co.elastic.clients.elasticsearch.ml.TrainedModelInferenceStats Maven / Gradle / Ivy
/*
* Licensed to Elasticsearch B.V. under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch B.V. licenses this file to you 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 IS GENERATED. MANUAL EDITS WILL BE LOST.
//----------------------------------------------------
package co.elastic.clients.elasticsearch.ml;
import co.elastic.clients.elasticsearch._types.Time;
import co.elastic.clients.json.JsonpDeserializable;
import co.elastic.clients.json.JsonpDeserializer;
import co.elastic.clients.json.JsonpMapper;
import co.elastic.clients.json.JsonpSerializable;
import co.elastic.clients.json.ObjectBuilderDeserializer;
import co.elastic.clients.json.ObjectDeserializer;
import co.elastic.clients.util.ApiTypeHelper;
import co.elastic.clients.util.ObjectBuilder;
import co.elastic.clients.util.WithJsonObjectBuilderBase;
import jakarta.json.stream.JsonGenerator;
import java.lang.Integer;
import java.util.Objects;
import java.util.function.Function;
import javax.annotation.Nullable;
// typedef: ml._types.TrainedModelInferenceStats
/**
*
* @see API
* specification
*/
@JsonpDeserializable
public class TrainedModelInferenceStats implements JsonpSerializable {
private final int cacheMissCount;
private final int failureCount;
private final int inferenceCount;
private final int missingAllFieldsCount;
private final Time timestamp;
// ---------------------------------------------------------------------------------------------
private TrainedModelInferenceStats(Builder builder) {
this.cacheMissCount = ApiTypeHelper.requireNonNull(builder.cacheMissCount, this, "cacheMissCount");
this.failureCount = ApiTypeHelper.requireNonNull(builder.failureCount, this, "failureCount");
this.inferenceCount = ApiTypeHelper.requireNonNull(builder.inferenceCount, this, "inferenceCount");
this.missingAllFieldsCount = ApiTypeHelper.requireNonNull(builder.missingAllFieldsCount, this,
"missingAllFieldsCount");
this.timestamp = ApiTypeHelper.requireNonNull(builder.timestamp, this, "timestamp");
}
public static TrainedModelInferenceStats of(Function> fn) {
return fn.apply(new Builder()).build();
}
/**
* Required - The number of times the model was loaded for inference and was not
* retrieved from the cache. If this number is close to the
* inference_count, the cache is not being appropriately used. This
* can be solved by increasing the cache size or its time-to-live (TTL). Refer
* to general machine learning settings for the appropriate settings.
*
* API name: {@code cache_miss_count}
*/
public final int cacheMissCount() {
return this.cacheMissCount;
}
/**
* Required - The number of failures when using the model for inference.
*
* API name: {@code failure_count}
*/
public final int failureCount() {
return this.failureCount;
}
/**
* Required - The total number of times the model has been called for inference.
* This is across all inference contexts, including all pipelines.
*
* API name: {@code inference_count}
*/
public final int inferenceCount() {
return this.inferenceCount;
}
/**
* Required - The number of inference calls where all the training features for
* the model were missing.
*
* API name: {@code missing_all_fields_count}
*/
public final int missingAllFieldsCount() {
return this.missingAllFieldsCount;
}
/**
* Required - The time when the statistics were last updated.
*
* API name: {@code timestamp}
*/
public final Time timestamp() {
return this.timestamp;
}
/**
* Serialize this object to JSON.
*/
public void serialize(JsonGenerator generator, JsonpMapper mapper) {
generator.writeStartObject();
serializeInternal(generator, mapper);
generator.writeEnd();
}
protected void serializeInternal(JsonGenerator generator, JsonpMapper mapper) {
generator.writeKey("cache_miss_count");
generator.write(this.cacheMissCount);
generator.writeKey("failure_count");
generator.write(this.failureCount);
generator.writeKey("inference_count");
generator.write(this.inferenceCount);
generator.writeKey("missing_all_fields_count");
generator.write(this.missingAllFieldsCount);
generator.writeKey("timestamp");
this.timestamp.serialize(generator, mapper);
}
// ---------------------------------------------------------------------------------------------
/**
* Builder for {@link TrainedModelInferenceStats}.
*/
public static class Builder extends WithJsonObjectBuilderBase
implements
ObjectBuilder {
private Integer cacheMissCount;
private Integer failureCount;
private Integer inferenceCount;
private Integer missingAllFieldsCount;
private Time timestamp;
/**
* Required - The number of times the model was loaded for inference and was not
* retrieved from the cache. If this number is close to the
* inference_count, the cache is not being appropriately used. This
* can be solved by increasing the cache size or its time-to-live (TTL). Refer
* to general machine learning settings for the appropriate settings.
*
* API name: {@code cache_miss_count}
*/
public final Builder cacheMissCount(int value) {
this.cacheMissCount = value;
return this;
}
/**
* Required - The number of failures when using the model for inference.
*
* API name: {@code failure_count}
*/
public final Builder failureCount(int value) {
this.failureCount = value;
return this;
}
/**
* Required - The total number of times the model has been called for inference.
* This is across all inference contexts, including all pipelines.
*
* API name: {@code inference_count}
*/
public final Builder inferenceCount(int value) {
this.inferenceCount = value;
return this;
}
/**
* Required - The number of inference calls where all the training features for
* the model were missing.
*
* API name: {@code missing_all_fields_count}
*/
public final Builder missingAllFieldsCount(int value) {
this.missingAllFieldsCount = value;
return this;
}
/**
* Required - The time when the statistics were last updated.
*
* API name: {@code timestamp}
*/
public final Builder timestamp(Time value) {
this.timestamp = value;
return this;
}
/**
* Required - The time when the statistics were last updated.
*
* API name: {@code timestamp}
*/
public final Builder timestamp(Function> fn) {
return this.timestamp(fn.apply(new Time.Builder()).build());
}
@Override
protected Builder self() {
return this;
}
/**
* Builds a {@link TrainedModelInferenceStats}.
*
* @throws NullPointerException
* if some of the required fields are null.
*/
public TrainedModelInferenceStats build() {
_checkSingleUse();
return new TrainedModelInferenceStats(this);
}
}
// ---------------------------------------------------------------------------------------------
/**
* Json deserializer for {@link TrainedModelInferenceStats}
*/
public static final JsonpDeserializer _DESERIALIZER = ObjectBuilderDeserializer
.lazy(Builder::new, TrainedModelInferenceStats::setupTrainedModelInferenceStatsDeserializer);
protected static void setupTrainedModelInferenceStatsDeserializer(
ObjectDeserializer op) {
op.add(Builder::cacheMissCount, JsonpDeserializer.integerDeserializer(), "cache_miss_count");
op.add(Builder::failureCount, JsonpDeserializer.integerDeserializer(), "failure_count");
op.add(Builder::inferenceCount, JsonpDeserializer.integerDeserializer(), "inference_count");
op.add(Builder::missingAllFieldsCount, JsonpDeserializer.integerDeserializer(), "missing_all_fields_count");
op.add(Builder::timestamp, Time._DESERIALIZER, "timestamp");
}
}