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[MRG] Regression metrics return arrays for multi-output cases #2493

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[MRG] Regression metrics return arrays for multi-output cases #2493

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a33a9d1
Regreesion metrics return arrays for multi-output cases
MechCoder Oct 6, 2013
ce91610
Added tests, hopefully resolved tests failures.
MechCoder Oct 7, 2013
5f0ed74
Changed True to micro
MechCoder Oct 7, 2013
1829ad9
Changed False to None, assert_array_equal
MechCoder Oct 7, 2013
a5614a9
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
MechCoder Oct 16, 2013
557f4a7
Added output_weights, docs, tests etc
MechCoder Oct 16, 2013
cd81cee
Minor changes
MechCoder Oct 17, 2013
1d6fc19
Change to docs
MechCoder Oct 17, 2013
76936ea
Extended multioutput to explained_variance_score
MechCoder Oct 17, 2013
887ed07
DOC: Minor change
MechCoder Oct 18, 2013
aabdc4d
Added custom_weights option
MechCoder Oct 21, 2013
057c0c4
Moved stuff to _check_reg_targets; Doc changes
MechCoder Oct 22, 2013
de43e6e
Minor changes
MechCoder Oct 23, 2013
b56ef15
Added docstring to advise the user to normalize y_true
MechCoder Oct 31, 2013
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57 changes: 53 additions & 4 deletions doc/modules/model_evaluation.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -920,6 +920,13 @@ variance is estimated as follow:

The best possible score is 1.0, lower values are worse.

The :func:`explained_variance_score` function has an `output_weights` keyword
with two possible values `None` and 'uniform'. If the value provided is `None`,
then the explained variance score is calculated for each dimension separately
and a numpy array is returned. If the value given is `uniform`, the
explained variance error is averaged over each dimension with a weight of
`1 / n_outputs`.

Here a small example of usage of the :func:`explained_variance_score`
function::

Expand All @@ -928,6 +935,14 @@ function::
>>> 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, output_weights=None)
... # doctest: +ELLIPSIS
array([ 0.967..., 1. ])
>>> explained_variance_score(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.990...

Mean absolute error
...................
Expand All @@ -945,6 +960,13 @@ and :math:`y_i` is the corresponding true value, then the mean absolute error

\text{MAE}(y, \hat{y}) = \frac{1}{n_{\text{samples}}} \sum_{i=0}^{n_{\text{samples}}-1} \left| y_i - \hat{y}_i \right|.

The :func:`mean_absolute_error` function has an `output_weights` keyword
with two possible values `None` and 'uniform'. If the value provided is
`None`, then the mean absolute error is calculated for each dimension
separately and a numpy array is returned. If the value given is `uniform`, the
mean absolute error is averaged over each dimension with a weight of
`1 / n_outputs`.

Here a small example of usage of the :func:`mean_absolute_error` function::

>>> from sklearn.metrics import mean_absolute_error
Expand All @@ -956,8 +978,11 @@ Here a small example of usage of the :func:`mean_absolute_error` function::
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75


>>> mean_absolute_error(y_true, y_pred, output_weights=None)
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849...

Mean squared error
...................
Expand All @@ -975,6 +1000,13 @@ and :math:`y_i` is the corresponding true value, then the mean squared error

\text{MSE}(y, \hat{y}) = \frac{1}{n_\text{samples}} \sum_{i=0}^{n_\text{samples} - 1} (y_i - \hat{y}_i)^2.

The :func:`mean_squared_error` function has an `output_weights` keyword
with two possible values `None` and 'uniform'. If the value provided is
`None`, then the mean squared error is calculated for each dimension
separately and a numpy array is returned. If the value given is `uniform`, the
mean squared error is averaged over each dimension with a weight of
`1 / n_outputs`.

Here a small example of usage of the :func:`mean_squared_error`
function::

Expand All @@ -987,6 +1019,12 @@ function::
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_squared_error(y_true, y_pred) # doctest: +ELLIPSIS
0.7083...
>>> mean_squared_error(y_true, y_pred, output_weights=None)
... # doctest: +ELLIPSIS
array([ 0.416..., 1. ])
>>> mean_squared_error(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.824...

.. topic:: Examples:

Expand All @@ -1012,6 +1050,12 @@ over :math:`n_{\text{samples}}` is defined as

where :math:`\bar{y} = \frac{1}{n_{\text{samples}}} \sum_{i=0}^{n_{\text{samples}} - 1} y_i`.

The :func:`r2_score` function has an `output_weights` keyword with two possible
values `None` and 'uniform'. If the value provided is `None`, then the r2 score
is calculated for each dimension separately and a numpy array is returned.
If the value given is `uniform`, the r2 score is averaged over each dimension
with a weight of `1 / n_outputs`.

Here a small example of usage of the :func:`r2_score` function::

>>> from sklearn.metrics import r2_score
Expand All @@ -1022,8 +1066,13 @@ Here a small example of usage of the :func:`r2_score` function::
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred) # doctest: +ELLIPSIS
0.938...

0.936...
>>> r2_score(y_true, y_pred, output_weights=None)
... # doctest: +ELLIPSIS
array([ 0.965..., 0.908...])
>>> r2_score(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.925...

.. topic:: Example:

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