water

Class Model<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>

java.lang.Object

water.Iced

water.Keyed

water.Lockable<M>

water.Model<M,P,O>

All Implemented Interfaces:

java.lang.Cloneable, Freezable

Direct Known Subclasses:

SupervisedModel

public abstract class Model<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>>
extends Lockable<M>

A Model models reality (hopefully). A model can be used to 'score' a row (make a prediction), or a collection of rows on any compatible dataset - meaning the row has all the columns with the same names as used to build the mode and any enum (categorical) columns can be adapted.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Model.ModelCategory
static class	Model.Output<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>> Model-specific output class.
static class	Model.Parameters<M extends Model<M,P,O>,P extends Model.Parameters<M,P,O>,O extends Model.Output<M,P,O>> Model-specific parameter class.

Field Summary

Fields

Modifier and Type	Field and Description
O	_output
P	_parms

Fields inherited from class water.Lockable

_lockers

Fields inherited from class water.Keyed

_key

Constructor Summary

Constructors

Constructor and Description
Model(Key selfKey, Frame fr, P parms, O output) Constructor from frame: Strips out the Vecs to just the names needed to match columns later for future datasets.
Model(Key selfKey, java.lang.String[] names, java.lang.String[][] domains, P parms, O output) Full constructor

Method Summary

Methods

Modifier and Type	Method and Description
Frame[]	adapt(Frame fr, boolean exact) Build an adapted Frame from the given Frame.
static int[][]	getDomainMapping(java.lang.String[] modelDom, java.lang.String[] colDom, boolean exact) Returns a mapping between values of model domains (modelDom) and given column domain.
static int[][]	getDomainMapping(java.lang.String colName, java.lang.String[] modelDom, java.lang.String[] colDom, boolean logNonExactMapping) Returns a mapping for given column according to given modelDom.
abstract ModelSchema	schema() Externally visible default schema TODO: this is in the wrong layer: the internals should not know anything about the schemas!!! This puts a reverse edge into the dependency graph.
double	score(double[] data)
Frame	score(Frame fr) Bulk score for given fr frame.
Frame	score(Frame fr, boolean adapt) Bulk score the frame fr, producing a Frame result; the 1st Vec is the predicted class, the remaining Vecs are the probability distributions.
float[]	score(Frame fr, boolean exact, int row) Single row scoring, on a compatible Frame.
float[]	score(int[][][] map, double[] row, float[] preds) Single row scoring, on a compatible set of data, given an adaption vector
float[]	score(java.lang.String[] names, java.lang.String[][] domains, boolean exact, double[] row) Single row scoring, on a compatible set of data.
protected abstract float[]	score0(Chunk[] chks, int row_in_chunk, double[] tmp, float[] preds) Bulk scoring API for one row.
protected abstract float[]	score0(double[] data, float[] preds) Subclasses implement the scoring logic.

Methods inherited from class water.Lockable

delete_and_lock, delete, delete, delete, errStr, read_lock, read_lock, unlock_all, unlock, update, write_lock

Methods inherited from class water.Keyed

remove_impl, remove, remove, remove, remove

Methods inherited from class water.Iced

clone, frozenType, read_impl, read, readJSON_impl, readJSON, write_impl, write, writeHTML_impl, writeHTML, writeJSON_impl, writeJSON

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

_parms

public P extends Model.Parameters<M,P,O> _parms

_output

public O extends Model.Output<M,P,O> _output

Constructor Detail

Model

public Model(Key selfKey,
     Frame fr,
     P parms,
     O output)

Constructor from frame: Strips out the Vecs to just the names needed to match columns later for future datasets.

Model

public Model(Key selfKey,
     java.lang.String[] names,
     java.lang.String[][] domains,
     P parms,
     O output)

Full constructor

Method Detail

schema

public abstract ModelSchema schema()

Externally visible default schema TODO: this is in the wrong layer: the internals should not know anything about the schemas!!! This puts a reverse edge into the dependency graph.

score

public final Frame score(Frame fr)

Bulk score for given fr frame. The frame is always adapted to this model.

Parameters:

fr - frame to be scored

Returns:

frame holding predicted values

See Also:

score(Frame, boolean)

score

public final Frame score(Frame fr,
          boolean adapt)

Bulk score the frame fr, producing a Frame result; the 1st Vec is the predicted class, the remaining Vecs are the probability distributions. For Regression (single-class) models, the 1st and only Vec is the prediction value. The flat adapt

Parameters:

fr - frame which should be scored

adapt - a flag enforcing an adaptation of fr to this model. If flag is false scoring code expect that fr is already adapted.

Returns:

a new frame containing a predicted values. For classification it contains a column with prediction and distribution for all response classes. For regression it contains only one column with predicted values.

score

public final float[] score(Frame fr,
            boolean exact,
            int row)

Single row scoring, on a compatible Frame.

score

public final float[] score(java.lang.String[] names,
            java.lang.String[][] domains,
            boolean exact,
            double[] row)

Single row scoring, on a compatible set of data. Fairly expensive to adapt.

score

public final float[] score(int[][][] map,
            double[] row,
            float[] preds)

Single row scoring, on a compatible set of data, given an adaption vector

adapt

public Frame[] adapt(Frame fr,
            boolean exact)

Build an adapted Frame from the given Frame. Useful for efficient bulk scoring of a new dataset to an existing model. Same adaption as above, but expressed as a Frame instead of as an int[][]. The returned Frame does not have a response column. It returns a two element array containing an adapted frame and a frame which contains only vectors which where adapted (the purpose of the second frame is to delete all adapted vectors with deletion of the frame).

getDomainMapping

public static int[][] getDomainMapping(java.lang.String[] modelDom,
                       java.lang.String[] colDom,
                       boolean exact)

Returns a mapping between values of model domains (modelDom) and given column domain.

See Also:

getDomainMapping(String, String[], String[], boolean)

getDomainMapping

public static int[][] getDomainMapping(java.lang.String colName,
                       java.lang.String[] modelDom,
                       java.lang.String[] colDom,
                       boolean logNonExactMapping)

Returns a mapping for given column according to given modelDom. In this case, modelDom is

Parameters:

colName - name of column which is mapped, can be null.

modelDom -

logNonExactMapping -

Returns:

score0

protected abstract float[] score0(Chunk[] chks,
             int row_in_chunk,
             double[] tmp,
             float[] preds)

Bulk scoring API for one row. Chunks are all compatible with the model, and expect the last Chunks are for the final distribution and prediction. Default method is to just load the data into the tmp array, then call subclass scoring logic.

score0

protected abstract float[] score0(double[] data,
             float[] preds)

Subclasses implement the scoring logic. The data is pre-loaded into a re-used temp array, in the order the model expects. The predictions are loaded into the re-used temp array, which is also returned.

score

public double score(double[] data)