How to use list_rules method in localstack

Best Python code snippet using localstack_python

discrimination.py

Source:discrimination.py Github

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1# coding: utf-82# python 3.53import sys4import os5sys.path.append('/​Users/​ooki/​git/​research_dr/​python/​MLEM2')6sys.path.append(os.path.dirname(os.path.abspath("__file__"))+'/​../​MLEM2')7from sklearn.metrics import accuracy_score8import copy9import importlib10import mlem211import LERS12importlib.reload(mlem2) 13importlib.reload(LERS) 14from rules_stat import getNumRulesClass15from rules_stat import getRulesValueCount16 17# =====================================18# 公正配慮すべき属性list_sをdecision_tableから削除する19# =====================================20def delDiscriminativeAttributes(decision_table, list_s):21 return(decision_table.drop(list_s, axis=1))22# =====================================23# Rules のうち 属性attr /​ 基本条件 e(属性attrの値v) を含むルールセットの数を返す24# =====================================25def getNumRulesIncludeAttr(list_rules, attr) :26 rules = [r for r in list_rules if attr in r.getKey()]27 return(len(rules))28def getNumRulesIncludeE(list_rules, attr, v) :29 rules = [r for r in list_rules if r.getValue(attr) == v]30 return(len(rules))31def getNumRulesClassIncludeAttr(list_rules, attr, cls) :32 rules = [r for r in list_rules if (attr in r.getKey()) and r.getConsequent() == cls]33 return(len(rules))34def getNumRulesClassIncludeE(list_rules, attr, v, cls) :35 rules = [r for r in list_rules if r.getValue(attr) == v and r.getConsequent() == cls]36 return(len(rules))37def getNumRulesIncludeMultipleE(list_rules, dict_attribute_value):38 tmp_rules = list_rules39 for attr in dict_attribute_value.keys():40 for v in dict_attribute_value[attr] : 41 tmp_rules = [r for r in tmp_rules if r.getValue(attr) == v]42 return(len(tmp_rules))43def getNumRulesClassIncludeMultipleE(list_rules, dict_attribute_value, cls):44 tmp_rules = list_rules45 for attr in dict_attribute_value.keys():46 for v in dict_attribute_value[attr] : 47 tmp_rules = [r for r in tmp_rules if r.getValue(attr) == v and r.getConsequent() == cls]48 return(len(tmp_rules))49# ======================================50# 分割表a, b, c, d を返す51# ======================================52def getContingencyTable(list_rules, dict_attribute_value, CLASSES):53 N = len(list_rules)54 n1 = getNumRulesClass(list_rules, CLASSES["bad"])55 n2 = getNumRulesClass(list_rules, CLASSES["good"])56 a = getNumRulesClassIncludeMultipleE(list_rules, dict_attribute_value, CLASSES["bad"])57 b = n1 - a58 c = getNumRulesClassIncludeMultipleE(list_rules, dict_attribute_value, CLASSES["good"])59 d = n2 - c60 return(a,b,c,d) 61 62# =====================================63# Rules のうち 属性attr /​ 基本条件 e(属性attrの値v) を含むルールセットを返す64# ===================================== 65def getRulesIncludeAttr(list_rules, attr) :66 rules = [r for r in list_rules if attr in r.getKey()]67 return(rules)68def getRulesIncludeE(list_rules, attr, v) :69 rules = [r for r in list_rules if r.getValue(attr) == v]70 return(rules)71 72# =====================================73# Rules のうち 属性attr /​ 基本条件e を 含まないルールセットを返す74# ===================================== 75def getRulesExcludeAttr(list_rules, attr) :76 rules = [r for r in list_rules if not attr in r.getKey()]77 return(rules)78 79def getRulesExcludeE(list_rules, attr, v) :80 rules = [r for r in list_rules if r.getValue(attr) != v]81 return(rules)82# =====================================83# Rules のうち 属性attr /​ 基本条件e を 削除したルールセットを返す84# Rule の 属性attr /​ 基本条件　e を削除したルールを返す85# =====================================86def getRulesDelAttr(list_rules, attr) :87 rules = [delAttrFromRule(r, attr) for r in list_rules]88 return(rules)89 90def getRulesDelE(list_rules, attr, v) :91 rules = [delEFromRule(r, attr, v) for r in list_rules]92 return(rules)93 94def delAttrFromRule(rule, attr) :95 rule_new = copy.deepcopy(rule)96 rule_new.delKey(attr)97 return(rule_new)98def delEFromRule(rule, attr, v) :99 if rule.getValue(attr) == v : return(delAttrFromRule(rule, attr))100 else : return(rule)101 102# =====================================103# alpha差別的な Rule を含まないルールセットを返す104# alpha差別的な Rule の 基本条件　e を削除したルールを返す105# ===================================== 106def getAlphaRulesExcludeE(list_rules, attr, v, decision_table, list_judgeNominal, alpha = 0) :107 rules = [r for r in list_rules if getElift(r, attr, v, decision_table, list_judgeNominal) <= alpha ]108 return(rules)109def getAlphaRulesDelE(list_rules, attr, v, decision_table, list_judgeNominal, alpha = 0) :110 rules = [delEFromAlphaRule(r, attr, v, decision_table, list_judgeNominal, alpha = 0) for r in list_rules]111 return(rules)112 113def delEFromAlphaRule(rule, attr, v, decision_table, list_judgeNominal, alpha = 0):114 if rule.getValue(attr) == v :115 elift = getElift(rule, attr, v, decision_table, list_judgeNominal)116 if elift > alpha : return(delAttrFromRule(rule, attr))117 else : return(rule)118 else : 119 return(rule)120# =====================================121# M差別的な Rule の を含まない /​ 基本条件　e を削除したルールセットを返す122# ===================================== 123def getMRulesFUN(list_rules, attr, v, target_cls, DELFUN, m = 0) :124 num_target_cls, num_other_cls, list_num_other_cls = 0, 0, []125 classes = mlem2.getEstimatedClass(list_rules)126 for cls in classes :127 if cls == target_cls :128 num_target_cls = getNumRulesClassIncludeE(list_rules, attr, v, cls)129 else :130 list_num_other_cls.append(getNumRulesClassIncludeE(list_rules, attr, v, cls))131 num_other_cls = sum(list_num_other_cls) /​ len(list_num_other_cls) #複数クラスの場合を考慮132 if (num_target_cls /​ (num_target_cls + num_other_cls)) > m : #m保護なら133 return(list_rules)134 else :135 return(DELFUN(list_rules, attr, v))136# =====================================137# 配慮変数sをもつ対象だけの決定表を作る138# =====================================139def createDTSuppoterdbyRule(list_rules, attr, v, cls, decision_table):140 target_indice = []141 target_rules = [r for r in list_rules if r.getValue(attr) == v and r.getConsequent() == cls]142 for rule in target_rules:143 target_indice.extend(rule.getSupport())144 target_indice = list(set(target_indice))145 target_indice = sorted(target_indice)146 new_decision_table = decision_table_train.ix[target_indice]147 new_decision_class = new_decision_table[new_decision_table.columns[-1]].values.tolist()148 return(new_decision_table, new_decision_class)149# 有利な決定クラスのルールを減らす関数 配慮変数sを150# =====================================151# Rule の 配慮変数s での decision_tableにおける　elift152# =====================================153def getElift(rule, attr, v, decision_table, list_judgeNominal):154 supp, conf = LERS.getSupportConfidence(rule, decision_table, list_judgeNominal)155 rule_s = delEFromRule(rule, attr, v)156 supp_s, conf_s = LERS.getSupportConfidence(rule_s, decision_table, list_judgeNominal)157 if conf_s == 0: elift = 999158 else : elift = conf /​ conf_s159 return(elift)160 161# =====================================162# Rule の 配慮変数s での decision_tableにおける　slift163# =====================================164def getSlift(rule, s, decision_table, operator):165 conditions = mlem2.getConditionValues(decision_table, s)166 clifts = [getClift(rule, s, c, decision_table) for c in conditions]167 slift = operator(clifts)168 return(slift)169# =====================================170# Rule の 配慮変数s と 代替する変数c での decision_tableにおける　clift171# =====================================172def getClift(rule, s, c, decision_table, list_judgeNominal):173 supp, conf = LERS.getSupportConfidence(rule, decision_table,list_judgeNominal)174 rule_c = mlem2.delEfromRule(rule,s)175 rule_c = rule_c.setValue(s,c)176 supp_c, conf_c = LERS.getSupportConfidence(rule_c, decision_table, list_judgeNominal)177 clift = conf /​ conf_c178 return(clift)179# ====================================180# Attribute Value dict を stringにして返す181# ====================================182def strAttributeValue(ATTRIBUTE_VALUE) :183 list_string = []184 for i in ATTRIBUTE_VALUE :185 list_string.append(i+"-".join(ATTRIBUTE_VALUE[i]))186 return("+".join(list_string))187# ====================================188# Attribute Value dict を stringにして返す189# ====================================190def getItemSet(rule_value) :191 itemset = set()192 for attr in rule_value :193 itemset.add(attr+"-".join(rule_value[attr]))194 return(itemset)195def jaccard(set1, set2):196 set_and = set1 & set2197 set_or = set1 | set2198 if len(set_or) == 0 :199 return(0)200 else :201 return(len(set_and)/​len(set_or))202# ========================================203# main204# ========================================205if __name__ == "__main__":206 # 設定207 DIR_UCI = '/​mnt/​data/​uci/​'208 FILENAME = 'german_credit_categorical' 209 iter1 = 1210 iter2 = 1211 212 # rule induction213 rules = mlem2.getRulesByMLEM2(FILENAME, iter1, iter2)214 # test data215 filepath = DIR_UCI+FILENAME+'/​'+FILENAME+'-test'+str(iter1)+'-'+str(iter2)+'.tsv'216 decision_table_test = mlem2.getDecisionTable(filepath)217 decision_table_test = decision_table_test.dropna()218 decision_class = decision_table_test[decision_table_test.columns[-1]].values.tolist()219 # nominal data220 filepath = DIR_UCI+FILENAME+'/​'+FILENAME+'.nominal'221 list_nominal = mlem2.getNominalList(filepath)222 list_judgeNominal = mlem2.getJudgeNominal(decision_table_test, list_nominal)223 224 # predict by LERS225 predictions = LERS.predictByLERS(rules, decision_table_test, list_judgeNominal)226 227 # 正答率を求める228 accuracy_score(decision_class, predictions)229 230 # rules の数を求める231 num = len(rules)232 # 平均の長さを求める233 mean_length = mlem2.getMeanLength(rules)234 # train data setup235 decision_table_train, decision_class = getData(FILENAME, iter1, iter2, T = "train")236 list_judgeNominal = getJudgeNominal(decision_table_train, FILENAME)237 # 平均支持度と平均確信度を求める238 mean_support, mean_conf = LERS.getSupportConfidenceRules(rules, decision_table_train, list_judgeNominal)239 # AccとRecallを求める240 acc_recall = LERS.getAccurayRecall(rules, decision_table_train, list_judgeNominal)241 for i,c in enumerate(mlem2.getEstimatedClass(rules)):242 print(str(acc_recall[i][0])+","+str(acc_recall[i][1]))243 244 ###### 公正配慮のテスト 245 246 # 基本条件を含むルールセット247 rules_sex_2 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status", "2.0")248 rules_sex_4 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status", "4.0") 249 # 条件を含まないルールセット 250 rules_exclude_sex = mlem2.getRulesExcludeAttr(rules, "Sex_Marital_Status")251 # 基本条件を含まないルールセット 252 rules_exclude_sex_1 = mlem2.getRulesExcludeE(rules, "Sex_Marital_Status", "1.0")253 # 条件を削除したルールセット254 rules_del_value = mlem2.getRulesDelAttr(rules, "Value_Savings_Stocks") 255 # 基本条件を削除したルールセット256 rules_del_value_1 = mlem2.getRulesDelE(rules, "Value_Savings_Stocks", "1.0") 257 258 # 条件を1つ削除する例259 rule = mlem2.delAttrFromRule(rules[12],'No_of_dependents')260 rule = mlem2.delAttrFromRule(rules[12],'Concurrent_Credits')261 262 263 # ====264 265 # read data266 filepath = '/​mnt/​data/​uci/​'+FILENAME+'/​'+FILENAME+'-train'+str(iter1)+'-'+str(iter2)+'.tsv'267 decision_table = mlem2.getDecisionTable(filepath)268 decision_table = decision_table.dropna()269 decision_table.index = range(decision_table.shape[0])270 # read nominal271 filepath = '/​mnt/​data/​uci/​'+'/​'+FILENAME+'/​'+FILENAME+'.nominal'272 list_nominal = mlem2.getNominalList(filepath)273 274 # ルールを満たすやつ ほとんどないな。。275 match_objects = decision_table.apply(lambda obj: isExplainRule(obj, rules[12], list_judgeNominal), axis=1) 276 # confidence277 getConfidence(rule, decision_table, list_judgeNominal)278 279 rules_sex_2 = mlem2.getRulesIncludeE(rules, "Sex_Marital_Status","2.0")...

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IDS_deterministic_local.py

Source:IDS_deterministic_local.py Github

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1# code for IDS with deterministic local search2# requires installation of python package apyori: https:/​/​pypi.org/​project/​apyori/​3import numpy as np4import pandas as pd5import math6from .apyori import apriori7# rule is of the form if A == a and B == b, then class_18# one of the member variables is itemset - a set of patterns {(A,a), (B,b)}9# the other member variable is class_label (e.g., class_1)10class rule:11 12 def __init__(self,feature_list,value_list,class_label):13 self.itemset = set()14 self.class_label = None15 self.add_item(feature_list,value_list)16 self.set_class_label(class_label)17 self._cover = None18 self._correct_cover = None19 self._incorrect_cover = None20 def add_item(self,feature_list,value_list):21 22 if len(feature_list) != len(value_list):23 print("Some error in inputting feature value pairs")24 return25 for i in range(0,len(feature_list)):26 self.itemset.add((feature_list[i],value_list[i]))27 28 def print_rule(self):29 s = "If "30 for item in self.itemset:31 s += str(item[0]) + " == " +str(item[1]) + " and "32 s = s[:-5]33 s += ", then "34 s += str(self.class_label)35 print(s)36 37 def all_predicates_same(self, r):38 return self.itemset == r.itemset39 40 def class_label_same(self,r):41 return self.class_label == r.class_label42 43 def set_class_label(self,label):44 self.class_label = label45 46 def get_length(self):47 return len(self.itemset)48 49 def get_cover(self, df, reuse=True):50 if reuse and self._cover is not None:51 return self._cover52 for pattern in self.itemset:53 df = df[df[pattern[0]] == int(pattern[1])]54 if reuse:55 self._cover = df.index.values56 return df.index.values57 def get_correct_cover(self, df, Y, reuse=True):58 if reuse and self._correct_cover is not None:59 return self._correct_cover, self._cover60 indexes_points_covered = self.get_cover(df) # indices of all points satisfying the rule61 Y_arr = pd.Series(Y) # make a series of all Y labels62 labels_covered_points = list(Y_arr[indexes_points_covered]) # get a list only of Y labels of the points covered63 correct_cover = []64 for ind in range(0,len(labels_covered_points)):65 if labels_covered_points[ind] == self.class_label:66 correct_cover.append(indexes_points_covered[ind])67 if reuse:68 self._correct_cover = correct_cover69 return correct_cover, indexes_points_covered70 71 def get_incorrect_cover(self, df, Y, reuse=True):72 if reuse and self._incorrect_cover is not None:73 return self._incorrect_cover74 correct_cover, full_cover = self.get_correct_cover(df, Y)75 ret = (sorted(list(set(full_cover) - set(correct_cover))))76 if reuse:77 self._incorrect_cover = ret78 return ret79# below function basically takes a data frame and a support threshold and returns itemsets which satisfy the threshold80def run_apriori(df, support_thres):81 # the idea is to basically make a list of strings out of df and run apriori api on it 82 # return the frequent itemsets83 dataset = []84 for i in range(0,df.shape[0]):85 temp = []86 # active_cols = df.columns.values[df.iloc[i].values == 1]87 # for col_name in active_cols:88 # temp.append(col_name)89 for col_name in df.columns:90 temp.append(col_name + "=" + str(df[col_name][i]))91 dataset.append(temp)92 #results = list(apriori(dataset, min_support=support_thres))93 results = []94 for ii,i in enumerate(apriori(dataset, min_support=support_thres)):95 if (ii+1) % 1000 == 0: print(ii)96 results.append(i)97 list_itemsets = []98 for ele in results:99 temp = []100 for pred in ele.items:101 temp.append(pred)102 list_itemsets.append(temp)103 return list_itemsets104# This function converts a list of itemsets (stored as list of lists of strings) into rule objects105def createrules(freq_itemsets, labels_set):106 # create a list of rule objects from frequent itemsets 107 list_of_rules = []108 for one_itemset in freq_itemsets:109 feature_list = []110 value_list = []111 for pattern in one_itemset:112 # feature_list.append(pattern)113 # value_list.append(1)114 fea_val = pattern.split("=")115 feature_list.append(fea_val[0])116 value_list.append(fea_val[1])117 for each_label in labels_set:118 temp_rule = rule(feature_list,value_list,each_label)119 list_of_rules.append(temp_rule)120 return list_of_rules121# compute the maximum length of any rule in the candidate rule set122def max_rule_length(list_rules):123 len_arr = []124 for r in list_rules:125 len_arr.append(r.get_length())126 return max(len_arr)127# compute the number of points which are covered both by r1 and r2 w.r.t. data frame df128def overlap(r1, r2, df):129 return sorted(list(set(r1.get_cover(df)).intersection(set(r2.get_cover(df)))))130# computes the objective value of a given solution set131def func_evaluation(soln_set, list_rules, df, Y, lambda_array):132 # evaluate the objective function based on rules in solution set 133 # soln set is a set of indexes which when used to index elements in list_rules point to the exact rules in the solution set134 # compute f1 through f7 and we assume there are 7 lambdas in lambda_array135 f = [] #stores values of f1 through f7; 136 137 # f0 term138 f0 = len(list_rules) - len(soln_set) # |S| - size(R)139 f.append(f0)140 141 # f1 term142 Lmax = max_rule_length(list_rules)143 sum_rule_length = 0.0144 for rule_index in soln_set:145 sum_rule_length += list_rules[rule_index].get_length()146 147 f1 = Lmax * len(list_rules) - sum_rule_length148 f.append(f1)149 # f2 term - intraclass overlap150 sum_overlap_intraclass = 0.0151 for r1_index in soln_set:152 for r2_index in soln_set:153 if r1_index >= r2_index:154 continue155 if list_rules[r1_index].class_label == list_rules[r2_index].class_label:156 sum_overlap_intraclass += len(overlap(list_rules[r1_index], list_rules[r2_index],df))157 f2 = df.shape[0] * len(list_rules) * len(list_rules) - sum_overlap_intraclass158 f.append(f2)159 # f3 term - interclass overlap160 sum_overlap_interclass = 0.0161 for r1_index in soln_set:162 for r2_index in soln_set:163 if r1_index >= r2_index:164 continue165 if list_rules[r1_index].class_label != list_rules[r2_index].class_label:166 sum_overlap_interclass += len(overlap(list_rules[r1_index], list_rules[r2_index],df))167 f3 = df.shape[0] * len(list_rules) * len(list_rules) - sum_overlap_interclass168 f.append(f3)169 # f4 term - coverage of all classes170 classes_covered = set() # set171 for index in soln_set:172 classes_covered.add(list_rules[index].class_label)173 f4 = len(classes_covered)174 f.append(f4)175 # f5 term - accuracy176 sum_incorrect_cover = 0.0177 for index in soln_set:178 sum_incorrect_cover += len(list_rules[index].get_incorrect_cover(df,Y))179 f5 = df.shape[0] * len(list_rules) - sum_incorrect_cover180 f.append(f5)181 #f6 term - cover correctly with at least one rule182 atleast_once_correctly_covered = set()183 for index in soln_set:184 correct_cover, full_cover = list_rules[index].get_correct_cover(df,Y)185 atleast_once_correctly_covered = atleast_once_correctly_covered.union(set(correct_cover))186 f6 = len(atleast_once_correctly_covered)187 f.append(f6)188 obj_val = 0.0189 for i in range(7):190 obj_val += f[i] * lambda_array[i]191 #print(f)192 return obj_val193# deterministic local search algorithm which returns a solution set as well as the corresponding objective value194def deterministic_local_search(list_rules, df, Y, lambda_array, epsilon):195 # step by step implementation of deterministic local search algorithm in the 196 # FOCS paper: https:/​/​people.csail.mit.edu/​mirrokni/​focs07.pdf (page 4-5)197 198 #initialize soln_set199 soln_set = set()200 n = len(list_rules)201 202 # step 1: find out the element with maximum objective function value and initialize soln set with it203 each_obj_val = []204 for ind in range(len(list_rules)):205 each_obj_val.append(func_evaluation(set([ind]), list_rules, df, Y, lambda_array))206 207 best_element = np.argmax(each_obj_val)208 soln_set.add(best_element)209 S_func_val = each_obj_val[best_element]210 print('best:', best_element)211 212 restart_step2 = False213 214 # step 2: if there exists an element which is good, add it to soln set and repeat215 while True:216 each_obj_val = []217 218 for ind in set(range(len(list_rules))) - soln_set:219 func_val = func_evaluation(soln_set.union(set([ind])), list_rules, df, Y, lambda_array)220 221 if func_val > (1.0 + epsilon/​(n*n)) * S_func_val:222 soln_set.add(ind)223 print("Adding rule "+str(ind))224 S_func_val = func_val225 restart_step2 = True226 break227 228 if restart_step2:229 restart_step2 = False230 continue231 232 for ind in soln_set:233 func_val = func_evaluation(soln_set - set([ind]), list_rules, df, Y, lambda_array)234 if func_val > (1.0 + epsilon/​(n*n)) * S_func_val:235 soln_set.remove(ind)236 print("Removing rule "+str(ind))237 S_func_val = func_val238 restart_step2 = True239 break240 241 if restart_step2:242 restart_step2 = False243 continue244 s1 = func_evaluation(soln_set, list_rules, df, Y, lambda_array)245 s2 = func_evaluation(set(range(len(list_rules))) - soln_set, list_rules, df, Y, lambda_array)246 print('s1', s1)247 print('s2', s2)248 if s1 >= s2:249 return soln_set, s1250 else: ...

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rules_list.py

Source:rules_list.py Github

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1from list_keywords import LEVELS2from definition import *3import re4# After the level 45# so what is not in quotes is code,6# and any keyword in the code can be colored independently7# of what is around it, so we use a general function8# This general function uses LEVELS9def rule_all(level):10 # get keyword by level11 data_level = LEVELS[level]12 # initialize with extra rules13 list_rules = data_level["extra_rules"]14 # Rule for comments :15 list_rules.append( { 'regex': '#.*$', 'token': 'comment', 'next': 'start' } )16 ## Rule for quoted string :17 # complete18 list_rules.append( { 'regex': '\"[^\"]*\"', 'token': 'constant.character', 'next': 'start' } )19 list_rules.append( { 'regex': "\'[^\']*\'", 'token': 'constant.character', 'next': 'start' } )20 # incomplete21 list_rules.append( { 'regex': '\"[^\"]*$', 'token': 'constant.character', 'next': 'start' } )22 list_rules.append( { 'regex': "\'[^\']*$", 'token': 'constant.character', 'next': 'start' } )23 # Rule for blanks marks :24 list_rules.append( { 'regex': '_\\?_', 'token': 'invalid', 'next': 'start' })25 list_rules.append( { 'regex': '(^| )(_)(?= |$)', 'token': ['text','invalid'], 'next': 'start' } )26 # Rules for numbers27 if (data_level["number"]) :28 if (data_level["number_with_decimal"]) :29 number_regex = '(' + DIGIT + '*\\.?' + DIGIT + '+)'30 else:31 number_regex = '(' + DIGIT + '+)'32 list_rules.append({'regex': START_WORD + number_regex + END_WORD, 'token': ['text','variable'], 'next':'start'} )33 # Special case of an number directly followed by a number 34 for command in data_level["space_before"]: 35 list_rules.append({36 'regex': START_WORD + get_translated_keyword(command) + number_regex + END_WORD,37 'token': ['text','keyword','variable'],38 'next': 'start',39 })40 for command in data_level["no_space"]:41 list_rules.append({42 'regex': get_translated_keyword(command) + number_regex + END_WORD,43 'token': ['keyword','variable'],44 'next': 'start',45 })46 # Rules for commands of space_before_and_after 47 # These are the keywords that must be "alone" so neither preceded nor followed directly by a word 48 for command in data_level["space_before_and_after"]:49 list_rules.append({50 'regex': START_WORD + get_translated_keyword(command) + END_WORD,51 'token': ["text","keyword"],52 'next': "start", 53 })54 55 # Rules for commands of no_space 56 # These are the keywords that are independent of the context (formerly the symbols57 # In particular, even if they are between 2 words, the syntax highlighting will select them58 for command in data_level["no_space"]:59 list_rules.append({60 'regex': get_translated_keyword(command),61 'token': ["keyword"],62 'next': "start", 63 })64 # Rules for commands of space_before 65 # This category of keywords allows you to have keywords that are not preced66 # by another word, but that can be followed immediately by another word. (see the PR #2413)*/​67 for command in data_level["space_before"]:68 list_rules.append({69 'regex': START_WORD + get_translated_keyword(command),70 'token': ["text","keyword"],71 'next': "start", 72 })73 # Rules for commands of space_after 74 # This category of keywords allows you to have keywords that can be preceded immediate75 # by another word, but that are not followed by another word.*/​76 for command in data_level["space_after"]:77 list_rules.append({78 'regex': get_translated_keyword(command) + END_WORD,79 'token': ["keyword"],80 'next': "start", 81 })82 # Rules for constants (colors, directions)83 for command in data_level['constant']:84 list_rules.append({85 'regex': START_WORD + get_translated_keyword(command) + END_WORD,86 'token': ["text",TOKEN_CONSTANT],87 'next': "start", 88 })...

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