software.amazon.awssdk.services.machinelearning.model.MLModel Maven / Gradle / Ivy
/*
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file 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.
*/
package software.amazon.awssdk.services.machinelearning.model;
import java.io.Serializable;
import java.time.Instant;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.function.BiConsumer;
import java.util.function.Consumer;
import java.util.function.Function;
import software.amazon.awssdk.annotations.Generated;
import software.amazon.awssdk.core.SdkField;
import software.amazon.awssdk.core.SdkPojo;
import software.amazon.awssdk.core.protocol.MarshallLocation;
import software.amazon.awssdk.core.protocol.MarshallingType;
import software.amazon.awssdk.core.traits.LocationTrait;
import software.amazon.awssdk.core.traits.MapTrait;
import software.amazon.awssdk.core.util.DefaultSdkAutoConstructMap;
import software.amazon.awssdk.core.util.SdkAutoConstructMap;
import software.amazon.awssdk.utils.ToString;
import software.amazon.awssdk.utils.builder.CopyableBuilder;
import software.amazon.awssdk.utils.builder.ToCopyableBuilder;
/**
*
* Represents the output of a GetMLModel operation.
*
*
* The content consists of the detailed metadata and the current status of the MLModel.
*
*/
@Generated("software.amazon.awssdk:codegen")
public final class MLModel implements SdkPojo, Serializable, ToCopyableBuilder {
private static final SdkField ML_MODEL_ID_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("MLModelId").getter(getter(MLModel::mlModelId)).setter(setter(Builder::mlModelId))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("MLModelId").build()).build();
private static final SdkField TRAINING_DATA_SOURCE_ID_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("TrainingDataSourceId").getter(getter(MLModel::trainingDataSourceId))
.setter(setter(Builder::trainingDataSourceId))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("TrainingDataSourceId").build())
.build();
private static final SdkField CREATED_BY_IAM_USER_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("CreatedByIamUser").getter(getter(MLModel::createdByIamUser)).setter(setter(Builder::createdByIamUser))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("CreatedByIamUser").build()).build();
private static final SdkField CREATED_AT_FIELD = SdkField. builder(MarshallingType.INSTANT)
.memberName("CreatedAt").getter(getter(MLModel::createdAt)).setter(setter(Builder::createdAt))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("CreatedAt").build()).build();
private static final SdkField LAST_UPDATED_AT_FIELD = SdkField. builder(MarshallingType.INSTANT)
.memberName("LastUpdatedAt").getter(getter(MLModel::lastUpdatedAt)).setter(setter(Builder::lastUpdatedAt))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("LastUpdatedAt").build()).build();
private static final SdkField NAME_FIELD = SdkField. builder(MarshallingType.STRING).memberName("Name")
.getter(getter(MLModel::name)).setter(setter(Builder::name))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Name").build()).build();
private static final SdkField STATUS_FIELD = SdkField. builder(MarshallingType.STRING).memberName("Status")
.getter(getter(MLModel::statusAsString)).setter(setter(Builder::status))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Status").build()).build();
private static final SdkField SIZE_IN_BYTES_FIELD = SdkField. builder(MarshallingType.LONG)
.memberName("SizeInBytes").getter(getter(MLModel::sizeInBytes)).setter(setter(Builder::sizeInBytes))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("SizeInBytes").build()).build();
private static final SdkField ENDPOINT_INFO_FIELD = SdkField
. builder(MarshallingType.SDK_POJO).memberName("EndpointInfo")
.getter(getter(MLModel::endpointInfo)).setter(setter(Builder::endpointInfo))
.constructor(RealtimeEndpointInfo::builder)
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("EndpointInfo").build()).build();
private static final SdkField> TRAINING_PARAMETERS_FIELD = SdkField
.> builder(MarshallingType.MAP)
.memberName("TrainingParameters")
.getter(getter(MLModel::trainingParameters))
.setter(setter(Builder::trainingParameters))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("TrainingParameters").build(),
MapTrait.builder()
.keyLocationName("key")
.valueLocationName("value")
.valueFieldInfo(
SdkField. builder(MarshallingType.STRING)
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD)
.locationName("value").build()).build()).build()).build();
private static final SdkField INPUT_DATA_LOCATION_S3_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("InputDataLocationS3").getter(getter(MLModel::inputDataLocationS3))
.setter(setter(Builder::inputDataLocationS3))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("InputDataLocationS3").build())
.build();
private static final SdkField ALGORITHM_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("Algorithm").getter(getter(MLModel::algorithmAsString)).setter(setter(Builder::algorithm))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Algorithm").build()).build();
private static final SdkField ML_MODEL_TYPE_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("MLModelType").getter(getter(MLModel::mlModelTypeAsString)).setter(setter(Builder::mlModelType))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("MLModelType").build()).build();
private static final SdkField SCORE_THRESHOLD_FIELD = SdkField. builder(MarshallingType.FLOAT)
.memberName("ScoreThreshold").getter(getter(MLModel::scoreThreshold)).setter(setter(Builder::scoreThreshold))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("ScoreThreshold").build()).build();
private static final SdkField SCORE_THRESHOLD_LAST_UPDATED_AT_FIELD = SdkField
. builder(MarshallingType.INSTANT)
.memberName("ScoreThresholdLastUpdatedAt")
.getter(getter(MLModel::scoreThresholdLastUpdatedAt))
.setter(setter(Builder::scoreThresholdLastUpdatedAt))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("ScoreThresholdLastUpdatedAt")
.build()).build();
private static final SdkField MESSAGE_FIELD = SdkField. builder(MarshallingType.STRING).memberName("Message")
.getter(getter(MLModel::message)).setter(setter(Builder::message))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Message").build()).build();
private static final SdkField COMPUTE_TIME_FIELD = SdkField. builder(MarshallingType.LONG)
.memberName("ComputeTime").getter(getter(MLModel::computeTime)).setter(setter(Builder::computeTime))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("ComputeTime").build()).build();
private static final SdkField FINISHED_AT_FIELD = SdkField. builder(MarshallingType.INSTANT)
.memberName("FinishedAt").getter(getter(MLModel::finishedAt)).setter(setter(Builder::finishedAt))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("FinishedAt").build()).build();
private static final SdkField STARTED_AT_FIELD = SdkField. builder(MarshallingType.INSTANT)
.memberName("StartedAt").getter(getter(MLModel::startedAt)).setter(setter(Builder::startedAt))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("StartedAt").build()).build();
private static final List> SDK_FIELDS = Collections.unmodifiableList(Arrays.asList(ML_MODEL_ID_FIELD,
TRAINING_DATA_SOURCE_ID_FIELD, CREATED_BY_IAM_USER_FIELD, CREATED_AT_FIELD, LAST_UPDATED_AT_FIELD, NAME_FIELD,
STATUS_FIELD, SIZE_IN_BYTES_FIELD, ENDPOINT_INFO_FIELD, TRAINING_PARAMETERS_FIELD, INPUT_DATA_LOCATION_S3_FIELD,
ALGORITHM_FIELD, ML_MODEL_TYPE_FIELD, SCORE_THRESHOLD_FIELD, SCORE_THRESHOLD_LAST_UPDATED_AT_FIELD, MESSAGE_FIELD,
COMPUTE_TIME_FIELD, FINISHED_AT_FIELD, STARTED_AT_FIELD));
private static final long serialVersionUID = 1L;
private final String mlModelId;
private final String trainingDataSourceId;
private final String createdByIamUser;
private final Instant createdAt;
private final Instant lastUpdatedAt;
private final String name;
private final String status;
private final Long sizeInBytes;
private final RealtimeEndpointInfo endpointInfo;
private final Map trainingParameters;
private final String inputDataLocationS3;
private final String algorithm;
private final String mlModelType;
private final Float scoreThreshold;
private final Instant scoreThresholdLastUpdatedAt;
private final String message;
private final Long computeTime;
private final Instant finishedAt;
private final Instant startedAt;
private MLModel(BuilderImpl builder) {
this.mlModelId = builder.mlModelId;
this.trainingDataSourceId = builder.trainingDataSourceId;
this.createdByIamUser = builder.createdByIamUser;
this.createdAt = builder.createdAt;
this.lastUpdatedAt = builder.lastUpdatedAt;
this.name = builder.name;
this.status = builder.status;
this.sizeInBytes = builder.sizeInBytes;
this.endpointInfo = builder.endpointInfo;
this.trainingParameters = builder.trainingParameters;
this.inputDataLocationS3 = builder.inputDataLocationS3;
this.algorithm = builder.algorithm;
this.mlModelType = builder.mlModelType;
this.scoreThreshold = builder.scoreThreshold;
this.scoreThresholdLastUpdatedAt = builder.scoreThresholdLastUpdatedAt;
this.message = builder.message;
this.computeTime = builder.computeTime;
this.finishedAt = builder.finishedAt;
this.startedAt = builder.startedAt;
}
/**
*
* The ID assigned to the MLModel at creation.
*
*
* @return The ID assigned to the MLModel at creation.
*/
public String mlModelId() {
return mlModelId;
}
/**
*
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*
*
* @return The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*/
public String trainingDataSourceId() {
return trainingDataSourceId;
}
/**
*
* The AWS user account from which the MLModel was created. The account type can be either an AWS root
* account or an AWS Identity and Access Management (IAM) user account.
*
*
* @return The AWS user account from which the MLModel was created. The account type can be either an
* AWS root account or an AWS Identity and Access Management (IAM) user account.
*/
public String createdByIamUser() {
return createdByIamUser;
}
/**
*
* The time that the MLModel was created. The time is expressed in epoch time.
*
*
* @return The time that the MLModel was created. The time is expressed in epoch time.
*/
public Instant createdAt() {
return createdAt;
}
/**
*
* The time of the most recent edit to the MLModel. The time is expressed in epoch time.
*
*
* @return The time of the most recent edit to the MLModel. The time is expressed in epoch time.
*/
public Instant lastUpdatedAt() {
return lastUpdatedAt;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @return A user-supplied name or description of the MLModel.
*/
public String name() {
return name;
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #status} will
* return {@link EntityStatus#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #statusAsString}.
*
*
* @return The current status of an MLModel. This element can have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The
* model isn't usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
* @see EntityStatus
*/
public EntityStatus status() {
return EntityStatus.fromValue(status);
}
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The model isn't
* usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #status} will
* return {@link EntityStatus#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #statusAsString}.
*
*
* @return The current status of an MLModel. This element can have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The
* model isn't usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
* @see EntityStatus
*/
public String statusAsString() {
return status;
}
/**
* Returns the value of the SizeInBytes property for this object.
*
* @return The value of the SizeInBytes property for this object.
*/
public Long sizeInBytes() {
return sizeInBytes;
}
/**
*
* The current endpoint of the MLModel.
*
*
* @return The current endpoint of the MLModel.
*/
public RealtimeEndpointInfo endpointInfo() {
return endpointInfo;
}
/**
* Returns true if the TrainingParameters property was specified by the sender (it may be empty), or false if the
* sender did not specify the value (it will be empty). For responses returned by the SDK, the sender is the AWS
* service.
*/
public boolean hasTrainingParameters() {
return trainingParameters != null && !(trainingParameters instanceof SdkAutoConstructMap);
}
/**
*
* A list of the training parameters in the MLModel. The list is implemented as a map of key-value
* pairs.
*
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the
* size of the model might affect its performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The default value is
* 33554432.
*
*
* -
*
* sgd.maxPasses - The number of times that the training process traverses the observations to build
* the MLModel. The value is an integer that ranges from 1 to 10000. The
* default value is 10.
*
*
* -
*
* sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a
* model's ability to find the optimal solution for a variety of data types. The valid values are auto
* and none. The default value is none.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the
* data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse
* feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1
* normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
* parameter, start by specifying a small value, such as 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2
* normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
*
*
*
*
* Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
*
*
* You can use {@link #hasTrainingParameters()} to see if a value was sent in this field.
*
*
* @return A list of the training parameters in the MLModel. The list is implemented as a map of
* key-value pairs.
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input
* data, the size of the model might affect its performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The default
* value is 33554432.
*
*
* -
*
* sgd.maxPasses - The number of times that the training process traverses the observations to
* build the MLModel. The value is an integer that ranges from 1 to
* 10000. The default value is 10.
*
*
* -
*
* sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves
* a model's ability to find the optimal solution for a variety of data types. The valid values are
* auto and none. The default value is none.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls
* overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to
* zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such
* as 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not
* use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls
* overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero
* values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not
* use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter
* sparingly.
*
*
*/
public Map trainingParameters() {
return trainingParameters;
}
/**
*
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*
*
* @return The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*/
public String inputDataLocationS3() {
return inputDataLocationS3;
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of
* the loss function.
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #algorithm} will
* return {@link Algorithm#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #algorithmAsString}.
*
*
* @return The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the
* gradient of the loss function.
* @see Algorithm
*/
public Algorithm algorithm() {
return Algorithm.fromValue(algorithm);
}
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of
* the loss function.
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #algorithm} will
* return {@link Algorithm#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #algorithmAsString}.
*
*
* @return The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the
* gradient of the loss function.
* @see Algorithm
*/
public String algorithmAsString() {
return algorithm;
}
/**
*
* Identifies the MLModel category. The following are the available types:
*
*
* -
REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #mlModelType} will
* return {@link MLModelType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #mlModelTypeAsString}.
*
*
* @return Identifies the MLModel category. The following are the available types:
*
* -
REGRESSION - Produces a numeric result. For example,
* "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
* @see MLModelType
*/
public MLModelType mlModelType() {
return MLModelType.fromValue(mlModelType);
}
/**
*
* Identifies the MLModel category. The following are the available types:
*
*
* -
REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #mlModelType} will
* return {@link MLModelType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #mlModelTypeAsString}.
*
*
* @return Identifies the MLModel category. The following are the available types:
*
* -
REGRESSION - Produces a numeric result. For example,
* "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
* @see MLModelType
*/
public String mlModelTypeAsString() {
return mlModelType;
}
/**
* Returns the value of the ScoreThreshold property for this object.
*
* @return The value of the ScoreThreshold property for this object.
*/
public Float scoreThreshold() {
return scoreThreshold;
}
/**
*
* The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
*
*
* @return The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
*/
public Instant scoreThresholdLastUpdatedAt() {
return scoreThresholdLastUpdatedAt;
}
/**
*
* A description of the most recent details about accessing the MLModel.
*
*
* @return A description of the most recent details about accessing the MLModel.
*/
public String message() {
return message;
}
/**
* Returns the value of the ComputeTime property for this object.
*
* @return The value of the ComputeTime property for this object.
*/
public Long computeTime() {
return computeTime;
}
/**
* Returns the value of the FinishedAt property for this object.
*
* @return The value of the FinishedAt property for this object.
*/
public Instant finishedAt() {
return finishedAt;
}
/**
* Returns the value of the StartedAt property for this object.
*
* @return The value of the StartedAt property for this object.
*/
public Instant startedAt() {
return startedAt;
}
@Override
public Builder toBuilder() {
return new BuilderImpl(this);
}
public static Builder builder() {
return new BuilderImpl();
}
public static Class serializableBuilderClass() {
return BuilderImpl.class;
}
@Override
public int hashCode() {
int hashCode = 1;
hashCode = 31 * hashCode + Objects.hashCode(mlModelId());
hashCode = 31 * hashCode + Objects.hashCode(trainingDataSourceId());
hashCode = 31 * hashCode + Objects.hashCode(createdByIamUser());
hashCode = 31 * hashCode + Objects.hashCode(createdAt());
hashCode = 31 * hashCode + Objects.hashCode(lastUpdatedAt());
hashCode = 31 * hashCode + Objects.hashCode(name());
hashCode = 31 * hashCode + Objects.hashCode(statusAsString());
hashCode = 31 * hashCode + Objects.hashCode(sizeInBytes());
hashCode = 31 * hashCode + Objects.hashCode(endpointInfo());
hashCode = 31 * hashCode + Objects.hashCode(hasTrainingParameters() ? trainingParameters() : null);
hashCode = 31 * hashCode + Objects.hashCode(inputDataLocationS3());
hashCode = 31 * hashCode + Objects.hashCode(algorithmAsString());
hashCode = 31 * hashCode + Objects.hashCode(mlModelTypeAsString());
hashCode = 31 * hashCode + Objects.hashCode(scoreThreshold());
hashCode = 31 * hashCode + Objects.hashCode(scoreThresholdLastUpdatedAt());
hashCode = 31 * hashCode + Objects.hashCode(message());
hashCode = 31 * hashCode + Objects.hashCode(computeTime());
hashCode = 31 * hashCode + Objects.hashCode(finishedAt());
hashCode = 31 * hashCode + Objects.hashCode(startedAt());
return hashCode;
}
@Override
public boolean equals(Object obj) {
return equalsBySdkFields(obj);
}
@Override
public boolean equalsBySdkFields(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (!(obj instanceof MLModel)) {
return false;
}
MLModel other = (MLModel) obj;
return Objects.equals(mlModelId(), other.mlModelId())
&& Objects.equals(trainingDataSourceId(), other.trainingDataSourceId())
&& Objects.equals(createdByIamUser(), other.createdByIamUser()) && Objects.equals(createdAt(), other.createdAt())
&& Objects.equals(lastUpdatedAt(), other.lastUpdatedAt()) && Objects.equals(name(), other.name())
&& Objects.equals(statusAsString(), other.statusAsString()) && Objects.equals(sizeInBytes(), other.sizeInBytes())
&& Objects.equals(endpointInfo(), other.endpointInfo())
&& hasTrainingParameters() == other.hasTrainingParameters()
&& Objects.equals(trainingParameters(), other.trainingParameters())
&& Objects.equals(inputDataLocationS3(), other.inputDataLocationS3())
&& Objects.equals(algorithmAsString(), other.algorithmAsString())
&& Objects.equals(mlModelTypeAsString(), other.mlModelTypeAsString())
&& Objects.equals(scoreThreshold(), other.scoreThreshold())
&& Objects.equals(scoreThresholdLastUpdatedAt(), other.scoreThresholdLastUpdatedAt())
&& Objects.equals(message(), other.message()) && Objects.equals(computeTime(), other.computeTime())
&& Objects.equals(finishedAt(), other.finishedAt()) && Objects.equals(startedAt(), other.startedAt());
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*/
@Override
public String toString() {
return ToString.builder("MLModel").add("MLModelId", mlModelId()).add("TrainingDataSourceId", trainingDataSourceId())
.add("CreatedByIamUser", createdByIamUser()).add("CreatedAt", createdAt()).add("LastUpdatedAt", lastUpdatedAt())
.add("Name", name()).add("Status", statusAsString()).add("SizeInBytes", sizeInBytes())
.add("EndpointInfo", endpointInfo())
.add("TrainingParameters", hasTrainingParameters() ? trainingParameters() : null)
.add("InputDataLocationS3", inputDataLocationS3()).add("Algorithm", algorithmAsString())
.add("MLModelType", mlModelTypeAsString()).add("ScoreThreshold", scoreThreshold())
.add("ScoreThresholdLastUpdatedAt", scoreThresholdLastUpdatedAt()).add("Message", message())
.add("ComputeTime", computeTime()).add("FinishedAt", finishedAt()).add("StartedAt", startedAt()).build();
}
public Optional getValueForField(String fieldName, Class clazz) {
switch (fieldName) {
case "MLModelId":
return Optional.ofNullable(clazz.cast(mlModelId()));
case "TrainingDataSourceId":
return Optional.ofNullable(clazz.cast(trainingDataSourceId()));
case "CreatedByIamUser":
return Optional.ofNullable(clazz.cast(createdByIamUser()));
case "CreatedAt":
return Optional.ofNullable(clazz.cast(createdAt()));
case "LastUpdatedAt":
return Optional.ofNullable(clazz.cast(lastUpdatedAt()));
case "Name":
return Optional.ofNullable(clazz.cast(name()));
case "Status":
return Optional.ofNullable(clazz.cast(statusAsString()));
case "SizeInBytes":
return Optional.ofNullable(clazz.cast(sizeInBytes()));
case "EndpointInfo":
return Optional.ofNullable(clazz.cast(endpointInfo()));
case "TrainingParameters":
return Optional.ofNullable(clazz.cast(trainingParameters()));
case "InputDataLocationS3":
return Optional.ofNullable(clazz.cast(inputDataLocationS3()));
case "Algorithm":
return Optional.ofNullable(clazz.cast(algorithmAsString()));
case "MLModelType":
return Optional.ofNullable(clazz.cast(mlModelTypeAsString()));
case "ScoreThreshold":
return Optional.ofNullable(clazz.cast(scoreThreshold()));
case "ScoreThresholdLastUpdatedAt":
return Optional.ofNullable(clazz.cast(scoreThresholdLastUpdatedAt()));
case "Message":
return Optional.ofNullable(clazz.cast(message()));
case "ComputeTime":
return Optional.ofNullable(clazz.cast(computeTime()));
case "FinishedAt":
return Optional.ofNullable(clazz.cast(finishedAt()));
case "StartedAt":
return Optional.ofNullable(clazz.cast(startedAt()));
default:
return Optional.empty();
}
}
@Override
public List> sdkFields() {
return SDK_FIELDS;
}
private static Function getter(Function g) {
return obj -> g.apply((MLModel) obj);
}
private static BiConsumer setter(BiConsumer s) {
return (obj, val) -> s.accept((Builder) obj, val);
}
public interface Builder extends SdkPojo, CopyableBuilder {
/**
*
* The ID assigned to the MLModel at creation.
*
*
* @param mlModelId
* The ID assigned to the MLModel at creation.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder mlModelId(String mlModelId);
/**
*
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
*
*
* @param trainingDataSourceId
* The ID of the training DataSource. The CreateMLModel operation uses the
* TrainingDataSourceId.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder trainingDataSourceId(String trainingDataSourceId);
/**
*
* The AWS user account from which the MLModel was created. The account type can be either an AWS
* root account or an AWS Identity and Access Management (IAM) user account.
*
*
* @param createdByIamUser
* The AWS user account from which the MLModel was created. The account type can be either
* an AWS root account or an AWS Identity and Access Management (IAM) user account.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder createdByIamUser(String createdByIamUser);
/**
*
* The time that the MLModel was created. The time is expressed in epoch time.
*
*
* @param createdAt
* The time that the MLModel was created. The time is expressed in epoch time.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder createdAt(Instant createdAt);
/**
*
* The time of the most recent edit to the MLModel. The time is expressed in epoch time.
*
*
* @param lastUpdatedAt
* The time of the most recent edit to the MLModel. The time is expressed in epoch time.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder lastUpdatedAt(Instant lastUpdatedAt);
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @param name
* A user-supplied name or description of the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder name(String name);
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @param status
* The current status of an MLModel. This element can have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The
* model isn't usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
* @see EntityStatus
* @return Returns a reference to this object so that method calls can be chained together.
* @see EntityStatus
*/
Builder status(String status);
/**
*
* The current status of an MLModel. This element can have one of the following values:
*
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The model
* isn't usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
*
*
* @param status
* The current status of an MLModel. This element can have one of the following values:
*
* -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an
* MLModel.
* -
INPROGRESS - The creation process is underway.
* -
FAILED - The request to create an MLModel didn't run to completion. The
* model isn't usable.
* -
COMPLETED - The creation process completed successfully.
* -
DELETED - The MLModel is marked as deleted. It isn't usable.
* @see EntityStatus
* @return Returns a reference to this object so that method calls can be chained together.
* @see EntityStatus
*/
Builder status(EntityStatus status);
/**
* Sets the value of the SizeInBytes property for this object.
*
* @param sizeInBytes
* The new value for the SizeInBytes property for this object.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder sizeInBytes(Long sizeInBytes);
/**
*
* The current endpoint of the MLModel.
*
*
* @param endpointInfo
* The current endpoint of the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder endpointInfo(RealtimeEndpointInfo endpointInfo);
/**
*
* The current endpoint of the MLModel.
*
* This is a convenience that creates an instance of the {@link RealtimeEndpointInfo.Builder} avoiding the need
* to create one manually via {@link RealtimeEndpointInfo#builder()}.
*
* When the {@link Consumer} completes, {@link RealtimeEndpointInfo.Builder#build()} is called immediately and
* its result is passed to {@link #endpointInfo(RealtimeEndpointInfo)}.
*
* @param endpointInfo
* a consumer that will call methods on {@link RealtimeEndpointInfo.Builder}
* @return Returns a reference to this object so that method calls can be chained together.
* @see #endpointInfo(RealtimeEndpointInfo)
*/
default Builder endpointInfo(Consumer endpointInfo) {
return endpointInfo(RealtimeEndpointInfo.builder().applyMutation(endpointInfo).build());
}
/**
*
* A list of the training parameters in the MLModel. The list is implemented as a map of key-value
* pairs.
*
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data,
* the size of the model might affect its performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The default value is
* 33554432.
*
*
* -
*
* sgd.maxPasses - The number of times that the training process traverses the observations to
* build the MLModel. The value is an integer that ranges from 1 to 10000
* . The default value is 10.
*
*
* -
*
* sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a
* model's ability to find the optimal solution for a variety of data types. The valid values are
* auto and none. The default value is none.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting
* the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in
* sparse feature set. If you use this parameter, start by specifying a small value, such as
* 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use
* L1 normalization. This parameter can't be used when L2 is specified. Use this parameter
* sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you
* use this parameter, start by specifying a small value, such as 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use
* L2 normalization. This parameter can't be used when L1 is specified. Use this parameter
* sparingly.
*
*
*
*
* @param trainingParameters
* A list of the training parameters in the MLModel. The list is implemented as a map of
* key-value pairs.
*
* The following is the current set of training parameters:
*
*
* -
*
* sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input
* data, the size of the model might affect its performance.
*
*
* The value is an integer that ranges from 100000 to 2147483648. The default
* value is 33554432.
*
*
* -
*
* sgd.maxPasses - The number of times that the training process traverses the observations
* to build the MLModel. The value is an integer that ranges from 1 to
* 10000. The default value is 10.
*
*
* -
*
* sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data
* improves a model's ability to find the optimal solution for a variety of data types. The valid values
* are auto and none. The default value is none.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls
* overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to
* zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value,
* such as 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to
* not use L1 normalization. This parameter can't be used when L2 is specified. Use this
* parameter sparingly.
*
*
* -
*
* sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls
* overfitting the data by penalizing large coefficients. This tends to drive coefficients to small,
* nonzero values. If you use this parameter, start by specifying a small value, such as
* 1.0E-08.
*
*
* The value is a double that ranges from 0 to MAX_DOUBLE. The default is to
* not use L2 normalization. This parameter can't be used when L1 is specified. Use this
* parameter sparingly.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder trainingParameters(Map trainingParameters);
/**
*
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
*
*
* @param inputDataLocationS3
* The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder inputDataLocationS3(String inputDataLocationS3);
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @param algorithm
* The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the
* gradient of the loss function.
* @see Algorithm
* @return Returns a reference to this object so that method calls can be chained together.
* @see Algorithm
*/
Builder algorithm(String algorithm);
/**
*
* The algorithm used to train the MLModel. The following algorithm is supported:
*
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient
* of the loss function.
*
*
* @param algorithm
* The algorithm used to train the MLModel. The following algorithm is supported:
*
* -
SGD -- Stochastic gradient descent. The goal of SGD is to minimize the
* gradient of the loss function.
* @see Algorithm
* @return Returns a reference to this object so that method calls can be chained together.
* @see Algorithm
*/
Builder algorithm(Algorithm algorithm);
/**
*
* Identifies the MLModel category. The following are the available types:
*
*
* -
REGRESSION - Produces a numeric result. For example,
* "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* @param mlModelType
* Identifies the MLModel category. The following are the available types:
*
* -
REGRESSION - Produces a numeric result. For example,
* "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
* @see MLModelType
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
Builder mlModelType(String mlModelType);
/**
*
* Identifies the MLModel category. The following are the available types:
*
*
* -
REGRESSION - Produces a numeric result. For example,
* "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
*
* @param mlModelType
* Identifies the MLModel category. The following are the available types:
*
* -
REGRESSION - Produces a numeric result. For example,
* "What price should a house be listed at?"
* -
BINARY - Produces one of two possible results. For example,
* "Is this a child-friendly web site?".
* -
MULTICLASS - Produces one of several possible results. For example,
* "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
*
* @see MLModelType
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
Builder mlModelType(MLModelType mlModelType);
/**
* Sets the value of the ScoreThreshold property for this object.
*
* @param scoreThreshold
* The new value for the ScoreThreshold property for this object.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder scoreThreshold(Float scoreThreshold);
/**
*
* The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
*
*
* @param scoreThresholdLastUpdatedAt
* The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch
* time.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder scoreThresholdLastUpdatedAt(Instant scoreThresholdLastUpdatedAt);
/**
*
* A description of the most recent details about accessing the MLModel.
*
*
* @param message
* A description of the most recent details about accessing the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder message(String message);
/**
* Sets the value of the ComputeTime property for this object.
*
* @param computeTime
* The new value for the ComputeTime property for this object.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder computeTime(Long computeTime);
/**
* Sets the value of the FinishedAt property for this object.
*
* @param finishedAt
* The new value for the FinishedAt property for this object.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder finishedAt(Instant finishedAt);
/**
* Sets the value of the StartedAt property for this object.
*
* @param startedAt
* The new value for the StartedAt property for this object.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder startedAt(Instant startedAt);
}
static final class BuilderImpl implements Builder {
private String mlModelId;
private String trainingDataSourceId;
private String createdByIamUser;
private Instant createdAt;
private Instant lastUpdatedAt;
private String name;
private String status;
private Long sizeInBytes;
private RealtimeEndpointInfo endpointInfo;
private Map trainingParameters = DefaultSdkAutoConstructMap.getInstance();
private String inputDataLocationS3;
private String algorithm;
private String mlModelType;
private Float scoreThreshold;
private Instant scoreThresholdLastUpdatedAt;
private String message;
private Long computeTime;
private Instant finishedAt;
private Instant startedAt;
private BuilderImpl() {
}
private BuilderImpl(MLModel model) {
mlModelId(model.mlModelId);
trainingDataSourceId(model.trainingDataSourceId);
createdByIamUser(model.createdByIamUser);
createdAt(model.createdAt);
lastUpdatedAt(model.lastUpdatedAt);
name(model.name);
status(model.status);
sizeInBytes(model.sizeInBytes);
endpointInfo(model.endpointInfo);
trainingParameters(model.trainingParameters);
inputDataLocationS3(model.inputDataLocationS3);
algorithm(model.algorithm);
mlModelType(model.mlModelType);
scoreThreshold(model.scoreThreshold);
scoreThresholdLastUpdatedAt(model.scoreThresholdLastUpdatedAt);
message(model.message);
computeTime(model.computeTime);
finishedAt(model.finishedAt);
startedAt(model.startedAt);
}
public final String getMlModelId() {
return mlModelId;
}
@Override
public final Builder mlModelId(String mlModelId) {
this.mlModelId = mlModelId;
return this;
}
public final void setMlModelId(String mlModelId) {
this.mlModelId = mlModelId;
}
public final String getTrainingDataSourceId() {
return trainingDataSourceId;
}
@Override
public final Builder trainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
return this;
}
public final void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
public final String getCreatedByIamUser() {
return createdByIamUser;
}
@Override
public final Builder createdByIamUser(String createdByIamUser) {
this.createdByIamUser = createdByIamUser;
return this;
}
public final void setCreatedByIamUser(String createdByIamUser) {
this.createdByIamUser = createdByIamUser;
}
public final Instant getCreatedAt() {
return createdAt;
}
@Override
public final Builder createdAt(Instant createdAt) {
this.createdAt = createdAt;
return this;
}
public final void setCreatedAt(Instant createdAt) {
this.createdAt = createdAt;
}
public final Instant getLastUpdatedAt() {
return lastUpdatedAt;
}
@Override
public final Builder lastUpdatedAt(Instant lastUpdatedAt) {
this.lastUpdatedAt = lastUpdatedAt;
return this;
}
public final void setLastUpdatedAt(Instant lastUpdatedAt) {
this.lastUpdatedAt = lastUpdatedAt;
}
public final String getName() {
return name;
}
@Override
public final Builder name(String name) {
this.name = name;
return this;
}
public final void setName(String name) {
this.name = name;
}
public final String getStatus() {
return status;
}
@Override
public final Builder status(String status) {
this.status = status;
return this;
}
@Override
public final Builder status(EntityStatus status) {
this.status(status == null ? null : status.toString());
return this;
}
public final void setStatus(String status) {
this.status = status;
}
public final Long getSizeInBytes() {
return sizeInBytes;
}
@Override
public final Builder sizeInBytes(Long sizeInBytes) {
this.sizeInBytes = sizeInBytes;
return this;
}
public final void setSizeInBytes(Long sizeInBytes) {
this.sizeInBytes = sizeInBytes;
}
public final RealtimeEndpointInfo.Builder getEndpointInfo() {
return endpointInfo != null ? endpointInfo.toBuilder() : null;
}
@Override
public final Builder endpointInfo(RealtimeEndpointInfo endpointInfo) {
this.endpointInfo = endpointInfo;
return this;
}
public final void setEndpointInfo(RealtimeEndpointInfo.BuilderImpl endpointInfo) {
this.endpointInfo = endpointInfo != null ? endpointInfo.build() : null;
}
public final Map getTrainingParameters() {
if (trainingParameters instanceof SdkAutoConstructMap) {
return null;
}
return trainingParameters;
}
@Override
public final Builder trainingParameters(Map trainingParameters) {
this.trainingParameters = TrainingParametersCopier.copy(trainingParameters);
return this;
}
public final void setTrainingParameters(Map trainingParameters) {
this.trainingParameters = TrainingParametersCopier.copy(trainingParameters);
}
public final String getInputDataLocationS3() {
return inputDataLocationS3;
}
@Override
public final Builder inputDataLocationS3(String inputDataLocationS3) {
this.inputDataLocationS3 = inputDataLocationS3;
return this;
}
public final void setInputDataLocationS3(String inputDataLocationS3) {
this.inputDataLocationS3 = inputDataLocationS3;
}
public final String getAlgorithm() {
return algorithm;
}
@Override
public final Builder algorithm(String algorithm) {
this.algorithm = algorithm;
return this;
}
@Override
public final Builder algorithm(Algorithm algorithm) {
this.algorithm(algorithm == null ? null : algorithm.toString());
return this;
}
public final void setAlgorithm(String algorithm) {
this.algorithm = algorithm;
}
public final String getMlModelType() {
return mlModelType;
}
@Override
public final Builder mlModelType(String mlModelType) {
this.mlModelType = mlModelType;
return this;
}
@Override
public final Builder mlModelType(MLModelType mlModelType) {
this.mlModelType(mlModelType == null ? null : mlModelType.toString());
return this;
}
public final void setMlModelType(String mlModelType) {
this.mlModelType = mlModelType;
}
public final Float getScoreThreshold() {
return scoreThreshold;
}
@Override
public final Builder scoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
return this;
}
public final void setScoreThreshold(Float scoreThreshold) {
this.scoreThreshold = scoreThreshold;
}
public final Instant getScoreThresholdLastUpdatedAt() {
return scoreThresholdLastUpdatedAt;
}
@Override
public final Builder scoreThresholdLastUpdatedAt(Instant scoreThresholdLastUpdatedAt) {
this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt;
return this;
}
public final void setScoreThresholdLastUpdatedAt(Instant scoreThresholdLastUpdatedAt) {
this.scoreThresholdLastUpdatedAt = scoreThresholdLastUpdatedAt;
}
public final String getMessage() {
return message;
}
@Override
public final Builder message(String message) {
this.message = message;
return this;
}
public final void setMessage(String message) {
this.message = message;
}
public final Long getComputeTime() {
return computeTime;
}
@Override
public final Builder computeTime(Long computeTime) {
this.computeTime = computeTime;
return this;
}
public final void setComputeTime(Long computeTime) {
this.computeTime = computeTime;
}
public final Instant getFinishedAt() {
return finishedAt;
}
@Override
public final Builder finishedAt(Instant finishedAt) {
this.finishedAt = finishedAt;
return this;
}
public final void setFinishedAt(Instant finishedAt) {
this.finishedAt = finishedAt;
}
public final Instant getStartedAt() {
return startedAt;
}
@Override
public final Builder startedAt(Instant startedAt) {
this.startedAt = startedAt;
return this;
}
public final void setStartedAt(Instant startedAt) {
this.startedAt = startedAt;
}
@Override
public MLModel build() {
return new MLModel(this);
}
@Override
public List> sdkFields() {
return SDK_FIELDS;
}
}
}