rdkit.ML.DecTree.ID3

Package rdkit :: Package ML :: Package DecTree :: Module ID3 |
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# Module ID3

source code

    ID3 Decision Trees

    contains an implementation of the ID3 decision tree algorithm
    as described in Tom Mitchell's book "Machine Learning"

    It relies upon the _Tree.TreeNode_ data structure (or something
      with the same API) defined locally to represent the trees

Functions | [hide private]
CalcTotalEntropy(examples, nPossibleVals) Calculates the total entropy of the data set (w.r.t. | source code
GenVarTable(examples, nPossibleVals, vars) Generates a list of variable tables for the examples passed in. | source code
ID3(examples, target, attrs, nPossibleVals, depth=0, maxDepth=-1, **kwargs) Implements the ID3 algorithm for constructing decision trees. | source code
ID3Boot(examples, attrs, nPossibleVals, initialVar=None, depth=0, maxDepth=-1, **kwargs) Bootstrapping code for the ID3 algorithm | source code

Variables | [hide private]
| __package__ = ``'``rdkit.ML.DecTree``'``

Imports: numpy, DecTree, entropy, range, xrange

Function Details | [hide private]

### CalcTotalEntropy(examples, nPossibleVals) | source code
    Calculates the total entropy of the data set (w.r.t. the results)

    **Arguments**

     - examples: a list (nInstances long) of lists of variable values + instance
               values
     - nPossibleVals: a list (nVars long) of the number of possible values each variable
       can adopt.

    **Returns**

      a float containing the informational entropy of the data set.

### GenVarTable(examples, nPossibleVals, vars) | source code
    Generates a list of variable tables for the examples passed in.

      The table for a given variable records the number of times each possible value
      of that variable appears for each possible result of the function.

    **Arguments**

      - examples: a list (nInstances long) of lists of variable values + instance
                values

      - nPossibleVals: a list containing the number of possible values of
                     each variable + the number of values of the function.

      - vars:  a list of the variables to include in the var table

    **Returns**

        a list of variable result tables. Each table is a Numeric array
          which is varValues x nResults

### ID3(examples, target, attrs, nPossibleVals, depth=0, maxDepth=-1, **kwargs) | source code
    Implements the ID3 algorithm for constructing decision trees.

    From Mitchell's book, page 56

    This is *slightly* modified from Mitchell's book because it supports
      multivalued (non-binary) results.

    **Arguments**

      - examples: a list (nInstances long) of lists 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.

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

      - maxDepth: (optional) the maximum depth to which the tree
                   will be grown

    **Returns**

     a DecTree.DecTreeNode with the decision tree

    **NOTE:** This code cannot bootstrap (start from nothing...)
          use _ID3Boot_ (below) for that.

### ID3Boot(examples, attrs, nPossibleVals, initialVar=None, depth=0, maxDepth=-1, **kwargs) | source code
    Bootstrapping code for the ID3 algorithm

    see ID3 for descriptions of the arguments

    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.