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/KMeansModelOutputV3.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 KMeansModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Cluster Centers[k][features]
*/
public TwoDimTableV3 centers;
/**
* Cluster Centers[k][features] on Standardized Data
*/
@SerializedName("centers_std")
public TwoDimTableV3 centersStd;
/*------------------------------------------------------------------------------------------------------------------
// 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 KMeansModelOutputV3() {
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/KMeansModelV3.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 KMeansModelV3 extends ModelSchemaV3<KMeansParametersV3, KMeansModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public KMeansParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public KMeansModelOutputV3 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 KMeansModelV3() {
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/KMeansParametersV3.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 KMeansParametersV3 extends ClusteringModelParametersSchemaV3 {
/**
* This option allows you to specify a dataframe, where each row represents an initial cluster center. The user-
* specified points must have the same number of columns as the training observations. The number of rows must equal
* the number of clusters
*/
@SerializedName("user_points")
public FrameKeyV3 userPoints;
/**
* Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration)
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Standardize columns before computing distances
*/
public boolean standardize;
/**
* RNG Seed
*/
public long seed;
/**
* Initialization mode
*/
public KMeansInitialization init;
/**
* Whether to estimate the number of clusters (<=k) iteratively and deterministically.
*/
@SerializedName("estimate_k")
public boolean estimateK;
/**
* An array specifying the minimum number of points that should be in each cluster. The length of the constraints
* array has to be the same as the number of clusters.
*/
@SerializedName("cluster_size_constraints")
public int[] clusterSizeConstraints;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find
// up to k centroids.
public int k;
// 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 KMeansParametersV3() {
maxIterations = 10;
standardize = true;
seed = -1L;
init = KMeansInitialization.Furthest;
estimateK = false;
k = 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/KMeansV3.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 KMeansV3 extends ClusteringModelBuilderSchema {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public KMeansParametersV3 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 KMeansV3() {
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/KeyV3.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 KeyV3 extends SchemaV3 {
/**
* 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.
*/
@SerializedName("URL")
public String url;
/**
* Public constructor
*/
public KeyV3() {
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/KeyValueV3.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 KeyValueV3 extends SchemaV3 {
/**
* Key
*/
public String key;
/**
* Value
*/
public double value;
/**
* Public constructor
*/
public KeyValueV3() {
key = "";
value = 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/KillMinus3V3.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 KillMinus3V3 extends RequestSchemaV3 {
/*------------------------------------------------------------------------------------------------------------------
// 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 KillMinus3V3() {
_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/LeaderboardV99.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 LeaderboardV99 extends SchemaV3 {
/**
* Identifier for models that should be grouped together in the leaderboard
*/
@SerializedName("project_name")
public String projectName;
/**
* List of models for this leaderboard, sorted by metric so that the best is first
*/
public ModelKeyV3[] models;
/**
* Frame for this leaderboard
*/
@SerializedName("leaderboard_frame")
public FrameKeyV3 leaderboardFrame;
/**
* Checksum for the Frame for this leaderboard
*/
@SerializedName("leaderboard_frame_checksum")
public long leaderboardFrameChecksum;
/**
* Sort metrics for the models in this leaderboard, in the same order as the models
*/
@SerializedName("sort_metrics")
public double[] sortMetrics;
/**
* Metric used to sort this leaderboard
*/
@SerializedName("sort_metric")
public String sortMetric;
/**
* Metric direction used in the sort
*/
@SerializedName("sort_decreasing")
public boolean sortDecreasing;
/**
* A table representation of this leaderboard, for easy rendering
*/
public TwoDimTableV3 table;
/**
* Public constructor
*/
public LeaderboardV99() {
projectName = "<default>";
leaderboardFrameChecksum = 0L;
sortMetric = "";
sortDecreasing = 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/LeaderboardsV99.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 LeaderboardsV99 extends RequestSchemaV3 {
/**
* Name of project of interest
*/
@SerializedName("project_name")
public String projectName;
/**
* List of extension columns to add to leaderboard
*/
public String[] extensions;
/**
* Leaderboards
*/
public LeaderboardV99[] leaderboards;
/*------------------------------------------------------------------------------------------------------------------
// 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 LeaderboardsV99() {
projectName = "";
_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/ListRequestV4.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 ListRequestV4 extends OutputSchemaV4 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Url describing the schema of the current object.
public String __schema;
*/
/**
* Public constructor
*/
public ListRequestV4() {
__schema = "/4/schemas/ListRequestV4";
}
/**
* 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/LogAndEchoV3.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 LogAndEchoV3 extends RequestSchemaV3 {
/**
* Message to be Logged and Echoed
*/
public String message;
/*------------------------------------------------------------------------------------------------------------------
// 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 LogAndEchoV3() {
message = "";
_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/LoggingLevel.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 LoggingLevel {
DEBUG,
ERROR,
INFO,
TRACE,
WARN,
}
|
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/LogsV3.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 LogsV3 extends RequestSchemaV3 {
/**
* Identifier of the node to get logs from. It can be either node index starting from (0-based), where -1 means
* current node, or IP and port.
*/
public String nodeidx;
/**
* Which specific log file to read from the log file directory. If left unspecified, the system chooses a default
* for you.
*/
public String name;
/**
* Content of log file
*/
public String log;
/*------------------------------------------------------------------------------------------------------------------
// 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 LogsV3() {
nodeidx = "";
name = "";
log = "";
_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/MakeGLMModelV3.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 MakeGLMModelV3 extends SchemaV3 {
/**
* source model
*/
public ModelKeyV3 model;
/**
* destination key
*/
public ModelKeyV3 dest;
/**
* coefficient names
*/
public String[] names;
/**
* new glm coefficients
*/
public double[] beta;
/**
* decision threshold for label-generation
*/
public float threshold;
/**
* Public constructor
*/
public MakeGLMModelV3() {
threshold = 0.5f;
}
/**
* 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/MetadataV3.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 MetadataV3 extends RequestSchemaV3 {
/**
* Number for specifying an endpoint
*/
public int num;
/**
* HTTP method (GET, POST, DELETE) if fetching by path
*/
@SerializedName("http_method")
public String httpMethod;
/**
* Path for specifying an endpoint
*/
public String path;
/**
* Class name, for fetching docs for a schema (DEPRECATED)
*/
public String classname;
/**
* Schema name (e.g., DocsV1), for fetching docs for a schema
*/
public String schemaname;
/**
* List of endpoint routes
*/
public RouteV3[] routes;
/**
* List of schemas
*/
public SchemaMetadataV3[] schemas;
/**
* Table of Contents Markdown
*/
public String markdown;
/*------------------------------------------------------------------------------------------------------------------
// 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 MetadataV3() {
num = 0;
httpMethod = "";
path = "";
classname = "";
schemaname = "";
markdown = "";
_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/MissingInserterV3.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 MissingInserterV3 extends RequestSchemaV3 {
/**
* dataset
*/
public FrameKeyV3 dataset;
/**
* Fraction of data to replace with a missing value
*/
public double fraction;
/**
* Seed
*/
public long seed;
/*------------------------------------------------------------------------------------------------------------------
// 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 MissingInserterV3() {
fraction = 0.0;
seed = -4773443660025852529L;
_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/ModelBuilderBuilderVisibility.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 ModelBuilderBuilderVisibility {
AlwaysVisible,
Beta,
Experimental,
}
|
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/ModelBuilderSchema.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 ModelBuilderSchema<P extends ModelParametersSchemaV3> extends RequestSchemaV3 {
/**
* Model builder parameters.
*/
public P 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).
*/
@SerializedName("algo_full_name")
public String algoFullName;
/**
* Model categories this ModelBuilder can build.
*/
@SerializedName("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
*/
@SerializedName("error_count")
public int errorCount;
/**
* HTTP status to return for this build.
*/
@SerializedName("__http_status")
public int __httpStatus;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelBuilderSchema() {
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/ModelBuilderV3.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 ModelBuilderV3 extends SchemaV3 {
/**
* Model builder parameters.
*/
public ModelParametersSchemaV3 parameters;
/**
* Info, warning and error messages; NOTE: can be appended to while the Job is running
*/
public ValidationMessageV3[] messages;
/**
* Count of error messages
*/
@SerializedName("error_count")
public int errorCount;
/**
* Public constructor
*/
public ModelBuilderV3() {
errorCount = 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/ModelBuildersV3.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 ModelBuildersV3 extends RequestSchemaV3 {
/**
* Algo of ModelBuilder of interest
*/
public String algo;
/**
* ModelBuilders
*/
@SerializedName("model_builders")
public Map<String,ModelBuilderSchema> modelBuilders;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelBuildersV3() {
algo = "";
_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/ModelCategory.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 ModelCategory {
AutoEncoder,
Binomial,
BinomialUplift,
Clustering,
DimReduction,
Multinomial,
Ordinal,
Regression,
Unknown,
WordEmbedding,
}
|
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/ModelContributionsContributionsOutputFormat.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 ModelContributionsContributionsOutputFormat {
Compact,
Original,
}
|
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/ModelExportV3.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 ModelExportV3 extends RequestSchemaV3 {
/**
* Name of Model of interest
*/
@SerializedName("model_id")
public ModelKeyV3 modelId;
/**
* Destination file (hdfs, s3, local)
*/
public String dir;
/**
* Overwrite destination file in case it exists or throw exception if set to false.
*/
public boolean force;
/**
* Flag indicating whether the exported model artifact should also include CV Holdout Frame predictions
*/
@SerializedName("export_cross_validation_predictions")
public boolean exportCrossValidationPredictions;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelExportV3() {
dir = "";
force = true;
exportCrossValidationPredictions = 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/ModelIdV3.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 ModelIdV3 extends SchemaV3 {
/**
* Model ID
*/
@SerializedName("model_id")
public String modelId;
/**
* Public constructor
*/
public ModelIdV3() {
modelId = "";
}
/**
* 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/ModelImportV3.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 ModelImportV3 extends RequestSchemaV3 {
/**
* Save imported model under given key into DKV.
*/
@SerializedName("model_id")
public ModelKeyV3 modelId;
/**
* Source directory (hdfs, s3, local) containing serialized model
*/
public String dir;
/**
* Override existing model in case it exists or throw exception if set to false
*/
public boolean force;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelImportV3() {
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/ModelInfoV4.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 ModelInfoV4 extends OutputSchemaV4 {
/**
* Algorithm name, such as 'gbm', 'deeplearning', etc.
*/
public String algo;
/**
* Development status of the algorithm: alpha, beta, or stable.
*/
public String maturity;
/**
* Does the model support generation of POJOs?
*/
@SerializedName("have_pojo")
public boolean havePojo;
/**
* Does the model support generation of MOJOs?
*/
@SerializedName("have_mojo")
public boolean haveMojo;
/**
* Mojo version number for this algorithm.
*/
@SerializedName("mojo_version")
public String mojoVersion;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Url describing the schema of the current object.
public String __schema;
*/
/**
* Public constructor
*/
public ModelInfoV4() {
algo = "";
maturity = "";
havePojo = false;
haveMojo = false;
mojoVersion = "";
__schema = "/4/schemas/ModelInfoV4";
}
/**
* 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/ModelKeyV3.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 ModelKeyV3 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 ModelKeyV3() {
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/ModelLeafNodeAssignmentLeafNodeAssignmentType.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 ModelLeafNodeAssignmentLeafNodeAssignmentType {
Node_ID,
Path,
}
|
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/ModelMetricsAnomalyV3.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 ModelMetricsAnomalyV3 extends ModelMetricsBaseV3 {
/**
* Mean Anomaly Score.
*/
@SerializedName("mean_score")
public double meanScore;
/**
* Mean Normalized Anomaly Score.
*/
@SerializedName("mean_normalized_score")
public double meanNormalizedScore;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsAnomalyV3() {
meanScore = 0.0;
meanNormalizedScore = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsAutoEncoderV3.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 ModelMetricsAutoEncoderV3 extends ModelMetricsBaseV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsAutoEncoderV3() {
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBaseV3.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 ModelMetricsBaseV3 extends SchemaV3 {
/**
* The model used for this scoring run.
*/
public ModelKeyV3 model;
/**
* The checksum for the model used for this scoring run.
*/
@SerializedName("model_checksum")
public long modelChecksum;
/**
* The frame used for this scoring run.
*/
public FrameKeyV3 frame;
/**
* The checksum for the frame used for this scoring run.
*/
@SerializedName("frame_checksum")
public long frameChecksum;
/**
* Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
*/
public String description;
/**
* The category (e.g., Clustering) for the model used for this scoring run.
*/
@SerializedName("model_category")
public ModelCategory modelCategory;
/**
* The time in mS since the epoch for the start of this scoring run.
*/
@SerializedName("scoring_time")
public long scoringTime;
/**
* Predictions Frame.
*/
public FrameV3 predictions;
/**
* The Mean Squared Error of the prediction for this scoring run.
*/
@SerializedName("MSE")
public double mse;
/**
* The Root Mean Squared Error of the prediction for this scoring run.
*/
@SerializedName("RMSE")
public double rmse;
/**
* Number of observations.
*/
public long nobs;
/**
* Name of custom metric
*/
@SerializedName("custom_metric_name")
public String customMetricName;
/**
* Value of custom metric
*/
@SerializedName("custom_metric_value")
public double customMetricValue;
/**
* Public constructor
*/
public ModelMetricsBaseV3() {
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBinomialGLMGenericV3.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 ModelMetricsBinomialGLMGenericV3 extends ModelMetricsBinomialGenericV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/**
* coefficients_table
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The logarithmic loss for this scoring run.
public double logloss;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The AUC for this scoring run.
public double auc;
// The precision-recall AUC for this scoring run.
public double prAuc;
// The Gini score for this scoring run.
public double gini;
// The mean misclassification error per class.
public double meanPerClassError;
// The class labels of the response.
public String[] domain;
// The ConfusionMatrix at the threshold for maximum F1.
public ConfusionMatrixV3 cm;
// The Metrics for various thresholds.
public TwoDimTableV3 thresholdsAndMetricScores;
// The Metrics for various criteria.
public TwoDimTableV3 maxCriteriaAndMetricScores;
// Gains and Lift table.
public TwoDimTableV3 gainsLiftTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsBinomialGLMGenericV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
auc = 0.0;
prAuc = 0.0;
gini = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBinomialGLMV3.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 ModelMetricsBinomialGLMV3 extends ModelMetricsBinomialV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The logarithmic loss for this scoring run.
public double logloss;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The AUC for this scoring run.
public double auc;
// The precision-recall AUC for this scoring run.
public double prAuc;
// The Gini score for this scoring run.
public double gini;
// The mean misclassification error per class.
public double meanPerClassError;
// The class labels of the response.
public String[] domain;
// The ConfusionMatrix at the threshold for maximum F1.
public ConfusionMatrixV3 cm;
// The Metrics for various thresholds.
public TwoDimTableV3 thresholdsAndMetricScores;
// The Metrics for various criteria.
public TwoDimTableV3 maxCriteriaAndMetricScores;
// Gains and Lift table.
public TwoDimTableV3 gainsLiftTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsBinomialGLMV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
auc = 0.0;
prAuc = 0.0;
gini = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBinomialGenericV3.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 ModelMetricsBinomialGenericV3 extends ModelMetricsBinomialV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The logarithmic loss for this scoring run.
public double logloss;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The AUC for this scoring run.
public double auc;
// The precision-recall AUC for this scoring run.
public double prAuc;
// The Gini score for this scoring run.
public double gini;
// The mean misclassification error per class.
public double meanPerClassError;
// The class labels of the response.
public String[] domain;
// The ConfusionMatrix at the threshold for maximum F1.
public ConfusionMatrixV3 cm;
// The Metrics for various thresholds.
public TwoDimTableV3 thresholdsAndMetricScores;
// The Metrics for various criteria.
public TwoDimTableV3 maxCriteriaAndMetricScores;
// Gains and Lift table.
public TwoDimTableV3 gainsLiftTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsBinomialGenericV3() {
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
auc = 0.0;
prAuc = 0.0;
gini = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBinomialUpliftGenericV3.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 ModelMetricsBinomialUpliftGenericV3 extends ModelMetricsBinomialUpliftV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Average Treatment Effect.
public double ate;
// Average Treatment Effect on the Treated.
public double att;
// Average Treatment Effect on the Control.
public double atc;
// The default AUUC for this scoring run.
public double auuc;
// The default normalized AUUC for this scoring run.
public double auucNormalized;
// The Qini value for this scoring run.
public double qini;
// The class labels of the response.
public String[] domain;
// The metrics for various thresholds.
public TwoDimTableV3 thresholdsAndMetricScores;
// Table of all types of AUUC.
public TwoDimTableV3 auucTable;
// Table of all types of AECU values.
public TwoDimTableV3 aecuTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsBinomialUpliftGenericV3() {
ate = 0.0;
att = 0.0;
atc = 0.0;
auuc = 0.0;
auucNormalized = 0.0;
qini = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBinomialUpliftV3.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 ModelMetricsBinomialUpliftV3 extends ModelMetricsBaseV3 {
/**
* Average Treatment Effect.
*/
public double ate;
/**
* Average Treatment Effect on the Treated.
*/
public double att;
/**
* Average Treatment Effect on the Control.
*/
public double atc;
/**
* The default AUUC for this scoring run.
*/
@SerializedName("AUUC")
public double auuc;
/**
* The default normalized AUUC for this scoring run.
*/
@SerializedName("auuc_normalized")
public double auucNormalized;
/**
* The Qini value for this scoring run.
*/
public double qini;
/**
* The class labels of the response.
*/
public String[] domain;
/**
* The metrics for various thresholds.
*/
@SerializedName("thresholds_and_metric_scores")
public TwoDimTableV3 thresholdsAndMetricScores;
/**
* Table of all types of AUUC.
*/
@SerializedName("auuc_table")
public TwoDimTableV3 auucTable;
/**
* Table of all types of AECU values.
*/
@SerializedName("aecu_table")
public TwoDimTableV3 aecuTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsBinomialUpliftV3() {
ate = 0.0;
att = 0.0;
atc = 0.0;
auuc = 0.0;
auucNormalized = 0.0;
qini = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsBinomialV3.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 ModelMetricsBinomialV3 extends ModelMetricsBaseV3 {
/**
* The R^2 for this scoring run.
*/
public double r2;
/**
* The logarithmic loss for this scoring run.
*/
public double logloss;
/**
* The logarithmic likelihood for this scoring run.
*/
public double loglikelihood;
/**
* The AIC for this scoring run.
*/
@SerializedName("AIC")
public double aic;
/**
* The AUC for this scoring run.
*/
@SerializedName("AUC")
public double auc;
/**
* The precision-recall AUC for this scoring run.
*/
@SerializedName("pr_auc")
public double prAuc;
/**
* The Gini score for this scoring run.
*/
@SerializedName("Gini")
public double gini;
/**
* The mean misclassification error per class.
*/
@SerializedName("mean_per_class_error")
public double meanPerClassError;
/**
* The class labels of the response.
*/
public String[] domain;
/**
* The ConfusionMatrix at the threshold for maximum F1.
*/
public ConfusionMatrixV3 cm;
/**
* The Metrics for various thresholds.
*/
@SerializedName("thresholds_and_metric_scores")
public TwoDimTableV3 thresholdsAndMetricScores;
/**
* The Metrics for various criteria.
*/
@SerializedName("max_criteria_and_metric_scores")
public TwoDimTableV3 maxCriteriaAndMetricScores;
/**
* Gains and Lift table.
*/
@SerializedName("gains_lift_table")
public TwoDimTableV3 gainsLiftTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsBinomialV3() {
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
auc = 0.0;
prAuc = 0.0;
gini = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsClusteringV3.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 ModelMetricsClusteringV3 extends ModelMetricsBaseV3 {
/**
* Within Cluster Sum of Square Error
*/
@SerializedName("tot_withinss")
public double totWithinss;
/**
* Total Sum of Square Error to Grand Mean
*/
public double totss;
/**
* Between Cluster Sum of Square Error
*/
public double betweenss;
/**
* Centroid Statistics
*/
@SerializedName("centroid_stats")
public TwoDimTableV3 centroidStats;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsClusteringV3() {
totWithinss = 0.0;
totss = 0.0;
betweenss = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsGLRMV99.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 ModelMetricsGLRMV99 extends ModelMetricsBaseV3 {
/**
* Sum of Squared Error (Numeric Cols)
*/
public double numerr;
/**
* Misclassification Error (Categorical Cols)
*/
public double caterr;
/**
* Number of Non-Missing Numeric Values
*/
public long numcnt;
/**
* Number of Non-Missing Categorical Values
*/
public long catcnt;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsGLRMV99() {
numerr = 0.0;
caterr = 0.0;
numcnt = 0L;
catcnt = 0L;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsListSchemaV3.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 ModelMetricsListSchemaV3 extends RequestSchemaV3 {
/**
* Key of Model of interest (optional)
*/
public ModelKeyV3 model;
/**
* Key of Frame of interest (optional)
*/
public FrameKeyV3 frame;
/**
* Key of predictions frame, if predictions are requested (optional)
*/
@SerializedName("predictions_frame")
public FrameKeyV3 predictionsFrame;
/**
* Key for the frame containing per-observation deviances (optional)
*/
@SerializedName("deviances_frame")
public FrameKeyV3 deviancesFrame;
/**
* Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)
*/
@SerializedName("reconstruction_error")
public boolean reconstructionError;
/**
* Compute reconstruction error per feature (optional, only for Deep Learning AutoEncoder models)
*/
@SerializedName("reconstruction_error_per_feature")
public boolean reconstructionErrorPerFeature;
/**
* Extract Deep Features for given hidden layer (optional, only for Deep Learning models)
*/
@SerializedName("deep_features_hidden_layer")
public int deepFeaturesHiddenLayer;
/**
* Extract Deep Features for given hidden layer by name (optional, only for Deep Water models)
*/
@SerializedName("deep_features_hidden_layer_name")
public String deepFeaturesHiddenLayerName;
/**
* Reconstruct original training frame (optional, only for GLRM models)
*/
@SerializedName("reconstruct_train")
public boolean reconstructTrain;
/**
* Project GLRM archetypes back into original feature space (optional, only for GLRM models)
*/
@SerializedName("project_archetypes")
public boolean projectArchetypes;
/**
* Reverse transformation applied during training to model output (optional, only for GLRM models)
*/
@SerializedName("reverse_transform")
public boolean reverseTransform;
/**
* Return the leaf node assignment (optional, only for DRF/GBM models)
*/
@SerializedName("leaf_node_assignment")
public boolean leafNodeAssignment;
/**
* Type of the leaf node assignment (optional, only for DRF/GBM models)
*/
@SerializedName("leaf_node_assignment_type")
public ModelLeafNodeAssignmentLeafNodeAssignmentType leafNodeAssignmentType;
/**
* Predict the class probabilities at each stage (optional, only for GBM models)
*/
@SerializedName("predict_staged_proba")
public boolean predictStagedProba;
/**
* Predict the feature contributions - Shapley values (optional, only for DRF, GBM and XGBoost models)
*/
@SerializedName("predict_contributions")
public boolean predictContributions;
/**
* Return which row is used in which tree (optional, only for GBM models)
*/
@SerializedName("row_to_tree_assignment")
public boolean rowToTreeAssignment;
/**
* Specify how to output feature contributions in XGBoost - XGBoost by default outputs contributions for 1-hot
* encoded features, specifying a Compact output format will produce a per-feature contribution
*/
@SerializedName("predict_contributions_output_format")
public ModelContributionsContributionsOutputFormat predictContributionsOutputFormat;
/**
* Only for predict_contributions function - sort Shapley values and return top_n highest (optional)
*/
@SerializedName("top_n")
public int topN;
/**
* Only for predict_contributions function - sort Shapley values and return bottom_n lowest (optional)
*/
@SerializedName("bottom_n")
public int bottomN;
/**
* Only for predict_contributions function - sort absolute Shapley values (optional)
*/
@SerializedName("compare_abs")
public boolean compareAbs;
/**
* Retrieve the feature frequencies on paths in trees in tree-based models (optional, only for GBM, DRF and
* Isolation Forest)
*/
@SerializedName("feature_frequencies")
public boolean featureFrequencies;
/**
* Retrieve all members for a given exemplar (optional, only for Aggregator models)
*/
@SerializedName("exemplar_index")
public int exemplarIndex;
/**
* Compute the deviances per row (optional, only for classification or regression models)
*/
public boolean deviances;
/**
* Reference to custom evaluation function, format: `language:keyName=funcName`
*/
@SerializedName("custom_metric_func")
public String customMetricFunc;
/**
* Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO",
* "WEIGHTED_OVO". Default is "NONE" (optional, only for multinomial classification).
*/
@SerializedName("auc_type")
public String aucType;
/**
* Set default AUUC type for uplift binomial classification. Must be one of: "AUTO", "qini", "lift", "gain". Default
* is "AUTO" (optional, only for uplift binomial classification).
*/
@SerializedName("auuc_type")
public String auucType;
/**
* Custom AUUC thresholds (for uplift binomial classification).
*/
@SerializedName("custom_auuc_thresholds")
public double[] customAuucThresholds;
/**
* Set number of bins to calculate AUUC. Must be -1 or higher than 0. Default is -1 which means 1000 (optional, only
* for uplift binomial classification).
*/
@SerializedName("auuc_nbins")
public int auucNbins;
/**
* Specify background frame used as a reference for calculating SHAP.
*/
@SerializedName("background_frame")
public FrameKeyV3 backgroundFrame;
/**
* If true, transform contributions so that they sum up to the difference in the output space (applicable iff
* contributions are in link space). Note that this transformation is an approximation and the contributions won't
* be exact SHAP values.
*/
@SerializedName("output_space")
public boolean outputSpace;
/**
* If true, return contributions against each background sample (aka reference), i.e. phi(feature, x, bg), otherwise
* return contributions averaged over the background sample (phi(feature, x) = E_{bg} phi(feature, x, bg))
*/
@SerializedName("output_per_reference")
public boolean outputPerReference;
/**
* ModelMetrics
*/
@SerializedName("model_metrics")
public ModelMetricsBaseV3[] modelMetrics;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelMetricsListSchemaV3() {
reconstructionError = false;
reconstructionErrorPerFeature = false;
deepFeaturesHiddenLayer = -1;
deepFeaturesHiddenLayerName = "";
reconstructTrain = false;
projectArchetypes = false;
reverseTransform = false;
leafNodeAssignment = false;
predictStagedProba = false;
predictContributions = false;
rowToTreeAssignment = false;
topN = 0;
bottomN = 0;
compareAbs = false;
featureFrequencies = false;
exemplarIndex = -1;
deviances = false;
customMetricFunc = "";
aucType = "";
auucType = "";
auucNbins = 0;
outputSpace = false;
outputPerReference = false;
modelMetrics = new ModelMetricsBaseV3[]{};
_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/ModelMetricsMakerSchemaV3.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 ModelMetricsMakerSchemaV3 extends SchemaV3 {
/**
* Predictions Frame.
*/
@SerializedName("predictions_frame")
public String predictionsFrame;
/**
* Actuals Frame.
*/
@SerializedName("actuals_frame")
public String actualsFrame;
/**
* Weights Frame.
*/
@SerializedName("weights_frame")
public String weightsFrame;
/**
* Treatment Frame.
*/
@SerializedName("treatment_frame")
public String treatmentFrame;
/**
* Domain (for classification).
*/
public String[] domain;
/**
* Distribution (for regression).
*/
public GenmodelutilsDistributionFamily distribution;
/**
* Default AUC type (for multinomial classification).
*/
@SerializedName("auc_type")
public MultinomialAucType aucType;
/**
* Default AUUC type (for uplift binomial classification).
*/
@SerializedName("auuc_type")
public AUUCType auucType;
/**
* Number of bins to calculate AUUC (for uplift binomial classification).
*/
@SerializedName("auuc_nbins")
public int auucNbins;
/**
* Custom AUUC thresholds (for uplift binomial classification).
*/
@SerializedName("custom_auuc_thresholds")
public double[] customAuucThresholds;
/**
* Model Metrics.
*/
@SerializedName("model_metrics")
public ModelMetricsBaseV3 modelMetrics;
/**
* Public constructor
*/
public ModelMetricsMakerSchemaV3() {
predictionsFrame = "";
actualsFrame = "";
weightsFrame = "";
treatmentFrame = "";
auucNbins = 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/ModelMetricsMultinomialGLMGenericV3.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 ModelMetricsMultinomialGLMGenericV3 extends ModelMetricsMultinomialV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/**
* coefficients_table
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The hit ratio table for this scoring run.
public TwoDimTableV3 hitRatioTable;
// The ConfusionMatrix object for this scoring run.
public ConfusionMatrixV3 cm;
// The logarithmic loss for this scoring run.
public double logloss;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The mean misclassification error per class.
public double meanPerClassError;
// The average AUC for this scoring run.
public double auc;
// The average precision-recall AUC for this scoring run.
public double prAuc;
// The multinomial AUC values.
public TwoDimTableV3 multinomialAucTable;
// The multinomial PR AUC values.
public TwoDimTableV3 multinomialAucprTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsMultinomialGLMGenericV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
meanPerClassError = 0.0;
auc = 0.0;
prAuc = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsMultinomialGLMV3.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 ModelMetricsMultinomialGLMV3 extends ModelMetricsMultinomialV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The hit ratio table for this scoring run.
public TwoDimTableV3 hitRatioTable;
// The ConfusionMatrix object for this scoring run.
public ConfusionMatrixV3 cm;
// The logarithmic loss for this scoring run.
public double logloss;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The mean misclassification error per class.
public double meanPerClassError;
// The average AUC for this scoring run.
public double auc;
// The average precision-recall AUC for this scoring run.
public double prAuc;
// The multinomial AUC values.
public TwoDimTableV3 multinomialAucTable;
// The multinomial PR AUC values.
public TwoDimTableV3 multinomialAucprTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsMultinomialGLMV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
meanPerClassError = 0.0;
auc = 0.0;
prAuc = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsMultinomialGenericV3.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 ModelMetricsMultinomialGenericV3 extends ModelMetricsMultinomialV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The hit ratio table for this scoring run.
public TwoDimTableV3 hitRatioTable;
// The ConfusionMatrix object for this scoring run.
public ConfusionMatrixV3 cm;
// The logarithmic loss for this scoring run.
public double logloss;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The mean misclassification error per class.
public double meanPerClassError;
// The average AUC for this scoring run.
public double auc;
// The average precision-recall AUC for this scoring run.
public double prAuc;
// The multinomial AUC values.
public TwoDimTableV3 multinomialAucTable;
// The multinomial PR AUC values.
public TwoDimTableV3 multinomialAucprTable;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsMultinomialGenericV3() {
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
meanPerClassError = 0.0;
auc = 0.0;
prAuc = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsMultinomialV3.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 ModelMetricsMultinomialV3 extends ModelMetricsBaseV3 {
/**
* The R^2 for this scoring run.
*/
public double r2;
/**
* The hit ratio table for this scoring run.
*/
@SerializedName("hit_ratio_table")
public TwoDimTableV3 hitRatioTable;
/**
* The ConfusionMatrix object for this scoring run.
*/
public ConfusionMatrixV3 cm;
/**
* The logarithmic loss for this scoring run.
*/
public double logloss;
/**
* The logarithmic likelihood for this scoring run.
*/
public double loglikelihood;
/**
* The AIC for this scoring run.
*/
@SerializedName("AIC")
public double aic;
/**
* The mean misclassification error per class.
*/
@SerializedName("mean_per_class_error")
public double meanPerClassError;
/**
* The average AUC for this scoring run.
*/
@SerializedName("AUC")
public double auc;
/**
* The average precision-recall AUC for this scoring run.
*/
@SerializedName("pr_auc")
public double prAuc;
/**
* The multinomial AUC values.
*/
@SerializedName("multinomial_auc_table")
public TwoDimTableV3 multinomialAucTable;
/**
* The multinomial PR AUC values.
*/
@SerializedName("multinomial_aucpr_table")
public TwoDimTableV3 multinomialAucprTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsMultinomialV3() {
r2 = 0.0;
logloss = 0.0;
loglikelihood = 0.0;
aic = 0.0;
meanPerClassError = 0.0;
auc = 0.0;
prAuc = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsOrdinalGLMGenericV3.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 ModelMetricsOrdinalGLMGenericV3 extends ModelMetricsOrdinalGenericV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/**
* coefficients_table
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The hit ratio table for this scoring run.
public TwoDimTableV3 hitRatioTable;
// The ConfusionMatrix object for this scoring run.
public ConfusionMatrixV3 cm;
// The logarithmic loss for this scoring run.
public double logloss;
// The mean misclassification error per class.
public double meanPerClassError;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsOrdinalGLMGenericV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
logloss = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsOrdinalGLMV3.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 ModelMetricsOrdinalGLMV3 extends ModelMetricsOrdinalV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The hit ratio table for this scoring run.
public TwoDimTableV3 hitRatioTable;
// The ConfusionMatrix object for this scoring run.
public ConfusionMatrixV3 cm;
// The logarithmic loss for this scoring run.
public double logloss;
// The mean misclassification error per class.
public double meanPerClassError;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsOrdinalGLMV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
logloss = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsOrdinalGenericV3.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 ModelMetricsOrdinalGenericV3 extends ModelMetricsBaseV3 {
/**
* The R^2 for this scoring run.
*/
public double r2;
/**
* The hit ratio table for this scoring run.
*/
@SerializedName("hit_ratio_table")
public TwoDimTableV3 hitRatioTable;
/**
* The ConfusionMatrix object for this scoring run.
*/
public ConfusionMatrixV3 cm;
/**
* The logarithmic loss for this scoring run.
*/
public double logloss;
/**
* The mean misclassification error per class.
*/
@SerializedName("mean_per_class_error")
public double meanPerClassError;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsOrdinalGenericV3() {
r2 = 0.0;
logloss = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsOrdinalV3.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 ModelMetricsOrdinalV3 extends ModelMetricsBaseV3 {
/**
* The R^2 for this scoring run.
*/
public double r2;
/**
* The hit ratio table for this scoring run.
*/
@SerializedName("hit_ratio_table")
public TwoDimTableV3 hitRatioTable;
/**
* The ConfusionMatrix object for this scoring run.
*/
public ConfusionMatrixV3 cm;
/**
* The logarithmic loss for this scoring run.
*/
public double logloss;
/**
* The mean misclassification error per class.
*/
@SerializedName("mean_per_class_error")
public double meanPerClassError;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsOrdinalV3() {
r2 = 0.0;
logloss = 0.0;
meanPerClassError = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsPCAV3.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 ModelMetricsPCAV3 extends ModelMetricsBaseV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsPCAV3() {
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionCoxPHGenericV3.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 ModelMetricsRegressionCoxPHGenericV3 extends ModelMetricsRegressionV3 {
/**
* Concordance metric (c-index)
*/
public double concordance;
/**
* Number of concordant pairs
*/
public long concordant;
/**
* Number of discordant pairs.
*/
public long discordant;
/**
* Number of tied pairs
*/
@SerializedName("tied_y")
public long tiedY;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The mean residual deviance for this scoring run.
public double meanResidualDeviance;
// The mean absolute error for this scoring run.
public double mae;
// The root mean squared log error for this scoring run.
public double rmsle;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionCoxPHGenericV3() {
concordance = 0.0;
concordant = 0L;
discordant = 0L;
tiedY = 0L;
r2 = 0.0;
meanResidualDeviance = 0.0;
mae = 0.0;
rmsle = 0.0;
loglikelihood = 0.0;
aic = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionCoxPHV3.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 ModelMetricsRegressionCoxPHV3 extends ModelMetricsRegressionV3 {
/**
* concordance index
*/
public double concordance;
/**
* number of concordant pairs
*/
public long concordant;
/**
* number of discordant pairs
*/
public long discordant;
/**
* number of pairs tied in Y value
*/
@SerializedName("tied_y")
public long tiedY;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The mean residual deviance for this scoring run.
public double meanResidualDeviance;
// The mean absolute error for this scoring run.
public double mae;
// The root mean squared log error for this scoring run.
public double rmsle;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionCoxPHV3() {
concordance = 0.0;
concordant = 0L;
discordant = 0L;
tiedY = 0L;
r2 = 0.0;
meanResidualDeviance = 0.0;
mae = 0.0;
rmsle = 0.0;
loglikelihood = 0.0;
aic = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionGLMGenericV3.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 ModelMetricsRegressionGLMGenericV3 extends ModelMetricsRegressionV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/**
* coefficients_table
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The mean residual deviance for this scoring run.
public double meanResidualDeviance;
// The mean absolute error for this scoring run.
public double mae;
// The root mean squared log error for this scoring run.
public double rmsle;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionGLMGenericV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
meanResidualDeviance = 0.0;
mae = 0.0;
rmsle = 0.0;
loglikelihood = 0.0;
aic = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionGLMV3.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 ModelMetricsRegressionGLMV3 extends ModelMetricsRegressionV3 {
/**
* residual deviance
*/
@SerializedName("residual_deviance")
public double residualDeviance;
/**
* null deviance
*/
@SerializedName("null_deviance")
public double nullDeviance;
/**
* AIC
*/
@SerializedName("AIC")
public double aic;
/**
* log likelihood
*/
public double loglikelihood;
/**
* null DOF
*/
@SerializedName("null_degrees_of_freedom")
public long nullDegreesOfFreedom;
/**
* residual DOF
*/
@SerializedName("residual_degrees_of_freedom")
public long residualDegreesOfFreedom;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The R^2 for this scoring run.
public double r2;
// The mean residual deviance for this scoring run.
public double meanResidualDeviance;
// The mean absolute error for this scoring run.
public double mae;
// The root mean squared log error for this scoring run.
public double rmsle;
// The logarithmic likelihood for this scoring run.
public double loglikelihood;
// The AIC for this scoring run.
public double aic;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionGLMV3() {
residualDeviance = 0.0;
nullDeviance = 0.0;
aic = 0.0;
loglikelihood = 0.0;
nullDegreesOfFreedom = 0L;
residualDegreesOfFreedom = 0L;
r2 = 0.0;
meanResidualDeviance = 0.0;
mae = 0.0;
rmsle = 0.0;
loglikelihood = 0.0;
aic = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionGenericV3.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 ModelMetricsRegressionGenericV3 extends ModelMetricsBaseV3 {
/**
* The mean residual deviance for this scoring run.
*/
@SerializedName("mean_residual_deviance")
public double meanResidualDeviance;
/**
* The mean absolute error for this scoring run.
*/
public double mae;
/**
* The root mean squared log error for this scoring run.
*/
public double rmsle;
/**
* The logarithmic likelihood for this scoring run.
*/
public double loglikelihood;
/**
* The AIC for this scoring run.
*/
@SerializedName("AIC")
public double aic;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionGenericV3() {
meanResidualDeviance = 0.0;
mae = 0.0;
rmsle = 0.0;
loglikelihood = 0.0;
aic = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionHGLMGenericV3.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 ModelMetricsRegressionHGLMGenericV3 extends ModelMetricsRegressionHGLMV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// fixed coefficient)
public double[] beta;
// random coefficients
public double[][] ubeta;
// log likelihood
public double logLikelihood;
// interclass correlation
public double[] icc;
// iterations taken to build model
public int iterations;
// covariance matrix of random effects
public double[][] tmat;
// variance of residual error
public double varResidual;
// mean square error of fixed effects only
public double mseFixed;
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionHGLMGenericV3() {
logLikelihood = 0.0;
iterations = 0;
varResidual = 0.0;
mseFixed = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionHGLMV3.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 ModelMetricsRegressionHGLMV3 extends ModelMetricsBaseV3 {
/**
* fixed coefficient)
*/
public double[] beta;
/**
* random coefficients
*/
public double[][] ubeta;
/**
* log likelihood
*/
@SerializedName("log_likelihood")
public double logLikelihood;
/**
* interclass correlation
*/
public double[] icc;
/**
* iterations taken to build model
*/
public int iterations;
/**
* covariance matrix of random effects
*/
public double[][] tmat;
/**
* variance of residual error
*/
@SerializedName("var_residual")
public double varResidual;
/**
* mean square error of fixed effects only
*/
@SerializedName("mse_fixed")
public double mseFixed;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionHGLMV3() {
logLikelihood = 0.0;
iterations = 0;
varResidual = 0.0;
mseFixed = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsRegressionV3.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 ModelMetricsRegressionV3 extends ModelMetricsBaseV3 {
/**
* The R^2 for this scoring run.
*/
public double r2;
/**
* The mean residual deviance for this scoring run.
*/
@SerializedName("mean_residual_deviance")
public double meanResidualDeviance;
/**
* The mean absolute error for this scoring run.
*/
public double mae;
/**
* The root mean squared log error for this scoring run.
*/
public double rmsle;
/**
* The logarithmic likelihood for this scoring run.
*/
public double loglikelihood;
/**
* The AIC for this scoring run.
*/
@SerializedName("AIC")
public double aic;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsRegressionV3() {
r2 = 0.0;
meanResidualDeviance = 0.0;
mae = 0.0;
rmsle = 0.0;
loglikelihood = 0.0;
aic = 0.0;
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelMetricsSVDV99.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 ModelMetricsSVDV99 extends ModelMetricsBaseV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The model used for this scoring run.
public ModelKeyV3 model;
// The checksum for the model used for this scoring run.
public long modelChecksum;
// The frame used for this scoring run.
public FrameKeyV3 frame;
// The checksum for the frame used for this scoring run.
public long frameChecksum;
// Optional description for this scoring run (to note out-of-bag, sampled data, etc.)
public String description;
// The category (e.g., Clustering) for the model used for this scoring run.
public ModelCategory modelCategory;
// The time in mS since the epoch for the start of this scoring run.
public long scoringTime;
// Predictions Frame.
public FrameV3 predictions;
// The Mean Squared Error of the prediction for this scoring run.
public double mse;
// The Root Mean Squared Error of the prediction for this scoring run.
public double rmse;
// Number of observations.
public long nobs;
// Name of custom metric
public String customMetricName;
// Value of custom metric
public double customMetricValue;
*/
/**
* Public constructor
*/
public ModelMetricsSVDV99() {
modelChecksum = 0L;
frameChecksum = 0L;
description = "";
scoringTime = 0L;
mse = 0.0;
rmse = 0.0;
nobs = 0L;
customMetricName = "";
customMetricValue = 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/ModelOutputSchemaV3.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 ModelOutputSchemaV3 extends SchemaV3 {
/**
* Column names
*/
public String[] names;
/**
* Original column names
*/
@SerializedName("original_names")
public String[] originalNames;
/**
* Column types
*/
@SerializedName("column_types")
public String[] columnTypes;
/**
* Domains for categorical columns
*/
public String[][] domains;
/**
* Cross-validation models (model ids)
*/
@SerializedName("cross_validation_models")
public ModelKeyV3[] crossValidationModels;
/**
* Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
* instead)
*/
@SerializedName("cross_validation_predictions")
public FrameKeyV3[] crossValidationPredictions;
/**
* Cross-validation holdout predictions (full out-of-sample predictions on training data)
*/
@SerializedName("cross_validation_holdout_predictions_frame_id")
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
/**
* Cross-validation fold assignment (each row is assigned to one holdout fold)
*/
@SerializedName("cross_validation_fold_assignment_frame_id")
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
/**
* Category of the model (e.g., Binomial)
*/
@SerializedName("model_category")
public ModelCategory modelCategory;
/**
* Model summary
*/
@SerializedName("model_summary")
public TwoDimTableV3 modelSummary;
/**
* Scoring history
*/
@SerializedName("scoring_history")
public TwoDimTableV3 scoringHistory;
/**
* Cross-Validation scoring history
*/
@SerializedName("cv_scoring_history")
public TwoDimTableV3[] cvScoringHistory;
/**
* Model reproducibility information
*/
@SerializedName("reproducibility_information_table")
public TwoDimTableV3[] reproducibilityInformationTable;
/**
* Training data model metrics
*/
@SerializedName("training_metrics")
public ModelMetricsBaseV3 trainingMetrics;
/**
* Validation data model metrics
*/
@SerializedName("validation_metrics")
public ModelMetricsBaseV3 validationMetrics;
/**
* Cross-validation model metrics
*/
@SerializedName("cross_validation_metrics")
public ModelMetricsBaseV3 crossValidationMetrics;
/**
* Cross-validation model metrics summary
*/
@SerializedName("cross_validation_metrics_summary")
public TwoDimTableV3 crossValidationMetricsSummary;
/**
* Job status
*/
public String status;
/**
* Start time in milliseconds
*/
@SerializedName("start_time")
public long startTime;
/**
* End time in milliseconds
*/
@SerializedName("end_time")
public long endTime;
/**
* Runtime in milliseconds
*/
@SerializedName("run_time")
public long runTime;
/**
* Default threshold used for predictions
*/
@SerializedName("default_threshold")
public double defaultThreshold;
/**
* Help information for output fields
*/
public Map<String,String> help;
/**
* Public constructor
*/
public ModelOutputSchemaV3() {
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/ModelParameterSchemaV3.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 ModelParameterSchemaV3 extends SchemaV3 {
/**
* name in the JSON, e.g. "lambda"
*/
public String name;
/**
* [DEPRECATED] same as name.
*/
public String label;
/**
* help for the UI, e.g. "regularization multiplier, typically used for foo bar baz etc."
*/
public String help;
/**
* the field is required
*/
public boolean required;
/**
* Java type, e.g. "double"
*/
public String type;
/**
* default value, e.g. 1
*/
@SerializedName("default_value")
public Object defaultValue;
/**
* actual value as set by the user and / or modified by the ModelBuilder, e.g., 10
*/
@SerializedName("actual_value")
public Object actualValue;
/**
* input value as set by the user, e.g., 10
*/
@SerializedName("input_value")
public Object inputValue;
/**
* the importance of the parameter, used by the UI, e.g. "critical", "extended" or "expert"
*/
public String level;
/**
* list of valid values for use by the front-end
*/
public String[] values;
/**
* For Vec-type fields this is the set of Frame-type fields which must contain the named column; for example, for a
* SupervisedModel the response_column must be in both the training_frame and (if it's set) the validation_frame
*/
@SerializedName("is_member_of_frames")
public String[] isMemberOfFrames;
/**
* For Vec-type fields this is the set of other Vec-type fields which must contain mutually exclusive values; for
* example, for a SupervisedModel the response_column must be mutually exclusive with the weights_column
*/
@SerializedName("is_mutually_exclusive_with")
public String[] isMutuallyExclusiveWith;
/**
* Parameter can be used in grid call
*/
public boolean gridable;
/**
* Public constructor
*/
public ModelParameterSchemaV3() {
name = "";
label = "";
help = "";
required = false;
type = "";
level = "";
gridable = 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/ModelParametersCategoricalEncodingScheme.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 ModelParametersCategoricalEncodingScheme {
AUTO,
Binary,
Eigen,
Enum,
EnumLimited,
LabelEncoder,
OneHotExplicit,
OneHotInternal,
SortByResponse,
}
|
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/ModelParametersFoldAssignmentScheme.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 ModelParametersFoldAssignmentScheme {
AUTO,
Modulo,
Random,
Stratified,
}
|
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/ModelParametersSchemaV3.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 ModelParametersSchemaV3 extends SchemaV3 {
/**
* Destination id for this model; auto-generated if not specified.
*/
@SerializedName("model_id")
public ModelKeyV3 modelId;
/**
* Id of the training data frame.
*/
@SerializedName("training_frame")
public FrameKeyV3 trainingFrame;
/**
* Id of the validation data frame.
*/
@SerializedName("validation_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.
*/
@SerializedName("keep_cross_validation_models")
public boolean keepCrossValidationModels;
/**
* Whether to keep the predictions of the cross-validation models.
*/
@SerializedName("keep_cross_validation_predictions")
public boolean keepCrossValidationPredictions;
/**
* Whether to keep the cross-validation fold assignment.
*/
@SerializedName("keep_cross_validation_fold_assignment")
public boolean keepCrossValidationFoldAssignment;
/**
* Allow parallel training of cross-validation models
*/
@SerializedName("parallelize_cross_validation")
public boolean parallelizeCrossValidation;
/**
* Distribution function
*/
public GenmodelutilsDistributionFamily distribution;
/**
* Tweedie power for Tweedie regression, must be between 1 and 2.
*/
@SerializedName("tweedie_power")
public double tweediePower;
/**
* Desired quantile for Quantile regression, must be between 0 and 1.
*/
@SerializedName("quantile_alpha")
public double quantileAlpha;
/**
* Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
*/
@SerializedName("huber_alpha")
public double huberAlpha;
/**
* Response variable column.
*/
@SerializedName("response_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.
*/
@SerializedName("weights_column")
public ColSpecifierV3 weightsColumn;
/**
* Offset column. This will be added to the combination of columns before applying the link function.
*/
@SerializedName("offset_column")
public ColSpecifierV3 offsetColumn;
/**
* Column with cross-validation fold index assignment per observation.
*/
@SerializedName("fold_column")
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.
*/
@SerializedName("fold_assignment")
public ModelParametersFoldAssignmentScheme foldAssignment;
/**
* Encoding scheme for categorical features
*/
@SerializedName("categorical_encoding")
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
/**
* For every categorical feature, only use this many most frequent categorical levels for model training. Only used
* for categorical_encoding == EnumLimited.
*/
@SerializedName("max_categorical_levels")
public int maxCategoricalLevels;
/**
* Names of columns to ignore for training.
*/
@SerializedName("ignored_columns")
public String[] ignoredColumns;
/**
* Ignore constant columns.
*/
@SerializedName("ignore_const_cols")
public boolean ignoreConstCols;
/**
* Whether to score during each iteration of model training.
*/
@SerializedName("score_each_iteration")
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)
*/
@SerializedName("stopping_rounds")
public int stoppingRounds;
/**
* Maximum allowed runtime in seconds for model training. Use 0 to disable.
*/
@SerializedName("max_runtime_secs")
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.
*/
@SerializedName("stopping_metric")
public ScoreKeeperStoppingMetric stoppingMetric;
/**
* Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
*/
@SerializedName("stopping_tolerance")
public double stoppingTolerance;
/**
* Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
*/
@SerializedName("gainslift_bins")
public int gainsliftBins;
/**
* Reference to custom evaluation function, format: `language:keyName=funcName`
*/
@SerializedName("custom_metric_func")
public String customMetricFunc;
/**
* Reference to custom distribution, format: `language:keyName=funcName`
*/
@SerializedName("custom_distribution_func")
public String customDistributionFunc;
/**
* Automatically export generated models to this directory.
*/
@SerializedName("export_checkpoints_dir")
public String exportCheckpointsDir;
/**
* Set default multinomial AUC type.
*/
@SerializedName("auc_type")
public MultinomialAucType aucType;
/**
* Public constructor
*/
public ModelParametersSchemaV3() {
nfolds = 0;
keepCrossValidationModels = false;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = false;
tweediePower = 0.0;
quantileAlpha = 0.0;
huberAlpha = 0.0;
maxCategoricalLevels = 0;
ignoreConstCols = false;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingTolerance = 0.0;
gainsliftBins = 0;
customMetricFunc = "";
customDistributionFunc = "";
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/ModelSchemaBaseV3.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 ModelSchemaBaseV3 extends SchemaV3 {
/**
* Model key
*/
@SerializedName("model_id")
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).
*/
@SerializedName("algo_full_name")
public String algoFullName;
/**
* The response column name for this Model (if applicable). Is null otherwise.
*/
@SerializedName("response_column_name")
public String responseColumnName;
/**
* The treatment column name for this Model (if applicable). Is null otherwise.
*/
@SerializedName("treatment_column_name")
public String treatmentColumnName;
/**
* The Model's training frame key
*/
@SerializedName("data_frame")
public FrameKeyV3 dataFrame;
/**
* Timestamp for when this model was completed
*/
public long timestamp;
/**
* Indicator, whether export to POJO is available
*/
@SerializedName("have_pojo")
public boolean havePojo;
/**
* Indicator, whether export to MOJO is available
*/
@SerializedName("have_mojo")
public boolean haveMojo;
/**
* Public constructor
*/
public ModelSchemaBaseV3() {
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/ModelSchemaV3.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 ModelSchemaV3<P extends ModelParametersSchemaV3, O extends ModelOutputSchemaV3> extends ModelSchemaBaseV3 {
/**
* The build parameters for the model (e.g. K for KMeans).
*/
public P parameters;
/**
* The build output for the model (e.g. the cluster centers for KMeans).
*/
public O output;
/**
* Compatible frames, if requested
*/
@SerializedName("compatible_frames")
public String[] compatibleFrames;
/**
* Checksum for all the things that go into building the Model.
*/
public long checksum;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// 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 ModelSchemaV3() {
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/ModelSelectionMode.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 ModelSelectionMode {
allsubsets,
backward,
maxr,
maxrsweep,
}
|
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/ModelSelectionModelOutputV3.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 ModelSelectionModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Names of predictors in the best predictor subset
*/
@SerializedName("best_predictors_subset")
public String[][] bestPredictorsSubset;
/**
* R2 values of all possible predictor subsets. Only for mode='allsubsets' or 'maxr'.
*/
@SerializedName("best_r2_values")
public double[] bestR2Values;
/**
* at each predictor subset size, the predictor added is collected in this array. Not for mode = 'backward'.
*/
@SerializedName("predictors_added_per_step")
public String[][] predictorsAddedPerStep;
/**
* at each predictor subset size, the predictor removed is collected in this array.
*/
@SerializedName("predictors_removed_per_step")
public String[][] predictorsRemovedPerStep;
/**
* p-values of chosen predictor subsets at each subset size. Only for model='backward'.
*/
@SerializedName("coef_p_values")
public double[][] coefPValues;
/**
* z-values of chosen predictor subsets at each subset size. Only for model='backward'.
*/
@SerializedName("z_values")
public double[][] zValues;
/**
* Key of models containing best 1-predictor model, best 2-predictors model, ....
*/
@SerializedName("best_model_ids")
public ModelKeyV3[] bestModelIds;
/**
* arrays of string arrays containing coefficient names of best 1-predictor model, best 2-predictors model, ....
*/
@SerializedName("coefficient_names")
public String[][] coefficientNames;
/**
* store coefficient values for each predictor subset. Only for maxrsweep when build_glm_model is false.
*/
@SerializedName("coefficient_values")
public double[][] coefficientValues;
/**
* store standardized coefficient values for each predictor subset. Only for maxrsweep when build_glm_model is
* false.
*/
@SerializedName("coefficient_values_normalized")
public double[][] coefficientValuesNormalized;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelSelectionModelOutputV3() {
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/ModelSelectionModelV3.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 ModelSelectionModelV3 extends ModelSchemaV3<ModelSelectionParametersV3, ModelSelectionModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public ModelSelectionParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public ModelSelectionModelOutputV3 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 ModelSelectionModelV3() {
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/ModelSelectionParametersV3.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 ModelSelectionParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/**
* Family. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported
*/
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;
/**
* Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
*/
@SerializedName("lambda_search")
public boolean lambdaSearch;
/**
* For maxrsweep only. If enabled, will attempt to perform sweeping action using multiple nodes in the cluster.
* Defaults to false.
*/
@SerializedName("multinode_mode")
public boolean multinodeMode;
/**
* For maxrsweep mode only. If true, will return full blown GLM models with the desired predictorsubsets. If
* false, only the predictor subsets, predictor coefficients are returned. This is forspeeding up the model
* selection process. The users can choose to build the GLM models themselvesby using the predictor subsets
* themselves. Defaults to false.
*/
@SerializedName("build_glm_model")
public boolean buildGlmModel;
/**
* 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;
/**
* 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 (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;
/**
* Double array to initialize coefficients for GLM.
*/
public double[] startval;
/**
* If true, will return likelihood function value for GLM.
*/
@SerializedName("calc_like")
public boolean calcLike;
/**
* Mode: Used to choose model selection algorithms to use. Options include 'allsubsets' for all subsets, 'maxr'
* that uses sequential replacement and GLM to build all models, slow but works with cross-validation, validation
* frames for more robust results, 'maxrsweep' that uses sequential replacement and sweeping action, much faster
* than 'maxr', 'backward' for backward selection.
*/
public ModelSelectionMode mode;
/**
* 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;
/**
* 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;
/**
* 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;
/**
* Maximum number of predictors to be considered when building GLM models. Defaults to 1.
*/
@SerializedName("max_predictor_number")
public int maxPredictorNumber;
/**
* For mode = 'backward' only. Minimum number of predictors to be considered when building GLM models starting with
* all predictors to be included. Defaults to 1.
*/
@SerializedName("min_predictor_number")
public int minPredictorNumber;
/**
* number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability
*/
public int nparallelism;
/**
* For mode='backward' only. If specified, will stop the model building process when all coefficientsp-values drop
* below this threshold
*/
@SerializedName("p_values_threshold")
public double pValuesThreshold;
/**
* If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset.
*/
public GLMInfluence influence;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelSelectionParametersV3() {
seed = -1L;
family = GLMFamily.AUTO;
tweedieVariancePower = 0.0;
tweedieLinkPower = 0.0;
theta = 0.0;
solver = GLMSolver.IRLSM;
lambda = new double[]{0.0};
lambdaSearch = false;
multinodeMode = false;
buildGlmModel = false;
earlyStopping = false;
nlambdas = 0;
scoreIterationInterval = 0;
standardize = true;
coldStart = false;
missingValuesHandling = GLMMissingValuesHandling.MeanImputation;
nonNegative = false;
maxIterations = 0;
betaEpsilon = 0.0001;
objectiveEpsilon = -1.0;
gradientEpsilon = -1.0;
objReg = -1.0;
link = GLMLink.family_default;
calcLike = false;
mode = ModelSelectionMode.maxr;
intercept = true;
prior = 0.0;
lambdaMinRatio = 0.0;
maxActivePredictors = -1;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
computePValues = false;
removeCollinearColumns = false;
maxPredictorNumber = 1;
minPredictorNumber = 1;
nparallelism = 0;
pValuesThreshold = 0.0;
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/ModelSelectionV3.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 ModelSelectionV3 extends ModelBuilderSchema<ModelSelectionParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public ModelSelectionParametersV3 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 ModelSelectionV3() {
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/ModelSynopsisV3.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 ModelSynopsisV3 extends ModelSchemaBaseV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// 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 ModelSynopsisV3() {
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/ModelsInfoV4.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 ModelsInfoV4 extends OutputSchemaV4 {
/**
* Generic information about each model supported in H2O.
*/
public ModelInfoV4[] models;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Url describing the schema of the current object.
public String __schema;
*/
/**
* Public constructor
*/
public ModelsInfoV4() {
__schema = "/4/schemas/ModelsInfoV4";
}
/**
* 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/ModelsKeyV3.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 ModelsKeyV3 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 ModelsKeyV3() {
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/ModelsV3.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 ModelsV3 extends RequestSchemaV3 {
/**
* Name of Model of interest
*/
@SerializedName("model_id")
public ModelKeyV3 modelId;
/**
* Return potentially abridged model suitable for viewing in a browser
*/
public boolean preview;
/**
* Find and return compatible frames?
*/
@SerializedName("find_compatible_frames")
public boolean findCompatibleFrames;
/**
* Models
*/
public ModelSchemaBaseV3[] models;
/**
* Compatible frames
*/
@SerializedName("compatible_frames")
public FrameV3[] compatibleFrames;
/**
* Flag indicating whether the exported model artifact should also include CV Holdout Frame predictions
*/
@SerializedName("export_cross_validation_predictions")
public boolean exportCrossValidationPredictions;
/*------------------------------------------------------------------------------------------------------------------
// 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 ModelsV3() {
preview = false;
findCompatibleFrames = false;
exportCrossValidationPredictions = 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/MultinomialAucType.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 MultinomialAucType {
AUTO,
MACRO_OVO,
MACRO_OVR,
NONE,
WEIGHTED_OVO,
WEIGHTED_OVR,
}
|
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/NaiveBayesModelOutputV3.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 NaiveBayesModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Categorical levels of the response
*/
public String[] levels;
/**
* A-priori probabilities of the response
*/
public TwoDimTableV3 apriori;
/**
* Conditional probabilities of the predictors
*/
public TwoDimTableV3[] pcond;
/*------------------------------------------------------------------------------------------------------------------
// 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 NaiveBayesModelOutputV3() {
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/NaiveBayesModelV3.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 NaiveBayesModelV3 extends ModelSchemaV3<NaiveBayesParametersV3, NaiveBayesModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public NaiveBayesParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public NaiveBayesModelOutputV3 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 NaiveBayesModelV3() {
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/NaiveBayesParametersV3.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 NaiveBayesParametersV3 extends ModelParametersSchemaV3 {
/**
* 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;
/**
* Laplace smoothing parameter
*/
public double laplace;
/**
* Min. standard deviation to use for observations with not enough data
*/
@SerializedName("min_sdev")
public double minSdev;
/**
* Cutoff below which standard deviation is replaced with min_sdev
*/
@SerializedName("eps_sdev")
public double epsSdev;
/**
* Min. probability to use for observations with not enough data
*/
@SerializedName("min_prob")
public double minProb;
/**
* Cutoff below which probability is replaced with min_prob
*/
@SerializedName("eps_prob")
public double epsProb;
/**
* Compute metrics on training data
*/
@SerializedName("compute_metrics")
public boolean computeMetrics;
/**
* Seed for pseudo random number generator (only used for cross-validation and fold_assignment="Random" or "AUTO")
*/
public long seed;
/*------------------------------------------------------------------------------------------------------------------
// 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 NaiveBayesParametersV3() {
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
laplace = 0.0;
minSdev = 0.001;
epsSdev = 0.0;
minProb = 0.001;
epsProb = 0.0;
computeMetrics = true;
seed = -1L;
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/NaiveBayesV3.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 NaiveBayesV3 extends ModelBuilderSchema<NaiveBayesParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public NaiveBayesParametersV3 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 NaiveBayesV3() {
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/NetworkBenchV3.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 NetworkBenchV3 extends RequestSchemaV3 {
/**
* NetworkBenchResults
*/
public TwoDimTableV3[] results;
/*------------------------------------------------------------------------------------------------------------------
// 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 NetworkBenchV3() {
_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/NetworkEvent.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 NetworkEvent extends EventV3 {
/**
* Boolean flag distinguishing between sends (true) and receives(false)
*/
@SerializedName("is_send")
public boolean isSend;
/**
* network protocol (UDP/TCP)
*/
public String protocol;
/**
* UDP type (exec,ack, ackack,...
*/
@SerializedName("msg_type")
public String msgType;
/**
* Sending node
*/
public String from;
/**
* Receiving node
*/
public String to;
/**
* Pretty print of the first few bytes of the msg payload. Contains class name for tasks.
*/
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 NetworkEvent() {
isSend = false;
protocol = "unknown";
msgType = "unknown";
from = "unknown";
to = "unknown";
data = "unknown";
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/NetworkTestV3.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 NetworkTestV3 extends RequestSchemaV3 {
/**
* Collective broadcast/reduce times in microseconds (for each message size)
*/
@SerializedName("microseconds_collective")
public double[] microsecondsCollective;
/**
* Collective bandwidths in Bytes/sec (for each message size, for each node)
*/
@SerializedName("bandwidths_collective")
public double[] bandwidthsCollective;
/**
* Round-trip times in microseconds (for each message size, for each node)
*/
public double[][] microseconds;
/**
* Bi-directional bandwidths in Bytes/sec (for each message size, for each node)
*/
public double[][] bandwidths;
/**
* Nodes
*/
public String[] nodes;
/**
* NetworkTestResults
*/
public TwoDimTableV3 table;
/*------------------------------------------------------------------------------------------------------------------
// 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 NetworkTestV3() {
_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/NodeMemoryInfoV3.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 NodeMemoryInfoV3 extends SchemaV3 {
/**
* IP address and port in the form a.b.c.d:e
*/
@SerializedName("ip_port")
public String ipPort;
/**
* Free heap
*/
@SerializedName("free_mem")
public long freeMem;
/**
* Public constructor
*/
public NodeMemoryInfoV3() {
ipPort = "";
freeMem = 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/NodePersistentStorageEntryV3.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 NodePersistentStorageEntryV3 extends SchemaV3 {
/**
* Category name
*/
public String category;
/**
* Key name
*/
public String name;
/**
* Size in bytes of value
*/
public long size;
/**
* Epoch time in milliseconds of when the value was written
*/
@SerializedName("timestamp_millis")
public long timestampMillis;
/**
* Public constructor
*/
public NodePersistentStorageEntryV3() {
category = "";
name = "";
size = 0L;
timestampMillis = 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/NodePersistentStorageV3.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 NodePersistentStorageV3 extends RequestSchemaV3 {
/**
* Category name
*/
public String category;
/**
* Key name
*/
public String name;
/**
* Value
*/
public String value;
/**
* Configured
*/
public boolean configured;
/**
* Exists
*/
public boolean exists;
/**
* List of entries
*/
public NodePersistentStorageEntryV3[] entries;
/*------------------------------------------------------------------------------------------------------------------
// 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 NodePersistentStorageV3() {
category = "";
name = "";
value = "";
configured = false;
exists = 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/NodeV3.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 NodeV3 extends SchemaV3 {
/**
* IP
*/
public String h2o;
/**
* IP address and port in the form a.b.c.d:e
*/
@SerializedName("ip_port")
public String ipPort;
/**
* (now-last_ping)<HeartbeatThread.TIMEOUT
*/
public boolean healthy;
/**
* Time (in msec) of last ping
*/
@SerializedName("last_ping")
public long lastPing;
/**
* PID
*/
public int pid;
/**
* num_cpus
*/
@SerializedName("num_cpus")
public int numCpus;
/**
* cpus_allowed
*/
@SerializedName("cpus_allowed")
public int cpusAllowed;
/**
* nthreads
*/
public int nthreads;
/**
* System load; average #runnables/#cores
*/
@SerializedName("sys_load")
public float sysLoad;
/**
* System CPU percentage used by this H2O process in last interval
*/
@SerializedName("my_cpu_pct")
public int myCpuPct;
/**
* System CPU percentage used by everything in last interval
*/
@SerializedName("sys_cpu_pct")
public int sysCpuPct;
/**
* Data on Node memory
*/
@SerializedName("mem_value_size")
public long memValueSize;
/**
* Temp (non Data) memory
*/
@SerializedName("pojo_mem")
public long pojoMem;
/**
* Free heap
*/
@SerializedName("free_mem")
public long freeMem;
/**
* Maximum memory size for node
*/
@SerializedName("max_mem")
public long maxMem;
/**
* Size of data on node's disk
*/
@SerializedName("swap_mem")
public long swapMem;
/**
* #local keys
*/
@SerializedName("num_keys")
public int numKeys;
/**
* Free disk
*/
@SerializedName("free_disk")
public long freeDisk;
/**
* Max disk
*/
@SerializedName("max_disk")
public long maxDisk;
/**
* Active Remote Procedure Calls
*/
@SerializedName("rpcs_active")
public int rpcsActive;
/**
* F/J Thread count, by priority
*/
public short[] fjthrds;
/**
* F/J Task count, by priority
*/
public short[] fjqueue;
/**
* Open TCP connections
*/
@SerializedName("tcps_active")
public int tcpsActive;
/**
* Open File Descripters
*/
@SerializedName("open_fds")
public int openFds;
/**
* Linpack GFlops
*/
public double gflops;
/**
* Memory Bandwidth
*/
@SerializedName("mem_bw")
public double memBw;
/**
* Public constructor
*/
public NodeV3() {
h2o = "";
ipPort = "";
healthy = false;
lastPing = 0L;
pid = 0;
numCpus = 0;
cpusAllowed = 0;
nthreads = 0;
sysLoad = 0.0f;
myCpuPct = 0;
sysCpuPct = 0;
memValueSize = 0L;
pojoMem = 0L;
freeMem = 0L;
maxMem = 0L;
swapMem = 0L;
numKeys = 0;
freeDisk = 0L;
maxDisk = 0L;
rpcsActive = 0;
tcpsActive = 0;
openFds = 0;
gflops = 0.0;
memBw = 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/OutputSchemaV4.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 OutputSchemaV4 {
/**
* Url describing the schema of the current object.
*/
public String __schema;
/**
* Public constructor
*/
public OutputSchemaV4() {
__schema = "/4/schemas/OutputSchemaV4";
}
/**
* 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/PCAImplementation.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 PCAImplementation {
JAMA,
MTJ_EVD_DENSEMATRIX,
MTJ_EVD_SYMMMATRIX,
MTJ_SVD_DENSEMATRIX,
}
|
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/PCAMethod.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 PCAMethod {
GLRM,
GramSVD,
Power,
Randomized,
}
|
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/PCAModelOutputV3.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 PCAModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Standard deviation and importance of each principal component
*/
public TwoDimTableV3 importance;
/**
* Principal components matrix
*/
public TwoDimTableV3 eigenvectors;
/**
* Final value of GLRM squared loss function
*/
public double objective;
/*------------------------------------------------------------------------------------------------------------------
// 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 PCAModelOutputV3() {
objective = 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/PCAModelV3.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 PCAModelV3 extends ModelSchemaV3<PCAParametersV3, PCAModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public PCAParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public PCAModelOutputV3 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 PCAModelV3() {
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/PCAParametersV3.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 PCAParametersV3 extends ModelParametersSchemaV3 {
/**
* Transformation of training data
*/
public DataInfoTransformType transform;
/**
* Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of
* the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method
* (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model
* with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental)
*/
@SerializedName("pca_method")
public PCAMethod pcaMethod;
/**
* Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue
* decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix
* using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue
* decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ -
* https://github.com/fommil/matrix-toolkits-java/
*/
@SerializedName("pca_impl")
public PCAImplementation pcaImpl;
/**
* Rank of matrix approximation
*/
public int k;
/**
* Maximum training iterations
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* RNG seed for initialization
*/
public long seed;
/**
* Whether first factor level is included in each categorical expansion
*/
@SerializedName("use_all_factor_levels")
public boolean useAllFactorLevels;
/**
* Whether to compute metrics on the training data
*/
@SerializedName("compute_metrics")
public boolean computeMetrics;
/**
* Whether to impute missing entries with the column mean
*/
@SerializedName("impute_missing")
public boolean imputeMissing;
/*------------------------------------------------------------------------------------------------------------------
// 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 PCAParametersV3() {
transform = DataInfoTransformType.NONE;
pcaMethod = PCAMethod.GramSVD;
k = 1;
maxIterations = 1000;
seed = -1L;
useAllFactorLevels = false;
computeMetrics = true;
imputeMissing = 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/PCAV3.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 PCAV3 extends ModelBuilderSchema<PCAParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public PCAParametersV3 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 PCAV3() {
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/PSVMModelOutputV3.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 PSVMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Total number of support vectors
*/
@SerializedName("svs_count")
public long svsCount;
/**
* Number of bounded support vectors
*/
@SerializedName("bsv_count")
public long bsvCount;
/**
* rho
*/
public double rho;
/**
* Weights of support vectors
*/
@SerializedName("alpha_key")
public FrameKeyV3 alphaKey;
/*------------------------------------------------------------------------------------------------------------------
// 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 PSVMModelOutputV3() {
svsCount = 0L;
bsvCount = 0L;
rho = 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/PSVMModelV3.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 PSVMModelV3 extends ModelSchemaV3<PSVMParametersV3, PSVMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public PSVMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public PSVMModelOutputV3 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 PSVMModelV3() {
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/PSVMParametersV3.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 PSVMParametersV3 extends ModelParametersSchemaV3 {
/**
* Penalty parameter C of the error term
*/
@SerializedName("hyper_param")
public double hyperParam;
/**
* Type of used kernel
*/
@SerializedName("kernel_type")
public GenmodelalgospsvmKernelType kernelType;
/**
* Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features)
*/
public double gamma;
/**
* Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)).
*/
@SerializedName("rank_ratio")
public double rankRatio;
/**
* Weight of positive (+1) class of observations
*/
@SerializedName("positive_weight")
public double positiveWeight;
/**
* Weight of positive (-1) class of observations
*/
@SerializedName("negative_weight")
public double negativeWeight;
/**
* Disable calculating training metrics (expensive on large datasets)
*/
@SerializedName("disable_training_metrics")
public boolean disableTrainingMetrics;
/**
* Threshold for accepting a candidate observation into the set of support vectors
*/
@SerializedName("sv_threshold")
public double svThreshold;
/**
* Maximum number of iteration of the algorithm
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Convergence threshold of the Incomplete Cholesky Factorization (ICF)
*/
@SerializedName("fact_threshold")
public double factThreshold;
/**
* Convergence threshold for primal-dual residuals in the IPM iteration
*/
@SerializedName("feasible_threshold")
public double feasibleThreshold;
/**
* Feasibility criterion of the surrogate duality gap (eta)
*/
@SerializedName("surrogate_gap_threshold")
public double surrogateGapThreshold;
/**
* Increasing factor mu
*/
@SerializedName("mu_factor")
public double muFactor;
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/*------------------------------------------------------------------------------------------------------------------
// 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 PSVMParametersV3() {
hyperParam = 1.0;
kernelType = GenmodelalgospsvmKernelType.gaussian;
gamma = -1.0;
rankRatio = -1.0;
positiveWeight = 1.0;
negativeWeight = 1.0;
disableTrainingMetrics = true;
svThreshold = 0.0001;
maxIterations = 200;
factThreshold = 1e-05;
feasibleThreshold = 0.001;
surrogateGapThreshold = 0.001;
muFactor = 10.0;
seed = -1L;
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/PSVMV3.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 PSVMV3 extends ModelBuilderSchema<PSVMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public PSVMParametersV3 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 PSVMV3() {
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/ParseSVMLightV3.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 ParseSVMLightV3 extends RequestSchemaV3 {
/**
* Final frame name
*/
@SerializedName("destination_frame")
public FrameKeyV3 destinationFrame;
/**
* Source frames
*/
@SerializedName("source_frames")
public FrameKeyV3[] sourceFrames;
/*------------------------------------------------------------------------------------------------------------------
// 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 ParseSVMLightV3() {
_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/ParseSetupV3.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 ParseSetupV3 extends RequestSchemaV3 {
/**
* Source frames
*/
@SerializedName("source_frames")
public FrameKeyV3[] sourceFrames;
/**
* Parser type
*/
@SerializedName("parse_type")
public ApiParseTypeValuesProvider parseType;
/**
* Field separator
*/
public byte separator;
/**
* Single quotes
*/
@SerializedName("single_quotes")
public boolean singleQuotes;
/**
* Check header: 0 means guess, +1 means 1st line is header not data, -1 means 1st line is data not header
*/
@SerializedName("check_header")
public int checkHeader;
/**
* Column names
*/
@SerializedName("column_names")
public String[] columnNames;
/**
* Skipped columns indices
*/
@SerializedName("skipped_columns")
public int[] skippedColumns;
/**
* Value types for columns
*/
@SerializedName("column_types")
public String[] columnTypes;
/**
* NA strings for columns
*/
@SerializedName("na_strings")
public String[][] naStrings;
/**
* Regex for names of columns to return
*/
@SerializedName("column_name_filter")
public String columnNameFilter;
/**
* Column offset to return
*/
@SerializedName("column_offset")
public int columnOffset;
/**
* Number of columns to return
*/
@SerializedName("column_count")
public int columnCount;
/**
* Suggested name
*/
@SerializedName("destination_frame")
public String destinationFrame;
/**
* Number of header lines found
*/
@SerializedName("header_lines")
public long headerLines;
/**
* Number of columns
*/
@SerializedName("number_columns")
public int numberColumns;
/**
* Sample data
*/
public String[][] data;
/**
* Warnings
*/
public String[] warnings;
/**
* Size of individual parse tasks
*/
@SerializedName("chunk_size")
public int chunkSize;
/**
* Total number of columns we would return with no column pagination
*/
@SerializedName("total_filtered_column_count")
public int totalFilteredColumnCount;
/**
* Custom characters to be treated as non-data line markers
*/
@SerializedName("custom_non_data_line_markers")
public String customNonDataLineMarkers;
/**
* Key-reference to an initialized instance of a Decryption Tool
*/
@SerializedName("decrypt_tool")
public DecryptionToolKeyV3 decryptTool;
/**
* Names of the columns the persisted dataset has been partitioned by.
*/
@SerializedName("partition_by")
public String[] partitionBy;
/**
* One ASCII character used to escape other characters.
*/
public byte escapechar;
/**
* If true, will force the column types to be either the ones in Parquet schema for Parquet files or the ones
* specified in column_types. This parameter is used for numerical columns only. Other column settings will happen
* without setting this parameter. Defaults to false.
*/
@SerializedName("force_col_types")
public boolean forceColTypes;
/**
* Adjust the imported time from GMT timezone to cluster timezone.
*/
@SerializedName("tz_adjust_to_local")
public boolean tzAdjustToLocal;
/*------------------------------------------------------------------------------------------------------------------
// 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 ParseSetupV3() {
parseType = ApiParseTypeValuesProvider.GUESS;
separator = 0;
singleQuotes = false;
checkHeader = 0;
columnNameFilter = "";
columnOffset = 0;
columnCount = 0;
destinationFrame = "";
headerLines = 0L;
numberColumns = 0;
chunkSize = 4194304;
totalFilteredColumnCount = 0;
customNonDataLineMarkers = "";
escapechar = 0;
forceColTypes = false;
tzAdjustToLocal = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
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