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test_treeParser.py
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# -*- coding: utf-8 -*-
'''
Created on 2018年3月28日
@author: STEVEN.CY.CHUANG
'''
import sys
import unittest
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
sys.path.append('../')
from util.treeParser import ClfParser
class Test_ClfParser(unittest.TestCase):
dfTree = pd.read_pickle("../file/dfTree")
def test_checkLTE(self):
bound = [np.nan, np.nan]
bound = ClfParser.deterBound(bound, 3.0, lte=True)
self.assertEqual(bound, [np.nan, 3.0])
bound = [np.nan, 3.0]
bound = ClfParser.deterBound(bound, 2.0, lte=True)
self.assertEqual(bound, [np.nan, 2.0])
bound = [3.0, np.nan]
try:
ClfParser.deterBound(bound, 4.0, lte=True)
except ValueError as msg:
self.assertEqual(str(msg), "value > max")
bound = [np.nan, np.nan]
bound = ClfParser.deterBound(bound, 3.0, lte=False)
self.assertEqual(bound, [3.0, np.nan])
bound = [1.2, 3.0]
bound = ClfParser.deterBound(bound, 2.0, lte=False)
self.assertEqual(bound, [2.0, 3.0])
bound = [2.0, 3.0]
try:
ClfParser.deterBound(bound, 1, lte=False)
except ValueError as msg:
self.assertEqual(str(msg), "value < min")
bound = [5.0, 3.0]
try:
ClfParser.deterBound(bound, 6, lte=False)
except ValueError as msg:
self.assertEqual(str(msg), "min > max")
def test_recurRulePath(self):
# Case 1, depth = 2 and 5 features
dfTree = self.dfTree
clf = DecisionTreeClassifier(max_depth=2, random_state=0)
nameFtrs = ['PINNUM', 'POSX', 'POSY', 'SIZEX', 'SIZEY']
clf.fit(dfTree[nameFtrs], dfTree['CODE'])
clfParser = ClfParser(clf, nameFtrs)
ans = { 2: {'POSX': [np.nan, 179.39999389648438], 'SIZEX': [np.nan, 1.4005000591278076]},
3: {'POSX': [179.39999389648438, np.nan], 'SIZEX': [np.nan, 1.4005000591278076]},
5: {'POSY': [np.nan, 32.650001525878906], 'SIZEX': [1.4005000591278076, np.nan]},
6: {'POSY': [32.650001525878906, np.nan], 'SIZEX': [1.4005000591278076, np.nan]}}
idNode = 0
factors = clfParser.recurRulePath(idNode, {}, {})
self.assertEqual(factors, ans)
# Case 2, depth = 3 and 5 features
dfTree = self.dfTree
clf = DecisionTreeClassifier(max_depth=3, random_state=0)
nameFtrs = ['PINNUM', 'POSX', 'POSY', 'SIZEX', 'SIZEY']
clf.fit(dfTree[nameFtrs], dfTree['CODE'])
clfParser = ClfParser(clf, nameFtrs)
ans = { 3: {'POSX': [np.nan, 179.39999389648438], 'SIZEX': [np.nan, 0.19349999725818634]},
4: {'POSX': [np.nan, 179.39999389648438], 'SIZEX': [0.19349999725818634, 1.4005000591278076]},
6: {'POSX': [179.39999389648438, np.nan], 'POSY': [np.nan, 112.14999389648438], 'SIZEX': [np.nan, 1.4005000591278076]},
7: {'POSX': [179.39999389648438, np.nan], 'POSY': [112.14999389648438, np.nan], 'SIZEX': [np.nan, 1.4005000591278076]},
10: {'POSY': [np.nan, 23.75], 'SIZEX': [1.4005000591278076, np.nan]},
11: {'POSY': [23.75, 32.650001525878906], 'SIZEX': [1.4005000591278076, np.nan]},
13: {'POSY': [32.650001525878906, 38.150001525878906], 'SIZEX': [1.4005000591278076, np.nan]},
14: {'POSY': [38.150001525878906, np.nan], 'SIZEX': [1.4005000591278076, np.nan]}}
idNode = 0
factors = clfParser.recurRulePath(idNode, {}, {})
self.assertEqual(factors, ans)
# Case 3, depth = 3 and 1 feature
clf = DecisionTreeClassifier(max_depth=3, random_state=0)
nameFtrs = ['SIZEX']
clf.fit(dfTree[nameFtrs], dfTree['CODE'])
clfParser = ClfParser(clf, nameFtrs)
ans = { 2: {'SIZEX': [np.nan, 0.19349999725818634]},
4: {'SIZEX': [0.19349999725818634, 0.76050001382827759]},
5: {'SIZEX': [0.76050001382827759, 1.4005000591278076]},
8: {'SIZEX': [1.4005000591278076, 1.4014999866485596]},
9: {'SIZEX': [1.4014999866485596, 1.5759999752044678]},
11: {'SIZEX': [1.5759999752044678, 2.5510001182556152]},
12: {'SIZEX': [2.5510001182556152, np.nan]}}
idNode = 0
factors = clfParser.recurRulePath(idNode, {}, {})
self.assertEqual(factors, ans)
def test_getLeaf(self):
# Case 1, depth = 2 and 5 features
dfTree = self.dfTree
clf = DecisionTreeClassifier(max_depth=2, random_state=0)
nameFtrs = ['PINNUM', 'POSX', 'POSY', 'SIZEX', 'SIZEY']
clf.fit(dfTree[nameFtrs], dfTree['CODE'])
clfParser = ClfParser(clf, nameFtrs)
leaf = clfParser.getLeaf()
print(leaf)
def test_bound2str(self):
factor = ClfParser.bound2str({"POSX": [179.39999389648438, np.nan], "POSY": [112.14999389648438, np.nan], "SIZEX": [np.nan, 1.4005000591278076]})
self.assertEqual(factor, {"POSX": "> 179.39999389648438", "POSY": "> 112.14999389648438", "SIZEX": "<= 1.4005000591278076"})
factor = ClfParser.bound2str({'POSY': [23.75, 32.650001525878906], 'SIZEX': [1.4005000591278076, np.nan]})
self.assertEqual(factor, {"POSY": "> 23.75, <= 32.650001525878906", "SIZEX": "> 1.4005000591278076"})
if __name__ == '__main__':
# Test_ClfParser().test_bound2str()
Test_ClfParser().test_getLeaf()
# Test_ClfParser().test_recurRulePath()
# unittest.main()