com.google.api.services.prediction.model.Training Maven / Gradle / Ivy
/*
* Copyright 2010 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
* in compliance with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software distributed under the License
* is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
* or implied. See the License for the specific language governing permissions and limitations under
* the License.
*/
/*
* This code was generated by https://code.google.com/p/google-apis-client-generator/
* (build: 2014-02-14 18:40:25 UTC)
* on 2014-03-30 at 15:45:53 UTC
* Modify at your own risk.
*/
package com.google.api.services.prediction.model;
/**
* Model definition for Training.
*
* This is the Java data model class that specifies how to parse/serialize into the JSON that is
* transmitted over HTTP when working with the Prediction API. For a detailed explanation see:
* http://code.google.com/p/google-http-java-client/wiki/JSON
*
*
* @author Google, Inc.
*/
@SuppressWarnings("javadoc")
public final class Training extends com.google.api.client.json.GenericJson {
/**
* Data Analysis.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private DataAnalysis dataAnalysis;
/**
* The unique name for the predictive model.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String id;
/**
* What kind of resource this is.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String kind;
/**
* Model metadata.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private ModelInfo modelInfo;
/**
* A URL to re-request this resource.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String selfLink;
/**
* Google storage location of the training data file.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String storageDataLocation;
/**
* Google storage location of the preprocessing pmml file.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String storagePMMLLocation;
/**
* Google storage location of the pmml model file.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String storagePMMLModelLocation;
/**
* The current status of the training job. This can be one of following: RUNNING; DONE; ERROR;
* ERROR: TRAINING JOB NOT FOUND
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String trainingStatus;
/**
* A class weighting function, which allows the importance weights for class labels to be
* specified [Categorical models only].
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.util.List> utility;
/**
* Data Analysis.
* @return value or {@code null} for none
*/
public DataAnalysis getDataAnalysis() {
return dataAnalysis;
}
/**
* Data Analysis.
* @param dataAnalysis dataAnalysis or {@code null} for none
*/
public Training setDataAnalysis(DataAnalysis dataAnalysis) {
this.dataAnalysis = dataAnalysis;
return this;
}
/**
* The unique name for the predictive model.
* @return value or {@code null} for none
*/
public java.lang.String getId() {
return id;
}
/**
* The unique name for the predictive model.
* @param id id or {@code null} for none
*/
public Training setId(java.lang.String id) {
this.id = id;
return this;
}
/**
* What kind of resource this is.
* @return value or {@code null} for none
*/
public java.lang.String getKind() {
return kind;
}
/**
* What kind of resource this is.
* @param kind kind or {@code null} for none
*/
public Training setKind(java.lang.String kind) {
this.kind = kind;
return this;
}
/**
* Model metadata.
* @return value or {@code null} for none
*/
public ModelInfo getModelInfo() {
return modelInfo;
}
/**
* Model metadata.
* @param modelInfo modelInfo or {@code null} for none
*/
public Training setModelInfo(ModelInfo modelInfo) {
this.modelInfo = modelInfo;
return this;
}
/**
* A URL to re-request this resource.
* @return value or {@code null} for none
*/
public java.lang.String getSelfLink() {
return selfLink;
}
/**
* A URL to re-request this resource.
* @param selfLink selfLink or {@code null} for none
*/
public Training setSelfLink(java.lang.String selfLink) {
this.selfLink = selfLink;
return this;
}
/**
* Google storage location of the training data file.
* @return value or {@code null} for none
*/
public java.lang.String getStorageDataLocation() {
return storageDataLocation;
}
/**
* Google storage location of the training data file.
* @param storageDataLocation storageDataLocation or {@code null} for none
*/
public Training setStorageDataLocation(java.lang.String storageDataLocation) {
this.storageDataLocation = storageDataLocation;
return this;
}
/**
* Google storage location of the preprocessing pmml file.
* @return value or {@code null} for none
*/
public java.lang.String getStoragePMMLLocation() {
return storagePMMLLocation;
}
/**
* Google storage location of the preprocessing pmml file.
* @param storagePMMLLocation storagePMMLLocation or {@code null} for none
*/
public Training setStoragePMMLLocation(java.lang.String storagePMMLLocation) {
this.storagePMMLLocation = storagePMMLLocation;
return this;
}
/**
* Google storage location of the pmml model file.
* @return value or {@code null} for none
*/
public java.lang.String getStoragePMMLModelLocation() {
return storagePMMLModelLocation;
}
/**
* Google storage location of the pmml model file.
* @param storagePMMLModelLocation storagePMMLModelLocation or {@code null} for none
*/
public Training setStoragePMMLModelLocation(java.lang.String storagePMMLModelLocation) {
this.storagePMMLModelLocation = storagePMMLModelLocation;
return this;
}
/**
* The current status of the training job. This can be one of following: RUNNING; DONE; ERROR;
* ERROR: TRAINING JOB NOT FOUND
* @return value or {@code null} for none
*/
public java.lang.String getTrainingStatus() {
return trainingStatus;
}
/**
* The current status of the training job. This can be one of following: RUNNING; DONE; ERROR;
* ERROR: TRAINING JOB NOT FOUND
* @param trainingStatus trainingStatus or {@code null} for none
*/
public Training setTrainingStatus(java.lang.String trainingStatus) {
this.trainingStatus = trainingStatus;
return this;
}
/**
* A class weighting function, which allows the importance weights for class labels to be
* specified [Categorical models only].
* @return value or {@code null} for none
*/
public java.util.List> getUtility() {
return utility;
}
/**
* A class weighting function, which allows the importance weights for class labels to be
* specified [Categorical models only].
* @param utility utility or {@code null} for none
*/
public Training setUtility(java.util.List> utility) {
this.utility = utility;
return this;
}
@Override
public Training set(String fieldName, Object value) {
return (Training) super.set(fieldName, value);
}
@Override
public Training clone() {
return (Training) super.clone();
}
/**
* Data Analysis.
*/
public static final class DataAnalysis extends com.google.api.client.json.GenericJson {
/**
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.util.List warnings;
/**
* @return value or {@code null} for none
*/
public java.util.List getWarnings() {
return warnings;
}
/**
* @param warnings warnings or {@code null} for none
*/
public DataAnalysis setWarnings(java.util.List warnings) {
this.warnings = warnings;
return this;
}
@Override
public DataAnalysis set(String fieldName, Object value) {
return (DataAnalysis) super.set(fieldName, value);
}
@Override
public DataAnalysis clone() {
return (DataAnalysis) super.clone();
}
}
/**
* Model metadata.
*/
public static final class ModelInfo extends com.google.api.client.json.GenericJson {
/**
* Estimated accuracy of model taking utility weights into account [Categorical models only].
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double classWeightedAccuracy;
/**
* A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the
* amount and quality of the training data, of the estimated prediction accuracy. You can use this
* is a guide to decide whether the results are accurate enough for your needs. This estimate will
* be more reliable if your real input data is similar to your training data [Categorical models
* only].
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double classificationAccuracy;
/**
* An output confusion matrix. This shows an estimate for how this model will do in predictions.
* This is first indexed by the true class label. For each true class label, this provides a pair
* {predicted_label, count}, where count is the estimated number of times the model will predict
* the predicted label given the true label. Will not output if more then 100 classes [Categorical
* models only].
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.util.Map> confusionMatrix;
/**
* A list of the confusion matrix row totals
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.util.Map confusionMatrixRowTotals;
/**
* An estimated mean squared error. The can be used to measure the quality of the predicted model
* [Regression models only].
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double meanSquaredError;
/**
* Type of predictive model (CLASSIFICATION or REGRESSION)
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.String modelType;
/**
* Number of valid data instances used in the trained model.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key @com.google.api.client.json.JsonString
private java.lang.Long numberInstances;
/**
* Number of class labels in the trained model [Categorical models only].
* The value may be {@code null}.
*/
@com.google.api.client.util.Key @com.google.api.client.json.JsonString
private java.lang.Long numberLabels;
/**
* Estimated accuracy of model taking utility weights into account [Categorical models only].
* @return value or {@code null} for none
*/
public java.lang.Double getClassWeightedAccuracy() {
return classWeightedAccuracy;
}
/**
* Estimated accuracy of model taking utility weights into account [Categorical models only].
* @param classWeightedAccuracy classWeightedAccuracy or {@code null} for none
*/
public ModelInfo setClassWeightedAccuracy(java.lang.Double classWeightedAccuracy) {
this.classWeightedAccuracy = classWeightedAccuracy;
return this;
}
/**
* A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the
* amount and quality of the training data, of the estimated prediction accuracy. You can use this
* is a guide to decide whether the results are accurate enough for your needs. This estimate will
* be more reliable if your real input data is similar to your training data [Categorical models
* only].
* @return value or {@code null} for none
*/
public java.lang.Double getClassificationAccuracy() {
return classificationAccuracy;
}
/**
* A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the
* amount and quality of the training data, of the estimated prediction accuracy. You can use this
* is a guide to decide whether the results are accurate enough for your needs. This estimate will
* be more reliable if your real input data is similar to your training data [Categorical models
* only].
* @param classificationAccuracy classificationAccuracy or {@code null} for none
*/
public ModelInfo setClassificationAccuracy(java.lang.Double classificationAccuracy) {
this.classificationAccuracy = classificationAccuracy;
return this;
}
/**
* An output confusion matrix. This shows an estimate for how this model will do in predictions.
* This is first indexed by the true class label. For each true class label, this provides a pair
* {predicted_label, count}, where count is the estimated number of times the model will predict
* the predicted label given the true label. Will not output if more then 100 classes [Categorical
* models only].
* @return value or {@code null} for none
*/
public java.util.Map> getConfusionMatrix() {
return confusionMatrix;
}
/**
* An output confusion matrix. This shows an estimate for how this model will do in predictions.
* This is first indexed by the true class label. For each true class label, this provides a pair
* {predicted_label, count}, where count is the estimated number of times the model will predict
* the predicted label given the true label. Will not output if more then 100 classes [Categorical
* models only].
* @param confusionMatrix confusionMatrix or {@code null} for none
*/
public ModelInfo setConfusionMatrix(java.util.Map> confusionMatrix) {
this.confusionMatrix = confusionMatrix;
return this;
}
/**
* A list of the confusion matrix row totals
* @return value or {@code null} for none
*/
public java.util.Map getConfusionMatrixRowTotals() {
return confusionMatrixRowTotals;
}
/**
* A list of the confusion matrix row totals
* @param confusionMatrixRowTotals confusionMatrixRowTotals or {@code null} for none
*/
public ModelInfo setConfusionMatrixRowTotals(java.util.Map confusionMatrixRowTotals) {
this.confusionMatrixRowTotals = confusionMatrixRowTotals;
return this;
}
/**
* An estimated mean squared error. The can be used to measure the quality of the predicted model
* [Regression models only].
* @return value or {@code null} for none
*/
public java.lang.Double getMeanSquaredError() {
return meanSquaredError;
}
/**
* An estimated mean squared error. The can be used to measure the quality of the predicted model
* [Regression models only].
* @param meanSquaredError meanSquaredError or {@code null} for none
*/
public ModelInfo setMeanSquaredError(java.lang.Double meanSquaredError) {
this.meanSquaredError = meanSquaredError;
return this;
}
/**
* Type of predictive model (CLASSIFICATION or REGRESSION)
* @return value or {@code null} for none
*/
public java.lang.String getModelType() {
return modelType;
}
/**
* Type of predictive model (CLASSIFICATION or REGRESSION)
* @param modelType modelType or {@code null} for none
*/
public ModelInfo setModelType(java.lang.String modelType) {
this.modelType = modelType;
return this;
}
/**
* Number of valid data instances used in the trained model.
* @return value or {@code null} for none
*/
public java.lang.Long getNumberInstances() {
return numberInstances;
}
/**
* Number of valid data instances used in the trained model.
* @param numberInstances numberInstances or {@code null} for none
*/
public ModelInfo setNumberInstances(java.lang.Long numberInstances) {
this.numberInstances = numberInstances;
return this;
}
/**
* Number of class labels in the trained model [Categorical models only].
* @return value or {@code null} for none
*/
public java.lang.Long getNumberLabels() {
return numberLabels;
}
/**
* Number of class labels in the trained model [Categorical models only].
* @param numberLabels numberLabels or {@code null} for none
*/
public ModelInfo setNumberLabels(java.lang.Long numberLabels) {
this.numberLabels = numberLabels;
return this;
}
@Override
public ModelInfo set(String fieldName, Object value) {
return (ModelInfo) super.set(fieldName, value);
}
@Override
public ModelInfo clone() {
return (ModelInfo) super.clone();
}
}
}
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