index
int64 | repo_id
string | file_path
string | content
string |
|---|---|---|---|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameBaseV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameBaseV3 extends RequestSchemaV3 {
/**
* Frame ID
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Total data size in bytes
*/
@SerializedName("byte_size")
public long byteSize;
/**
* Is this Frame raw unparsed data?
*/
@SerializedName("is_text")
public boolean isText;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FrameBaseV3() {
byteSize = 0L;
isText = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameChunkV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameChunkV3 extends SchemaV3 {
/**
* An identifier unique in scope of a given frame
*/
@SerializedName("chunk_id")
public int chunkId;
/**
* Number of rows represented byt the chunk
*/
@SerializedName("row_count")
public int rowCount;
/**
* Index of H2O node where the chunk is located in
*/
@SerializedName("node_idx")
public int nodeIdx;
/**
* Public constructor
*/
public FrameChunkV3() {
chunkId = 0;
rowCount = 0;
nodeIdx = 0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameChunksV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameChunksV3 extends SchemaV3 {
/**
* ID of a given frame
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Description of particular chunks
*/
public FrameChunkV3[] chunks;
/**
* Public constructor
*/
public FrameChunksV3() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameKeyV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameKeyV3 extends KeyV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Name (string representation) for this Key.
public String name;
// Name (string representation) for the type of Keyed this Key points to.
public String type;
// URL for the resource that this Key points to, if one exists.
public String url;
*/
/**
* Public constructor
*/
public FrameKeyV3() {
name = "";
type = "";
url = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameLoadV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameLoadV3 extends RequestSchemaV3 {
/**
* Import frame under given key into DKV.
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Source directory (hdfs, s3, local) containing serialized frame
*/
public String dir;
/**
* Override existing frame in case it exists or throw exception if set to false
*/
public boolean force;
/**
* Job indicating progress
*/
public JobV3 job;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FrameLoadV3() {
dir = "";
force = true;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameSaveV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameSaveV3 extends RequestSchemaV3 {
/**
* Name of Frame of interest
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Destination directory (hdfs, s3, local)
*/
public String dir;
/**
* Overwrite destination file in case it exists or throw exception if set to false.
*/
public boolean force;
/**
* Job indicating progress
*/
public JobV3 job;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FrameSaveV3() {
dir = "";
force = true;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameSynopsisV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameSynopsisV3 extends FrameBaseV3 {
/**
* Number of rows in the Frame
*/
public long rows;
/**
* Number of columns in the Frame
*/
public long columns;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Frame ID
public FrameKeyV3 frameId;
// Total data size in bytes
public long byteSize;
// Is this Frame raw unparsed data?
public boolean isText;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FrameSynopsisV3() {
rows = 0L;
columns = 0L;
byteSize = 0L;
isText = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FrameV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FrameV3 extends FrameBaseV3 {
/**
* Row offset to display
*/
@SerializedName("row_offset")
public long rowOffset;
/**
* Number of rows to display
*/
@SerializedName("row_count")
public int rowCount;
/**
* Column offset to return
*/
@SerializedName("column_offset")
public int columnOffset;
/**
* Number of columns to return
*/
@SerializedName("column_count")
public int columnCount;
/**
* Number of full columns to return. The columns between full_column_count and column_count will be returned without
* the data
*/
@SerializedName("full_column_count")
public int fullColumnCount;
/**
* Total number of columns in the Frame
*/
@SerializedName("total_column_count")
public int totalColumnCount;
/**
* checksum
*/
public long checksum;
/**
* Number of rows in the Frame
*/
public long rows;
/**
* Number of columns in the Frame
*/
@SerializedName("num_columns")
public long numColumns;
/**
* Default percentiles, from 0 to 1
*/
@SerializedName("default_percentiles")
public double[] defaultPercentiles;
/**
* Columns in the Frame
*/
public ColV3[] columns;
/**
* Compatible models, if requested
*/
@SerializedName("compatible_models")
public String[] compatibleModels;
/**
* Chunk summary
*/
@SerializedName("chunk_summary")
public TwoDimTableV3 chunkSummary;
/**
* Distribution summary
*/
@SerializedName("distribution_summary")
public TwoDimTableV3 distributionSummary;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Frame ID
public FrameKeyV3 frameId;
// Total data size in bytes
public long byteSize;
// Is this Frame raw unparsed data?
public boolean isText;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FrameV3() {
rowOffset = 0L;
rowCount = 0;
columnOffset = 0;
columnCount = 0;
fullColumnCount = 0;
totalColumnCount = 0;
checksum = 0L;
rows = 0L;
numColumns = 0L;
byteSize = 0L;
isText = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FramesListV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FramesListV3 extends RequestSchemaV3 {
/**
* Name of Frame of interest
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Frames
*/
public FrameBaseV3[] frames;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FramesListV3() {
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FramesV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FramesV3 extends RequestSchemaV3 {
/**
* Name of Frame of interest
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Name of column of interest
*/
public String column;
/**
* Row offset to return
*/
@SerializedName("row_offset")
public long rowOffset;
/**
* Number of rows to return
*/
@SerializedName("row_count")
public int rowCount;
/**
* Column offset to return
*/
@SerializedName("column_offset")
public int columnOffset;
/**
* Number of full columns to return. The columns between full_column_count and column_count will be returned without
* the data
*/
@SerializedName("full_column_count")
public int fullColumnCount;
/**
* Number of columns to return
*/
@SerializedName("column_count")
public int columnCount;
/**
* Find and return compatible models?
*/
@SerializedName("find_compatible_models")
public boolean findCompatibleModels;
/**
* File output path
*/
public String path;
/**
* Overwrite existing file
*/
public boolean force;
/**
* Number of part files to use (1=single file,-1=automatic)
*/
@SerializedName("num_parts")
public int numParts;
/**
* Use parallel export to a single file (doesn't apply when num_parts != 1, creates temporary files in the
* destination directory)
*/
public boolean parallel;
/**
* Output file format. Defaults to 'csv'.
*/
public UtilExportFileFormat format;
/**
* Compression method (default none; gzip, bzip2, zstd and snappy available depending on runtime environment)
*/
public String compression;
/**
* Specifies if checksum should be written next to data files on export (if supported by export format).
*/
@SerializedName("write_checksum")
public boolean writeChecksum;
/**
* Specifies if the timezone should be adjusted from local to UTC timezone (parquet only).
*/
@SerializedName("tz_adjust_from_local")
public boolean tzAdjustFromLocal;
/**
* Field separator (default ',')
*/
public byte separator;
/**
* Use header (default true)
*/
public boolean header;
/**
* Quote column names in header line (default true)
*/
@SerializedName("quote_header")
public boolean quoteHeader;
/**
* Job for export file
*/
public JobV3 job;
/**
* Frames
*/
public FrameV3[] frames;
/**
* Compatible models
*/
@SerializedName("compatible_models")
public ModelSchemaV3[] compatibleModels;
/**
* Domains
*/
public String[][] domain;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FramesV3() {
column = "";
rowOffset = 0L;
rowCount = -1;
columnOffset = 0;
fullColumnCount = -1;
columnCount = -1;
findCompatibleModels = false;
path = "";
force = false;
numParts = 1;
parallel = false;
compression = "";
writeChecksum = true;
tzAdjustFromLocal = false;
separator = 44;
header = true;
quoteHeader = true;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FriedmanPopescusHV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FriedmanPopescusHV3 extends RequestSchemaV3 {
/**
* Model id of interest
*/
@SerializedName("model_id")
public ModelKeyV3 modelId;
/**
* Frame the model has been fitted to
*/
public FrameV3 frame;
/**
* Variables of interest
*/
public String[] variables;
/**
* Value of H statistic
*/
public double h;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FriedmanPopescusHV3() {
h = 0.0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GAMModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GAMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Table of Coefficients
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/**
* Table of Coefficients without centering
*/
@SerializedName("coefficients_table_no_centering")
public TwoDimTableV3 coefficientsTableNoCentering;
/**
* GLM scoring history
*/
@SerializedName("glm_scoring_history")
public TwoDimTableV3 glmScoringHistory;
/**
* GLM model summary
*/
@SerializedName("glm_model_summary")
public TwoDimTableV3 glmModelSummary;
/**
* Table of Standardized Coefficients Magnitudes
*/
@SerializedName("standardized_coefficient_magnitudes")
public TwoDimTableV3 standardizedCoefficientMagnitudes;
/**
* Variable Importances
*/
@SerializedName("variable_importances")
public TwoDimTableV3 variableImportances;
/**
* key storing gam columns and predictor columns. For debugging purposes only
*/
@SerializedName("gam_transformed_center_key")
public String gamTransformedCenterKey;
/**
* GLM Z values. For debugging purposes only
*/
@SerializedName("glm_zvalues")
public double[] glmZvalues;
/**
* GLM p values. For debugging purposes only
*/
@SerializedName("glm_pvalues")
public double[] glmPvalues;
/**
* GLM standard error values. For debugging purposes only
*/
@SerializedName("glm_std_err")
public double[] glmStdErr;
/**
* knot locations for all gam columns.
*/
@SerializedName("knot_locations")
public double[][] knotLocations;
/**
* Gam column names for knots stored in knot_locations
*/
@SerializedName("gam_knot_column_names")
public String[] gamKnotColumnNames;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public GAMModelOutputV3() {
gamTransformedCenterKey = "";
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GAMModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GAMModelV3 extends ModelSchemaV3<GAMParametersV3, GAMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public GAMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public GAMModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public GAMModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GAMParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GAMParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/**
* Family. Use binomial for classification with logistic regression, others are for regression problems.
*/
public GLMFamily family;
/**
* Tweedie variance power
*/
@SerializedName("tweedie_variance_power")
public double tweedieVariancePower;
/**
* Tweedie link power
*/
@SerializedName("tweedie_link_power")
public double tweedieLinkPower;
/**
* Theta
*/
public double theta;
/**
* AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small
* number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
*/
public GLMSolver solver;
/**
* Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents
* Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing
* between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.
*/
public double[] alpha;
/**
* Regularization strength
*/
public double[] lambda;
/**
* double array to initialize coefficients for GAM.
*/
public double[] startval;
/**
* Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
*/
@SerializedName("lambda_search")
public boolean lambdaSearch;
/**
* Stop early when there is no more relative improvement on train or validation (if provided)
*/
@SerializedName("early_stopping")
public boolean earlyStopping;
/**
* Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True,
* the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
*/
public int nlambdas;
/**
* Standardize numeric columns to have zero mean and unit variance
*/
public boolean standardize;
/**
* Handling of missing values. Either MeanImputation, Skip or PlugValues.
*/
@SerializedName("missing_values_handling")
public GLMMissingValuesHandling missingValuesHandling;
/**
* Plug Values (a single row frame containing values that will be used to impute missing values of the
* training/validation frame, use with conjunction missing_values_handling = PlugValues)
*/
@SerializedName("plug_values")
public FrameKeyV3 plugValues;
/**
* Restrict coefficients (not intercept) to be non-negative
*/
@SerializedName("non_negative")
public boolean nonNegative;
/**
* Maximum number of iterations
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
*/
@SerializedName("beta_epsilon")
public double betaEpsilon;
/**
* Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value
* of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value
* of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is
* set to .0001.
*/
@SerializedName("objective_epsilon")
public double objectiveEpsilon;
/**
* Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default
* indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is
* equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values
* above are 1E-8 and 1E-6 respectively.
*/
@SerializedName("gradient_epsilon")
public double gradientEpsilon;
/**
* Likelihood divider in objective value computation, default is 1/nobs
*/
@SerializedName("obj_reg")
public double objReg;
/**
* Link function.
*/
public GLMLink link;
/**
* Include constant term in the model
*/
public boolean intercept;
/**
* Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of
* response does not reflect reality.
*/
public double prior;
/**
* Only applicable to multiple alpha/lambda values when calling GLM from GAM. If false, build the next model for
* next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model
* from scratch.
*/
@SerializedName("cold_start")
public boolean coldStart;
/**
* Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all
* coefficients to zero). Default indicates: if the number of observations is greater than the number of variables,
* then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then
* lambda_min_ratio is set to 0.01.
*/
@SerializedName("lambda_min_ratio")
public double lambdaMinRatio;
/**
* Beta constraints
*/
@SerializedName("beta_constraints")
public FrameKeyV3 betaConstraints;
/**
* Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model
* building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors
* is set to 5000 otherwise it is set to 100000000.
*/
@SerializedName("max_active_predictors")
public int maxActivePredictors;
/**
* A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
*/
public String[] interactions;
/**
* A list of pairwise (first order) column interactions.
*/
@SerializedName("interaction_pairs")
public StringPairV3[] interactionPairs;
/**
* Balance training data class counts via over/under-sampling (for imbalanced data).
*/
@SerializedName("balance_classes")
public boolean balanceClasses;
/**
* Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be
* automatically computed to obtain class balance during training. Requires balance_classes.
*/
@SerializedName("class_sampling_factors")
public float[] classSamplingFactors;
/**
* Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
* balance_classes.
*/
@SerializedName("max_after_balance_size")
public float maxAfterBalanceSize;
/**
* [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
*/
@SerializedName("max_confusion_matrix_size")
public int maxConfusionMatrixSize;
/**
* Request p-values computation, p-values work only with IRLSM solver and no regularization
*/
@SerializedName("compute_p_values")
public boolean computePValues;
/**
* In case of linearly dependent columns, remove some of the dependent columns
*/
@SerializedName("remove_collinear_columns")
public boolean removeCollinearColumns;
/**
* If set to true, will return knot locations as double[][] array for gam column names found knots_for_gam. Default
* to false.
*/
@SerializedName("store_knot_locations")
public boolean storeKnotLocations;
/**
* Number of knots for gam predictors. If specified, must specify one for each gam predictor. For monotone
* I-splines, mininum = 2, for cs spline, minimum = 3. For thin plate, minimum is size of polynomial basis + 2.
*/
@SerializedName("num_knots")
public int[] numKnots;
/**
* Order of I-splines or NBSplineTypeI M-splines used for gam predictors. If specified, must be the same size as
* gam_columns. For I-splines, the spline_orders will be the same as the polynomials used to generate the splines.
* For M-splines, the polynomials used to generate the splines will be spline_order-1. Values for bs=0 or 1 will be
* ignored.
*/
@SerializedName("spline_orders")
public int[] splineOrders;
/**
* Valid for I-spline (bs=2) only. True if the I-splines are monotonically increasing (and monotonically non-
* decreasing) and False if the I-splines are monotonically decreasing (and monotonically non-increasing). If
* specified, must be the same size as gam_columns. Values for other spline types will be ignored. Default to
* true.
*/
@SerializedName("splines_non_negative")
public boolean[] splinesNonNegative;
/**
* Arrays of predictor column names for gam for smoothers using single or multiple predictors like
* {{'c1'},{'c2','c3'},{'c4'},...}
*/
@SerializedName("gam_columns")
public String[][] gamColumns;
/**
* Smoothing parameter for gam predictors. If specified, must be of the same length as gam_columns
*/
public double[] scale;
/**
* Basis function type for each gam predictors, 0 for cr, 1 for thin plate regression with knots, 2 for monotone
* I-splines, 3 for NBSplineTypeI M-splines (refer to doc here: https://github.com/h2oai/h2o-3/issues/6926). If
* specified, must be the same size as gam_columns
*/
public int[] bs;
/**
* Save keys of model matrix
*/
@SerializedName("keep_gam_cols")
public boolean keepGamCols;
/**
* standardize tp (thin plate) predictor columns
*/
@SerializedName("standardize_tp_gam_cols")
public boolean standardizeTpGamCols;
/**
* Scale penalty matrix for tp (thin plate) smoothers as in R
*/
@SerializedName("scale_tp_penalty_mat")
public boolean scaleTpPenaltyMat;
/**
* Array storing frame keys of knots. One for each gam column set specified in gam_columns
*/
@SerializedName("knot_ids")
public String[] knotIds;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public GAMParametersV3() {
seed = -1L;
family = GLMFamily.AUTO;
tweedieVariancePower = 0.0;
tweedieLinkPower = 0.0;
theta = 0.0;
solver = GLMSolver.AUTO;
lambdaSearch = false;
earlyStopping = true;
nlambdas = -1;
standardize = false;
missingValuesHandling = GLMMissingValuesHandling.MeanImputation;
nonNegative = false;
maxIterations = -1;
betaEpsilon = 0.0001;
objectiveEpsilon = -1.0;
gradientEpsilon = -1.0;
objReg = -1.0;
link = GLMLink.family_default;
intercept = true;
prior = -1.0;
coldStart = false;
lambdaMinRatio = -1.0;
maxActivePredictors = -1;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
computePValues = false;
removeCollinearColumns = false;
storeKnotLocations = false;
keepGamCols = false;
standardizeTpGamCols = false;
scaleTpPenaltyMat = false;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GAMV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GAMV3 extends ModelBuilderSchema<GAMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public GAMParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public GAMV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
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0
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GBMModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GBMModelOutputV3 extends SharedTreeModelOutputV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Variable Importances
public TwoDimTableV3 variableImportances;
// The Intercept term, the initial model function value to which trees make adjustments
public double initF;
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public GBMModelOutputV3() {
initF = 0.0;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GBMModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GBMModelV3 extends SharedTreeModelV3<GBMParametersV3, GBMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public GBMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public GBMModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public GBMModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
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0
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java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GBMParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GBMParametersV3 extends SharedTreeParametersV3 {
/**
* Learning rate (from 0.0 to 1.0)
*/
@SerializedName("learn_rate")
public double learnRate;
/**
* Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999)
*/
@SerializedName("learn_rate_annealing")
public double learnRateAnnealing;
/**
* Row sample rate per tree (from 0.0 to 1.0)
*/
@SerializedName("sample_rate")
public double sampleRate;
/**
* Column sample rate (from 0.0 to 1.0)
*/
@SerializedName("col_sample_rate")
public double colSampleRate;
/**
* A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a
* decreasing constraint.
*/
@SerializedName("monotone_constraints")
public KeyValueV3[] monotoneConstraints;
/**
* Maximum absolute value of a leaf node prediction
*/
@SerializedName("max_abs_leafnode_pred")
public double maxAbsLeafnodePred;
/**
* Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions
*/
@SerializedName("pred_noise_bandwidth")
public double predNoiseBandwidth;
/**
* A set of allowed column interactions.
*/
@SerializedName("interaction_constraints")
public String[][] interactionConstraints;
/**
* Allow automatic rebalancing of training and validation datasets
*/
@SerializedName("auto_rebalance")
public boolean autoRebalance;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Balance training data class counts via over/under-sampling (for imbalanced data).
public boolean balanceClasses;
// Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be
// automatically computed to obtain class balance during training. Requires balance_classes.
public float[] classSamplingFactors;
// Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
// balance_classes.
public float maxAfterBalanceSize;
// [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
public int maxConfusionMatrixSize;
// Number of trees.
public int ntrees;
// Maximum tree depth (0 for unlimited).
public int maxDepth;
// Fewest allowed (weighted) observations in a leaf.
public double minRows;
// For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
public int nbins;
// For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease
// by factor of two per level
public int nbinsTopLevel;
// For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher
// values can lead to more overfitting.
public int nbinsCats;
// r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and
// stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds
// this
public double r2Stopping;
// Seed for pseudo random number generator (if applicable)
public long seed;
// Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
public boolean buildTreeOneNode;
// A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
public double[] sampleRatePerClass;
// Column sample rate per tree (from 0.0 to 1.0)
public double colSampleRatePerTree;
// Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
public double colSampleRateChangePerLevel;
// Score the model after every so many trees. Disabled if set to 0.
public int scoreTreeInterval;
// Minimum relative improvement in squared error reduction for a split to happen
public double minSplitImprovement;
// What type of histogram to use for finding optimal split points
public TreeSharedTreeModelSharedTreeParametersHistogramType histogramType;
// Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can
// provide more accurate estimates of class probabilities.
public boolean calibrateModel;
// Data for model calibration
public FrameKeyV3 calibrationFrame;
// Calibration method to use
public TreeCalibrationHelperCalibrationMethod calibrationMethod;
// Check if response column is constant. If enabled, then an exception is thrown if the response column is a
// constant value.If disabled, then model will train regardless of the response column being a constant value or
// not.
public boolean checkConstantResponse;
// Create checkpoints into defined directory while training process is still running. In case of cluster shutdown,
// this checkpoint can be used to restart training.
public String inTrainingCheckpointsDir;
// Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is
// defined
public int inTrainingCheckpointsTreeInterval;
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public GBMParametersV3() {
learnRate = 0.1;
learnRateAnnealing = 1.0;
sampleRate = 1.0;
colSampleRate = 1.0;
maxAbsLeafnodePred = 1.7976931348623157e+308;
predNoiseBandwidth = 0.0;
autoRebalance = true;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
ntrees = 50;
maxDepth = 5;
minRows = 10.0;
nbins = 20;
nbinsTopLevel = 1024;
nbinsCats = 1024;
r2Stopping = 1.7976931348623157e+308;
seed = -1L;
buildTreeOneNode = false;
colSampleRatePerTree = 1.0;
colSampleRateChangePerLevel = 1.0;
scoreTreeInterval = 0;
minSplitImprovement = 1e-05;
histogramType = TreeSharedTreeModelSharedTreeParametersHistogramType.AUTO;
calibrateModel = false;
calibrationMethod = TreeCalibrationHelperCalibrationMethod.AUTO;
checkConstantResponse = true;
inTrainingCheckpointsDir = "";
inTrainingCheckpointsTreeInterval = 1;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GBMV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GBMV3 extends SharedTreeV3<GBMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public GBMParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public GBMV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMDispersionMethod.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GLMDispersionMethod {
deviance,
ml,
pearson,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMFamily.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GLMFamily {
AUTO,
binomial,
fractionalbinomial,
gamma,
gaussian,
multinomial,
negativebinomial,
ordinal,
poisson,
quasibinomial,
tweedie,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMInfluence.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GLMInfluence {
dfbetas,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMLink.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GLMLink {
family_default,
identity,
inverse,
log,
logit,
ologit,
tweedie,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMMissingValuesHandling.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GLMMissingValuesHandling {
MeanImputation,
PlugValues,
Skip,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Table of Coefficients
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/**
* Table of Coefficients with coefficients denoted with class names for GLM multinonimals only.
*/
@SerializedName("coefficients_table_multinomials_with_class_names")
public TwoDimTableV3 coefficientsTableMultinomialsWithClassNames;
/**
* Standardized Coefficient Magnitudes
*/
@SerializedName("standardized_coefficient_magnitudes")
public TwoDimTableV3 standardizedCoefficientMagnitudes;
/**
* Variable Importances
*/
@SerializedName("variable_importances")
public TwoDimTableV3 variableImportances;
/**
* Lambda minimizing the objective value, only applicable with lambda search or when arrays of alpha and lambdas are
* provided
*/
@SerializedName("lambda_best")
public double lambdaBest;
/**
* Alpha minimizing the objective value, only applicable when arrays of alphas are given
*/
@SerializedName("alpha_best")
public double alphaBest;
/**
* submodel index minimizing the objective value, only applicable for arrays of alphas/lambda
*/
@SerializedName("best_submodel_index")
public int bestSubmodelIndex;
/**
* Lambda best + 1 standard error. Only applicable with lambda search and cross-validation
*/
@SerializedName("lambda_1se")
public double lambda1se;
/**
* Minimum lambda value calculated that may be used for lambda search. Early-stop may happen and the minimum lambda
* value will not be used in this case.
*/
@SerializedName("lambda_min")
public double lambdaMin;
/**
* Starting lambda value used when lambda search is enabled.
*/
@SerializedName("lambda_max")
public double lambdaMax;
/**
* Dispersion parameter, only applicable to Tweedie family (input/output) and fractional Binomial (output only)
*/
public double dispersion;
/**
* Predictor names where variable inflation factors are calculated.
*/
@SerializedName("vif_predictor_names")
public String[] vifPredictorNames;
/**
* GLM model coefficients names.
*/
@SerializedName("coefficient_names")
public String[] coefficientNames;
/**
* predictor variable inflation factors.
*/
@SerializedName("variable_inflation_factors")
public double[] variableInflationFactors;
/**
* Beta (if exists) and linear constraints states
*/
@SerializedName("linear_constraint_states")
public String[] linearConstraintStates;
/**
* Table of beta (if exists) and linear constraints values and status
*/
@SerializedName("linear_constraints_table")
public TwoDimTableV3 linearConstraintsTable;
/**
* Contains the original dataset and the dfbetas calculated for each predictor.
*/
@SerializedName("regression_influence_diagnostics")
public FrameKeyV3 regressionInfluenceDiagnostics;
/**
* True if all constraints conditions are satisfied. Otherwise, false.
*/
@SerializedName("all_constraints_satisfied")
public boolean allConstraintsSatisfied;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public GLMModelOutputV3() {
lambdaBest = -1.0;
alphaBest = -1.0;
bestSubmodelIndex = 0;
lambda1se = -1.0;
lambdaMin = -1.0;
lambdaMax = -1.0;
dispersion = 0.0;
allConstraintsSatisfied = false;
modelCategory = ModelCategory.Regression;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.5;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLMModelV3 extends ModelSchemaV3<GLMParametersV3, GLMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public GLMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public GLMModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public GLMModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLMParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for pseudo random number generator (if applicable).
*/
public long seed;
/**
* Family. Use binomial for classification with logistic regression, others are for regression problems.
*/
public GLMFamily family;
/**
* Tweedie variance power
*/
@SerializedName("tweedie_variance_power")
public double tweedieVariancePower;
/**
* Dispersion learning rate is only valid for tweedie family dispersion parameter estimation using ml. It must be >
* 0. This controls how much the dispersion parameter estimate is to be changed when the calculated loglikelihood
* actually decreases with the new dispersion. In this case, instead of setting new dispersion = dispersion +
* change, we set new dispersion = dispersion + dispersion_learning_rate * change. Defaults to 0.5.
*/
@SerializedName("dispersion_learning_rate")
public double dispersionLearningRate;
/**
* Tweedie link power.
*/
@SerializedName("tweedie_link_power")
public double tweedieLinkPower;
/**
* Theta
*/
public double theta;
/**
* AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small
* number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
*/
public GLMSolver solver;
/**
* Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents
* Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing
* between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.
*/
public double[] alpha;
/**
* Regularization strength
*/
public double[] lambda;
/**
* Use lambda search starting at lambda max, given lambda is then interpreted as lambda min.
*/
@SerializedName("lambda_search")
public boolean lambdaSearch;
/**
* Stop early when there is no more relative improvement on train or validation (if provided).
*/
@SerializedName("early_stopping")
public boolean earlyStopping;
/**
* Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True,
* the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
*/
public int nlambdas;
/**
* Perform scoring for every score_iteration_interval iterations.
*/
@SerializedName("score_iteration_interval")
public int scoreIterationInterval;
/**
* Standardize numeric columns to have zero mean and unit variance.
*/
public boolean standardize;
/**
* Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda
* values starting from the values provided by current model. If true will start GLM model from scratch.
*/
@SerializedName("cold_start")
public boolean coldStart;
/**
* Handling of missing values. Either MeanImputation, Skip or PlugValues.
*/
@SerializedName("missing_values_handling")
public GLMMissingValuesHandling missingValuesHandling;
/**
* If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset.
*/
public GLMInfluence influence;
/**
* Plug Values (a single row frame containing values that will be used to impute missing values of the
* training/validation frame, use with conjunction missing_values_handling = PlugValues).
*/
@SerializedName("plug_values")
public FrameKeyV3 plugValues;
/**
* Restrict coefficients (not intercept) to be non-negative.
*/
@SerializedName("non_negative")
public boolean nonNegative;
/**
* Maximum number of iterations. Value should >=1. A value of 0 is only set when only the model coefficient names
* and model coefficient dimensions are needed.
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Converge if beta changes less (using L-infinity norm) than beta esilon. ONLY applies to IRLSM solver.
*/
@SerializedName("beta_epsilon")
public double betaEpsilon;
/**
* Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True
* the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero,
* the value of objective_epsilon is set to .000001, for any other value of lambda the default value of
* objective_epsilon is set to .0001.
*/
@SerializedName("objective_epsilon")
public double objectiveEpsilon;
/**
* Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of
* -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of
* gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the
* conditional values above are 1E-8 and 1E-6 respectively.
*/
@SerializedName("gradient_epsilon")
public double gradientEpsilon;
/**
* Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs.
*/
@SerializedName("obj_reg")
public double objReg;
/**
* Link function.
*/
public GLMLink link;
/**
* Method used to estimate the dispersion parameter for Tweedie, Gamma and Negative Binomial only.
*/
@SerializedName("dispersion_parameter_method")
public GLMDispersionMethod dispersionParameterMethod;
/**
* double array to initialize coefficients for GLM. If standardize is true, the standardized coefficients should be
* used. Otherwise, use the regular coefficients.
*/
public double[] startval;
/**
* if true, will return likelihood function value.
*/
@SerializedName("calc_like")
public boolean calcLike;
/**
* if true, will generate variable inflation factors for numerical predictors. Default to false.
*/
@SerializedName("generate_variable_inflation_factors")
public boolean generateVariableInflationFactors;
/**
* Include constant term in the model
*/
public boolean intercept;
/**
* If set, will build a model with only the intercept. Default to false.
*/
@SerializedName("build_null_model")
public boolean buildNullModel;
/**
* Only used for Tweedie, Gamma and Negative Binomial GLM. If set, will use the dispsersion parameter in
* init_dispersion_parameter as the standard error and use it to calculate the p-values. Default to false.
*/
@SerializedName("fix_dispersion_parameter")
public boolean fixDispersionParameter;
/**
* Only used for Tweedie, Gamma and Negative Binomial GLM. Store the initial value of dispersion parameter. If
* fix_dispersion_parameter is set, this value will be used in the calculation of p-values.
*/
@SerializedName("init_dispersion_parameter")
public double initDispersionParameter;
/**
* Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of
* response does not reflect reality.
*/
public double prior;
/**
* Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all
* coefficients to zero). Default indicates: if the number of observations is greater than the number of variables,
* then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then
* lambda_min_ratio is set to 0.01.
*/
@SerializedName("lambda_min_ratio")
public double lambdaMinRatio;
/**
* Beta constraints
*/
@SerializedName("beta_constraints")
public FrameKeyV3 betaConstraints;
/**
* Linear constraints: used to specify linear constraints involving more than one coefficients in standard form. It
* is only supported for solver IRLSM. It contains four columns: names (strings for coefficient names or constant),
* values, types ( strings of 'Equal' or 'LessThanEqual'), constraint_numbers (0 for first linear constraint, 1 for
* second linear constraint, ...).
*/
@SerializedName("linear_constraints")
public FrameKeyV3 linearConstraints;
/**
* Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model
* building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors
* is set to 5000 otherwise it is set to 100000000.
*/
@SerializedName("max_active_predictors")
public int maxActivePredictors;
/**
* A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
*/
public String[] interactions;
/**
* A list of pairwise (first order) column interactions.
*/
@SerializedName("interaction_pairs")
public StringPairV3[] interactionPairs;
/**
* Balance training data class counts via over/under-sampling (for imbalanced data).
*/
@SerializedName("balance_classes")
public boolean balanceClasses;
/**
* Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be
* automatically computed to obtain class balance during training. Requires balance_classes.
*/
@SerializedName("class_sampling_factors")
public float[] classSamplingFactors;
/**
* Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
* balance_classes.
*/
@SerializedName("max_after_balance_size")
public float maxAfterBalanceSize;
/**
* [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.
*/
@SerializedName("max_confusion_matrix_size")
public int maxConfusionMatrixSize;
/**
* Request p-values computation, p-values work only with IRLSM solver.
*/
@SerializedName("compute_p_values")
public boolean computePValues;
/**
* If true, will fix tweedie variance power value to the value set in tweedie_variance_power.
*/
@SerializedName("fix_tweedie_variance_power")
public boolean fixTweedieVariancePower;
/**
* In case of linearly dependent columns, remove the dependent columns.
*/
@SerializedName("remove_collinear_columns")
public boolean removeCollinearColumns;
/**
* If changes in dispersion parameter estimation or loglikelihood value is smaller than dispersion_epsilon, will
* break out of the dispersion parameter estimation loop using maximum likelihood.
*/
@SerializedName("dispersion_epsilon")
public double dispersionEpsilon;
/**
* In estimating tweedie dispersion parameter using maximum likelihood, this is used to choose the lower and upper
* indices in the approximating of the infinite series summation.
*/
@SerializedName("tweedie_epsilon")
public double tweedieEpsilon;
/**
* Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood.
*/
@SerializedName("max_iterations_dispersion")
public int maxIterationsDispersion;
/**
* If set to true, will generate scoring history for GLM. This may significantly slow down the algo.
*/
@SerializedName("generate_scoring_history")
public boolean generateScoringHistory;
/**
* If true, will initialize coefficients with values derived from GLM runs without linear constraints. Only
* available for linear constraints.
*/
@SerializedName("init_optimal_glm")
public boolean initOptimalGlm;
/**
* If true, will keep the beta constraints and linear constraints separate. After new coefficients are found, first
* beta constraints will be applied followed by the application of linear constraints. Note that the beta
* constraints in this case will not be part of the objective function. If false, will combine the beta and linear
* constraints.
*/
@SerializedName("separate_linear_beta")
public boolean separateLinearBeta;
/**
* For constrained GLM only. It affects the setting of eta_k+1=eta_0/power(ck+1, alpha).
*/
@SerializedName("constraint_eta0")
public double constraintEta0;
/**
* For constrained GLM only. It affects the setting of c_k+1=tau*c_k.
*/
@SerializedName("constraint_tau")
public double constraintTau;
/**
* For constrained GLM only. It affects the setting of eta_k = eta_0/pow(c_0, alpha).
*/
@SerializedName("constraint_alpha")
public double constraintAlpha;
/**
* For constrained GLM only. It affects the setting of eta_k+1 = eta_k/pow(c_k, beta).
*/
@SerializedName("constraint_beta")
public double constraintBeta;
/**
* For constrained GLM only. It affects the initial setting of epsilon_k = 1/c_0.
*/
@SerializedName("constraint_c0")
public double constraintC0;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public GLMParametersV3() {
seed = -1L;
family = GLMFamily.AUTO;
tweedieVariancePower = 0.0;
dispersionLearningRate = 0.5;
tweedieLinkPower = 1.0;
theta = 1e-10;
solver = GLMSolver.AUTO;
lambdaSearch = false;
earlyStopping = true;
nlambdas = -1;
scoreIterationInterval = -1;
standardize = true;
coldStart = false;
missingValuesHandling = GLMMissingValuesHandling.MeanImputation;
nonNegative = false;
maxIterations = -1;
betaEpsilon = 0.0001;
objectiveEpsilon = -1.0;
gradientEpsilon = -1.0;
objReg = -1.0;
link = GLMLink.family_default;
dispersionParameterMethod = GLMDispersionMethod.pearson;
calcLike = false;
generateVariableInflationFactors = false;
intercept = true;
buildNullModel = false;
fixDispersionParameter = false;
initDispersionParameter = 1.0;
prior = -1.0;
lambdaMinRatio = -1.0;
maxActivePredictors = -1;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
computePValues = false;
fixTweedieVariancePower = true;
removeCollinearColumns = false;
dispersionEpsilon = 0.0001;
tweedieEpsilon = 8e-17;
maxIterationsDispersion = 3000;
generateScoringHistory = false;
initOptimalGlm = false;
separateLinearBeta = false;
constraintEta0 = 0.1258925;
constraintTau = 10.0;
constraintAlpha = 0.1;
constraintBeta = 0.9;
constraintC0 = 10.0;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMRegularizationPathV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLMRegularizationPathV3 extends SchemaV3 {
/**
* source model
*/
public ModelKeyV3 model;
/**
* Computed lambda values
*/
public double[] lambdas;
/**
* alpha values used in building submodels
*/
public double[] alphas;
/**
* explained deviance on the training set
*/
@SerializedName("explained_deviance_train")
public double[] explainedDevianceTrain;
/**
* explained deviance on the validation set
*/
@SerializedName("explained_deviance_valid")
public double[] explainedDevianceValid;
/**
* coefficients for all lambdas
*/
public double[][] coefficients;
/**
* standardized coefficients for all lambdas
*/
@SerializedName("coefficients_std")
public double[][] coefficientsStd;
/**
* coefficient names
*/
@SerializedName("coefficient_names")
public String[] coefficientNames;
/**
* z-values
*/
@SerializedName("z_values")
public double[][] zValues;
/**
* p-values
*/
@SerializedName("p_values")
public double[][] pValues;
/**
* standard error
*/
@SerializedName("std_errs")
public double[][] stdErrs;
/**
* Public constructor
*/
public GLMRegularizationPathV3() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMSolver.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GLMSolver {
AUTO,
COORDINATE_DESCENT,
COORDINATE_DESCENT_NAIVE,
GRADIENT_DESCENT_LH,
GRADIENT_DESCENT_SQERR,
IRLSM,
L_BFGS,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLMV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLMV3 extends ModelBuilderSchema<GLMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public GLMParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public GLMV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLRMModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLRMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Number of iterations executed
*/
public int iterations;
/**
* Number of updates executed
*/
public int updates;
/**
* Current value of the objective function
*/
public double objective;
/**
* Average change in objective value on final iteration
*/
@SerializedName("avg_change_obj")
public double avgChangeObj;
/**
* Final step size
*/
@SerializedName("step_size")
public double stepSize;
/**
* Mapping from lower dimensional k-space to training features (Y)
*/
public TwoDimTableV3 archetypes;
/**
* Singular values of XY matrix
*/
@SerializedName("singular_vals")
public double[] singularVals;
/**
* Eigenvectors of XY matrix
*/
public TwoDimTableV3 eigenvectors;
/**
* Frame key name for X matrix
*/
@SerializedName("representation_name")
public String representationName;
/**
* Standard deviation and importance of each principal component
*/
public TwoDimTableV3 importance;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public GLRMModelOutputV3() {
iterations = 0;
updates = 0;
objective = 0.0;
avgChangeObj = 0.0;
stepSize = 0.0;
representationName = "";
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLRMModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLRMModelV3 extends ModelSchemaV3<GLRMParametersV3, GLRMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public GLRMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public GLRMModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public GLRMModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLRMParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLRMParametersV3 extends ModelParametersSchemaV3 {
/**
* Transformation of training data
*/
public DataInfoTransformType transform;
/**
* Rank of matrix approximation
*/
public int k;
/**
* Numeric loss function
*/
public GenmodelalgosglrmGlrmLoss loss;
/**
* Categorical loss function
*/
@SerializedName("multi_loss")
public GenmodelalgosglrmGlrmLoss multiLoss;
/**
* Loss function by column (override)
*/
@SerializedName("loss_by_col")
public GenmodelalgosglrmGlrmLoss[] lossByCol;
/**
* Loss function by column index (override)
*/
@SerializedName("loss_by_col_idx")
public int[] lossByColIdx;
/**
* Length of period (only used with periodic loss function)
*/
public int period;
/**
* Regularization function for X matrix
*/
@SerializedName("regularization_x")
public GenmodelalgosglrmGlrmRegularizer regularizationX;
/**
* Regularization function for Y matrix
*/
@SerializedName("regularization_y")
public GenmodelalgosglrmGlrmRegularizer regularizationY;
/**
* Regularization weight on X matrix
*/
@SerializedName("gamma_x")
public double gammaX;
/**
* Regularization weight on Y matrix
*/
@SerializedName("gamma_y")
public double gammaY;
/**
* Maximum number of iterations
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Maximum number of updates, defaults to 2*max_iterations
*/
@SerializedName("max_updates")
public int maxUpdates;
/**
* Initial step size
*/
@SerializedName("init_step_size")
public double initStepSize;
/**
* Minimum step size
*/
@SerializedName("min_step_size")
public double minStepSize;
/**
* RNG seed for initialization
*/
public long seed;
/**
* Initialization mode
*/
public GenmodelalgosglrmGlrmInitialization init;
/**
* Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable)
*/
@SerializedName("svd_method")
public SVDMethod svdMethod;
/**
* User-specified initial Y
*/
@SerializedName("user_y")
public FrameKeyV3 userY;
/**
* User-specified initial X
*/
@SerializedName("user_x")
public FrameKeyV3 userX;
/**
* [Deprecated] Use representation_name instead. Frame key to save resulting X.
*/
@SerializedName("loading_name")
public String loadingName;
/**
* Frame key to save resulting X
*/
@SerializedName("representation_name")
public String representationName;
/**
* Expand categorical columns in user-specified initial Y
*/
@SerializedName("expand_user_y")
public boolean expandUserY;
/**
* Reconstruct original training data by reversing transform
*/
@SerializedName("impute_original")
public boolean imputeOriginal;
/**
* Recover singular values and eigenvectors of XY
*/
@SerializedName("recover_svd")
public boolean recoverSvd;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public GLRMParametersV3() {
transform = DataInfoTransformType.NONE;
k = 1;
loss = GenmodelalgosglrmGlrmLoss.Quadratic;
multiLoss = GenmodelalgosglrmGlrmLoss.Categorical;
period = 1;
regularizationX = GenmodelalgosglrmGlrmRegularizer.None;
regularizationY = GenmodelalgosglrmGlrmRegularizer.None;
gammaX = 0.0;
gammaY = 0.0;
maxIterations = 1000;
maxUpdates = 2000;
initStepSize = 1.0;
minStepSize = 0.0001;
seed = -1L;
init = GenmodelalgosglrmGlrmInitialization.PlusPlus;
svdMethod = SVDMethod.Randomized;
loadingName = "";
representationName = "";
expandUserY = true;
imputeOriginal = false;
recoverSvd = false;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GLRMV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GLRMV3 extends ModelBuilderSchema<GLRMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public GLRMParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public GLRMV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GarbageCollectV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GarbageCollectV3 extends SchemaV3 {
/**
* Public constructor
*/
public GarbageCollectV3() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenericModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GenericModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Variable Importances
*/
@SerializedName("variable_importances")
public TwoDimTableV3 variableImportances;
/**
* Short identifier of the original algorithm name
*/
@SerializedName("original_model_identifier")
public String originalModelIdentifier;
/**
* Full, potentially long name of the original agorithm
*/
@SerializedName("original_model_full_name")
public String originalModelFullName;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public GenericModelOutputV3() {
originalModelIdentifier = "";
originalModelFullName = "";
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenericModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GenericModelV3 extends ModelSchemaV3<GenericParametersV3, GenericModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public GenericParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public GenericModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public GenericModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenericParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GenericParametersV3 extends ModelParametersSchemaV3 {
/**
* Path to file with self-contained model archive.
*/
public String path;
/**
* Key to the self-contained model archive already uploaded to H2O.
*/
@SerializedName("model_key")
public FrameKeyV3 modelKey;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public GenericParametersV3() {
path = "";
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenericV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GenericV3 extends ModelBuilderSchema<GenericParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public GenericParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public GenericV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenmodelalgosglrmGlrmInitialization.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GenmodelalgosglrmGlrmInitialization {
PlusPlus,
Random,
SVD,
User,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenmodelalgosglrmGlrmLoss.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GenmodelalgosglrmGlrmLoss {
Absolute,
Categorical,
Hinge,
Huber,
Logistic,
Ordinal,
Periodic,
Poisson,
Quadratic,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenmodelalgosglrmGlrmRegularizer.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GenmodelalgosglrmGlrmRegularizer {
L1,
L2,
NonNegative,
None,
OneSparse,
Quadratic,
Simplex,
UnitOneSparse,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenmodelalgospsvmKernelType.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GenmodelalgospsvmKernelType {
gaussian,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GenmodelutilsDistributionFamily.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GenmodelutilsDistributionFamily {
AUTO,
bernoulli,
custom,
gamma,
gaussian,
huber,
laplace,
multinomial,
ordinal,
poisson,
quantile,
quasibinomial,
tweedie,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GramV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GramV3 {
/**
* source data
*/
@SerializedName("X")
public FrameKeyV3 x;
/**
* weight vector
*/
@SerializedName("W")
public ColSpecifierV3 w;
/**
* use all factor levels when doing 1-hot encoding
*/
@SerializedName("use_all_factor_levels")
public boolean useAllFactorLevels;
/**
* standardize data
*/
public boolean standardize;
/**
* skip missing values
*/
@SerializedName("skip_missing")
public boolean skipMissing;
/**
* Destination key for the resulting matrix.
*/
@SerializedName("destination_frame")
public FrameKeyV3 destinationFrame;
/**
* Public constructor
*/
public GramV3() {
useAllFactorLevels = false;
standardize = false;
skipMissing = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GrepModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GrepModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Matching strings
*/
public String[] matches;
/**
* Byte offsets of matches
*/
public long[] offsets;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public GrepModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GrepModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GrepModelV3 extends ModelSchemaV3<GrepParametersV3, GrepModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public GrepParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public GrepModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public GrepModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GrepParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GrepParametersV3 extends ModelParametersSchemaV3 {
/**
* regex
*/
public String regex;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public GrepParametersV3() {
regex = "";
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GrepV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GrepV3 extends ModelBuilderSchema<GrepParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public GrepParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public GrepV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridExportV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GridExportV3 extends SchemaV3 {
/**
* ID of the Grid to load from the directory
*/
@SerializedName("grid_id")
public String gridId;
/**
* Path to the directory with saved Grid search
*/
@SerializedName("grid_directory")
public String gridDirectory;
/**
* True if objects referenced by params should also be saved.
*/
@SerializedName("save_params_references")
public boolean saveParamsReferences;
/**
* Flag indicating whether the exported model artifacts should also include CV Holdout Frame predictions
*/
@SerializedName("export_cross_validation_predictions")
public boolean exportCrossValidationPredictions;
/**
* Public constructor
*/
public GridExportV3() {
gridId = "";
gridDirectory = "";
saveParamsReferences = false;
exportCrossValidationPredictions = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridHyperSpaceSearchCriteriaStrategy.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum GridHyperSpaceSearchCriteriaStrategy {
Cartesian,
RandomDiscrete,
Sequential,
Unknown,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridImportV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GridImportV3 extends SchemaV3 {
/**
* Full path to the file containing saved Grid
*/
@SerializedName("grid_path")
public String gridPath;
/**
* If true will also load saved objects referenced by params. Will fail with an error if grid was saved without
* objects referenced by params.
*/
@SerializedName("load_params_references")
public boolean loadParamsReferences;
/**
* Public constructor
*/
public GridImportV3() {
gridPath = "";
loadParamsReferences = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridKeyV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GridKeyV3 extends KeyV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Name (string representation) for this Key.
public String name;
// Name (string representation) for the type of Keyed this Key points to.
public String type;
// URL for the resource that this Key points to, if one exists.
public String url;
*/
/**
* Public constructor
*/
public GridKeyV3() {
name = "";
type = "";
url = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridSchemaV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GridSchemaV99 extends SchemaV3 {
/**
* Grid id
*/
@SerializedName("grid_id")
public GridKeyV3 gridId;
/**
* Model performance metric to sort by. Examples: logloss, residual_deviance, mse, rmse, mae,rmsle, auc, r2, f1,
* recall, precision, accuracy, mcc, err, err_count, lift_top_group, max_per_class_error
*/
@SerializedName("sort_by")
public String sortBy;
/**
* Specify whether sort order should be decreasing.
*/
public boolean decreasing;
/**
* Model IDs built by a grid search
*/
@SerializedName("model_ids")
public ModelKeyV3[] modelIds;
/**
* Used hyper parameters.
*/
@SerializedName("hyper_names")
public String[] hyperNames;
/**
* List of failed parameters
*/
@SerializedName("failed_params")
public ModelParametersSchemaV3[] failedParams;
/**
* List of detailed warning messages
*/
@SerializedName("warning_details")
public String[] warningDetails;
/**
* List of detailed failure messages
*/
@SerializedName("failure_details")
public String[] failureDetails;
/**
* List of detailed failure stack traces
*/
@SerializedName("failure_stack_traces")
public String[] failureStackTraces;
/**
* List of raw parameters causing model building failure
*/
@SerializedName("failed_raw_params")
public String[][] failedRawParams;
/**
* Training model metrics for the returned models; only returned if sort_by is set
*/
@SerializedName("training_metrics")
public ModelMetricsBaseV3[] trainingMetrics;
/**
* Validation model metrics for the returned models; only returned if sort_by is set
*/
@SerializedName("validation_metrics")
public ModelMetricsBaseV3[] validationMetrics;
/**
* Cross validation model metrics for the returned models; only returned if sort_by is set
*/
@SerializedName("cross_validation_metrics")
public ModelMetricsBaseV3[] crossValidationMetrics;
/**
* Cross validation model metrics summary for the returned models; only returned if sort_by is set
*/
@SerializedName("cross_validation_metrics_summary")
public TwoDimTableV3[] crossValidationMetricsSummary;
/**
* Directory for Grid automatic checkpointing
*/
@SerializedName("export_checkpoints_dir")
public String exportCheckpointsDir;
/**
* Summary
*/
@SerializedName("summary_table")
public TwoDimTableV3 summaryTable;
/**
* Scoring history
*/
@SerializedName("scoring_history")
public TwoDimTableV3 scoringHistory;
/**
* Public constructor
*/
public GridSchemaV99() {
sortBy = "";
decreasing = false;
exportCheckpointsDir = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridSearchSchema.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
import java.util.Map;
public class GridSearchSchema extends SchemaV3 {
/**
* Basic model builder parameters.
*/
public ModelParametersSchemaV3 parameters;
/**
* Grid search parameters.
*/
@SerializedName("hyper_parameters")
public Map<Object,Object> hyperParameters;
/**
* Destination id for this grid; auto-generated if not specified.
*/
@SerializedName("grid_id")
public GridKeyV3 gridId;
/**
* Hyperparameter search criteria, including strategy and early stopping directives. If it is not given, exhaustive
* Cartesian is used.
*/
@SerializedName("search_criteria")
public HyperSpaceSearchCriteriaV99 searchCriteria;
/**
* Level of parallelism during grid model building. 1 = sequential building (default). 0 for adaptive parallelism.
* Any number > 1 sets the exact number of models built in parallel.
*/
public int parallelism;
/**
* Path to a directory where grid will save everything necessary to resume training after cluster crash.
*/
@SerializedName("recovery_dir")
public String recoveryDir;
/**
* Key to use for the Job handling this GridSearch (internal use only).
*/
@SerializedName("job_id")
public JobKeyV3 jobId;
/**
* Number of all models generated by grid search.
*/
@SerializedName("total_models")
public int totalModels;
/**
* Job Key.
*/
public JobV3 job;
/**
* Public constructor
*/
public GridSearchSchema() {
parallelism = 0;
recoveryDir = "";
totalModels = 0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/GridsV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class GridsV99 extends SchemaV3 {
/**
* Grids
*/
public GridSchemaV99[] grids;
/**
* Public constructor
*/
public GridsV99() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/H2OErrorV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
import java.util.Map;
public class H2OErrorV3 extends SchemaV3 {
/**
* Milliseconds since the epoch for the time that this H2OError instance was created. Generally this is a short
* time since the underlying error ocurred.
*/
public long timestamp;
/**
* Error url
*/
@SerializedName("error_url")
public String errorUrl;
/**
* Message intended for the end user (a data scientist).
*/
public String msg;
/**
* Potentially more detailed message intended for a developer (e.g. a front end engineer or someone designing a
* language binding).
*/
@SerializedName("dev_msg")
public String devMsg;
/**
* HTTP status code for this error.
*/
@SerializedName("http_status")
public int httpStatus;
/**
* Any values that are relevant to reporting or handling this error. Examples are a key name if the error is on a
* key, or a field name and object name if it's on a specific field.
*/
public Map<String,Object> values;
/**
* Exception type, if any.
*/
@SerializedName("exception_type")
public String exceptionType;
/**
* Raw exception message, if any.
*/
@SerializedName("exception_msg")
public String exceptionMsg;
/**
* Stacktrace, if any.
*/
public String[] stacktrace;
/**
* Public constructor
*/
public H2OErrorV3() {
timestamp = 0L;
errorUrl = "";
msg = "";
devMsg = "";
httpStatus = 0;
exceptionType = "";
exceptionMsg = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/H2OModelBuilderErrorV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class H2OModelBuilderErrorV3 extends H2OErrorV3 {
/**
* Model builder parameters.
*/
public ModelParametersSchemaV3 parameters;
/**
* Parameter validation messages
*/
public ValidationMessageV3[] messages;
/**
* Count of parameter validation errors
*/
@SerializedName("error_count")
public int errorCount;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Milliseconds since the epoch for the time that this H2OError instance was created. Generally this is a short
// time since the underlying error ocurred.
public long timestamp;
// Error url
public String errorUrl;
// Message intended for the end user (a data scientist).
public String msg;
// Potentially more detailed message intended for a developer (e.g. a front end engineer or someone designing a
// language binding).
public String devMsg;
// HTTP status code for this error.
public int httpStatus;
// Any values that are relevant to reporting or handling this error. Examples are a key name if the error is on a
// key, or a field name and object name if it's on a specific field.
public Map<String,Object> values;
// Exception type, if any.
public String exceptionType;
// Raw exception message, if any.
public String exceptionMsg;
// Stacktrace, if any.
public String[] stacktrace;
*/
/**
* Public constructor
*/
public H2OModelBuilderErrorV3() {
errorCount = 0;
timestamp = 0L;
errorUrl = "";
msg = "";
devMsg = "";
httpStatus = 0;
exceptionType = "";
exceptionMsg = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/H2oautomlStepDefinitionAlias.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum H2oautomlStepDefinitionAlias {
all,
defaults,
exploitation,
grids,
optionals,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/H2oautomleventsEventLogEntryStage.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum H2oautomleventsEventLogEntryStage {
DataImport,
FeatureAnalysis,
FeatureCreation,
FeatureReduction,
ModelSelection,
ModelTraining,
Validation,
Workflow,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/H2oautomlpreprocessingPreprocessingStepDefinitionType.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum H2oautomlpreprocessingPreprocessingStepDefinitionType {
TargetEncoding,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/H2otargetencodingTargetEncoderModelDataLeakageHandlingStrategy.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum H2otargetencodingTargetEncoderModelDataLeakageHandlingStrategy {
KFold,
LeaveOneOut,
None,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HGLMMethod.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum HGLMMethod {
EM,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HGLMModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class HGLMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Table of Fixed Coefficients
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/**
* Table of Random Coefficients
*/
@SerializedName("random_coefficients_table")
public TwoDimTableV3 randomCoefficientsTable;
/**
* Table of Scoring History for Validation Dataset
*/
@SerializedName("scoring_history_valid")
public TwoDimTableV3 scoringHistoryValid;
/**
* Fixed Effects Coefficient Names
*/
@SerializedName("coefficient_names")
public String[] coefficientNames;
/**
* Random Effects Coefficient Names
*/
@SerializedName("random_coefficient_names")
public String[] randomCoefficientNames;
/**
* Level 2 Indice Names
*/
@SerializedName("group_column_names")
public String[] groupColumnNames;
/**
* Fixed Effects Coefficients
*/
public double[] beta;
/**
* Random Effects Coefficients
*/
public double[][] ubeta;
/**
* Covariance Matrix for Random Effects (= Tj in section II.I of the doc
*/
public double[][] tmat;
/**
* Ratio of each random effect variance and (sum of all random effect variances plus the residual noise variance).
*/
public double[] icc;
/**
* Residual noise variance
*/
@SerializedName("residual_variance")
public double residualVariance;
/**
* Mean residual error with fixed effect coefficients only
*/
@SerializedName("mean_residual_fixed")
public double meanResidualFixed;
/**
* Mean residual error with fixed effect coefficients only
*/
@SerializedName("mean_residual_fixed_valid")
public double meanResidualFixedValid;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public HGLMModelOutputV3() {
residualVariance = 0.0;
meanResidualFixed = 0.0;
meanResidualFixedValid = 0.0;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HGLMModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class HGLMModelV3 extends ModelSchemaV3<HGLMParametersV3, HGLMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public HGLMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public HGLMModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public HGLMModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HGLMParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class HGLMParametersV3 extends ModelParametersSchemaV3 {
/**
* Perform scoring for every score_iteration_interval iterations.
*/
@SerializedName("score_iteration_interval")
public int scoreIterationInterval;
/**
* Seed for pseudo random number generator (if applicable).
*/
public long seed;
/**
* Handling of missing values. Either MeanImputation, Skip or PlugValues.
*/
@SerializedName("missing_values_handling")
public GLMMissingValuesHandling missingValuesHandling;
/**
* Plug Values (a single row frame containing values that will be used to impute missing values of the
* training/validation frame, use with conjunction missing_values_handling = PlugValues).
*/
@SerializedName("plug_values")
public FrameKeyV3 plugValues;
/**
* Family. Only gaussian is supported now.
*/
public GLMFamily family;
/**
* Set distribution of random effects. Only Gaussian is implemented now.
*/
@SerializedName("rand_family")
public GLMFamily randFamily;
/**
* Maximum number of iterations. Value should >=1. A value of 0 is only set when only the model coefficient names
* and model coefficient dimensions are needed.
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* An array that contains initial values of the fixed effects coefficient.
*/
@SerializedName("initial_fixed_effects")
public double[] initialFixedEffects;
/**
* A H2OFrame id that contains initial values of the random effects coefficient. The row names shouldbe the random
* coefficient names. If you are not sure what the random coefficient names are, build HGLM model with
* max_iterations = 0 and checkout the model output field random_coefficient_names. The number of rows of this
* frame should be the number of level 2 units. Again, to figure this out, build HGLM model with max_iterations=0
* and check out the model output field group_column_names. The number of rows should equal the length of
* thegroup_column_names.
*/
@SerializedName("initial_random_effects")
public FrameKeyV3 initialRandomEffects;
/**
* A H2OFrame id that contains initial values of the T matrix. It should be a positive symmetric matrix.
*/
@SerializedName("initial_t_matrix")
public FrameKeyV3 initialTMatrix;
/**
* Initial variance of random coefficient effects. If set, should provide a value > 0.0. If not set, will be
* randomly set in the model building process.
*/
@SerializedName("tau_u_var_init")
public double tauUVarInit;
/**
* Initial variance of random noise. If set, should provide a value > 0.0. If not set, will be randomly set in the
* model building process.
*/
@SerializedName("tau_e_var_init")
public double tauEVarInit;
/**
* Random columns indices for HGLM.
*/
@SerializedName("random_columns")
public String[] randomColumns;
/**
* We only implemented EM as a method to obtain the fixed, random coefficients and the various variances.
*/
public HGLMMethod method;
/**
* Converge if beta/ubeta/tmat/tauEVar changes less (using L-infinity norm) than em esilon. ONLY applies to EM
* method.
*/
@SerializedName("em_epsilon")
public double emEpsilon;
/**
* If true, will allow random component to the GLM coefficients.
*/
@SerializedName("random_intercept")
public boolean randomIntercept;
/**
* Group column is the column that is categorical and used to generate the groups in HGLM
*/
@SerializedName("group_column")
public String groupColumn;
/**
* If true, add gaussian noise with variance specified in parms._tau_e_var_init.
*/
@SerializedName("gen_syn_data")
public boolean genSynData;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public HGLMParametersV3() {
scoreIterationInterval = 5;
seed = -1L;
missingValuesHandling = GLMMissingValuesHandling.MeanImputation;
family = GLMFamily.gaussian;
maxIterations = -1;
tauUVarInit = 0.0;
tauEVarInit = 0.0;
method = HGLMMethod.EM;
emEpsilon = 0.001;
randomIntercept = true;
groupColumn = "";
genSynData = false;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HGLMV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class HGLMV3 extends ModelBuilderSchema<HGLMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public HGLMParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public HGLMV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HeartBeatEvent.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class HeartBeatEvent extends EventV3 {
/**
* number of sent heartbeats
*/
public int sends;
/**
* number of received heartbeats
*/
public int recvs;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Time when the event was recorded. Format is hh:mm:ss:ms
public String date;
// Time in nanos
public long nanos;
// type of recorded event
public TimelineEventEventType type;
*/
/**
* Public constructor
*/
public HeartBeatEvent() {
sends = -1;
recvs = -1;
date = "";
nanos = -1L;
type = TimelineEventEventType.unknown;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/HyperSpaceSearchCriteriaV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class HyperSpaceSearchCriteriaV99 extends SchemaV3 {
/**
* Hyperparameter space search strategy.
*/
public GridHyperSpaceSearchCriteriaStrategy strategy;
/**
* Public constructor
*/
public HyperSpaceSearchCriteriaV99() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IOEvent.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IOEvent extends EventV3 {
/**
* flavor of the recorded io (ice/hdfs/...)
*/
@SerializedName("io_flavor")
public String ioFlavor;
/**
* node where this io event happened
*/
public String node;
/**
* data info
*/
public String data;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Time when the event was recorded. Format is hh:mm:ss:ms
public String date;
// Time in nanos
public long nanos;
// type of recorded event
public TimelineEventEventType type;
*/
/**
* Public constructor
*/
public IOEvent() {
ioFlavor = "unknown";
node = "unknown";
data = "unknown";
date = "23:59:59:999";
nanos = -1L;
type = TimelineEventEventType.io;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ImportFilesMultiV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ImportFilesMultiV3 extends RequestSchemaV3 {
/**
* paths
*/
public String[] paths;
/**
* pattern
*/
public String pattern;
/**
* files
*/
public String[] files;
/**
* names
*/
@SerializedName("destination_frames")
public String[] destinationFrames;
/**
* fails
*/
public String[] fails;
/**
* dels
*/
public String[] dels;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ImportFilesMultiV3() {
pattern = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ImportFilesV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ImportFilesV3 extends RequestSchemaV3 {
/**
* path
*/
public String path;
/**
* pattern
*/
public String pattern;
/**
* files
*/
public String[] files;
/**
* names
*/
@SerializedName("destination_frames")
public String[] destinationFrames;
/**
* fails
*/
public String[] fails;
/**
* dels
*/
public String[] dels;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ImportFilesV3() {
path = "";
pattern = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ImportHiveTableV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ImportHiveTableV3 extends RequestSchemaV3 {
/**
* database
*/
public String database;
/**
* table
*/
public String table;
/**
* partitions
*/
public String[][] partitions;
/**
* partitions
*/
@SerializedName("allow_multi_format")
public boolean allowMultiFormat;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ImportHiveTableV3() {
database = "";
table = "";
allowMultiFormat = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ImportSQLTableV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ImportSQLTableV99 extends RequestSchemaV3 {
/**
* connection_url
*/
@SerializedName("connection_url")
public String connectionUrl;
/**
* table
*/
public String table;
/**
* select_query
*/
@SerializedName("select_query")
public String selectQuery;
/**
* use_temp_table
*/
@SerializedName("use_temp_table")
public String useTempTable;
/**
* temp_table_name
*/
@SerializedName("temp_table_name")
public String tempTableName;
/**
* username
*/
public String username;
/**
* password
*/
public String password;
/**
* columns
*/
public String columns;
/**
* Mode for data loading. All modes may not be supported by all databases.
*/
@SerializedName("fetch_mode")
public String fetchMode;
/**
* Desired number of chunks for the target Frame. Optional.
*/
@SerializedName("num_chunks_hint")
public String numChunksHint;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ImportSQLTableV99() {
connectionUrl = "";
table = "";
selectQuery = "";
useTempTable = "";
tempTableName = "";
username = "";
password = "";
columns = "*";
fetchMode = "";
numChunksHint = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InfogramAlgorithm.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum InfogramAlgorithm {
AUTO,
deeplearning,
drf,
gbm,
glm,
xgboost,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InfogramModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InfogramModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Array of conditional mutual information for admissible features normalized to 0.0 and 1.0
*/
@SerializedName("admissible_cmi")
public double[] admissibleCmi;
/**
* Array of conditional mutual information for admissible features raw and not normalized to 0.0 and 1.0
*/
@SerializedName("admissible_cmi_raw")
public double[] admissibleCmiRaw;
/**
* Array of variable importance for admissible features
*/
@SerializedName("admissible_relevance")
public double[] admissibleRelevance;
/**
* Array containing names of admissible features for the user
*/
@SerializedName("admissible_features")
public String[] admissibleFeatures;
/**
* Array containing names of admissible features for the user from the validation dataset.
*/
@SerializedName("admissible_features_valid")
public String[] admissibleFeaturesValid;
/**
* Array containing names of admissible features for the user from cross-validation.
*/
@SerializedName("admissible_features_xval")
public String[] admissibleFeaturesXval;
/**
* Array of raw conditional mutual information for all features excluding sensitive attributes if applicable
*/
@SerializedName("cmi_raw")
public double[] cmiRaw;
/**
* Array of conditional mutual information for all features excluding sensitive attributes if applicable normalized
* to 0.0 and 1.0
*/
public double[] cmi;
/**
* Array containing names of all features excluding sensitive attributes if applicable corresponding to CMI and
* relevance
*/
@SerializedName("all_predictor_names")
public String[] allPredictorNames;
/**
* Array of variable importance for all features excluding sensitive attributes if applicable
*/
public double[] relevance;
/**
* Frame key that stores the predictor names, net CMI and relevance.
*/
@SerializedName("admissible_score_key")
public FrameKeyV3 admissibleScoreKey;
/**
* Frame key that stores the predictor names, net CMI and relevance calculated from validation dataset.
*/
@SerializedName("admissible_score_key_valid")
public FrameKeyV3 admissibleScoreKeyValid;
/**
* Frame key that stores the predictor names, net CMI and relevance from cross-validation.
*/
@SerializedName("admissible_score_key_xval")
public FrameKeyV3 admissibleScoreKeyXval;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public InfogramModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InfogramModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InfogramModelV3 extends ModelSchemaV3<InfogramParametersV3, InfogramModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public InfogramParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public InfogramModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public InfogramModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InfogramParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InfogramParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for pseudo random number generator (if applicable).
*/
public long seed;
/**
* Standardize numeric columns to have zero mean and unit variance.
*/
public boolean standardize;
/**
* Plug Values (a single row frame containing values that will be used to impute missing values of the
* training/validation frame, use with conjunction missing_values_handling = PlugValues).
*/
@SerializedName("plug_values")
public FrameKeyV3 plugValues;
/**
* Maximum number of iterations.
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of
* response does not reflect reality.
*/
public double prior;
/**
* Balance training data class counts via over/under-sampling (for imbalanced data).
*/
@SerializedName("balance_classes")
public boolean balanceClasses;
/**
* Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be
* automatically computed to obtain class balance during training. Requires balance_classes.
*/
@SerializedName("class_sampling_factors")
public float[] classSamplingFactors;
/**
* Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
* balance_classes.
*/
@SerializedName("max_after_balance_size")
public float maxAfterBalanceSize;
/**
* Type of machine learning algorithm used to build the infogram. Options include 'AUTO' (gbm), 'deeplearning' (Deep
* Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default
* parameters), 'glm' (GLM with default parameters), or 'xgboost' (if available, XGBoost with default parameters).
*/
public InfogramAlgorithm algorithm;
/**
* Customized parameters for the machine learning algorithm specified in the algorithm parameter.
*/
@SerializedName("algorithm_params")
public String algorithmParams;
/**
* Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if applicable.
* These features (e.g. race, gender, etc) should not drive the prediction of the response.
*/
@SerializedName("protected_columns")
public String[] protectedColumns;
/**
* A number between 0 and 1 representing a threshold for total information, defaulting to 0.1. For a specific
* feature, if the total information is higher than this threshold, and the corresponding net information is also
* higher than the threshold ``net_information_threshold``, that feature will be considered admissible. The total
* information is the x-axis of the Core Infogram. Default is -1 which gets set to 0.1.
*/
@SerializedName("total_information_threshold")
public double totalInformationThreshold;
/**
* A number between 0 and 1 representing a threshold for net information, defaulting to 0.1. For a specific
* feature, if the net information is higher than this threshold, and the corresponding total information is also
* higher than the total_information_threshold, that feature will be considered admissible. The net information is
* the y-axis of the Core Infogram. Default is -1 which gets set to 0.1.
*/
@SerializedName("net_information_threshold")
public double netInformationThreshold;
/**
* A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1. This is only used
* when ``protected_columns`` is set by the user. For a specific feature, if the relevance index value is higher
* than this threshold, and the corresponding safety index is also higher than the safety_index_threshold``, that
* feature will be considered admissible. The relevance index is the x-axis of the Fair Infogram. Default is -1
* which gets set to 0.1.
*/
@SerializedName("relevance_index_threshold")
public double relevanceIndexThreshold;
/**
* A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1. This is only used
* when protected_columns is set by the user. For a specific feature, if the safety index value is higher than this
* threshold, and the corresponding relevance index is also higher than the relevance_index_threshold, that feature
* will be considered admissible. The safety index is the y-axis of the Fair Infogram. Default is -1 which gets set
* to 0.1.
*/
@SerializedName("safety_index_threshold")
public double safetyIndexThreshold;
/**
* The fraction of training frame to use to build the infogram model. Defaults to 1.0, and any value greater than 0
* and less than or equal to 1.0 is acceptable.
*/
@SerializedName("data_fraction")
public double dataFraction;
/**
* An integer specifying the number of columns to evaluate in the infogram. The columns are ranked by variable
* importance, and the top N are evaluated. Defaults to 50.
*/
@SerializedName("top_n_features")
public int topNFeatures;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public InfogramParametersV3() {
seed = -1L;
standardize = false;
maxIterations = 0;
prior = 0.0;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
algorithm = InfogramAlgorithm.AUTO;
algorithmParams = "";
totalInformationThreshold = -1.0;
netInformationThreshold = -1.0;
relevanceIndexThreshold = -1.0;
safetyIndexThreshold = -1.0;
dataFraction = 1.0;
topNFeatures = 50;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InfogramV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InfogramV3 extends ModelBuilderSchema<InfogramParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public InfogramParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public InfogramV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InitIDV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InitIDV3 extends RequestSchemaV3 {
/**
* Session ID
*/
@SerializedName("session_key")
public String sessionKey;
/**
* Indicates whether users are allowed to set and modify session properties
*/
@SerializedName("session_properties_allowed")
public boolean sessionPropertiesAllowed;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public InitIDV3() {
sessionKey = "";
sessionPropertiesAllowed = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InputSchemaV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InputSchemaV4 {
/**
* Filter on the set of output fields: if you set _fields="foo,bar,baz", then only those fields will be included in
* the output; or you can specify _fields="-goo,gee" to include all fields except goo and gee. If the result
* contains nested data structures, then you can refer to the fields within those structures as well. For example if
* you specify _fields="foo(oof),bar(-rab)", then only fields foo and bar will be included, and within foo there
* will be only field oof, whereas within bar all fields except rab will be reported.
*/
public String _fields;
/**
* Public constructor
*/
public InputSchemaV4() {
_fields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/InteractionV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class InteractionV3 extends RequestSchemaV3 {
/**
* destination key
*/
public FrameKeyV3 dest;
/**
* Input data frame
*/
@SerializedName("source_frame")
public FrameKeyV3 sourceFrame;
/**
* Factor columns
*/
@SerializedName("factor_columns")
public String[] factorColumns;
/**
* Whether to create pairwise quadratic interactions between factors (otherwise create one higher-order
* interaction). Only applicable if there are 3 or more factors.
*/
public boolean pairwise;
/**
* Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be
* made)
*/
@SerializedName("max_factors")
public int maxFactors;
/**
* Min. occurrence threshold for factor levels in pair-wise interaction terms
*/
@SerializedName("min_occurrence")
public int minOccurrence;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public InteractionV3() {
pairwise = false;
maxFactors = 100;
minOccurrence = 1;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IoStatsEntry.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IoStatsEntry extends SchemaV3 {
/**
* Back end type
*/
public String backend;
/**
* Number of store events
*/
@SerializedName("store_count")
public long storeCount;
/**
* Cumulative stored bytes
*/
@SerializedName("store_bytes")
public long storeBytes;
/**
* Number of delete events
*/
@SerializedName("delete_count")
public long deleteCount;
/**
* Number of load events
*/
@SerializedName("load_count")
public long loadCount;
/**
* Cumulative loaded bytes
*/
@SerializedName("load_bytes")
public long loadBytes;
/**
* Public constructor
*/
public IoStatsEntry() {
backend = "";
storeCount = 0L;
storeBytes = 0L;
deleteCount = 0L;
loadCount = 0L;
loadBytes = 0L;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsolationForestModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsolationForestModelOutputV3 extends SharedTreeModelOutputV3 {
/**
* Variable Splits
*/
@SerializedName("variable_splits")
public TwoDimTableV3 variableSplits;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Variable Importances
public TwoDimTableV3 variableImportances;
// The Intercept term, the initial model function value to which trees make adjustments
public double initF;
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public IsolationForestModelOutputV3() {
initF = 0.0;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsolationForestModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsolationForestModelV3 extends SharedTreeModelV3<IsolationForestParametersV3, IsolationForestModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public IsolationForestParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public IsolationForestModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public IsolationForestModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsolationForestParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsolationForestParametersV3 extends SharedTreeParametersV3 {
/**
* Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters
* sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored.
*/
@SerializedName("sample_size")
public long sampleSize;
/**
* Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to
* 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead.
*/
@SerializedName("sample_rate")
public double sampleRate;
/**
* Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of
* predictors)/3.
*/
public int mtries;
/**
* Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function
* will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined).
*/
public double contamination;
/**
* (experimental) Name of the response column in the validation frame. Response column should be binary and indicate
* not anomaly/anomaly.
*/
@SerializedName("validation_response_column")
public ColSpecifierV3 validationResponseColumn;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Balance training data class counts via over/under-sampling (for imbalanced data).
public boolean balanceClasses;
// Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be
// automatically computed to obtain class balance during training. Requires balance_classes.
public float[] classSamplingFactors;
// Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
// balance_classes.
public float maxAfterBalanceSize;
// [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
public int maxConfusionMatrixSize;
// Number of trees.
public int ntrees;
// Maximum tree depth (0 for unlimited).
public int maxDepth;
// Fewest allowed (weighted) observations in a leaf.
public double minRows;
// For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
public int nbins;
// For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease
// by factor of two per level
public int nbinsTopLevel;
// For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher
// values can lead to more overfitting.
public int nbinsCats;
// r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and
// stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds
// this
public double r2Stopping;
// Seed for pseudo random number generator (if applicable)
public long seed;
// Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
public boolean buildTreeOneNode;
// A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
public double[] sampleRatePerClass;
// Column sample rate per tree (from 0.0 to 1.0)
public double colSampleRatePerTree;
// Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
public double colSampleRateChangePerLevel;
// Score the model after every so many trees. Disabled if set to 0.
public int scoreTreeInterval;
// Minimum relative improvement in squared error reduction for a split to happen
public double minSplitImprovement;
// What type of histogram to use for finding optimal split points
public TreeSharedTreeModelSharedTreeParametersHistogramType histogramType;
// Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can
// provide more accurate estimates of class probabilities.
public boolean calibrateModel;
// Data for model calibration
public FrameKeyV3 calibrationFrame;
// Calibration method to use
public TreeCalibrationHelperCalibrationMethod calibrationMethod;
// Check if response column is constant. If enabled, then an exception is thrown if the response column is a
// constant value.If disabled, then model will train regardless of the response column being a constant value or
// not.
public boolean checkConstantResponse;
// Create checkpoints into defined directory while training process is still running. In case of cluster shutdown,
// this checkpoint can be used to restart training.
public String inTrainingCheckpointsDir;
// Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is
// defined
public int inTrainingCheckpointsTreeInterval;
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public IsolationForestParametersV3() {
sampleSize = 256L;
sampleRate = -1.0;
mtries = -1;
contamination = -1.0;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
ntrees = 50;
maxDepth = 8;
minRows = 1.0;
nbins = 2;
nbinsTopLevel = 1024;
nbinsCats = 2;
r2Stopping = 1.7976931348623157e+308;
seed = -1L;
buildTreeOneNode = false;
colSampleRatePerTree = 1.0;
colSampleRateChangePerLevel = 1.0;
scoreTreeInterval = 0;
minSplitImprovement = 0.0;
histogramType = TreeSharedTreeModelSharedTreeParametersHistogramType.Random;
calibrateModel = false;
calibrationMethod = TreeCalibrationHelperCalibrationMethod.AUTO;
checkConstantResponse = true;
inTrainingCheckpointsDir = "";
inTrainingCheckpointsTreeInterval = 1;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.gaussian;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.01;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsolationForestV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsolationForestV3 extends SharedTreeV3<IsolationForestParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public IsolationForestParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public IsolationForestV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsotonicRegressionModelOutOfBoundsHandling.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum IsotonicRegressionModelOutOfBoundsHandling {
NA,
clip,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsotonicRegressionModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsotonicRegressionModelOutputV3 extends ModelOutputSchemaV3 {
/**
* thresholds y
*/
@SerializedName("thresholds_y")
public double[] thresholdsY;
/**
* thresholds X
*/
@SerializedName("thresholds_x")
public double[] thresholdsX;
/**
* min X
*/
@SerializedName("min_x")
public double minX;
/**
* max X
*/
@SerializedName("max_x")
public double maxX;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public IsotonicRegressionModelOutputV3() {
minX = 0.0;
maxX = 0.0;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsotonicRegressionModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsotonicRegressionModelV3 extends ModelSchemaV3<IsotonicRegressionParametersV3, IsotonicRegressionModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public IsotonicRegressionParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public IsotonicRegressionModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public IsotonicRegressionModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsotonicRegressionParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsotonicRegressionParametersV3 extends ModelParametersSchemaV3 {
/**
* Method of handling values of X predictor that are outside of the bounds seen in training.
*/
@SerializedName("out_of_bounds")
public IsotonicRegressionModelOutOfBoundsHandling outOfBounds;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public IsotonicRegressionParametersV3() {
outOfBounds = IsotonicRegressionModelOutOfBoundsHandling.NA;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/IsotonicRegressionV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class IsotonicRegressionV3 extends ModelBuilderSchema<IsotonicRegressionParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public IsotonicRegressionParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public IsotonicRegressionV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JStackV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class JStackV3 extends RequestSchemaV3 {
/**
* Stacktraces
*/
public DStackTraceV3[] traces;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public JStackV3() {
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JobIV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class JobIV4 extends InputSchemaV4 {
/**
* Id of the job to fetch.
*/
@SerializedName("job_id")
public String jobId;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Filter on the set of output fields: if you set _fields="foo,bar,baz", then only those fields will be included in
// the output; or you can specify _fields="-goo,gee" to include all fields except goo and gee. If the result
// contains nested data structures, then you can refer to the fields within those structures as well. For example if
// you specify _fields="foo(oof),bar(-rab)", then only fields foo and bar will be included, and within foo there
// will be only field oof, whereas within bar all fields except rab will be reported.
public String _fields;
*/
/**
* Public constructor
*/
public JobIV4() {
jobId = "";
_fields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JobKeyV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class JobKeyV3 extends KeyV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Name (string representation) for this Key.
public String name;
// Name (string representation) for the type of Keyed this Key points to.
public String type;
// URL for the resource that this Key points to, if one exists.
public String url;
*/
/**
* Public constructor
*/
public JobKeyV3() {
name = "";
type = "";
url = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JobStatus.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum JobStatus {
CANCELLED,
DONE,
FAILED,
RUNNING,
STOPPING,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JobV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class JobV3 extends SchemaV3 {
/**
* Job Key
*/
public JobKeyV3 key;
/**
* Job description
*/
public String description;
/**
* job status
*/
public String status;
/**
* progress, from 0 to 1
*/
public float progress;
/**
* current progress status description
*/
@SerializedName("progress_msg")
public String progressMsg;
/**
* Start time
*/
@SerializedName("start_time")
public long startTime;
/**
* Runtime in milliseconds
*/
public long msec;
/**
* destination key
*/
public KeyV3 dest;
/**
* exception
*/
public String[] warnings;
/**
* exception
*/
public String exception;
/**
* stacktrace
*/
public String stacktrace;
/**
* recoverable
*/
@SerializedName("auto_recoverable")
public boolean autoRecoverable;
/**
* ready for view
*/
@SerializedName("ready_for_view")
public boolean readyForView;
/**
* Public constructor
*/
public JobV3() {
description = "";
status = "";
progress = 0.0f;
progressMsg = "";
startTime = 0L;
msec = 0L;
exception = "";
stacktrace = "";
autoRecoverable = false;
readyForView = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JobV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class JobV4 extends OutputSchemaV4 {
/**
* Job id
*/
@SerializedName("job_id")
public String jobId;
/**
* Job status
*/
public JobStatus status;
/**
* Current progress, a number going from 0 to 1
*/
public float progress;
/**
* Current progress status description
*/
@SerializedName("progress_msg")
public String progressMsg;
/**
* Start time
*/
@SerializedName("start_time")
public long startTime;
/**
* Runtime in milliseconds
*/
public long duration;
/**
* Id of the target object (being created by this Job)
*/
@SerializedName("target_id")
public String targetId;
/**
* Type of the target: Frame, Model, etc.
*/
@SerializedName("target_type")
public String targetType;
/**
* Exception message, if an exception occurred
*/
public String exception;
/**
* Stacktrace
*/
public String stacktrace;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Url describing the schema of the current object.
public String __schema;
*/
/**
* Public constructor
*/
public JobV4() {
jobId = "";
progress = 0.0f;
progressMsg = "";
startTime = 0L;
duration = 0L;
targetId = "";
targetType = "";
exception = "";
stacktrace = "";
__schema = "/4/schemas/JobV4";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/JobsV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class JobsV3 extends RequestSchemaV3 {
/**
* Optional Job identifier
*/
@SerializedName("job_id")
public JobKeyV3 jobId;
/**
* jobs
*/
public JobV3[] jobs;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public JobsV3() {
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/KMeansInitialization.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum KMeansInitialization {
Furthest,
PlusPlus,
Random,
User,
}
|
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