"""
This is a module to be used as a reference for building other modules
"""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
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.utils.estimator_checks import parametrize_with_checks


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)

        # Check non-negative X for illustration
        if np.any(X < 0):
            raise ValueError("This classifier needs non-negative X")

        # Store the classes seen during fit
        self.classes_ = unique_labels(y)
        self.n_features_in_ = X.shape[1]
        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]

    def _more_tags(self):
        return {
            'requires_y': True,
            'requires_positive_X': True,
        }


@parametrize_with_checks([TemplateClassifier()])
def test_sklearn_compatible_estimator(estimator, check):
    check(estimator)