rdkit.ML.GrowComposite module

command line utility for growing composite models

Usage

_GrowComposite [optional args] filename_

Command Line Arguments

  • -n count: number of new models to build

  • -C pickle file name: name of file containing composite upon which to build.

  • –inNote note: note to be used in loading composite models from the database

    for growing

  • –balTable table name: table from which to take the original data set

    (for balancing)

  • –balWeight weight: (between 0 and 1) weighting factor for the new data

    (for balancing). OR, weight can be a list of weights

  • –balCnt count: number of individual models in the balanced composite

    (for balancing)

  • –balH: use only the holdout set from the original data set in the balancing

    (for balancing)

  • –balT: use only the training set from the original data set in the balancing

    (for balancing)

  • -S: shuffle the original data set

    (for balancing)

  • -r: randomize the activities of the original data set

    (for balancing)

  • -N note: note to be attached to the grown composite when it’s saved in the

    database

  • –outNote note: equivalent to -N

  • -o filename: name of an output file to hold the pickled composite after

    it has been grown. If multiple balance weights are used, the weights will be added to the filenames.

  • -L limit: provide an (integer) limit on individual model complexity

  • -d database name: instead of reading the data from a QDAT file,

    pull it from a database. In this case, the _filename_ argument provides the name of the database table containing the data set.

  • -p tablename: store persistence data in the database

    in table tablename

  • -l: locks the random number generator to give consistent sets

    of training and hold-out data. This is primarily intended for testing purposes.

  • -g: be less greedy when training the models.

  • -G number: force trees to be rooted at descriptor number.

  • -D: show a detailed breakdown of the composite model performance

    across the training and, when appropriate, hold-out sets.

  • -t threshold value: use high-confidence predictions for the final

    analysis of the hold-out data.

  • -q list string: Add QuantTrees to the composite and use the list

    specified in list string as the number of target quantization bounds for each descriptor. Don’t forget to include 0’s at the beginning and end of list string for the name and value fields. For example, if there are 4 descriptors and you want 2 quant bounds apiece, you would use _-q “[0,2,2,2,2,0]”_. Two special cases:

    1. If you would like to ignore a descriptor in the model building, use ‘-1’ for its number of quant bounds.
    2. If you have integer valued data that should not be quantized further, enter 0 for that descriptor.

  • -V: print the version number and exit

rdkit.ML.GrowComposite.BalanceComposite(details, composite, data1=None, data2=None)

balances the composite using the parameters provided in details

Arguments

  • details a _CompositeRun.RunDetails_ object
  • composite: the composite model to be balanced
  • data1: (optional) if provided, this should be the data set used to construct the original models
  • data2: (optional) if provided, this should be the data set used to construct the new individual models
rdkit.ML.GrowComposite.GetComposites(details)
rdkit.ML.GrowComposite.GrowIt(details, composite, progressCallback=None, saveIt=1, setDescNames=0, data=None)

does the actual work of building a composite model

Arguments

  • details: a _CompositeRun.CompositeRun_ object containing details (options, parameters, etc.) about the run
  • composite: the composite model to grow
  • progressCallback: (optional) a function which is called with a single argument (the number of models built so far) after each model is built.
  • saveIt: (optional) if this is nonzero, the resulting model will be pickled and dumped to the filename specified in _details.outName_
  • setDescNames: (optional) if nonzero, the composite’s _SetInputOrder()_ method will be called using the results of the data set’s _GetVarNames()_ method; it is assumed that the details object has a _descNames attribute which is passed to the composites _SetDescriptorNames()_ method. Otherwise (the default), _SetDescriptorNames()_ gets the results of _GetVarNames()_.
  • data: (optional) the data set to be used. If this is not provided, the data set described in details will be used.

Returns

the enlarged composite model
rdkit.ML.GrowComposite.ParseArgs(runDetails)

parses command line arguments and updates _runDetails_

Arguments

  • runDetails: a _CompositeRun.CompositeRun_ object.
rdkit.ML.GrowComposite.SetDefaults(runDetails=None)

initializes a details object with default values

Arguments

  • details: (optional) a _CompositeRun.CompositeRun_ object. If this is not provided, the global _runDetails will be used.

Returns

the initialized _CompositeRun_ object.
rdkit.ML.GrowComposite.ShowVersion(includeArgs=0)

prints the version number

rdkit.ML.GrowComposite.Usage()

provides a list of arguments for when this is used from the command line

rdkit.ML.GrowComposite.message(msg)

emits messages to _sys.stdout_ override this in modules which import this one to redirect output

Arguments

  • msg: the string to be displayed