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])