classification Package

classification Package

AdvancedSampler Module

class WORC.classification.AdvancedSampler.AdvancedSampler(param_distributions, n_iter, random_state=None, method='Halton')[source]

Bases: object

Generator on parameters sampled from given distributions using numerical sequences. Based on the sklearn ParameterSampler.

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.

Note that before SciPy 0.16, the scipy.stats.distributions do not accept a custom RNG instance and always use the singleton RNG from numpy.random. Hence setting random_state will not guarantee a deterministic iteration whenever scipy.stats distributions are used to define the parameter search space. Deterministic behavior is however guaranteed from SciPy 0.16 onwards.

Read more in the User Guide.

Parameters

param_distributionsdict

Dictionary where the keys are parameters and values are distributions from which a parameter is to be sampled. Distributions either have to provide a rvs function to sample from them, or can be given as a list of values, where a uniform distribution is assumed.

n_iterinteger

Number of parameter settings that are produced.

random_stateint or RandomState

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

Returns

paramsdict of string to any

Yields dictionaries mapping each estimator parameter to as sampled value.

Examples

>>> from WORC.classification.AdvancedSampler import HaltonSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> np.random.seed(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(HaltonSampler(param_grid, n_iter=4))
>>> 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
__dict__ = mappingproxy({'__module__': 'WORC.classification.AdvancedSampler', '__doc__': "Generator on parameters sampled from given distributions using\n    numerical sequences. Based on the sklearn ParameterSampler.\n\n    Non-deterministic iterable over random candidate combinations for hyper-\n    parameter search. If all parameters are presented as a list,\n    sampling without replacement is performed. If at least one parameter\n    is given as a distribution, sampling with replacement is used.\n    It is highly recommended to use continuous distributions for continuous\n    parameters.\n\n    Note that before SciPy 0.16, the ``scipy.stats.distributions`` do not\n    accept a custom RNG instance and always use the singleton RNG from\n    ``numpy.random``. Hence setting ``random_state`` will not guarantee a\n    deterministic iteration whenever ``scipy.stats`` distributions are used to\n    define the parameter search space. Deterministic behavior is however\n    guaranteed from SciPy 0.16 onwards.\n\n    Read more in the :ref:`User Guide <search>`.\n\n    Parameters\n    ----------\n    param_distributions : dict\n        Dictionary where the keys are parameters and values\n        are distributions from which a parameter is to be sampled.\n        Distributions either have to provide a ``rvs`` function\n        to sample from them, or can be given as a list of values,\n        where a uniform distribution is assumed.\n\n    n_iter : integer\n        Number of parameter settings that are produced.\n\n    random_state : int or RandomState\n        Pseudo random number generator state used for random uniform sampling\n        from lists of possible values instead of scipy.stats distributions.\n\n    Returns\n    -------\n    params : dict of string to any\n        **Yields** dictionaries mapping each estimator parameter to\n        as sampled value.\n\n    Examples\n    --------\n    >>> from WORC.classification.AdvancedSampler import HaltonSampler\n    >>> from scipy.stats.distributions import expon\n    >>> import numpy as np\n    >>> np.random.seed(0)\n    >>> param_grid = {'a':[1, 2], 'b': expon()}\n    >>> param_list = list(HaltonSampler(param_grid, n_iter=4))\n    >>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())\n    ...                 for d in param_list]\n    >>> rounded_list == [{'b': 0.89856, 'a': 1},\n    ...                  {'b': 0.923223, 'a': 1},\n    ...                  {'b': 1.878964, 'a': 2},\n    ...                  {'b': 1.038159, 'a': 2}]\n    True\n    ", '__init__': <function AdvancedSampler.__init__>, '__iter__': <function AdvancedSampler.__iter__>, '__len__': <function AdvancedSampler.__len__>, '__dict__': <attribute '__dict__' of 'AdvancedSampler' objects>, '__weakref__': <attribute '__weakref__' of 'AdvancedSampler' objects>, '__annotations__': {}})
__init__(param_distributions, n_iter, random_state=None, method='Halton')[source]
__iter__()[source]
__len__()[source]

Number of points that will be sampled.

__module__ = 'WORC.classification.AdvancedSampler'
__weakref__

list of weak references to the object (if defined)

class WORC.classification.AdvancedSampler.boolean_uniform(loc=0, scale=1, threshold=0.5)[source]

Bases: object

Uniform distribution thresholded at a certain value to output booleans.

Note: as Booleans cannot be saved in JSOn, which WORC later does, this object returns strings.

__dict__ = mappingproxy({'__module__': 'WORC.classification.AdvancedSampler', '__doc__': '\n    Uniform distribution thresholded at a certain value to output booleans.\n\n    Note: as Booleans cannot be saved in JSOn, which WORC later does, this\n    object returns strings.\n\n    ', '__init__': <function boolean_uniform.__init__>, 'rvs': <function boolean_uniform.rvs>, '__dict__': <attribute '__dict__' of 'boolean_uniform' objects>, '__weakref__': <attribute '__weakref__' of 'boolean_uniform' objects>, '__annotations__': {}})
__init__(loc=0, scale=1, threshold=0.5)[source]
__module__ = 'WORC.classification.AdvancedSampler'
__weakref__

list of weak references to the object (if defined)

rvs(size=None, random_state=None)[source]
class WORC.classification.AdvancedSampler.discrete_uniform(loc=-1, scale=0)[source]

Bases: object

__dict__ = mappingproxy({'__module__': 'WORC.classification.AdvancedSampler', '__init__': <function discrete_uniform.__init__>, 'rvs': <function discrete_uniform.rvs>, '__dict__': <attribute '__dict__' of 'discrete_uniform' objects>, '__weakref__': <attribute '__weakref__' of 'discrete_uniform' objects>, '__doc__': None, '__annotations__': {}})
__init__(loc=-1, scale=0)[source]
__module__ = 'WORC.classification.AdvancedSampler'
__weakref__

list of weak references to the object (if defined)

rvs(size=None, random_state=None)[source]
class WORC.classification.AdvancedSampler.exp_uniform(loc=-1, scale=0, base=2.718281828459045)[source]

Bases: object

__dict__ = mappingproxy({'__module__': 'WORC.classification.AdvancedSampler', '__init__': <function exp_uniform.__init__>, 'rvs': <function exp_uniform.rvs>, '__dict__': <attribute '__dict__' of 'exp_uniform' objects>, '__weakref__': <attribute '__weakref__' of 'exp_uniform' objects>, '__doc__': None, '__annotations__': {}})
__init__(loc=-1, scale=0, base=2.718281828459045)[source]
__module__ = 'WORC.classification.AdvancedSampler'
__weakref__

list of weak references to the object (if defined)

rvs(size=None, random_state=None)[source]
class WORC.classification.AdvancedSampler.log_uniform(loc=-1, scale=0, base=10)[source]

Bases: object

__dict__ = mappingproxy({'__module__': 'WORC.classification.AdvancedSampler', '__init__': <function log_uniform.__init__>, 'rvs': <function log_uniform.rvs>, '__dict__': <attribute '__dict__' of 'log_uniform' objects>, '__weakref__': <attribute '__weakref__' of 'log_uniform' objects>, '__doc__': None, '__annotations__': {}})
__init__(loc=-1, scale=0, base=10)[source]
__module__ = 'WORC.classification.AdvancedSampler'
__weakref__

list of weak references to the object (if defined)

rvs(size=None, random_state=None)[source]

ObjectSampler Module

class WORC.classification.ObjectSampler.ObjectSampler(method, random_seed, sampling_strategy='auto', n_jobs=1, n_neighbors=3, k_neighbors=5, threshold_cleaning=0.5, verbose=True)[source]

Bases: object

Samples objects for learning based on various under-, over- and combined sampling methods.

The choice of included methods is largely based on:

He, Haibo, and Edwardo A. Garcia. “Learning from imbalanced data.” IEEE Transactions on Knowledge & Data Engineering 9 (2008): 1263-1284.

__dict__ = mappingproxy({'__module__': 'WORC.classification.ObjectSampler', '__doc__': '\n    Samples objects for learning based on various under-, over- and combined sampling methods.\n\n    The choice of included methods is largely based on:\n\n    He, Haibo, and Edwardo A. Garcia. "Learning from imbalanced data."\n    IEEE Transactions on Knowledge & Data Engineering 9 (2008): 1263-1284.\n\n    ', '__init__': <function ObjectSampler.__init__>, 'init_RandomUnderSampling': <function ObjectSampler.init_RandomUnderSampling>, 'init_NearMiss': <function ObjectSampler.init_NearMiss>, 'init_NeighbourhoodCleaningRule': <function ObjectSampler.init_NeighbourhoodCleaningRule>, 'init_RandomOverSampling': <function ObjectSampler.init_RandomOverSampling>, 'init_ADASYN': <function ObjectSampler.init_ADASYN>, 'init_BorderlineSMOTE': <function ObjectSampler.init_BorderlineSMOTE>, 'init_SMOTE': <function ObjectSampler.init_SMOTE>, 'init_SMOTEENN': <function ObjectSampler.init_SMOTEENN>, 'init_SMOTETomek': <function ObjectSampler.init_SMOTETomek>, 'fit': <function ObjectSampler.fit>, 'transform': <function ObjectSampler.transform>, '__dict__': <attribute '__dict__' of 'ObjectSampler' objects>, '__weakref__': <attribute '__weakref__' of 'ObjectSampler' objects>, '__annotations__': {}})
__init__(method, random_seed, sampling_strategy='auto', n_jobs=1, n_neighbors=3, k_neighbors=5, threshold_cleaning=0.5, verbose=True)[source]

Initialize object.

__module__ = 'WORC.classification.ObjectSampler'
__weakref__

list of weak references to the object (if defined)

fit(*args, **kwargs)[source]

Fit a sampler object.

init_ADASYN(sampling_strategy, n_neighbors, n_jobs)[source]

Creata a ADASYN sampler object.

init_BorderlineSMOTE(k_neighbors, n_jobs)[source]

Creata a BorderlineSMOTE sampler object.

init_NearMiss(sampling_strategy, n_jobs)[source]

Creata a near miss sampler object.

init_NeighbourhoodCleaningRule(sampling_strategy, n_neighbors, n_jobs, threshold_cleaning)[source]

Creata a NeighbourhoodCleaningRule sampler object.

init_RandomOverSampling(sampling_strategy)[source]

Creata a random over sampler object.

init_RandomUnderSampling(sampling_strategy)[source]

Creata a random under sampler object.

init_SMOTE(k_neighbors, n_jobs)[source]

Creata a SMOTE sampler object.

init_SMOTEENN(sampling_strategy)[source]

Creata a SMOTEEN sampler object.

init_SMOTETomek(sampling_strategy)[source]

Creata a SMOTE Tomek sampler object.

transform(*args, **kwargs)[source]

Transform objects with a fitted sampler.

SearchCV Module

class WORC.classification.SearchCV.BaseSearchCV(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, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_training_workflows=False, ensemble_validation_score=None, refit_validation_workflows=False)[source]

Bases: BaseEstimator, MetaEstimatorMixin

Base class for hyper parameter search with cross-validation.

__abstractmethods__ = frozenset({'__init__'})
abstract __init__(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, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_training_workflows=False, ensemble_validation_score=None, refit_validation_workflows=False)[source]

Initialize SearchCV Object.

__module__ = 'WORC.classification.SearchCV'
create_ensemble(X_train, Y_train, verbose=None, initialize=False, scoring=None, method='top_N', size=50, overfit_scaler=False)[source]

Create an (optimal) ensemble of a combination of hyperparameter settings and the associated groupsels, PCAs, estimators etc.

# The following ensemble methods are supported:

# Single: # only use the single best classifier. Performance is computed # using the same predict function as during the optimization # top_N: # make an ensemble of the best N individual classifiers, where N is # given as an input. If N==1, then only the single best classifier is # used, but it is evaluated using predict_proba. # FitNumber: # make an ensemble of the best N individual classifiers, choosing N # that gives the highest performance # ForwardSelection: # add the model that optimizes the total ensemble performance, # then repeat with replacement until there is no more improvement # in performance # Caruana: # for a fixed number of iterations, add the model that optimizes # the total ensemble performance, then choose the ensemble size # which gave the best performance # Bagging: # same as Caruana method, but the final ensemble is a weighted average # of a number of ensembles that each use only a subset of the available # models

decision_function(X)[source]

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

inverse_transform(Xt)[source]

Call inverse_transform on the estimator with the best found params.

Only available if the underlying estimator implements inverse_transform and refit=True.

Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X)[source]

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_log_proba(X)[source]

Call predict_log_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_log_proba.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_proba(X)[source]

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

preprocess(X, y=None, training=False)[source]

Apply the available preprocssing methods to the features.

process_fit(n_splits, parameters_all, test_sample_counts, test_score_dicts, train_score_dicts, fit_time, score_time, cv_iter, X, y, fitted_workflows=[], fitted_validation_workflows=[], use_smac=False)[source]

Process a fit.

Process the outcomes of a SearchCV fit and find the best settings over all cross validations from all hyperparameters tested

Very similar to the _format_results function or the original SearchCV.

refit_and_score(X, y, parameters_all, train, test, verbose=None)[source]

Refit the base estimator and attributes such as GroupSel.

Parameters

X: array, mandatory

Array containingfor each object (rows) the feature values (1st Column) and the associated feature label (2nd Column).

y: list(?), mandatory

List containing the labels of the objects.

parameters_all: dictionary, mandatory

Contains the settings used for the all preprocessing functions and the fitting. TODO: Create a default object and show the fields.

train: list, mandatory

Indices of the objects to be used as training set.

test: list, mandatory

Indices of the objects to be used as testing set.

score(X, y=None)[source]

Compute the score (i.e. probability) on a given data.

This uses the score defined by scoring where provided, and the best_estimator_.score method otherwise.

Parameters

Xarray-like, shape = [n_samples, n_features]

Input data, where n_samples is the number of samples and n_features is the number of features.

yarray-like, shape = [n_samples] or [n_samples, n_output], optional

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

Returns

score : float

transform(X)[source]

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

class WORC.classification.SearchCV.BaseSearchCVJoblib(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, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_training_workflows=False, ensemble_validation_score=None, refit_validation_workflows=False)[source]

Bases: BaseSearchCV

Base class for hyper parameter search with cross-validation.

__abstractmethods__ = frozenset({'__init__'})
__module__ = 'WORC.classification.SearchCV'
class WORC.classification.SearchCV.BaseSearchCVSMAC(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, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_training_workflows=False, ensemble_validation_score=None, refit_validation_workflows=False)[source]

Bases: BaseSearchCV

Base class for Bayesian hyper parameter search with cross-validation.

__abstractmethods__ = frozenset({'__init__'})
__module__ = 'WORC.classification.SearchCV'
class WORC.classification.SearchCV.BaseSearchCVfastr(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, n_jobspercore=100, maxlen=100, fastr_plugin=None, memory='2G', ranking_score='test_score', refit_training_workflows=False, ensemble_validation_score=None, refit_validation_workflows=False)[source]

Bases: BaseSearchCV

Base class for hyper parameter search with cross-validation.

__abstractmethods__ = frozenset({'__init__'})
__module__ = 'WORC.classification.SearchCV'
class WORC.classification.SearchCV.Ensemble(estimators)[source]

Bases: BaseEstimator, MetaEstimatorMixin

Ensemble of BaseSearchCV Estimators.

__abstractmethods__ = frozenset({})
__init__(estimators)[source]

Initialize object with list of estimators.

__module__ = 'WORC.classification.SearchCV'
decision_function(X)[source]

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

inverse_transform(Xt)[source]

Call inverse_transform on the estimator with the best found params.

Only available if the underlying estimator implements inverse_transform and refit=True.

Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X)[source]

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_log_proba(X)[source]

Call predict_log_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_log_proba.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_proba(X)[source]

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

transform(X)[source]

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

Parameters

Xindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

class WORC.classification.SearchCV.GridSearchCVJoblib(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: BaseSearchCVJoblib

Exhaustive search over specified parameter values for an estimator.

Important members are fit, predict.

GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.

Read more in the sklearn user guide.

Parameters

estimatorestimator 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_griddict 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.

scoringstring, 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_paramsdict, optional

Parameters to pass to the fit method.

n_jobsint, default=1

Number of jobs to run in parallel.

pre_dispatchint, 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’

iidboolean, 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.

cvint, 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.

Refer sklearn user guide for the various cross-validation strategies that can be used here.

refitboolean, default=True

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

verboseinteger

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_scoreboolean, default=True

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

Examples

>>> from sklearn import svm, datasets
>>> from sklearn.model_selection import GridSearchCV
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVC()
>>> clf = GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target)
...                             
GridSearchCV(cv=None, error_score=...,
       estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
                     decision_function_shape=None, degree=..., gamma=...,
                     kernel='rbf', max_iter=-1, probability=False,
                     random_state=None, shrinking=True, tol=...,
                     verbose=False),
       fit_params={}, iid=..., n_jobs=1,
       param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,
       scoring=..., verbose=...)
>>> sorted(clf.cv_results_.keys())
...                             
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
 'mean_train_score', 'param_C', 'param_kernel', 'params',...
 'rank_test_score', 'split0_test_score',...
 'split0_train_score', 'split1_test_score', 'split1_train_score',...
 'split2_test_score', 'split2_train_score',...
 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]

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 below given 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).

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.

If n_jobs was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

See Also

ParameterGrid:

generates all the combinations of a hyperparameter grid.

sklearn.model_selection.train_test_split():

utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.

sklearn.metrics.make_scorer():

Make a scorer from a performance metric or loss function.

__abstractmethods__ = frozenset({})
__init__(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]

Initialize SearchCV Object.

__module__ = 'WORC.classification.SearchCV'
fit(X, y=None, groups=None)[source]

Run fit with all sets of parameters.

Parameters

Xarray-like, shape = [n_samples, n_features]

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

yarray-like, shape = [n_samples] or [n_samples, n_output], optional

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

groupsarray-like, with shape (n_samples,), optional

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

class WORC.classification.SearchCV.GridSearchCVfastr(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: BaseSearchCVfastr

Exhaustive search over specified parameter values for an estimator.

Important members are fit, predict.

GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.

Read more in the sklearn user guide.

Parameters

estimatorestimator 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_griddict 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.

scoringstring, 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_paramsdict, optional

Parameters to pass to the fit method.

n_jobsint, default=1

Number of jobs to run in parallel.

pre_dispatchint, 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’

iidboolean, 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.

cvint, 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.

Refer the sklearn user guide for the various cross-validation strategies that can be used here.

refitboolean, default=True

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

verboseinteger

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_scoreboolean, default=True

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

Examples

>>> from sklearn import svm, datasets
>>> from sklearn.model_selection import GridSearchCV
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVC()
>>> clf = GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target)
...                             
GridSearchCV(cv=None, error_score=...,
       estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
                     decision_function_shape=None, degree=..., gamma=...,
                     kernel='rbf', max_iter=-1, probability=False,
                     random_state=None, shrinking=True, tol=...,
                     verbose=False),
       fit_params={}, iid=..., n_jobs=1,
       param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,
       scoring=..., verbose=...)
>>> sorted(clf.cv_results_.keys())
...                             
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
 'mean_train_score', 'param_C', 'param_kernel', 'params',...
 'rank_test_score', 'split0_test_score',...
 'split0_train_score', 'split1_test_score', 'split1_train_score',...
 'split2_test_score', 'split2_train_score',...
 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]

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 below given 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).

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.

If n_jobs was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

See Also

ParameterGrid:

generates all the combinations of a hyperparameter grid.

sklearn.model_selection.train_test_split():

utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.

sklearn.metrics.make_scorer():

Make a scorer from a performance metric or loss function.

__abstractmethods__ = frozenset({})
__init__(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]

Initialize SearchCV Object.

__module__ = 'WORC.classification.SearchCV'
fit(X, y=None, groups=None)[source]

Run fit with all sets of parameters.

Parameters

Xarray-like, shape = [n_samples, n_features]

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

yarray-like, shape = [n_samples] or [n_samples, n_output], optional

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

groupsarray-like, with shape (n_samples,), optional

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

class WORC.classification.SearchCV.GuidedSearchCVSMAC(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, n_jobspercore=100, fastr_plugin=None, maxlen=100, ranking_score='test_score', features=None, labels=None, refit_training_workflows=False, refit_validation_workflows=False, smac_result_file=None)[source]

Bases: BaseSearchCVSMAC

Guided search on hyperparameters.

GuidedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings.

The optimization is performed using the Sequential Model-based Algorithm Configuration (SMAC) method. A probabilistic model of the objective function is constructed and updated with each function evaluation.

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.

Parameters

param_distributionsdict

Dictionary with parameter names (string) as keys and details of their domains as values. From this dictionary the complete search space will later be constructed.

n_iterint, default=10

Number of function evaluations allowed in each optimization sequence of SMAC.

scoringstring, 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_paramsdict, optional

Parameters to pass to the fit method.

n_jobsint, default=1

Number of jobs to run in parallel.

pre_dispatchint, 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’

iidboolean, 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.

cvint, 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.

Refer User Guide for the various cross-validation strategies that can be used here.

refitboolean, default=True

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

verboseinteger

Controls the verbosity: the higher, the more messages.

random_stateint 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_scoreboolean, 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 below given 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).

Notes

The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.

If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

See Also

GridSearchCV:

Does exhaustive search over a grid of parameters.

ParameterSampler:

A generator over parameter settings, constructed from param_distributions.

__abstractmethods__ = frozenset({})
__init__(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, n_jobspercore=100, fastr_plugin=None, maxlen=100, ranking_score='test_score', features=None, labels=None, refit_training_workflows=False, refit_validation_workflows=False, smac_result_file=None)[source]

Initialize SearchCV Object.

__module__ = 'WORC.classification.SearchCV'
fit(X, y=None, groups=None)[source]

Run fit on the estimator with randomly drawn parameters.

Parameters

Xarray-like, shape = [n_samples, n_features]

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

yarray-like, shape = [n_samples] or [n_samples, n_output], optional

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

groupsarray-like, with shape (n_samples,), optional

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

class WORC.classification.SearchCV.RandomizedSearchCVJoblib(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, n_jobspercore=100, maxlen=100, ranking_score='test_score')[source]

Bases: BaseSearchCVJoblib

Randomized search on hyper parameters.

RandomizedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

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. It is highly recommended to use continuous distributions for continuous parameters.

Read more in the sklearn user guide.

Parameters

estimatorestimator 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_distributionsdict

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_iterint, default=10

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

scoringstring, 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_paramsdict, optional

Parameters to pass to the fit method.

n_jobsint, default=1

Number of jobs to run in parallel.

pre_dispatchint, 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’

iidboolean, 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.

cvint, 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.

Refer sklearn user guide for the various cross-validation strategies that can be used here.

refitboolean, default=True

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

verboseinteger

Controls the verbosity: the higher, the more messages.

random_stateint 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_scoreboolean, 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 below given 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).

Notes

The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.

If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

See Also

GridSearchCV:

Does exhaustive search over a grid of parameters.

ParameterSampler:

A generator over parameter settins, constructed from param_distributions.

__abstractmethods__ = frozenset({})
__init__(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, n_jobspercore=100, maxlen=100, ranking_score='test_score')[source]

Initialize SearchCV Object.

__module__ = 'WORC.classification.SearchCV'
fit(X, y=None, groups=None)[source]

Run fit on the estimator with randomly drawn parameters.

Parameters

Xarray-like, shape = [n_samples, n_features]

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

yarray-like, shape = [n_samples] or [n_samples, n_output], optional

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

groupsarray-like, with shape (n_samples,), optional

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

class WORC.classification.SearchCV.RandomizedSearchCVfastr(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, n_jobspercore=100, fastr_plugin=None, memory='2G', maxlen=100, ranking_score='test_score', refit_training_workflows=False, refit_validation_workflows=False)[source]

Bases: BaseSearchCVfastr

Randomized search on hyper parameters.

RandomizedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

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. It is highly recommended to use continuous distributions for continuous parameters.

Read more in the sklearn user guide.

Parameters

estimatorestimator 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_distributionsdict

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_iterint, default=10

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

scoringstring, 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_paramsdict, optional

Parameters to pass to the fit method.

n_jobsint, default=1

Number of jobs to run in parallel.

pre_dispatchint, 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’

iidboolean, 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.

cvint, 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.

Refer the sklearn user guide for the various cross-validation strategies that can be used here.

refitboolean, default=True

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

verboseinteger

Controls the verbosity: the higher, the more messages.

random_stateint 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_scoreboolean, 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 below given 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).

Notes

The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.

If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

See Also

GridSearchCV:

Does exhaustive search over a grid of parameters.

ParameterSampler:

A generator over parameter settings, constructed from param_distributions.

__abstractmethods__ = frozenset({})
__init__(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, n_jobspercore=100, fastr_plugin=None, memory='2G', maxlen=100, ranking_score='test_score', refit_training_workflows=False, refit_validation_workflows=False)[source]

Initialize SearchCV Object.

__module__ = 'WORC.classification.SearchCV'
fit(X, y=None, groups=None)[source]

Randomized model selection and hyperparameter search.

Parameters

Xarray-like, shape = [n_samples, n_features]

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

yarray-like, shape = [n_samples] or [n_samples, n_output], optional

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

groupsarray-like, with shape (n_samples,), optional

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

WORC.classification.SearchCV.chunks(l, n)[source]

Yield successive n-sized chunks from l.

WORC.classification.SearchCV.chunksdict(data, SIZE)[source]

Split a dictionary in equal parts of certain slice.

WORC.classification.SearchCV.rms_score(truth, prediction)[source]

Root-mean-square-error metric.

WORC.classification.SearchCV.sar_score(truth, prediction)[source]

SAR metric from Caruana et al. 2004.

construct_classifier Module

WORC.classification.construct_classifier.construct_SVM(config, regression=False)[source]

Construct a SVM classifier.

Args:

config (dict): Dictionary of the required config settings features (pandas dataframe): A pandas dataframe containing the features

to be used for classification

Returns:

SVM/SVR classifier, parameter grid

WORC.classification.construct_classifier.construct_classifier(config)[source]

Interface to create classification.

Different classifications can be created using this common interface

Parameters

config: dict, mandatory

Contains the required config settings. See the Github Wiki for all available fields.

Returns:

Constructed classifier

WORC.classification.construct_classifier.create_param_grid(config)[source]

Create a parameter grid for the WORC classifiers.

createfixedsplits Module

WORC.classification.createfixedsplits.createfixedsplits(label_file=None, label_type=None, patient_IDs=None, test_size=0.2, N_iterations=1, regression=False, stratify=None, modus='singlelabel', output=None)[source]

Create fixed splits for a cross validation.

crossval Module

WORC.classification.crossval.LOO_cross_validation(image_features, feature_labels, classes, patient_ids, param_grid, config, modus, test_size, start=0, save_data=None, tempsave=False, tempfolder=None, fixedsplits=None, fixed_seed=False, use_fastr=None, fastr_plugin=None, use_SMAC=False, smac_result_file=None)[source]

Cross-validation in which each sample is once used as the test set.

Mostly based on the default sklearn object.

Parameters

Returns

WORC.classification.crossval.crossval(config, label_data, image_features, param_grid=None, use_fastr=False, fastr_plugin=None, tempsave=False, fixedsplits=None, ensemble={'Use': False}, outputfolder=None, modus='singlelabel', use_SMAC=False, smac_result_file=None)[source]

Constructs multiple individual classifiers based on the label settings.

Parameters

config: dict, mandatory

Dictionary with config settings. See the Github Wiki for the available fields and formatting.

label_data: dict, mandatory

Should contain the following: patient_ids (list): ids of the patients, used to keep track of test and

training sets, and label data

label (list): List of lists, where each list contains the

label status for that patient for each label

label_name (list): Contains the different names that are stored

in the label object

image_features: numpy array, mandatory

Consists of a tuple of two lists for each patient: (feature_values, feature_labels)

param_grid: dictionary, optional

Contains the parameters and their values wich are used in the grid or randomized search hyperparamater optimization. See the construct_classifier function for some examples.

use_fastr: boolean, default False

If False, parallel execution through Joblib is used for fast execution of the hyperparameter optimization. Especially suited for execution on mutlicore (H)PC’s. The settings used are specified in the config.ini file in the IOparser folder, which you can adjust to your system.

If True, fastr is used to split the hyperparameter optimization in separate jobs. Parameters for the splitting can be specified in the config file. Especially suited for clusters.

fastr_plugin: string, default None

Determines which plugin is used for fastr executions. When None, uses the default plugin from the fastr config.

tempsave: boolean, default False

If True, create a .hdf5 file after each Cross-validation containing the classifier and results from that that split. This is written to the GSOut folder in your fastr output mount. If False, only the result of all combined Cross-validations will be saved to a .hdf5 file. This will also be done if set to True.

fixedsplits: string, optional

By default, random split Cross-validation is used to train and evaluate the machine learning methods. Optionally, you can provide a .xlsx file containing fixed splits to be used. See the Github Wiki for the format.

ensemble: dictionary, optional

Contains the configuration for constructing an ensemble.

modus: string, default ‘singlelabel’

Determine whether one-vs-all classification (or regression) for each single label is used (‘singlelabel’) or if multilabel classification is performed (‘multilabel’).

Returns

panda_data: pandas dataframe

Contains all information on the trained classifier.

WORC.classification.crossval.nocrossval(config, label_data_train, label_data_test, image_features_train, image_features_test, param_grid=None, use_fastr=False, fastr_plugin=None, ensemble={'Use': False}, modus='singlelabel')[source]

Constructs multiple individual classifiers based on the label settings.

Arguments:

config (Dict): Dictionary with config settings label_data (Dict): should contain: patient_ids (list): ids of the patients, used to keep track of test and

training sets, and label data

label (list): List of lists, where each list contains the

label status for that patient for each label

label_name (list): Contains the different names that are stored

in the label object

image_features (numpy array): Consists of a tuple of two lists for each patient:

(feature_values, feature_labels)

ensemble: dictionary, optional

Contains the configuration for constructing an ensemble.

modus: string, default ‘singlelabel’

Determine whether one-vs-all classification (or regression) for each single label is used (‘singlelabel’) or if multilabel classification is performed (‘multilabel’).

Returns:

classifier_data (pandas dataframe)

WORC.classification.crossval.random_split_cross_validation(image_features, feature_labels, classes, patient_ids, n_iterations, param_grid, config, modus, test_size, start=0, save_data=None, tempsave=False, tempfolder=None, fixedsplits=None, fixed_seed=False, use_fastr=None, fastr_plugin=None, use_SMAC=False, smac_result_file=None)[source]

Cross-validation in which data is randomly split in each iteration.

Due to options of doing single-label and multi-label classification, stratified splitting, and regression, we use a manual loop instead of the default scikit-learn object.

Parameters

Returns

WORC.classification.crossval.test_RS_Ensemble(estimator_input, X_train, Y_train, X_test, Y_test, feature_labels, output_json, verbose=False, RSs=None, ensembles=None, maxlen=100)[source]

Test performance for different random search and ensemble sizes.

This function is written for conducting a specific experiment from the WORC paper to test how the performance varies with varying random search and ensemble sizes. We do not recommend usage in general of this part.

maxlen = 100 # max ensembles numeric

fitandscore Module

WORC.classification.fitandscore.delete_cc_para(para)[source]

Delete all parameters that are involved in classifier construction.

WORC.classification.fitandscore.delete_nonestimator_parameters(parameters)[source]

Delete non-estimator parameters.

Delete all parameters in a parameter dictionary that are not used for the actual estimator.

WORC.classification.fitandscore.fit_and_score(X, y, scoring, train, test, parameters, fit_params=None, return_train_score=True, return_n_test_samples=True, return_times=True, return_parameters=False, return_estimator=False, error_score='raise', verbose=False, return_all=True, refit_training_workflows=False, refit_validation_workflows=False, skip=False)[source]

Fit an estimator to a dataset and score the performance.

The following methods can currently be applied as preprocessing before fitting, in this order: 0. Apply OneHotEncoder 1. Apply feature imputation 2. Select features based on feature type group (e.g. shape, histogram). 3. Scale features with e.g. z-scoring. 4. Apply feature selection based on variance of feature among patients. 5. Univariate statistical testing (e.g. t-test, Wilcoxon). 6. Use Relief feature selection. 7. Select features based on a fit with a LASSO model. 8. Select features using PCA. 9. Resampling 10. If a SingleLabel classifier is used for a MultiLabel problem,

a OneVsRestClassifier is employed around it.

All of the steps are optional.

Parameters

estimator: sklearn estimator, mandatory

Unfitted estimator which will be fit.

X: array, mandatory

Array containingfor each object (rows) the feature values (1st Column) and the associated feature label (2nd Column).

y: list(?), mandatory

List containing the labels of the objects.

scorer: sklearn scorer, mandatory

Function used as optimization criterion for the hyperparamater optimization.

train: list, mandatory

Indices of the objects to be used as training set.

test: list, mandatory

Indices of the objects to be used as testing set.

parameters: dictionary, mandatory

Contains the settings used for the above preprocessing functions and the fitting. TODO: Create a default object and show the fields.

fit_params:dictionary, default None

Parameters supplied to the estimator for fitting. See the SKlearn site for the parameters of the estimators.

return_train_score: boolean, default True

Save the training score to the final SearchCV object.

return_n_test_samples: boolean, default True

Save the number of times each sample was used in the test set to the final SearchCV object.

return_times: boolean, default True

Save the time spend for each fit to the final SearchCV object.

return_parameters: boolean, default True

Return the parameters used in the final fit to the final SearchCV object.

return_estimatorbool, default=False

Whether to return the fitted estimator.

error_score: numeric or “raise” by default

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.

verbose: boolean, default=True

If True, print intermediate progress to command line. Warnings are always printed.

return_all: boolean, default=True

If False, only the ret object containing the performance will be returned. If True, the ret object plus all fitted objects will be returned.

Returns

Depending on the return_all input parameter, either only ret or all objects below are returned.

ret: list

Contains optionally the train_scores and the test_scores, fit_time, score_time, parameters_est and parameters_all.

GroupSel: WORC GroupSel Object

Either None if the groupwise feature selection is not used, or the fitted object.

VarSel: WORC VarSel Object

Either None if the variance threshold feature selection is not used, or the fitted object.

SelectModel: WORC SelectModel Object

Either None if the feature selection based on a fittd model is not used, or the fitted object.

feature_labels: list

Labels of the features. Only one list is returned, not one per feature object, as we assume all samples have the same feature names.

scaler: scaler object

Either None if feature scaling is not used, or the fitted object.

encoder: WORC Encoder Object

Either None if feature OneHotEncoding is not used, or the fitted object.

imputer: WORC Imputater Object

Either None if feature imputation is not used, or the fitted object.

pca: WORC PCA Object

Either None if PCA based feature selection is not used, or the fitted object.

StatisticalSel: WORC StatisticalSel Object

Either None if the statistical test feature selection is not used, or the fitted object.

RFESel: WORC RFESel Object

Either None if the recursive feature elimination feature selection is not used, or the fitted object.

ReliefSel: WORC ReliefSel Object

Either None if the RELIEF feature selection is not used, or the fitted object.

Sampler: WORC ObjectSampler Object

Either None if no resampling is used, or an ObjectSampler object

WORC.classification.fitandscore.replacenan(image_features, verbose=True, feature_labels=None)[source]

Replace the NaNs in an image feature matrix.

metrics Module

WORC.classification.metrics.ICC(M, ICCtype='inter')[source]
Input:

M is matrix of observations. Rows: patients, columns: observers. type: ICC type, currently “inter” or “intra”.

WORC.classification.metrics.ICC_anova(Y, ICCtype='inter', more=False)[source]

Adopted from Nipype with a slight alteration to distinguish inter and intra. the data Y are entered as a ‘table’ ie subjects are in rows and repeated measures in columns One Sample Repeated measure ANOVA Y = XB + E with X = [FaTor / Subjects]

WORC.classification.metrics.check_multimetric_scoring(estimator, scoring=None)[source]

Wrapper around sklearn function to enable more scoring options.

Check the scoring parameter in cases when multiple metrics are allowed

Parameters

estimatorsklearn estimator instance

The estimator for which the scoring will be applied.

scoringstring, callable, list/tuple, dict or None, default: None

A single string (see scoring_parameter) or a callable (see scoring) to evaluate the predictions on the test set. For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values. NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each. See multimetric_grid_search for an example. If None the estimator’s score method is used. The return value in that case will be {'score': <default_scorer>}. If the estimator’s score method is not available, a TypeError is raised.

Returns

scorers_dictdict

A dict mapping each scorer name to its validated scorer.

is_multimetricbool

True if scorer is a list/tuple or dict of callables False if scorer is None/str/callable

WORC.classification.metrics.check_scoring(estimator, scoring=None, allow_none=False)[source]

Surrogate for sklearn’s check_scoring to enable use of some other scoring metrics.

WORC.classification.metrics.f1_weighted_predictproba(y_truth, y_score)[source]

Calculate f1-score, but based on predict_proba instead of predict.

Probabilities are thresholded at 0.5.

WORC.classification.metrics.multi_class_auc(y_truth, y_score)[source]
WORC.classification.metrics.multi_class_auc_score(y_truth, y_score)[source]
WORC.classification.metrics.pairwise_auc(y_truth, y_score, class_i, class_j)[source]
WORC.classification.metrics.performance_multilabel(y_truth, y_prediction, y_score=None, beta=1)[source]

Multiclass performance metrics.

y_truth and y_prediction should both be lists with the multiclass label of each object, e.g.

y_truth = [0, 0, 0, 0, 0, 0, 2, 2, 1, 1, 2] ### Groundtruth y_prediction = [0, 0, 0, 0, 0, 0, 1, 2, 1, 2, 2] ### Predicted labels y_score = [[0.3, 0.3, 0.4], [0.2, 0.6, 0.2], … ] # Normalized score per patient for all labels (three in this example)

Calculation of accuracy accorading to formula suggested in CAD Dementia Grand Challege http://caddementia.grand-challenge.org and the TADPOLE challenge https://tadpole.grand-challenge.org/Performance_Metrics/ Calculation of Multi Class AUC according to classpy: https://bitbucket.org/bigr_erasmusmc/classpy/src/master/classpy/multi_class_auc.py

WORC.classification.metrics.performance_singlelabel(y_truth, y_prediction, y_score, regression=False)[source]

Singleclass performance metrics

parameter_optimization Module

WORC.classification.parameter_optimization.guided_search_parameters(features, labels, N_iter, test_size, parameters, scoring_method, n_splits=5, n_jobspercore=200, use_fastr=False, n_cores=1, fastr_plugin=None, memory='2G', maxlen=100, ranking_score='test_score', random_seed=None, refit_training_workflows=False, refit_validation_workflows=False, smac_result_file=None)[source]

Train a classifier and simultaneously optimizes hyperparameters using a Bayesian optimization approach.

Arguments:

features: numpy array containing the training features. labels: list containing the object labels to be trained on. N_iter: integer listing the number of iterations to be used in the

hyperparameter optimization.

test_size: float listing the test size percentage used in the cross

validation.

classifier: sklearn classifier to be tested param_grid: dictionary containing all possible hyperparameters and their

values or distrubitions.

scoring_method: string defining scoring method used in optimization,

e.g. f1_weighted for a SVM.

n_jobsperscore: integer listing the number of jobs that are ran on a

single core when using the fastr randomized search.

use_fastr: Boolean determining of either fastr or joblib should be used

for the opimization.

fastr_plugin: determines which plugin is used for fastr executions.

When None, uses the default plugin from the fastr config.

Returns:

guided_search: object containing the results

WORC.classification.parameter_optimization.random_search_parameters(features, labels, N_iter, test_size, param_grid, scoring_method, n_splits=5, n_jobspercore=200, use_fastr=False, n_cores=1, fastr_plugin=None, memory='2G', maxlen=100, ranking_score='test_score', random_seed=None, refit_training_workflows=False, refit_validation_workflows=False)[source]

Train a classifier and simultaneously optimizes hyperparameters using a randomized search.

Arguments:

features: numpy array containing the training features. labels: list containing the object labels to be trained on. N_iter: integer listing the number of iterations to be used in the

hyperparameter optimization.

test_size: float listing the test size percentage used in the cross

validation.

classifier: sklearn classifier to be tested param_grid: dictionary containing all possible hyperparameters and their

values or distrubitions.

scoring_method: string defining scoring method used in optimization,

e.g. f1_weighted for a SVM.

n_jobsperscore: integer listing the number of jobs that are ran on a

single core when using the fastr randomized search.

use_fastr: Boolean determining of either fastr or joblib should be used

for the opimization.

fastr_plugin: determines which plugin is used for fastr executions.

When None, uses the default plugin from the fastr config.

Returns:

random_search: sklearn randomsearch object containing the results.

regressors Module

smac Module

WORC.classification.smac.build_smac_config(parameters)[source]

Reads a parameter dictionary and constructs a SMAC configuration object from it, to be used as input for the Bayesian optimization

Parameters

parameters: dict, mandatory

Contains the required config settings

Returns:

ConfigurationSpace object that defines the search space of the hyperparameter optimization

trainclassifier Module

WORC.classification.trainclassifier.add_parameters_to_grid(param_grid, config)[source]

Add non-classifier parameters from config to param grid.

WORC.classification.trainclassifier.trainclassifier(feat_train, patientinfo_train, config, output_hdf, feat_test=None, patientinfo_test=None, fixedsplits=None, output_smac=None, verbose=True)[source]

Train a classifier using machine learning from features.

By default, if no split in training and test is supplied, a cross validation will be performed.

Parameters

feat_train: string, mandatory

contains the paths to all .hdf5 feature files used. modalityname1=file1,file2,file3,… modalityname2=file1,… Thus, modalities names are always between a space and a equal sign, files are split by commas. We assume that the lists of files for each modality has the same length. Files on the same position on each list should belong to the same patient.

patientinfo: string, mandatory

Contains the path referring to a .txt file containing the patient label(s) and value(s) to be used for learning. See the Github Wiki for the format.

config: string, mandatory

path referring to a .ini file containing the parameters used for feature extraction. See the Github Wiki for the possible fields and their description.

output_hdf: string, mandatory

path refering to a .hdf5 file to which the final classifier and it’s properties will be written to.

feat_test: string, optional

When this argument is supplied, the machine learning will not be trained using a cross validation, but rather using a fixed training and text split. This field should contain paths of the test set feature files, similar to the feat_train argument.

patientinfo_test: string, optional

When feat_test is supplied, you can supply optionally a patient label file through which the performance will be evaluated.

fixedsplits: string, optional

By default, random split cross validation is used to train and evaluate the machine learning methods. Optionally, you can provide a .xlsx file containing fixed splits to be used. See the Github Wiki for the format.

verbose: boolean, default True

print final feature values and labels to command line or not.