# Author: Gael Varoquaux
# License: BSD

import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal

from nose.tools import assert_true
from nose.tools import assert_false
from nose.tools import assert_equal
from nose.tools import assert_raises
from sklearn.base import BaseEstimator, clone, is_classifier
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV


#############################################################################
# A few test classes
class MyEstimator(BaseEstimator):

    def __init__(self, l1=0, empty=None):
        self.l1 = l1
        self.empty = empty


class K(BaseEstimator):
    def __init__(self, c=None, d=None):
        self.c = c
        self.d = d


class T(BaseEstimator):
    def __init__(self, a=None, b=None):
        self.a = a
        self.b = b


class Buggy(BaseEstimator):
    " A buggy estimator that does not set its parameters right. "

    def __init__(self, a=None):
        self.a = 1


#############################################################################
# The tests

def test_clone():
    """Tests that clone creates a correct deep copy.

    We create an estimator, make a copy of its original state
    (which, in this case, is the current state of the setimator),
    and check that the obtained copy is a correct deep copy.

    """
    from sklearn.feature_selection import SelectFpr, f_classif

    selector = SelectFpr(f_classif, alpha=0.1)
    new_selector = clone(selector)
    assert_true(selector is not new_selector)
    assert_equal(selector.get_params(), new_selector.get_params())

    selector = SelectFpr(f_classif, alpha=np.zeros((10, 2)))
    new_selector = clone(selector)
    assert_true(selector is not new_selector)


def test_clone_2():
    """Tests that clone doesn't copy everything.

    We first create an estimator, give it an own attribute, and
    make a copy of its original state. Then we check that the copy doesn't
    have the specific attribute we manually added to the initial estimator.
    """
    from sklearn.feature_selection import SelectFpr, f_classif

    selector = SelectFpr(f_classif, alpha=0.1)
    selector.own_attribute = "test"
    new_selector = clone(selector)
    assert_false(hasattr(new_selector, "own_attribute"))


def test_clone_buggy():
    """Check that clone raises an error on buggy estimators."""
    buggy = Buggy()
    buggy.a = 2
    assert_raises(RuntimeError, clone, buggy)


def test_clone_empty_array():
    """Regression test for cloning estimators with empty arrays"""
    clf = MyEstimator(empty=np.array([]))
    clf2 = clone(clf)
    assert_array_equal(clf.empty, clf2.empty)

    clf = MyEstimator(empty=sp.csr_matrix(np.array([[0]])))
    clf2 = clone(clf)
    assert_array_equal(clf.empty.data, clf2.empty.data)


def test_repr():
    """Smoke test the repr of the base estimator."""
    my_estimator = MyEstimator()
    repr(my_estimator)
    test = T(K(), K())
    assert_equal(
        repr(test),
        "T(a=K(c=None, d=None), b=K(c=None, d=None))"
    )


def test_str():
    """Smoke test the str of the base estimator"""
    my_estimator = MyEstimator()
    str(my_estimator)


def test_get_params():
    test = T(K(), K())

    assert_true('a__d' in test.get_params(deep=True))
    assert_true('a__d' not in test.get_params(deep=False))

    test.set_params(a__d=2)
    assert_true(test.a.d == 2)
    assert_raises(ValueError, test.set_params, a__a=2)


def test_is_classifier():
    svc = SVC()
    assert_true(is_classifier(svc))
    assert_true(is_classifier(GridSearchCV(svc, {'C': [0.1, 1]})))
    assert_true(is_classifier(Pipeline([('svc', svc)])))
    assert_true(is_classifier(Pipeline([('svc_cv',
                              GridSearchCV(svc, {'C': [0.1, 1]}))])))