def classify(fm_dir, cl_dir):
'''
PRE: required features are available in fe_dir!
'''
f_pre = os.path.basename(os.path.dirname(os.path.dirname(fm_dir)))
# create dir to store the classification output and feature calc...
out_dir = os.path.join(cl_dir, 'class_output')
if not(os.path.exists(out_dir)):
os.makedirs(out_dir)
# read feature ids that were used to train the classifier
cl_settings_f = os.path.join(cl_dir, 'settings.txt')
settings_dict = file_io.read_settings_dict(cl_settings_f)
feature_ids = settings_dict['feature_names']
# obtain feature matrix STANDARDIZED DATA
fm = featmat.FeatureMatrix.load_from_dir(fm_dir)
feat_is = fm.feature_indices(feature_ids)
object_is = range(len(fm.object_ids))
data = fm.standardized_slice(feat_is, object_is)
# load trained classifier
cl_f = os.path.join(cl_dir, 'classifier.joblib.pkl')
classifier = joblib.load(cl_f)
# run classify method
preds, probas = classification.classify(data, classifier)
pred_f = os.path.join(out_dir, '%s_pred.txt' % (f_pre))
proba_f = os.path.join(out_dir, '%s_proba.txt' % (f_pre))
file_io.write_tuple_list(pred_f, zip(fm.object_ids, preds))
file_io.write_tuple_list(proba_f, zip(fm.object_ids, probas))
def classify(fm_dir, cl_dir):
'''
PRE: required features are available in fe_dir!
'''
f_pre = os.path.basename(os.path.dirname(os.path.dirname(fm_dir)))
# create dir to store the classification output and feature calc...
out_dir = os.path.join(cl_dir, 'class_output')
if not(os.path.exists(out_dir)):
os.makedirs(out_dir)
# read feature ids that were used to train the classifier
cl_settings_f = os.path.join(cl_dir, 'settings.txt')
settings_dict = file_io.read_settings_dict(cl_settings_f)
feature_ids = settings_dict['feature_names']
# obtain feature matrix STANDARDIZED DATA
fm = featmat.FeatureMatrix.load_from_dir(fm_dir)
feat_is = fm.feature_indices(feature_ids)
object_is = range(len(fm.object_ids))
data = fm.standardized_slice(feat_is, object_is)
# load trained classifier
cl_f = os.path.join(cl_dir, 'classifier.joblib.pkl')
classifier = joblib.load(cl_f)
# run classify method
preds, probas = classification.classify(data, classifier)
pred_f = os.path.join(out_dir, '%s_pred.txt' % (f_pre))
proba_f = os.path.join(out_dir, '%s_proba.txt' % (f_pre))
file_io.write_tuple_list(pred_f, zip(fm.object_ids, preds))
file_io.write_tuple_list(proba_f, zip(fm.object_ids, probas))
def get_classifier_settings(self, cl_id):
settings_f = os.path.join(self.get_cl_dir(cl_id), 'settings.txt')
return file_io.read_settings_dict(settings_f)
def get_classifier_settings(self, cl_id):
settings_f = os.path.join(self.get_cl_dir(cl_id), 'settings.txt')
return file_io.read_settings_dict(settings_f)
