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Merged
merged 8 commits into from
Jan 11, 2020
Merged

DOC Docstring improvements to confusion_matrix and splitters #16072

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35ef4ea
make docstring of confusion_matrix more clear
keyianpai Jan 9, 2020
b37d305
Update _classification.py
keyianpai Jan 9, 2020
2db42e1
Update sklearn/metrics/_classification.py
keyianpai Jan 10, 2020
3f1a3d2
Update sklearn/metrics/_classification.py
keyianpai Jan 10, 2020
d33a61d
Update _classification.py
keyianpai Jan 11, 2020
e0bee3b
Update _classification.py
keyianpai Jan 11, 2020
c15a12d
Merge remote-tracking branch 'upstream/master'
keyianpai Jan 11, 2020
c6f2751
DOC improve random_state docstring model selection modue (#15576)
keyianpai Jan 11, 2020
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5 changes: 4 additions & 1 deletion sklearn/metrics/_classification.py
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Expand Up @@ -232,7 +232,10 @@ def confusion_matrix(y_true, y_pred, labels=None, sample_weight=None,
Returns
-------
C : ndarray of shape (n_classes, n_classes)
Confusion matrix.
Confusion matrix whose i-th row and j-th
column entry indicates the number of
samples with true label being i-th class
and prediced label being j-th class.

References
----------
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18 changes: 8 additions & 10 deletions sklearn/model_selection/_search.py
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Expand Up @@ -204,13 +204,12 @@ class ParameterSampler:
n_iter : integer
Number of parameter settings that are produced.

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance or None, default=None
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions.
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Returns
-------
Expand Down Expand Up @@ -1296,13 +1295,12 @@ class RandomizedSearchCV(BaseSearchCV):
verbose : integer
Controls the verbosity: the higher, the more messages.

random_state : int, RandomState instance or None, optional, default=None
random_state : int, RandomState instance or None, default=None
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions.
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
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80 changes: 37 additions & 43 deletions sklearn/model_selection/_split.py
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Expand Up @@ -380,12 +380,12 @@ class KFold(_BaseKFold):
Whether to shuffle the data before splitting into batches.
Note that the samples within each split will not be shuffled.

random_state : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Only used when ``shuffle`` is True. This should be left
random_state : int, RandomState instance or None, default=None
Only used when ``shuffle`` is True. This should be left
to None if ``shuffle`` is False.
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -587,12 +587,12 @@ class StratifiedKFold(_BaseKFold):
Whether to shuffle each class's samples before splitting into batches.
Note that the samples within each split will not be shuffled.

random_state : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Only used when ``shuffle`` is True. This should be left
random_state : int, RandomState instance or None, default=None
Only used when ``shuffle`` is True. This should be left
to None if ``shuffle`` is False.
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -1090,11 +1090,10 @@ class _RepeatedSplits(metaclass=ABCMeta):
n_repeats : int, default=10
Number of times cross-validator needs to be repeated.

random_state : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

**cvargs : additional params
Constructor parameters for cv. Must not contain random_state
Expand Down Expand Up @@ -1195,11 +1194,9 @@ class RepeatedKFold(_RepeatedSplits):
n_repeats : int, default=10
Number of times cross-validator needs to be repeated.

random_state : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -1249,9 +1246,10 @@ class RepeatedStratifiedKFold(_RepeatedSplits):
n_repeats : int, default=10
Number of times cross-validator needs to be repeated.

random_state : None, int or RandomState, default=None
Random state to be used to generate random state for each
repetition.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -1389,11 +1387,10 @@ class ShuffleSplit(BaseShuffleSplit):
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -1491,11 +1488,10 @@ class GroupShuffleSplit(ShuffleSplit):
int, represents the absolute number of train groups. If None,
the value is automatically set to the complement of the test size.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -1604,11 +1600,10 @@ class StratifiedShuffleSplit(BaseShuffleSplit):
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

Examples
--------
Expand Down Expand Up @@ -2048,11 +2043,10 @@ def train_test_split(*arrays, **options):
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

shuffle : boolean, optional (default=True)
Whether or not to shuffle the data before splitting. If shuffle=False
Expand Down
19 changes: 9 additions & 10 deletions sklearn/model_selection/_validation.py
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Expand Up @@ -1003,11 +1003,10 @@ def permutation_test_score(estimator, X, y, groups=None, cv=None,
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None, optional (default=0)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
random_state : int, RandomState instance or None, default=None
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

verbose : integer, optional
The verbosity level.
Expand Down Expand Up @@ -1173,11 +1172,11 @@ def learning_curve(estimator, X, y, groups=None,
Whether to shuffle training data before taking prefixes of it
based on``train_sizes``.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Used when ``shuffle`` is True.
random_state : int, RandomState instance or None, default=None
Used when ``shuffle`` is True.
Pass an int for reproducible output across multiple
function calls.
See :term:`Glossary <random_state>`.

error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
Expand Down