PREDICT.processing package

Submodules

PREDICT.processing.AdvancedSampler module

class PREDICT.processing.AdvancedSampler.AdvancedSampler(param_distributions, n_iter, random_state=None, method='Halton')

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.

param_distributions : dict
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_iter : integer
Number of parameter settings that are produced.
random_state : int or RandomState
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.
params : dict of string to any
Yields dictionaries mapping each estimator parameter to as sampled value.
>>> from PREDICT.processing.HaltonSampler 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
class PREDICT.processing.AdvancedSampler.exp_uniform(loc=-1, scale=0, base=2.718281828459045)
rvs(size=None, random_state=None)
class PREDICT.processing.AdvancedSampler.log_uniform(loc=-1, scale=0, base=10)
rvs(size=None, random_state=None)

PREDICT.processing.ICC module

PREDICT.processing.ICC.ICC(M, ICCtype='inter')
Input:
M is matrix of observations. Rows: patients, columns: observers. type: ICC type, currently “inter” or “intra”.
PREDICT.processing.ICC.ICC_anova(Y, ICCtype='inter', more=False)

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]

PREDICT.processing.Imputer module

PREDICT.processing.SearchCV module

class PREDICT.processing.SearchCV.BaseSearchCV(estimator, param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None)

Bases: abc.NewBase

Base class for hyper parameter search with cross-validation.

best_params_
best_score_
create_ensemble(X_train, Y_train, verbose=None, initialize=True, scoring=None, method=50)

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

Based on Caruana et al. 2004, but a little different:

  1. Recreate the training/validation splits for a n-fold cross validation.
  2. For each fold:
    1. Start with an empty ensemble
    2. Create starting ensemble by adding N individually best performing models on the validation set. N is tuned on the validation set.
    3. Add model that improves ensemble performance on validation set the most, with replacement.
    4. Repeat (c) untill performance does not increase

The performance metric is the same as for the original hyperparameter search, i.e. probably the F1-score for classification and r2-score for regression. However, we recommend using the SAR score, as this is more universal.

Method: top50 or Caruana

decision_function(*args, **kwargs)

Call decision_function on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
grid_scores_
inverse_transform(*args, **kwargs)

Call inverse_transform on the estimator with the best found params.

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

Xt : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict(*args, **kwargs)

Call predict on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict_log_proba(*args, **kwargs)

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.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict_proba(*args, **kwargs)

Call predict_proba on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
preprocess(X)

Apply the available preprocssing methods to the features

process_fit(n_splits, parameters_est, parameters_all, test_sample_counts, test_scores, train_scores, fit_time, score_time, cv_iter, base_estimator, X, y)

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

refit_and_score(X, y, parameters_all, parameters_est, train, test, verbose=None)

Refit the base estimator and attributes such as GroupSel

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.
parameters_est: dictionary, mandatory
Contains the settings used for the base estimator
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)

Returns the score on the given data, if the estimator has been refit.

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

X : array-like, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.

score : float

transform(*args, **kwargs)

Call transform on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
class PREDICT.processing.SearchCV.BaseSearchCVJoblib(estimator, param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None)

Bases: PREDICT.processing.SearchCV.BaseSearchCV

Base class for hyper parameter search with cross-validation.

class PREDICT.processing.SearchCV.BaseSearchCVfastr(estimator, param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, maxlen=100, fastr_plugin=None)

Bases: PREDICT.processing.SearchCV.BaseSearchCV

Base class for hyper parameter search with cross-validation.

class PREDICT.processing.SearchCV.Ensemble(estimators)

Bases: abc.NewBase

Ensemble of BaseSearchCV Estimators.

decision_function(X)

Call decision_function on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
inverse_transform(Xt)

Call inverse_transform on the estimator with the best found params.

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

Xt : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict(X)

Call predict on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict_log_proba(X)

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.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict_proba(X)

Call predict_proba on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
transform(X)

Call transform on the estimator with the best found parameters.

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

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
class PREDICT.processing.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)

Bases: PREDICT.processing.SearchCV.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 User Guide.

estimator : estimator object.
This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional

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

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
iid : boolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional

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

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

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

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

refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this GridSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
error_score : ‘raise’ (default) or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
return_train_score : boolean, default=True
If 'False', the cv_results_ attribute will not include training scores.
>>> 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'...]
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).

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.

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.
fit(X, y=None, groups=None)

Run fit with all sets of parameters.

X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
class PREDICT.processing.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)

Bases: PREDICT.processing.SearchCV.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 User Guide.

estimator : estimator object.
This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional

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

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
iid : boolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional

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

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

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

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

refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this GridSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
error_score : ‘raise’ (default) or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
return_train_score : boolean, default=True
If 'False', the cv_results_ attribute will not include training scores.
>>> 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'...]
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).

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.

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.
fit(X, y=None, groups=None)

Run fit with all sets of parameters.

X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
class PREDICT.processing.SearchCV.RandomizedSearchCVJoblib(estimator, param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100)

Bases: PREDICT.processing.SearchCV.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 User Guide.

estimator : estimator object.
A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_distributions : dict
Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly.
n_iter : int, default=10
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional

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

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
iid : boolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional

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

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

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

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

refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
random_state : int or RandomState
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.
error_score : ‘raise’ (default) or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
return_train_score : boolean, default=True
If 'False', the cv_results_ attribute will not include training scores.
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).

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.

GridSearchCV:
Does exhaustive search over a grid of parameters.
ParameterSampler:
A generator over parameter settins, constructed from param_distributions.
fit(X, y=None, groups=None)

Run fit on the estimator with randomly drawn parameters.

X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
class PREDICT.processing.SearchCV.RandomizedSearchCVfastr(estimator, param_distributions={}, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True, n_jobspercore=100, fastr_plugin=None)

Bases: PREDICT.processing.SearchCV.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 User Guide.

estimator : estimator object.
A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_distributions : dict
Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly.
n_iter : int, default=10
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional

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

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
iid : boolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional

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

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

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

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

refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
random_state : int or RandomState
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.
error_score : ‘raise’ (default) or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
return_train_score : boolean, default=True
If 'False', the cv_results_ attribute will not include training scores.
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).

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.

GridSearchCV:
Does exhaustive search over a grid of parameters.
ParameterSampler:
A generator over parameter settings, constructed from param_distributions.
fit(X, y=None, groups=None)

Run fit on the estimator with randomly drawn parameters.

X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
PREDICT.processing.SearchCV.chunks(l, n)

Yield successive n-sized chunks from l.

PREDICT.processing.SearchCV.chunksdict(data, SIZE)

Split a dictionary in equal parts of certain slice

PREDICT.processing.SearchCV.rms_score(truth, prediction)

Root-mean-square-error metric

PREDICT.processing.SearchCV.sar_score(truth, prediction)

SAR metric from Caruana et al. 2004

PREDICT.processing.fitandscore module

PREDICT.processing.fitandscore.delete_nonestimator_parameters(parameters)

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

PREDICT.processing.fitandscore.fit_and_score(estimator, X, y, scorer, train, test, para, fit_params=None, return_train_score=True, return_n_test_samples=True, return_times=True, return_parameters=True, error_score='raise', verbose=True, return_all=True)

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

a OneVsRestClassifier is employed around it.

All of the steps are optional.

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.
para: 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.
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.

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, test_sample_counts, fit_time, score_time, parameters_est and parameters_all.
GroupSel: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.
VarSel: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.
SelectModel: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.

feature_labels

scaler

imputer: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.
pca: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.
StatisticalSel: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.
ReliefSel: PREDICT GroupSel Object
Either None if the GroupSelFitted GroupSel Object.
PREDICT.processing.fitandscore.replacenan(image_features, verbose=True, feature_labels=None)

Replace the NaNs in an image feature matrix.

Module contents