rdkit.ML.DecTree.BuildQuantTree module

rdkit.ML.DecTree.BuildQuantTree.BuildQuantTree(examples, target, attrs, nPossibleVals, nBoundsPerVar, depth=0, maxDepth=-1, exIndices=None, **kwargs)

Arguments

• examples: a list of lists (nInstances x nVariables+1) of variable values + instance values

• target: an int

• attrs: a list of ints indicating which variables can be used in the tree

• nPossibleVals: a list containing the number of possible values of

  every variable.

• nBoundsPerVar: the number of bounds to include for each variable

• depth: (optional) the current depth in the tree

• maxDepth: (optional) the maximum depth to which the tree

  will be grown

Returns

a QuantTree.QuantTreeNode with the decision tree

NOTE: This code cannot bootstrap (start from nothing...)

use _QuantTreeBoot_ (below) for that.

rdkit.ML.DecTree.BuildQuantTree.FindBest(resCodes, examples, nBoundsPerVar, nPossibleRes, nPossibleVals, attrs, exIndices=None, **kwargs)

rdkit.ML.DecTree.BuildQuantTree.QuantTreeBoot(examples, attrs, nPossibleVals, nBoundsPerVar, initialVar=None, maxDepth=-1, **kwargs)

Bootstrapping code for the QuantTree

If _initialVar_ is not set, the algorithm will automatically

choose the first variable in the tree (the standard greedy approach). Otherwise, _initialVar_ will be used as the first split.

rdkit.ML.DecTree.BuildQuantTree.TestQuantTree()

Testing code for named trees

The created pkl file is required by the unit test code.

rdkit.ML.DecTree.BuildQuantTree.TestQuantTree2()

testing code for named trees

The created pkl file is required by the unit test code.

rdkit.ML.DecTree.BuildQuantTree.TestTree()

testing code for named trees