def get_weights():
# Read validation labels
_, labels, _, _, _ = utils.load_data()
skf = StratifiedKFold(labels, n_folds=5, random_state=23)
test_index = None
for _, test_idx in skf:
test_index = np.append(
test_index, test_idx) if test_index is not None else test_idx
val_labels = labels[test_index]
# Read predictions on validation set
val_predictions = []
prediction_files = utils.get_prediction_files()
for preds_file in prediction_files:
vp = np.genfromtxt(os.path.join(consts.BLEND_PATH, preds_file),
delimiter=',')
val_predictions.append(vp)
# Minimize blending function
p0 = [1.] * len(prediction_files)
p = fmin_cobyla(error,
p0,
args=(val_predictions, val_labels),
cons=[constraint],
rhoend=1e-5)
return p
def get_weights():
# Read validation labels
_, labels, _, _, _ = utils.load_data()
skf = StratifiedKFold(labels, n_folds=5, random_state=23)
test_index = None
for _, test_idx in skf:
test_index = np.append(test_index, test_idx) if test_index is not None else test_idx
val_labels = labels[test_index]
# Read predictions on validation set
val_predictions = []
prediction_files = utils.get_prediction_files()
for preds_file in prediction_files:
vp = np.genfromtxt(os.path.join(consts.BLEND_PATH, preds_file), delimiter=',')
val_predictions.append(vp)
# Minimize blending function
p0 = [1.] * len(prediction_files)
p = fmin_cobyla(error, p0, args=(val_predictions, val_labels), cons=[constraint], rhoend=1e-5)
return p
import csv
import numpy as np
import os
import blender
import consts
import utils
if __name__ == '__main__':
weights = blender.get_weights()
prediction_files = utils.get_prediction_files()
with open(os.path.join(consts.OUTPUT_PATH, 'ensembler_weighted_models.csv'), 'wb') as f_out:
writer = csv.writer(f_out)
readers = []
f_ins = []
for fpred in prediction_files:
f_in = open(os.path.join(consts.ENSEMBLE_PATH, fpred), 'rb')
f_ins.append(f_in)
readers.append(csv.reader(f_in))
# Copy header
writer.writerow(readers[0].next())
for r in readers[1:]:
r.next()
# Merge content
for line in readers[0]:
file_name = line[0]
preds = weights[0] * np.array(map(float, line[1:]))
for i, r in enumerate(readers[1:]):
preds += weights[i+1] * np.array(map(float, r.next()[1:]))
import csv
import numpy as np
import os
import blender
import consts
import utils
if __name__ == '__main__':
weights = blender.get_weights()
prediction_files = utils.get_prediction_files()
with open(
os.path.join(consts.OUTPUT_PATH, 'ensembler_weighted_models.csv'),
'wb') as f_out:
writer = csv.writer(f_out)
readers = []
f_ins = []
for fpred in prediction_files:
f_in = open(os.path.join(consts.ENSEMBLE_PATH, fpred), 'rb')
f_ins.append(f_in)
readers.append(csv.reader(f_in))
# Copy header
writer.writerow(readers[0].next())
for r in readers[1:]:
r.next()
# Merge content
for line in readers[0]:
file_name = line[0]
preds = weights[0] * np.array(map(float, line[1:]))
for i, r in enumerate(readers[1:]):
