#!/usr/bin/env python
# Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
import numpy as np
[docs]class SelectGroups(BaseEstimator, 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.
'''
[docs] def __init__(self, parameters, toolboxes=['PREDICT']):
'''
Parameters
----------
parameters: dict, mandatory
Contains the settings for the groups to be selected. Should
contain the settings for the following groups:
- histogram_features
- shape_features
- orientation_features
- semantic_features
- dicom_features
- coliage_features
- phase_features
- vessel_features
- texture_Gabor_features
- texture_GLCM_features
- texture_GLCMMS_features
- texture_GLRLM_features
- texture_GLSZM_features
- texture_GLDZM_features
- texture_NGTDM_features
- texture_NGLDM_features
- texture_LBP_features
- fractal_features
- location_features
- RGRD_features
Also, should contain a parameter for selecting per feature toolbox:
- PREDICT
- PyRadiomics
And a parameter to select whether transformation have been applied:
- original_features
- wavelet_features
- log_features
'''
params = list()
if parameters['histogram_features'] == 'True':
params.append('hf_')
if parameters['shape_features'] == 'True':
params.append('sf_')
if parameters['orientation_features'] == 'True':
params.append('of_')
if parameters['semantic_features'] == 'True':
params.append('semf_')
if parameters['dicom_features'] == 'True':
params.append('df_')
if parameters['coliage_features'] == 'True':
params.append('cf_')
if parameters['phase_features'] == 'True':
params.append('phasef_')
if parameters['vessel_features'] == 'True':
params.append('vf_')
if parameters['fractal_features'] == 'True':
params.append('fracf_')
if parameters['location_features'] == 'True':
params.append('locf_')
if parameters['rgrd_features'] == 'True':
params.append('rgrdf_')
transform_params = list()
if parameters['original_features'] == 'False':
transform_params.append('_original')
if parameters['wavelet_features'] == 'False':
transform_params.append('_wavelet')
if parameters['log_features'] == 'False':
transform_params.append('_log')
if 'texture_features' in parameters.keys():
# Backwards compatability
if parameters['texture_features'] == 'True':
params.append('tf_')
elif parameters['texture_features'] == 'False':
pass
else:
params.append('tf_' + parameters['texture_features'])
else:
# Hyperparameter per feature group
if parameters['texture_gabor_features'] == 'True':
params.append('Gabor')
if parameters['texture_glcm_features'] == 'True':
params.append('GLCM_')
if parameters['texture_gldm_features'] == 'True':
params.append('GLDM')
if parameters['texture_glcmms_features'] == 'True':
params.append('GLCMMS')
if parameters['texture_glrlm_features'] == 'True':
params.append('GLRLM')
if parameters['texture_glszm_features'] == 'True':
params.append('GLSZM')
if parameters['texture_gldzm_features'] == 'True':
params.append('GLDZM')
if parameters['texture_ngtdm_features'] == 'True':
params.append('NGTDM')
if parameters['texture_ngldm_features'] == 'True':
params.append('NGLDM')
if parameters['texture_lbp_features'] == 'True':
params.append('LBP')
self.parameters = params
self.transform_parameters = transform_params
# Select per feature toolbox
if toolboxes == 'All':
self.toolboxes = '_'
else:
self.toolboxes = toolboxes
[docs] def fit(self, feature_labels):
'''
Select only features specificed by parameters per patient.
Parameters
----------
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.
'''
# Remove NAN
selectrows = list()
for num, l in enumerate(feature_labels):
if any(x in l for x in self.parameters):
if any(x in l for x in self.toolboxes):
if not any(x in l for x in self.transform_parameters):
selectrows.append(num)
self.selectrows = selectrows
def _get_support_mask(self):
# NOTE: Method is required for the Selector class, but can be empty
pass