#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Apr 30 10:16:33 2020 @author: ljia """ import pandas as pd import numpy as np import os DS_SYMB = ['MUTAG', 'Monoterpenoides', 'MAO_symb'] DS_NON_SYMB = ['Letter-high', 'Letter-med', 'Letter-low', 'COIL-RAG', 'PAH'] DS_UNLABELED = ['PAH_unlabeled'] def rounder(x, decimals): x_strs = str(x).split('.') if len(x_strs) == 2: before = x_strs[0] after = x_strs[1] if len(after) > decimals: if int(after[decimals]) >= 5: after0s = '' for c in after: if c == '0': after0s += '0' elif c != '0': break after = after0s + str(int(after[0:decimals]) + 1)[-decimals:] else: after = after[0:decimals] elif len(after) < decimals: after += '0' * (decimals - len(after)) return before + '.' + after elif len(x_strs) == 1: return x_strs[0] def replace_nth(string, sub, wanted, n): import re where = [m.start() for m in re.finditer(sub, string)][n-1] before = string[:where] after = string[where:] after = after.replace(sub, wanted, 1) newString = before + after return newString def df_to_latex_table(df): ltx = df.to_latex(index=True, escape=False, multirow=True) # modify middle lines. ltx = ltx.replace('\\cline{1-9}\n\\cline{2-9}', '\\toprule') ltx = ltx.replace('\\cline{2-9}', '\\cmidrule(l){2-9}') # modify first row. i_start = ltx.find('\n\\toprule\n') i_end = ltx.find('\\\\\n\\midrule\n') ltx = ltx.replace(ltx[i_start:i_end+12], '\n\\toprule\nDatasets & Graph Kernels & Algorithms & $d_\\mathcal{F}$ SM & $d_\\mathcal{F}$ SM (UO) & $d_\\mathcal{F}$ GM & $d_\\mathcal{F}$ GM (UO) & Runtime & Runtime (UO) \\\\\n\\midrule\n', 1) # add row numbers. ltx = ltx.replace('lllllllll', 'lllllllll|@{\\makebox[2em][r]{\\textit{\\rownumber\\space}}}', 1) ltx = replace_nth(ltx, '\\\\\n', '\\gdef\\rownumber{\\stepcounter{magicrownumbers}\\arabic{magicrownumbers}} \\\\\n', 1) return ltx def beautify_df(df): df = df.sort_values(by=['Datasets', 'Graph Kernels']) df = df.set_index(['Datasets', 'Graph Kernels', 'Algorithms']) # index = pd.MultiIndex.from_frame(df[['Datasets', 'Graph Kernels', 'Algorithms']]) # bold the best results. for ds in df.index.get_level_values('Datasets').unique(): for gk in df.loc[ds].index.get_level_values('Graph Kernels').unique(): min_val = np.inf min_indices = [] min_labels = [] for index, row in df.loc[(ds, gk)].iterrows(): for label in ['$d_\mathcal{F}$ SM', '$d_\mathcal{F}$ GM', '$d_\mathcal{F}$ GM (UO)']: value = row[label] if value != '-': value = float(value.strip('/same')) if value < min_val: min_val = value min_indices = [index] min_labels = [label] elif value == min_val: min_indices.append(index) min_labels.append(label) for idx, index in enumerate(min_indices): df.loc[(ds, gk, index), min_labels[idx]] = '\\textbf{' + df.loc[(ds, gk, index), min_labels[idx]] + '}' return df def get_results(data_dir, ds_name, gkernel): # get results from .csv. file_name = data_dir + 'results_summary.' + ds_name + '.' + gkernel + '.csv' try: df_summary = pd.read_csv(file_name) except FileNotFoundError: return None df_results = pd.DataFrame(index=None, columns=['d_F SM', 'd_F GM', 'runtime']) for index, row in df_summary.iterrows(): if row['target'] == 'all' and row['fit method'] == 'k-graphs': df_results.loc['From median set'] = ['-', rounder(row['min dis_k gi'], 3), '-'] if_uo = (int(row['mge num decrease order']) > 0 or int(row['mge num increase order']) > 0) df_results.loc['Optimized'] = [rounder(row['dis_k SM'], 3), rounder(row['dis_k GM'], 3) if if_uo else (rounder(row['dis_k GM'], 3) + '/same'), rounder(row['time total'], 2)] if row['target'] == 'all' and row['fit method'] == 'expert': if_uo = (int(row['mge num decrease order']) > 0 or int(row['mge num increase order']) > 0) df_results.loc['IAM: expert costs'] = [rounder(row['dis_k SM'], 3), rounder(row['dis_k GM'], 3) if if_uo else (rounder(row['dis_k GM'], 3) + '/same'), rounder(row['time total'], 2)] # get results from random summary .csv. random_fini = True file_name = data_dir + 'summary_for_random_edit_costs.csv' try: df_random = pd.read_csv(file_name) except FileNotFoundError: random_fini = False if random_fini: for index, row in df_random.iterrows(): if row['measure'] == 'mean': if_uo = (float(row['mge num decrease order']) > 0 or float(row['mge num increase order']) > 0) df_results.loc['IAM: random costs'] = [rounder(row['dis_k SM'], 3), rounder(row['dis_k GM'], 3) if if_uo else (rounder(row['dis_k GM'], 3) + '/same'), rounder(row['time total'], 2)] # sort index. df_results = df_results.reindex([item for item in ['From median set', 'IAM: random costs', 'IAM: expert costs', 'Optimized'] if item in df_results.index]) return df_results def get_results_of_one_xp(data_dir, ds_name, gkernel): df_results = pd.DataFrame() df_tmp_uo = None if not os.path.isfile(data_dir + 'update_order/error.txt'): df_tmp_uo = get_results(data_dir + 'update_order/', ds_name, gkernel) df_tmp = None if not os.path.isfile(data_dir + 'error.txt'): df_tmp = get_results(data_dir, ds_name, gkernel) if (df_tmp_uo is not None and not df_tmp_uo.empty) or (df_tmp is not None and not df_tmp.empty): df_results = pd.DataFrame(index=['From median set', 'IAM: random costs', 'IAM: expert costs', 'Optimized'], columns=['$d_\mathcal{F}$ SM', '$d_\mathcal{F}$ SM (UO)', '$d_\mathcal{F}$ GM', '$d_\mathcal{F}$ GM (UO)', 'Runtime', 'Runtime (UO)']) if df_tmp_uo is not None and not df_tmp_uo.empty: for index, row in df_tmp_uo.iterrows(): for algo in df_results.index: if index == algo: df_results.at[algo, '$d_\mathcal{F}$ SM (UO)'] = row['d_F SM'] df_results.at[algo, '$d_\mathcal{F}$ GM (UO)'] = row['d_F GM'] df_results.at[algo, 'Runtime (UO)'] = row['runtime'] if df_tmp is not None and not df_tmp.empty: for index, row in df_tmp.iterrows(): for algo in df_results.index: if index == algo: df_results.at[algo, '$d_\mathcal{F}$ SM'] = row['d_F SM'] df_results.at[algo, '$d_\mathcal{F}$ GM'] = row['d_F GM'].strip('/same') df_results.at[algo, 'Runtime'] = row['runtime'] df_results = df_results.dropna(axis=0, how='all') df_results = df_results.fillna(value='-') df_results = df_results.reset_index().rename(columns={'index': 'Algorithms'}) return df_results def get_results_for_all_experiments(root_dir): columns=['Datasets', 'Graph Kernels', 'Algorithms', '$d_\mathcal{F}$ SM', '$d_\mathcal{F}$ SM (UO)', '$d_\mathcal{F}$ GM', '$d_\mathcal{F}$ GM (UO)', 'Runtime', 'Runtime (UO)'] df_symb = pd.DataFrame(columns=columns) df_nonsymb = pd.DataFrame(columns=columns) df_unlabeled = pd.DataFrame(columns=columns) dir_list = [i for i in os.listdir(root_dir) if os.path.isdir(root_dir + i)] for dir_name in dir_list: sp_tmp = dir_name.split('.') gkernel = sp_tmp[1] ds_name = sp_tmp[0].strip('[error]') suffix = '' if sp_tmp[-1] == 'unlabeled': suffix = '_unlabeled' elif sp_tmp[-1] == 'symb': suffix = '_symb' df_results = get_results_of_one_xp(root_dir + dir_name + '/', ds_name, gkernel) if not df_results.empty: ds_name += suffix if ds_name in DS_SYMB: for index, row in df_results.iterrows(): df_symb.loc[len(df_symb)] = [ds_name.replace('_', '\_'), gkernel] + row.tolist() elif ds_name in DS_NON_SYMB: for index, row in df_results.iterrows(): df_nonsymb.loc[len(df_nonsymb)] = [ds_name.replace('_', '\_'), gkernel] + row.tolist() elif ds_name in DS_UNLABELED: for index, row in df_results.iterrows(): df_unlabeled.loc[len(df_unlabeled)] = [ds_name.replace('_', '\_'), gkernel] + row.tolist() else: raise Exception('dataset' + ds_name + 'is not pre-defined.') # sort. df_symb = beautify_df(df_symb) df_nonsymb = beautify_df(df_nonsymb) df_unlabeled = beautify_df(df_unlabeled) # convert dfs to latex strings. ltx_symb = df_to_latex_table(df_symb) ltx_nonsymb = df_to_latex_table(df_nonsymb) ltx_unlabeled = df_to_latex_table(df_unlabeled) return ltx_symb, ltx_nonsymb, ltx_unlabeled if __name__ == '__main__': # root_dir = '../results/xp_median_preimage.init20/' root_dir = '../../results/CRIANN/xp_median_preimage.init10/' ltx_symb, ltx_nonsymb, ltx_unlabeled = get_results_for_all_experiments(root_dir)