Bayesian (cdk 1.5.15 API)

java.lang.Object
- org.openscience.cdk.fingerprint.model.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:
master

Belongs to CDK module:
standard

Keywords:

fingerprint, bayesian, model

Created on:

2015-01-05

Field Summary

Fields
Modifier and Type	Field and Description
`protected ArrayList<Boolean>`	`activity`
`protected Map<Integer,Double>`	`contribs`
`protected double[]`	`estimates`
`protected double`	`highThresh`
`protected Map<Integer,int[]>`	`inHash`
`protected double`	`invRange`
`protected double`	`lowThresh`
`protected double`	`range`
`protected double`	`rocAUC`
`protected String`	`rocType`
`protected float[]`	`rocX`
`protected float[]`	`rocY`
`protected ArrayList<int[]>`	`training`
`protected int`	`trainingActives`
`protected int`	`trainingSize`

Constructor Summary

Constructors
Constructor and 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

Methods
Modifier and Type	Method and 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`	`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.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - inHash
```
protected Map<Integer,int[]> inHash
```
  - training
```
protected ArrayList<int[]> training
```
  - activity
```
protected ArrayList<Boolean> activity
```
  - contribs
```
protected Map<Integer,Double> contribs
```
  - 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
  - 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

Class Bayesian

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

inHash

training

activity

contribs

lowThresh

highThresh

range

invRange

estimates

rocX

rocY

rocType

rocAUC

trainingSize

trainingActives

Constructor Detail

Bayesian

Bayesian

Method Detail

getClassType

getFolding

addMolecule

build

predict

scalePredictor

validateLeaveOneOut

validateFiveFold

validateThreeFold

clearTraining

getTrainingSize

getTrainingActives

getROCAUC

getROCType

getRocX

getRocY

getNoteTitle

setNoteTitle

getNoteOrigin

setNoteOrigin

getNoteComments

setNoteComments

serialise

deserialise

deserialise