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File Content: `regression.cpython-35.pyc`



��(X�D������������������@���s����d��Z��d�d�l�m�Z�d�d�l�Z�d�d�l�m�Z�m�Z�d�d�l�m�Z�d�d�l	�m
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����Z�d�d�d�d���Z�d�d�d�d���Z�d�S)z�Metrics to assess performance on regression task

Functions named as ``*_score`` return a scalar value to maximize: the higher
the better

Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
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�����)�divisionN����)�check_array�check_consistent_length)�column_or_1d)�string_types�mean_absolute_error�mean_squared_error�median_absolute_error�r2_score�explained_variance_scorec�������������C���sO��t��|��|���t�|��d�d��}��t�|�d�d��}�|��j�d�k�rO�|��j�d���}��|�j�d�k�rm�|�j�d���}�|��j�d�|�j�d�k�r��t�d�j�|��j�d�|�j�d�������|��j�d�}�d�}�|�|�k�r't�|�d�d��}�|�d�k�r��t�d	�����n.�|�t�|���k�r't�d
�t�|���|�f�����|�d�k�r9d�n�d�}�|�|��|�|�f�S)a���Check that y_true and y_pred belong to the same regression task

    Parameters
    ----------
    y_true : array-like,

    y_pred : array-like,

    multioutput : array-like or string in ['raw_values', uniform_average',
        'variance_weighted'] or None
        None is accepted due to backward compatibility of r2_score().

    Returns
    -------
    type_true : one of {'continuous', continuous-multioutput'}
        The type of the true target data, as output by
        'utils.multiclass.type_of_target'

    y_true : array-like of shape = (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples, n_outputs)
        Estimated target values.

    multioutput : array-like of shape = (n_outputs) or string in ['raw_values',
        uniform_average', 'variance_weighted'] or None
        Custom output weights if ``multioutput`` is array-like or
        just the corresponding argument if ``multioutput`` is a
        correct keyword.

    Z	ensure_2dF����z<y_true and y_pred have different number of output ({0}!={1})N�
raw_values�uniform_average�variance_weightedz5Custom weights are useful only in multi-output cases.z?There must be equally many custom weights (%d) as outputs (%d).Z
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�r���r���c�������������C���s����t��|��|�|���\�}�}��}�}�t�j�t�j�|�|����d�|�d�d��}�t�|�t���rw�|�d�k�re�|�S|�d�k�rw�d�}�t�j�|�d�|��S)a��Mean absolute error regression loss

    Read more in the :ref:`User Guide <mean_absolute_error>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Estimated target values.

    sample_weight : array-like of shape = (n_samples), optional
        Sample weights.

    multioutput : string in ['raw_values', 'uniform_average']
        or array-like of shape (n_outputs)
        Defines aggregating of multiple output values.
        Array-like value defines weights used to average errors.

        'raw_values' :
            Returns a full set of errors in case of multioutput input.

        'uniform_average' :
            Errors of all outputs are averaged with uniform weight.


    Returns
    -------
    loss : float or ndarray of floats
        If multioutput is 'raw_values', then mean absolute error is returned
        for each output separately.
        If multioutput is 'uniform_average' or an ndarray of weights, then the
        weighted average of all output errors is returned.

        MAE output is non-negative floating point. The best value is 0.0.

    Examples
    --------
    >>> from sklearn.metrics import mean_absolute_error
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> mean_absolute_error(y_true, y_pred)
    0.5
    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
    >>> mean_absolute_error(y_true, y_pred)
    0.75
    >>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
    array([ 0.5,  1. ])
    >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
    ... # doctest: +ELLIPSIS
    0.849...
    �weights�axisr���r���r���N)r����np�average�abs�
isinstancer���)r���r����
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output_errorsr���r���r���r���i���s����9c�������������C���s����t��|��|�|���\�}�}��}�}�t�j�|��|�d�d�d�d�|��}�t�|�t���rr�|�d�k�r`�|�S|�d�k�rr�d�}�t�j�|�d�|��S)a���Mean squared error regression loss

    Read more in the :ref:`User Guide <mean_squared_error>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Estimated target values.

    sample_weight : array-like of shape = (n_samples), optional
        Sample weights.

    multioutput : string in ['raw_values', 'uniform_average']
        or array-like of shape (n_outputs)
        Defines aggregating of multiple output values.
        Array-like value defines weights used to average errors.

        'raw_values' :
            Returns a full set of errors in case of multioutput input.

        'uniform_average' :
            Errors of all outputs are averaged with uniform weight.

    Returns
    -------
    loss : float or ndarray of floats
        A non-negative floating point value (the best value is 0.0), or an
        array of floating point values, one for each individual target.

    Examples
    --------
    >>> from sklearn.metrics import mean_squared_error
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> mean_squared_error(y_true, y_pred)
    0.375
    >>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
    >>> y_pred = [[0, 2],[-1, 2],[8, -5]]
    >>> mean_squared_error(y_true, y_pred)  # doctest: +ELLIPSIS
    0.708...
    >>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
    ... # doctest: +ELLIPSIS
    array([ 0.416...,  1.        ])
    >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
    ... # doctest: +ELLIPSIS
    0.824...

    r���r���r���r���r���r���N)r���r ���r!���r#���r���)r���r���r$���r���r���r%���r���r���r���r	�������s����6	c�������������C���sP���t��|��|�d���\�}�}��}�}�|�d�k�r6�t�d�����t�j�t�j�|�|������S)ar��Median absolute error regression loss

    Read more in the :ref:`User Guide <median_absolute_error>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples)
        Estimated target values.

    Returns
    -------
    loss : float
        A positive floating point value (the best value is 0.0).

    Examples
    --------
    >>> from sklearn.metrics import median_absolute_error
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> median_absolute_error(y_true, y_pred)
    0.5

    r���zcontinuous-multioutputz2Multioutput not supported in median_absolute_error)r���r���r ���Zmedianr"���)r���r���r����_r���r���r���r
�������s
����	c�������������C���se��t��|��|�|���\�}�}��}�}�t�j�|��|�d�|�d�d��}�t�j�|��|�|�d�d�|�d�d��}�t�j�|��d�|�d�d��}�t�j�|��|�d�d�|�d�d��}�|�d�k�}	�|�d�k�}
�|	�|
�@}�t�j�|��j�d���}�d�|�|�|�|�|�|�<d�|�|	�|
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��S)a���Explained variance regression score function

    Best possible score is 1.0, lower values are worse.

    Read more in the :ref:`User Guide <explained_variance_score>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Estimated target values.

    sample_weight : array-like of shape = (n_samples), optional
        Sample weights.

    multioutput : string in ['raw_values', 'uniform_average',                 'variance_weighted'] or array-like of shape (n_outputs)
        Defines aggregating of multiple output scores.
        Array-like value defines weights used to average scores.

        'raw_values' :
            Returns a full set of scores in case of multioutput input.

        'uniform_average' :
            Scores of all outputs are averaged with uniform weight.

        'variance_weighted' :
            Scores of all outputs are averaged, weighted by the variances
            of each individual output.

    Returns
    -------
    score : float or ndarray of floats
        The explained variance or ndarray if 'multioutput' is 'raw_values'.

    Notes
    -----
    This is not a symmetric function.

    Examples
    --------
    >>> from sklearn.metrics import explained_variance_score
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> explained_variance_score(y_true, y_pred)  # doctest: +ELLIPSIS
    0.957...
    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
    >>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
    ... # doctest: +ELLIPSIS
    0.983...

    r���r���r���r���r
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������C���s���t��|��|�|���\�}�}��}�}�|�d�k	�rR�t�|���}�|�d�d���t�j�f�}�n�d�}�|�|��|�d�j�d�d�d�t�j���}�|�|��t�j�|��d�d�d�|��d�j�d�d�d�t�j���}�|�d�k�}�|�d�k�}	�|�|	�@}
�t�j�|��j�d�g���}�d�|�|
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�|�|
�<d	�|�|	�|�@<|�d�k�rT|��j�d�d�k�rTt	�j
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��R^2 (coefficient of determination) regression score function.

    Best possible score is 1.0 and it can be negative (because the
    model can be arbitrarily worse). A constant model that always
    predicts the expected value of y, disregarding the input features,
    would get a R^2 score of 0.0.

    Read more in the :ref:`User Guide <r2_score>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Estimated target values.

    sample_weight : array-like of shape = (n_samples), optional
        Sample weights.

    multioutput : string in ['raw_values', 'uniform_average', 'variance_weighted'] or None or array-like of shape (n_outputs)

        Defines aggregating of multiple output scores.
        Array-like value defines weights used to average scores.
        Default value corresponds to 'variance_weighted', this behaviour is
        deprecated since version 0.17 and will be changed to 'uniform_average'
        starting from 0.19.

        'raw_values' :
            Returns a full set of scores in case of multioutput input.

        'uniform_average' :
            Scores of all outputs are averaged with uniform weight.

        'variance_weighted' :
            Scores of all outputs are averaged, weighted by the variances
            of each individual output.

    Returns
    -------
    z : float or ndarray of floats
        The R^2 score or ndarray of scores if 'multioutput' is
        'raw_values'.

    Notes
    -----
    This is not a symmetric function.

    Unlike most other scores, R^2 score may be negative (it need not actually
    be the square of a quantity R).

    References
    ----------
    .. [1] `Wikipedia entry on the Coefficient of determination
            <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_

    Examples
    --------
    >>> from sklearn.metrics import r2_score
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> r2_score(y_true, y_pred)  # doctest: +ELLIPSIS
    0.948...
    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
    >>> r2_score(y_true, y_pred, multioutput='variance_weighted')  # doctest: +ELLIPSIS
    0.938...
    >>> y_true = [1,2,3]
    >>> y_pred = [1,2,3]
    >>> r2_score(y_true, y_pred)
    1.0
    >>> y_true = [1,2,3]
    >>> y_pred = [2,2,2]
    >>> r2_score(y_true, y_pred)
    0.0
    >>> y_true = [1,2,3]
    >>> y_pred = [3,2,1]
    >>> r2_score(y_true, y_pred)
    -3.0
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File Content: regression.cpython-35.pyc

File Content: `regression.cpython-35.pyc`
