def features_one_file(f): file_name = os.path.basename(f).split(".")[0] treatment, animal = file_name.split("_") pol = pr.polygraph_from_pkl(f) pol = pol.normalise() pol = pr.preprocess_eegs(pol) print "processing " + f tmp_df = feature_factory.make_features_for_epochs(pol,10,LAG_WINDOW, add_major_annotations=True) tmp_df["animal"] = animal tmp_df["treatment"] = treatment return tmp_df
import glob from non_package_stuff.classifiers.eeg_vs_emg import EEGsvEMG import pyrem as pr DATA_FILE_PATTERN = "/data/pyrem/eeg_vs_emg_data/*.pkl" from sklearn.externals import joblib if __name__ == "__main__": classif = EEGsvEMG() classif.train_from_polygraph_file_list(DATA_FILE_PATTERN) classif.save("EEGvsEMG.pkl") #prediction verification: classif = joblib.load("EEGvsEMG.pkl") files = glob.glob(DATA_FILE_PATTERN) for f in sorted(files): print "Predicting: " + f polyg = pr.polygraph_from_pkl(f) a, proba = classif.predict(polyg) print a, proba # #
import glob from non_package_stuff.classifiers.eeg_vs_emg import EEGsvEMG import pyrem as pr DATA_FILE_PATTERN= "/data/pyrem/eeg_vs_emg_data/*.pkl" from sklearn.externals import joblib if __name__ == "__main__": classif = EEGsvEMG() classif.train_from_polygraph_file_list(DATA_FILE_PATTERN) classif.save("EEGvsEMG.pkl") #prediction verification: classif = joblib.load("EEGvsEMG.pkl") files = glob.glob(DATA_FILE_PATTERN) for f in sorted(files): print "Predicting: " + f polyg = pr.polygraph_from_pkl(f) a, proba = classif.predict(polyg) print a, proba # #