Source code for WORC.featureprocessing.Decomposition

#!/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.

import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt

import numpy as np
import WORC.IOparser.config_io_classifier as config_io
import os
from WORC.IOparser.file_io import load_features
from sklearn.decomposition import PCA, SparsePCA, KernelPCA
from sklearn.manifold import TSNE


[docs]def Decomposition(features, patientinfo, config, output, label_type=None, verbose=True): """ Perform decompositions to two components of the feature space. Useage is similar to StatisticalTestFeatures. Parameters ---------- features: 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. # TODO: outputs verbose: boolean, default True print final feature values and labels to command line or not. """ # Load variables from the config file config = config_io.load_config(config) # Create output folder if required if not os.path.exists(os.path.dirname(output)): os.makedirs(os.path.dirname(output)) if label_type is None: label_type = config['Labels']['label_names'] # Read the features and classification data print("Reading features and label data.") label_data, image_features =\ load_features(features, patientinfo, label_type) # Extract feature labels and put values in an array feature_labels = image_features[0][1] feature_values = np.zeros([len(image_features), len(feature_labels)]) for num, x in enumerate(image_features): feature_values[num, :] = x[0] # ----------------------------------------------------------------------- # Perform decomposition print("Performing decompositions.") label_value = label_data['label'] label_name = label_data['label_name'] # Reduce to two components for plotting n_components = 2 for i_class, i_name in zip(label_value, label_name): classlabels = i_class.ravel() class1 = [i for j, i in enumerate(feature_values) if classlabels[j] == 1] class2 = [i for j, i in enumerate(feature_values) if classlabels[j] == 0] f = plt.figure(figsize=(20, 15)) # ------------------------------------------------------- # Fit PCA pca = PCA(n_components=n_components) pca.fit(feature_values) explained_variance_ratio = np.sum(pca.explained_variance_ratio_) class1_pca = pca.transform(class1) class2_pca = pca.transform(class2) # Plot PCA ax = plt.subplot(2, 3, 1) plt.subplots_adjust(hspace=0.3, wspace=0.2) ax.scatter(class1_pca[:, 0], class1_pca[:, 1], color='blue') ax.scatter(class2_pca[:, 0], class2_pca[:, 1], color='green') ax.set_title(f'PCA: {round(explained_variance_ratio, 3)} variance.') # ------------------------------------------------------- # Fit Sparse PCA pca = SparsePCA(n_components=n_components) pca.fit(feature_values) class1_pca = pca.transform(class1) class2_pca = pca.transform(class2) # Plot Sparse PCA ax = plt.subplot(2, 3, 2) plt.subplots_adjust(hspace=0.3, wspace=0.2) ax.scatter(class1_pca[:, 0], class1_pca[:, 1], color='blue') ax.scatter(class2_pca[:, 0], class2_pca[:, 1], color='green') ax.set_title('Sparse PCA.') # ------------------------------------------------------- # Fit Kernel PCA fnum = 3 for kernel in ['linear', 'poly', 'rbf']: pca = KernelPCA(n_components=n_components, kernel=kernel) pca.fit(feature_values) class1_pca = pca.transform(class1) class2_pca = pca.transform(class2) # Plot Sparse PCA ax = plt.subplot(2, 3, fnum) plt.subplots_adjust(hspace=0.3, wspace=0.2) ax.scatter(class1_pca[:, 0], class1_pca[:, 1], color='blue') ax.scatter(class2_pca[:, 0], class2_pca[:, 1], color='green') ax.set_title(('Kernel PCA: {} .').format(kernel)) fnum += 1 # ------------------------------------------------------- # Fit t-SNE tSNE = TSNE(n_components=n_components) class_all = class1 + class2 class_all_tsne = tSNE.fit_transform(class_all) class1_tSNE = class_all_tsne[0:len(class1)] class2_tSNE = class_all_tsne[len(class1):] # Plot Sparse tSNE ax = plt.subplot(2, 3, 6) plt.subplots_adjust(hspace=0.3, wspace=0.2) ax.scatter(class1_tSNE[:, 0], class1_tSNE[:, 1], color='blue') ax.scatter(class2_tSNE[:, 0], class2_tSNE[:, 1], color='green') ax.set_title('t-SNE.') # ------------------------------------------------------- # Maximize figure to get correct spacings # mng = plt.get_current_fig_manager() # mng.resize(*mng.window.maxsize()) # High DTI to make sure we save the maximized image f.savefig(output, dpi=600) print(("Decomposition saved as {} !").format(output))