Rohit Garg
71 linhas
2.9 KiB
Python
71 linhas
2.9 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf-8
|
|
|
|
# In[ ]:
|
|
|
|
|
|
# Script to get the feature ranking and electrode ranking through
|
|
# Method A :- Random Forest Regressor
|
|
# Method B :- F score based Ranking
|
|
# Method C :- Random Forest Importances approach
|
|
# Main function
|
|
|
|
from ImportUtils import *
|
|
from TopNByFSMethods import *
|
|
from TopNByClassifier import *
|
|
from args_eeg import args as my_args
|
|
|
|
if __name__ == '__main__':
|
|
|
|
# args object to fetch command line inputs
|
|
args = my_args()
|
|
print(args.__dict__)
|
|
pwd = os.getcwd()
|
|
|
|
dataset = args.dataset
|
|
window = args.window
|
|
stride = args.stride
|
|
sfreq = args.sfreq
|
|
model = args.model
|
|
label = args.label
|
|
approach = args.approach #byclassifier or byfs
|
|
ml_algo = args.ml_algo #classification or regression
|
|
top = args.top #e or f or ef
|
|
fs_method = args.fs_method
|
|
|
|
#feature extraction
|
|
getEpochedFeatures(dataset, window, stride, sfreq, label)
|
|
if(top == "e"):
|
|
clf = RandomForestRegressor()
|
|
topElectrodeRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False)
|
|
topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest')
|
|
topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='RandomForest')
|
|
plt.legend(["Method A","Method B", "Method C"])
|
|
|
|
if(label == 1):
|
|
plt.savefig(pwd + "/" + dataset + "/arousal_plots/" + "CorrectedElectrodewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight')
|
|
plt.show()
|
|
plt.clf()
|
|
|
|
else:
|
|
plt.savefig(pwd + "/" + dataset + "/plots/" + "CorrectedElectrodewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight')
|
|
plt.show()
|
|
plt.clf()
|
|
|
|
elif(top == "f"):
|
|
clf = RandomForestRegressor()
|
|
topFeaturesRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False)
|
|
topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest')
|
|
topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='RandomForest')
|
|
if(label == 1):
|
|
plt.legend(["Method A","Method B", "Method C"])
|
|
plt.savefig(pwd + "/" + dataset + "/arousal_plots/" + "CorrectedFeaturewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight')
|
|
plt.show()
|
|
plt.clf()
|
|
else:
|
|
plt.legend(["Method A","Method B", "Method C"])
|
|
plt.savefig(pwd + "/" + dataset + "/plots/" + "CorrectedFeaturewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight')
|
|
plt.show()
|
|
plt.clf()
|
|
|