def run_trainer():
with open('SETTINGS.json') as f:
settings_dict = json.load(f)
reg_list = [10000000, 100, 10, 1.0, 0.1, 0.01]
for reg_C in reg_list:
print reg_C
data_path = settings_dict['path']['processed_data_path'] + '/' + create_fft_data_name(settings_dict)
submission_path = settings_dict['path']['submission_path'] + '/logreg_' + str(
reg_C) + '_' + create_fft_data_name(settings_dict)
if not os.path.exists(data_path):
fft.run_fft_preprocessor()
if not os.path.exists(submission_path):
os.makedirs(submission_path)
subjects = ['Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2']
for subject in subjects:
print subject
model, data_scaler, = train(subject, data_path, reg_C)
predict(subject, model, data_scaler, data_path, submission_path)
merge_csv_files(submission_path, subjects, 'submission')
merge_csv_files(submission_path, subjects, 'submission_softmax')
merge_csv_files(submission_path, subjects, 'submission_minmax')
merge_csv_files(submission_path, subjects, 'submission_median')
def run_trainer():
with open('SETTINGS.json') as f:
settings_dict = json.load(f)
data_path = settings_dict['path']['processed_data_path'] + '/' + create_fft_data_name(settings_dict)
submission_path = settings_dict['path']['submission_path'] + '/LDA_' + create_fft_data_name(settings_dict)
print data_path
if not os.path.exists(data_path):
fft.run_fft_preprocessor()
if not os.path.exists(submission_path):
os.makedirs(submission_path)
test_labels_path = '/mnt/sda4/CODING/python/kaggle_data/test_labels.csv'
test_labels = load_test_labels(test_labels_path)
subjects = ['Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2']
coef_list = []
for subject in subjects:
print '***********************', subject, '***************************'
model, data_scaler, coefs = train(subject, data_path)
predict(subject, model, data_scaler, data_path, submission_path, test_labels[subject]['preictal'])
coef_list.append(coefs)
merge_csv_files(submission_path, subjects, 'submission')
merge_csv_files(submission_path, subjects, 'submission_softmax')
merge_csv_files(submission_path, subjects, 'submission_minmax')
merge_csv_files(submission_path, subjects, 'submission_median')
def run_trainer():
with open('SETTINGS.json') as f:
settings_dict = json.load(f)
data_path = settings_dict['path'][
'processed_data_path'] + '/' + create_fft_data_name(settings_dict)
submission_path = settings_dict['path'][
'submission_path'] + '/LDA_' + create_fft_data_name(settings_dict)
print data_path
if not os.path.exists(data_path):
fft.run_fft_preprocessor()
if not os.path.exists(submission_path):
os.makedirs(submission_path)
test_labels_path = '/mnt/sda4/CODING/python/kaggle_data/test_labels.csv'
test_labels = load_test_labels(test_labels_path)
subjects = [
'Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2'
]
coef_list = []
for subject in subjects:
print '***********************', subject, '***************************'
model, data_scaler, coefs = train(subject, data_path)
predict(subject, model, data_scaler, data_path, submission_path,
test_labels[subject]['preictal'])
coef_list.append(coefs)
merge_csv_files(submission_path, subjects, 'submission')
merge_csv_files(submission_path, subjects, 'submission_softmax')
merge_csv_files(submission_path, subjects, 'submission_minmax')
merge_csv_files(submission_path, subjects, 'submission_median')
