rdkit.ML.BuildComposite module — The RDKit 2016.09.1 documentation

# rdkit.ML.BuildComposite module

command line utility for building composite models

#DOC

Usage

> BuildComposite [optional args] filename

Unless indicated otherwise (via command line arguments), _filename_ is a QDAT file.

Command Line Arguments

> - -o filename: name of the output file for the pickled composite

> - -n num: number of separate models to add to the composite

> - -p tablename: store persistence data in the database

>     in table tablename

> - -N note: attach some arbitrary text to the persistence data

> - -b filename: name of the text file to hold examples from the

>     holdout set which are misclassified

> - -s: split the data into training and hold-out sets before building

>     the composite

> - -f frac: the fraction of data to use in the training set when the

>     data is split

> - -r: randomize the activities (for testing purposes). This ignores

>     the initial distribution of activity values and produces each possible activity value with equal likliehood.

> - -S: shuffle the activities (for testing purposes) This produces

>     a permutation of the input activity values.

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

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

> - -B: use a so-called Bayesian composite model.

> - -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.

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

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

> - -P pickle file name: write out the pickled data set to the file

> - -F filter frac: filters the data before training to change the

>     distribution of activity values in the training set. filter frac is the fraction of the training set that should have the target value. See note below on data filtering.

> - -v filter value: filters the data before training to change the

>     distribution of activity values in the training set. filter value is the target value to use in filtering. See note below on data filtering.

> - –modelFiltFrac model filter frac: Similar to filter frac above,

>     in this case the data is filtered for each model in the composite rather than a single overall filter for a composite. model filter frac is the fraction of the training set for each model that should have the target value (model filter value).

> - –modelFiltVal model filter value: target value to use for

>     filtering data before training each model in the composite.

> - -t threshold value: use high-confidence predictions for the

>     final analysis of the hold-out data.

> - -Q list string: the values of quantization bounds for the

>     activity value. See the _-q_ argument for the format of list string.

> - –nRuns count: build count composite models

> - –prune: prune any models built

> - -h: print a usage message and exit.

> - -V: print the version number and exit

> -------- Tree-Related Options --------

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

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

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

>     complexity

> - -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.

> - –recycle: allow descriptors to be used more than once in a tree

> - –randomDescriptors=val: toggles growing random forests with val

>     randomly-selected descriptors available at each node.

> -------- KNN-Related Options --------

> - –doKnn: use K-Nearest Neighbors models
> - –knnK=*value*: the value of K to use in the KNN models
> - –knnTanimoto: use the Tanimoto metric in KNN models
> - –knnEuclid: use a Euclidean metric in KNN models

> ------- Naive Bayes Classifier Options --------* - –doNaiveBayes : use Naive Bayes classifiers

> - –mEstimateVal
>   : the value to be used in the m-estimate formula

>     If this is greater than 0.0, we use it to compute the conditional probabilities by the m-estimate

> -------- SVM-Related Options --------

> **** NOTE: THESE ARE DISABLED ****

# # - –doSVM: use Support-vector machines

# # - –svmKernel=*kernel*: choose the type of kernel to be used for # # the SVMs. Options are: # # The default is:

# # - –svmType=*type*: choose the type of support-vector machine # # to be used. Options are: # # The default is:

# # - –svmGamma=*gamma*: provide the gamma value for the SVMs. If this # # is not provided, a grid search will be carried out to determine an # # optimal gamma value for each SVM.

# # - –svmCost=*cost*: provide the cost value for the SVMs. If this is # # not provided, a grid search will be carried out to determine an # # optimal cost value for each SVM.

# # - –svmWeights=*weights*: provide the weight values for the # # activities. If provided this should be a sequence of (label, # # weight) 2-tuples nActs long. If not provided, a weight of 1 # # will be used for each activity.

# # - –svmEps=*epsilon*: provide the epsilon value used to determine # # when the SVM has converged. Defaults to 0.001

# # - –svmDegree=*degree*: provide the degree of the kernel (when # # sensible) Defaults to 3

# # - –svmCoeff=*coeff*: provide the coefficient for the kernel (when # # sensible) Defaults to 0

# # - –svmNu=*nu*: provide the nu value for the kernel (when sensible) # # Defaults to 0.5

# # - –svmDataType=*float*: if the data is contains only 1 and 0 s, specify by # # using binary. Defaults to float

# # - –svmCache=*cache*: provide the size of the memory cache (in MB) # # to be used while building the SVM. Defaults to 40

Notes

> - Data filtering: When there is a large disparity between the numbers of points with various activity levels present in the training set it is sometimes desirable to train on a more homogeneous data set. This can be accomplished using filtering. The filtering process works by selecting a particular target fraction and target value. For example, in a case where 95% of the original training set has activity 0 and ony 5% activity 1, we could filter (by randomly removing points with activity 0) so that 30% of the data set used to build the composite has activity 1.

`rdkit.ML.BuildComposite.` `GetCommandLine`(details)

  #DOC

`rdkit.ML.BuildComposite.` `ParseArgs`(runDetails)

  parses command line arguments and updates _runDetails_

  Arguments

  > - runDetails: a _CompositeRun.CompositeRun_ object.

`rdkit.ML.BuildComposite.` `RunIt`(details, progressCallback=None, saveIt=1, setDescNames=0)

  does the actual work of building a composite model

  Arguments

  > - details: a _CompositeRun.CompositeRun_ object containing details (options, parameters, etc.) about the run
  > - 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()_.

  Returns

  > the composite model constructed

`rdkit.ML.BuildComposite.` `RunOnData`(details, data, progressCallback=None, saveIt=1, setDescNames=0)

`rdkit.ML.BuildComposite.` `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.BuildComposite.` `ShowVersion`(includeArgs=0)

  prints the version number

`rdkit.ML.BuildComposite.` `Usage`()

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

`rdkit.ML.BuildComposite.` `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

`rdkit.ML.BuildComposite.` `testall`(composite, examples, badExamples=[])

  screens a number of examples past a composite

  Arguments

  > - composite: a composite model
  > - examples: a list of examples (with results) to be screened
  > - badExamples: a list to which misclassified examples are appended

  Returns

  > a list of 2-tuples containing:

  > > 1. a vote
  > > 2. a confidence

  > these are the votes and confidence levels for misclassified examples