neulab/docprompting-conala huggingface dataset:
class sklearn.model_selection.ParameterGrid(param_grid) [source]
 
Grid of parameters with a discrete number of values for each. Can be used to iterate over parameter value combinations with the Python built-in function iter. The order of the generated parameter combinations is deterministic. Read more in the User Guide.  Parameters 
 
param_griddict of str to sequence, or sequence of such 

The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. An empty dict signifies default parameters. A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below.      See also  
GridSearchCV


Uses ParameterGrid to perform a full parallelized parameter search.    Examples >>> from sklearn.model_selection import ParameterGrid
>>> param_grid = {'a': [1, 2], 'b': [True, False]}
>>> list(ParameterGrid(param_grid)) == (
...    [{'a': 1, 'b': True}, {'a': 1, 'b': False},
...     {'a': 2, 'b': True}, {'a': 2, 'b': False}])
True
 >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
>>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
...                               {'kernel': 'rbf', 'gamma': 1},
...                               {'kernel': 'rbf', 'gamma': 10}]
True
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True
sklearn.model_selection.ParameterGrid  
class sklearn.model_selection.ParameterGrid(param_grid) [source]
 
Grid of parameters with a discrete number of values for each. Can be used to iterate over parameter value combinations with the Python built-in function iter. The order of the generated parameter combinations is deterministic. Read more in the User Guide.  Parameters 
 
param_griddict of str to sequence, or sequence of such 

The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. An empty dict signifies default parameters. A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below.      See also  
GridSearchCV


Uses ParameterGrid to perform a full parallelized parameter search.    Examples >>> from sklearn.model_selection import ParameterGrid
>>> param_grid = {'a': [1, 2], 'b': [True, False]}
>>> list(ParameterGrid(param_grid)) == (
...    [{'a': 1, 'b': True}, {'a': 1, 'b': False},
...     {'a': 2, 'b': True}, {'a': 2, 'b': False}])
True
 >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
>>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
...                               {'kernel': 'rbf', 'gamma': 1},
...                               {'kernel': 'rbf', 'gamma': 10}]
True
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True
class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_state=None) [source]
 
Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide.  Parameters 
 
param_distributionsdict 

Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.  
n_iterint 

Number of parameter settings that are produced.  
random_stateint, 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. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
paramsdict of str to any 

Yields dictionaries mapping each estimator parameter to as sampled value.     Examples >>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4,
...                                    random_state=rng))
>>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
...                 for d in param_list]
>>> rounded_list == [{'b': 0.89856, 'a': 1},
...                  {'b': 0.923223, 'a': 1},
...                  {'b': 1.878964, 'a': 2},
...                  {'b': 1.038159, 'a': 2}]
True
sklearn.model_selection.ParameterSampler  
class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_state=None) [source]
 
Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide.  Parameters 
 
param_distributionsdict 

Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.  
n_iterint 

Number of parameter settings that are produced.  
random_stateint, 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. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
paramsdict of str to any 

Yields dictionaries mapping each estimator parameter to as sampled value.     Examples >>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4,
...                                    random_state=rng))
>>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
...                 for d in param_list]
>>> rounded_list == [{'b': 0.89856, 'a': 1},
...                  {'b': 0.923223, 'a': 1},
...                  {'b': 1.878964, 'a': 2},
...                  {'b': 1.038159, 'a': 2}]
True
class sklearn.model_selection.PredefinedSplit(test_fold) [source]
 
Predefined split cross-validator Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold parameter. Read more in the User Guide.  New in version 0.16.   Parameters 
 
test_foldarray-like of shape (n_samples,) 

The entry test_fold[i] represents the index of the test set that sample i belongs to. It is possible to exclude sample i from any test set (i.e. include sample i in every training set) by setting test_fold[i] equal to -1.     Examples >>> import numpy as np
>>> from sklearn.model_selection import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> test_fold = [0, 1, -1, 1]
>>> ps = PredefinedSplit(test_fold)
>>> ps.get_n_splits()
2
>>> print(ps)
PredefinedSplit(test_fold=array([ 0,  1, -1,  1]))
>>> for train_index, test_index in ps.split():
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
TRAIN: [1 2 3] TEST: [0]
TRAIN: [0 2] TEST: [1 3]
 Methods  
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator  
split([X, y, groups]) Generate indices to split data into training and test set.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.     
  
split(X=None, y=None, groups=None) [source]
 
Generate indices to split data into training and test set.  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.
sklearn.model_selection.PredefinedSplit  
class sklearn.model_selection.PredefinedSplit(test_fold) [source]
 
Predefined split cross-validator Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold parameter. Read more in the User Guide.  New in version 0.16.   Parameters 
 
test_foldarray-like of shape (n_samples,) 

The entry test_fold[i] represents the index of the test set that sample i belongs to. It is possible to exclude sample i from any test set (i.e. include sample i in every training set) by setting test_fold[i] equal to -1.     Examples >>> import numpy as np
>>> from sklearn.model_selection import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> test_fold = [0, 1, -1, 1]
>>> ps = PredefinedSplit(test_fold)
>>> ps.get_n_splits()
2
>>> print(ps)
PredefinedSplit(test_fold=array([ 0,  1, -1,  1]))
>>> for train_index, test_index in ps.split():
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
TRAIN: [1 2 3] TEST: [0]
TRAIN: [0 2] TEST: [1 3]
 Methods  
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator  
split([X, y, groups]) Generate indices to split data into training and test set.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.     
  
split(X=None, y=None, groups=None) [source]
 
Generate indices to split data into training and test set.  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.
split(X=None, y=None, groups=None) [source]
 
Generate indices to split data into training and test set.  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.
class sklearn.model_selection.RepeatedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
 
Repeated K-Fold cross validator. Repeats K-Fold n times with different randomization in each repetition. Read more in the User Guide.  Parameters 
 
n_splitsint, default=5 

Number of folds. Must be at least 2.  
n_repeatsint, default=10 

Number of times cross-validator needs to be repeated.  
random_stateint, RandomState instance or None, default=None 

Controls the randomness of each repeated cross-validation instance. Pass an int for reproducible output across multiple function calls. See Glossary.      See also  
RepeatedStratifiedKFold


Repeats Stratified K-Fold n times.    Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer. Examples >>> import numpy as np
>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> for train_index, test_index in rkf.split(X):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
...
TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
 Methods  
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator  
split(X[, y, groups]) Generates indices to split data into training and test set.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
yobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.     
  
split(X, y=None, groups=None) [source]
 
Generates indices to split data into training and test set.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples,) 

The target variable for supervised learning problems.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.
sklearn.model_selection.RepeatedKFold  
class sklearn.model_selection.RepeatedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
 
Repeated K-Fold cross validator. Repeats K-Fold n times with different randomization in each repetition. Read more in the User Guide.  Parameters 
 
n_splitsint, default=5 

Number of folds. Must be at least 2.  
n_repeatsint, default=10 

Number of times cross-validator needs to be repeated.  
random_stateint, RandomState instance or None, default=None 

Controls the randomness of each repeated cross-validation instance. Pass an int for reproducible output across multiple function calls. See Glossary.      See also  
RepeatedStratifiedKFold


Repeats Stratified K-Fold n times.    Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer. Examples >>> import numpy as np
>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> for train_index, test_index in rkf.split(X):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
...
TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
 Methods  
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator  
split(X[, y, groups]) Generates indices to split data into training and test set.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
yobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.     
  
split(X, y=None, groups=None) [source]
 
Generates indices to split data into training and test set.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples,) 

The target variable for supervised learning problems.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.     
 
 Examples using sklearn.model_selection.RepeatedKFold
 
  Common pitfalls in interpretation of coefficients of linear models
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
yobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
 
Generates indices to split data into training and test set.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples,) 

The target variable for supervised learning problems.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.
class sklearn.model_selection.RepeatedStratifiedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
 
Repeated Stratified K-Fold cross validator. Repeats Stratified K-Fold n times with different randomization in each repetition. Read more in the User Guide.  Parameters 
 
n_splitsint, default=5 

Number of folds. Must be at least 2.  
n_repeatsint, default=10 

Number of times cross-validator needs to be repeated.  
random_stateint, RandomState instance or None, default=None 

Controls the generation of the random states for each repetition. Pass an int for reproducible output across multiple function calls. See Glossary.      See also  
RepeatedKFold


Repeats K-Fold n times.    Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer. Examples >>> import numpy as np
>>> from sklearn.model_selection import RepeatedStratifiedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=2,
...     random_state=36851234)
>>> for train_index, test_index in rskf.split(X, y):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
...
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]
 Methods  
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator  
split(X[, y, groups]) Generates indices to split data into training and test set.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
yobject 

Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.     
  
split(X, y=None, groups=None) [source]
 
Generates indices to split data into training and test set.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples,) 

The target variable for supervised learning problems.  
groupsarray-like of shape (n_samples,), default=None 

Group labels for the samples used while splitting the dataset into train/test set.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

The testing set indices for that split.