Package rdkit :: Package ML

Package ML

module containing machine learning code

Submodules

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rdkit.ML.AnalyzeComposite: command line utility to report on the contributions of descriptors to tree-based composite models
rdkit.ML.BuildComposite: command line utility for building composite models
rdkit.ML.Cluster
- rdkit.ML.Cluster.Butina: Implementation of the clustering algorithm published in:...
- rdkit.ML.Cluster.ClusterUtils: utility functions for clustering
- rdkit.ML.Cluster.ClusterVis: Cluster tree visualization using Sping
- rdkit.ML.Cluster.Clustering
- rdkit.ML.Cluster.Clusters: contains the Cluster class for representing hierarchical cluster trees
- rdkit.ML.Cluster.Murtagh: Interface to the C++ Murtagh hierarchic clustering code
- rdkit.ML.Cluster.Resemblance: code for dealing with resemblance (metric) matrices
- rdkit.ML.Cluster.Standardize: contains code for standardization of data matrices for clustering
rdkit.ML.Composite
- rdkit.ML.Composite.AdjustComposite: functionality to allow adjusting composite model contents
- rdkit.ML.Composite.BayesComposite: code for dealing with Bayesian composite models
- rdkit.ML.Composite.Composite: code for dealing with composite models
- rdkit.ML.Composite.CompositeCOM: the COM server for Composite models
rdkit.ML.CompositeRun: contains a class to store parameters for and results from...
rdkit.ML.Data
- rdkit.ML.Data.DataUtils: Utilities for data manipulation
- rdkit.ML.Data.FindQuantBounds
- rdkit.ML.Data.MLData: classes to be used to help work with data sets
- rdkit.ML.Data.Quantize: Automatic search for quantization bounds
- rdkit.ML.Data.SplitData
- rdkit.ML.Data.Stats: various statistical operations on data
- rdkit.ML.Data.Transforms
- rdkit.ML.Data.cQuantize
rdkit.ML.DecTree: Here we're implementing the Decision Tree stuff found in Chapter 3 of Tom Mitchell's Machine Learning Book.
- rdkit.ML.DecTree.BuildQuantTree:
- rdkit.ML.DecTree.BuildSigTree:
- rdkit.ML.DecTree.CrossValidate: handles doing cross validation with decision trees
- rdkit.ML.DecTree.DecTree: Defines the class _DecTreeNode_, used to represent decision trees
- rdkit.ML.DecTree.Forest: code for dealing with forests (collections) of decision trees
- rdkit.ML.DecTree.ID3: ID3 Decision Trees
- rdkit.ML.DecTree.PruneTree: Contains functionality for doing tree pruning
- rdkit.ML.DecTree.QuantTree: Defines the class _QuantTreeNode_, used to represent decision trees with automatic quantization bounds
- rdkit.ML.DecTree.SigTree: Defines the class SigTreeNode, used to represent trees that use signatures (bit vectors) to represent data.
- rdkit.ML.DecTree.Tree: Implements a class used to represent N-ary trees
- rdkit.ML.DecTree.TreeUtils: Utilities for working with trees
- rdkit.ML.DecTree.TreeVis: functionality for drawing trees on sping canvases
rdkit.ML.Descriptors
- rdkit.ML.Descriptors.CompoundDescriptors: descriptor calculator for compounds defined by a composition alone...
- rdkit.ML.Descriptors.Descriptors: Various bits and pieces for calculating descriptors
- rdkit.ML.Descriptors.DescriptorsCOM: the COM server for Descriptor Calculators
- rdkit.ML.Descriptors.MoleculeDescriptors: Various bits and pieces for calculating Molecular descriptors
- rdkit.ML.Descriptors.Parser: The "parser" for compound descriptors.
rdkit.ML.EnrichPlot: Command line tool to construct an enrichment plot from saved composite models
rdkit.ML.FeatureSelect
- rdkit.ML.FeatureSelect.CMIM: for the moment this is using Francois Fleuret's cmim library to do the feature selection
- rdkit.ML.FeatureSelect.rdFeatSelect: Module containing functions for feature selection
rdkit.ML.GrowComposite: command line utility for growing composite models
rdkit.ML.InfoTheory: Information Theory functionality
- rdkit.ML.InfoTheory.BitClusterer
- rdkit.ML.InfoTheory.BitRank: Functionality for ranking bits using info gains
- rdkit.ML.InfoTheory.cEntropy
- rdkit.ML.InfoTheory.entropy: Informational Entropy functions
- rdkit.ML.InfoTheory.rdInfoTheory: Module containing bunch of functions for information metrics and a ranker to rank bits
rdkit.ML.KNN:
- rdkit.ML.KNN.CrossValidate: handles doing cross validation with k-nearest neighbors model
- rdkit.ML.KNN.DistFunctions
- rdkit.ML.KNN.KNNClassificationModel: Define the class _KNNClassificationModel_, used to represent a k-nearest neighbhors classification model
- rdkit.ML.KNN.KNNModel: Define the class _KNNModel_, used to represent a k-nearest neighbhors model
- rdkit.ML.KNN.KNNRegressionModel: Define the class _KNNRegressionModel_, used to represent a k-nearest neighbhors regression model
rdkit.ML.MLUtils
- rdkit.ML.MLUtils.VoteImg: functionality for generating an image showing the results of a composite model voting on a data set
rdkit.ML.MatOps: Matrix operations which may or may not come in handy some day
rdkit.ML.ModelPackage
- rdkit.ML.ModelPackage.PackageUtils
- rdkit.ML.ModelPackage.Packager
rdkit.ML.NaiveBayes:
- rdkit.ML.NaiveBayes.ClassificationModel: Defines Naive Baysean classification model...
- rdkit.ML.NaiveBayes.CrossValidate: handles doing cross validation with naive bayes models...
rdkit.ML.Neural
- rdkit.ML.Neural.ActFuncs: Activation functions for neural network nodes
- rdkit.ML.Neural.CrossValidate: handles doing cross validation with neural nets
- rdkit.ML.Neural.NetNode: Contains the class _NetNode_ which is used to represent nodes in neural nets
- rdkit.ML.Neural.Network: Contains the class _Network_ which is used to represent neural nets
- rdkit.ML.Neural.Trainers: Training algorithms for feed-forward neural nets
rdkit.ML.SLT
- rdkit.ML.SLT.Risk: code for calculating empirical risk
rdkit.ML.ScreenComposite: command line utility for screening composite models
rdkit.ML.files: Generic file manipulation stuff

Variables

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__package__ = None

Imports: Cluster, Data, InfoTheory