com.oracle.bmc.aianomalydetection.model.ModelTrainingDetails Maven / Gradle / Ivy
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This project contains the SDK distribution used for Oracle Cloud Infrastructure, and all the dependencies that can be shaded. It also has Maven dependencies that cannot be shaded. Therefore, use this module to depend on the shaded distribution via Maven -- it will shade everything that can be shaded, and automatically pull in the other dependencies.
/**
* Copyright (c) 2016, 2024, Oracle and/or its affiliates. All rights reserved.
* This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
*/
package com.oracle.bmc.aianomalydetection.model;
/**
* Specifies the details of the MSET model during the create call.
* Note: Objects should always be created or deserialized using the {@link Builder}. This model
* distinguishes fields that are {@code null} because they are unset from fields that are explicitly
* set to {@code null}. This is done in the setter methods of the {@link Builder}, which maintain a
* set of all explicitly set fields called {@link Builder#__explicitlySet__}. The {@link
* #hashCode()} and {@link #equals(Object)} methods are implemented to take the explicitly set
* fields into account. The constructor, on the other hand, does not take the explicitly set fields
* into account (since the constructor cannot distinguish explicit {@code null} from unset {@code
* null}).
*/
@jakarta.annotation.Generated(value = "OracleSDKGenerator", comments = "API Version: 20210101")
@com.fasterxml.jackson.databind.annotation.JsonDeserialize(
builder = ModelTrainingDetails.Builder.class)
@com.fasterxml.jackson.annotation.JsonFilter(
com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel.EXPLICITLY_SET_FILTER_NAME)
public final class ModelTrainingDetails
extends com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel {
@Deprecated
@java.beans.ConstructorProperties({
"algorithmHint",
"targetFap",
"trainingFraction",
"windowSize",
"dataAssetIds"
})
public ModelTrainingDetails(
AlgorithmHint algorithmHint,
Float targetFap,
Float trainingFraction,
Integer windowSize,
java.util.List dataAssetIds) {
super();
this.algorithmHint = algorithmHint;
this.targetFap = targetFap;
this.trainingFraction = trainingFraction;
this.windowSize = windowSize;
this.dataAssetIds = dataAssetIds;
}
@com.fasterxml.jackson.databind.annotation.JsonPOJOBuilder(withPrefix = "")
public static class Builder {
/** User can choose specific algorithm for training. */
@com.fasterxml.jackson.annotation.JsonProperty("algorithmHint")
private AlgorithmHint algorithmHint;
/**
* User can choose specific algorithm for training.
*
* @param algorithmHint the value to set
* @return this builder
*/
public Builder algorithmHint(AlgorithmHint algorithmHint) {
this.algorithmHint = algorithmHint;
this.__explicitlySet__.add("algorithmHint");
return this;
}
/** A target model accuracy metric user provides as their requirement */
@com.fasterxml.jackson.annotation.JsonProperty("targetFap")
private Float targetFap;
/**
* A target model accuracy metric user provides as their requirement
*
* @param targetFap the value to set
* @return this builder
*/
public Builder targetFap(Float targetFap) {
this.targetFap = targetFap;
this.__explicitlySet__.add("targetFap");
return this;
}
/**
* Fraction of total data that is used for training the model. The remaining is used for
* validation of the model.
*/
@com.fasterxml.jackson.annotation.JsonProperty("trainingFraction")
private Float trainingFraction;
/**
* Fraction of total data that is used for training the model. The remaining is used for
* validation of the model.
*
* @param trainingFraction the value to set
* @return this builder
*/
public Builder trainingFraction(Float trainingFraction) {
this.trainingFraction = trainingFraction;
this.__explicitlySet__.add("trainingFraction");
return this;
}
/** This value would determine the window size of the training algorithm. */
@com.fasterxml.jackson.annotation.JsonProperty("windowSize")
private Integer windowSize;
/**
* This value would determine the window size of the training algorithm.
*
* @param windowSize the value to set
* @return this builder
*/
public Builder windowSize(Integer windowSize) {
this.windowSize = windowSize;
this.__explicitlySet__.add("windowSize");
return this;
}
/**
* The list of OCIDs of the data assets to train the model. The dataAssets have to be in the
* same project where the ai model would reside.
*/
@com.fasterxml.jackson.annotation.JsonProperty("dataAssetIds")
private java.util.List dataAssetIds;
/**
* The list of OCIDs of the data assets to train the model. The dataAssets have to be in the
* same project where the ai model would reside.
*
* @param dataAssetIds the value to set
* @return this builder
*/
public Builder dataAssetIds(java.util.List dataAssetIds) {
this.dataAssetIds = dataAssetIds;
this.__explicitlySet__.add("dataAssetIds");
return this;
}
@com.fasterxml.jackson.annotation.JsonIgnore
private final java.util.Set __explicitlySet__ = new java.util.HashSet();
public ModelTrainingDetails build() {
ModelTrainingDetails model =
new ModelTrainingDetails(
this.algorithmHint,
this.targetFap,
this.trainingFraction,
this.windowSize,
this.dataAssetIds);
for (String explicitlySetProperty : this.__explicitlySet__) {
model.markPropertyAsExplicitlySet(explicitlySetProperty);
}
return model;
}
@com.fasterxml.jackson.annotation.JsonIgnore
public Builder copy(ModelTrainingDetails model) {
if (model.wasPropertyExplicitlySet("algorithmHint")) {
this.algorithmHint(model.getAlgorithmHint());
}
if (model.wasPropertyExplicitlySet("targetFap")) {
this.targetFap(model.getTargetFap());
}
if (model.wasPropertyExplicitlySet("trainingFraction")) {
this.trainingFraction(model.getTrainingFraction());
}
if (model.wasPropertyExplicitlySet("windowSize")) {
this.windowSize(model.getWindowSize());
}
if (model.wasPropertyExplicitlySet("dataAssetIds")) {
this.dataAssetIds(model.getDataAssetIds());
}
return this;
}
}
/** Create a new builder. */
public static Builder builder() {
return new Builder();
}
public Builder toBuilder() {
return new Builder().copy(this);
}
/** User can choose specific algorithm for training. */
public enum AlgorithmHint implements com.oracle.bmc.http.internal.BmcEnum {
MultivariateMset("MULTIVARIATE_MSET"),
UnivariateOcsvm("UNIVARIATE_OCSVM"),
/**
* This value is used if a service returns a value for this enum that is not recognized by
* this version of the SDK.
*/
UnknownEnumValue(null);
private static final org.slf4j.Logger LOG =
org.slf4j.LoggerFactory.getLogger(AlgorithmHint.class);
private final String value;
private static java.util.Map map;
static {
map = new java.util.HashMap<>();
for (AlgorithmHint v : AlgorithmHint.values()) {
if (v != UnknownEnumValue) {
map.put(v.getValue(), v);
}
}
}
AlgorithmHint(String value) {
this.value = value;
}
@com.fasterxml.jackson.annotation.JsonValue
public String getValue() {
return value;
}
@com.fasterxml.jackson.annotation.JsonCreator
public static AlgorithmHint create(String key) {
if (map.containsKey(key)) {
return map.get(key);
}
LOG.warn(
"Received unknown value '{}' for enum 'AlgorithmHint', returning UnknownEnumValue",
key);
return UnknownEnumValue;
}
};
/** User can choose specific algorithm for training. */
@com.fasterxml.jackson.annotation.JsonProperty("algorithmHint")
private final AlgorithmHint algorithmHint;
/**
* User can choose specific algorithm for training.
*
* @return the value
*/
public AlgorithmHint getAlgorithmHint() {
return algorithmHint;
}
/** A target model accuracy metric user provides as their requirement */
@com.fasterxml.jackson.annotation.JsonProperty("targetFap")
private final Float targetFap;
/**
* A target model accuracy metric user provides as their requirement
*
* @return the value
*/
public Float getTargetFap() {
return targetFap;
}
/**
* Fraction of total data that is used for training the model. The remaining is used for
* validation of the model.
*/
@com.fasterxml.jackson.annotation.JsonProperty("trainingFraction")
private final Float trainingFraction;
/**
* Fraction of total data that is used for training the model. The remaining is used for
* validation of the model.
*
* @return the value
*/
public Float getTrainingFraction() {
return trainingFraction;
}
/** This value would determine the window size of the training algorithm. */
@com.fasterxml.jackson.annotation.JsonProperty("windowSize")
private final Integer windowSize;
/**
* This value would determine the window size of the training algorithm.
*
* @return the value
*/
public Integer getWindowSize() {
return windowSize;
}
/**
* The list of OCIDs of the data assets to train the model. The dataAssets have to be in the
* same project where the ai model would reside.
*/
@com.fasterxml.jackson.annotation.JsonProperty("dataAssetIds")
private final java.util.List dataAssetIds;
/**
* The list of OCIDs of the data assets to train the model. The dataAssets have to be in the
* same project where the ai model would reside.
*
* @return the value
*/
public java.util.List getDataAssetIds() {
return dataAssetIds;
}
@Override
public String toString() {
return this.toString(true);
}
/**
* Return a string representation of the object.
*
* @param includeByteArrayContents true to include the full contents of byte arrays
* @return string representation
*/
public String toString(boolean includeByteArrayContents) {
java.lang.StringBuilder sb = new java.lang.StringBuilder();
sb.append("ModelTrainingDetails(");
sb.append("super=").append(super.toString());
sb.append("algorithmHint=").append(String.valueOf(this.algorithmHint));
sb.append(", targetFap=").append(String.valueOf(this.targetFap));
sb.append(", trainingFraction=").append(String.valueOf(this.trainingFraction));
sb.append(", windowSize=").append(String.valueOf(this.windowSize));
sb.append(", dataAssetIds=").append(String.valueOf(this.dataAssetIds));
sb.append(")");
return sb.toString();
}
@Override
public boolean equals(Object o) {
if (this == o) {
return true;
}
if (!(o instanceof ModelTrainingDetails)) {
return false;
}
ModelTrainingDetails other = (ModelTrainingDetails) o;
return java.util.Objects.equals(this.algorithmHint, other.algorithmHint)
&& java.util.Objects.equals(this.targetFap, other.targetFap)
&& java.util.Objects.equals(this.trainingFraction, other.trainingFraction)
&& java.util.Objects.equals(this.windowSize, other.windowSize)
&& java.util.Objects.equals(this.dataAssetIds, other.dataAssetIds)
&& super.equals(other);
}
@Override
public int hashCode() {
final int PRIME = 59;
int result = 1;
result =
(result * PRIME)
+ (this.algorithmHint == null ? 43 : this.algorithmHint.hashCode());
result = (result * PRIME) + (this.targetFap == null ? 43 : this.targetFap.hashCode());
result =
(result * PRIME)
+ (this.trainingFraction == null ? 43 : this.trainingFraction.hashCode());
result = (result * PRIME) + (this.windowSize == null ? 43 : this.windowSize.hashCode());
result = (result * PRIME) + (this.dataAssetIds == null ? 43 : this.dataAssetIds.hashCode());
result = (result * PRIME) + super.hashCode();
return result;
}
}
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