rdkit.ML.Composite.BayesComposite module — The RDKit 2016.09.1 documentation

# rdkit.ML.Composite.BayesComposite module

code for dealing with Bayesian composite models

For a model to be useable here, it should support the following API:

> - _ClassifyExample(example)_, returns a classification

Other compatibility notes:

> 1. To use _Composite.Grow_ there must be some kind of builder functionality which returns a 2-tuple containing (model,percent accuracy).
> 2. The models should be pickleable
> 3. It would be very happy if the models support the __cmp__ method so that membership tests used to make sure models are unique work.

class `rdkit.ML.Composite.BayesComposite.` `BayesComposite`

  Bases: `rdkit.ML.Composite.Composite.Composite`

  a composite model using Bayesian statistics in the Decision Proxy

  Notes

  - typical usage:

    > 1. grow the composite with AddModel until happy with it
    > 2. call AverageErrors to calculate the average error values
    > 3. call SortModels to put things in order by either error or count
    > 4. call Train to update the Bayesian stats.

  `ClassifyExample`(example, threshold=0, verbose=0, appendExample=0)

    classifies the given example using the entire composite

    Arguments

    > - example: the data to be classified

    > - threshold: if this is a number greater than zero, then a

    >     classification will only be returned if the confidence is above _threshold_. Anything lower is returned as -1.

    Returns

    > a (result,confidence) tuple

  `Train`(data, verbose=0)

`rdkit.ML.Composite.BayesComposite.` `BayesCompositeToComposite`(obj)

  converts a BayesComposite to a Composite.Composite

`rdkit.ML.Composite.BayesComposite.` `CompositeToBayesComposite`(obj)

  converts a Composite to a BayesComposite

  if _obj_ is already a BayesComposite or if it is not a _Composite.Composite_ ,
    nothing will be done.