CSV_FOLDER, CSV_ALL, CONFIG_FILE, Q_NPZ, SKILL_WINS, SKILL_FAILS = dataio.build_new_paths( DATASET_NAME) config = dataio.get_config(CONFIG_FILE) experiment_args = vars(options) df_train, df_val, df_test = dataio.get_data(DATASET_NAME) try: skill_wins = load_npz(SKILL_WINS) skill_fails = load_npz(SKILL_FAILS) except: skill_wins = None skill_fails = None short_legend, full_legend, latex_legend, active_agents = dataio.get_legend( experiment_args) EXPERIMENT_FOLDER = os.path.join(CSV_FOLDER, short_legend) dataio.prepare_folder(EXPERIMENT_FOLDER) def df_to_sparse(df, filename): SPARSE_NPZ = os.path.join(EXPERIMENT_FOLDER, filename) if os.path.isfile(SPARSE_NPZ): X_fm = load_npz(SPARSE_NPZ) return X_fm X = {} nb_events, _ = df.shape rows = list(range(nb_events)) for key in ['users', 'items', 'speech']: X[key] = coo_matrix(([1] * nb_events, (rows, df[key])), shape=(nb_events, config['NUM'][key]))
# Tables labels = {} latex_labels = {} TABLE_TEX = os.path.join(CSV_FOLDER, '{:s}-table.tex'.format(DATASET_NAME)) array = [] acc_values = {} nll_values = {} #with open(TABLE_TEX, 'w') as latex: for filename in experiments: df = pd.read_csv(filename.replace('results.json', 'rlog.csv')) acc_values[filename] = df['accuracy'] nll_values[filename] = df['ll_mcmc_all'] with open(filename) as f: results = json.load(f) short_legend, full_legend, latex_legend, active_agents = dataio.get_legend( results['args']) # full_legend = full_legend latex_legend = latex_legend.replace( 'users, items, skills, wins, fails, item_wins, item_fails', 'u, i, s, w, f, item_wins, item_fails').replace('_', r'\_') print(filename) print(latex_legend) labels[filename] = full_legend latex_labels[filename] = latex_legend + " $d = {:d}$".format( results['args']['d']) # line = '{:s} & '.format(latex_legend) # line += ' & '.join('{:.4f}'.format(results['metrics'][metric]) for metric in ['ACC', 'AUC', 'NLL']) + r'\\' row = [latex_legend, results['args']['d']] + [ results['metrics'][metric] for metric in ['ACC', 'AUC', 'NLL'] ] # MCMC NLL is different