estimators.py

import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import euclidean_distances
from sklearn.svm import SVC


class QuantumKernelEstimator(BaseEstimator):
    """ A template estimator to be used as a reference implementation.
    For more information regarding how to build your own estimator, read more
    in the :ref:`User Guide <user_guide>`.
    Parameters
    ----------
    demo_param : str, default='demo_param'
        A parameter used for demonstation of how to pass and store paramters.
    Examples
    --------
    >>> from skltemplate import TemplateEstimator
    >>> import numpy as np
    >>> X = np.arange(100).reshape(100, 1)
    >>> y = np.zeros((100, ))
    >>> estimator = TemplateEstimator()
    >>> estimator.fit(X, y)
    TemplateEstimator(demo_param='demo_param')
    """
    def __init__(self, C=10, p=0, mu=10):
        self.C = C
        self.p = p
        self.mu = mu
        self.clf = SVC(kernel='precomputed', C=C, random_state=76)

    def fit(self, X, y):
        """A reference implementation of a fitting function.
        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The training input samples.
        y : array-like, shape (n_samples,) or (n_samples, n_outputs)
            The target values (class labels in classification, real numbers in
            regression).
        Returns
        -------
        self : object
            Returns self.
        """
        #X, y = check_X_y(X, y, accept_sparse=True)
        K = np.exp(-self.mu * (self.p * X[:,:,0] + (1 - self.p) * X[:,:,1]))
        self.clf.fit(K, y)
        self.is_fitted_ = True
        # `fit` should always return `self`
        return self

    def predict(self, X):
        """ A reference implementation of a predicting function.
        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The training input samples.
        Returns
        -------
        y : ndarray, shape (n_samples,)
            Returns an array of ones.
        """
        K = np.exp(-self.mu * (self.p * X[:,:,0] + (1 - self.p) * X[:,:,1]))
        #X = check_array(X, accept_sparse=True)
        check_is_fitted(self, 'is_fitted_')
        return self.clf.predict(K)


class TemplateClassifier(ClassifierMixin, BaseEstimator):
    """ An example classifier which implements a 1-NN algorithm.
    For more information regarding how to build your own classifier, read more
    in the :ref:`User Guide <user_guide>`.
    Parameters
    ----------
    demo_param : str, default='demo'
        A parameter used for demonstation of how to pass and store paramters.
    Attributes
    ----------
    X_ : ndarray, shape (n_samples, n_features)
        The input passed during :meth:`fit`.
    y_ : ndarray, shape (n_samples,)
        The labels passed during :meth:`fit`.
    classes_ : ndarray, shape (n_classes,)
        The classes seen at :meth:`fit`.
    """
    def __init__(self, demo_param='demo'):
        self.demo_param = demo_param

    def fit(self, X, y):
        """A reference implementation of a fitting function for a classifier.
        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The training input samples.
        y : array-like, shape (n_samples,)
            The target values. An array of int.
        Returns
        -------
        self : object
            Returns self.
        """
        # Check that X and y have correct shape
        X, y = check_X_y(X, y)
        # Store the classes seen during fit
        self.classes_ = unique_labels(y)

        self.X_ = X
        self.y_ = y
        # Return the classifier
        return self

    def predict(self, X):
        """ A reference implementation of a prediction for a classifier.
        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The input samples.
        Returns
        -------
        y : ndarray, shape (n_samples,)
            The label for each sample is the label of the closest sample
            seen during fit.
        """
        # Check is fit had been called
        check_is_fitted(self, ['X_', 'y_'])

        # Input validation
        X = check_array(X)

        closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
        return self.y_[closest]


class TemplateTransformer(TransformerMixin, BaseEstimator):
    """ An example transformer that returns the element-wise square root.
    For more information regarding how to build your own transformer, read more
    in the :ref:`User Guide <user_guide>`.
    Parameters
    ----------
    demo_param : str, default='demo'
        A parameter used for demonstation of how to pass and store paramters.
    Attributes
    ----------
    n_features_ : int
        The number of features of the data passed to :meth:`fit`.
    """
    def __init__(self, demo_param='demo'):
        self.demo_param = demo_param

    def fit(self, X, y=None):
        """A reference implementation of a fitting function for a transformer.
        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The training input samples.
        y : None
            There is no need of a target in a transformer, yet the pipeline API
            requires this parameter.
        Returns
        -------
        self : object
            Returns self.
        """
        X = check_array(X, accept_sparse=True)

        self.n_features_ = X.shape[1]

        # Return the transformer
        return self

    def transform(self, X):
        """ A reference implementation of a transform function.
        Parameters
        ----------
        X : {array-like, sparse-matrix}, shape (n_samples, n_features)
            The input samples.
        Returns
        -------
        X_transformed : array, shape (n_samples, n_features)
            The array containing the element-wise square roots of the values
            in ``X``.
        """
        # Check is fit had been called
        check_is_fitted(self, 'n_features_')

        # Input validation
        X = check_array(X, accept_sparse=True)

        # Check that the input is of the same shape as the one passed
        # during fit.
        if X.shape[1] != self.n_features_:
            raise ValueError('Shape of input is different from what was seen'
                             'in `fit`')
        return np.sqrt(X)