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
# #