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')
if __name__ == '__main__':
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']
t, sn, sp = [], [], []
for subject in subjects:
print '***********************', subject, '***************************'
t_i, sn_i, sp_i = curve_per_subject(subject, data_path, test_labels[subject]['preictal'])
t.append(t_i)
sn.append(sn_i)
sp.append(sp_i)
ax = plt.subplot(111)
plt.xlim([-0.01, 1.0])
plt.ylim([- 0.01, 1.06])
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(25)
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'
]
t, sn, sp = [], [], []
for subject in subjects:
print '***********************', subject, '***************************'
t_i, sn_i, sp_i = curve_per_subject(subject, data_path,
test_labels[subject]['preictal'])
t.append(t_i)
sn.append(sn_i)
sp.append(sp_i)
ax = plt.subplot(111)
plt.xlim([-0.01, 1.0])
