dislib.model_selection¶

class
dislib.model_selection.
GridSearchCV
(estimator, param_grid, scoring=None, cv=None, refit=True)[source]¶ Bases:
dislib.model_selection._search.BaseSearchCV
Exhaustive search over specified parameter values for an estimator.
GridSearchCV implements a “fit” and a “score” method.
The parameters of the estimator used to apply these methods are optimized by crossvalidated gridsearch over a parameter grid.
Parameters:  estimator (estimator object.) – This is assumed to implement the scikitlearn estimator interface.
Either estimator needs to provide a
score
function, orscoring
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 (callable, dict or None, optional (default=None)) – A callable to evaluate the predictions on the test set. It should take 3 parameters, estimator, x and y, and return a score (higher meaning better). For evaluating multiple metrics, give a dict with names as keys and callables as values. If None, the estimator’s score method is used.
 cv (int or cv generator, optional (default=None)) – Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 5fold cross validation,  integer, to specify the number of folds in a KFold,  custom cv generator.
 refit (boolean, string, or callable, optional (default=True)) – Refit an estimator using the best found parameters on the whole
dataset.
For multiple metric evaluation, this needs to be a string denoting the
scorer that would be used to find the best parameters for refitting
the estimator at the end.
Where there are considerations other than maximum score in
choosing a best estimator,
refit
can be set to a function which returns the selectedbest_index_
givencv_results_
. The refitted estimator is made available at thebest_estimator_
attribute and permits usingpredict
directly on thisGridSearchCV
instance. Also for multiple metric evaluation, the attributesbest_index_
,best_score_
andbest_params_
will only be available ifrefit
is set and all of them will be determined w.r.t this specific scorer.best_score_
is not returned if refit is callable. Seescoring
parameter to know more about multiple metric evaluation.
Examples
>>> import dislib as ds >>> import numpy as np >>> from dislib.model_selection import GridSearchCV >>> from dislib.classification import RandomForestClassifier >>> from sklearn import datasets >>> x_np, y_np = datasets.load_iris(return_X_y=True) >>> x = ds.array(x_np, (30, 4)) >>> y = ds.array(y_np[:, np.newaxis], (30, 1)) >>> param_grid = {'n_estimators': (2, 4), 'max_depth': range(3, 5)} >>> rf = RandomForestClassifier() >>> searcher = GridSearchCV(rf, param_grid) >>> searcher.fit(x, y) >>> searcher.cv_results_
Variables:  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_degree split0_test_score … rank_t… ’poly’ 2 0.80 … 2 ’poly’ 3 0.70 … 4 ’rbf’ – 0.80 … 3 ’rbf’ – 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_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, 0.68, 0.78], 'split2_test_score' : [0.79, 0.55, 0.71, 0.93], ... '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], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], }
NOTES:
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
andstd_score_time
are all in seconds.For multimetric 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 or dict) – 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
. Seerefit
parameter for more information on allowed values.  best_score (float) – Mean crossvalidated score of the best_estimator
For multimetric evaluation, this is present only if
refit
is specified.  best_params (dict) – Parameter setting that gave the best results on the hold out data.
For multimetric evaluation, this is present only if
refit
is specified.  best_index (int) – The index (of the
cv_results_
arrays) which corresponds to the best candidate parameter setting. The dict atsearch.cv_results_['params'][search.best_index_]
gives the parameter setting for the best model, that gives the highest mean score (search.best_score_
). For multimetric evaluation, this is present only ifrefit
is specified.  scorer (function or a dict) – Scorer function used on the held out data to choose the best
parameters for the model.
For multimetric evaluation, this attribute holds the validated
scoring
dict which maps the scorer key to the scorer callable.  n_splits (int) – The number of crossvalidation splits (folds/iterations).
 estimator (estimator object.) – This is assumed to implement the scikitlearn estimator interface.
Either estimator needs to provide a

class
dislib.model_selection.
RandomizedSearchCV
(estimator, param_distributions, n_iter=10, scoring=None, cv=None, refit=True, random_state=None)[source]¶ Bases:
dislib.model_selection._search.BaseSearchCV
Randomized search on hyper parameters.
RandomizedSearchCV implements a “fit” and a “score” method.
The parameters of the estimator used to apply these methods are optimized by crossvalidated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.
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.
Parameters:  estimator (estimator object.) – This is assumed to implement the scikitlearn estimator interface.
Either estimator needs to provide a
score
function, orscoring
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, optional (default=10)) – Number of parameter settings that are sampled.
 scoring (callable, dict or None, optional (default=None)) – A callable to evaluate the predictions on the test set. It should take 3 parameters, estimator, x and y, and return a score (higher meaning better). For evaluating multiple metrics, give a dict with names as keys and callables as values. If None, the estimator’s score method is used.
 cv (int or cv generator, optional (default=None)) – Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 5fold cross validation,  integer, to specify the number of folds in a KFold,  custom cv generator.
 refit (boolean, string, or callable, optional (default=True)) – Refit an estimator using the best found parameters on the whole
dataset.
For multiple metric evaluation, this needs to be a string denoting the
scorer that would be used to find the best parameters for refitting
the estimator at the end.
Where there are considerations other than maximum score in
choosing a best estimator,
refit
can be set to a function which returns the selectedbest_index_
givencv_results_
. The refitted estimator is made available at thebest_estimator_
attribute and permits usingpredict
directly on thisGridSearchCV
instance. Also for multiple metric evaluation, the attributesbest_index_
,best_score_
andbest_params_
will only be available ifrefit
is set and all of them will be determined w.r.t this specific scorer.best_score_
is not returned if refit is callable. Seescoring
parameter to know more about multiple metric evaluation.  random_state (int, RandomState instance or None, optional, default=None) – Pseudo random number generator state used for random sampling of params in param_distributions. This is not passed to each estimator. 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.
Examples
>>> import dislib as ds >>> import numpy as np >>> from dislib.model_selection import RandomizedSearchCV >>> from dislib.classification import CascadeSVM >>> from sklearn import datasets >>> import scipy.stats as stats >>> x_np, y_np = datasets.load_iris(return_X_y=True) >>> p = np.random.permutation(len(x_np)) # Preshuffling required for CSVM >>> x = ds.array(x_np[p], (30, 4)) >>> y = ds.array((y_np[p] == 0)[:, np.newaxis], (30, 1)) >>> param_distributions = {'c': stats.expon(scale=0.5), >>> 'gamma': stats.expon(scale=10)} >>> csvm = CascadeSVM() >>> searcher = RandomizedSearchCV(csvm, param_distributions, n_iter=10) >>> searcher.fit(x, y) >>> searcher.cv_results_
Variables:  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_c param_gamma split0_test_score … rank_test_score 0.193 1.883 0.82 … 3 1.452 0.327 0.81 … 2 0.926 3.452 0.94 … 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' : [0.82, 0.81, 0.94], 'split1_test_score' : [0.66, 0.75, 0.79], 'split2_test_score' : [0.82, 0.87, 0.84], ... 'mean_test_score' : [0.76, 0.84, 0.86], 'std_test_score' : [0.01, 0.20, 0.04], 'rank_test_score' : [3, 2, 1], 'params' : [{'c' : 0.193, 'gamma' : 1.883}, ...], }
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
andstd_score_time
are all in seconds.For multimetric 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 or dict) –
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
.For multimetric evaluation, this attribute is present only if
refit
is specified.See
refit
parameter for more information on allowed values.  best_score (float) –
Mean crossvalidated score of the best_estimator.
For multimetric evaluation, this is not available if
refit
isFalse
. Seerefit
parameter for more information.  best_params (dict) –
Parameter setting that gave the best results on the hold out data.
For multimetric evaluation, this is not available if
refit
isFalse
. Seerefit
parameter for more information.  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_
).For multimetric evaluation, this is not available if
refit
isFalse
. Seerefit
parameter for more information.  scorer (function or a dict) –
Scorer function used on the held out data to choose the best parameters for the model.
For multimetric evaluation, this attribute holds the validated
scoring
dict which maps the scorer key to the scorer callable.  n_splits (int) – The number of crossvalidation splits (folds/iterations).
 estimator (estimator object.) – This is assumed to implement the scikitlearn estimator interface.
Either estimator needs to provide a

class
dislib.model_selection.
KFold
(n_splits=5, shuffle=False, random_state=None)[source]¶ Bases:
object
Kfold splitter for crossvalidation
Returns k partitions of the dataset into train and validation datasets. The dataset is shuffled and split into k folds; each fold is used once as validation dataset while the k  1 remaining folds form the training dataset.
Each fold contains n//k or n//k + 1 samples, where n is the number of samples in the input dataset.
Parameters:  n_splits (int, optional (default=5)) – Number of folds. Must be at least 2.
 shuffle (boolean, optional (default=False)) – Shuffles and balances the data before splitting into batches.
 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
== True.

get_n_splits
()[source]¶ Get the number of CV splits that this splitter does.
Returns: n_splits – The number of splits performed by this CV splitter. Return type: int

split
(x, y=None)[source]¶ Generates Kfold splits.
Parameters:  x (dsarray) – Samples array.
 y (dsarray, optional (default=None)) – Corresponding labels or values.
Yields:  train_data (train_x, train_y) – The training dsarrays for that split. If y is None, train_y is None.
 test_data (test_x, test_y) – The testing dsarrays data for that split. If y is None, test_y is None.