├── .gitignore ├── images ├── install_graphviz.png ├── DecisionTree │ └── DecisionTree.jpeg └── RandomForest │ ├── EnsembleTrees_No1.jpeg │ ├── EnsembleTrees_No2.jpeg │ └── EnsembleTrees_No3.jpeg ├── rule.py ├── README.md ├── rule_extraction.py ├── Demo1_Rule_Extraction_from_Trees.ipynb └── dataset └── titanic.csv /.gitignore: -------------------------------------------------------------------------------- 1 | rule_extraction 20181014.py 2 | __pycache__ 3 | .ipynb_checkpoints 4 | .gitignore.bak 5 | history -------------------------------------------------------------------------------- /images/install_graphviz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Yimeng-Zhang/Rule_Extraction_from_Trees/HEAD/images/install_graphviz.png -------------------------------------------------------------------------------- /images/DecisionTree/DecisionTree.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Yimeng-Zhang/Rule_Extraction_from_Trees/HEAD/images/DecisionTree/DecisionTree.jpeg -------------------------------------------------------------------------------- /images/RandomForest/EnsembleTrees_No1.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Yimeng-Zhang/Rule_Extraction_from_Trees/HEAD/images/RandomForest/EnsembleTrees_No1.jpeg -------------------------------------------------------------------------------- /images/RandomForest/EnsembleTrees_No2.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Yimeng-Zhang/Rule_Extraction_from_Trees/HEAD/images/RandomForest/EnsembleTrees_No2.jpeg -------------------------------------------------------------------------------- /images/RandomForest/EnsembleTrees_No3.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Yimeng-Zhang/Rule_Extraction_from_Trees/HEAD/images/RandomForest/EnsembleTrees_No3.jpeg -------------------------------------------------------------------------------- /rule.py: -------------------------------------------------------------------------------- 1 | class Rule: 2 | """ An object modelizing a logical rule and add factorization methods. 3 | It is used to simplify rules and deduplicate them. 4 | 将一段决策树规则分解并简化，去除规则中冗余的部分 5 | eg： 6 | >>> r = 'feature1 > 3.0 and feature2 < 10.0 and feature3 == 3.5 and feature1 > 4.0' 7 | >>> result = Rule(r, args=None) 8 | >>> print(result) 9 | feature1 > 4.0 and feature2 < 10.0 and feature3 == 3.5 10 | 11 | Parameters 12 | ---------- 13 | 14 | rule : str 15 | The logical rule that is interpretable by a pandas query. 16 | 一段能被 pandas query 方法解析的规则字符串 17 | 18 | args : object, optional 19 | Arguments associated to the rule, it is not used for factorization 20 | but it takes part of the output when the rule is converted to an array. 21 | 额外的参数，不用于规则分解，但会与最后的结果一起输出 22 | """ 23 | 24 | def __init__(self, rule, args=None): 25 | self.rule = rule 26 | self.args = args 27 | self.terms = [t.split(' ') for t in self.rule.split(' and ')] 28 | self.agg_dict = {} 29 | self.factorize() 30 | self.rule = str(self) 31 | 32 | # 重新定义类中的'=='行为，但没看出哪里派用场了 33 | # def __eq__(self, other): 34 | # return self.agg_dict == other.agg_dict 35 | 36 | # 暂时不知道用途 37 | # def __hash__(self): 38 | # # FIXME : Easier method ? 39 | # return hash(tuple(sorted(((i, j) for i, j in self.agg_dict.items())))) 40 | 41 | def factorize(self): 42 | """ 43 | 将决策树的规则分解为 字段名 + 判断符号 + 阈值，并进行合并简化 44 | """ 45 | for feature, symbol, value in self.terms: 46 | if (feature, symbol) not in self.agg_dict: 47 | if symbol != '==': 48 | self.agg_dict[(feature, symbol)] = str(float(value)) 49 | else: 50 | self.agg_dict[(feature, symbol)] = value 51 | else: 52 | if symbol[0] == '<': 53 | self.agg_dict[(feature, symbol)] = str(min( 54 | float(self.agg_dict[(feature, symbol)]), 55 | float(value))) 56 | elif symbol[0] == '>': 57 | self.agg_dict[(feature, symbol)] = str(max( 58 | float(self.agg_dict[(feature, symbol)]), 59 | float(value))) 60 | else: # Handle the c0 == c0 case 61 | self.agg_dict[(feature, symbol)] = value 62 | #print(self.agg_dict) 63 | 64 | def __iter__(self): 65 | yield str(self) 66 | yield self.args 67 | 68 | def __repr__(self): 69 | return ' and '.join([' '.join( 70 | [feature, symbol, str(self.agg_dict[(feature, symbol)])]) 71 | for feature, symbol in sorted(self.agg_dict.keys()) 72 | ]) 73 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Rule_Extraction_From_Trees 2 | 3 | A toolkit for extracting comprehensible rules and selecting the best performing rule set from tree-based algorithms, based on [Skope-rules](https://github.com/scikit-learn-contrib/skope-rules). Currently only supports 2-classes classification task. 4 | 5 | **Major groups of functionalities:** 6 | 7 | 1. Visualize tree structures and output as images; 8 | 2. Rule extraction from trained tree models; 9 | 3. Filter rules based on recall/precision threshold on a given dataset; 10 | 4. Make predictions by rule voting. 11 | 12 | **Model supported:** 13 | 14 | 1. DecisionTreeClassifier/DecisionTreeRegressor 15 | 2. BaggingClassifier/BaggingRegressor 16 | 3. RandomForestClassifier/RandomForestRegressor 17 | 4. ExtraTreesClassifier/ ExtraTreeRegressor 18 | 19 | 20 | 21 | ## Installation 22 | 23 | This project requires: 24 | 25 | - Python (>= 2.7 or >= 3.3) 26 | - NumPy (>= 1.10.4) 27 | - SciPy (>= 0.17.0) 28 | - Pandas (>= 0.18.1) 29 | - Scikit-Learn (>= 0.17.1) 30 | - pydotplus (>=2.0.2) 31 | - graphviz (>=0.8.2) 32 | 33 | 34 | 35 | **Installing graphviz (for windows user):** 36 | 37 | 1. Download and install executable from https://graphviz.gitlab.io/_pages/Download/Download_windows.html 38 | 39 | 2. Set the PATH variable as follows 40 | 41 | ![install_graphviz](images/install_graphviz.png) 42 | 43 | 3. Restart your currently running application that requires the path 44 | 45 | 4. pip install pydotplus 46 | 47 | 48 | 49 | ## Quick Start 50 | 51 | See **Demo1** [here](https://github.com/Yimeng-Zhang/Rule_Extraction_from_Trees/blob/master/Demo1_Rule_Extraction_from_Trees.ipynb) for a detailed example. 52 | 53 | First download the code into your project folder. 54 | 55 | 1. Train or load a tree-based model. Having the dataset that is trained on is better. 56 | 57 | ``` 58 | import pandas as pd 59 | import numpy as np 60 | from sklearn.model_selection import train_test_split 61 | from sklearn import tree,ensemble,metrics 62 | 63 | from rule import Rule 64 | from rule_extraction import rule_extract,draw_tree 65 | 66 | # Train the model 67 | model = tree.DecisionTreeClassifier(criterion='gini',max_depth=3) 68 | model.fit(X_train,y_train) 69 | ``` 70 | 71 | 2. Extract all the rules from the tree (all paths from root node to leaves) 72 | 73 | ```python 74 | rules, _ = rule_extract(model=model,feature_names=X_train.columns) 75 | for i in rules: 76 | print(i) 77 | 78 | # output 79 | Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare > 26.125 80 | Sex_ordered <= 0.4722778648138046 and Age > 13.0 and Pclass_ordered <= 0.5564569681882858 81 | Sex_ordered <= 0.4722778648138046 and Age <= 13.0 and Pclass_ordered <= 0.3504907488822937 82 | Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Fare <= 20.800000190734863 83 | Sex_ordered <= 0.4722778648138046 and Age > 13.0 and Pclass_ordered > 0.5564569681882858 84 | Sex_ordered <= 0.4722778648138046 and Age <= 13.0 and Pclass_ordered > 0.3504907488822937 85 | Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare <= 26.125 86 | Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Fare > 20.800000190734863 87 | 88 | ``` 89 | 90 | 3. Draw the structure of the tree 91 | 92 | ```python 93 | # blue (class=1) denote the node make prediction of class 1 94 | # orange (class=0) denote the node make prediction of class 0 95 | # the darker the color, the more purity the node has 96 | draw_tree(model=model, 97 | outdir='./images/DecisionTree/', 98 | feature_names=X_train.columns, 99 | proportion=False, # show [proportion] or [number of samples] from a node 100 | class_names=['0','1']) 101 | ``` 102 | 103 | 104 | 105 | ![](./images/DecisionTree/DecisionTree.jpeg) 106 | 107 | 108 | 109 | 4. Filter rules base on recall/precision on dataset 110 | 111 | ```python 112 | rules, rule_dict = rule_extract(model=model_tree_clf, 113 | feature_names=X_train.columns, 114 | x_test=X_test, 115 | y_test=y_test, 116 | recall_min_c0=0.9, # recall threshold on class 1 117 | precision_min_c0=0.6) # precision threshold on class 1 118 | 119 | for i in rule_dict: 120 | print(i) 121 | # return:(rule, recall on 1-class, prec on 1-class, recall on 0-class, prec on 0-class, nb) 122 | ('Fare > 26.125 and Pclass_ordered > 0.3504907488822937 and Sex_ordered > 0.4722778648138046', (0.328125, 0.9130434782608695, 0.9746835443037974, 0.6416666666666667, 1)) 123 | ('Fare <= 26.125 and Pclass_ordered > 0.3504907488822937 and Sex_ordered > 0.4722778648138046', (0.21875, 0.875, 0.9746835443037974, 0.6062992125984252, 1)) 124 | 125 | ``` 126 | 127 | 128 | 129 | ## API Reference 130 | 131 | TODO 132 | -------------------------------------------------------------------------------- /rule_extraction.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sklearn 3 | from sklearn.tree import _tree 4 | from warnings import warn 5 | from sklearn import tree 6 | import pydotplus 7 | from sklearn.externals.six import StringIO 8 | import numpy as np 9 | import pandas as pd 10 | 11 | from rule import Rule 12 | 13 | # 2018.10.14 Created by Eamon.Zhang 14 | # 2018.10.15 Add rule filtering function, which allows future functions such as prediction based on selected sets of rules 15 | # 2018.10.15 Issue: DataFrame.Query has a max length limit of 32 to the query string, see https://github.com/PyTables/PyTables/issues/286. 16 | # will raise error if rules have >32 conditiions. Unsovlebale yet. 17 | # 2018.10.28 Add tree visualize function. Output the tree structure to images. 18 | # 2018.12.15 fix bug on calculating precision on class 0 19 | # 2018.12.15 add rule voting 20 | 21 | 22 | # 2018.10.28 Visualize a decision tree or trees from ensembles 23 | def draw_tree(model,outdir,feature_names=None,proportion=True,class_names=['0','1']): 24 | """ 25 | Visualize a decision tree or trees from ensembles 26 | and output a jpeg image 27 | 28 | Parameters 29 | ---------- 30 | model : 31 | pre-trained model 32 | outdir: 33 | output path of the image 34 | feature_names： 35 | Feature names. If None, returns X1,X2... 36 | proportion: 37 | the values of the output graph is absolute number of samples belongs to 38 | that node or the proportion. Default is proportion 39 | class_names： 40 | label of the prediction of each node. Default is ['0','1'] 41 | 42 | """ 43 | 44 | # if input model is a single decision tree/classifier 45 | if isinstance(model,(sklearn.tree.tree.DecisionTreeClassifier,sklearn.tree.tree.DecisionTreeRegressor)): 46 | dot_data = StringIO() 47 | tree.export_graphviz(decision_tree=model, 48 | out_file=dot_data, 49 | filled=True, 50 | rounded=True, 51 | special_characters=True, 52 | feature_names=feature_names, 53 | proportion=proportion, 54 | class_names=class_names) 55 | 56 | graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 57 | # print(graph) 58 | outfile = os.path.join(outdir,'DecisionTree.jpeg') 59 | graph.write_jpeg(outfile) 60 | 61 | # if input model is a tree ensemble 62 | elif isinstance(model,(sklearn.ensemble.bagging.BaggingClassifier, 63 | sklearn.ensemble.bagging.BaggingRegressor, 64 | sklearn.ensemble.forest.RandomForestClassifier, 65 | sklearn.ensemble.forest.RandomForestRegressor, 66 | sklearn.ensemble.forest.ExtraTreesClassifier, 67 | sklearn.ensemble.forest.ExtraTreeRegressor)): 68 | i = 0 69 | # visulaize each tree from the whole ensemble 70 | for estimator in model.estimators_: 71 | i += 1 72 | dot_data = StringIO() 73 | tree.export_graphviz(decision_tree=estimator, 74 | out_file=dot_data, 75 | filled=True, 76 | rounded=True, 77 | special_characters=True, 78 | feature_names=feature_names, 79 | proportion=proportion, 80 | class_names=class_names) 81 | 82 | graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 83 | #print(graph) 84 | outfile = os.path.join(outdir,'EnsembleTrees_No%s.jpeg' % str(i)) 85 | graph.write_jpeg(outfile) 86 | else: 87 | raise ValueError('Unsupported model type!') 88 | return 89 | 90 | 91 | # 2018.10.15 rule extraction from a tree model with filtering criterions based on recall/precision on a given dataset 92 | # 2018.10.30 allow extract all paths of a tree with no filtering 93 | def rule_extract(model, feature_names, x_test=None, y_test=None, sort_key=0, recall_min_c1=0., precision_min_c1=0., recall_min_c0=0., precision_min_c0=0.): 94 | """ 95 | Extract rules from Tree based Algorithm and filter rules based 96 | on recall/precision threshold on a given dataset. 97 | Return rules and rules with its performance. 98 | 99 | Parameters 100 | ---------- 101 | model : 102 | 用户训练好的模型 103 | 104 | feature_names: 105 | 特征名称 106 | 107 | x_test : pandas.DataFrame. 108 | 用来测试的样本的特征集 109 | 110 | y_test : pandas.DataFrame. 111 | 用来测试的样本的y标签 112 | 113 | sort_key: 按哪一个指标排序，default = 0 114 | 0： 按label=1样本的 recall 降序 115 | 1： 按label=1样本的 precision 降序 116 | 2： 按label=0样本的 recall 降序 117 | 3： 按label=0样本的 precision 降序 118 | 119 | recall_min_c1：对1类样本的召回率表现筛选 120 | 121 | precision_min_c1：对1类样本的准确率表现筛选 122 | 123 | recall_min_c0：对0类样本的召回率表现筛选 124 | 125 | precision_min_c0：对0类样本的准确率表现筛选 126 | 127 | Returns 128 | ------- 129 | rules: list of str. 130 | eg: ['Fare > 26 and Age > 10','Fare <= 20 and Age <= 18'] 131 | rules_dict: list of tuples (rule, recall on 1-class, prec on 1-class, recall on 0-class, prec on 0-class, nb). 132 | eg:[('Fare > 26 and Age > 10', (0.32, 0.91, 0.97, 0.6, 1)), 133 | ('Fare <= 20 and Age <= 18', (0.22, 0.71, 0.17, 0.5, 1))] 134 | 135 | """ 136 | 137 | rules_dict = {} 138 | rules_ = [] 139 | #feature_names = feature_names if feature_names is not None 140 | # else ['X' + x for x in np.arange(X.shape[1]).astype(str)] 141 | 142 | # if input model is a single decision tree/classifier 143 | if isinstance(model,(sklearn.tree.tree.DecisionTreeClassifier,sklearn.tree.tree.DecisionTreeRegressor)): 144 | rules_from_tree = _tree_to_rules(model,feature_names) 145 | 146 | # if no test data is given, return the rules 147 | if x_test is None: 148 | rules_ = [(r) for r in set(rules_from_tree)] 149 | return rules_, rules_dict 150 | # if test data is given, filter the rules based on precision/recall 151 | else: 152 | rules_from_tree = [(r, _eval_rule_perf(r, x_test, y_test)) for r in set(rules_from_tree)] 153 | rules_ = rules_from_tree 154 | 155 | # 根据评估指标进行规则筛选 156 | rules_dict = _rule_filter(rules_,rules_dict, recall_min_c1, precision_min_c1, recall_min_c0, precision_min_c0) 157 | 158 | # 按 recall_1 降序排列 159 | rules_dict = sorted(rules_dict.items(), 160 | key=lambda x: (x[1][sort_key]), reverse=True) 161 | rules_= [i[0] for i in rules_dict] 162 | return rules_, rules_dict 163 | 164 | elif isinstance(model,(sklearn.ensemble.bagging.BaggingClassifier, 165 | sklearn.ensemble.bagging.BaggingRegressor, 166 | sklearn.ensemble.forest.RandomForestClassifier, 167 | sklearn.ensemble.forest.RandomForestRegressor, 168 | sklearn.ensemble.forest.ExtraTreesClassifier, 169 | sklearn.ensemble.forest.ExtraTreeRegressor)): 170 | if x_test is None: 171 | for estimator in model.estimators_: 172 | rules_from_tree = _tree_to_rules(estimator,feature_names) 173 | rules_from_tree = [(r) for r in set(rules_from_tree)] 174 | rules_ += rules_from_tree 175 | return rules_, rules_dict 176 | else: 177 | for estimator in model.estimators_: 178 | rules_from_tree = _tree_to_rules(estimator,feature_names) 179 | rules_from_tree = [(r, _eval_rule_perf(r, x_test, y_test)) for r in set(rules_from_tree)] 180 | rules_ += rules_from_tree 181 | 182 | # 根据评估指标进行规则筛选 183 | rules_dict = _rule_filter(rules_, rules_dict, recall_min_c1, precision_min_c1, recall_min_c0, precision_min_c0) 184 | 185 | # 按 recall_1 降序排列 186 | rules_dict = sorted(rules_dict.items(), 187 | key=lambda x: (x[1][sort_key]), reverse=True) 188 | rules_= [i[0] for i in rules_dict] 189 | return rules_, rules_dict 190 | 191 | else: 192 | raise ValueError('Unsupported model type!') 193 | return 194 | 195 | 196 | # 2018.10.15 对规则筛选。目前由于测试集固定，实际上同一条规则的表现不会波动。 197 | def _rule_filter(rules_,rules_dict,recall_min_c1,precision_min_c1,recall_min_c0,precision_min_c0): 198 | # Factorize rules before semantic tree filtering 199 | rules_ = [ 200 | tuple(rule) 201 | for rule in 202 | [Rule(r, args=args) for r, args in rules_]] 203 | 204 | # 根据0/1类样本的recall/precision进行筛选。对于重复出现的规则，则更新其表现。 205 | for rule, score in rules_: 206 | if score[0] >= recall_min_c1 and score[1] >= precision_min_c1 and score[2]>=recall_min_c0 and score[3]>=precision_min_c0: 207 | 208 | if rule in rules_dict: 209 | # update the score to the new mean 210 | # Moving Average Calculation 211 | e = rules_dict[rule][4] + 1 # counter 212 | d = rules_dict[rule][3] + 1. / e * ( 213 | score[3] - rules_dict[rule][3]) 214 | c = rules_dict[rule][2] + 1. / e * ( 215 | score[2] - rules_dict[rule][2]) 216 | b = rules_dict[rule][1] + 1. / e * ( 217 | score[1] - rules_dict[rule][1]) 218 | a = rules_dict[rule][0] + 1. / e * ( 219 | score[0] - rules_dict[rule][0]) 220 | 221 | rules_dict[rule] = (a, b, c, d, e) 222 | else: 223 | rules_dict[rule] = (score[0], score[1], score[2], score[3], 1) 224 | 225 | return rules_dict 226 | 227 | 228 | # 2018.10.14 评估一条单独规则的指标 229 | def _eval_rule_perf(rule, X, y): 230 | """ 231 | 衡量每一条单独规则的评价指标，目前支持 0/1 两类样本的precision/recall 232 | 233 | Parameters 234 | ---------- 235 | 236 | rule : str 237 | 从决策树中提取出的单条规则 238 | 239 | X : pandas.DataFrame. 240 | 用来测试的样本的特征集 241 | 242 | y : pandas.DataFrame. 243 | 用来测试的样本的y标签 244 | 245 | """ 246 | 247 | detected_index = list(X.query(rule).index) 248 | # print(detected_index) 249 | if len(detected_index) <= 0: 250 | warn("rule %s reach no samples" % str(rule)) 251 | return (0.,0.,0.,0.) 252 | 253 | y_detected = y[detected_index] 254 | true_pos = y_detected[y_detected > 0].count() 255 | false_pos = y_detected[y_detected == 0].count() 256 | 257 | pos = y[y > 0].count() 258 | neg = y[y == 0].count() 259 | # recall_0 = str('recall for class 0 is: '+ str(1- (float(false_pos) /neg))) 260 | # prec_0 = str('prec for class 0 is: ' + str((neg-false_pos) / (len(y)-y_detected.sum()))) 261 | # recall_1 = str('recall for class 1 is: '+ str(float(true_pos) / pos)) 262 | # prec_1 = str('prec for class 1 is: ' + str(y_detected.mean())) 263 | recall_0 = (1- (float(false_pos) /neg)) 264 | prec_0 = ((neg-false_pos) / (len(y)-y_detected.sum()-false_pos)) 265 | recall_1 = (float(true_pos) / pos) 266 | prec_1 = (y_detected.mean()) 267 | #print(rule) 268 | #print(pos,neg,true_pos,false_pos,len(y),y_detected.sum()) 269 | return recall_1, prec_1, recall_0, prec_0 270 | 271 | 272 | # 2018.10.14 从sklearn 的 tree_ 对象中提取规则 273 | # direct copied from https://github.com/scikit-learn-contrib/skope-rules/tree/master/skrules 274 | def _tree_to_rules(tree, feature_names): 275 | """ 276 | Return a list of rules from a tree 277 | 278 | Parameters 279 | ---------- 280 | tree : Decision Tree Classifier/Regressor 281 | feature_names: list of variable names 282 | 283 | Returns 284 | ------- 285 | rules : list of rules. 286 | """ 287 | # XXX todo: check the case where tree is build on subset of features, 288 | # ie max_features != None 289 | 290 | tree_ = tree.tree_ 291 | feature_name = [ 292 | feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!" 293 | for i in tree_.feature] 294 | rules = [] 295 | 296 | def recurse(node, base_name): 297 | if tree_.feature[node] != _tree.TREE_UNDEFINED: 298 | name = feature_name[node] 299 | symbol = '<=' 300 | symbol2 = '>' 301 | threshold = tree_.threshold[node] 302 | text = base_name + ["{} {} {}".format(name, symbol, threshold)] 303 | recurse(tree_.children_left[node], text) 304 | 305 | text = base_name + ["{} {} {}".format(name, symbol2, 306 | threshold)] 307 | recurse(tree_.children_right[node], text) 308 | else: 309 | rule = str.join(' and ', base_name) 310 | rule = (rule if rule != '' 311 | else ' == '.join([feature_names[0]] * 2)) 312 | # a rule selecting all is set to "c0==c0" 313 | rules.append(rule) 314 | 315 | recurse(0, []) 316 | 317 | return rules if len(rules) > 0 else 'True' 318 | 319 | # 2018.12.14 added by Eamon.Zhang 320 | def rules_vote(X,rules): 321 | """ 322 | Score representing a vote of the base classifiers (rules) 323 | The score of an input sample is computed as the sum of the binary 324 | rules outputs: a score of k means than k rules have voted positively. 325 | 326 | Parameters 327 | ---------- 328 | X : array-like, shape (n_samples, n_features) 329 | The training input samples. 330 | 331 | Returns 332 | ------- 333 | scores : array, shape (n_samples,) 334 | 335 | """ 336 | scores = np.zeros(X.shape[0]) 337 | for r in rules: 338 | scores[list(X.query(r).index)] +=1 #w[0] 339 | scores=pd.DataFrame(scores) 340 | 341 | return scores -------------------------------------------------------------------------------- /Demo1_Rule_Extraction_from_Trees.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import numpy as np\n", 13 | "from sklearn.model_selection import train_test_split\n", 14 | "from sklearn import tree,ensemble,metrics\n", 15 | "\n", 16 | "from rule import Rule\n", 17 | "from rule_extraction import rule_extract,draw_tree,rules_vote" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## 1. Data Preparation" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 2, 30 | "metadata": {}, 31 | "outputs": [ 32 | { 33 | "name": "stdout", 34 | "output_type": "stream", 35 | "text": [ 36 | "(571, 5) (143, 5)\n", 37 | " Age Fare Sex Pclass Survived\n", 38 | "387 36.0 13.0000 female 2 1\n", 39 | "685 25.0 41.5792 male 2 0\n", 40 | "20 35.0 26.0000 male 2 0\n", 41 | "331 45.5 28.5000 male 1 0\n", 42 | "396 31.0 7.8542 female 3 0\n" 43 | ] 44 | } 45 | ], 46 | "source": [ 47 | "# fetch dataset\n", 48 | "data = pd.read_csv('./dataset/titanic.csv', usecols = ['Age', 'Fare','Sex','Pclass','Survived'])\n", 49 | "\n", 50 | "# drop NA records since most Tree algorithm cannot handle\n", 51 | "data.dropna(inplace=True)\n", 52 | "\n", 53 | "# split training/test sets\n", 54 | "X_train, X_test, y_train, y_test = train_test_split(data[['Age', 'Fare','Sex','Pclass','Survived']], \n", 55 | " data.Survived, test_size=0.2, random_state=0)\n", 56 | "\n", 57 | "# dataset shape\n", 58 | "print(X_train.shape, X_test.shape)\n", 59 | "print(X_train.head(5))" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "## 2. Categorical Variable encoding using Mean Encoding" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 3, 72 | "metadata": { 73 | "collapsed": true 74 | }, 75 | "outputs": [], 76 | "source": [ 77 | "# Pclass\n", 78 | "X_train.groupby(['Pclass'])['Survived'].mean()\n", 79 | "ordered_labels = X_train.groupby(['Pclass'])['Survived'].mean().to_dict()\n", 80 | "ordered_labels\n", 81 | "\n", 82 | "# Mean Encoding\n", 83 | "X_train['Pclass_ordered'] = X_train.Pclass.map(ordered_labels)\n", 84 | "X_test['Pclass_ordered'] = X_test.Pclass.map(ordered_labels)\n", 85 | "\n", 86 | "# Sex\n", 87 | "X_train.groupby(['Sex'])['Survived'].mean()\n", 88 | "ordered_labels = X_train.groupby(['Sex'])['Survived'].mean().to_dict()\n", 89 | "ordered_labels\n", 90 | "\n", 91 | "# Mean Encoding\n", 92 | "X_train['Sex_ordered'] = X_train.Sex.map(ordered_labels)\n", 93 | "X_test['Sex_ordered'] = X_test.Sex.map(ordered_labels)\n" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "## 3. Final training data" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 4, 106 | "metadata": {}, 107 | "outputs": [ 108 | { 109 | "name": "stdout", 110 | "output_type": "stream", 111 | "text": [ 112 | " Age Fare Sex_ordered Pclass_ordered\n", 113 | "387 36.0 13.0000 0.740196 0.460432\n", 114 | "685 25.0 41.5792 0.204360 0.460432\n", 115 | "20 35.0 26.0000 0.204360 0.460432\n", 116 | "331 45.5 28.5000 0.204360 0.652482\n", 117 | "396 31.0 7.8542 0.740196 0.240550\n" 118 | ] 119 | } 120 | ], 121 | "source": [ 122 | "X_train_proceeded = X_train[['Age', 'Fare','Sex_ordered','Pclass_ordered']]\n", 123 | "X_test_proceeded = X_test[['Age', 'Fare','Sex_ordered','Pclass_ordered']]\n", 124 | "print(X_train_proceeded.head())" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "## 4. Training a Single Decision Tree" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 5, 137 | "metadata": {}, 138 | "outputs": [ 139 | { 140 | "data": { 141 | "text/plain": [ 142 | "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n", 143 | " max_features=None, max_leaf_nodes=None,\n", 144 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 145 | " min_samples_leaf=1, min_samples_split=2,\n", 146 | " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", 147 | " splitter='best')" 148 | ] 149 | }, 150 | "execution_count": 5, 151 | "metadata": {}, 152 | "output_type": "execute_result" 153 | } 154 | ], 155 | "source": [ 156 | "# API refer to http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n", 157 | "\n", 158 | "model_tree_clf = tree.DecisionTreeClassifier(criterion='gini',max_depth=3)\n", 159 | "model_tree_clf.fit(X_train_proceeded,y_train)" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 6, 165 | "metadata": {}, 166 | "outputs": [ 167 | { 168 | "name": "stdout", 169 | "output_type": "stream", 170 | "text": [ 171 | "[[311 34]\n", 172 | " [ 67 159]]\n" 173 | ] 174 | } 175 | ], 176 | "source": [ 177 | "# model performance on training set\n", 178 | "y_pred = model_tree_clf.predict(X_train_proceeded)\n", 179 | "print(metrics.confusion_matrix(y_train,y_pred))" 180 | ] 181 | }, 182 | { 183 | "cell_type": "markdown", 184 | "metadata": {}, 185 | "source": [ 186 | "## 4.1 Extract all rules without filtering\n", 187 | "8 rules in total, corresponding to 8 paths from root to leaf node" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 7, 193 | "metadata": {}, 194 | "outputs": [ 195 | { 196 | "name": "stdout", 197 | "output_type": "stream", 198 | "text": [ 199 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare > 26.125\n", 200 | "Sex_ordered <= 0.4722778648138046 and Age > 13.0 and Pclass_ordered <= 0.5564569681882858\n", 201 | "Sex_ordered <= 0.4722778648138046 and Age <= 13.0 and Pclass_ordered <= 0.3504907488822937\n", 202 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Fare <= 20.800000190734863\n", 203 | "Sex_ordered <= 0.4722778648138046 and Age > 13.0 and Pclass_ordered > 0.5564569681882858\n", 204 | "Sex_ordered <= 0.4722778648138046 and Age <= 13.0 and Pclass_ordered > 0.3504907488822937\n", 205 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare <= 26.125\n", 206 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Fare > 20.800000190734863\n" 207 | ] 208 | } 209 | ], 210 | "source": [ 211 | "rule, _ = rule_extract(model=model_tree_clf,feature_names=X_train_proceeded.columns)\n", 212 | "for i in rule:\n", 213 | " print(i)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "## 4.2 Output the tree sturcture\n", 221 | "compared with the confusion matrix on training data:\n", 222 | "\n", 223 | " | pred=0 | pred=1\n", 224 | "- | :-: | -: \n", 225 | "true=0 | 311 | 34\n", 226 | "true=1| 67 | 159\n", 227 | "\n", 228 | "the graph's result match perfectly" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 8, 234 | "metadata": { 235 | "collapsed": true 236 | }, 237 | "outputs": [], 238 | "source": [ 239 | "# blue node (class=1) denote the node make prediction of class 1\n", 240 | "# orange node (class=0) denote the node make prediction of class 0\n", 241 | "# the darker the color, the more purity the node has \n", 242 | "# values refer to the absolute number of labeled samples in that node\n", 243 | "# eg, the 1st leaf node [12,7] means that 12 class 0 samples and 7 class 1 samples are in that node\n", 244 | "draw_tree(model=model_tree_clf,\n", 245 | " outdir='./images/DecisionTree/',\n", 246 | " feature_names=X_train_proceeded.columns,\n", 247 | " proportion=False,\n", 248 | " class_names=['0','1'])" 249 | ] 250 | }, 251 | { 252 | "cell_type": "markdown", 253 | "metadata": {}, 254 | "source": [ 255 | "![title](images/DecisionTree/DecisionTree.jpeg) " 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "## 5. Extract rule with filtering\n", 263 | "rule_dict: rule, recall on 1-class, prec on 1-class, recall on 0-class, prec on 0-class, nb\n" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": 9, 269 | "metadata": {}, 270 | "outputs": [ 271 | { 272 | "name": "stdout", 273 | "output_type": "stream", 274 | "text": [ 275 | "('Fare > 26.125 and Pclass_ordered > 0.3504907488822937 and Sex_ordered > 0.4722778648138046', (0.328125, 0.9130434782608695, 0.9746835443037974, 0.6416666666666667, 1))\n", 276 | "('Fare <= 26.125 and Pclass_ordered > 0.3504907488822937 and Sex_ordered > 0.4722778648138046', (0.21875, 0.875, 0.9746835443037974, 0.6062992125984252, 1))\n" 277 | ] 278 | } 279 | ], 280 | "source": [ 281 | "# filter rule\n", 282 | "rules, rule_dict = rule_extract(model=model_tree_clf,\n", 283 | " feature_names=X_train_proceeded.columns,\n", 284 | " x_test=X_test_proceeded,\n", 285 | " y_test=y_test,\n", 286 | " sort_key=0,\n", 287 | " recall_min_c1=0.,\n", 288 | " precision_min_c1=0.,\n", 289 | " recall_min_c0=0.9,\n", 290 | " precision_min_c0=0.6)\n", 291 | "for i in rule_dict:\n", 292 | " print(i)" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "### 5.1 Random Forest" 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "execution_count": 10, 305 | "metadata": {}, 306 | "outputs": [ 307 | { 308 | "data": { 309 | "text/plain": [ 310 | "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", 311 | " max_depth=3, max_features='auto', max_leaf_nodes=None,\n", 312 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 313 | " min_samples_leaf=1, min_samples_split=2,\n", 314 | " min_weight_fraction_leaf=0.0, n_estimators=3, n_jobs=None,\n", 315 | " oob_score=False, random_state=9, verbose=0, warm_start=False)" 316 | ] 317 | }, 318 | "execution_count": 10, 319 | "metadata": {}, 320 | "output_type": "execute_result" 321 | } 322 | ], 323 | "source": [ 324 | "model_RF_clf = ensemble.RandomForestClassifier(max_depth=3,n_estimators=3,random_state=9)\n", 325 | "model_RF_clf.fit(X_train_proceeded,y_train)" 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": 11, 331 | "metadata": {}, 332 | "outputs": [ 333 | { 334 | "name": "stdout", 335 | "output_type": "stream", 336 | "text": [ 337 | "[[72 7]\n", 338 | " [23 41]]\n" 339 | ] 340 | } 341 | ], 342 | "source": [ 343 | "# model performance on test set\n", 344 | "y_pred_test = model_RF_clf.predict(X_test_proceeded)\n", 345 | "print(metrics.confusion_matrix(y_test,y_pred_test))" 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": 12, 351 | "metadata": {}, 352 | "outputs": [ 353 | { 354 | "name": "stdout", 355 | "output_type": "stream", 356 | "text": [ 357 | "Fare > 15.64585018157959 and Age > 53.5 and Fare > 133.36874771118164\n", 358 | "Fare <= 15.64585018157959 and Sex_ordered <= 0.4722778648138046 and Pclass_ordered > 0.3504907488822937\n", 359 | "Fare > 15.64585018157959 and Age > 53.5 and Fare <= 133.36874771118164\n", 360 | "Fare <= 15.64585018157959 and Sex_ordered > 0.4722778648138046 and Fare > 10.481249809265137\n", 361 | "Fare > 15.64585018157959 and Age <= 53.5 and Pclass_ordered <= 0.3504907488822937\n", 362 | "Fare <= 15.64585018157959 and Sex_ordered > 0.4722778648138046 and Fare <= 10.481249809265137\n", 363 | "Fare <= 15.64585018157959 and Sex_ordered <= 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937\n", 364 | "Fare > 15.64585018157959 and Age <= 53.5 and Pclass_ordered > 0.3504907488822937\n", 365 | "Pclass_ordered > 0.3504907488822937 and Age > 17.5 and Sex_ordered > 0.4722778648138046\n", 366 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered <= 0.4722778648138046 and Age <= 9.5\n", 367 | "Pclass_ordered > 0.3504907488822937 and Age <= 17.5\n", 368 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered > 0.4722778648138046 and Fare > 13.464600086212158\n", 369 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered > 0.4722778648138046 and Fare <= 13.464600086212158\n", 370 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered <= 0.4722778648138046 and Age > 9.5\n", 371 | "Pclass_ordered > 0.3504907488822937 and Age > 17.5 and Sex_ordered <= 0.4722778648138046\n", 372 | "Pclass_ordered > 0.3504907488822937 and Age <= 17.5 and Fare <= 12.5\n", 373 | "Pclass_ordered > 0.3504907488822937 and Age > 17.5 and Sex_ordered > 0.4722778648138046\n", 374 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered > 0.4722778648138046 and Fare > 13.466650009155273\n", 375 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered <= 0.4722778648138046 and Fare > 7.012500047683716\n", 376 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered <= 0.4722778648138046 and Fare <= 7.012500047683716\n", 377 | "Pclass_ordered <= 0.3504907488822937 and Sex_ordered > 0.4722778648138046 and Fare <= 13.466650009155273\n", 378 | "Pclass_ordered > 0.3504907488822937 and Age <= 17.5 and Fare > 12.5\n", 379 | "Pclass_ordered > 0.3504907488822937 and Age > 17.5 and Sex_ordered <= 0.4722778648138046\n" 380 | ] 381 | } 382 | ], 383 | "source": [ 384 | "rules,_ = rule_extract(model=model_RF_clf,feature_names=X_train_proceeded.columns)\n", 385 | "for i in rules:\n", 386 | " print(i)" 387 | ] 388 | }, 389 | { 390 | "cell_type": "markdown", 391 | "metadata": {}, 392 | "source": [ 393 | "### 5.2 Output the tree sturcture" 394 | ] 395 | }, 396 | { 397 | "cell_type": "code", 398 | "execution_count": 13, 399 | "metadata": { 400 | "collapsed": true 401 | }, 402 | "outputs": [], 403 | "source": [ 404 | "draw_tree(model=model_RF_clf,\n", 405 | " outdir='./images/RandomForest/',\n", 406 | " feature_names=X_train_proceeded.columns,\n", 407 | " proportion=False,\n", 408 | " class_names=['0','1'])" 409 | ] 410 | }, 411 | { 412 | "cell_type": "markdown", 413 | "metadata": {}, 414 | "source": [ 415 | "### Tree 1\n", 416 | "![title](images/RandomForest/EnsembleTrees_No1.jpeg) " 417 | ] 418 | }, 419 | { 420 | "cell_type": "markdown", 421 | "metadata": {}, 422 | "source": [ 423 | "### Tree2\n", 424 | "![title](images/RandomForest/EnsembleTrees_No2.jpeg) " 425 | ] 426 | }, 427 | { 428 | "cell_type": "markdown", 429 | "metadata": {}, 430 | "source": [ 431 | "### Tree3\n", 432 | "![title](images/RandomForest/EnsembleTrees_No3.jpeg) " 433 | ] 434 | }, 435 | { 436 | "cell_type": "markdown", 437 | "metadata": {}, 438 | "source": [ 439 | "### 5.3 BaggingClassifier" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": 14, 445 | "metadata": {}, 446 | "outputs": [ 447 | { 448 | "data": { 449 | "text/plain": [ 450 | "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n", 451 | " max_features=None, max_leaf_nodes=None,\n", 452 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 453 | " min_samples_leaf=1, min_samples_split=2,\n", 454 | " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", 455 | " splitter='best'),\n", 456 | " bootstrap=True, bootstrap_features=False, max_features=1.0,\n", 457 | " max_samples=1.0, n_estimators=2, n_jobs=-1, oob_score=False,\n", 458 | " random_state=0, verbose=0, warm_start=False)" 459 | ] 460 | }, 461 | "execution_count": 14, 462 | "metadata": {}, 463 | "output_type": "execute_result" 464 | } 465 | ], 466 | "source": [ 467 | "model_bagging_clf = ensemble.BaggingClassifier(\n", 468 | " base_estimator=tree.DecisionTreeClassifier(max_depth=3),\n", 469 | " n_estimators=2,\n", 470 | " n_jobs=-1,\n", 471 | " random_state=0)\n", 472 | "model_bagging_clf.fit(X_train_proceeded,y_train)" 473 | ] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": 15, 478 | "metadata": {}, 479 | "outputs": [ 480 | { 481 | "name": "stdout", 482 | "output_type": "stream", 483 | "text": [ 484 | "[[75 4]\n", 485 | " [27 37]]\n" 486 | ] 487 | } 488 | ], 489 | "source": [ 490 | "# model performance on test set\n", 491 | "y_pred_test = model_bagging_clf.predict(X_test_proceeded)\n", 492 | "print(metrics.confusion_matrix(y_test,y_pred_test))" 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 16, 498 | "metadata": {}, 499 | "outputs": [ 500 | { 501 | "name": "stdout", 502 | "output_type": "stream", 503 | "text": [ 504 | "Sex_ordered <= 0.4722778648138046 and Fare <= 56.19790077209473 and Age <= 9.5\n", 505 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare > 22.0\n", 506 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Age > 35.0\n", 507 | "Sex_ordered <= 0.4722778648138046 and Fare > 56.19790077209473 and Fare > 59.08749961853027\n", 508 | "Sex_ordered <= 0.4722778648138046 and Fare > 56.19790077209473 and Fare <= 59.08749961853027\n", 509 | "Sex_ordered <= 0.4722778648138046 and Fare <= 56.19790077209473 and Age > 9.5\n", 510 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare <= 22.0\n", 511 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Age <= 35.0\n", 512 | "Sex_ordered <= 0.4722778648138046 and Age <= 5.5 and Pclass_ordered <= 0.3504907488822937\n", 513 | "Sex_ordered <= 0.4722778648138046 and Age > 5.5 and Pclass_ordered > 0.5564569681882858\n", 514 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare > 13.75\n", 515 | "Sex_ordered <= 0.4722778648138046 and Age > 5.5 and Pclass_ordered <= 0.5564569681882858\n", 516 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered > 0.3504907488822937 and Fare <= 13.75\n", 517 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Fare <= 20.800000190734863\n", 518 | "Sex_ordered > 0.4722778648138046 and Pclass_ordered <= 0.3504907488822937 and Fare > 20.800000190734863\n", 519 | "Sex_ordered <= 0.4722778648138046 and Age <= 5.5 and Pclass_ordered > 0.3504907488822937\n" 520 | ] 521 | } 522 | ], 523 | "source": [ 524 | "rules,_ = rule_extract(model=model_bagging_clf,feature_names=X_train_proceeded.columns)\n", 525 | "for i in rules:\n", 526 | " print(i)" 527 | ] 528 | }, 529 | { 530 | "cell_type": "markdown", 531 | "metadata": {}, 532 | "source": [ 533 | "### 5.4 ExtraTree " 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 17, 539 | "metadata": {}, 540 | "outputs": [ 541 | { 542 | "data": { 543 | "text/plain": [ 544 | "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n", 545 | " max_depth=3, max_features='auto', max_leaf_nodes=None,\n", 546 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 547 | " min_samples_leaf=1, min_samples_split=2,\n", 548 | " min_weight_fraction_leaf=0.0, n_estimators=2, n_jobs=None,\n", 549 | " oob_score=False, random_state=0, verbose=0, warm_start=False)" 550 | ] 551 | }, 552 | "execution_count": 17, 553 | "metadata": {}, 554 | "output_type": "execute_result" 555 | } 556 | ], 557 | "source": [ 558 | "model_extratree_clf = ensemble.ExtraTreesClassifier(max_depth=3,n_estimators=2,random_state=0)\n", 559 | "model_extratree_clf.fit(X_train_proceeded,y_train)" 560 | ] 561 | }, 562 | { 563 | "cell_type": "code", 564 | "execution_count": 18, 565 | "metadata": {}, 566 | "outputs": [ 567 | { 568 | "name": "stdout", 569 | "output_type": "stream", 570 | "text": [ 571 | "[[68 11]\n", 572 | " [18 46]]\n" 573 | ] 574 | } 575 | ], 576 | "source": [ 577 | "# model performance on test set\n", 578 | "y_pred_test = model_extratree_clf.predict(X_test_proceeded)\n", 579 | "print(metrics.confusion_matrix(y_test,y_pred_test))" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": 19, 585 | "metadata": {}, 586 | "outputs": [ 587 | { 588 | "name": "stdout", 589 | "output_type": "stream", 590 | "text": [ 591 | "Sex_ordered > 0.5859209424696681 and Pclass_ordered > 0.506024637348139 and Age > 42.70739467800939\n", 592 | "Sex_ordered <= 0.5859209424696681 and Pclass_ordered > 0.6300343487048754 and Fare <= 487.86732759797127\n", 593 | "Sex_ordered > 0.5859209424696681 and Pclass_ordered > 0.506024637348139 and Age <= 42.70739467800939\n", 594 | "Sex_ordered <= 0.5859209424696681 and Pclass_ordered <= 0.6300343487048754 and Fare > 19.768909903834068\n", 595 | "Sex_ordered > 0.5859209424696681 and Pclass_ordered <= 0.506024637348139 and Age <= 27.126815941243812\n", 596 | "Sex_ordered <= 0.5859209424696681 and Pclass_ordered <= 0.6300343487048754 and Fare <= 19.768909903834068\n", 597 | "Sex_ordered <= 0.5859209424696681 and Pclass_ordered > 0.6300343487048754 and Fare > 487.86732759797127\n", 598 | "Sex_ordered > 0.5859209424696681 and Pclass_ordered <= 0.506024637348139 and Age > 27.126815941243812\n", 599 | "Pclass_ordered > 0.4895107363075585 and Sex_ordered > 0.3002771553876842 and Age <= 16.99989479733626\n", 600 | "Pclass_ordered > 0.4895107363075585 and Sex_ordered > 0.3002771553876842 and Age > 16.99989479733626\n", 601 | "Pclass_ordered <= 0.4895107363075585 and Sex_ordered > 0.3898439314490967 and Age > 61.69978473554355\n", 602 | "Pclass_ordered > 0.4895107363075585 and Sex_ordered <= 0.3002771553876842 and Fare <= 322.528637502558\n", 603 | "Pclass_ordered <= 0.4895107363075585 and Sex_ordered > 0.3898439314490967 and Age <= 61.69978473554355\n", 604 | "Pclass_ordered > 0.4895107363075585 and Sex_ordered <= 0.3002771553876842 and Fare > 322.528637502558\n", 605 | "Pclass_ordered <= 0.4895107363075585 and Sex_ordered <= 0.3898439314490967 and Age <= 28.78645799781408\n", 606 | "Pclass_ordered <= 0.4895107363075585 and Sex_ordered <= 0.3898439314490967 and Age > 28.78645799781408\n" 607 | ] 608 | } 609 | ], 610 | "source": [ 611 | "rules, _ = rule_extract(model=model_extratree_clf,feature_names=X_train_proceeded.columns)\n", 612 | "for i in rules:\n", 613 | " print(i)" 614 | ] 615 | }, 616 | { 617 | "cell_type": "markdown", 618 | "metadata": { 619 | "collapsed": true 620 | }, 621 | "source": [ 622 | "## 6.Rule Voting" 623 | ] 624 | }, 625 | { 626 | "cell_type": "markdown", 627 | "metadata": {}, 628 | "source": [ 629 | "### 6.1 Testing our filtering method" 630 | ] 631 | }, 632 | { 633 | "cell_type": "markdown", 634 | "metadata": {}, 635 | "source": [ 636 | "in section 5, we have a rule with performance on test set:\n", 637 | "\n", 638 | "('Fare > 26.125 and Pclass_ordered > 0.3504907488822937 and Sex_ordered > 0.4722778648138046', \n", 639 | "\n", 640 | "recall on 1-class, prec on 1-class, recall on 0-class, prec on 0-class, nb \n", 641 | "(0.328125, 0.9130434782608695, 0.9746835443037974, 0.6416666666666667, 1))\n", 642 | "\n", 643 | "let's check if the result is correct" 644 | ] 645 | }, 646 | { 647 | "cell_type": "code", 648 | "execution_count": 20, 649 | "metadata": {}, 650 | "outputs": [ 651 | { 652 | "name": "stdout", 653 | "output_type": "stream", 654 | "text": [ 655 | "0.0 120\n", 656 | "1.0 23\n", 657 | "Name: 0, dtype: int64\n" 658 | ] 659 | } 660 | ], 661 | "source": [ 662 | "one_rule = ['Fare > 26.125 and Pclass_ordered > 0.3504907488822937 and Sex_ordered > 0.4722778648138046']\n", 663 | "X_test_proceeded_reindex = X_test_proceeded.reset_index(drop=True)\n", 664 | "score = rules_vote(X=X_test_proceeded_reindex,rules=one_rule)\n", 665 | "score = pd.DataFrame(score)\n", 666 | "print(score[0].value_counts())\n", 667 | "score['predict'] = score[0]\n", 668 | "score['predict'][score[0]==1] = 1\n", 669 | "# this single rule has predicted 23 cases to be positive in test data" 670 | ] 671 | }, 672 | { 673 | "cell_type": "code", 674 | "execution_count": 21, 675 | "metadata": {}, 676 | "outputs": [ 677 | { 678 | "name": "stdout", 679 | "output_type": "stream", 680 | "text": [ 681 | "[[77 2]\n", 682 | " [43 21]]\n", 683 | "recall in 1-class: 0.328125\n", 684 | "prec in 1-class: 0.9130434782608695\n", 685 | "recall in 0-class: 0.9746835443037974\n", 686 | "prec in 0-class: 0.6416666666666667\n" 687 | ] 688 | } 689 | ], 690 | "source": [ 691 | "print(metrics.confusion_matrix(y_test,score.predict))\n", 692 | "print('recall in 1-class: ', 21/(21+43))\n", 693 | "print('prec in 1-class: ', 21/(21+2))\n", 694 | "print('recall in 0-class: ', 77/(77+2))\n", 695 | "print('prec in 0-class: ', 77/(77+43))" 696 | ] 697 | }, 698 | { 699 | "cell_type": "markdown", 700 | "metadata": {}, 701 | "source": [ 702 | "### 6.2 Random Forest" 703 | ] 704 | }, 705 | { 706 | "cell_type": "code", 707 | "execution_count": 22, 708 | "metadata": {}, 709 | "outputs": [ 710 | { 711 | "data": { 712 | "text/plain": [ 713 | "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", 714 | " max_depth=3, max_features='auto', max_leaf_nodes=None,\n", 715 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 716 | " min_samples_leaf=1, min_samples_split=2,\n", 717 | " min_weight_fraction_leaf=0.0, n_estimators=3, n_jobs=None,\n", 718 | " oob_score=False, random_state=9, verbose=0, warm_start=False)" 719 | ] 720 | }, 721 | "execution_count": 22, 722 | "metadata": {}, 723 | "output_type": "execute_result" 724 | } 725 | ], 726 | "source": [ 727 | "# we train a random forest\n", 728 | "model_RF_clf = ensemble.RandomForestClassifier(max_depth=3,n_estimators=3,random_state=9)\n", 729 | "model_RF_clf.fit(X_train_proceeded,y_train)" 730 | ] 731 | }, 732 | { 733 | "cell_type": "code", 734 | "execution_count": 23, 735 | "metadata": {}, 736 | "outputs": [ 737 | { 738 | "name": "stdout", 739 | "output_type": "stream", 740 | "text": [ 741 | "[[72 7]\n", 742 | " [23 41]]\n" 743 | ] 744 | } 745 | ], 746 | "source": [ 747 | "# model performance on test set\n", 748 | "y_pred_test = model_RF_clf.predict(X_test_proceeded)\n", 749 | "print(metrics.confusion_matrix(y_test,y_pred_test))" 750 | ] 751 | }, 752 | { 753 | "cell_type": "code", 754 | "execution_count": 24, 755 | "metadata": {}, 756 | "outputs": [ 757 | { 758 | "name": "stdout", 759 | "output_type": "stream", 760 | "text": [ 761 | "5\n" 762 | ] 763 | } 764 | ], 765 | "source": [ 766 | "# we extract rules from the ensemble with filtering\n", 767 | "rules,rule_dict = rule_extract(model=model_RF_clf,\n", 768 | " feature_names=X_train_proceeded.columns,\n", 769 | " x_test=X_train_proceeded,\n", 770 | " y_test=y_train,\n", 771 | " sort_key=0,\n", 772 | " recall_min_c1=0.1,\n", 773 | " precision_min_c1=0.6,\n", 774 | " recall_min_c0=0.1,\n", 775 | " precision_min_c0=0.5)\n", 776 | "print(len(rules))\n", 777 | "\n", 778 | "# we have 5 rule that have prec on class 1>0.6 and recall>0.1" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 25, 784 | "metadata": {}, 785 | "outputs": [ 786 | { 787 | "name": "stdout", 788 | "output_type": "stream", 789 | "text": [ 790 | "0.0 72\n", 791 | "1.0 36\n", 792 | "2.0 29\n", 793 | "3.0 6\n", 794 | "Name: 0, dtype: int64\n" 795 | ] 796 | } 797 | ], 798 | "source": [ 799 | "# use the 5 above rules to make prediction again!\n", 800 | "X_test_proceeded_reindex = X_test_proceeded.reset_index(drop=True)\n", 801 | "#print(X_test_proceeded_reindex)\n", 802 | "score = rules_vote(X=X_test_proceeded_reindex,rules=rules)\n", 803 | "score = pd.DataFrame(score)\n", 804 | "print(score[0].value_counts())\n", 805 | "\n", 806 | "# 6 cases have been voted 3 times. they should be class 1 with greater confidence" 807 | ] 808 | }, 809 | { 810 | "cell_type": "code", 811 | "execution_count": 26, 812 | "metadata": {}, 813 | "outputs": [ 814 | { 815 | "data": { 816 | "text/html": [ 817 | "
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0predict
01.01.0
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\n", 867 | "
" 868 | ], 869 | "text/plain": [ 870 | " 0 predict\n", 871 | "0 1.0 1.0\n", 872 | "1 2.0 1.0\n", 873 | "2 3.0 1.0\n", 874 | "3 0.0 0.0\n", 875 | "4 1.0 1.0" 876 | ] 877 | }, 878 | "execution_count": 26, 879 | "metadata": {}, 880 | "output_type": "execute_result" 881 | } 882 | ], 883 | "source": [ 884 | "# we predict all cases that have been voted at least once to be class 1\n", 885 | "# if we are more preservative, we can predict class 1 only if they have been \n", 886 | "# voted more times\n", 887 | "\n", 888 | "score['predict'] = score[0]\n", 889 | "score['predict'][score[0]>0] = 1\n", 890 | "score.head()" 891 | ] 892 | }, 893 | { 894 | "cell_type": "code", 895 | "execution_count": 27, 896 | "metadata": {}, 897 | "outputs": [ 898 | { 899 | "name": "stdout", 900 | "output_type": "stream", 901 | "text": [ 902 | "confusion matrix of RF model\n", 903 | "[[72 7]\n", 904 | " [23 41]]\n", 905 | "confusion matrix of the 5 rules\n", 906 | "[[58 21]\n", 907 | " [14 50]]\n" 908 | ] 909 | } 910 | ], 911 | "source": [ 912 | "# compare this result with the confusion matrix made by the RF model itself\n", 913 | "# we can see that 5 rules have a better performace on predicting class 1 samples\n", 914 | "print('confusion matrix of RF model')\n", 915 | "print(metrics.confusion_matrix(y_test,y_pred_test))\n", 916 | "print('confusion matrix of the 5 rules')\n", 917 | "print(metrics.confusion_matrix(y_test,score.predict))" 918 | ] 919 | }, 920 | { 921 | "cell_type": "code", 922 | "execution_count": null, 923 | "metadata": { 924 | "collapsed": true 925 | }, 926 | "outputs": [], 927 | "source": [] 928 | } 929 | ], 930 | "metadata": { 931 | "kernelspec": { 932 | "display_name": "Python 3", 933 | "language": "python", 934 | "name": "python3" 935 | }, 936 | "language_info": { 937 | "codemirror_mode": { 938 | "name": "ipython", 939 | "version": 3 940 | }, 941 | "file_extension": ".py", 942 | "mimetype": "text/x-python", 943 | "name": "python", 944 | "nbconvert_exporter": "python", 945 | "pygments_lexer": "ipython3", 946 | "version": "3.6.1" 947 | } 948 | }, 949 | "nbformat": 4, 950 | "nbformat_minor": 2 951 | } 952 | -------------------------------------------------------------------------------- /dataset/titanic.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 5 | 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 6 | 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 7 | 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 8 | 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 9 | 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 10 | 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 11 | 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 12 | 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 13 | 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 14 | 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 15 | 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 16 | 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 17 | 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 18 | 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 19 | 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 20 | 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 21 | 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 22 | 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 23 | 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 24 | 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 25 | 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 26 | 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 27 | 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 28 | 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 29 | 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 30 | 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 31 | 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 32 | 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C 33 | 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C 34 | 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q 35 | 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S 36 | 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C 37 | 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S 38 | 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C 39 | 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S 40 | 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S 41 | 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C 42 | 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S 43 | 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S 44 | 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C 45 | 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C 46 | 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q 47 | 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 48 | 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 49 | 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 50 | 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 51 | 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 52 | 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S 53 | 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S 54 | 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C 55 | 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S 56 | 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C 57 | 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S 58 | 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S 59 | 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C 60 | 59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S 61 | 60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S 62 | 61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C 63 | 62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28, 64 | 63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S 65 | 64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S 66 | 65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C 67 | 66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C 68 | 67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S 69 | 68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S 70 | 69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S 71 | 70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S 72 | 71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S 73 | 72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S 74 | 73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S 75 | 74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C 76 | 75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S 77 | 76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S 78 | 77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S 79 | 78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S 80 | 79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S 81 | 80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S 82 | 81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S 83 | 82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S 84 | 83,1,3,"McDermott, Miss. 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