def main():
exp_dir = defines.exp_dir
exp_name = 'bayes_opt_LR_alphas_reuse'
df = pd.DataFrame()
basenames = ['test_acc.csv', 'test_micro_f1.csv', 'test_macro_f1.csv', 'test_pp.csv']
rownames = ['model accuracy', 'model micro f1', 'model macro f1', 'model percent perfect']
for i, basename in enumerate(basenames):
rowname = rownames[i]
files = glob.glob(os.path.join(exp_dir, '*', 'test_fold_0', exp_name, 'results', basename))
gather_results(df, files, rowname)
files = glob.glob(os.path.join(defines.data_raw_labels_dir, '*.csv'))
for file in files:
dataset = fh.get_basename_wo_ext(file)
codes = label_reader.get_dataset_labels(dataset)
if dataset in df.columns:
df.loc['Number of responses', dataset] = codes.shape[0]
df.loc['Number of labels', dataset] = codes.shape[1]
output_dir = '/Users/dcard/Dropbox/CMU/DAP/results/'
output_filename = fh.make_filename(output_dir, exp_name, 'csv')
df.to_csv(output_filename)
def find_most_errors(dataset, filename):
predicted = pd.read_csv(filename)
df_labels = label_reader.get_dataset_labels(dataset)
for i in predicted.index:
print np.sum(np.abs(predicted.loc[i, :] - df_labels.loc[i, :]))