New translations preimage_results_to_latex_tables.py (Chinese Simplified)

lang/zh/gklearn/preimage/experiments/tools/preimage_results_to_latex_tables.py

@@ -0,0 +1,228 @@
+#!/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)