1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
|
- from typing import List
- import numpy as np
- import pandas as pd
- from imodels.rule_set.skope_rules import SkopeRulesClassifier
- from imodels.util.convert import itemsets_to_rules
- from imodels.util.extract import extract_fpgrowth
- from imodels.util.rule import Rule
- from imodels.util.score import score_precision_recall
- class FPSkopeClassifier(SkopeRulesClassifier):
- def __init__(self,
- minsupport=0.1,
- maxcardinality=2,
- verbose=False,
- precision_min=0.5,
- recall_min=0.01,
- n_estimators=10,
- max_samples=.8,
- max_samples_features=1.,
- bootstrap=False,
- bootstrap_features=False,
- max_depth=3,
- max_depth_duplication=None,
- max_features=1.,
- min_samples_split=2,
- n_jobs=1,
- random_state=None):
- super().__init__(precision_min,
- recall_min,
- n_estimators,
- max_samples,
- max_samples_features,
- bootstrap,
- bootstrap_features,
- max_depth,
- max_depth_duplication,
- max_features,
- min_samples_split,
- n_jobs,
- random_state,
- verbose)
- self.minsupport = minsupport
- self.maxcardinality = maxcardinality
- self.verbose = verbose
- def fit(self, X, y=None, feature_names=None, undiscretized_features=[], sample_weight=None):
- self.undiscretized_features = undiscretized_features
- super().fit(X, y, feature_names=feature_names, sample_weight=sample_weight)
- return self
- def _extract_rules(self, X, y) -> List[str]:
- X = pd.DataFrame(X, columns=self.feature_placeholders)
- itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
- maxcardinality=self.maxcardinality,
- verbose=self.verbose)
- return [itemsets_to_rules(itemsets)], [np.arange(X.shape[0])], [np.arange(len(self.feature_names))]
- def _score_rules(self, X, y, rules) -> List[Rule]:
- return score_precision_recall(X, y,
- rules,
- self.estimators_samples_,
- self.estimators_features_,
- self.feature_placeholders,
- oob=False)
|