rdkit.ML.Composite.BayesComposite

Package rdkit :: Package ML :: Package Composite :: Module BayesComposite |
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# Source Code for Module rdkit.ML.Composite.BayesComposite

      1  # $Id$
      2  #
      3  #  Copyright (C) 2000-2008  greg Landrum and Rational Discovery LLC
      4  #   All Rights Reserved
      5  #
      6  """ code for dealing with Bayesian composite models
      7
      8  For a model to be useable here, it should support the following API:
      9
     10    - _ClassifyExample(example)_, returns a classification
     11
     12  Other compatibility notes:
     13
     14   1) To use _Composite.Grow_ there must be some kind of builder
     15      functionality which returns a 2-tuple containing (model,percent accuracy).
     16
     17   2) The models should be pickleable
     18
     19   3) It would be very happy if the models support the __cmp__ method so that
     20      membership tests used to make sure models are unique work.
     21
     22
     23
     24  """
     25  from __future__ import print_function
     26  import numpy
     27  from rdkit.ML.Composite import Composite
     28
     29
     30 -class BayesComposite(Composite.Composite):
     31    """a composite model using Bayesian statistics in the Decision Proxy
     32
     33
     34      **Notes**
     35
     36      - typical usage:
     37
     38         1) grow the composite with AddModel until happy with it
     39
     40         2) call AverageErrors to calculate the average error values
     41
     42         3) call SortModels to put things in order by either error or count
     43
     44         4) call Train to update the Bayesian stats.
     45
     46    """
     47 -  def Train(self,data,verbose=0):
     48      # FIX: this is wrong because it doesn't take the counts of each model into account
     49      nModels = len(self)
     50      nResults = self.nPossibleVals[-1]
     51      self.resultProbs = numpy.zeros(nResults,numpy.float)
     52      self.condProbs = [None]*nModels
     53
     54      for i in range(nModels):
     55        self.condProbs[i] = numpy.zeros((nResults,nResults),numpy.float)
     56      # FIX: this is a quick hack which may slow things down a lot
     57      for example in data:
     58        act = self.QuantizeActivity(example)[-1]
     59        self.resultProbs[int(act)] += 1
     60
     61      for example in data:
     62        if self._mapOrder is not None:
     63          example = self._RemapInput(example)
     64        if self.GetActivityQuantBounds():
     65          example = self.QuantizeActivity(example)
     66        if self.quantBounds is not None and 1 in self.quantizationRequirements:
     67          quantExample = self.QuantizeExample(example,self.quantBounds)
     68        else:
     69          quantExample = []
     70
     71        trueRes = int(example[-1])
     72
     73        votes = self.CollectVotes(example,quantExample)
     74
     75        for i in range(nModels):
     76          self.condProbs[i][votes[i],trueRes] += 1
     77
     78        #self.condProbs /= self.resultProbs
     79      for i in range(nModels):
     80        for j in range(nResults):
     81          self.condProbs[i][j] /= sum(self.condProbs[i][j])
     82        #self.condProbs[i] /= self.resultProbs
     83
     84
     85      self.resultProbs /= sum(self.resultProbs)
     86
     87      if verbose:
     88        print('**** Bayesian Results')
     89        print('Result probabilities')
     90        print('\t',self.resultProbs)
     91        print('Model by model breakdown of conditional probs')
     92        for mat in self.condProbs:
     93          for row in mat:
     94            print('\t',row)
     95          print()
     96
     97
     98
     99 -  def ClassifyExample(self,example,threshold=0,verbose=0,appendExample=0):
    100      """ classifies the given example using the entire composite
    101
    102        **Arguments**
    103
    104         - example: the data to be classified
    105
    106         - threshold:  if this is a number greater than zero, then a
    107            classification will only be returned if the confidence is
    108            above _threshold_.  Anything lower is returned as -1.
    109
    110        **Returns**
    111
    112          a (result,confidence) tuple
    113
    114      """
    115      if self._mapOrder is not None:
    116        example = self._RemapInput(example)
    117      if self.GetActivityQuantBounds():
    118        example = self.QuantizeActivity(example)
    119      if self.quantBounds is not None and 1 in self.quantizationRequirements:
    120        quantExample = self.QuantizeExample(example,self.quantBounds)
    121      else:
    122        quantExample = []
    123      self.modelVotes = self.CollectVotes(example,quantExample,appendExample=appendExample)
    124
    125      nPossibleRes = self.nPossibleVals[-1]
    126      votes = [0.]*nPossibleRes
    127      for i in range(len(self)):
    128        predict = self.modelVotes[i]
    129        for j in range(nPossibleRes):
    130          votes[j] += self.condProbs[i][predict,j]
    131
    132      #totVotes = sum(votes)
    133      res = numpy.argmax(votes)
    134      conf = votes[res] / len(self)
    135      if verbose:
    136        print(votes,conf,example[-1])
    137      if conf > threshold:
    138        return res,conf
    139      else:
    140        return -1,conf
    141
    142
    143 -  def __init__(self):
    144      Composite.Composite.__init__(self)
    145      self.resultProbs = None
    146      self.condProbs = None
    147
    148 -def CompositeToBayesComposite(obj):
    149    """ converts a Composite to a BayesComposite
    150
    151     if _obj_ is already a BayesComposite or if it is not a _Composite.Composite_ ,
    152      nothing will be done.
    153
    154    """
    155    if obj.__class__ == BayesComposite:
    156      return
    157    elif obj.__class__ == Composite.Composite:
    158      obj.__class__ = BayesComposite
    159      obj.resultProbs = None
    160      obj.condProbs = None
    161
    162 -def BayesCompositeToComposite(obj):
    163    """ converts a BayesComposite to a Composite.Composite
    164
    165    """
    166    if obj.__class__ == Composite.Composite:
    167      return
    168    elif obj.__class__ == BayesComposite:
    169      obj.__class__ = Composite.Composite
    170      obj.resultProbs = None
    171      obj.condProbs = None
    172