Python sklearn.metrics.mean_squared_log_error() Examples

The following are 16 code examples of sklearn.metrics.mean_squared_log_error(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.metrics , or try the search function .
Example #1
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_multioutput_regression():
    y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
    y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])

    error = mean_squared_error(y_true, y_pred)
    assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)

    error = mean_squared_log_error(y_true, y_pred)
    assert_almost_equal(error, 0.200, decimal=2)

    # mean_absolute_error and mean_squared_error are equal because
    # it is a binary problem.
    error = mean_absolute_error(y_true, y_pred)
    assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)

    error = r2_score(y_true, y_pred, multioutput='variance_weighted')
    assert_almost_equal(error, 1. - 5. / 2)
    error = r2_score(y_true, y_pred, multioutput='uniform_average')
    assert_almost_equal(error, -.875)
Example #2
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_regression_metrics_at_limits():
    assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(max_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
    assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
    assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be "
                        "used when targets contain negative values.",
                        mean_squared_log_error, [-1.], [-1.])
    assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be "
                        "used when targets contain negative values.",
                        mean_squared_log_error, [1., 2., 3.], [1., -2., 3.])
    assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be "
                        "used when targets contain negative values.",
                        mean_squared_log_error, [1., -2., 3.], [1., 2., 3.])
Example #3
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_regression_custom_weights():
    y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
    y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]

    msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
    maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
    rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
    evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])

    assert_almost_equal(msew, 0.39, decimal=2)
    assert_almost_equal(maew, 0.475, decimal=3)
    assert_almost_equal(rw, 0.94, decimal=2)
    assert_almost_equal(evsw, 0.94, decimal=2)

    # Handling msle separately as it does not accept negative inputs.
    y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
    y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
    msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
    msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
                               multioutput=[0.3, 0.7])
    assert_almost_equal(msle, msle2, decimal=2)
Example #4
Source File: test_metrics.py    From python-dlpy with Apache License 2.0 6 votes vote down vote up 
def test_mean_squared_log_error(self):

        try:
            from sklearn.metrics import mean_squared_log_error as skmsle
        except:
            unittest.TestCase.skipTest(self, "sklearn is not found in the libraries")

        skmsle_score1 = skmsle(self.local_reg1.target, self.local_reg1.p_target)
        dlpymsle_score1 = mean_squared_log_error('target', 'p_target', castable=self.reg_table1)

        self.assertAlmostEqual(skmsle_score1, dlpymsle_score1)

        skmsle_score2 = skmsle(self.local_reg1.target, self.local_reg2.p_target)
        dlpymsle_score2 = mean_squared_log_error(self.reg_table1.target, self.reg_table2.p_target,
                                                 id_vars='id1')
        dlpymsle_score2_1 = mean_squared_log_error(self.reg_table1.target, self.reg_table2.p_target)

        self.assertAlmostEqual(skmsle_score2, dlpymsle_score2)
Example #5
Source File: test_regression.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_multioutput_regression():
    y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
    y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])

    error = mean_squared_error(y_true, y_pred)
    assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)

    error = mean_squared_log_error(y_true, y_pred)
    assert_almost_equal(error, 0.200, decimal=2)

    # mean_absolute_error and mean_squared_error are equal because
    # it is a binary problem.
    error = mean_absolute_error(y_true, y_pred)
    assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)

    error = r2_score(y_true, y_pred, multioutput='variance_weighted')
    assert_almost_equal(error, 1. - 5. / 2)
    error = r2_score(y_true, y_pred, multioutput='uniform_average')
    assert_almost_equal(error, -.875)
Example #6
Source File: test_regression.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_regression_custom_weights():
    y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
    y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]

    msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
    maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
    rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
    evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])

    assert_almost_equal(msew, 0.39, decimal=2)
    assert_almost_equal(maew, 0.475, decimal=3)
    assert_almost_equal(rw, 0.94, decimal=2)
    assert_almost_equal(evsw, 0.94, decimal=2)

    # Handling msle separately as it does not accept negative inputs.
    y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
    y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
    msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
    msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
                               multioutput=[0.3, 0.7])
    assert_almost_equal(msle, msle2, decimal=2)
Example #7
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_regression_metrics(n_samples=50):
    y_true = np.arange(n_samples)
    y_pred = y_true + 1

    assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
    assert_almost_equal(mean_squared_log_error(y_true, y_pred),
                        mean_squared_error(np.log(1 + y_true),
                                           np.log(1 + y_pred)))
    assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
    assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
    assert_almost_equal(max_error(y_true, y_pred), 1.)
    assert_almost_equal(r2_score(y_true, y_pred),  0.995, 2)
    assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
Example #8
Source File: mean_squared_log_error.py    From driverlessai-recipes with Apache License 2.0 5 votes vote down vote up 
def score(self,
              actual: np.array,
              predicted: np.array,
              sample_weight: typing.Optional[np.array] = None,
              labels: typing.Optional[np.array] = None,
              **kwargs) -> float:
        if not ((actual >= 0).all() and (predicted >= 0).all()):
            return 1e36
        return mean_squared_log_error(actual, predicted)
Example #9
Source File: custom_scores.py    From Auto_ViML with Apache License 2.0 5 votes vote down vote up 
def gini_msle(truth, predictions):
    score = mean_squared_log_error(truth, predictions)
    return score
Example #10
Source File: custom_scores_HO.py    From Auto_ViML with Apache License 2.0 5 votes vote down vote up 
def gini_msle(truth, predictions):
    score = np.sqrt(mean_squared_log_error(truth, predictions))
    return score
Example #11
Source File: tf_sparse.py    From mercari-solution with MIT License 5 votes vote down vote up 
def get_rmsle(y_true, y_pred):
    return np.sqrt(mean_squared_log_error(np.expm1(y_true), np.expm1(y_pred)))
Example #12
Source File: mercari_golf.py    From mercari-solution with MIT License 5 votes vote down vote up 
def main():
    vectorizer = make_union(
        on_field('name', Tfidf(max_features=100000, token_pattern='\w+')),
        on_field('text', Tfidf(max_features=100000, token_pattern='\w+', ngram_range=(1, 2))),
        on_field(['shipping', 'item_condition_id'],
                 FunctionTransformer(to_records, validate=False), DictVectorizer()),
        n_jobs=4)
    y_scaler = StandardScaler()
    with timer('process train'):
        train = pd.read_table('../input/train.tsv')
        train = train[train['price'] > 0].reset_index(drop=True)
        cv = KFold(n_splits=20, shuffle=True, random_state=42)
        train_ids, valid_ids = next(cv.split(train))
        train, valid = train.iloc[train_ids], train.iloc[valid_ids]
        y_train = y_scaler.fit_transform(np.log1p(train['price'].values.reshape(-1, 1)))
        X_train = vectorizer.fit_transform(preprocess(train)).astype(np.float32)
        print(f'X_train: {X_train.shape} of {X_train.dtype}')
        del train
    with timer('process valid'):
        X_valid = vectorizer.transform(preprocess(valid)).astype(np.float32)
    with ThreadPool(processes=4) as pool:
        Xb_train, Xb_valid = [x.astype(np.bool).astype(np.float32) for x in [X_train, X_valid]]
        xs = [[Xb_train, Xb_valid], [X_train, X_valid]] * 2
        y_pred = np.mean(pool.map(partial(fit_predict, y_train=y_train), xs), axis=0)
    y_pred = np.expm1(y_scaler.inverse_transform(y_pred.reshape(-1, 1))[:, 0])
    print('Valid RMSLE: {:.4f}'.format(np.sqrt(mean_squared_log_error(valid['price'], y_pred))))
Example #13
Source File: test_regression.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_regression_metrics(n_samples=50):
    y_true = np.arange(n_samples)
    y_pred = y_true + 1

    assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
    assert_almost_equal(mean_squared_log_error(y_true, y_pred),
                        mean_squared_error(np.log(1 + y_true),
                                           np.log(1 + y_pred)))
    assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
    assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
    assert_almost_equal(r2_score(y_true, y_pred),  0.995, 2)
    assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
Example #14
Source File: test_regression.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_regression_metrics_at_limits():
    assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
    assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
    assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
    assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be "
                        "used when targets contain negative values.",
                        mean_squared_log_error, [-1.], [-1.])
Example #15
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
def test_regression_multioutput_array():
    y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
    y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]

    mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
    mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
    r = r2_score(y_true, y_pred, multioutput='raw_values')
    evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')

    assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
    assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
    assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
    assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)

    # mean_absolute_error and mean_squared_error are equal because
    # it is a binary problem.
    y_true = [[0, 0]]*4
    y_pred = [[1, 1]]*4
    mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
    mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
    r = r2_score(y_true, y_pred, multioutput='raw_values')
    assert_array_almost_equal(mse, [1., 1.], decimal=2)
    assert_array_almost_equal(mae, [1., 1.], decimal=2)
    assert_array_almost_equal(r, [0., 0.], decimal=2)

    r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values')
    assert_array_almost_equal(r, [0, -3.5], decimal=2)
    assert_equal(np.mean(r), r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
                 multioutput='uniform_average'))
    evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
                                   multioutput='raw_values')
    assert_array_almost_equal(evs, [0, -1.25], decimal=2)

    # Checking for the condition in which both numerator and denominator is
    # zero.
    y_true = [[1, 3], [-1, 2]]
    y_pred = [[1, 4], [-1, 1]]
    r2 = r2_score(y_true, y_pred, multioutput='raw_values')
    assert_array_almost_equal(r2, [1., -3.], decimal=2)
    assert_equal(np.mean(r2), r2_score(y_true, y_pred,
                 multioutput='uniform_average'))
    evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
    assert_array_almost_equal(evs, [1., -3.], decimal=2)
    assert_equal(np.mean(evs), explained_variance_score(y_true, y_pred))

    # Handling msle separately as it does not accept negative inputs.
    y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
    y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
    msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
    msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
                               multioutput='raw_values')
    assert_array_almost_equal(msle, msle2, decimal=2)
Example #16
Source File: test_regression.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
def test_regression_multioutput_array():
    y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
    y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]

    mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
    mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
    r = r2_score(y_true, y_pred, multioutput='raw_values')
    evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')

    assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
    assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
    assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
    assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)

    # mean_absolute_error and mean_squared_error are equal because
    # it is a binary problem.
    y_true = [[0, 0]]*4
    y_pred = [[1, 1]]*4
    mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
    mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
    r = r2_score(y_true, y_pred, multioutput='raw_values')
    assert_array_almost_equal(mse, [1., 1.], decimal=2)
    assert_array_almost_equal(mae, [1., 1.], decimal=2)
    assert_array_almost_equal(r, [0., 0.], decimal=2)

    r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values')
    assert_array_almost_equal(r, [0, -3.5], decimal=2)
    assert_equal(np.mean(r), r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
                 multioutput='uniform_average'))
    evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
                                   multioutput='raw_values')
    assert_array_almost_equal(evs, [0, -1.25], decimal=2)

    # Checking for the condition in which both numerator and denominator is
    # zero.
    y_true = [[1, 3], [-1, 2]]
    y_pred = [[1, 4], [-1, 1]]
    r2 = r2_score(y_true, y_pred, multioutput='raw_values')
    assert_array_almost_equal(r2, [1., -3.], decimal=2)
    assert_equal(np.mean(r2), r2_score(y_true, y_pred,
                 multioutput='uniform_average'))
    evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
    assert_array_almost_equal(evs, [1., -3.], decimal=2)
    assert_equal(np.mean(evs), explained_variance_score(y_true, y_pred))

    # Handling msle separately as it does not accept negative inputs.
    y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
    y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
    msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
    msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
                               multioutput='raw_values')
    assert_array_almost_equal(msle, msle2, decimal=2)