rdkit.ML.GrowComposite

Package rdkit :: Package ML :: Module GrowComposite |
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# Source Code for Module rdkit.ML.GrowComposite

      1  # $Id$
      2  #
      3  #  Copyright (C) 2003-2006  greg Landrum and Rational Discovery LLC
      4  #
      5  #   @@ All Rights Reserved @@
      6  #  This file is part of the RDKit.
      7  #  The contents are covered by the terms of the BSD license
      8  #  which is included in the file license.txt, found at the root
      9  #  of the RDKit source tree.
     10  #
     11
     12  """ command line utility for growing composite models
     13
     14  **Usage**
     15
     16    _GrowComposite [optional args] filename_
     17
     18  **Command Line Arguments**
     19
     20    - -n *count*: number of new models to build
     21
     22    - -C *pickle file name*:  name of file containing composite upon which to build.
     23
     24    - --inNote *note*: note to be used in loading composite models from the database
     25        for growing
     26
     27    - --balTable *table name*:  table from which to take the original data set
     28       (for balancing)
     29
     30    - --balWeight *weight*: (between 0 and 1) weighting factor for the new data
     31       (for balancing). OR, *weight* can be a list of weights
     32
     33    - --balCnt *count*: number of individual models in the balanced composite
     34       (for balancing)
     35
     36    - --balH: use only the holdout set from the original data set in the balancing
     37       (for balancing)
     38
     39    - --balT: use only the training set from the original data set in the balancing
     40       (for balancing)
     41
     42    - -S: shuffle the original data set
     43       (for balancing)
     44
     45    - -r: randomize the activities of the original data set
     46       (for balancing)
     47
     48    - -N *note*: note to be attached to the grown composite when it's saved in the
     49       database
     50
     51    - --outNote *note*: equivalent to -N
     52
     53    - -o *filename*: name of an output file to hold the pickled composite after
     54       it has been grown.
     55       If multiple balance weights are used, the weights will be added to
     56       the filenames.
     57
     58    - -L *limit*: provide an (integer) limit on individual model complexity
     59
     60    - -d *database name*: instead of reading the data from a QDAT file,
     61       pull it from a database.  In this case, the _filename_ argument
     62       provides the name of the database table containing the data set.
     63
     64    - -p *tablename*: store persistence data in the database
     65       in table *tablename*
     66
     67    - -l: locks the random number generator to give consistent sets
     68       of training and hold-out data.  This is primarily intended
     69       for testing purposes.
     70
     71    - -g: be less greedy when training the models.
     72
     73    - -G *number*: force trees to be rooted at descriptor *number*.
     74
     75    - -D: show a detailed breakdown of the composite model performance
     76       across the training and, when appropriate, hold-out sets.
     77
     78    - -t *threshold value*: use high-confidence predictions for the final
     79       analysis of the hold-out data.
     80
     81    - -q *list string*:  Add QuantTrees to the composite and use the list
     82       specified in *list string* as the number of target quantization
     83       bounds for each descriptor.  Don't forget to include 0's at the
     84       beginning and end of *list string* for the name and value fields.
     85       For example, if there are 4 descriptors and you want 2 quant bounds
     86       apiece, you would use _-q "[0,2,2,2,2,0]"_.
     87       Two special cases:
     88         1) If you would like to ignore a descriptor in the model building,
     89            use '-1' for its number of quant bounds.
     90         2) If you have integer valued data that should not be quantized
     91            further, enter 0 for that descriptor.
     92
     93    - -V: print the version number and exit
     94
     95  """
     96  from __future__ import print_function
     97  from rdkit import RDConfig
     98  import numpy
     99  from rdkit.ML.Data import DataUtils,SplitData
    100  from rdkit.ML import ScreenComposite,BuildComposite
    101  from rdkit.ML.Composite import AdjustComposite
    102  from rdkit.Dbase.DbConnection import DbConnect
    103  from rdkit.ML import CompositeRun
    104  from rdkit.six.moves import cPickle
    105  import sys,time,types
    106
    107  _runDetails = CompositeRun.CompositeRun()
    108
    109  __VERSION_STRING="0.5.0"
    110
    111  _verbose = 1
    112 -def message(msg):
    113    """ emits messages to _sys.stdout_
    114      override this in modules which import this one to redirect output
    115
    116      **Arguments**
    117
    118        - msg: the string to be displayed
    119
    120    """
    121    if _verbose: sys.stdout.write('%s\n'%(msg))
    122
    123 -def GrowIt(details,composite,progressCallback=None,
    124             saveIt=1,setDescNames=0,data=None):
    125    """ does the actual work of building a composite model
    126
    127      **Arguments**
    128
    129        - details:  a _CompositeRun.CompositeRun_ object containing details
    130          (options, parameters, etc.) about the run
    131
    132        - composite: the composite model to grow
    133
    134        - progressCallback: (optional) a function which is called with a single
    135          argument (the number of models built so far) after each model is built.
    136
    137        - saveIt: (optional) if this is nonzero, the resulting model will be pickled
    138          and dumped to the filename specified in _details.outName_
    139
    140        - setDescNames: (optional) if nonzero, the composite's _SetInputOrder()_ method
    141          will be called using the results of the data set's _GetVarNames()_ method;
    142          it is assumed that the details object has a _descNames attribute which
    143          is passed to the composites _SetDescriptorNames()_ method.  Otherwise
    144          (the default), _SetDescriptorNames()_ gets the results of _GetVarNames()_.
    145
    146        - data: (optional) the data set to be used.  If this is not provided, the
    147          data set described in details will be used.
    148
    149      **Returns**
    150
    151        the enlarged composite model
    152
    153
    154    """
    155    details.rundate = time.asctime()
    156
    157    if data is None:
    158      fName = details.tableName.strip()
    159      if details.outName == '':
    160        details.outName = fName + '.pkl'
    161      if details.dbName == '':
    162        data = DataUtils.BuildQuantDataSet(fName)
    163      elif details.qBounds != []:
    164        details.tableName = fName
    165        data = details.GetDataSet()
    166      else:
    167        data = DataUtils.DBToQuantData(details.dbName,fName,quantName=details.qTableName,
    168                                       user=details.dbUser,password=details.dbPassword)
    169
    170    nExamples = data.GetNPts()
    171    seed = composite._randomSeed
    172    DataUtils.InitRandomNumbers(seed)
    173    testExamples = []
    174    if details.shuffleActivities == 1:
    175      DataUtils.RandomizeActivities(data,shuffle=1,runDetails=details)
    176    elif details.randomActivities == 1:
    177      DataUtils.RandomizeActivities(data,shuffle=0,runDetails=details)
    178
    179    namedExamples = data.GetNamedData()
    180    trainExamples = namedExamples
    181    nExamples = len(trainExamples)
    182    message('Training with %d examples'%(nExamples))
    183    message('\t%d descriptors'%(len(trainExamples[0])-2))
    184    nVars = data.GetNVars()
    185    nPossibleVals = composite.nPossibleVals
    186    attrs = range(1,nVars+1)
    187
    188    if details.useTrees:
    189      from rdkit.ML.DecTree import CrossValidate,PruneTree
    190      if details.qBounds != []:
    191        from rdkit.ML.DecTree import BuildQuantTree
    192        builder = BuildQuantTree.QuantTreeBoot
    193      else:
    194        from rdkit.ML.DecTree import ID3
    195        builder = ID3.ID3Boot
    196      driver = CrossValidate.CrossValidationDriver
    197      pruner = PruneTree.PruneTree
    198
    199      if setDescNames:
    200        composite.SetInputOrder(data.GetVarNames())
    201      composite.Grow(trainExamples,attrs,[0]+nPossibleVals,
    202                     buildDriver=driver,
    203                     pruner=pruner,
    204                     nTries=details.nModels,pruneIt=details.pruneIt,
    205                     lessGreedy=details.lessGreedy,needsQuantization=0,
    206                     treeBuilder=builder,nQuantBounds=details.qBounds,
    207                     startAt=details.startAt,
    208                     maxDepth=details.limitDepth,
    209                     progressCallback=progressCallback,
    210                     silent=not _verbose)
    211
    212
    213    else:
    214      from rdkit.ML.Neural import CrossValidate
    215      driver = CrossValidate.CrossValidationDriver
    216      composite.Grow(trainExamples,attrs,[0]+nPossibleVals,nTries=details.nModels,
    217                     buildDriver=driver,needsQuantization=0)
    218
    219    composite.AverageErrors()
    220    composite.SortModels()
    221    modelList,counts,avgErrs = composite.GetAllData()
    222    counts = numpy.array(counts)
    223    avgErrs = numpy.array(avgErrs)
    224    composite._varNames = data.GetVarNames()
    225
    226    for i in range(len(modelList)):
    227      modelList[i].NameModel(composite._varNames)
    228
    229    # do final statistics
    230    weightedErrs = counts*avgErrs
    231    averageErr = sum(weightedErrs)/sum(counts)
    232    devs = (avgErrs - averageErr)
    233    devs = devs * counts
    234    devs = numpy.sqrt(devs*devs)
    235    avgDev = sum(devs)/sum(counts)
    236    if _verbose:
    237      message('# Overall Average Error: %%% 5.2f, Average Deviation: %%% 6.2f'%(100.*averageErr,100.*avgDev))
    238
    239    if details.bayesModel:
    240      composite.Train(trainExamples,verbose=0)
    241
    242    badExamples = []
    243    if not details.detailedRes:
    244      if _verbose:
    245        message('Testing all examples')
    246      wrong = BuildComposite.testall(composite,namedExamples,badExamples)
    247      if _verbose:
    248        message('%d examples (%% %5.2f) were misclassified'%(len(wrong),100.*float(len(wrong))/float(len(namedExamples))))
    249      _runDetails.overall_error = float(len(wrong))/len(namedExamples)
    250
    251    if details.detailedRes:
    252      if _verbose:
    253        message('\nEntire data set:')
    254      resTup = ScreenComposite.ShowVoteResults(range(data.GetNPts()),data,composite,
    255                                               nPossibleVals[-1],details.threshold)
    256      nGood,nBad,nSkip,avgGood,avgBad,avgSkip,voteTab = resTup
    257      nPts = len(namedExamples)
    258      nClass = nGood+nBad
    259      _runDetails.overall_error = float(nBad) / nClass
    260      _runDetails.overall_correct_conf = avgGood
    261      _runDetails.overall_incorrect_conf = avgBad
    262      _runDetails.overall_result_matrix = repr(voteTab)
    263      nRej = nClass-nPts
    264      if nRej > 0:
    265        _runDetails.overall_fraction_dropped = float(nRej)/nPts
    266
    267    return composite
    268
    269 -def GetComposites(details):
    270    res = []
    271    if details.persistTblName and details.inNote:
    272      conn = DbConnect(details.dbName,details.persistTblName)
    273      mdls = conn.GetData(fields='MODEL',where="where note='%s'"%(details.inNote))
    274      for row in mdls:
    275        rawD = row[0]
    276        res.append(cPickle.loads(str(rawD)))
    277    elif details.composFileName:
    278      res.append(cPickle.load(open(details.composFileName,'rb')))
    279    return res
    280
    281 -def BalanceComposite(details,composite,data1=None,data2=None):
    282    """ balances the composite using the parameters provided in details
    283
    284     **Arguments**
    285
    286       - details a _CompositeRun.RunDetails_ object
    287
    288       - composite: the composite model to be balanced
    289
    290       - data1: (optional) if provided, this should be the
    291         data set used to construct the original models
    292
    293       - data2: (optional) if provided, this should be the
    294         data set used to construct the new individual models
    295
    296    """
    297    if not details.balCnt or details.balCnt > len(composite):
    298      return composite
    299    message("Balancing Composite")
    300
    301    #
    302    # start by getting data set 1: which is the data set used to build the
    303    #  original models
    304    #
    305    if data1 is None:
    306      message("\tReading First Data Set")
    307      fName = details.balTable.strip()
    308      tmp = details.tableName
    309      details.tableName = fName
    310      dbName = details.dbName
    311      details.dbName = details.balDb
    312      data1 = details.GetDataSet()
    313      details.tableName = tmp
    314      details.dbName = dbName
    315    if data1 is None:
    316      return composite
    317    details.splitFrac = composite._splitFrac
    318    details.randomSeed = composite._randomSeed
    319    DataUtils.InitRandomNumbers(details.randomSeed)
    320    if details.shuffleActivities == 1:
    321      DataUtils.RandomizeActivities(data1,shuffle=1,runDetails=details)
    322    elif details.randomActivities == 1:
    323      DataUtils.RandomizeActivities(data1,shuffle=0,runDetails=details)
    324    namedExamples = data1.GetNamedData()
    325    if details.balDoHoldout or details.balDoTrain:
    326      trainIdx,testIdx = SplitData.SplitIndices(len(namedExamples),details.splitFrac,
    327                                                silent=1)
    328      trainExamples = [namedExamples[x] for x in trainIdx]
    329      testExamples = [namedExamples[x] for x in testIdx]
    330      if details.filterFrac != 0.0:
    331        trainIdx,temp = DataUtils.FilterData(trainExamples,details.filterVal,
    332                                             details.filterFrac,-1,
    333                                             indicesOnly=1)
    334        tmp = [trainExamples[x] for x in trainIdx]
    335        testExamples += [trainExamples[x] for x in temp]
    336        trainExamples = tmp
    337      if details.balDoHoldout:
    338        testExamples,trainExamples = trainExamples,testExamples
    339    else:
    340      trainExamples = namedExamples
    341    dataSet1 = trainExamples
    342    cols1 = [x.upper() for x in data1.GetVarNames()]
    343    data1 = None
    344
    345    #
    346    # now grab data set 2: the data used to build the new individual models
    347    #
    348    if data2 is None:
    349      message("\tReading Second Data Set")
    350      data2 = details.GetDataSet()
    351    if data2 is None:
    352      return composite
    353    details.splitFrac = composite._splitFrac
    354    details.randomSeed = composite._randomSeed
    355    DataUtils.InitRandomNumbers(details.randomSeed)
    356    if details.shuffleActivities == 1:
    357      DataUtils.RandomizeActivities(data2,shuffle=1,runDetails=details)
    358    elif details.randomActivities == 1:
    359      DataUtils.RandomizeActivities(data2,shuffle=0,runDetails=details)
    360    dataSet2 = data2.GetNamedData()
    361    cols2 = [x.upper() for x in data2.GetVarNames()]
    362    data2 = None
    363
    364    # and balance it:
    365    res = []
    366    weights = details.balWeight
    367    if type(weights) not in (types.TupleType,types.ListType):
    368      weights = (weights,)
    369    for weight in weights:
    370      message("\tBalancing with Weight: %.4f"%(weight))
    371      res.append(AdjustComposite.BalanceComposite(composite,dataSet1,dataSet2,
    372                                                  weight,
    373                                                  details.balCnt,
    374                                                  names1=cols1,names2=cols2))
    375    return res
    376
    377 -def ShowVersion(includeArgs=0):
    378    """ prints the version number
    379
    380    """
    381    print('This is GrowComposite.py version %s'%(__VERSION_STRING))
    382    if includeArgs:
    383      import sys
    384      print('command line was:')
    385      print(' '.join(sys.argv))
    386
    387 -def Usage():
    388    """ provides a list of arguments for when this is used from the command line
    389
    390    """
    391    import sys
    392    print(__doc__)
    393    sys.exit(-1)
    394
    395 -def SetDefaults(runDetails=None):
    396    """  initializes a details object with default values
    397
    398        **Arguments**
    399
    400          - details:  (optional) a _CompositeRun.CompositeRun_ object.
    401            If this is not provided, the global _runDetails will be used.
    402
    403        **Returns**
    404
    405          the initialized _CompositeRun_ object.
    406
    407
    408    """
    409    if runDetails is None: runDetails = _runDetails
    410    return CompositeRun.SetDefaults(runDetails)
    411
    412 -def ParseArgs(runDetails):
    413    """ parses command line arguments and updates _runDetails_
    414
    415        **Arguments**
    416
    417          - runDetails:  a _CompositeRun.CompositeRun_ object.
    418
    419    """
    420    import getopt
    421    args,extra = getopt.getopt(sys.argv[1:],'P:o:n:p:b:sf:F:v:hlgd:rSTt:Q:q:DVG:L:C:N:',
    422                               ['inNote=','outNote=','balTable=','balWeight=','balCnt=',
    423                                'balH','balT','balDb=',])
    424    runDetails.inNote=''
    425    runDetails.composFileName=''
    426    runDetails.balTable=''
    427    runDetails.balWeight=(0.5,)
    428    runDetails.balCnt=0
    429    runDetails.balDoHoldout=0
    430    runDetails.balDoTrain=0
    431    runDetails.balDb=''
    432    for arg,val in args:
    433      if arg == '-n':
    434        runDetails.nModels = int(val)
    435      elif arg == '-C':
    436        runDetails.composFileName=val
    437      elif arg=='--balTable':
    438        runDetails.balTable=val
    439      elif arg=='--balWeight':
    440        runDetails.balWeight=eval(val)
    441        if type(runDetails.balWeight) not in (types.TupleType,types.ListType):
    442          runDetails.balWeight=(runDetails.balWeight,)
    443      elif arg=='--balCnt':
    444        runDetails.balCnt=int(val)
    445      elif arg=='--balH':
    446        runDetails.balDoHoldout=1
    447      elif arg=='--balT':
    448        runDetails.balDoTrain=1
    449      elif arg=='--balDb':
    450        runDetails.balDb=val
    451      elif arg == '--inNote':
    452        runDetails.inNote=val
    453      elif arg == '-N' or arg=='--outNote':
    454        runDetails.note=val
    455      elif arg == '-o':
    456        runDetails.outName = val
    457      elif arg == '-p':
    458        runDetails.persistTblName=val
    459      elif arg == '-r':
    460        runDetails.randomActivities = 1
    461      elif arg == '-S':
    462        runDetails.shuffleActivities = 1
    463      elif arg == '-h':
    464        Usage()
    465      elif arg == '-l':
    466        runDetails.lockRandom = 1
    467      elif arg == '-g':
    468        runDetails.lessGreedy=1
    469      elif arg == '-G':
    470        runDetails.startAt = int(val)
    471      elif arg == '-d':
    472        runDetails.dbName=val
    473      elif arg == '-T':
    474        runDetails.useTrees = 0
    475      elif arg == '-t':
    476        runDetails.threshold=float(val)
    477      elif arg == '-D':
    478        runDetails.detailedRes = 1
    479      elif arg == '-L':
    480        runDetails.limitDepth = int(val)
    481      elif arg == '-q':
    482        qBounds = eval(val)
    483        assert type(qBounds) in (types.TupleType,types.ListType),'bad argument type for -q, specify a list as a string'
    484        runDetails.qBoundCount=val
    485        runDetails.qBounds = qBounds
    486      elif arg == '-Q':
    487        qBounds = eval(val)
    488        assert type(qBounds) in [type([]),type(())],'bad argument type for -Q, specify a list as a string'
    489        runDetails.activityBounds=qBounds
    490        runDetails.activityBoundsVals=val
    491      elif arg == '-V':
    492        ShowVersion()
    493        sys.exit(0)
    494      else:
    495        print('bad argument:',arg,file=sys.stderr)
    496        Usage()
    497    runDetails.tableName=extra[0]
    498    if not runDetails.balDb:
    499      runDetails.balDb=runDetails.dbName
    500  if __name__ == '__main__':
    501    if len(sys.argv) < 2:
    502      Usage()
    503
    504    _runDetails.cmd = ' '.join(sys.argv)
    505    SetDefaults(_runDetails)
    506    ParseArgs(_runDetails)
    507
    508    ShowVersion(includeArgs=1)
    509
    510    initModels = GetComposites(_runDetails)
    511    nModels = len(initModels)
    512    if nModels>1:
    513      for i in range(nModels):
    514        sys.stderr.write('---------------------------------\n\tDoing %d of %d\n---------------------------------\n'%(i+1,nModels))
    515        composite = GrowIt(_runDetails,initModels[i],setDescNames=1)
    516        if _runDetails.balTable and _runDetails.balCnt:
    517          composites = BalanceComposite(_runDetails,composite)
    518        else:
    519          composites=[composite]
    520        for mdl in composites:
    521          mdl.ClearModelExamples()
    522        if _runDetails.outName:
    523          nWeights = len(_runDetails.balWeight)
    524          if nWeights==1:
    525            outName = _runDetails.outName
    526            composites[0].Pickle(outName)
    527          else:
    528            for i in range(nWeights):
    529              weight = int(100*_runDetails.balWeight[i])
    530              model = composites[i]
    531              outName = '%s.%d.pkl'%(_runDetails.outName.split('.pkl')[0],weight)
    532              model.Pickle(outName)
    533        if _runDetails.persistTblName and _runDetails.dbName:
    534          message('Updating results table %s:%s'%(_runDetails.dbName,_runDetails.persistTblName))
    535          if(len(_runDetails.balWeight))>1:
    536            message('WARNING: updating results table with models having different weights')
    537          # save the composite
    538          for i in range(len(composites)):
    539            _runDetails.model = cPickle.dumps(composites[i])
    540            _runDetails.Store(db=_runDetails.dbName,table=_runDetails.persistTblName)
    541    elif nModels==1:
    542      composite = GrowIt(_runDetails,initModels[0],setDescNames=1)
    543      if _runDetails.balTable and _runDetails.balCnt:
    544        composites = BalanceComposite(_runDetails,composite)
    545      else:
    546        composites=[composite]
    547      for mdl in composites:
    548        mdl.ClearModelExamples()
    549      if _runDetails.outName:
    550        nWeights = len(_runDetails.balWeight)
    551        if nWeights==1:
    552          outName = _runDetails.outName
    553          composites[0].Pickle(outName)
    554        else:
    555          for i in range(nWeights):
    556            weight = int(100*_runDetails.balWeight[i])
    557            model = composites[i]
    558            outName = '%s.%d.pkl'%(_runDetails.outName.split('.pkl')[0],weight)
    559            model.Pickle(outName)
    560      if _runDetails.persistTblName and _runDetails.dbName:
    561        message('Updating results table %s:%s'%(_runDetails.dbName,_runDetails.persistTblName))
    562        if(len(composites))>1:
    563          message('WARNING: updating results table with models having different weights')
    564        for i in range(len(composites)):
    565          _runDetails.model = cPickle.dumps(composites[i])
    566          _runDetails.Store(db=_runDetails.dbName,table=_runDetails.persistTblName)
    567    else:
    568      message("No models found")
    569