dislib.model_selection#

class dislib.model_selection.GridSearchCV(estimator, param_grid, sort='max', scoring=None, cv=None, refit=True)[source]#

Bases: 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 cross-validated grid-search over a parameter grid.

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 (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.
sort (string, optional (default=”max”)) – Specifies the order used for the rank of the results. By default it sorts from highest value to lowest. The other possible value to set is “min” and will sort from lowest to highest.
cv (int or cv generator, optional (default=None)) – Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold 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 selected best_index_ given cv_results_. The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this GridSearchCV instance. Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer. best_score_ is not returned if refit is callable. See scoring parameter to know more about multiple metric evaluation.

Examples

>>> import dislib as ds
>>> from dislib.model_selection import GridSearchCV
>>> from dislib.classification import RandomForestClassifier
>>> import numpy as np
>>> from sklearn import datasets
>>>
>>>
>>> if __name__ == '__main__':
>>>     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 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 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. See refit parameter for more information on allowed values.
best_score (float) – Mean cross-validated score of the best_estimator For multi-metric 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 multi-metric 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 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 multi-metric evaluation, this is present only if refit is specified.
scorer (function or a dict) – Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.
n_splits (int) – The number of cross-validation splits (folds/iterations).

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

fit(x, y=None, **fit_params)[source]#

Run fit with all sets of parameters.

Parameters:

x (ds-array) – Training data samples.
y (ds-array, optional (default = None)) – Training data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

score(x=None, y=None, **fit_params)[source]#

Compute score for the trained sets of parameters.

Parameters:

x (ds-array) – Test data samples.
y (ds-array, optional (default = None)) – Test data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

train_candidates(x, y, **fit_params)[source]#

Run fit with all sets of parameters.

Parameters:

x (ds-array) – Training data samples.
y (ds-array, optional (default = None)) – Training data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

class dislib.model_selection.KFold(n_splits=5, shuffle=False, random_state=None)[source]#

Bases: object

K-fold splitter for cross-validation

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 K-fold splits.

Parameters:

x (ds-array) – Samples array.
y (ds-array, optional (default=None)) – Corresponding labels or values.

Yields:

train_data (train_x, train_y) – The training ds-arrays for that split. If y is None, train_y is None.
test_data (test_x, test_y) – The testing ds-arrays data for that split. If y is None, test_y is None.

class dislib.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, sort='max', scoring=None, cv=None, refit=True, random_state=None)[source]#

Bases: 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 cross-validated 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 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, 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.
sort (string, optional (default=”max”)) – Specifies the order used for the rank of the results. By default it sorts from highest value to lowest. The other possible value to set is “min” and will sort from lowest to highest.
cv (int or cv generator, optional (default=None)) – Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold 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 selected best_index_ given cv_results_. The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this GridSearchCV instance. Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer. best_score_ is not returned if refit is callable. See scoring 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
>>> from dislib.model_selection import RandomizedSearchCV
>>> from dislib.classification import CascadeSVM
>>> import numpy as np
>>> import scipy.stats as stats
>>> from sklearn import datasets
>>>
>>>
>>> if __name__ == '__main__':
>>>     x_np, y_np = datasets.load_iris(return_X_y=True)
>>>     # Pre-shuffling required for CSVM
>>>     p = np.random.permutation(len(x_np))
>>>     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 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 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 multi-metric evaluation, this attribute is present only if refit is specified.

See refit parameter for more information on allowed values.
best_score (float) –
Mean cross-validated score of the best_estimator.

For multi-metric evaluation, this is not available if refit is False. See refit parameter for more information.
best_params (dict) –
Parameter setting that gave the best results on the hold out data.

For multi-metric evaluation, this is not available if refit is False. See refit 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 multi-metric evaluation, this is not available if refit is False. See refit 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 multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.
n_splits (int) – The number of cross-validation splits (folds/iterations).

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

fit(x, y=None, **fit_params)[source]#

Run fit with all sets of parameters.

Parameters:

x (ds-array) – Training data samples.
y (ds-array, optional (default = None)) – Training data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

score(x=None, y=None, **fit_params)[source]#

Compute score for the trained sets of parameters.

Parameters:

x (ds-array) – Test data samples.
y (ds-array, optional (default = None)) – Test data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

train_candidates(x, y, **fit_params)[source]#

Run fit with all sets of parameters.

Parameters:

x (ds-array) – Training data samples.
y (ds-array, optional (default = None)) – Training data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

class dislib.model_selection.SimulationGridSearch(estimator, param_grid, sim_number=1, order='max')[source]#

Bases: object

Exhaustive execution of all combinations of specified parameters values: in parallel simulations.

SimulationGridSearch implements a “fit” method.

Parameters:

estimator (simulator object.) – This should receive the parameters specified in param_grid and use that parameters for the corresponding operation.
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.
sim_number (Integer) – Number of simulations that are going to be executed with each of the parameter combination.
order (string “max” or “min”.) – String that specifies how to order the results obtained from the simulation, “max” will set first the highest values and “min” the lowest values.

Examples

>>> import dislib as ds
>>> from dislib.model_selection import SimulationGridSearch
>>> from dislib.classification import RandomForestClassifier
>>> import numpy as np
>>> from sklearn import datasets
>>> def my_simulation(a, b):
>>>    return (a*a)/(b*b)+a*(a+b)-b*(2*b)
>>>
>>> param_grid = {'a': [-1.1, -0.1, 1.5, 2.5], 'b': [0.1, 1.5, 2.5, 3.5]}
>>> searcher = SimulationGridSearch(my_simulation, param_grid, order="min")
>>> searcher.fit(None)
>>> best_params = searcher.best_params_
>>>

Variables:

raw_results (list of objects) – List containing the results obtained from the different simulations. In the list the results are saved as returned from the simulation, with no changes in the format.

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_a	param_b	params	…	rank_t…
-1.1	0.1	{‘a’: -1.1, ‘b’: 0.1}	…	2
-1.1	1.5	{‘a’: -1.1, ‘b’: 1.5}	…	4
-0.1	0.1	{‘a’: -0.1, ‘b’: 0.1}	…	3
-0.1	1.5	{‘a’: -0.1, ‘b’: 1.5}	…	1

will be represented by a cv_results_ dict of:

{
'param_kernel': masked_array(data = [-1.1, -1.1, -0.1, -0.1],
                             mask = [False False False False]...),
'param_degree': masked_array(data = [0.1 1.5 0.1 1.5],
                             mask = [False False  False  False]
                             ...),
...
'mean_test_simulation'    : [122.08, -4.40, 0.98, -4.63],
'std_test_simulation'     : [0.0, --, --, --],
'rank_test_score'    : [2, 4, 3, 1],
'params'             : [{'a': '-1.1', 'b': 0.1}, ...],
}

NOTES:

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

best_score (float) – Best value obtained from a simulation, if several runs of each simulation are done the best mean of the values obtained is used
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_).

fit(x, y=None, **fit_params)[source]#

Run fit with all sets of parameters.

Parameters:

x (ds-array) – Training data samples.
y (ds-array, optional (default = None)) – Training data labels or values.
**fit_params (dict of string -> object) – Parameters passed to the fit method of the estimator

dislib.model_selection.train_test_split(x, y=None, test_size=None, train_size=None, random_state=None)[source]#

Randomly shuffles the rows of data.

Parameters:

x (ds-array) – Data to be split.
y (ds-array, optional (default=None)) – Additional array to split using the same permutations, usually for labels or values. It is required that y.shape[0] == x.shape[0].
test_size (float) – Number between 0 and 1 that defines the percentage of rows used as test data
train_size (float) – Number between 0 and 1 that defines the percentage of rows used as train data
random_state (int or RandomState, optional (default = None)) – Seed or numpy.random.RandomState instance to use in the generation of splits in the blocks.

Returns:

train (ds-array) – A new ds-array containing the rows of x that correspond to train data.
test (ds-array) – A new ds-array containing the rows of x that correspond to test data.
train_y (ds-array, optional) – A new ds-array containing the rows of y that correspond to the rows in train.
test_y (ds-array, optional) – A new ds-array containing the rows of y that correspond to the rows in test.