Python metrics.median_absolute_error方法代码示例

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
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.]) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def mae_cv(self, cv):
        """
        This method performs cross-validation over median absolute error.
        
        Parameters
        ----------
        * cv : integer
          The number of cross validation folds to perform

        Returns
        -------
        Returns a scores of the k-fold median absolute error.
        """

        mae = metrics.make_scorer(metrics.median_absolute_error)
        result = cross_validate(self.reg, self.X,
                                self.y, cv=cv,
                                scoring=(mae))
        return self.get_test_score(result) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def print_evaluation_metrics(trained_model, trained_model_name, X_test, y_test):
    print('--------- For Model: ', trained_model_name, ' ---------\n')
    predicted_values = trained_model.predict(X_test)
    print("Mean absolute error: ",
          metrics.mean_absolute_error(y_test, predicted_values))
    print("Median absolute error: ",
          metrics.median_absolute_error(y_test, predicted_values))
    print("Mean squared error: ", metrics.mean_squared_error(
        y_test, predicted_values))
    print("R2: ", metrics.r2_score(y_test, predicted_values))
    plt.scatter(y_test, predicted_values, color='black')
    # plt.plot(x, y_pred, color='blue', linewidth=3)
    plt.title(trained_model_name)
    plt.xlabel('$y_{test}$')
    plt.ylabel('$y_{predicted}/y_{test}$')
    plt.savefig('%s.png' %trained_model_name, bbox_inches='tight')
    print("---------------------------------------\n") 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def print_evaluation_metrics2(trained_model, trained_model_name, X_test, y_test):
    print('--------- For Model: ', trained_model_name, ' --------- (Train Data)\n')
    predicted_values = trained_model.predict(X_test)
    print("Mean absolute error: ",
          metrics.mean_absolute_error(y_test, predicted_values))
    print("Median absolute error: ",
          metrics.median_absolute_error(y_test, predicted_values))
    print("Mean squared error: ", metrics.mean_squared_error(
        y_test, predicted_values))
    print("R2: ", metrics.r2_score(y_test, predicted_values))
    plt.scatter(y_test, predicted_values/y_test, color='black')
    # plt.plot(x, y_pred, color='blue', linewidth=3)
    plt_name = trained_model_name + " (Train Data)"
    plt.title(plt_name)
    plt.xlabel('$y_{test}$')
    plt.ylabel('$y_{predicted}/y_{test}$')
    plt.savefig('%s.png' %plt_name, bbox_inches='tight')
    print("---------------------------------------\n") 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def eval_metrics_on(predictions, labels):
    '''
    assuming this is a regression task; labels are continuous-valued floats
    
    returns most regression-related scores for the given predictions/targets as a dictionary:
    
        r2, mean_abs_error, mse, rmse, median_absolute_error, explained_variance_score
    '''
    if len(labels[0])==2: #labels is list of data/labels pairs
        labels = np.concatenate([l[1] for l in labels])
    predictions = predictions[:,0]
    
    r2                       = metrics.r2_score(labels, predictions)
    mean_abs_error           = np.abs(predictions - labels).mean()
    mse                      = ((predictions - labels)**2).mean()
    rmse                     = np.sqrt(mse)
    median_absolute_error    = metrics.median_absolute_error(labels, predictions) # robust to outliers
    explained_variance_score = metrics.explained_variance_score(labels, predictions) # best score = 1, lower is worse
    return {'r2':r2, 'mean_abs_error':mean_abs_error, 'mse':mse, 'rmse':rmse, 
            'median_absolute_error':median_absolute_error, 
            'explained_variance_score':explained_variance_score} 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def test_corrupted_regression(loss, weighting):
    reg = RobustWeightedEstimator(
        SGDRegressor(),
        loss=loss,
        max_iter=50,
        weighting=weighting,
        k=4,
        c=None,
        random_state=rng,
    )
    reg.fit(X_rc, y_rc)
    score = median_absolute_error(reg.predict(X_rc), y_rc)
    assert score < 0.2 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
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.) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def score(self,
              actual: np.array,
              predicted: np.array,
              sample_weight: typing.Optional[np.array] = None,
              labels: typing.Optional[np.array] = None,
              **kwargs) -> float:
        return median_absolute_error(actual, predicted) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def mae_upper_boundary(self, upper_boundary):
        y_pred = self.reg.predict(self.X)
        if metrics.median_absolute_error(self.y, y_pred) > upper_boundary:
            return False
        return True 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def cross_val_mae_result(self, reg, cv=3):
        y_pred = cross_val_predict(reg, self.X, self.y)
        return metrics.median_absolute_error(self.y, y_pred) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def mae_result(self, reg):
        y_pred = reg.predict(self.X)
        return metrics.median_absolute_error(self.y, y_pred) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def mae_upper_boundary(upper_boundary):
        y_pred = self.reg.predict(self.X)
        if metrics.median_absolute_error(self.y, y_pred) > upper_boundary:
            return False
        return True 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def compute(labels, pred_scores):
        return median_absolute_error(labels, pred_scores) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def gini_meae(truth, predictions):
    score = median_absolute_error(truth, predictions)
    return score 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import median_absolute_error [as 别名]
def print_evaluation_metrics(trained_model, trained_model_name, X_test, y_test):
    print('--------- For Model: ', trained_model_name, ' ---------\n')
    predicted_values = trained_model.predict(X_test)
    print("Mean absolute error: ",
          metrics.mean_absolute_error(y_test, predicted_values))
    print("Median absolute error: ",
          metrics.median_absolute_error(y_test, predicted_values))
    print("Mean squared error: ", metrics.mean_squared_error(
        y_test, predicted_values))
    print("R2: ", metrics.r2_score(y_test, predicted_values))