from __future__ import print_function

import warnings
import sys
import traceback
import inspect
import pickle
from copy import deepcopy

import numpy as np
from scipy import sparse
import struct

from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import META_ESTIMATORS
from sklearn.utils.testing import set_random_state
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import ignore_warnings

from sklearn.base import clone, ClassifierMixin
from sklearn.metrics import accuracy_score, adjusted_rand_score, f1_score

from sklearn.lda import LDA
from sklearn.random_projection import BaseRandomProjection
from sklearn.feature_selection import SelectKBest
from sklearn.svm.base import BaseLibSVM
from sklearn.pipeline import make_pipeline

from sklearn.utils.validation import DataConversionWarning, NotFittedError
from sklearn.cross_validation import train_test_split

from sklearn.utils import shuffle
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris, load_boston, make_blobs


BOSTON = None
CROSS_DECOMPOSITION = ['PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD']


def _boston_subset(n_samples=200):
    global BOSTON
    if BOSTON is None:
        boston = load_boston()
        X, y = boston.data, boston.target
        X, y = shuffle(X, y, random_state=0)
        X, y = X[:n_samples], y[:n_samples]
        X = StandardScaler().fit_transform(X)
        BOSTON = X, y
    return BOSTON


def set_fast_parameters(estimator):
    # speed up some estimators
    params = estimator.get_params()
    if ("n_iter" in params
            and estimator.__class__.__name__ != "TSNE"):
        estimator.set_params(n_iter=5)
    if "max_iter" in params:
        # NMF
        if estimator.max_iter is not None:
            estimator.set_params(max_iter=min(5, estimator.max_iter))
        # LinearSVR
        if estimator.__class__.__name__ == 'LinearSVR':
            estimator.set_params(max_iter=20)
    if "n_resampling" in params:
        # randomized lasso
        estimator.set_params(n_resampling=5)
    if "n_estimators" in params:
        # especially gradient boosting with default 100
        estimator.set_params(n_estimators=min(5, estimator.n_estimators))
    if "max_trials" in params:
        # RANSAC
        estimator.set_params(max_trials=10)
    if "n_init" in params:
        # K-Means
        estimator.set_params(n_init=2)

    if estimator.__class__.__name__ == "SelectFdr":
        # be tolerant of noisy datasets (not actually speed)
        estimator.set_params(alpha=.5)

    if estimator.__class__.__name__ == "TheilSenRegressor":
        estimator.max_subpopulation = 100

    if isinstance(estimator, BaseRandomProjection):
        # Due to the jl lemma and often very few samples, the number
        # of components of the random matrix projection will be probably
        # greater than the number of features.
        # So we impose a smaller number (avoid "auto" mode)
        estimator.set_params(n_components=1)

    if isinstance(estimator, SelectKBest):
        # SelectKBest has a default of k=10
        # which is more feature than we have in most case.
        estimator.set_params(k=1)


class NotAnArray(object):
    " An object that is convertable to an array"

    def __init__(self, data):
        self.data = data

    def __array__(self, dtype=None):
        return self.data


def _is_32bit():
    """Detect if process is 32bit Python."""
    return struct.calcsize('P') * 8 == 32


def check_estimator_sparse_data(name, Estimator):
    rng = np.random.RandomState(0)
    X = rng.rand(40, 10)
    X[X < .8] = 0
    X = sparse.csr_matrix(X)
    y = (4 * rng.rand(40)).astype(np.int)
    # catch deprecation warnings
    with warnings.catch_warnings():
        if name in ['Scaler', 'StandardScaler']:
            estimator = Estimator(with_mean=False)
        else:
            estimator = Estimator()
    set_fast_parameters(estimator)
    # fit and predict
    try:
        estimator.fit(X, y)
        if hasattr(estimator, "predict"):
            estimator.predict(X)
        if hasattr(estimator, 'predict_proba'):
            estimator.predict_proba(X)
    except TypeError as e:
        if 'sparse' not in repr(e):
            print("Estimator %s doesn't seem to fail gracefully on "
                  "sparse data: error message state explicitly that "
                  "sparse input is not supported if this is not the case."
                  % name)
            raise
    except Exception:
        print("Estimator %s doesn't seem to fail gracefully on "
              "sparse data: it should raise a TypeError if sparse input "
              "is explicitly not supported." % name)
        raise


def check_dtype_object(name, Estimator):
    # check that estimators treat dtype object as numeric if possible
    rng = np.random.RandomState(0)
    X = rng.rand(40, 10).astype(object)
    y = (X[:, 0] * 4).astype(np.int)
    y = multioutput_estimator_convert_y_2d(name, y)
    with warnings.catch_warnings():
        estimator = Estimator()
    set_fast_parameters(estimator)

    estimator.fit(X, y)
    if hasattr(estimator, "predict"):
        estimator.predict(X)

    if hasattr(estimator, "transform"):
        estimator.transform(X)

    try:
        estimator.fit(X, y.astype(object))
    except Exception as e:
        if "Unknown label type" not in str(e):
            raise

    X[0, 0] = {'foo': 'bar'}
    assert_raise_message(TypeError, "string or a number", estimator.fit, X, y)


def check_transformer(name, Transformer):
    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    X = StandardScaler().fit_transform(X)
    X -= X.min()
    _check_transformer(name, Transformer, X, y)
    _check_transformer(name, Transformer, X.tolist(), y.tolist())


def check_transformer_data_not_an_array(name, Transformer):
    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    X = StandardScaler().fit_transform(X)
    # We need to make sure that we have non negative data, for things
    # like NMF
    X -= X.min() - .1
    this_X = NotAnArray(X)
    this_y = NotAnArray(np.asarray(y))
    _check_transformer(name, Transformer, this_X, this_y)


def check_transformers_unfitted(name, Transformer):
    X, y = _boston_subset()

    with warnings.catch_warnings(record=True):
        transformer = Transformer()

    assert_raises(NotFittedError, transformer.transform, X)


def _check_transformer(name, Transformer, X, y):
    if name in ('CCA', 'LocallyLinearEmbedding', 'KernelPCA') and _is_32bit():
        # Those transformers yield non-deterministic output when executed on
        # a 32bit Python. The same transformers are stable on 64bit Python.
        # FIXME: try to isolate a minimalistic reproduction case only depending
        # on numpy & scipy and/or maybe generate a test dataset that does not
        # cause such unstable behaviors.
        msg = name + ' is non deterministic on 32bit Python'
        raise SkipTest(msg)
    n_samples, n_features = np.asarray(X).shape
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        transformer = Transformer()
    set_random_state(transformer)
    set_fast_parameters(transformer)

    # fit

    if name in CROSS_DECOMPOSITION:
        y_ = np.c_[y, y]
        y_[::2, 1] *= 2
    else:
        y_ = y

    transformer.fit(X, y_)
    X_pred = transformer.fit_transform(X, y=y_)
    if isinstance(X_pred, tuple):
        for x_pred in X_pred:
            assert_equal(x_pred.shape[0], n_samples)
    else:
        assert_equal(X_pred.shape[0], n_samples)

    if hasattr(transformer, 'transform'):
        if name in CROSS_DECOMPOSITION:
            X_pred2 = transformer.transform(X, y_)
            X_pred3 = transformer.fit_transform(X, y=y_)
        else:
            X_pred2 = transformer.transform(X)
            X_pred3 = transformer.fit_transform(X, y=y_)
        if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple):
            for x_pred, x_pred2, x_pred3 in zip(X_pred, X_pred2, X_pred3):
                assert_array_almost_equal(
                    x_pred, x_pred2, 2,
                    "fit_transform and transform outcomes not consistent in %s"
                    % Transformer)
                assert_array_almost_equal(
                    x_pred, x_pred3, 2,
                    "consecutive fit_transform outcomes not consistent in %s"
                    % Transformer)
        else:
            assert_array_almost_equal(
                X_pred, X_pred2, 2,
                "fit_transform and transform outcomes not consistent in %s"
                % Transformer)
            assert_array_almost_equal(
                X_pred, X_pred3, 2,
                "consecutive fit_transform outcomes not consistent in %s"
                % Transformer)

        # raises error on malformed input for transform
        if hasattr(X, 'T'):
            # If it's not an array, it does not have a 'T' property
            assert_raises(ValueError, transformer.transform, X.T)


@ignore_warnings
def check_pipeline_consistency(name, Estimator):
    if name in ('CCA', 'LocallyLinearEmbedding', 'KernelPCA') and _is_32bit():
        # Those transformers yield non-deterministic output when executed on
        # a 32bit Python. The same transformers are stable on 64bit Python.
        # FIXME: try to isolate a minimalistic reproduction case only depending
        # scipy and/or maybe generate a test dataset that does not
        # cause such unstable behaviors.
        msg = name + ' is non deterministic on 32bit Python'
        raise SkipTest(msg)

    # check that make_pipeline(est) gives same score as est
    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    X -= X.min()
    y = multioutput_estimator_convert_y_2d(name, y)
    estimator = Estimator()
    set_fast_parameters(estimator)
    set_random_state(estimator)
    pipeline = make_pipeline(estimator)
    estimator.fit(X, y)
    pipeline.fit(X, y)
    funcs = ["score", "fit_transform"]
    for func_name in funcs:
        func = getattr(estimator, func_name, None)
        if func is not None:
            func_pipeline = getattr(pipeline, func_name)
            result = func(X, y)
            result_pipe = func_pipeline(X, y)
            assert_array_almost_equal(result, result_pipe)


@ignore_warnings
def check_fit_score_takes_y(name, Estimator):
    # check that all estimators accept an optional y
    # in fit and score so they can be used in pipelines
    rnd = np.random.RandomState(0)
    X = rnd.uniform(size=(10, 3))
    y = np.arange(10) % 3
    y = multioutput_estimator_convert_y_2d(name, y)
    estimator = Estimator()
    set_fast_parameters(estimator)
    set_random_state(estimator)
    funcs = ["fit", "score", "partial_fit", "fit_predict", "fit_transform"]

    for func_name in funcs:
        func = getattr(estimator, func_name, None)
        if func is not None:
            func(X, y)
            args = inspect.getargspec(func).args
            assert_true(args[2] in ["y", "Y"])


@ignore_warnings
def check_estimators_dtypes(name, Estimator):
    rnd = np.random.RandomState(0)
    X_train_32 = 3 * rnd.uniform(size=(20, 5)).astype(np.float32)
    X_train_64 = X_train_32.astype(np.float64)
    X_train_int_64 = X_train_32.astype(np.int64)
    X_train_int_32 = X_train_32.astype(np.int32)
    y = X_train_int_64[:, 0]
    y = multioutput_estimator_convert_y_2d(name, y)
    for X_train in [X_train_32, X_train_64, X_train_int_64, X_train_int_32]:
        with warnings.catch_warnings(record=True):
            estimator = Estimator()
        set_fast_parameters(estimator)
        set_random_state(estimator, 1)
        estimator.fit(X_train, y)

        for method in ["predict", "transform", "decision_function",
                       "predict_proba"]:
            if hasattr(estimator, method):
                getattr(estimator, method)(X_train)


def check_estimators_empty_data_messages(name, Estimator):
    e = Estimator()
    set_fast_parameters(e)
    set_random_state(e, 1)

    X_zero_samples = np.empty(0).reshape(0, 3)
    # The precise message can change depending on whether X or y is
    # validated first. Let us test the type of exception only:
    assert_raises(ValueError, e.fit, X_zero_samples, [])

    X_zero_features = np.empty(0).reshape(3, 0)
    # the following y should be accepted by both classifiers and regressors
    # and ignored by unsupervised models
    y = multioutput_estimator_convert_y_2d(name, np.array([1, 0, 1]))
    msg = "0 feature(s) (shape=(3, 0)) while a minimum of 1 is required."
    assert_raise_message(ValueError, msg, e.fit, X_zero_features, y)


def check_estimators_nan_inf(name, Estimator):
    rnd = np.random.RandomState(0)
    X_train_finite = rnd.uniform(size=(10, 3))
    X_train_nan = rnd.uniform(size=(10, 3))
    X_train_nan[0, 0] = np.nan
    X_train_inf = rnd.uniform(size=(10, 3))
    X_train_inf[0, 0] = np.inf
    y = np.ones(10)
    y[:5] = 0
    y = multioutput_estimator_convert_y_2d(name, y)
    error_string_fit = "Estimator doesn't check for NaN and inf in fit."
    error_string_predict = ("Estimator doesn't check for NaN and inf in"
                            " predict.")
    error_string_transform = ("Estimator doesn't check for NaN and inf in"
                              " transform.")
    for X_train in [X_train_nan, X_train_inf]:
        # catch deprecation warnings
        with warnings.catch_warnings(record=True):
            estimator = Estimator()
            set_fast_parameters(estimator)
            set_random_state(estimator, 1)
            # try to fit
            try:
                estimator.fit(X_train, y)
            except ValueError as e:
                if 'inf' not in repr(e) and 'NaN' not in repr(e):
                    print(error_string_fit, Estimator, e)
                    traceback.print_exc(file=sys.stdout)
                    raise e
            except Exception as exc:
                print(error_string_fit, Estimator, exc)
                traceback.print_exc(file=sys.stdout)
                raise exc
            else:
                raise AssertionError(error_string_fit, Estimator)
            # actually fit
            estimator.fit(X_train_finite, y)

            # predict
            if hasattr(estimator, "predict"):
                try:
                    estimator.predict(X_train)
                except ValueError as e:
                    if 'inf' not in repr(e) and 'NaN' not in repr(e):
                        print(error_string_predict, Estimator, e)
                        traceback.print_exc(file=sys.stdout)
                        raise e
                except Exception as exc:
                    print(error_string_predict, Estimator, exc)
                    traceback.print_exc(file=sys.stdout)
                else:
                    raise AssertionError(error_string_predict, Estimator)

            # transform
            if hasattr(estimator, "transform"):
                try:
                    estimator.transform(X_train)
                except ValueError as e:
                    if 'inf' not in repr(e) and 'NaN' not in repr(e):
                        print(error_string_transform, Estimator, e)
                        traceback.print_exc(file=sys.stdout)
                        raise e
                except Exception as exc:
                    print(error_string_transform, Estimator, exc)
                    traceback.print_exc(file=sys.stdout)
                else:
                    raise AssertionError(error_string_transform, Estimator)


def check_transformer_pickle(name, Transformer):
    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    n_samples, n_features = X.shape
    X = StandardScaler().fit_transform(X)
    X -= X.min()
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        transformer = Transformer()
    if not hasattr(transformer, 'transform'):
        return
    set_random_state(transformer)
    set_fast_parameters(transformer)

    # fit
    if name in CROSS_DECOMPOSITION:
        random_state = np.random.RandomState(seed=12345)
        y_ = np.vstack([y, 2 * y + random_state.randint(2, size=len(y))])
        y_ = y_.T
    else:
        y_ = y

    transformer.fit(X, y_)
    X_pred = transformer.fit(X, y_).transform(X)
    pickled_transformer = pickle.dumps(transformer)
    unpickled_transformer = pickle.loads(pickled_transformer)
    pickled_X_pred = unpickled_transformer.transform(X)

    assert_array_almost_equal(pickled_X_pred, X_pred)


def check_estimators_partial_fit_n_features(name, Alg):
    # check if number of features changes between calls to partial_fit.
    if not hasattr(Alg, 'partial_fit'):
        return
    X, y = make_blobs(n_samples=50, random_state=1)
    X -= X.min()
    with warnings.catch_warnings(record=True):
        alg = Alg()
    set_fast_parameters(alg)
    if isinstance(alg, ClassifierMixin):
        classes = np.unique(y)
        alg.partial_fit(X, y, classes=classes)
    else:
        alg.partial_fit(X, y)

    assert_raises(ValueError, alg.partial_fit, X[:, :-1], y)


def check_clustering(name, Alg):
    X, y = make_blobs(n_samples=50, random_state=1)
    X, y = shuffle(X, y, random_state=7)
    X = StandardScaler().fit_transform(X)
    n_samples, n_features = X.shape
    # catch deprecation and neighbors warnings
    with warnings.catch_warnings(record=True):
        alg = Alg()
    set_fast_parameters(alg)
    if hasattr(alg, "n_clusters"):
        alg.set_params(n_clusters=3)
    set_random_state(alg)
    if name == 'AffinityPropagation':
        alg.set_params(preference=-100)
        alg.set_params(max_iter=100)

    # fit
    alg.fit(X)
    # with lists
    alg.fit(X.tolist())

    assert_equal(alg.labels_.shape, (n_samples,))
    pred = alg.labels_
    assert_greater(adjusted_rand_score(pred, y), 0.4)
    # fit another time with ``fit_predict`` and compare results
    if name is 'SpectralClustering':
        # there is no way to make Spectral clustering deterministic :(
        return
    set_random_state(alg)
    with warnings.catch_warnings(record=True):
        pred2 = alg.fit_predict(X)
    assert_array_equal(pred, pred2)


def check_clusterer_compute_labels_predict(name, Clusterer):
    """Check that predict is invariant of compute_labels"""
    X, y = make_blobs(n_samples=20, random_state=0)
    clusterer = Clusterer()

    if hasattr(clusterer, "compute_labels"):
        # MiniBatchKMeans
        if hasattr(clusterer, "random_state"):
            clusterer.set_params(random_state=0)

        X_pred1 = clusterer.fit(X).predict(X)
        clusterer.set_params(compute_labels=False)
        X_pred2 = clusterer.fit(X).predict(X)
        assert_array_equal(X_pred1, X_pred2)


def check_classifiers_one_label(name, Classifier):
    error_string_fit = "Classifier can't train when only one class is present."
    error_string_predict = ("Classifier can't predict when only one class is "
                            "present.")
    rnd = np.random.RandomState(0)
    X_train = rnd.uniform(size=(10, 3))
    X_test = rnd.uniform(size=(10, 3))
    y = np.ones(10)
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        classifier = Classifier()
        set_fast_parameters(classifier)
        # try to fit
        try:
            classifier.fit(X_train, y)
        except ValueError as e:
            if 'class' not in repr(e):
                print(error_string_fit, Classifier, e)
                traceback.print_exc(file=sys.stdout)
                raise e
            else:
                return
        except Exception as exc:
            print(error_string_fit, Classifier, exc)
            traceback.print_exc(file=sys.stdout)
            raise exc
        # predict
        try:
            assert_array_equal(classifier.predict(X_test), y)
        except Exception as exc:
            print(error_string_predict, Classifier, exc)
            raise exc


def check_classifiers_train(name, Classifier):
    X_m, y_m = make_blobs(random_state=0)
    X_m, y_m = shuffle(X_m, y_m, random_state=7)
    X_m = StandardScaler().fit_transform(X_m)
    # generate binary problem from multi-class one
    y_b = y_m[y_m != 2]
    X_b = X_m[y_m != 2]
    for (X, y) in [(X_m, y_m), (X_b, y_b)]:
        # catch deprecation warnings
        classes = np.unique(y)
        n_classes = len(classes)
        n_samples, n_features = X.shape
        with warnings.catch_warnings(record=True):
            classifier = Classifier()
        if name in ['BernoulliNB', 'MultinomialNB']:
            X -= X.min()
        set_fast_parameters(classifier)
        set_random_state(classifier)
        # raises error on malformed input for fit
        assert_raises(ValueError, classifier.fit, X, y[:-1])

        # fit
        classifier.fit(X, y)
        # with lists
        classifier.fit(X.tolist(), y.tolist())
        assert_true(hasattr(classifier, "classes_"))
        y_pred = classifier.predict(X)
        assert_equal(y_pred.shape, (n_samples,))
        # training set performance
        if name not in ['BernoulliNB', 'MultinomialNB']:
            assert_greater(accuracy_score(y, y_pred), 0.83)

        # raises error on malformed input for predict
        assert_raises(ValueError, classifier.predict, X.T)
        if hasattr(classifier, "decision_function"):
            try:
                # decision_function agrees with predict
                decision = classifier.decision_function(X)
                if n_classes is 2:
                    assert_equal(decision.shape, (n_samples,))
                    dec_pred = (decision.ravel() > 0).astype(np.int)
                    assert_array_equal(dec_pred, y_pred)
                if (n_classes is 3
                        and not isinstance(classifier, BaseLibSVM)):
                    # 1on1 of LibSVM works differently
                    assert_equal(decision.shape, (n_samples, n_classes))
                    assert_array_equal(np.argmax(decision, axis=1), y_pred)

                # raises error on malformed input
                assert_raises(ValueError,
                              classifier.decision_function, X.T)
                # raises error on malformed input for decision_function
                assert_raises(ValueError,
                              classifier.decision_function, X.T)
            except NotImplementedError:
                pass
        if hasattr(classifier, "predict_proba"):
            # predict_proba agrees with predict
            y_prob = classifier.predict_proba(X)
            assert_equal(y_prob.shape, (n_samples, n_classes))
            assert_array_equal(np.argmax(y_prob, axis=1), y_pred)
            # check that probas for all classes sum to one
            assert_array_almost_equal(np.sum(y_prob, axis=1),
                                      np.ones(n_samples))
            # raises error on malformed input
            assert_raises(ValueError, classifier.predict_proba, X.T)
            # raises error on malformed input for predict_proba
            assert_raises(ValueError, classifier.predict_proba, X.T)


def check_estimators_unfitted(name, Estimator):
    """Check if NotFittedError is raised when calling predict and related
    functions"""

    # Common test for Regressors as well as Classifiers
    X, y = _boston_subset()

    with warnings.catch_warnings(record=True):
        est = Estimator()

    assert_raises(NotFittedError, est.predict, X)

    if hasattr(est, 'predict'):
        assert_raises(NotFittedError, est.predict, X)

    if hasattr(est, 'decision_function'):
        assert_raises(NotFittedError, est.decision_function, X)

    if hasattr(est, 'predict_proba'):
        assert_raises(NotFittedError, est.predict_proba, X)

    if hasattr(est, 'predict_log_proba'):
        assert_raises(NotFittedError, est.predict_log_proba, X)


def check_classifiers_input_shapes(name, Classifier):
    iris = load_iris()
    X, y = iris.data, iris.target
    X, y = shuffle(X, y, random_state=1)
    X = StandardScaler().fit_transform(X)
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        classifier = Classifier()
    set_fast_parameters(classifier)
    set_random_state(classifier)
    # fit
    classifier.fit(X, y)
    y_pred = classifier.predict(X)

    set_random_state(classifier)
    # Check that when a 2D y is given, a DataConversionWarning is
    # raised
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always", DataConversionWarning)
        warnings.simplefilter("ignore", RuntimeWarning)
        classifier.fit(X, y[:, np.newaxis])
    msg = "expected 1 DataConversionWarning, got: %s" % (
        ", ".join([str(w_x) for w_x in w]))
    assert_equal(len(w), 1, msg)
    assert_array_equal(y_pred, classifier.predict(X))


def check_classifiers_classes(name, Classifier):
    X, y = make_blobs(n_samples=30, random_state=0, cluster_std=0.1)
    X, y = shuffle(X, y, random_state=7)
    X = StandardScaler().fit_transform(X)
    # We need to make sure that we have non negative data, for things
    # like NMF
    X -= X.min() - .1
    y_names = np.array(["one", "two", "three"])[y]

    for y_names in [y_names, y_names.astype('O')]:
        if name in ["LabelPropagation", "LabelSpreading"]:
            # TODO some complication with -1 label
            y_ = y
        else:
            y_ = y_names

        classes = np.unique(y_)
        # catch deprecation warnings
        with warnings.catch_warnings(record=True):
            classifier = Classifier()
        if name == 'BernoulliNB':
            classifier.set_params(binarize=X.mean())
        set_fast_parameters(classifier)
        # fit
        classifier.fit(X, y_)

        y_pred = classifier.predict(X)
        # training set performance
        assert_array_equal(np.unique(y_), np.unique(y_pred))
        if np.any(classifier.classes_ != classes):
            print("Unexpected classes_ attribute for %r: "
                  "expected %s, got %s" %
                  (classifier, classes, classifier.classes_))


def check_classifiers_pickle(name, Classifier):
    X, y = make_blobs(random_state=0)
    X, y = shuffle(X, y, random_state=7)
    X -= X.min()

    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        classifier = Classifier()
    set_fast_parameters(classifier)
    # raises error on malformed input for fit
    assert_raises(ValueError, classifier.fit, X, y[:-1])

    # fit
    classifier.fit(X, y)
    y_pred = classifier.predict(X)
    pickled_classifier = pickle.dumps(classifier)
    unpickled_classifier = pickle.loads(pickled_classifier)
    pickled_y_pred = unpickled_classifier.predict(X)

    assert_array_almost_equal(pickled_y_pred, y_pred)


def check_regressors_int(name, Regressor):
    X, _ = _boston_subset()
    X = X[:50]
    rnd = np.random.RandomState(0)
    y = rnd.randint(3, size=X.shape[0])
    y = multioutput_estimator_convert_y_2d(name, y)
    rnd = np.random.RandomState(0)
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        # separate estimators to control random seeds
        regressor_1 = Regressor()
        regressor_2 = Regressor()
    set_fast_parameters(regressor_1)
    set_fast_parameters(regressor_2)
    set_random_state(regressor_1)
    set_random_state(regressor_2)

    if name in CROSS_DECOMPOSITION:
        y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
        y_ = y_.T
    else:
        y_ = y

    # fit
    regressor_1.fit(X, y_)
    pred1 = regressor_1.predict(X)
    regressor_2.fit(X, y_.astype(np.float))
    pred2 = regressor_2.predict(X)
    assert_array_almost_equal(pred1, pred2, 2, name)


def check_regressors_train(name, Regressor):
    X, y = _boston_subset()
    y = StandardScaler().fit_transform(y)   # X is already scaled
    y = multioutput_estimator_convert_y_2d(name, y)
    rnd = np.random.RandomState(0)
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        regressor = Regressor()
    set_fast_parameters(regressor)
    if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
        # linear regressors need to set alpha, but not generalized CV ones
        regressor.alpha = 0.01
    if name == 'PassiveAggressiveRegressor':
        regressor.C = 0.01

    # raises error on malformed input for fit
    assert_raises(ValueError, regressor.fit, X, y[:-1])
    # fit
    if name in CROSS_DECOMPOSITION:
        y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
        y_ = y_.T
    else:
        y_ = y
    set_random_state(regressor)
    regressor.fit(X, y_)
    regressor.fit(X.tolist(), y_.tolist())
    regressor.predict(X)

    # TODO: find out why PLS and CCA fail. RANSAC is random
    # and furthermore assumes the presence of outliers, hence
    # skipped
    if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'):
        print(regressor)
        assert_greater(regressor.score(X, y_), 0.5)


def check_regressors_pickle(name, Regressor):
    X, y = _boston_subset()
    y = StandardScaler().fit_transform(y)   # X is already scaled
    y = multioutput_estimator_convert_y_2d(name, y)
    rnd = np.random.RandomState(0)
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        regressor = Regressor()
    set_fast_parameters(regressor)
    if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
        # linear regressors need to set alpha, but not generalized CV ones
        regressor.alpha = 0.01

    if name in CROSS_DECOMPOSITION:
        y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
        y_ = y_.T
    else:
        y_ = y
    regressor.fit(X, y_)
    y_pred = regressor.predict(X)
    # store old predictions
    pickled_regressor = pickle.dumps(regressor)
    unpickled_regressor = pickle.loads(pickled_regressor)
    pickled_y_pred = unpickled_regressor.predict(X)
    assert_array_almost_equal(pickled_y_pred, y_pred)


def check_class_weight_classifiers(name, Classifier):
    for n_centers in [2, 3]:
        # create a very noisy dataset
        X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                            random_state=0)
        n_centers = len(np.unique(y_train))

        if n_centers == 2:
            class_weight = {0: 1000, 1: 0.0001}
        else:
            class_weight = {0: 1000, 1: 0.0001, 2: 0.0001}

        with warnings.catch_warnings(record=True):
            classifier = Classifier(class_weight=class_weight)
        if hasattr(classifier, "n_iter"):
            classifier.set_params(n_iter=100)
        if hasattr(classifier, "min_weight_fraction_leaf"):
            classifier.set_params(min_weight_fraction_leaf=0.01)

        set_random_state(classifier)
        classifier.fit(X_train, y_train)
        y_pred = classifier.predict(X_test)
        assert_greater(np.mean(y_pred == 0), 0.89)


def check_class_weight_auto_classifiers(name, Classifier, X_train, y_train,
                                        X_test, y_test, weights):
    with warnings.catch_warnings(record=True):
        classifier = Classifier()
    if hasattr(classifier, "n_iter"):
        classifier.set_params(n_iter=100)

    set_random_state(classifier)
    classifier.fit(X_train, y_train)
    y_pred = classifier.predict(X_test)

    classifier.set_params(class_weight='auto')
    classifier.fit(X_train, y_train)
    y_pred_auto = classifier.predict(X_test)
    assert_greater(f1_score(y_test, y_pred_auto, average='weighted'),
                   f1_score(y_test, y_pred, average='weighted'))


def check_class_weight_auto_linear_classifier(name, Classifier):
    """Test class weights with non-contiguous class labels."""
    X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
                  [1.0, 1.0], [1.0, 0.0]])
    y = [1, 1, 1, -1, -1]

    with warnings.catch_warnings(record=True):
        classifier = Classifier()
    if hasattr(classifier, "n_iter"):
        # This is a very small dataset, default n_iter are likely to prevent
        # convergence
        classifier.set_params(n_iter=1000)
    set_random_state(classifier)

    # Let the model compute the class frequencies
    classifier.set_params(class_weight='auto')
    coef_auto = classifier.fit(X, y).coef_.copy()

    # Count each label occurrence to reweight manually
    mean_weight = (1. / 3 + 1. / 2) / 2
    class_weight = {
        1: 1. / 3 / mean_weight,
        -1: 1. / 2 / mean_weight,
    }
    classifier.set_params(class_weight=class_weight)
    coef_manual = classifier.fit(X, y).coef_.copy()

    assert_array_almost_equal(coef_auto, coef_manual)


def check_estimators_overwrite_params(name, Estimator):
    X, y = make_blobs(random_state=0, n_samples=9)
    y = multioutput_estimator_convert_y_2d(name, y)
    # some want non-negative input
    X -= X.min()
    with warnings.catch_warnings(record=True):
        # catch deprecation warnings
        estimator = Estimator()

    if name == 'MiniBatchDictLearning' or name == 'MiniBatchSparsePCA':
        # FIXME
        # for MiniBatchDictLearning and MiniBatchSparsePCA
        estimator.batch_size = 1

    set_fast_parameters(estimator)
    set_random_state(estimator)

    # Make a physical copy of the orginal estimator parameters before fitting.
    params = estimator.get_params()
    original_params = deepcopy(params)

    # Fit the model
    estimator.fit(X, y)

    # Compare the state of the model parameters with the original parameters
    new_params = estimator.get_params()
    for param_name, original_value in original_params.items():
        new_value = new_params[param_name]

        # We should never change or mutate the internal state of input
        # parameters by default. To check this we use the joblib.hash function
        # that introspects recursively any subobjects to compute a checksum.
        # The only exception to this rule of immutable constructor parameters
        # is possible RandomState instance but in this check we explicitly
        # fixed the random_state params recursively to be integer seeds.
        assert_equal(hash(new_value), hash(original_value),
                     "Estimator %s should not change or mutate "
                     " the parameter %s from %s to %s during fit."
                     % (name, param_name, original_value, new_value))


def check_sparsify_coefficients(name, Estimator):
    X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1],
                  [-1, -2], [2, 2], [-2, -2]])
    y = [1, 1, 1, 2, 2, 2, 3, 3, 3]
    est = Estimator()

    est.fit(X, y)
    pred_orig = est.predict(X)

    # test sparsify with dense inputs
    est.sparsify()
    assert_true(sparse.issparse(est.coef_))
    pred = est.predict(X)
    assert_array_equal(pred, pred_orig)

    # pickle and unpickle with sparse coef_
    est = pickle.loads(pickle.dumps(est))
    assert_true(sparse.issparse(est.coef_))
    pred = est.predict(X)
    assert_array_equal(pred, pred_orig)


def check_classifier_data_not_an_array(name, Estimator):
    X = np.array([[3, 0], [0, 1], [0, 2], [1, 1], [1, 2], [2, 1]])
    y = [1, 1, 1, 2, 2, 2]
    y = multioutput_estimator_convert_y_2d(name, y)
    check_estimators_data_not_an_array(name, Estimator, X, y)


def check_regressor_data_not_an_array(name, Estimator):
    X, y = _boston_subset(n_samples=50)
    y = multioutput_estimator_convert_y_2d(name, y)
    check_estimators_data_not_an_array(name, Estimator, X, y)


def check_estimators_data_not_an_array(name, Estimator, X, y):

    if name in CROSS_DECOMPOSITION:
        raise SkipTest
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        # separate estimators to control random seeds
        estimator_1 = Estimator()
        estimator_2 = Estimator()
    set_fast_parameters(estimator_1)
    set_fast_parameters(estimator_2)
    set_random_state(estimator_1)
    set_random_state(estimator_2)

    y_ = NotAnArray(np.asarray(y))
    X_ = NotAnArray(np.asarray(X))

    # fit
    estimator_1.fit(X_, y_)
    pred1 = estimator_1.predict(X_)
    estimator_2.fit(X, y)
    pred2 = estimator_2.predict(X)
    assert_array_almost_equal(pred1, pred2, 2, name)


def check_parameters_default_constructible(name, Estimator):
    classifier = LDA()
    # test default-constructibility
    # get rid of deprecation warnings
    with warnings.catch_warnings(record=True):
        if name in META_ESTIMATORS:
            estimator = Estimator(classifier)
        else:
            estimator = Estimator()
        # test cloning
        clone(estimator)
        # test __repr__
        repr(estimator)
        # test that set_params returns self
        assert_true(isinstance(estimator.set_params(), Estimator))

        # test if init does nothing but set parameters
        # this is important for grid_search etc.
        # We get the default parameters from init and then
        # compare these against the actual values of the attributes.

        # this comes from getattr. Gets rid of deprecation decorator.
        init = getattr(estimator.__init__, 'deprecated_original',
                       estimator.__init__)
        try:
            args, varargs, kws, defaults = inspect.getargspec(init)
        except TypeError:
            # init is not a python function.
            # true for mixins
            return
        params = estimator.get_params()
        if name in META_ESTIMATORS:
            # they need a non-default argument
            args = args[2:]
        else:
            args = args[1:]
        if args:
            # non-empty list
            assert_equal(len(args), len(defaults))
        else:
            return
        for arg, default in zip(args, defaults):
            if arg not in params.keys():
                # deprecated parameter, not in get_params
                assert_true(default is None)
                continue

            if isinstance(params[arg], np.ndarray):
                assert_array_equal(params[arg], default)
            else:
                assert_equal(params[arg], default)


def multioutput_estimator_convert_y_2d(name, y):
    # Estimators in mono_output_task_error raise ValueError if y is of 1-D
    # Convert into a 2-D y for those estimators.
    if name in (['MultiTaskElasticNetCV', 'MultiTaskLassoCV',
                 'MultiTaskLasso', 'MultiTaskElasticNet']):
        return y[:, np.newaxis]
    return y


def check_non_transformer_estimators_n_iter(name, estimator,
                                            multi_output=False):
    # Check if all iterative solvers, run for more than one iteratiom

    iris = load_iris()
    X, y_ = iris.data, iris.target

    if multi_output:
        y_ = y_[:, np.newaxis]

    set_random_state(estimator, 0)
    if name == 'AffinityPropagation':
        estimator.fit(X)
    else:
        estimator.fit(X, y_)
    assert_greater(estimator.n_iter_, 0)


def check_transformer_n_iter(name, estimator):
    if name in CROSS_DECOMPOSITION:
        # Check using default data
        X = [[0., 0., 1.], [1., 0., 0.], [2., 2., 2.], [2., 5., 4.]]
        y_ = [[0.1, -0.2], [0.9, 1.1], [0.1, -0.5], [0.3, -0.2]]

    else:
        X, y_ = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                           random_state=0, n_features=2, cluster_std=0.1)
        X -= X.min() - 0.1
    set_random_state(estimator, 0)
    estimator.fit(X, y_)

    # These return a n_iter per component.
    if name in CROSS_DECOMPOSITION:
        for iter_ in estimator.n_iter_:
            assert_greater(iter_, 1)
    else:
        assert_greater(estimator.n_iter_, 1)
