PREDICT.featureselection package¶
Submodules¶
PREDICT.featureselection.Relief module¶
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class
PREDICT.featureselection.Relief.
SelectMulticlassRelief
(n_neighbours=3, sample_size=1, distance_p=2, numf=None)¶ Bases:
sklearn.base.BaseEstimator
,sklearn.feature_selection.base.SelectorMixin
Object to fit feature selection based on the type group the feature belongs to. The label for the feature is used for this procedure.
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fit
(X, y)¶ Select only features specificed by parameters per patient.
- feature_values: numpy array, mandatory
- Array containing feature values used for model_selection. Number of objects on first axis, features on second axis.
- feature_labels: list, mandatory
- Contains the labels of all features used. The index in this list will be used in the transform funtion to select features.
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multi_class_relief
(feature_set, label_set, nb=3, sample_size=1, distance_p=2, numf=None)¶
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single_class_relief
(feature_set, label_set, nb=3, sample_size=1, distance_p=2, numf=None)¶
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transform
(inputarray)¶ Transform the inputarray to select only the features based on the result from the fit function.
- inputarray: numpy array, mandatory
- Array containing the items to use selection on. The type of item in this list does not matter, e.g. floats, strings etc.
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PREDICT.featureselection.SelectGroups module¶
-
class
PREDICT.featureselection.SelectGroups.
SelectGroups
(parameters)¶ Bases:
sklearn.base.BaseEstimator
,sklearn.feature_selection.base.SelectorMixin
Object to fit feature selection based on the type group the feature belongs to. The label for the feature is used for this procedure.
-
fit
(feature_labels)¶ Select only features specificed by parameters per patient.
- feature_labels: list, optional
- Contains the labels of all features used. The index in this list will be used in the transform funtion to select features.
-
transform
(inputarray)¶ Transform the inputarray to select only the features based on the result from the fit function.
- inputarray: numpy array, mandatory
- Array containing the items to use selection on. The type of item in this list does not matter, e.g. floats, strings etc.
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PREDICT.featureselection.SelectIndividuals module¶
-
class
PREDICT.featureselection.SelectIndividuals.
SelectIndividuals
(parameters=['hf_mean', 'sf_compactness'])¶ Bases:
sklearn.base.BaseEstimator
,sklearn.feature_selection.base.SelectorMixin
Object to fit feature selection based on the type group the feature belongs to. The label for the feature is used for this procedure.
-
fit
(feature_labels)¶ Select only features specificed by parameters per patient.
- feature_labels: list, optional
- Contains the labels of all features used. The index in this list will be used in the transform funtion to select features.
-
transform
(inputarray)¶ Transform the inputarray to select only the features based on the result from the fit function.
- inputarray: numpy array, mandatory
- Array containing the items to use selection on. The type of item in this list does not matter, e.g. floats, strings etc.
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PREDICT.featureselection.StatisticalTestThreshold module¶
-
class
PREDICT.featureselection.StatisticalTestThreshold.
StatisticalTestThreshold
(metric='ttest', threshold=0.05)¶ Bases:
sklearn.base.BaseEstimator
,sklearn.feature_selection.base.SelectorMixin
Object to fit feature selection based on statistical tests.
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fit
(X_train, Y_train)¶ Select only features specificed by the metric and threshold per patient.
- X_train: numpy array, mandatory
- Array containing feature values used for model_selection. Number of objects on first axis, features on second axis.
- Y_train: numpy array, mandatory
- Array containing the binary labels for each object in X_train.
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transform
(inputarray)¶ Transform the inputarray to select only the features based on the result from the fit function.
- inputarray: numpy array, mandatory
- Array containing the items to use selection on. The type of item in this list does not matter, e.g. floats, strings etc.
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PREDICT.featureselection.VarianceThreshold module¶
-
class
PREDICT.featureselection.VarianceThreshold.
VarianceThresholdMean
(threshold)¶ Bases:
sklearn.base.BaseEstimator
,sklearn.feature_selection.base.SelectorMixin
Select features based on variance among objects. Similar to VarianceThreshold from sklearn, but does take the mean of the feature into account.
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fit
(image_features)¶
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transform
(inputarray)¶ Transform the inputarray to select only the features based on the result from the fit function. Parameters ———- inputarray: numpy array, mandatory
Array containing the items to use selection on. The type of item in this list does not matter, e.g. floats, strings etc.
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PREDICT.featureselection.VarianceThreshold.
selfeat_variance
(image_features, labels=None, thresh=0.99, method='nomean')¶ Select features using a variance threshold.
- image_features: numpy array, mandatory
- Array containing the feature values to apply the variance threshold selection on. The rows correspond to the patients, the column to the features.
- labels: numpy array, optional
- Array containing the labels of the corresponding features. Array should therefore have the same shape as the image_features array.
- thresh: float, default 0.99
- Threshold to be used as lower boundary for feature variance among patients.
- method: string, default nomean.
- Method to use for selection. Default: do not use the mean of the features. Other valid option is ‘mean’.
- image_features: numpy array
- Transformed features array.
- labels: list or None
- When labels are given, returns the transformed labels. That object contains a list of all label names kept.
- sel: VarianceThreshold object
- The fitted variance threshold object.