Python sklearn.metrics.mean_squared_error() Examples

The following are 30 code examples for showing how to use sklearn.metrics.mean_squared_error(). These examples are extracted from open source projects. 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.

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Example 1
def Train(data, modelcount, censhu, yanzhgdata):
    model = xgb.XGBRegressor(max_depth=censhu, learning_rate=0.1, n_estimators=modelcount, silent=True, objective='reg:gamma')

    model.fit(data[:, :-1], data[:, -1])
    # 给出训练数据的预测值
    train_out = model.predict(data[:, :-1])
    # 计算MSE
    train_mse = mse(data[:, -1], train_out)

    # 给出验证数据的预测值
    add_yan = model.predict(yanzhgdata[:, :-1])
    # 计算MSE
    add_mse = mse(yanzhgdata[:, -1], add_yan)
    print(train_mse, add_mse)
    return train_mse, add_mse

# 最终确定组合的函数 
Example 2
Project: malss   Author: canard0328   File: test.py    License: MIT License 6 votes vote down vote up
def test_regression_small():
    X, y = make_regression(n_samples=2000,
                           n_features=10,
                           n_informative=5,
                           noise=30.0,
                           random_state=0)
    X = pd.DataFrame(X)
    y = pd.Series(y)
    cls = MALSS('regression').fit(X, y, 'test_regression_small')
    cls.generate_module_sample()

    from sklearn.metrics import mean_squared_error
    pred = cls.predict(X)
    print(mean_squared_error(y, pred))

    assert len(cls.algorithms) == 4
    assert cls.algorithms[0].best_score is not None 
Example 3
Project: malss   Author: canard0328   File: test.py    License: MIT License 6 votes vote down vote up
def test_regression_medium():
    X, y = make_regression(n_samples=20000,
                           n_features=10,
                           n_informative=5,
                           noise=30.0,
                           random_state=0)
    X = pd.DataFrame(X)
    y = pd.Series(y)
    cls = MALSS('regression').fit(X, y, 'test_regression_medium')
    cls.generate_module_sample()

    from sklearn.metrics import mean_squared_error
    pred = cls.predict(X)
    print(mean_squared_error(y, pred))

    assert len(cls.algorithms) == 2
    assert cls.algorithms[0].best_score is not None 
Example 4
Project: malss   Author: canard0328   File: test.py    License: MIT License 6 votes vote down vote up
def test_regression_big():
    X, y = make_regression(n_samples=200000,
                           n_features=10,
                           n_informative=5,
                           noise=30.0,
                           random_state=0)
    X = pd.DataFrame(X)
    y = pd.Series(y)
    cls = MALSS('regression').fit(X, y, 'test_regression_big')
    cls.generate_module_sample()

    from sklearn.metrics import mean_squared_error
    pred = cls.predict(X)
    print(mean_squared_error(y, pred))

    assert len(cls.algorithms) == 1
    assert cls.algorithms[0].best_score is not None 
Example 5
Project: healthcareai-py   Author: HealthCatalyst   File: model_eval.py    License: MIT License 6 votes vote down vote up
def calculate_regression_metrics(trained_sklearn_estimator, x_test, y_test):
    """
    Given a trained estimator, calculate metrics.

    Args:
        trained_sklearn_estimator (sklearn.base.BaseEstimator): a scikit-learn estimator that has been `.fit()`
        y_test (numpy.ndarray): A 1d numpy array of the y_test set (predictions)
        x_test (numpy.ndarray): A 2d numpy array of the x_test set (features)

    Returns:
        dict: A dictionary of metrics objects
    """
    # Get predictions
    predictions = trained_sklearn_estimator.predict(x_test)

    # Calculate individual metrics
    mean_squared_error = skmetrics.mean_squared_error(y_test, predictions)
    mean_absolute_error = skmetrics.mean_absolute_error(y_test, predictions)

    result = {'mean_squared_error': mean_squared_error, 'mean_absolute_error': mean_absolute_error}

    return result 
Example 6
Project: snape   Author: mbernico   File: score_dataset.py    License: Apache License 2.0 6 votes vote down vote up
def score_regression(y, y_hat, report=True):
    """
    Create regression score
    :param y:
    :param y_hat:
    :return:
    """
    r2 = r2_score(y, y_hat)
    rmse = sqrt(mean_squared_error(y, y_hat))
    mae = mean_absolute_error(y, y_hat)

    report_string = "---Regression Score--- \n"
    report_string += "R2 = " + str(r2) + "\n"
    report_string += "RMSE = " + str(rmse) + "\n"
    report_string += "MAE = " + str(mae) + "\n"

    if report:
        print(report_string)

    return mae, report_string 
Example 7
Project: emmental   Author: SenWu   File: mean_squared_error.py    License: MIT License 6 votes vote down vote up
def mean_squared_error_scorer(
    golds: ndarray,
    probs: ndarray,
    preds: Optional[ndarray],
    uids: Optional[List[str]] = None,
) -> Dict[str, float]:
    """Mean squared error regression loss.

    Args:
      golds: Ground truth values.
      probs: Predicted probabilities.
      preds: Predicted values.
      uids: Unique ids, defaults to None.

    Returns:
      Mean squared error regression loss.
    """
    return {"mean_squared_error": float(mean_squared_error(golds, probs))} 
Example 8
def Train(data, treecount, tezh, yanzhgdata):
    model = RF(n_estimators=treecount, max_features=tezh)
    model.fit(data[:, :-1], data[:, -1])
    # 给出训练数据的预测值
    train_out = model.predict(data[:, :-1])
    # 计算MSE
    train_mse = mse(data[:, -1], train_out)

    # 给出验证数据的预测值
    add_yan = model.predict(yanzhgdata[:, :-1])
    # 计算MSE
    add_mse = mse(yanzhgdata[:, -1], add_yan)
    print(train_mse, add_mse)
    return train_mse, add_mse

# 最终确定组合的函数 
Example 9
def Train(data, modelcount, censhu, yanzhgdata):
    model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=censhu),
                              n_estimators=modelcount, learning_rate=0.8)

    model.fit(data[:, :-1], data[:, -1])
    # 给出训练数据的预测值
    train_out = model.predict(data[:, :-1])
    # 计算MSE
    train_mse = mse(data[:, -1], train_out)

    # 给出验证数据的预测值
    add_yan = model.predict(yanzhgdata[:, :-1])
    # 计算MSE
    add_mse = mse(yanzhgdata[:, -1], add_yan)
    print(train_mse, add_mse)
    return train_mse, add_mse

# 最终确定组合的函数 
Example 10
def Train(data, modelcount, censhu, yanzhgdata):
    model = lgbm.LGBMRegressor(boosting_type='gbdt', objective='regression', num_leaves=1200,
                                learning_rate=0.17, n_estimators=modelcount, max_depth=censhu,
                                metric='rmse', bagging_fraction=0.8, feature_fraction=0.8, reg_lambda=0.9)

    model.fit(data[:, :-1], data[:, -1])
    # 给出训练数据的预测值
    train_out = model.predict(data[:, :-1])
    # 计算MSE
    train_mse = mse(data[:, -1], train_out)

    # 给出验证数据的预测值
    add_yan = model.predict(yanzhgdata[:, :-1])
    # 计算MSE
    add_mse = mse(yanzhgdata[:, -1], add_yan)
    print(train_mse, add_mse)
    return train_mse, add_mse

# 最终确定组合的函数 
Example 11
Project: gordo   Author: equinor   File: test_utils.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def test_metrics_wrapper():
    # make the features in y be in different scales
    y = np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]) * [1, 100]

    # With no scaler provided it is relevant which of the two series gets an 80% error
    metric_func_noscaler = model_utils.metric_wrapper(mean_squared_error)

    mse_feature_one_wrong = metric_func_noscaler(y, y * [0.8, 1])
    mse_feature_two_wrong = metric_func_noscaler(y, y * [1, 0.8])

    assert not np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)

    # With a scaler provided it is not relevant which of the two series gets an 80%
    # error
    scaler = MinMaxScaler().fit(y)
    metric_func_scaler = model_utils.metric_wrapper(mean_squared_error, scaler=scaler)

    mse_feature_one_wrong = metric_func_scaler(y, y * [0.8, 1])
    mse_feature_two_wrong = metric_func_scaler(y, y * [1, 0.8])

    assert np.isclose(mse_feature_one_wrong, mse_feature_two_wrong) 
Example 12
Project: gordo   Author: equinor   File: test_builder.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def test_get_metrics_dict_scaler(scaler, mock):
    mock_model = mock
    metrics_list = [sklearn.metrics.mean_squared_error]
    # make the features in y be in different scales
    y = pd.DataFrame(
        np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]) * [1, 100],
        columns=["Tag 1", "Tag 2"],
    )
    metrics_dict = ModelBuilder.build_metrics_dict(metrics_list, y, scaler=scaler)
    metric_func = metrics_dict["mean-squared-error"]

    mock_model.predict = lambda _y: _y * [0.8, 1]
    mse_feature_one_wrong = metric_func(mock_model, y, y)
    mock_model.predict = lambda _y: _y * [1, 0.8]
    mse_feature_two_wrong = metric_func(mock_model, y, y)

    if scaler:
        assert np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)
    else:
        assert not np.isclose(mse_feature_one_wrong, mse_feature_two_wrong) 
Example 13
Project: gordo   Author: equinor   File: test_builder.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
def test_metrics_from_list():
    """
    Check getting functions from a list of metric names
    """
    default = ModelBuilder.metrics_from_list()
    assert default == [
        metrics.explained_variance_score,
        metrics.r2_score,
        metrics.mean_squared_error,
        metrics.mean_absolute_error,
    ]

    specifics = ModelBuilder.metrics_from_list(
        ["sklearn.metrics.adjusted_mutual_info_score", "sklearn.metrics.r2_score"]
    )
    assert specifics == [metrics.adjusted_mutual_info_score, metrics.r2_score] 
Example 14
Project: nyaggle   Author: nyanp   File: test_averaging.py    License: MIT License 6 votes vote down vote up
def test_averaging_opt_minimize():
    X, y = make_regression_df(n_samples=1024)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    oof, test = _make_1st_stage_preds(X_train, y_train, X_test)

    best_single_model = min(mean_squared_error(y_train, oof[0]),
                            mean_squared_error(y_train, oof[1]),
                            mean_squared_error(y_train, oof[2]))

    result = averaging_opt(test, oof, y_train, mean_squared_error, higher_is_better=False)

    assert result.score <= best_single_model

    result_simple_avg = averaging(test, oof, y_train, eval_func=mean_squared_error)

    assert result.score <= result_simple_avg.score 
Example 15
Project: nyaggle   Author: nyanp   File: test_run.py    License: MIT License 6 votes vote down vote up
def test_experiment_lgb_regressor(tmpdir_name):
    X, y = make_regression_df(n_samples=1024, n_num_features=10, n_cat_features=2,
                              random_state=0, id_column='user_id')

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

    params = {
        'objective': 'regression',
        'max_depth': 8
    }

    result = run_experiment(params, X_train, y_train, X_test, tmpdir_name)

    assert len(np.unique(result.oof_prediction)) > 5  # making sure prediction is not binarized
    assert len(np.unique(result.test_prediction)) > 5
    assert mean_squared_error(y_train, result.oof_prediction) == result.metrics[-1]

    _check_file_exists(tmpdir_name) 
Example 16
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_tree.py    License: MIT License 6 votes vote down vote up
def test_boston():
    # Check consistency on dataset boston house prices.

    for (name, Tree), criterion in product(REG_TREES.items(), REG_CRITERIONS):
        reg = Tree(criterion=criterion, random_state=0)
        reg.fit(boston.data, boston.target)
        score = mean_squared_error(boston.target, reg.predict(boston.data))
        assert_less(score, 1,
                    "Failed with {0}, criterion = {1} and score = {2}"
                    "".format(name, criterion, score))

        # using fewer features reduces the learning ability of this tree,
        # but reduces training time.
        reg = Tree(criterion=criterion, max_features=6, random_state=0)
        reg.fit(boston.data, boston.target)
        score = mean_squared_error(boston.target, reg.predict(boston.data))
        assert_less(score, 2,
                    "Failed with {0}, criterion = {1} and score = {2}"
                    "".format(name, criterion, score)) 
Example 17
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_regression.py    License: 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 18
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_regression.py    License: 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 19
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_multioutput.py    License: MIT License 6 votes vote down vote up
def test_base_chain_crossval_fit_and_predict():
    # Fit chain with cross_val_predict and verify predict
    # performance
    X, Y = generate_multilabel_dataset_with_correlations()

    for chain in [ClassifierChain(LogisticRegression()),
                  RegressorChain(Ridge())]:
        chain.fit(X, Y)
        chain_cv = clone(chain).set_params(cv=3)
        chain_cv.fit(X, Y)
        Y_pred_cv = chain_cv.predict(X)
        Y_pred = chain.predict(X)

        assert Y_pred_cv.shape == Y_pred.shape
        assert not np.all(Y_pred == Y_pred_cv)
        if isinstance(chain, ClassifierChain):
            assert jaccard_score(Y, Y_pred_cv, average='samples') > .4
        else:
            assert mean_squared_error(Y, Y_pred_cv) < .25 
Example 20
Project: dzetsaka   Author: nkarasiak   File: domainAdaptation.py    License: GNU General Public License v3.0 6 votes vote down vote up
def __init__(self, transportAlgorithm="MappingTransport",
                 scaler=False, params=None, feedback=True):
        try:
            from sklearn.metrics import mean_squared_error
            from itertools import product
            from sklearn.metrics import (
                f1_score, cohen_kappa_score, accuracy_score)
        except BaseException:
            raise ImportError('Please install itertools and scikit-learn')

        self.transportAlgorithm = transportAlgorithm
        self.feedback = feedback

        self.params_ = params

        if scaler:
            from sklearn.preprocessing import MinMaxScaler
            self.scaler = MinMaxScaler(feature_range=(-1, 1))
            self.scalerTarget = MinMaxScaler(feature_range=(-1, 1))
        else:
            self.scaler = scaler 
Example 21
Project: drifter_ml   Author: EricSchles   File: regression_tests.py    License: MIT License 6 votes vote down vote up
def mse_cv(self, cv):
        """
        This method performs cross-validation over mean squared error.
        
        Parameters
        ----------
        * cv : integer
          The number of cross validation folds to perform

        Returns
        -------
        Returns a scores of the k-fold mean squared error.
        """
        mse = metrics.make_scorer(metrics.mean_squared_error)
        result = cross_validate(self.reg, self.X,
                                self.y, cv=cv,
                                scoring=(mse))
        return self.get_test_score(result) 
Example 22
Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: weather_forecasting2018_eval.py    License: Apache License 2.0 5 votes vote down vote up
def rmse(a, b):
    return sqrt(mean_squared_error(a, b)) 
Example 23
Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: eval_details_rmse.py    License: Apache License 2.0 5 votes vote down vote up
def rmse(a, b):
    return sqrt(mean_squared_error(a, b)) 
Example 24
def rmse(a, b):
    return sqrt(mean_squared_error(a, b)) 
Example 25
Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: eval_details_score.py    License: Apache License 2.0 5 votes vote down vote up
def rmse(a, b):
    return sqrt(mean_squared_error(a, b)) 
Example 26
Project: Deep_Learning_Weather_Forecasting   Author: BruceBinBoxing   File: helper.py    License: Apache License 2.0 5 votes vote down vote up
def rmse(y_pred, y_true):
    y_pred = y_pred.reshape(-1)
    y_true = y_true.reshape(-1)
    #print(y_true.shape)
    return np.sqrt(mean_squared_error(y_pred, y_true)) 
Example 27
Project: Kaggler   Author: jeongyoonlee   File: regression.py    License: MIT License 5 votes vote down vote up
def rmse(y, p):
    """Root Mean Squared Error (RMSE).

    Args:
        y (numpy.array): target
        p (numpy.array): prediction

    Returns:
        e (numpy.float64): RMSE
    """

    # check and get number of samples
    assert y.shape == p.shape

    return np.sqrt(mse(y, p)) 
Example 28
Project: astroalign   Author: toros-astro   File: time_regression.py    License: MIT License 5 votes vote down vote up
def plot(results, ax):
    df = results[["size", "time"]]

    df.plot.scatter(x='size', y='time', c='LightBlue', ax=ax, marker=".")

    # linear regression
    x = df["size"].values.reshape((-1, 1))
    y = df["time"].values
    linear = LinearRegression().fit(x, y)
    y_pred = linear.predict(x)

    mqe = mean_squared_error(y, y_pred)
    r2 = r2_score(y, y_pred)

    ax.plot(x, y_pred, color='DarkBlue', linewidth=2)

    ax.set_title(
        "Linear regression between size and time "
        f"\n$mse={mqe:.3f}$ - $R^2={r2:.3f}$")
    ax.set_xlabel("Size")
    ax.set_ylabel("Seconds")

    return ax


# =============================================================================
# CLI MAIN
# ============================================================================= 
Example 29
Project: tensorflow-XNN   Author: ChenglongChen   File: metrics.py    License: MIT License 5 votes vote down vote up
def rmse(y_true, y_pred):
    assert y_true.shape == y_pred.shape
    return np.sqrt(mean_squared_error(y_true, y_pred)) 
Example 30
Project: sagemaker-xgboost-container   Author: aws   File: custom_metrics.py    License: Apache License 2.0 5 votes vote down vote up
def mse(preds, dtrain):
    """Compute mean squared error.

    For more information see: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
    :param preds: Prediction values
    :param dtrain: Training data with labels
    :return: Metric name, mean squared error
    """
    labels = dtrain.get_label()
    return 'mse', mean_squared_error(labels, preds)