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4Freye merged 1 commit into from
Dec 31, 2025
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removed artifacts from copying from sklearn #100

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133 changes: 0 additions & 133 deletions panelsplit/model_selection/model_selection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -988,9 +988,6 @@ class GridSearch(BaseSearch):
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors.

.. versionchanged:: v0.20
`n_jobs` default changed from 1 to None

refit : bool, str, or callable, default=True
Refit an estimator using the best found parameters on the whole
dataset.
Expand Down Expand Up @@ -1054,72 +1051,12 @@ class GridSearch(BaseSearch):
expensive and is not strictly required to select the parameters that
yield the best generalization performance.

.. versionadded:: 0.19

.. versionchanged:: 0.21
Default value was changed from ``True`` to ``False``

Attributes
----------
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.

For instance the below given table

+------------+-----------+------------+-----------------+---+---------+
|param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|
+============+===========+============+=================+===+=========+
| 'poly' | -- | 2 | 0.80 |...| 2 |
+------------+-----------+------------+-----------------+---+---------+
| 'poly' | -- | 3 | 0.70 |...| 4 |
+------------+-----------+------------+-----------------+---+---------+
| 'rbf' | 0.1 | -- | 0.80 |...| 3 |
+------------+-----------+------------+-----------------+---+---------+
| 'rbf' | 0.2 | -- | 0.93 |...| 1 |
+------------+-----------+------------+-----------------+---+---------+

will be represented by a ``cv_results_`` dict of::

{
'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
mask = [False False False False]...)
'param_gamma': masked_array(data = [-- -- 0.1 0.2],
mask = [ True True False False]...),
'param_degree': masked_array(data = [2.0 3.0 -- --],
mask = [False False True True]...),
'split0_test_score' : [0.80, 0.70, 0.80, 0.93],
'split1_test_score' : [0.82, 0.50, 0.70, 0.78],
'mean_test_score' : [0.81, 0.60, 0.75, 0.85],
'std_test_score' : [0.01, 0.10, 0.05, 0.08],
'rank_test_score' : [2, 4, 3, 1],
'split0_train_score' : [0.80, 0.92, 0.70, 0.93],
'split1_train_score' : [0.82, 0.55, 0.70, 0.87],
'mean_train_score' : [0.81, 0.74, 0.70, 0.90],
'std_train_score' : [0.01, 0.19, 0.00, 0.03],
'mean_fit_time' : [0.73, 0.63, 0.43, 0.49],
'std_fit_time' : [0.01, 0.02, 0.01, 0.01],
'mean_score_time' : [0.01, 0.06, 0.04, 0.04],
'std_score_time' : [0.00, 0.00, 0.00, 0.01],
'params' : [{'kernel': 'poly', 'degree': 2}, ...],
}

For an example of visualization and interpretation of GridSearch results,
see :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`.

NOTE

The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.

The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.

For multi-metric evaluation, the scores for all the scorers are
available in the ``cv_results_`` dict at the keys ending with that
scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
above. ('split0_test_precision', 'mean_train_precision' etc.)

best_estimator_ : estimator
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
Expand Down Expand Up @@ -1167,8 +1104,6 @@ class GridSearch(BaseSearch):

This is present only if ``refit`` is not False.

.. versionadded:: 0.20

multimetric_ : bool
Whether or not the scorers compute several metrics.

Expand All @@ -1182,16 +1117,12 @@ class GridSearch(BaseSearch):
parameter for more details) and that `best_estimator_` exposes
`n_features_in_` when fit.

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if
`best_estimator_` is defined (see the documentation for the `refit`
parameter for more details) and that `best_estimator_` exposes
`feature_names_in_` when fit.

.. versionadded:: 1.0

See Also
--------
ParameterGrid : Generates all the combinations of a hyperparameter grid.
Expand Down Expand Up @@ -1341,9 +1272,6 @@ class RandomizedSearch(BaseSearch):
``-1`` means using all processors.
for more details.

.. versionchanged:: v0.20
`n_jobs` default changed from 1 to None

refit : bool, str, or callable, default=True
Refit an estimator using the best found parameters on the whole
dataset.
Expand Down Expand Up @@ -1413,67 +1341,12 @@ class RandomizedSearch(BaseSearch):
expensive and is not strictly required to select the parameters that
yield the best generalization performance.

.. versionadded:: 0.19

.. versionchanged:: 0.21
Default value was changed from ``True`` to ``False``

Attributes
----------
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.

For instance the below given table

+--------------+-------------+-------------------+---+---------------+
| param_kernel | param_gamma | split0_test_score |...|rank_test_score|
+==============+=============+===================+===+===============+
| 'rbf' | 0.1 | 0.80 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.2 | 0.84 |...| 3 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.3 | 0.70 |...| 2 |
+--------------+-------------+-------------------+---+---------------+

will be represented by a ``cv_results_`` dict of::

{
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
mask = False),
'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score' : [0.80, 0.84, 0.70],
'split1_test_score' : [0.82, 0.50, 0.70],
'mean_test_score' : [0.81, 0.67, 0.70],
'std_test_score' : [0.01, 0.24, 0.00],
'rank_test_score' : [1, 3, 2],
'split0_train_score' : [0.80, 0.92, 0.70],
'split1_train_score' : [0.82, 0.55, 0.70],
'mean_train_score' : [0.81, 0.74, 0.70],
'std_train_score' : [0.01, 0.19, 0.00],
'mean_fit_time' : [0.73, 0.63, 0.43],
'std_fit_time' : [0.01, 0.02, 0.01],
'mean_score_time' : [0.01, 0.06, 0.04],
'std_score_time' : [0.00, 0.00, 0.00],
'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}

For an example of analysing ``cv_results_``,
see :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`.

NOTE

The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.

The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.

For multi-metric evaluation, the scores for all the scorers are
available in the ``cv_results_`` dict at the keys ending with that
scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
above. ('split0_test_precision', 'mean_train_precision' etc.)

best_estimator_ : estimator
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
Expand Down Expand Up @@ -1524,8 +1397,6 @@ class RandomizedSearch(BaseSearch):

This is present only if ``refit`` is not False.

.. versionadded:: 0.20

multimetric_ : bool
Whether or not the scorers compute several metrics.

Expand All @@ -1539,16 +1410,12 @@ class RandomizedSearch(BaseSearch):
parameter for more details) and that `best_estimator_` exposes
`n_features_in_` when fit.

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if
`best_estimator_` is defined (see the documentation for the `refit`
parameter for more details) and that `best_estimator_` exposes
`feature_names_in_` when fit.

.. versionadded:: 1.0

See Also
--------
GridSearch : Does exhaustive search over a grid of parameters.
Expand Down