rdkit.ML.Neural.CrossValidate module — The RDKit 2016.09.1 documentation

# rdkit.ML.Neural.CrossValidate module

handles doing cross validation with neural nets

This is, perhaps, a little misleading. For the purposes of this module, cross validation == evaluating the accuracy of a net.

`rdkit.ML.Neural.CrossValidate.` `CrossValidate`(net, testExamples, tolerance, appendExamples=0)
  Determines the classification error for the testExamples

    Arguments

    > - tree: a decision tree (or anything supporting a _ClassifyExample()_ method)

    > - testExamples: a list of examples to be used for testing

    > - appendExamples: a toggle which is ignored, it’s just here to maintain

    >     the same API as the decision tree code.

    Returns

    > a 2-tuple consisting of:

    > > 1. the percent error of the net
    > > 2. a list of misclassified examples

  Note
    At the moment, this is specific to nets with only one output

`rdkit.ML.Neural.CrossValidate.` `CrossValidationDriver`(examples, attrs=[], nPossibleVals=[], holdOutFrac=0.3, silent=0, tolerance=0.3, calcTotalError=0, hiddenSizes=None, **kwargs)

  Arguments

  > - examples: the full set of examples

  > - attrs: a list of attributes to consider in the tree building

  >     This argument is ignored

  > - nPossibleVals: a list of the number of possible values each variable can adopt

  >     This argument is ignored

  > - holdOutFrac: the fraction of the data which should be reserved for the hold-out set

  >     (used to calculate the error)

  > - silent: a toggle used to control how much visual noise this makes as it goes.

  > - tolerance: the tolerance for convergence of the net

  > - calcTotalError: if this is true the entire data set is used to calculate

  >     accuracy of the net

  > - hiddenSizes: a list containing the size(s) of the hidden layers in the network.

  >     if _hiddenSizes_ is None, one hidden layer containing the same number of nodes as the input layer will be used

  Returns

  > a 2-tuple containing:

  > > 1. the net
  > > 2. the cross-validation error of the net

  Note
    At the moment, this is specific to nets with only one output