#!/usr/bin/env python
# Copyright 2016-2019 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 SelectIndividuals(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=['hf_mean', 'sf_compactness']):
'''
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
- patient_features
- coliage_features
- phase_features
- vessel_features
- log_features
- texture_features
'''
self.parameters = parameters
[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):
selectrows.append(num)
self.selectrows = selectrows
def _get_support_mask(self):
# NOTE: Method is required for the Selector class, but can be empty
pass