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Feature Importance using Forest of Trees¶
This example shows how to compute feature importance for a classification task using forest of trees
# import matplotlib
# matplotlib.use('TkAgg')
from os.path import isdir, join
import sab_dataset
import seaborn as sns
sns.set()
sns.set_context('paper')
Load the data : sab dataset
sab_dataset_dirpath = join('pySAB', 'sample_data') if isdir('pySAB') else join('..', '..', 'pySAB', 'sample_data')
sab_dataset_filename = 'sab_dataset_rec_subject_id_040119_1153.p'
rec_dataset = sab_dataset.load_sab_dataset(join(sab_dataset_dirpath, sab_dataset_filename))
Downsample the data
rec_dataset.downsample(2)
Out:
New sampling rate is 256.0
Construct the features from the SabDataset object - Select only ‘hits’ and ‘correct rejects’ trials and keep only 1 electrode of interest :
time_features = rec_dataset.create_features(trial_sel=(rec_dataset.hits | rec_dataset.correct_rejects))
print(time_features)
Out:
Time Features subject_id_rec - 7 features, 180 time points, 356 trials
2 labels : {1: 'Hits', 2: 'Correct rejects'}
Feature types : Amp
Compute feature importance using forest of decision trees
time_features.compute_feature_importance([1, 2])
Total running time of the script: ( 0 minutes 45.056 seconds)