# coding=utf8
"""
Label propagation in the context of this module refers to a set of
semi-supervised classification algorithms. At a high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.

These algorithms perform very well in practice. The cost of running can be very
expensive, at approximately O(N^3) where N is the number of (labeled and
unlabeled) points. The theory (why they perform so well) is motivated by
intuitions from random walk algorithms and geometric relationships in the data.
For more information see the references below.

Model Features
--------------
Label clamping:
  The algorithm tries to learn distributions of labels over the dataset given
  label assignments over an initial subset. In one variant, the algorithm does
  not allow for any errors in the initial assignment (hard-clamping) while
  in another variant, the algorithm allows for some wiggle room for the initial
  assignments, allowing them to change by a fraction alpha in each iteration
  (soft-clamping).

Kernel:
  A function which projects a vector into some higher dimensional space. This
  implementation supports RBF and KNN kernels. Using the RBF kernel generates
  a dense matrix of size O(N^2). KNN kernel will generate a sparse matrix of
  size O(k*N) which will run much faster. See the documentation for SVMs for
  more info on kernels.

Examples
--------
>>> import numpy as np
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> iris = datasets.load_iris()
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
LabelPropagation(...)

Notes
-----
References:
[1] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised
Learning (2006), pp. 193-216

[2] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient
Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005
"""

# Authors: Clay Woolam <clay@woolam.org>
#          Utkarsh Upadhyay <mail@musicallyut.in>
# License: BSD
from abc import ABCMeta, abstractmethod

import warnings
import numpy as np
from scipy import sparse
from scipy.sparse import csgraph

from ..base import BaseEstimator, ClassifierMixin
from ..metrics.pairwise import rbf_kernel
from ..neighbors import NearestNeighbors
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted, check_array
from ..utils.validation import _deprecate_positional_args
from ..exceptions import ConvergenceWarning


class BaseLabelPropagation(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):
    """Base class for label propagation module.

    Parameters
    ----------
    kernel : {'knn', 'rbf'} or callable, default='rbf'
        String identifier for kernel function to use or the kernel function
        itself. Only 'rbf' and 'knn' strings are valid inputs. The function
        passed should take two inputs, each of shape (n_samples, n_features),
        and return a (n_samples, n_samples) shaped weight matrix.

    gamma : float, default=20
        Parameter for rbf kernel.

    n_neighbors : int, default=7
        Parameter for knn kernel. Need to be strictly positive.

    alpha : float, default=1.0
        Clamping factor.

    max_iter : int, default=30
        Change maximum number of iterations allowed.

    tol : float, default=1e-3
        Convergence tolerance: threshold to consider the system at steady
        state.

   n_jobs : int, default=None
        The number of parallel jobs to run.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.
    """

    @_deprecate_positional_args
    def __init__(self, kernel='rbf', *, gamma=20, n_neighbors=7,
                 alpha=1, max_iter=30, tol=1e-3, n_jobs=None):

        self.max_iter = max_iter
        self.tol = tol

        # kernel parameters
        self.kernel = kernel
        self.gamma = gamma
        self.n_neighbors = n_neighbors

        # clamping factor
        self.alpha = alpha

        self.n_jobs = n_jobs

    def _get_kernel(self, X, y=None):
        if self.kernel == "rbf":
            if y is None:
                return rbf_kernel(X, X, gamma=self.gamma)
            else:
                return rbf_kernel(X, y, gamma=self.gamma)
        elif self.kernel == "knn":
            if self.nn_fit is None:
                self.nn_fit = NearestNeighbors(n_neighbors=self.n_neighbors,
                                               n_jobs=self.n_jobs).fit(X)
            if y is None:
                return self.nn_fit.kneighbors_graph(self.nn_fit._fit_X,
                                                    self.n_neighbors,
                                                    mode='connectivity')
            else:
                return self.nn_fit.kneighbors(y, return_distance=False)
        elif callable(self.kernel):
            if y is None:
                return self.kernel(X, X)
            else:
                return self.kernel(X, y)
        else:
            raise ValueError("%s is not a valid kernel. Only rbf and knn"
                             " or an explicit function "
                             " are supported at this time." % self.kernel)

    @abstractmethod
    def _build_graph(self):
        raise NotImplementedError("Graph construction must be implemented"
                                  " to fit a label propagation model.")

    def predict(self, X):
        """Performs inductive inference across the model.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The data matrix.

        Returns
        -------
        y : ndarray of shape (n_samples,)
            Predictions for input data.
        """
        probas = self.predict_proba(X)
        return self.classes_[np.argmax(probas, axis=1)].ravel()

    def predict_proba(self, X):
        """Predict probability for each possible outcome.

        Compute the probability estimates for each single sample in X
        and each possible outcome seen during training (categorical
        distribution).

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The data matrix.

        Returns
        -------
        probabilities : ndarray of shape (n_samples, n_classes)
            Normalized probability distributions across
            class labels.
        """
        check_is_fitted(self)

        X_2d = check_array(X, accept_sparse=['csc', 'csr', 'coo', 'dok',
                                             'bsr', 'lil', 'dia'])
        weight_matrices = self._get_kernel(self.X_, X_2d)
        if self.kernel == 'knn':
            probabilities = np.array([
                np.sum(self.label_distributions_[weight_matrix], axis=0)
                for weight_matrix in weight_matrices])
        else:
            weight_matrices = weight_matrices.T
            probabilities = safe_sparse_dot(
                    weight_matrices, self.label_distributions_)
        normalizer = np.atleast_2d(np.sum(probabilities, axis=1)).T
        probabilities /= normalizer
        return probabilities

    def fit(self, X, y):
        """Fit a semi-supervised label propagation model based

        All the input data is provided matrix X (labeled and unlabeled)
        and corresponding label matrix y with a dedicated marker value for
        unlabeled samples.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            A matrix of shape (n_samples, n_samples) will be created from this.

        y : array-like of shape (n_samples,)
            `n_labeled_samples` (unlabeled points are marked as -1)
            All unlabeled samples will be transductively assigned labels.

        Returns
        -------
        self : object
        """
        X, y = self._validate_data(X, y)
        self.X_ = X
        check_classification_targets(y)

        # actual graph construction (implementations should override this)
        graph_matrix = self._build_graph()

        # label construction
        # construct a categorical distribution for classification only
        classes = np.unique(y)
        classes = (classes[classes != -1])
        self.classes_ = classes

        n_samples, n_classes = len(y), len(classes)

        alpha = self.alpha
        if self._variant == 'spreading' and \
                (alpha is None or alpha <= 0.0 or alpha >= 1.0):
            raise ValueError('alpha=%s is invalid: it must be inside '
                             'the open interval (0, 1)' % alpha)
        y = np.asarray(y)
        unlabeled = y == -1

        # initialize distributions
        self.label_distributions_ = np.zeros((n_samples, n_classes))
        for label in classes:
            self.label_distributions_[y == label, classes == label] = 1

        y_static = np.copy(self.label_distributions_)
        if self._variant == 'propagation':
            # LabelPropagation
            y_static[unlabeled] = 0
        else:
            # LabelSpreading
            y_static *= 1 - alpha

        l_previous = np.zeros((self.X_.shape[0], n_classes))

        unlabeled = unlabeled[:, np.newaxis]
        if sparse.isspmatrix(graph_matrix):
            graph_matrix = graph_matrix.tocsr()

        for self.n_iter_ in range(self.max_iter):
            if np.abs(self.label_distributions_ - l_previous).sum() < self.tol:
                break

            l_previous = self.label_distributions_
            self.label_distributions_ = safe_sparse_dot(
                graph_matrix, self.label_distributions_)

            if self._variant == 'propagation':
                normalizer = np.sum(
                    self.label_distributions_, axis=1)[:, np.newaxis]
                self.label_distributions_ /= normalizer
                self.label_distributions_ = np.where(unlabeled,
                                                     self.label_distributions_,
                                                     y_static)
            else:
                # clamp
                self.label_distributions_ = np.multiply(
                    alpha, self.label_distributions_) + y_static
        else:
            warnings.warn(
                'max_iter=%d was reached without convergence.' % self.max_iter,
                category=ConvergenceWarning
            )
            self.n_iter_ += 1

        normalizer = np.sum(self.label_distributions_, axis=1)[:, np.newaxis]
        normalizer[normalizer == 0] = 1
        self.label_distributions_ /= normalizer

        # set the transduction item
        transduction = self.classes_[np.argmax(self.label_distributions_,
                                               axis=1)]
        self.transduction_ = transduction.ravel()
        return self


class LabelPropagation(BaseLabelPropagation):
    """Label Propagation classifier

    Read more in the :ref:`User Guide <label_propagation>`.

    Parameters
    ----------
    kernel : {'knn', 'rbf'} or callable, default='rbf'
        String identifier for kernel function to use or the kernel function
        itself. Only 'rbf' and 'knn' strings are valid inputs. The function
        passed should take two inputs, each of shape (n_samples, n_features),
        and return a (n_samples, n_samples) shaped weight matrix.

    gamma : float, default=20
        Parameter for rbf kernel.

    n_neighbors : int, default=7
        Parameter for knn kernel which need to be strictly positive.

    max_iter : int, default=1000
        Change maximum number of iterations allowed.

    tol : float, 1e-3
        Convergence tolerance: threshold to consider the system at steady
        state.

    n_jobs : int, default=None
        The number of parallel jobs to run.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    Attributes
    ----------
    X_ : ndarray of shape (n_samples, n_features)
        Input array.

    classes_ : ndarray of shape (n_classes,)
        The distinct labels used in classifying instances.