rdkit.ML.BuildComposite

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# Source Code for Module rdkit.ML.BuildComposite

       1  # $Id$
       2  #
       3  #  Copyright (C) 2000-2008  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  """ command line utility for building composite models
      12
      13  #DOC
      14
      15  **Usage**
      16
      17    BuildComposite [optional args] filename
      18
      19  Unless indicated otherwise (via command line arguments), _filename_ is
      20  a QDAT file.
      21
      22  **Command Line Arguments**
      23
      24    - -o *filename*: name of the output file for the pickled composite
      25
      26    - -n *num*: number of separate models to add to the composite
      27
      28    - -p *tablename*: store persistence data in the database
      29       in table *tablename*
      30
      31    - -N *note*: attach some arbitrary text to the persistence data
      32
      33    - -b *filename*: name of the text file to hold examples from the
      34       holdout set which are misclassified
      35
      36    - -s: split the data into training and hold-out sets before building
      37       the composite
      38
      39    - -f *frac*: the fraction of data to use in the training set when the
      40       data is split
      41
      42    - -r: randomize the activities (for testing purposes).  This ignores
      43       the initial distribution of activity values and produces each
      44       possible activity value with equal likliehood.
      45
      46    - -S: shuffle the activities (for testing purposes) This produces
      47       a permutation of the input activity values.
      48
      49    - -l: locks the random number generator to give consistent sets
      50       of training and hold-out data.  This is primarily intended
      51       for testing purposes.
      52
      53    - -B: use a so-called Bayesian composite model.
      54
      55    - -d *database name*: instead of reading the data from a QDAT file,
      56       pull it from a database.  In this case, the _filename_ argument
      57       provides the name of the database table containing the data set.
      58
      59    - -D: show a detailed breakdown of the composite model performance
      60       across the training and, when appropriate, hold-out sets.
      61
      62    - -P *pickle file name*: write out the pickled data set to the file
      63
      64    - -F *filter frac*: filters the data before training to change the
      65       distribution of activity values in the training set.  *filter
      66       frac* is the fraction of the training set that should have the
      67       target value.  **See note below on data filtering.**
      68
      69    - -v *filter value*: filters the data before training to change the
      70       distribution of activity values in the training set. *filter
      71       value* is the target value to use in filtering.  **See note below
      72       on data filtering.**
      73
      74    - --modelFiltFrac *model filter frac*: Similar to filter frac above,
      75       in this case the data is filtered for each model in the composite
      76       rather than a single overall filter for a composite. *model
      77       filter frac* is the fraction of the training set for each model
      78       that should have the target value (*model filter value*).
      79
      80    - --modelFiltVal *model filter value*: target value to use for
      81       filtering data before training each model in the composite.
      82
      83    - -t *threshold value*: use high-confidence predictions for the
      84       final analysis of the hold-out data.
      85
      86    - -Q *list string*: the values of quantization bounds for the
      87       activity value.  See the _-q_ argument for the format of *list
      88       string*.
      89
      90    - --nRuns *count*: build *count* composite models
      91
      92    - --prune: prune any models built
      93
      94    - -h: print a usage message and exit.
      95
      96    - -V: print the version number and exit
      97
      98    *-*-*-*-*-*-*-*- Tree-Related Options -*-*-*-*-*-*-*-*
      99
     100    - -g: be less greedy when training the models.
     101
     102    - -G *number*: force trees to be rooted at descriptor *number*.
     103
     104    - -L *limit*: provide an (integer) limit on individual model
     105       complexity
     106
     107    - -q *list string*: Add QuantTrees to the composite and use the list
     108       specified in *list string* as the number of target quantization
     109       bounds for each descriptor.  Don't forget to include 0's at the
     110       beginning and end of *list string* for the name and value fields.
     111       For example, if there are 4 descriptors and you want 2 quant
     112       bounds apiece, you would use _-q "[0,2,2,2,2,0]"_.
     113       Two special cases:
     114         1) If you would like to ignore a descriptor in the model
     115            building, use '-1' for its number of quant bounds.
     116         2) If you have integer valued data that should not be quantized
     117            further, enter 0 for that descriptor.
     118
     119    - --recycle: allow descriptors to be used more than once in a tree
     120
     121    - --randomDescriptors=val: toggles growing random forests with val
     122        randomly-selected descriptors available at each node.
     123
     124
     125    *-*-*-*-*-*-*-*- KNN-Related Options -*-*-*-*-*-*-*-*
     126
     127    - --doKnn: use K-Nearest Neighbors models
     128
     129    - --knnK=*value*: the value of K to use in the KNN models
     130
     131    - --knnTanimoto: use the Tanimoto metric in KNN models
     132
     133    - --knnEuclid: use a Euclidean metric in KNN models
     134
     135    *-*-*-*-*-*-*- Naive Bayes Classifier Options -*-*-*-*-*-*-*-*
     136    - --doNaiveBayes : use Naive Bayes classifiers
     137
     138    - --mEstimateVal : the value to be used in the m-estimate formula
     139        If this is greater than 0.0, we use it to compute the conditional
     140        probabilities by the m-estimate
     141
     142    *-*-*-*-*-*-*-*- SVM-Related Options -*-*-*-*-*-*-*-*
     143
     144    **** NOTE: THESE ARE DISABLED ****
     145
     146  ##   - --doSVM: use Support-vector machines
     147
     148  ##   - --svmKernel=*kernel*: choose the type of kernel to be used for
     149  ##     the SVMs.  Options are:
     150  ##     The default is:
     151
     152  ##   - --svmType=*type*: choose the type of support-vector machine
     153  ##     to be used.  Options are:
     154  ##     The default is:
     155
     156  ##   - --svmGamma=*gamma*: provide the gamma value for the SVMs.  If this
     157  ##     is not provided, a grid search will be carried out to determine an
     158  ##     optimal *gamma* value for each SVM.
     159
     160  ##   - --svmCost=*cost*: provide the cost value for the SVMs.  If this is
     161  ##     not provided, a grid search will be carried out to determine an
     162  ##     optimal *cost* value for each SVM.
     163
     164  ##   - --svmWeights=*weights*: provide the weight values for the
     165  ##     activities.  If provided this should be a sequence of (label,
     166  ##     weight) 2-tuples *nActs* long.  If not provided, a weight of 1
     167  ##     will be used for each activity.
     168
     169  ##   - --svmEps=*epsilon*: provide the epsilon value used to determine
     170  ##     when the SVM has converged.  Defaults to 0.001
     171
     172  ##   - --svmDegree=*degree*: provide the degree of the kernel (when
     173  ##     sensible) Defaults to 3
     174
     175  ##   - --svmCoeff=*coeff*: provide the coefficient for the kernel (when
     176  ##     sensible) Defaults to 0
     177
     178  ##   - --svmNu=*nu*: provide the nu value for the kernel (when sensible)
     179  ##     Defaults to 0.5
     180
     181  ##   - --svmDataType=*float*: if the data is contains only 1 and 0 s, specify by
     182  ##     using binary. Defaults to float
     183
     184  ##   - --svmCache=*cache*: provide the size of the memory cache (in MB)
     185  ##     to be used while building the SVM.  Defaults to 40
     186
     187  **Notes**
     188
     189    - *Data filtering*: When there is a large disparity between the
     190      numbers of points with various activity levels present in the
     191      training set it is sometimes desirable to train on a more
     192      homogeneous data set.  This can be accomplished using filtering.
     193      The filtering process works by selecting a particular target
     194      fraction and target value.  For example, in a case where 95% of
     195      the original training set has activity 0 and ony 5% activity 1, we
     196      could filter (by randomly removing points with activity 0) so that
     197      30% of the data set used to build the composite has activity 1.
     198
     199
     200  """
     201  from __future__ import print_function
     202  import sys,time
     203  import math
     204  import numpy
     205  from rdkit.six.moves import cPickle
     206  from rdkit import RDConfig
     207  from rdkit.utils import listutils
     208  from rdkit.ML.Composite import Composite,BayesComposite
     209  #from ML.SVM import SVMClassificationModel as SVM
     210  from rdkit.ML.Data import DataUtils,SplitData
     211  from rdkit.ML import ScreenComposite
     212  from rdkit.Dbase import DbModule
     213  from rdkit.Dbase.DbConnection import DbConnect
     214  from rdkit.ML import CompositeRun
     215  from rdkit import DataStructs
     216
     217  _runDetails = CompositeRun.CompositeRun()
     218
     219  __VERSION_STRING="3.2.3"
     220
     221  _verbose = 1
     222 -def message(msg):
     223    """ emits messages to _sys.stdout_
     224      override this in modules which import this one to redirect output
     225
     226      **Arguments**
     227
     228        - msg: the string to be displayed
     229
     230    """
     231    if _verbose: sys.stdout.write('%s\n'%(msg))
     232
     233
     234 -def testall(composite,examples,badExamples=[]):
     235    """ screens a number of examples past a composite
     236
     237      **Arguments**
     238
     239        - composite: a composite model
     240
     241        - examples: a list of examples (with results) to be screened
     242
     243        - badExamples: a list to which misclassified examples are appended
     244
     245      **Returns**
     246
     247        a list of 2-tuples containing:
     248
     249          1) a vote
     250
     251          2) a confidence
     252
     253        these are the votes and confidence levels for **misclassified** examples
     254
     255    """
     256    wrong = []
     257    for example in examples:
     258      if composite.GetActivityQuantBounds():
     259        answer = composite.QuantizeActivity(example)[-1]
     260      else:
     261        answer = example[-1]
     262      res,conf = composite.ClassifyExample(example)
     263      if res != answer:
     264        wrong.append((res,conf))
     265        badExamples.append(example)
     266
     267    return wrong
     268
     269 -def GetCommandLine(details):
     270    """ #DOC
     271
     272    """
     273    args = ['BuildComposite']
     274    args.append('-n %d'%(details.nModels))
     275    if details.filterFrac != 0.0: args.append('-F %.3f -v %d'%(details.filterFrac,details.filterVal))
     276    if details.modelFilterFrac != 0.0: args.append('--modelFiltFrac=%.3f --modelFiltVal=%d'%(details.modelFilterFrac,
     277                                                                    details.modelFilterVal))
     278    if details.splitRun: args.append('-s -f %.3f'%(details.splitFrac))
     279    if details.shuffleActivities: args.append('-S')
     280    if details.randomActivities: args.append('-r')
     281    if details.threshold > 0.0: args.append('-t %.3f'%(details.threshold))
     282    if details.activityBounds: args.append('-Q "%s"'%(details.activityBoundsVals))
     283    if details.dbName: args.append('-d %s'%(details.dbName))
     284    if details.detailedRes: args.append('-D')
     285    if hasattr(details,'noScreen') and details.noScreen: args.append('--noScreen')
     286    if details.persistTblName and details.dbName:
     287      args.append('-p %s'%(details.persistTblName))
     288    if details.note:
     289      args.append('-N %s'%(details.note))
     290    if details.useTrees:
     291      if details.limitDepth>0: args.append('-L %d'%(details.limitDepth))
     292      if details.lessGreedy: args.append('-g')
     293      if details.qBounds:
     294        shortBounds = listutils.CompactListRepr(details.qBounds)
     295        if details.qBounds: args.append('-q "%s"'%(shortBounds))
     296      else:
     297        if details.qBounds: args.append('-q "%s"'%(details.qBoundCount))
     298
     299      if details.pruneIt: args.append('--prune')
     300      if details.startAt: args.append('-G %d'%details.startAt)
     301      if details.recycleVars: args.append('--recycle')
     302      if details.randomDescriptors: args.append('--randomDescriptors=%d'%details.randomDescriptors)
     303    if details.useSigTrees:
     304      args.append('--doSigTree')
     305      if details.limitDepth>0: args.append('-L %d'%(details.limitDepth))
     306      if details.randomDescriptors:
     307        args.append('--randomDescriptors=%d'%details.randomDescriptors)
     308
     309    if details.useKNN:
     310      args.append('--doKnn --knnK %d'%(details.knnNeighs))
     311      if details.knnDistFunc=='Tanimoto':
     312        args.append('--knnTanimoto')
     313      else:
     314        args.append('--knnEuclid')
     315
     316    if details.useNaiveBayes:
     317      args.append('--doNaiveBayes')
     318      if details.mEstimateVal >= 0.0 :
     319        args.append('--mEstimateVal=%.3f'%details.mEstimateVal)
     320
     321  ##   if details.useSVM:
     322  ##     args.append('--doSVM')
     323  ##     if details.svmKernel:
     324  ##       for k in SVM.kernels.keys():
     325  ##         if SVM.kernels[k]==details.svmKernel:
     326  ##           args.append('--svmKernel=%s'%k)
     327  ##           break
     328  ##     if details.svmType:
     329  ##       for k in SVM.machineTypes.keys():
     330  ##         if SVM.machineTypes[k]==details.svmType:
     331  ##           args.append('--svmType=%s'%k)
     332  ##           break
     333  ##     if details.svmGamma:
     334  ##       args.append('--svmGamma=%f'%details.svmGamma)
     335  ##     if details.svmCost:
     336  ##       args.append('--svmCost=%f'%details.svmCost)
     337  ##     if details.svmWeights:
     338  ##       args.append("--svmWeights='%s'"%str(details.svmWeights))
     339  ##     if details.svmDegree:
     340  ##       args.append('--svmDegree=%d'%details.svmDegree)
     341  ##     if details.svmCoeff:
     342  ##       args.append('--svmCoeff=%d'%details.svmCoeff)
     343  ##     if details.svmEps:
     344  ##       args.append('--svmEps=%f'%details.svmEps)
     345  ##     if details.svmNu:
     346  ##       args.append('--svmNu=%f'%details.svmNu)
     347  ##     if details.svmCache:
     348  ##       args.append('--svmCache=%d'%details.svmCache)
     349  ##     if detail.svmDataType:
     350  ##       args.append('--svmDataType=%s'%details.svmDataType)
     351  ##     if not details.svmShrink:
     352  ##       args.append('--svmShrink')
     353
     354    if details.replacementSelection: args.append('--replacementSelection')
     355
     356
     357    # this should always be last:
     358    if details.tableName: args.append(details.tableName)
     359
     360    return ' '.join(args)
     361
     362 -def RunOnData(details,data,progressCallback=None,saveIt=1,setDescNames=0):
     363    nExamples = data.GetNPts()
     364    if details.lockRandom:
     365      seed = details.randomSeed
     366    else:
     367      import random
     368      seed = (random.randint(0,1e6),random.randint(0,1e6))
     369    DataUtils.InitRandomNumbers(seed)
     370    testExamples = []
     371    if details.shuffleActivities == 1:
     372      DataUtils.RandomizeActivities(data,shuffle=1,runDetails=details)
     373    elif details.randomActivities == 1:
     374      DataUtils.RandomizeActivities(data,shuffle=0,runDetails=details)
     375
     376    namedExamples = data.GetNamedData()
     377    if details.splitRun == 1:
     378      trainIdx,testIdx = SplitData.SplitIndices(len(namedExamples),details.splitFrac,
     379                                                silent=not _verbose)
     380
     381      trainExamples = [namedExamples[x] for x in trainIdx]
     382      testExamples = [namedExamples[x] for x in testIdx]
     383    else:
     384      testExamples = []
     385      testIdx = []
     386      trainIdx = range(len(namedExamples))
     387      trainExamples = namedExamples
     388
     389    if details.filterFrac != 0.0:
     390      # if we're doing quantization on the fly, we need to handle that here:
     391      if hasattr(details,'activityBounds') and details.activityBounds:
     392        tExamples = []
     393        bounds = details.activityBounds
     394        for pt in trainExamples:
     395          pt = pt[:]
     396          act = pt[-1]
     397          placed=0
     398          bound=0
     399          while not placed and bound < len(bounds):
     400            if act < bounds[bound]:
     401              pt[-1] = bound
     402              placed = 1
     403            else:
     404              bound += 1
     405          if not placed:
     406            pt[-1] = bound
     407          tExamples.append(pt)
     408      else:
     409        bounds = None
     410        tExamples = trainExamples
     411      trainIdx,temp = DataUtils.FilterData(tExamples,details.filterVal,
     412                                           details.filterFrac,-1,
     413                                           indicesOnly=1)
     414      tmp = [trainExamples[x] for x in trainIdx]
     415      testExamples += [trainExamples[x] for x in temp]
     416      trainExamples = tmp
     417
     418      counts = DataUtils.CountResults(trainExamples,bounds=bounds)
     419      ks = counts.keys()
     420      ks.sort()
     421      message('Result Counts in training set:')
     422      for k in ks:
     423        message(str((k, counts[k])))
     424      counts = DataUtils.CountResults(testExamples,bounds=bounds)
     425      ks = counts.keys()
     426      ks.sort()
     427      message('Result Counts in test set:')
     428      for k in ks:
     429        message(str((k, counts[k])))
     430    nExamples = len(trainExamples)
     431    message('Training with %d examples'%(nExamples))
     432
     433    nVars = data.GetNVars()
     434    attrs = range(1,nVars+1)
     435    nPossibleVals = data.GetNPossibleVals()
     436    for i in range(1,len(nPossibleVals)):
     437      if nPossibleVals[i-1] == -1:
     438        attrs.remove(i)
     439
     440    if details.pickleDataFileName != '':
     441      pickleDataFile = open(details.pickleDataFileName,'wb+')
     442      cPickle.dump(trainExamples,pickleDataFile)
     443      cPickle.dump(testExamples,pickleDataFile)
     444      pickleDataFile.close()
     445
     446    if details.bayesModel:
     447      composite = BayesComposite.BayesComposite()
     448    else:
     449      composite = Composite.Composite()
     450
     451    composite._randomSeed = seed
     452    composite._splitFrac = details.splitFrac
     453    composite._shuffleActivities = details.shuffleActivities
     454    composite._randomizeActivities = details.randomActivities
     455
     456    if hasattr(details,'filterFrac'):
     457      composite._filterFrac = details.filterFrac
     458    if hasattr(details,'filterVal'):
     459      composite._filterVal = details.filterVal
     460
     461    composite.SetModelFilterData(details.modelFilterFrac, details.modelFilterVal)
     462
     463    composite.SetActivityQuantBounds(details.activityBounds)
     464    nPossibleVals = data.GetNPossibleVals()
     465    if details.activityBounds:
     466      nPossibleVals[-1] = len(details.activityBounds)+1
     467
     468    if setDescNames:
     469      composite.SetInputOrder(data.GetVarNames())
     470      composite.SetDescriptorNames(details._descNames)
     471    else:
     472      composite.SetDescriptorNames(data.GetVarNames())
     473    composite.SetActivityQuantBounds(details.activityBounds)
     474    if details.nModels==1:
     475      details.internalHoldoutFrac=0.0
     476    if details.useTrees:
     477      from rdkit.ML.DecTree import CrossValidate,PruneTree
     478      if details.qBounds != []:
     479        from rdkit.ML.DecTree import BuildQuantTree
     480        builder = BuildQuantTree.QuantTreeBoot
     481      else:
     482        from rdkit.ML.DecTree import ID3
     483        builder = ID3.ID3Boot
     484      driver = CrossValidate.CrossValidationDriver
     485      pruner = PruneTree.PruneTree
     486
     487      composite.SetQuantBounds(details.qBounds)
     488      nPossibleVals = data.GetNPossibleVals()
     489      if details.activityBounds:
     490        nPossibleVals[-1] = len(details.activityBounds)+1
     491      composite.Grow(trainExamples,attrs,nPossibleVals=[0]+nPossibleVals,
     492                     buildDriver=driver,
     493                     pruner=pruner,
     494                     nTries=details.nModels,pruneIt=details.pruneIt,
     495                     lessGreedy=details.lessGreedy,needsQuantization=0,
     496                     treeBuilder=builder,nQuantBounds=details.qBounds,
     497                     startAt=details.startAt,
     498                     maxDepth=details.limitDepth,
     499                     progressCallback=progressCallback,
     500                     holdOutFrac=details.internalHoldoutFrac,
     501                     replacementSelection=details.replacementSelection,
     502                     recycleVars=details.recycleVars,
     503                     randomDescriptors=details.randomDescriptors,
     504                     silent=not _verbose)
     505
     506    elif details.useSigTrees:
     507      from rdkit.ML.DecTree import CrossValidate
     508      from rdkit.ML.DecTree import BuildSigTree
     509      builder = BuildSigTree.SigTreeBuilder
     510      driver = CrossValidate.CrossValidationDriver
     511      nPossibleVals = data.GetNPossibleVals()
     512      if details.activityBounds:
     513        nPossibleVals[-1] = len(details.activityBounds)+1
     514      if hasattr(details,'sigTreeBiasList'):
     515        biasList = details.sigTreeBiasList
     516      else:
     517        biasList=None
     518      if hasattr(details,'useCMIM'):
     519        useCMIM=details.useCMIM
     520      else:
     521        useCMIM=0
     522      if hasattr(details,'allowCollections'):
     523        allowCollections = details.allowCollections
     524      else:
     525        allowCollections=False
     526      composite.Grow(trainExamples,attrs,nPossibleVals=[0]+nPossibleVals,
     527                     buildDriver=driver,
     528                     nTries=details.nModels,
     529                     needsQuantization=0,
     530                     treeBuilder=builder,
     531                     maxDepth=details.limitDepth,
     532                     progressCallback=progressCallback,
     533                     holdOutFrac=details.internalHoldoutFrac,
     534                     replacementSelection=details.replacementSelection,
     535                     recycleVars=details.recycleVars,
     536                     randomDescriptors=details.randomDescriptors,
     537                     biasList=biasList,
     538                     useCMIM=useCMIM,
     539                     allowCollection=allowCollections,
     540                     silent=not _verbose)
     541
     542    elif details.useKNN:
     543      from rdkit.ML.KNN import CrossValidate
     544      from rdkit.ML.KNN import DistFunctions
     545
     546      driver = CrossValidate.CrossValidationDriver
     547      dfunc = ''
     548      if (details.knnDistFunc == "Euclidean") :
     549        dfunc = DistFunctions.EuclideanDist
     550      elif (details.knnDistFunc == "Tanimoto"):
     551        dfunc = DistFunctions.TanimotoDist
     552      else:
     553        assert 0,"Bad KNN distance metric value"
     554
     555
     556      composite.Grow(trainExamples, attrs, nPossibleVals=[0]+nPossibleVals,
     557                     buildDriver=driver, nTries=details.nModels,
     558                     needsQuantization=0,
     559                     numNeigh=details.knnNeighs,
     560                     holdOutFrac=details.internalHoldoutFrac,
     561                     distFunc=dfunc)
     562
     563    elif details.useNaiveBayes or details.useSigBayes:
     564      from rdkit.ML.NaiveBayes import CrossValidate
     565      driver = CrossValidate.CrossValidationDriver
     566      if not (hasattr(details,'useSigBayes') and details.useSigBayes):
     567        composite.Grow(trainExamples, attrs, nPossibleVals=[0]+nPossibleVals,
     568                       buildDriver=driver, nTries=details.nModels,
     569                       needsQuantization=0, nQuantBounds=details.qBounds,
     570                       holdOutFrac=details.internalHoldoutFrac,
     571                       replacementSelection=details.replacementSelection,
     572                       mEstimateVal=details.mEstimateVal,
     573                       silent=not _verbose)
     574      else:
     575        if hasattr(details,'useCMIM'):
     576          useCMIM=details.useCMIM
     577        else:
     578          useCMIM=0
     579
     580        composite.Grow(trainExamples, attrs, nPossibleVals=[0]+nPossibleVals,
     581                       buildDriver=driver, nTries=details.nModels,
     582                       needsQuantization=0, nQuantBounds=details.qBounds,
     583                       mEstimateVal=details.mEstimateVal,
     584                       useSigs=True,useCMIM=useCMIM,
     585                       holdOutFrac=details.internalHoldoutFrac,
     586                       replacementSelection=details.replacementSelection,
     587                       silent=not _verbose)
     588
     589
     590
     591  ##   elif details.useSVM:
     592  ##     from rdkit.ML.SVM import CrossValidate
     593  ##     driver = CrossValidate.CrossValidationDriver
     594  ##     composite.Grow(trainExamples, attrs, nPossibleVals=[0]+nPossibleVals,
     595  ##                    buildDriver=driver, nTries=details.nModels,
     596  ##                    needsQuantization=0,
     597  ##                    cost=details.svmCost,gamma=details.svmGamma,
     598  ##                    weights=details.svmWeights,degree=details.svmDegree,
     599  ##                    type=details.svmType,kernelType=details.svmKernel,
     600  ##                    coef0=details.svmCoeff,eps=details.svmEps,nu=details.svmNu,
     601  ##                    cache_size=details.svmCache,shrinking=details.svmShrink,
     602  ##                    dataType=details.svmDataType,
     603  ##                    holdOutFrac=details.internalHoldoutFrac,
     604  ##                    replacementSelection=details.replacementSelection,
     605  ##                    silent=not _verbose)
     606
     607    else:
     608      from rdkit.ML.Neural import CrossValidate
     609      driver = CrossValidate.CrossValidationDriver
     610      composite.Grow(trainExamples,attrs,[0]+nPossibleVals,nTries=details.nModels,
     611                     buildDriver=driver,needsQuantization=0)
     612
     613    composite.AverageErrors()
     614    composite.SortModels()
     615    modelList,counts,avgErrs = composite.GetAllData()
     616    counts = numpy.array(counts)
     617    avgErrs = numpy.array(avgErrs)
     618    composite._varNames = data.GetVarNames()
     619
     620    for i in range(len(modelList)):
     621      modelList[i].NameModel(composite._varNames)
     622
     623    # do final statistics
     624    weightedErrs = counts*avgErrs
     625    averageErr = sum(weightedErrs)/sum(counts)
     626    devs = (avgErrs - averageErr)
     627    devs = devs * counts
     628    devs = numpy.sqrt(devs*devs)
     629    avgDev = sum(devs)/sum(counts)
     630    message('# Overall Average Error: %%% 5.2f, Average Deviation: %%% 6.2f'%(100.*averageErr,100.*avgDev))
     631
     632    if details.bayesModel:
     633      composite.Train(trainExamples,verbose=0)
     634
     635    # blow out the saved examples and then save the composite:
     636    composite.ClearModelExamples()
     637    if saveIt:
     638      composite.Pickle(details.outName)
     639    details.model = DbModule.binaryHolder(cPickle.dumps(composite))
     640
     641    badExamples = []
     642    if not details.detailedRes and (not hasattr(details,'noScreen') or not details.noScreen):
     643      if details.splitRun:
     644        message('Testing all hold-out examples')
     645        wrong = testall(composite,testExamples,badExamples)
     646        message('%d examples (%% %5.2f) were misclassified'%(len(wrong),
     647                                                             100.*float(len(wrong))/float(len(testExamples))))
     648        _runDetails.holdout_error = float(len(wrong))/len(testExamples)
     649      else:
     650        message('Testing all examples')
     651        wrong = testall(composite,namedExamples,badExamples)
     652        message('%d examples (%% %5.2f) were misclassified'%(len(wrong),
     653                                                             100.*float(len(wrong))/float(len(namedExamples))))
     654        _runDetails.overall_error = float(len(wrong))/len(namedExamples)
     655
     656    if details.detailedRes:
     657      message('\nEntire data set:')
     658      resTup = ScreenComposite.ShowVoteResults(range(data.GetNPts()),data,composite,
     659                                               nPossibleVals[-1],details.threshold)
     660      nGood,nBad,nSkip,avgGood,avgBad,avgSkip,voteTab = resTup
     661      nPts = len(namedExamples)
     662      nClass = nGood+nBad
     663      _runDetails.overall_error = float(nBad) / nClass
     664      _runDetails.overall_correct_conf = avgGood
     665      _runDetails.overall_incorrect_conf = avgBad
     666      _runDetails.overall_result_matrix = repr(voteTab)
     667      nRej = nClass-nPts
     668      if nRej > 0:
     669        _runDetails.overall_fraction_dropped = float(nRej)/nPts
     670
     671      if details.splitRun:
     672        message('\nHold-out data:')
     673        resTup = ScreenComposite.ShowVoteResults(range(len(testExamples)),testExamples,
     674                                                 composite,
     675                                                 nPossibleVals[-1],details.threshold)
     676        nGood,nBad,nSkip,avgGood,avgBad,avgSkip,voteTab = resTup
     677        nPts = len(testExamples)
     678        nClass = nGood+nBad
     679        _runDetails.holdout_error = float(nBad) / nClass
     680        _runDetails.holdout_correct_conf = avgGood
     681        _runDetails.holdout_incorrect_conf = avgBad
     682        _runDetails.holdout_result_matrix = repr(voteTab)
     683        nRej = nClass-nPts
     684        if nRej > 0:
     685          _runDetails.holdout_fraction_dropped = float(nRej)/nPts
     686
     687    if details.persistTblName and details.dbName:
     688      message('Updating results table %s:%s'%(details.dbName,details.persistTblName))
     689      details.Store(db=details.dbName,table=details.persistTblName)
     690
     691    if details.badName != '':
     692      badFile = open(details.badName,'w+')
     693      for i in range(len(badExamples)):
     694        ex = badExamples[i]
     695        vote = wrong[i]
     696        outStr = '%s\t%s\n'%(ex,vote)
     697        badFile.write(outStr)
     698      badFile.close()
     699
     700    composite.ClearModelExamples()
     701    return composite
     702
     703 -def RunIt(details,progressCallback=None,saveIt=1,setDescNames=0):
     704    """ does the actual work of building a composite model
     705
     706      **Arguments**
     707
     708        - details:  a _CompositeRun.CompositeRun_ object containing details
     709          (options, parameters, etc.) about the run
     710
     711        - progressCallback: (optional) a function which is called with a single
     712          argument (the number of models built so far) after each model is built.
     713
     714        - saveIt: (optional) if this is nonzero, the resulting model will be pickled
     715          and dumped to the filename specified in _details.outName_
     716
     717        - setDescNames: (optional) if nonzero, the composite's _SetInputOrder()_ method
     718          will be called using the results of the data set's _GetVarNames()_ method;
     719          it is assumed that the details object has a _descNames attribute which
     720          is passed to the composites _SetDescriptorNames()_ method.  Otherwise
     721          (the default), _SetDescriptorNames()_ gets the results of _GetVarNames()_.
     722
     723      **Returns**
     724
     725        the composite model constructed
     726
     727
     728    """
     729    details.rundate = time.asctime()
     730
     731    fName = details.tableName.strip()
     732    if details.outName == '':
     733      details.outName = fName + '.pkl'
     734    if not details.dbName:
     735      if details.qBounds != []:
     736        data = DataUtils.TextFileToData(fName)
     737      else:
     738        data = DataUtils.BuildQuantDataSet(fName)
     739    elif details.useSigTrees or details.useSigBayes:
     740      details.tableName = fName
     741      data = details.GetDataSet(pickleCol=0,pickleClass=DataStructs.ExplicitBitVect)
     742    elif details.qBounds != [] or not details.useTrees:
     743      details.tableName = fName
     744      data = details.GetDataSet()
     745    else:
     746      data = DataUtils.DBToQuantData(details.dbName,fName,quantName=details.qTableName,
     747                                     user=details.dbUser,password=details.dbPassword)
     748
     749    composite = RunOnData(details,data,progressCallback=progressCallback,
     750                          saveIt=saveIt,setDescNames=setDescNames)
     751    return composite
     752
     753
     754 -def ShowVersion(includeArgs=0):
     755    """ prints the version number
     756
     757    """
     758    print('This is BuildComposite.py version %s' % (__VERSION_STRING))
     759    if includeArgs:
     760      import sys
     761      print('command line was:')
     762      print(' '.join(sys.argv))
     763
     764 -def Usage():
     765    """ provides a list of arguments for when this is used from the command line
     766
     767    """
     768    import sys
     769    print(__doc__)
     770    sys.exit(-1)
     771
     772 -def SetDefaults(runDetails=None):
     773    """  initializes a details object with default values
     774
     775        **Arguments**
     776
     777          - details:  (optional) a _CompositeRun.CompositeRun_ object.
     778            If this is not provided, the global _runDetails will be used.
     779
     780        **Returns**
     781
     782          the initialized _CompositeRun_ object.
     783
     784
     785    """
     786    if runDetails is None: runDetails = _runDetails
     787    return CompositeRun.SetDefaults(runDetails)
     788
     789 -def ParseArgs(runDetails):
     790    """ parses command line arguments and updates _runDetails_
     791
     792        **Arguments**
     793
     794          - runDetails:  a _CompositeRun.CompositeRun_ object.
     795
     796    """
     797    import getopt
     798    args,extra = getopt.getopt(sys.argv[1:],'P:o:n:p:b:sf:F:v:hlgd:rSTt:BQ:q:DVG:N:L:',
     799                               ['nRuns=','prune','profile',
     800                                'seed=','noScreen',
     801
     802                                'modelFiltFrac=', 'modelFiltVal=',
     803
     804                                'recycle','randomDescriptors=',
     805
     806                                'doKnn','knnK=','knnTanimoto','knnEuclid',
     807
     808                                'doSigTree','allowCollections',
     809
     810                                'doNaiveBayes', 'mEstimateVal=',
     811                                'doSigBayes',
     812
     813  ##                               'doSVM','svmKernel=','svmType=','svmGamma=',
     814  ##                               'svmCost=','svmWeights=','svmDegree=',
     815  ##                               'svmCoeff=','svmEps=','svmNu=','svmCache=',
     816  ##                               'svmShrink','svmDataType=',
     817
     818                                'replacementSelection',
     819
     820                                ])
     821    runDetails.profileIt=0
     822    for arg,val in args:
     823      if arg == '-n':
     824        runDetails.nModels = int(val)
     825      elif arg == '-N':
     826        runDetails.note=val
     827      elif arg == '-o':
     828        runDetails.outName = val
     829      elif arg == '-Q':
     830        qBounds = eval(val)
     831        assert type(qBounds) in [type([]),type(())],'bad argument type for -Q, specify a list as a string'
     832        runDetails.activityBounds=qBounds
     833        runDetails.activityBoundsVals=val
     834      elif arg == '-p':
     835        runDetails.persistTblName=val
     836      elif arg == '-P':
     837        runDetails.pickleDataFileName= val
     838      elif arg == '-r':
     839        runDetails.randomActivities = 1
     840      elif arg == '-S':
     841        runDetails.shuffleActivities = 1
     842      elif arg == '-b':
     843        runDetails.badName = val
     844      elif arg == '-B':
     845        runDetails.bayesModels=1
     846      elif arg == '-s':
     847        runDetails.splitRun = 1
     848      elif arg == '-f':
     849        runDetails.splitFrac=float(val)
     850      elif arg == '-F':
     851        runDetails.filterFrac=float(val)
     852      elif arg == '-v':
     853        runDetails.filterVal=float(val)
     854      elif arg == '-l':
     855        runDetails.lockRandom = 1
     856      elif arg == '-g':
     857        runDetails.lessGreedy=1
     858      elif arg == '-G':
     859        runDetails.startAt = int(val)
     860      elif arg == '-d':
     861        runDetails.dbName=val
     862      elif arg == '-T':
     863        runDetails.useTrees = 0
     864      elif arg == '-t':
     865        runDetails.threshold=float(val)
     866      elif arg == '-D':
     867        runDetails.detailedRes = 1
     868      elif arg == '-L':
     869        runDetails.limitDepth = int(val)
     870      elif arg == '-q':
     871        qBounds = eval(val)
     872        assert type(qBounds) in [type([]),type(())],'bad argument type for -q, specify a list as a string'
     873        runDetails.qBoundCount=val
     874        runDetails.qBounds = qBounds
     875      elif arg == '-V':
     876        ShowVersion()
     877        sys.exit(0)
     878      elif arg == '--nRuns':
     879        runDetails.nRuns = int(val)
     880      elif arg == '--modelFiltFrac':
     881        runDetails.modelFilterFrac=float(val)
     882      elif arg == '--modelFiltVal':
     883        runDetails.modelFilterVal=float(val)
     884      elif arg == '--prune':
     885        runDetails.pruneIt=1
     886      elif arg == '--profile':
     887        runDetails.profileIt=1
     888
     889      elif arg == '--recycle':
     890        runDetails.recycleVars=1
     891      elif arg == '--randomDescriptors':
     892        runDetails.randomDescriptors=int(val)
     893
     894      elif arg == '--doKnn':
     895        runDetails.useKNN=1
     896        runDetails.useTrees=0
     897  ##      runDetails.useSVM=0
     898        runDetails.useNaiveBayes=0
     899      elif arg == '--knnK':
     900        runDetails.knnNeighs = int(val)
     901      elif arg == '--knnTanimoto':
     902        runDetails.knnDistFunc="Tanimoto"
     903      elif arg == '--knnEuclid':
     904        runDetails.knnDistFunc="Euclidean"
     905
     906      elif arg == '--doSigTree':
     907  ##      runDetails.useSVM=0
     908        runDetails.useKNN=0
     909        runDetails.useTrees=0
     910        runDetails.useNaiveBayes=0
     911        runDetails.useSigTrees=1
     912      elif arg == '--allowCollections':
     913        runDetails.allowCollections=True
     914
     915      elif arg == '--doNaiveBayes':
     916        runDetails.useNaiveBayes=1
     917  ##      runDetails.useSVM=0
     918        runDetails.useKNN=0
     919        runDetails.useTrees=0
     920        runDetails.useSigBayes=0
     921      elif arg == '--doSigBayes':
     922        runDetails.useSigBayes=1
     923        runDetails.useNaiveBayes=0
     924  ##      runDetails.useSVM=0
     925        runDetails.useKNN=0
     926        runDetails.useTrees=0
     927      elif arg == '--mEstimateVal':
     928        runDetails.mEstimateVal=float(val)
     929
     930  ##     elif arg == '--doSVM':
     931  ##       runDetails.useSVM=1
     932  ##       runDetails.useKNN=0
     933  ##       runDetails.useTrees=0
     934  ##       runDetails.useNaiveBayes=0
     935  ##     elif arg == '--svmKernel':
     936  ##       if val not in SVM.kernels.keys():
     937  ##         message('kernel %s not in list of available kernels:\n%s\n'%(val,SVM.kernels.keys()))
     938  ##         sys.exit(-1)
     939  ##       else:
     940  ##         runDetails.svmKernel=SVM.kernels[val]
     941  ##     elif arg == '--svmType':
     942  ##       if val not in SVM.machineTypes.keys():
     943  ##         message('type %s not in list of available machines:\n%s\n'%(val,SVM.machineTypes.keys()))
     944  ##         sys.exit(-1)
     945  ##       else:
     946  ##         runDetails.svmType=SVM.machineTypes[val]
     947  ##     elif arg == '--svmGamma':
     948  ##       runDetails.svmGamma = float(val)
     949  ##     elif arg == '--svmCost':
     950  ##       runDetails.svmCost = float(val)
     951  ##     elif arg == '--svmWeights':
     952  ##       # FIX: this is dangerous
     953  ##       runDetails.svmWeights = eval(val)
     954  ##     elif arg == '--svmDegree':
     955  ##       runDetails.svmDegree = int(val)
     956  ##     elif arg == '--svmCoeff':
     957  ##       runDetails.svmCoeff = float(val)
     958  ##     elif arg == '--svmEps':
     959  ##       runDetails.svmEps = float(val)
     960  ##     elif arg == '--svmNu':
     961  ##       runDetails.svmNu = float(val)
     962  ##     elif arg == '--svmCache':
     963  ##       runDetails.svmCache = int(val)
     964  ##     elif arg == '--svmShrink':
     965  ##       runDetails.svmShrink = 0
     966  ##     elif arg == '--svmDataType':
     967  ##       runDetails.svmDataType=val
     968
     969      elif arg== '--seed':
     970        # FIX: dangerous
     971        runDetails.randomSeed = eval(val)
     972
     973      elif arg== '--noScreen':
     974        runDetails.noScreen=1
     975
     976      elif arg== '--replacementSelection':
     977        runDetails.replacementSelection = 1
     978
     979      elif arg == '-h':
     980        Usage()
     981
     982      else:
     983        Usage()
     984    runDetails.tableName=extra[0]
     985
     986  if __name__ == '__main__':
     987    if len(sys.argv) < 2:
     988      Usage()
     989
     990    _runDetails.cmd = ' '.join(sys.argv)
     991    SetDefaults(_runDetails)
     992    ParseArgs(_runDetails)
     993
     994
     995    ShowVersion(includeArgs=1)
     996
     997    if _runDetails.nRuns > 1:
     998      for i in range(_runDetails.nRuns):
     999        sys.stderr.write('---------------------------------\n\tDoing %d of %d\n---------------------------------\n'%(i+1,_runDetails.nRuns))
    1000        RunIt(_runDetails)
    1001    else:
    1002      if _runDetails.profileIt:
    1003        import hotshot,hotshot.stats
    1004        prof=hotshot.Profile('prof.dat')
    1005        prof.runcall(RunIt,_runDetails)
    1006        stats = hotshot.stats.load('prof.dat')
    1007        stats.strip_dirs()
    1008        stats.sort_stats('time','calls')
    1009        stats.print_stats(30)
    1010      else:
    1011        RunIt(_runDetails)
    1012