied regressor
        """
        if (self.regressor is None):
            self.coefs_ = safe_sparse_dot(pinv2(self.hidden_activations_), y)
        else:
            self.regressor.fit(self.hidden_activations_, y)

        self.fitted_ = True

    def fit(self, X, y):
        """
        Fit the model using X, y as training data.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.

        y : array-like of shape [n_samples, n_outputs]
            Target values (class labels in classification, real numbers in
            regression)

        Returns
        -------
        self : object

            Returns an instance of self.
        """
        # fit random hidden layer and compute the hidden layer activations
        self.hidden_activations_ = self.hidden_layer.fit_transform(X)

        # solve the regression from hidden activations to outputs
        self._fit_regression(as_float_array(y, copy=True))

        return self

    def _get_predictions(self):
        """get predictions using internal least squares/supplied regressor"""
        if (self.regressor is None):
            preds = safe_sparse_dot(self.hidden_activations_, self.coefs_)
        else:
            preds = self.regressor.predict(self.hidden_activations_)

        return preds

    def predict(self, X):
        """
        Predict values using the model

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]

        Returns
        -------
        C : numpy array of shape [n_samples, n_outputs]
            Predicted values.
        """
        if (not self.fitted_):
            raise ValueError("ELMRegressor not fitted")

        # compute hidden layer activations
        self.hidden_activations_ = self.hidden_layer.transform(X)

        # compute output predictions for new hidden activations
        predictions = self._get_predictions()

        return predictions


class GenELMClassifier(BaseELM, ClassifierMixin):
    """
    GenELMClassifier is a classifier based on the Extreme Learning Machine.

    An Extreme Learning Machine (ELM) is a single layer feedforward
    network with a random hidden layer components and ordinary linear
    least squares fitting of the hidden->output weights by default.
    [1][2]

    Parameters
    ----------
    `hidden_layer` : random_layer instance, optional
        (default=MLPRandomLayer(random_state=0))

    `binarizer` : LabelBinarizer, optional
        (default=LabelBinarizer(-1, 1))

    `regressor`    : regressor instance, optional (default=None)
        If provided, this object is used to perform the regression from hidden
        unit activations to the outputs and subsequent predictions.  If not
        present, an ordinary linear least squares fit is performed

    Attributes
    ----------
    `classes_` : numpy array of shape [n_classes]
        Array of class labels

    `genelm_regressor_` : ELMRegressor instance
        Performs actual fit of binarized values

    See Also
    --------
    RBFRandomLayer, MLPRandomLayer, ELMRegressor, ELMClassifier

    References
    ----------
    .. [1] http://www.extreme-learning-machines.org
    .. [2] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme Learning Machine:
              Theory and Applications", Neurocomputing, vol. 70, pp. 489-501,
              2006.
    """
    def __init__(self,
                 hidden_layer=MLPRandomLayer(random_state=0),
                 binarizer=LabelBinarizer(-1, 1),
                 regressor=None):

        super(GenELMClassifier, self).__init__(hidden_layer, regressor)

        self.binarizer = binarizer

        self.classes_ = None
        self.genelm_regressor_ = GenELMRegressor(hidden_layer, regressor)

    def decision_function(self, X):
        """
        This function return the decision function values related to each
        class on an array of test vectors X.

        Parameters
        ----------
        X : array-like of shape [n_samples, n_features]

        Returns
        -------
        C : array of shape [n_samples, n_classes] or [n_samples,]
            Decision function values related to each class, per sample.
            In the two-class case, the shape is [n_samples,]
        """
        return self.genelm_regressor_.predict(X)

    def fit(self, X, y,sample_weight=0.0):
        """
        Fit the model using X, y as training data.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.

        y : array-like of shape [n_samples, n_outputs]
            Target values (class labels in classification, real numbers in
            regression)

        Returns
        -------
        self : object

            Returns an instance of self.
        """

        self.classes_ = np.unique(y)

        y_bin = self.binarizer.fit_transform(y)

        self.genelm_regressor_.fit(X, y_bin)
        return self

    def predict(self, X):
        """Predict values using the model

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]

        Returns
        -------
        C : numpy array of shape [n_samples, n_outputs]
            Predicted values.
        """
        raw_predictions = self.decision_function(X)
        class_predictions = self.binarizer.inverse_transform(raw_predictions)

        return class_predictions


# ELMRegressor with default RandomLayer
class ELMRegressor(BaseEstimator, RegressorMixin):
    """
    ELMRegressor is a regressor based on the Extreme Learning Machine.

    An Extreme Learning Machine (ELM) is a single layer feedforward
    network with a random hidden layer components and ordinary linear
    least squares fitting of the hidden->output weights by default.
    [1][2]

    ELMRegressor is a wrapper for an GenELMRegressor that uses a
    RandomLayer and passes the __init__ parameters through
    to the hidden layer generated by the fit() method.

    Parameters
    ----------
    `n_hidden` : int, optional (default=20)
        Number of units to generate in the SimpleRandomLayer

    `alpha` : float, optional (default=0.5)
        Mixing coefficient for distance and dot product input activations:
        activation = alpha*mlp_activation + (1-alpha)*rbf_width*rbf_activation

    `rbf_width` : float, optional (default=1.0)
        multiplier on rbf_activation

    `activation_func` : {callable, string} optional (default='tanh')
        Function used to transform input activation

        It must be one of 'tanh', 'sine', 'tribas', 'inv_tribase', 'sigmoid',
        'hardlim', 'softlim', 'gaussian', 'multiquadric', 'inv_multiquadric' or
        a callable.  If none is given, 'tanh' will be used. If a callable
        is given, it will be used to compute the hidden unit activations.

    `activation_args` : dictionary, optional (default=None)
        Supplies keyword arguments for a callable activation_func

    `user_components`: dictionary, optional (default=None)
        dictionary containing values for components that woud otherwise be
        randomly generated.  Valid key/value pairs are as follows:
           'radii'  : array-like of shape [n_hidden]
           'centers': array-like of shape [n_hidden, n_features]
           'biases' : array-like of shape [n_hidden]
           'weights': array-like of shape [n_hidden, n_features]

    `regressor`    : regressor instance, optional (default=None)
        If provided, this object is used to perform the regression from hidden
        unit activations to the outputs and subsequent predictions.  If not
        present, an ordinary linear least squares fit is performed

    `random_state`  : int, RandomState instance or None (default=None)
        Control the pseudo random number generator used to generate the
        hidden unit weights at fit time.

    Attributes
    ----------
    `genelm_regressor_` : GenELMRegressor object
        Wrapped object that actually performs the fit.

    See Also
    --------
    RandomLayer, RBFRandomLayer, MLPRandomLayer,
    GenELMRegressor, GenELMClassifier, ELMClassifier

    References
    ----------
    .. [1] http://www.extreme-learning-machines.org
    .. [2] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme Learning Machine:
          Theory and Applications", Neurocomputing, vol. 70, pp. 489-501,
              2006.
    """

    def __init__(self, n_hidden=20, alpha=0.5, rbf_width=1.0,
                 activation_func='tanh', activation_args=None,
                 user_components=None, regressor=None, random_state=None):

        self.n_hidden = n_hidden
        self.alpha = alpha
        self.random_state = random_state
        self.activation_func = activation_func
        self.activation_args = activation_args
        self.user_components = user_components
        self.rbf_width = rbf_width
        self.regressor = regressor

        self._genelm_regressor = None

    def _create_random_layer(self):
        """Pass init params to RandomLayer"""

        return RandomLayer(n_hidden=self.n_hidden,
                           alpha=self.alpha, random_state=self.random_state,
                           activation_func=self.activation_func,
                           activation_args=self.activation_args,
                           user_components=self.user_components,
                           rbf_width=self.rbf_width)

    def fit(self, X, y):
        """
        Fit the model using X, y as training data.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.

        y : array-like of shape [n_samples, n_outputs]
            Target values (class labels in classification, real numbers in
            regression)

        Returns
        -------
        self : object

            Returns an instance of self.
        """
        rhl = self._create_random_layer()
        self._genelm_regressor = GenELMRegressor(hidden_layer=rhl,
                                                 regressor=self.regressor)
        self._genelm_regressor.fit(X, y)
        return self

    def predict(self, X):
        """
        Predict values using the model

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]

        Returns
        -------
        C : numpy array of shape [n_samples, n_outputs]
            Predicted values.
        """
        if (self._genelm_regressor is None):
            raise ValueError("SimpleELMRegressor not fitted")

        return self._genelm_regressor.predict(X)


class ELMClassifier(ELMRegressor):
    """
    ELMClassifier is a classifier based on the Extreme Learning Machine.

    An Extreme Learning Machine (ELM) is a single layer feedforward
    network with a random hidden layer components and ordinary linear
    least squares fitting of the hidden->output weights by default.
    [1][2]

    ELMClassifier is an ELMRegressor subclass that first binarizes the
    data, then uses the superclass to compute the decision function that
    is then unbinarized to yield the prediction.

    The params for the RandomLayer used in the input transform are
    exposed in the ELMClassifier constructor.

    Parameters
    ----------
    `n_hidden` : int, optional (default=20)
        Number of units to generate in the SimpleRandomLayer

    `activation_func` : {callable, string} optional (default='tanh')
        Function used to transform input activation

        It must be one of 'tanh', 'sine', 'tribas', 'inv_tribase', 'sigmoid',
        'hardlim', 'softlim', 'gaussian', 'multiquadric', 'inv_multiquadric' or
        a callable.  If none is given, 'tanh' will be used. If a callable
        is given, it will be used to compute the hidden unit activations.

    `activation_args` : dictionary, optional (default=None)
        Supplies keyword arguments for a callable activation_func

    `random_state`  : int, RandomState instance or None (default=None)
        Control the pseudo random number generator used to generate the
        hidden unit weights at fit time.

    Attributes
    ----------
    `classes_` : numpy array of shape [n_classes]
        Array of class labels

    See Also
    --------
    RandomLayer, RBFRandomLayer, MLPRandomLayer,
    GenELMRegressor, GenELMClassifier, ELMClassifier

    References
    ----------
    .. [1] http://www.extreme-learning-machines.org
    .. [2] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme Learning Machine:
          Theory and Applications", Neurocomputing, vol. 70, pp. 489-501,
              2006.
    """

    def __init__(self, n_hidden=20, alpha=0.5, rbf_width=1.0,
                 activation_func='tanh', activation_args=None,
                 user_components=None, regressor=None,
                 binarizer=LabelBinarizer(-1, 1),
                 random_state=None):

        super(ELMClassifier, self).__init__(n_hidden=n_hidden,
                                            alpha=alpha,
                                            random_state=random_state,
                                            activation_func=activation_func,
                                            activation_args=activation_args,
                                            user_components=user_components,
                                            rbf_width=rbf_width,
                                            regressor=regressor)

        self.classes_ = None
        self.binarizer = binarizer

    def decision_function(self, X):
        """
        This function return the decision function values related to each
        class on an array of test vectors X.

        Parameters
        ----------
        X : array-like of shape [n_samples, n_features]

        Returns
        -------
        C : array of shape [n_samples, n_classes] or [n_samples,]
            Decision function values related to each class, per sample.
            In the two-class case, the shape is [n_samples,]
        """
        return super(ELMClassifier, self).predict(X)

    def fit(self, X, y):
        """
        Fit the model using X, y as training data.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.

        y : array-like of shape [n_samples, n_outputs]
            Target values (class labels in classification, real numbers in
            regression)

        Returns
        -------
        self : object

            Returns an instance of self.
        """
        self.classes_ = np.unique(y)

        y_bin = self.binarizer.fit_transform(y)

        super(ELMClassifier, self).fit(X, y_bin)

        return self

    def predict(self, X):
        """
        Predict values using the model

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape [n_samples, n_features]

        Returns
        -------
        C : numpy array of shape [n_samples, n_outputs]
            Predicted values.
        """
        raw_predictions = self.decision_function(X)
        class_predictions = self.binarizer.inverse_transform(raw_predictions)

        return class_predictions

    def score(self, X, y):
        """Force use of accuracy score since we don't inherit
           from ClassifierMixin"""

        from sklearn.metrics import accuracy_score
        return accuracy_score(y, self.predict(X))