"""Base classes for all estimators."""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause

import copy
import warnings

import numpy as np
from scipy import sparse
from .externals import six
from .utils.fixes import signature
from .utils.deprecation import deprecated
from .exceptions import ChangedBehaviorWarning as ChangedBehaviorWarning_


class ChangedBehaviorWarning(ChangedBehaviorWarning_):
    pass

ChangedBehaviorWarning = deprecated("ChangedBehaviorWarning has been moved "
                                    "into the sklearn.exceptions module. "
                                    "It will not be available here from "
                                    "version 0.19")(ChangedBehaviorWarning)


##############################################################################
def clone(estimator, safe=True):
    """Constructs a new estimator with the same parameters.

    Clone does a deep copy of the model in an estimator
    without actually copying attached data. It yields a new estimator
    with the same parameters that has not been fit on any data.

    Parameters
    ----------
    estimator: estimator object, or list, tuple or set of objects
        The estimator or group of estimators to be cloned

    safe: boolean, optional
        If safe is false, clone will fall back to a deepcopy on objects
        that are not estimators.

    """
    estimator_type = type(estimator)
    # XXX: not handling dictionaries
    if estimator_type in (list, tuple, set, frozenset):
        return estimator_type([clone(e, safe=safe) for e in estimator])
    elif not hasattr(estimator, 'get_params'):
        if not safe:
            return copy.deepcopy(estimator)
        else:
            raise TypeError("Cannot clone object '%s' (type %s): "
                            "it does not seem to be a scikit-learn estimator "
                            "as it does not implement a 'get_params' methods."
                            % (repr(estimator), type(estimator)))
    klass = estimator.__class__
    new_object_params = estimator.get_params(deep=False)
    for name, param in six.iteritems(new_object_params):
        new_object_params[name] = clone(param, safe=False)
    new_object = klass(**new_object_params)
    params_set = new_object.get_params(deep=False)

    # quick sanity check of the parameters of the clone
    for name in new_object_params:
        param1 = new_object_params[name]
        param2 = params_set[name]
        if isinstance(param1, np.ndarray):
            # For most ndarrays, we do not test for complete equality
            if not isinstance(param2, type(param1)):
                equality_test = False
            elif (param1.ndim > 0
                    and param1.shape[0] > 0
                    and isinstance(param2, np.ndarray)
                    and param2.ndim > 0
                    and param2.shape[0] > 0):
                equality_test = (
                    param1.shape == param2.shape
                    and param1.dtype == param2.dtype
                    # We have to use '.flat' for 2D arrays
                    and param1.flat[0] == param2.flat[0]
                    and param1.flat[-1] == param2.flat[-1]
                )
            else:
                equality_test = np.all(param1 == param2)
        elif sparse.issparse(param1):
            # For sparse matrices equality doesn't work
            if not sparse.issparse(param2):
                equality_test = False
            elif param1.size == 0 or param2.size == 0:
                equality_test = (
                    param1.__class__ == param2.__class__
                    and param1.size == 0
                    and param2.size == 0
                )
            else:
                equality_test = (
                    param1.__class__ == param2.__class__
                    and param1.data[0] == param2.data[0]
                    and param1.data[-1] == param2.data[-1]
                    and param1.nnz == param2.nnz
                    and param1.shape == param2.shape
                )
        else:
            new_obj_val = new_object_params[name]
            params_set_val = params_set[name]
            # The following construct is required to check equality on special
            # singletons such as np.nan that are not equal to them-selves:
            equality_test = (new_obj_val == params_set_val or
                             new_obj_val is params_set_val)
        if not equality_test:
            raise RuntimeError('Cannot clone object %s, as the constructor '
                               'does not seem to set parameter %s' %
                               (estimator, name))

    return new_object


###############################################################################
def _pprint(params, offset=0, printer=repr):
    """Pretty print the dictionary 'params'

    Parameters
    ----------
    params: dict
        The dictionary to pretty print

    offset: int
        The offset in characters to add at the begin of each line.

    printer:
        The function to convert entries to strings, typically
        the builtin str or repr

    """
    # Do a multi-line justified repr:
    options = np.get_printoptions()
    np.set_printoptions(precision=5, threshold=64, edgeitems=2)
    params_list = list()
    this_line_length = offset
    line_sep = ',\n' + (1 + offset // 2) * ' '
    for i, (k, v) in enumerate(sorted(six.iteritems(params))):
        if type(v) is float:
            # use str for representing floating point numbers
            # this way we get consistent representation across
            # architectures and versions.
            this_repr = '%s=%s' % (k, str(v))
        else:
            # use repr of the rest
            this_repr = '%s=%s' % (k, printer(v))
        if len(this_repr) > 500:
            this_repr = this_repr[:300] + '...' + this_repr[-100:]
        if i > 0:
            if (this_line_length + len(this_repr) >= 75 or '\n' in this_repr):
                params_list.append(line_sep)
                this_line_length = len(line_sep)
            else:
                params_list.append(', ')
                this_line_length += 2
        params_list.append(this_repr)
        this_line_length += len(this_repr)

    np.set_printoptions(**options)
    lines = ''.join(params_list)
    # Strip trailing space to avoid nightmare in doctests
    lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n'))
    return lines


###############################################################################
class BaseEstimator(object):
    """Base class for all estimators in scikit-learn

    Notes
    -----
    All estimators should specify all the parameters that can be set
    at the class level in their ``__init__`` as explicit keyword
    arguments (no ``*args`` or ``**kwargs``).
    """

    @classmethod
    def _get_param_names(cls):
        """Get parameter names for the estimator"""
        # fetch the constructor or the original constructor before
        # deprecation wrapping if any
        init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
        if init is object.__init__:
            # No explicit constructor to introspect
            return []

        # introspect the constructor arguments to find the model parameters
        # to represent
        init_signature = signature(init)
        # Consider the constructor parameters excluding 'self'
        parameters = [p for p in init_signature.parameters.values()
                      if p.name != 'self' and p.kind != p.VAR_KEYWORD]
        for p in parameters:
            if p.kind == p.VAR_POSITIONAL:
                raise RuntimeError("scikit-learn estimators should always "
                                   "specify their parameters in the signature"
                                   " of their __init__ (no varargs)."
                                   " %s with constructor %s doesn't "
                                   " follow this convention."
                                   % (cls, init_signature))
        # Extract and sort argument names excluding 'self'
        return sorted([p.name for p in parameters])

    def get_params(self, deep=True):
        """Get parameters for this estimator.

        Parameters
        ----------
        deep: boolean, optional
            If True, will return the parameters for this estimator and
            contained subobjects that are estimators.

        Returns
        -------
        params : mapping of string to any
            Parameter names mapped to their values.
        """
        out = dict()
        for key in self._get_param_names():
            # We need deprecation warnings to always be on in order to
            # catch deprecated param values.
            # This is set in utils/__init__.py but it gets overwritten
            # when running under python3 somehow.
            warnings.simplefilter("always", DeprecationWarning)
            try:
                with warnings.catch_warnings(record=True) as w:
                    value = getattr(self, key, None)
                if len(w) and w[0].category == DeprecationWarning:
                    # if the parameter is deprecated, don't show it
                    continue
            finally:
                warnings.filters.pop(0)

            # XXX: should we rather test if instance of estimator?
            if deep and hasattr(value, 'get_params'):
                deep_items = value.get_params().items()
                out.update((key + '__' + k, val) for k, val in deep_items)
            out[key] = value
        return out

    def set_params(self, **params):
        """Set the parameters of this estimator.

        The method works on simple estimators as well as on nested objects
        (such as pipelines). The former have parameters of the form
        ``<component>__<parameter>`` so that it's possible to update each
        component of a nested object.

        Returns
        -------
        self
        """
        if not params:
            # Simple optimisation to gain speed (inspect is slow)
            return self
        valid_params = self.get_params(deep=True)
        for key, value in six.iteritems(params):
            split = key.split('__', 1)
            if len(split) > 1:
                # nested objects case
                name, sub_name = split
                if name not in valid_params:
                    raise ValueError('Invalid parameter %s for estimator %s. '
                                     'Check the list of available parameters '
                                     'with `estimator.get_params().keys()`.' %
                                     (name, self))
                sub_object = valid_params[name]
                sub_object.set_params(**{sub_name: value})
            else:
                # simple objects case
                if key not in valid_params:
                    raise ValueError('Invalid parameter %s for estimator %s. '
                                     'Check the list of available parameters '
                                     'with `estimator.get_params().keys()`.' %
                                     (key, self.__class__.__name__))
                setattr(self, key, value)
        return self

    def __repr__(self):
        class_name = self.__class__.__name__
        return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
                                               offset=len(class_name),),)


###############################################################################
class ClassifierMixin(object):
    """Mixin class for all classifiers in scikit-learn."""
    _estimator_type = "classifier"

    def score(self, X, y, sample_weight=None):
        """Returns the mean accuracy on the given test data and labels.

        In multi-label classification, this is the subset accuracy
        which is a harsh metric since you require for each sample that
        each label set be correctly predicted.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Test samples.

        y : array-like, shape = (n_samples) or (n_samples, n_outputs)
            True labels for X.

        sample_weight : array-like, shape = [n_samples], optional
            Sample weights.

        Returns
        -------
        score : float
            Mean accuracy of self.predict(X) wrt. y.

        """
        from .metrics import accuracy_score
        return accuracy_score(y, self.predict(X), sample_weight=sample_weight)


###############################################################################
class RegressorMixin(object):
    """Mixin class for all regression estimators in scikit-learn."""
    _estimator_type = "regressor"

    def score(self, X, y, sample_weight=None):
        """Returns the coefficient of determination R^2 of the prediction.

        The coefficient R^2 is defined as (1 - u/v), where u is the regression
        sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
        sum of squares ((y_true - y_true.mean()) ** 2).sum().
        Best possible score is 1.0 and it can be negative (because the
        model can be arbitrarily worse). A constant model that always
        predicts the expected value of y, disregarding the input features,
        would get a R^2 score of 0.0.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Test samples.

        y : array-like, shape = (n_samples) or (n_samples, n_outputs)
            True values for X.

        sample_weight : array-like, shape = [n_samples], optional
            Sample weights.

        Returns
        -------
        score : float
            R^2 of self.predict(X) wrt. y.
        """

        from .metrics import r2_score
        return r2_score(y, self.predict(X), sample_weight=sample_weight,
                        multioutput='variance_weighted')


###############################################################################
class ClusterMixin(object):
    """Mixin class for all cluster estimators in scikit-learn."""
    _estimator_type = "clusterer"

    def fit_predict(self, X, y=None):
        """Performs clustering on X and returns cluster labels.

        Parameters
        ----------
        X : ndarray, shape (n_samples, n_features)
            Input data.

        Returns
        -------
        y : ndarray, shape (n_samples,)
            cluster labels
        """
        # non-optimized default implementation; override when a better
        # method is possible for a given clustering algorithm
        self.fit(X)
        return self.labels_


class BiclusterMixin(object):
    """Mixin class for all bicluster estimators in scikit-learn"""

    @property
    def biclusters_(self):
        """Convenient way to get row and column indicators together.

        Returns the ``rows_`` and ``columns_`` members.
        """
        return self.rows_, self.columns_

    def get_indices(self, i):
        """Row and column indices of the i'th bicluster.

        Only works if ``rows_`` and ``columns_`` attributes exist.

        Returns
        -------
        row_ind : np.array, dtype=np.intp
            Indices of rows in the dataset that belong to the bicluster.
        col_ind : np.array, dtype=np.intp
            Indices of columns in the dataset that belong to the bicluster.

        """
        rows = self.rows_[i]
        columns = self.columns_[i]
        return np.nonzero(rows)[0], np.nonzero(columns)[0]

    def get_shape(self, i):
        """Shape of the i'th bicluster.

        Returns
        -------
        shape : (int, int)
            Number of rows and columns (resp.) in the bicluster.
        """
        indices = self.get_indices(i)
        return tuple(len(i) for i in indices)

    def get_submatrix(self, i, data):
        """Returns the submatrix corresponding to bicluster `i`.

        Works with sparse matrices. Only works if ``rows_`` and
        ``columns_`` attributes exist.

        """
        from .utils.validation import check_array
        data = check_array(data, accept_sparse='csr')
        row_ind, col_ind = self.get_indices(i)
        return data[row_ind[:, np.newaxis], col_ind]


###############################################################################
class TransformerMixin(object):
    """Mixin class for all transformers in scikit-learn."""

    def fit_transform(self, X, y=None, **fit_params):
        """Fit to data, then transform it.

        Fits transformer to X and y with optional parameters fit_params
        and returns a transformed version of X.

        Parameters
        ----------
        X : numpy array of shape [n_samples, n_features]
            Training set.

        y : numpy array of shape [n_samples]
            Target values.

        Returns
        -------
        X_new : numpy array of shape [n_samples, n_features_new]
            Transformed array.

        """
        # non-optimized default implementation; override when a better
        # method is possible for a given clustering algorithm
        if y is None:
            # fit method of arity 1 (unsupervised transformation)
            return self.fit(X, **fit_params).transform(X)
        else:
            # fit method of arity 2 (supervised transformation)
            return self.fit(X, y, **fit_params).transform(X)


###############################################################################
class MetaEstimatorMixin(object):
    """Mixin class for all meta estimators in scikit-learn."""
    # this is just a tag for the moment


###############################################################################

def is_classifier(estimator):
    """Returns True if the given estimator is (probably) a classifier."""
    return getattr(estimator, "_estimator_type", None) == "classifier"


def is_regressor(estimator):
    """Returns True if the given estimator is (probably) a regressor."""
    return getattr(estimator, "_estimator_type", None) == "regressor"
