com.google.api.services.bigquery.model.RankingMetrics Maven / Gradle / Ivy
/*
* 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://github.com/googleapis/google-api-java-client-services/
* Modify at your own risk.
*/
package com.google.api.services.bigquery.model;
/**
* Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.
*
* 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 BigQuery API. For a detailed explanation see:
* https://developers.google.com/api-client-library/java/google-http-java-client/json
*
*
* @author Google, Inc.
*/
@SuppressWarnings("javadoc")
public final class RankingMetrics extends com.google.api.client.json.GenericJson {
/**
* Determines the goodness of a ranking by computing the percentile rank from the predicted
* confidence and dividing it by the original rank.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double averageRank;
/**
* Calculates a precision per user for all the items by ranking them and then averages all the
* precisions across all the users.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double meanAveragePrecision;
/**
* Similar to the mean squared error computed in regression and explicit recommendation models
* except instead of computing the rating directly, the output from evaluate is computed against a
* preference which is 1 or 0 depending on if the rating exists or not.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double meanSquaredError;
/**
* A metric to determine the goodness of a ranking calculated from the predicted confidence by
* comparing it to an ideal rank measured by the original ratings.
* The value may be {@code null}.
*/
@com.google.api.client.util.Key
private java.lang.Double normalizedDiscountedCumulativeGain;
/**
* Determines the goodness of a ranking by computing the percentile rank from the predicted
* confidence and dividing it by the original rank.
* @return value or {@code null} for none
*/
public java.lang.Double getAverageRank() {
return averageRank;
}
/**
* Determines the goodness of a ranking by computing the percentile rank from the predicted
* confidence and dividing it by the original rank.
* @param averageRank averageRank or {@code null} for none
*/
public RankingMetrics setAverageRank(java.lang.Double averageRank) {
this.averageRank = averageRank;
return this;
}
/**
* Calculates a precision per user for all the items by ranking them and then averages all the
* precisions across all the users.
* @return value or {@code null} for none
*/
public java.lang.Double getMeanAveragePrecision() {
return meanAveragePrecision;
}
/**
* Calculates a precision per user for all the items by ranking them and then averages all the
* precisions across all the users.
* @param meanAveragePrecision meanAveragePrecision or {@code null} for none
*/
public RankingMetrics setMeanAveragePrecision(java.lang.Double meanAveragePrecision) {
this.meanAveragePrecision = meanAveragePrecision;
return this;
}
/**
* Similar to the mean squared error computed in regression and explicit recommendation models
* except instead of computing the rating directly, the output from evaluate is computed against a
* preference which is 1 or 0 depending on if the rating exists or not.
* @return value or {@code null} for none
*/
public java.lang.Double getMeanSquaredError() {
return meanSquaredError;
}
/**
* Similar to the mean squared error computed in regression and explicit recommendation models
* except instead of computing the rating directly, the output from evaluate is computed against a
* preference which is 1 or 0 depending on if the rating exists or not.
* @param meanSquaredError meanSquaredError or {@code null} for none
*/
public RankingMetrics setMeanSquaredError(java.lang.Double meanSquaredError) {
this.meanSquaredError = meanSquaredError;
return this;
}
/**
* A metric to determine the goodness of a ranking calculated from the predicted confidence by
* comparing it to an ideal rank measured by the original ratings.
* @return value or {@code null} for none
*/
public java.lang.Double getNormalizedDiscountedCumulativeGain() {
return normalizedDiscountedCumulativeGain;
}
/**
* A metric to determine the goodness of a ranking calculated from the predicted confidence by
* comparing it to an ideal rank measured by the original ratings.
* @param normalizedDiscountedCumulativeGain normalizedDiscountedCumulativeGain or {@code null} for none
*/
public RankingMetrics setNormalizedDiscountedCumulativeGain(java.lang.Double normalizedDiscountedCumulativeGain) {
this.normalizedDiscountedCumulativeGain = normalizedDiscountedCumulativeGain;
return this;
}
@Override
public RankingMetrics set(String fieldName, Object value) {
return (RankingMetrics) super.set(fieldName, value);
}
@Override
public RankingMetrics clone() {
return (RankingMetrics) super.clone();
}
}