Source Code for Module rdkit.ML.Composite.AdjustComposite

 1  # $Id$ 
 2  # 
 3  #  Copyright (C) 2003  greg Landrum and Rational Discovery LLC 
 4  #   All Rights Reserved 
 5  # 
 6  """ functionality to allow adjusting composite model contents 
 7   
 8  """ 
 9  from __future__ import print_function 
10   
11  import copy 
12   
13  import numpy 
14   
15   
16 -def BalanceComposite(model, set1, set2, weight, targetSize, names1=None, names2=None): 
17    """ adjusts the contents of the composite model so as to maximize 
18      the weighted classification accuracty across the two data sets. 
19   
20      The resulting composite model, with _targetSize_ models, is returned. 
21   
22      **Notes**: 
23   
24        - if _names1_ and _names2_ are not provided, _set1_ and _set2_ should 
25          have the same ordering of columns and _model_ should have already 
26          have had _SetInputOrder()_ called. 
27   
28    """ 
29    # 
30    # adjust the weights to be proportional to the size of the two data sets 
31    #   The normalization we do here assures that a perfect model contributes 
32    #   a score of S1+S2 to the final 
33    # 
34    S1 = len(set1) 
35    S2 = len(set2) 
36    weight1 = float(S1 + S2) * (1 - weight) / S1 
37    weight2 = float(S1 + S2) * weight / S2 
38    # print('\t:::', S1, S2, weight1, weight2) 
39    # print('nModels:', len(model)) 
40    # start with a copy so that we get all the additional schnick-schnack 
41    res = copy.copy(model) 
42    res.modelList = [] 
43    res.errList = [] 
44    res.countList = [] 
45    res.quantizationRequirements = [] 
46   
47    startSize = len(model) 
48    scores = numpy.zeros(startSize, numpy.float) 
49    actQuantBounds = model.GetActivityQuantBounds() 
50    if names1 is not None: 
51      model.SetInputOrder(names1) 
52    for pt in set1: 
53      pred, conf = model.ClassifyExample(pt) 
54      if actQuantBounds: 
55        ans = model.QuantizeActivity(pt)[-1] 
56      else: 
57        ans = pt[-1] 
58      votes = model.GetVoteDetails() 
59      for i in range(startSize): 
60        if votes[i] == ans: 
61          scores[i] += weight1 
62    if names2 is not None: 
63      model.SetInputOrder(names2) 
64    for pt in set2: 
65      pred, conf = model.ClassifyExample(pt) 
66      if actQuantBounds: 
67        ans = model.QuantizeActivity(pt)[-1] 
68      else: 
69        ans = pt[-1] 
70      votes = model.GetVoteDetails() 
71      for i in range(startSize): 
72        if votes[i] == ans: 
73          scores[i] += weight2 
74    # normalize the scores 
75    nPts = S1 + S2 
76    scores /= nPts 
77    # sort them: 
78    bestOrder = list(numpy.argsort(scores)) 
79    bestOrder.reverse() 
80    print('\tTAKE:', bestOrder[:targetSize]) 
81    # and now take the best set: 
82    for i in range(targetSize): 
83      idx = bestOrder[i] 
84      mdl = model.modelList[idx] 
85      res.modelList.append(mdl) 
86      res.errList.append(1. - scores[idx]) 
87      res.countList.append(1) 
88      # FIX: this should probably be more general: 
89      res.quantizationRequirements.append(0) 
90    return res 
91