from scipy import sparse

import numpy as np
from scipy import sparse

from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal

from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_almost_equal
from sklearn.linear_model import LinearRegression, RANSACRegressor, Lasso
from sklearn.linear_model.ransac import _dynamic_max_trials


# Generate coordinates of line
X = np.arange(-200, 200)
y = 0.2 * X + 20
data = np.column_stack([X, y])

# Add some faulty data
outliers = np.array((10, 30, 200))
data[outliers[0], :] = (1000, 1000)
data[outliers[1], :] = (-1000, -1000)
data[outliers[2], :] = (-100, -50)

X = data[:, 0][:, np.newaxis]
y = data[:, 1]


def test_ransac_inliers_outliers():

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)

    # Estimate parameters of corrupted data
    ransac_estimator.fit(X, y)

    # Ground truth / reference inlier mask
    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


def test_ransac_is_data_valid():
    def is_data_valid(X, y):
        assert_equal(X.shape[0], 2)
        assert_equal(y.shape[0], 2)
        return False

    X = np.random.rand(10, 2)
    y = np.random.rand(10, 1)

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5,
                                       is_data_valid=is_data_valid,
                                       random_state=0)

    assert_raises(ValueError, ransac_estimator.fit, X, y)


def test_ransac_is_model_valid():
    def is_model_valid(estimator, X, y):
        assert_equal(X.shape[0], 2)
        assert_equal(y.shape[0], 2)
        return False

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5,
                                       is_model_valid=is_model_valid,
                                       random_state=0)

    assert_raises(ValueError, ransac_estimator.fit, X, y)


def test_ransac_max_trials():
    base_estimator = LinearRegression()

    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, max_trials=0,
                                       random_state=0)
    assert_raises(ValueError, ransac_estimator.fit, X, y)

    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, max_trials=11,
                                       random_state=0)
    assert getattr(ransac_estimator, 'n_trials_', None) is None
    ransac_estimator.fit(X, y)
    assert_equal(ransac_estimator.n_trials_, 2)


def test_ransac_stop_n_inliers():
    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, stop_n_inliers=2,
                                       random_state=0)
    ransac_estimator.fit(X, y)

    assert_equal(ransac_estimator.n_trials_, 1)


def test_ransac_stop_score():
    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, stop_score=0,
                                       random_state=0)
    ransac_estimator.fit(X, y)

    assert_equal(ransac_estimator.n_trials_, 1)


def test_ransac_score():
    X = np.arange(100)[:, None]
    y = np.zeros((100, ))
    y[0] = 1
    y[1] = 100

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=0.5, random_state=0)
    ransac_estimator.fit(X, y)

    assert_equal(ransac_estimator.score(X[2:], y[2:]), 1)
    assert_less(ransac_estimator.score(X[:2], y[:2]), 1)


def test_ransac_predict():
    X = np.arange(100)[:, None]
    y = np.zeros((100, ))
    y[0] = 1
    y[1] = 100

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=0.5, random_state=0)
    ransac_estimator.fit(X, y)

    assert_equal(ransac_estimator.predict(X), np.zeros(100))


def test_ransac_resid_thresh_no_inliers():
    # When residual_threshold=0.0 there are no inliers and a
    # ValueError with a message should be raised
    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=0.0, random_state=0)

    assert_raises_regexp(ValueError,
                    "No inliers.*residual_threshold.*0\.0",
                    ransac_estimator.fit, X, y)


def test_ransac_sparse_coo():
    X_sparse = sparse.coo_matrix(X)

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)
    ransac_estimator.fit(X_sparse, y)

    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


def test_ransac_sparse_csr():
    X_sparse = sparse.csr_matrix(X)

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)
    ransac_estimator.fit(X_sparse, y)

    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


def test_ransac_sparse_csc():
    X_sparse = sparse.csc_matrix(X)

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)
    ransac_estimator.fit(X_sparse, y)

    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


def test_ransac_none_estimator():

    base_estimator = LinearRegression()

    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)
    ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0)

    ransac_estimator.fit(X, y)
    ransac_none_estimator.fit(X, y)

    assert_array_almost_equal(ransac_estimator.predict(X),
                              ransac_none_estimator.predict(X))


def test_ransac_min_n_samples():
    base_estimator = LinearRegression()
    ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0)
    ransac_estimator2 = RANSACRegressor(base_estimator,
                                        min_samples=2. / X.shape[0],
                                        residual_threshold=5, random_state=0)
    ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1,
                                        residual_threshold=5, random_state=0)
    ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2,
                                        residual_threshold=5, random_state=0)
    ransac_estimator5 = RANSACRegressor(base_estimator, min_samples=2.0,
                                        residual_threshold=5, random_state=0)
    ransac_estimator6 = RANSACRegressor(base_estimator,
                                        residual_threshold=5, random_state=0)
    ransac_estimator7 = RANSACRegressor(base_estimator,
                                        min_samples=X.shape[0] + 1,
                                        residual_threshold=5, random_state=0)

    ransac_estimator1.fit(X, y)
    ransac_estimator2.fit(X, y)
    ransac_estimator5.fit(X, y)
    ransac_estimator6.fit(X, y)

    assert_array_almost_equal(ransac_estimator1.predict(X),
                              ransac_estimator2.predict(X))
    assert_array_almost_equal(ransac_estimator1.predict(X),
                              ransac_estimator5.predict(X))
    assert_array_almost_equal(ransac_estimator1.predict(X),
                              ransac_estimator6.predict(X))

    assert_raises(ValueError, ransac_estimator3.fit, X, y)
    assert_raises(ValueError, ransac_estimator4.fit, X, y)
    assert_raises(ValueError, ransac_estimator7.fit, X, y)


def test_ransac_multi_dimensional_targets():

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       residual_threshold=5, random_state=0)

    # 3-D target values
    yyy = np.column_stack([y, y, y])

    # Estimate parameters of corrupted data
    ransac_estimator.fit(X, yyy)

    # Ground truth / reference inlier mask
    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


# XXX: Remove in 0.20
def test_ransac_residual_metric():
    residual_metric1 = lambda dy: np.sum(np.abs(dy), axis=1)
    residual_metric2 = lambda dy: np.sum(dy ** 2, axis=1)

    yyy = np.column_stack([y, y, y])

    base_estimator = LinearRegression()
    ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0)
    ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        residual_metric=residual_metric1)
    ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        residual_metric=residual_metric2)

    # multi-dimensional
    ransac_estimator0.fit(X, yyy)
    assert_warns(DeprecationWarning, ransac_estimator1.fit, X, yyy)
    assert_warns(DeprecationWarning, ransac_estimator2.fit, X, yyy)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator1.predict(X))
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))

    # one-dimensional
    ransac_estimator0.fit(X, y)
    assert_warns(DeprecationWarning, ransac_estimator2.fit, X, y)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))

def test_ransac_residual_loss():
    loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1)
    loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1)

    loss_mono = lambda y_true, y_pred : np.abs(y_true - y_pred)
    yyy = np.column_stack([y, y, y])

    base_estimator = LinearRegression()
    ransac_estimator0 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0)
    ransac_estimator1 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        loss=loss_multi1)
    ransac_estimator2 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        loss=loss_multi2)

    # multi-dimensional
    ransac_estimator0.fit(X, yyy)
    ransac_estimator1.fit(X, yyy)
    ransac_estimator2.fit(X, yyy)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator1.predict(X))
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))

    # one-dimensional
    ransac_estimator0.fit(X, y)
    ransac_estimator2.loss = loss_mono
    ransac_estimator2.fit(X, y)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))
    ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=2,
                                        residual_threshold=5, random_state=0,
                                        loss="squared_loss")
    ransac_estimator3.fit(X, y)
    assert_array_almost_equal(ransac_estimator0.predict(X),
                              ransac_estimator2.predict(X))


def test_ransac_default_residual_threshold():
    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       random_state=0)

    # Estimate parameters of corrupted data
    ransac_estimator.fit(X, y)

    # Ground truth / reference inlier mask
    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False

    assert_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)


def test_ransac_dynamic_max_trials():
    # Numbers hand-calculated and confirmed on page 119 (Table 4.3) in
    #   Hartley, R.~I. and Zisserman, A., 2004,
    #   Multiple View Geometry in Computer Vision, Second Edition,
    #   Cambridge University Press, ISBN: 0521540518

    # e = 0%, min_samples = X
    assert_equal(_dynamic_max_trials(100, 100, 2, 0.99), 1)

    # e = 5%, min_samples = 2
    assert_equal(_dynamic_max_trials(95, 100, 2, 0.99), 2)
    # e = 10%, min_samples = 2
    assert_equal(_dynamic_max_trials(90, 100, 2, 0.99), 3)
    # e = 30%, min_samples = 2
    assert_equal(_dynamic_max_trials(70, 100, 2, 0.99), 7)
    # e = 50%, min_samples = 2
    assert_equal(_dynamic_max_trials(50, 100, 2, 0.99), 17)

    # e = 5%, min_samples = 8
    assert_equal(_dynamic_max_trials(95, 100, 8, 0.99), 5)
    # e = 10%, min_samples = 8
    assert_equal(_dynamic_max_trials(90, 100, 8, 0.99), 9)
    # e = 30%, min_samples = 8
    assert_equal(_dynamic_max_trials(70, 100, 8, 0.99), 78)
    # e = 50%, min_samples = 8
    assert_equal(_dynamic_max_trials(50, 100, 8, 0.99), 1177)

    # e = 0%, min_samples = 10
    assert_equal(_dynamic_max_trials(1, 100, 10, 0), 0)
    assert_equal(_dynamic_max_trials(1, 100, 10, 1), float('inf'))

    base_estimator = LinearRegression()
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       stop_probability=-0.1)
    assert_raises(ValueError, ransac_estimator.fit, X, y)
    ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                       stop_probability=1.1)
    assert_raises(ValueError, ransac_estimator.fit, X, y)


def test_ransac_fit_sample_weight():
    ransac_estimator = RANSACRegressor(random_state=0)
    n_samples = y.shape[0]
    weights = np.ones(n_samples)
    ransac_estimator.fit(X, y, weights)
    # sanity check
    assert_equal(ransac_estimator.inlier_mask_.shape[0], n_samples)

    ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_
                                   ).astype(np.bool_)
    ref_inlier_mask[outliers] = False
    # check that mask is correct
    assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)

    # check that fit(X)  = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
    #   X = X1 repeated n1 times, X2 repeated n2 times and so forth
    random_state = check_random_state(0)
    X_ = random_state.randint(0, 200, [10, 1])
    y_ = np.ndarray.flatten(0.2 * X_ + 2)
    sample_weight = random_state.randint(0, 10, 10)
    outlier_X = random_state.randint(0, 1000, [1, 1])
    outlier_weight = random_state.randint(0, 10, 1)
    outlier_y = random_state.randint(-1000, 0, 1)

    X_flat = np.append(np.repeat(X_, sample_weight, axis=0),
                       np.repeat(outlier_X, outlier_weight, axis=0), axis=0)
    y_flat = np.ndarray.flatten(np.append(np.repeat(y_, sample_weight, axis=0),
                                np.repeat(outlier_y, outlier_weight, axis=0),
                                          axis=0))
    ransac_estimator.fit(X_flat, y_flat)
    ref_coef_ = ransac_estimator.estimator_.coef_

    sample_weight = np.append(sample_weight, outlier_weight)
    X_ = np.append(X_, outlier_X, axis=0)
    y_ = np.append(y_, outlier_y)
    ransac_estimator.fit(X_, y_, sample_weight)

    assert_almost_equal(ransac_estimator.estimator_.coef_, ref_coef_)

    # check that if base_estimator.fit doesn't support
    # sample_weight, raises error
    base_estimator = Lasso()
    ransac_estimator = RANSACRegressor(base_estimator)
    assert_raises(ValueError, ransac_estimator.fit, X, y, weights)
