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
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
|
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_text
import joblib
import sys
from sklearn.tree import _tree
sys.setrecursionlimit(4000)
loaded_model = joblib.load(sys.argv[1])
headers = joblib.load(sys.argv[2])
output_file = sys.argv[3]
finalHeaders = []
with open(output_file, 'r') as f:
for line in f:
if line[0] == "[" and len(line) > 1000:
print(line)
finalHeaders = line[1:-1].split(",")
for i in range(len(finalHeaders)):
finalHeaders[i] = finalHeaders[i].strip()
finalHeaders[i] = finalHeaders[i].replace("'", "")
print(finalHeaders)
categories = ['A', 'C', 'G', 'T', 'N', '-']
classes = loaded_model.classes_
def getClass(weightArray):
weightArray = weightArray[0]
maxValue = 0
maxIndex = 0
for i in range(len(weightArray)):
if weightArray[i] > maxValue:
maxValue = weightArray[i]
maxIndex = i
return classes[maxIndex]
def reformatRule(sign, name, threshold):
position = name.split("_")[0]
letter = name.split("_")[1]
symbol = "=="
if sign == "<=":
symbol = "!="
return position + symbol + "'" + letter + "'"
outcomesToRules = dict()
def tree_to_code(tree, feature_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
prevRules = []
def recurse(node, depth, prevRules):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
prevRulesRight = prevRules.copy()
prevRulesLeft = prevRules.copy()
prevRulesLeft.append(reformatRule("<=", name, threshold))
prevRulesRight.append(reformatRule(">", name, threshold))
recurse(tree_.children_left[node], depth + 1, prevRulesLeft)
recurse(tree_.children_right[node], depth + 1, prevRulesRight)
else:
rulesString = prevRules[0]
for rule in prevRules[1:]:
rule = rule.strip()
rulesString = rulesString + "," + rule
outcome = getClass(tree_.value[node])
if outcome not in outcomesToRules:
outcomesToRules[outcome] = []
print(outcome + "\t" + rulesString)
recurse(0, 1, prevRules)
tree_to_code(loaded_model, finalHeaders)
def formatCommonRules(ruleSets):
splitRuleSets = []
totalRules = []
commonRules = []
for r in ruleSets:
splitRuleSets.append(r.split(","))
for i in r.split(","):
totalRules.append(i)
for i in totalRules:
addRule = True
for r in splitRuleSets:
if i not in r:
addRule = False
if addRule:
commonRules.append(i)
for r in splitRuleSets:
r.remove(i)
retString = ""
if len(commonRules) > 0:
retString = commonRules[0]
for c in commonRules[1:]:
retString = retString + "," + c
retString = retString + " AND "
for s in splitRuleSets:
if len(s) > 0:
retString = retString + "(" + s[0]
for r in s[1:]:
retString = retString + "," + r
retString = retString + ") OR "
return retString[:-3]
|
