skutil.model_selection module

In scikit-learn 0.18, sklearn.grid_search was deprecated. Since skutil handles the deprecation issues in skutil.utils.fixes, the skutil.model_selection module merely provides the same import functionality as sklearn 0.18, so sklearn users can seamlessly migrate to skutil for grid_search imports.

class skutil.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score=True)[source]

Bases: sklearn.model_selection._search.GridSearchCV

Exhaustive search over specified parameter values for an estimator. This class is a skutil fix of the sklearn 0.18 GridSearchCV module, and allows use with SelectiveMixins and other skutil classes that don’t interact so kindly with other sklearn 0.18 structures (i.e. when as_df is True in many transformers, predicting on a column vector from a pd.DataFrame will cause issues in sklearn).

Parameters:

estimator : estimator object.

This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.

param_grid : dict or list of dictionaries

Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.

scoring : string, callable or None, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.

fit_params : dict, optional

Parameters to pass to the fit method.

n_jobs : int, default=1

Number of jobs to run in parallel.

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’

iid : boolean, default=True

If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the default 3-fold cross validation,
  • integer, to specify the number of folds in a (Stratified)KFold,
  • An object to be used as a cross-validation generator.
  • An iterable yielding train, test splits.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

refit : boolean, default=True

Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this GridSearchCV instance after fitting.

verbose : integer

Controls the verbosity: the higher, the more messages.

error_score : ‘raise’ (default) or numeric

Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.

return_train_score : boolean, default=True

If 'False', the cv_results_ attribute will not include training scores.
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 following table

param_kernel param_gamma param_degree split0_test_score ... rank_....
‘poly’ – 2 0.8 ... 2
‘poly’ – 3 0.7 ... 4
‘rbf’ 0.1 – 0.8 ... 3
‘rbf’ 0.2 – 0.9 ... 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’ : [0.8, 0.7, 0.8, 0.9], ‘split1_test_score’ : [0.82, 0.5, 0.7, 0.78], ‘mean_test_score’ : [0.81, 0.60, 0.75, 0.82], ‘std_test_score’ : [0.02, 0.01, 0.03, 0.03], ‘rank_test_score’ : [2, 4, 3, 1], ‘split0_train_score’ : [0.8, 0.9, 0.7], ‘split1_train_score’ : [0.82, 0.5, 0.7], ‘mean_train_score’ : [0.81, 0.7, 0.7], ‘std_train_score’ : [0.03, 0.03, 0.04], ‘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’ : [0.007, 0.06, 0.04, 0.04], ‘std_score_time’ : [0.001, 0.002, 0.003, 0.005], ‘params’ : [{‘kernel’: ‘poly’, ‘degree’: 2}, ...],

}

NOTE that the key 'params' is used to store a list of parameter settings dict for all the parameter candidates. The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

best_estimator_ : estimator

Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

best_score_ : float

Score of best_estimator on the left out data.

best_params_ : dict

Parameter setting that gave the best results on the hold out data.

best_index_ : int

The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting. The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

scorer_ : function

Scorer function used on the held out data to choose the best parameters for the model.

n_splits_ : int

The number of cross-validation splits (folds/iterations).

Methods

decision_function(\*args, \*\*kwargs) Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups]) Run fit with all sets of parameters.
get_params([deep]) Get parameters for this estimator.
inverse_transform(\*args, \*\*kwargs) Call inverse_transform on the estimator with the best found params.
predict(\*args, \*\*kwargs) Call predict on the estimator with the best found parameters.
predict_log_proba(\*args, \*\*kwargs) Call predict_log_proba on the estimator with the best found parameters.
predict_proba(\*args, \*\*kwargs) Call predict_proba on the estimator with the best found parameters.
score(X[, y]) Returns the score on the given data, if the estimator has been refit.
set_params(\*\*params) Set the parameters of this estimator.
transform(\*args, \*\*kwargs) Call transform on the estimator with the best found parameters.
fit(X, y=None, groups=None)[source]

Run fit with all sets of parameters.

Parameters:

X : array-like, shape=(n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape=(n_samples,) or (n_samples, n_output), optional (default=None)

Target relative to X for classification or regression; None for unsupervised learning.

groups : array-like, shape=(n_samples,), optional (default=None)

Group labels for the samples used while splitting the dataset into train/test set.
class skutil.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True)[source]

Bases: sklearn.model_selection._search.RandomizedSearchCV

Randomized search on hyper parameters. This class is a skutil fix of the sklearn 0.18 RandomizedSearchCV module, and allows use with SelectiveMixins and other skutil classes that don’t interact so kindly with other sklearn 0.18 structures (i.e. when as_df is True in many transformers, predicting on a column vector from a pd.DataFrame will cause issues in sklearn).

Parameters:

estimator : estimator object.

A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.

param_distributions : dict

Dictionary with parameters names (string) 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.

n_iter : int, default=10

Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.

scoring : string, callable or None, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.

fit_params : dict, optional

Parameters to pass to the fit method.

n_jobs : int, default=1

Number of jobs to run in parallel.

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’

iid : boolean, default=True

If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the default 3-fold cross validation,
  • integer, to specify the number of folds in a (Stratified)KFold,
  • An object to be used as a cross-validation generator.
  • An iterable yielding train, test splits.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

refit : boolean, default=True

Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.

verbose : integer

Controls the verbosity: the higher, the more messages.

random_state : int or RandomState

Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.

error_score : ‘raise’ (default) or numeric

Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.

return_train_score : boolean, default=True

If 'False', the cv_results_ attribute will not include training scores.
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 following table:

param_kernel param_gamma split0_test_score ... rank_test_score
‘rbf’ 0.1 0.8 ... 2
‘rbf’ 0.2 0.9 ... 1
‘rbf’ 0.3 0.7 ... 1

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’ : [0.8, 0.9, 0.7], ‘split1_test_score’ : [0.82, 0.5, 0.7], ‘mean_test_score’ : [0.81, 0.7, 0.7], ‘std_test_score’ : [0.02, 0.2, 0.], ‘rank_test_score’ : [3, 1, 1], ‘split0_train_score’ : [0.8, 0.9, 0.7], ‘split1_train_score’ : [0.82, 0.5, 0.7], ‘mean_train_score’ : [0.81, 0.7, 0.7], ‘std_train_score’ : [0.03, 0.03, 0.04], ‘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’ : [0.007, 0.06, 0.04, 0.04], ‘std_score_time’ : [0.001, 0.002, 0.003, 0.005], ‘params’ : [{‘kernel’ : ‘rbf’, ‘gamma’ : 0.1}, ...],

}

NOTE that the key 'params' is used to store a list of parameter settings dict for all the parameter candidates. The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

best_estimator_ : estimator

Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

best_score_ : float

Score of best_estimator on the left out data.

best_params_ : dict

Parameter setting that gave the best results on the hold out data.

best_index_ : int

The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting. The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

scorer_ : function

Scorer function used on the held out data to choose the best parameters for the model.

n_splits_ : int

The number of cross-validation splits (folds/iterations).

Methods

decision_function(\*args, \*\*kwargs) Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups]) Run fit on the estimator with randomly drawn parameters.
get_params([deep]) Get parameters for this estimator.
inverse_transform(\*args, \*\*kwargs) Call inverse_transform on the estimator with the best found params.
predict(\*args, \*\*kwargs) Call predict on the estimator with the best found parameters.
predict_log_proba(\*args, \*\*kwargs) Call predict_log_proba on the estimator with the best found parameters.
predict_proba(\*args, \*\*kwargs) Call predict_proba on the estimator with the best found parameters.
score(X[, y]) Returns the score on the given data, if the estimator has been refit.
set_params(\*\*params) Set the parameters of this estimator.
transform(\*args, \*\*kwargs) Call transform on the estimator with the best found parameters.
fit(X, y=None, groups=None)[source]

Run fit on the estimator with randomly drawn parameters.

Parameters:

X : array-like, shape=(n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape=(n_samples,) or (n_samples, n_output), optional (default=None)

Target relative to X for classification or regression; None for unsupervised learning.

groups : array-like, shape=(n_samples,), optional (default=None)

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