Package org.openscience.cdk.fingerprint.model

Class Bayesian

  • public class Bayesian
extends Object
    • Bayesian models using fingerprints: provides model creation, analysis, prediction and serialisation.
    • Uses a variation of the classic Bayesian model, using a Laplacian correction, which sums log values of ratios rather than multiplying them together. This is an effective way to work with large numbers of fingerprints without running into extreme numerical precision issues, but it also means that the outgoing predictor is an arbitrary value rather than a probability, which introduces the need for an additional calibration step prior to interpretation.
    • For more information about the method, see: J. Chem. Inf. Model, v.46, pp.1124-1133 (2006) J. Biomol. Screen., v.10, pp.682-686 (2005) Molec. Divers., v.10, pp.283-299 (2006)
    • Currently only the CircularFingerprinter fingerprints are supported (i.e. ECFP_n and FCFP_n).
    • Model building is done by selecting the fingerprinting method and folding size, then providing a series of molecules & responses. Individual model contributions are kept around in order to produce the analysis data (e.g. the ROC curve), but is discarded during serialise/deserialise cycles.
    • Fingerprint "folding" is optional, but recommended, because it places an upper limit on the model size. If folding is not used (folding=0) then the entire 32-bits are used, which means that in the diabolical case, the number of Bayesian contributions that needs to be stored is 4 billion. In practice the improvement in predictivity tends to plateaux out at around 1024 bits, so values of 2048 or 4096 are generally safe. Folding values must be integer powers of 2.
    Author:
    am.clark
    Source code:
    main
    Belongs to CDK module:
    standard
    Keywords:
    fingerprint, bayesian, model
    Created on:
    2015-01-05

    • Constructor Summary

      Constructors 
      Constructor Description
      Bayesian​(int classType)
      Instantiate a Bayesian model with no data.
      Bayesian​(int classType, int folding)
      Instantiate a Bayesian model with no data.

    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      void addMolecule​(IAtomContainer mol, boolean active)
      Appends a new row to the model source data, which consists of a molecule and whether or not it is considered active.
      void build()
      Performs that Bayesian model generation, using the {molecule:activity} pairs that have been submitted up to this point.
      void clearTraining()
      Clears out the training set, to free up memory.
      static Bayesian deserialise​(BufferedReader rdr)
      Reads the incoming stream and attempts to convert it into an instantiated model.
      static Bayesian deserialise​(String str)
      Converts a given string into a Bayesian model instance, or throws an exception if it is not valid.
      int getClassType()
      Access to the fingerprint type.
      int getFolding()
      Access to the fingerprint folding extent.
      String[] getNoteComments()
      Returns the optional comments, which is a list of arbitrary text strings.
      String getNoteOrigin()
      Returns the optional description of the source for the model.
      String getNoteTitle()
      Returns the optional title used to describe the model.
      double getROCAUC()
      Returns the integral of the area-under-the-curve of the receiver-operator-characteristic.
      String getROCType()
      Returns a string description of the method used to create the ROC curve (e.g.
      float[] getRocX()
      Returns X-values that can be used to plot the ROC-curve.
      float[] getRocY()
      Returns Y-values that can be used to plot the ROC-curve.
      int getTrainingActives()
      Returns the number of actives in the training set that was used to create the model.
      int getTrainingSize()
      Returns the size of the training set, i.e.
      double predict​(IAtomContainer mol)
      For a given molecule, determines its fingerprints and uses them to calculate a Bayesian prediction.
      double scalePredictor​(double pred)
      Converts a raw Bayesian prediction and transforms it into a probability-like range, i.e.
      String serialise()
      Converts the current model into a serialised string representation.
      void setNoteComments​(String[] comments)
      Sets the comments for the model, which is a list of strings containing arbitrary content.
      void setNoteOrigin​(String origin)
      Provides an arbitrary string that briefly describes the model origin, which may include authors, data source keywords, or other pertinent information.
      void setNoteTitle​(String title)
      Provides an arbitrary title string that briefly summarises the model.
      void setPerceiveStereo​(boolean val)
      Sets whether stereochemistry should be re-perceived from 2D/3D coordinates.
      void validateFiveFold()
      Produces a ROC validation set by partitioning the inputs into 5 groups, and performing five separate 80% in/20% out model simulations.
      void validateLeaveOneOut()
      Produces an ROC validation set, using the inputs provided prior to the model building, using leave-one-out.
      void validateThreeFold()
      Produces a ROC validation set by partitioning the inputs into 3 groups, and performing three separate 66% in/33% out model simulations.

    • Field Detail

      • inHash

        protected final Map<Integer,​int[]> inHash

      • training

        protected final ArrayList<int[]> training

      • lowThresh

        protected double lowThresh

      • highThresh

        protected double highThresh

      • range

        protected double range

      • invRange

        protected double invRange

      • estimates

        protected double[] estimates

      • rocX

        protected float[] rocX

      • rocY

        protected float[] rocY

      • rocType

        protected String rocType

      • rocAUC

        protected double rocAUC

      • trainingSize

        protected int trainingSize

      • trainingActives

        protected int trainingActives

    • Constructor Detail

      • Bayesian

        public Bayesian​(int classType)
        Instantiate a Bayesian model with no data.
        Parameters:
        classType - one of the CircularFingerprinter.CLASS_* constants

      • Bayesian

        public Bayesian​(int classType,
                int folding)
        Instantiate a Bayesian model with no data. * @param classType one of the CircularFingerprinter.CLASS_* constants
        Parameters:
        folding - the maximum number of fingerprint bits, which must be a power of 2 (e.g. 1024, 2048) or 0 for no folding

    • Method Detail

      • setPerceiveStereo

        public void setPerceiveStereo​(boolean val)
        Sets whether stereochemistry should be re-perceived from 2D/3D coordinates. By default stereochemistry encoded as IStereoElements are used.
        Parameters:
        val - perceived from 2D

      • getClassType

        public int getClassType()
        Access to the fingerprint type.
        Returns:
        fingerprint class, one of CircularFingerprinter.CLASS_*

      • getFolding

        public int getFolding()
        Access to the fingerprint folding extent.
        Returns:
        folding extent, either 0 (for none) or a power of 2

      • addMolecule

        public void addMolecule​(IAtomContainer mol,
                        boolean active)
                 throws CDKException
        Appends a new row to the model source data, which consists of a molecule and whether or not it is considered active.
        Parameters:
        mol - molecular structure, which must be non-blank
        active - whether active or not
        Throws:
        CDKException

      • build

        public void build()
           throws CDKException
        Performs that Bayesian model generation, using the {molecule:activity} pairs that have been submitted up to this point. Once this method has finished, the object can be used to generate predictions, validation data or to serialise for later use.
        Throws:
        CDKException

      • predict

        public double predict​(IAtomContainer mol)
               throws CDKException
        For a given molecule, determines its fingerprints and uses them to calculate a Bayesian prediction. Note that this value is unscaled, and so it only has relative meaning within the confines of the model, i.e. higher is more likely to be active.
        Parameters:
        mol - molecular structure which cannot be blank or null
        Returns:
        predictor value
        Throws:
        CDKException

      • scalePredictor

        public double scalePredictor​(double pred)
        Converts a raw Bayesian prediction and transforms it into a probability-like range, i.e. most values within the domain are between 0..1, and assigning a cutoff of activie = scaled_prediction > 0.5 is reasonable. The transform (scale/translation) is determined by the ROC-analysis, if any. The resulting value can be used as a probability by capping the values so that 0 ≤ p ≤ 1.
        Parameters:
        pred - raw prediction, as provided by the predict(..) method
        Returns:
        scaled prediction

      • validateLeaveOneOut

        public void validateLeaveOneOut()
        Produces an ROC validation set, using the inputs provided prior to the model building, using leave-one-out. Note that this should only be used for small datasets, since it is very thorough, and scales as O(N^2) relative to training set size.

      • validateFiveFold

        public void validateFiveFold()
        Produces a ROC validation set by partitioning the inputs into 5 groups, and performing five separate 80% in/20% out model simulations. This is quite efficient, and takes approximately 5 times as long as building the original model: it should be used for larger datasets.

      • validateThreeFold

        public void validateThreeFold()
        Produces a ROC validation set by partitioning the inputs into 3 groups, and performing three separate 66% in/33% out model simulations. This is quite efficient, and takes approximately 3 times as long as building the original model: it should be used for larger datasets.

      • clearTraining

        public void clearTraining()
        Clears out the training set, to free up memory.

      • getTrainingSize

        public int getTrainingSize()
        Returns the size of the training set, i.e. the total number of molecules used to create the model.
        Returns:
        training set size

      • getTrainingActives

        public int getTrainingActives()
        Returns the number of actives in the training set that was used to create the model.
        Returns:
        actives in training set

      • getROCAUC

        public double getROCAUC()
        Returns the integral of the area-under-the-curve of the receiver-operator-characteristic. A value of 1 means perfect recall, 0.5 is pretty much random.
        Returns:
        ROC area under the curve, between 0 and 1

      • getROCType

        public String getROCType()
        Returns a string description of the method used to create the ROC curve (e.g. "leave-one-out" or "five-fold").
        Returns:
        validation method

      • getRocX

        public float[] getRocX()
        Returns X-values that can be used to plot the ROC-curve.

      • getRocY

        public float[] getRocY()
        Returns Y-values that can be used to plot the ROC-curve.

      • getNoteTitle

        public String getNoteTitle()
        Returns the optional title used to describe the model.
        Returns:
        title (may be null)

      • setNoteTitle

        public void setNoteTitle​(String title)
        Provides an arbitrary title string that briefly summarises the model.
        Parameters:
        title - short text description (no newlines or tabs); use null if none

      • getNoteOrigin

        public String getNoteOrigin()
        Returns the optional description of the source for the model.
        Returns:
        origin (may be null)

      • setNoteOrigin

        public void setNoteOrigin​(String origin)
        Provides an arbitrary string that briefly describes the model origin, which may include authors, data source keywords, or other pertinent information.
        Parameters:
        origin - short text description (no newlines or tabs); use null if none

      • getNoteComments

        public String[] getNoteComments()
        Returns the optional comments, which is a list of arbitrary text strings.
        Returns:
        comment list (may be null)

      • setNoteComments

        public void setNoteComments​(String[] comments)
        Sets the comments for the model, which is a list of strings containing arbitrary content. This may embellish upon the title or origin, or provide other human-readable information, such as references and links.
        Parameters:
        comments - list of strings; use null or empty array if none

      • serialise

        public String serialise()
        Converts the current model into a serialised string representation. The serialised form omits the original data that was used to build the model, but otherwise contains all of the information necessary to recreate the model and use it to make predictions against new molecules. The format used is a concise text-based format that is easy to recognise by its prefix, and is reasonably efficient with regard to storage space.
        Returns:
        serialised model

      • deserialise

        public static Bayesian deserialise​(String str)
                            throws IOException
        Converts a given string into a Bayesian model instance, or throws an exception if it is not valid.
        Parameters:
        str - string containing the serialised model
        Returns:
        instantiated model that can be used for predictions
        Throws:
        IOException

      • deserialise

        public static Bayesian deserialise​(BufferedReader rdr)
                            throws IOException
        Reads the incoming stream and attempts to convert it into an instantiated model. The input most be compatible with the format used by the serialise() method, otherwise an exception will be thrown.
        Parameters:
        rdr - reader
        Returns:
        instantiated model that can be used for predictions
        Throws:
        IOException