software.amazon.awssdk.services.machinelearning.model.CreateMlModelRequest 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.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.awscore.AwsRequestOverrideConfiguration;
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;
/**
*/
@Generated("software.amazon.awssdk:codegen")
public final class CreateMlModelRequest extends MachineLearningRequest implements
ToCopyableBuilder {
private static final SdkField ML_MODEL_ID_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("MLModelId").getter(getter(CreateMlModelRequest::mlModelId)).setter(setter(Builder::mlModelId))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("MLModelId").build()).build();
private static final SdkField ML_MODEL_NAME_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("MLModelName").getter(getter(CreateMlModelRequest::mlModelName)).setter(setter(Builder::mlModelName))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("MLModelName").build()).build();
private static final SdkField ML_MODEL_TYPE_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("MLModelType").getter(getter(CreateMlModelRequest::mlModelTypeAsString))
.setter(setter(Builder::mlModelType))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("MLModelType").build()).build();
private static final SdkField> PARAMETERS_FIELD = SdkField
.> builder(MarshallingType.MAP)
.memberName("Parameters")
.getter(getter(CreateMlModelRequest::parameters))
.setter(setter(Builder::parameters))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Parameters").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 TRAINING_DATA_SOURCE_ID_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("TrainingDataSourceId").getter(getter(CreateMlModelRequest::trainingDataSourceId))
.setter(setter(Builder::trainingDataSourceId))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("TrainingDataSourceId").build())
.build();
private static final SdkField RECIPE_FIELD = SdkField. builder(MarshallingType.STRING).memberName("Recipe")
.getter(getter(CreateMlModelRequest::recipe)).setter(setter(Builder::recipe))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("Recipe").build()).build();
private static final SdkField RECIPE_URI_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("RecipeUri").getter(getter(CreateMlModelRequest::recipeUri)).setter(setter(Builder::recipeUri))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("RecipeUri").build()).build();
private static final List> SDK_FIELDS = Collections.unmodifiableList(Arrays.asList(ML_MODEL_ID_FIELD,
ML_MODEL_NAME_FIELD, ML_MODEL_TYPE_FIELD, PARAMETERS_FIELD, TRAINING_DATA_SOURCE_ID_FIELD, RECIPE_FIELD,
RECIPE_URI_FIELD));
private final String mlModelId;
private final String mlModelName;
private final String mlModelType;
private final Map parameters;
private final String trainingDataSourceId;
private final String recipe;
private final String recipeUri;
private CreateMlModelRequest(BuilderImpl builder) {
super(builder);
this.mlModelId = builder.mlModelId;
this.mlModelName = builder.mlModelName;
this.mlModelType = builder.mlModelType;
this.parameters = builder.parameters;
this.trainingDataSourceId = builder.trainingDataSourceId;
this.recipe = builder.recipe;
this.recipeUri = builder.recipeUri;
}
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
*
* @return A user-supplied ID that uniquely identifies the MLModel.
*/
public String mlModelId() {
return mlModelId;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @return A user-supplied name or description of the MLModel.
*/
public String mlModelName() {
return mlModelName;
}
/**
*
* The category of supervised learning that this MLModel will address. Choose from the following types:
*
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon Machine
* Learning Developer Guide.
*
*
* 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 The category of supervised learning that this MLModel will address. Choose from the
* following types:
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon
* Machine Learning Developer Guide.
* @see MLModelType
*/
public MLModelType mlModelType() {
return MLModelType.fromValue(mlModelType);
}
/**
*
* The category of supervised learning that this MLModel will address. Choose from the following types:
*
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon Machine
* Learning Developer Guide.
*
*
* 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 The category of supervised learning that this MLModel will address. Choose from the
* following types:
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon
* Machine Learning Developer Guide.
* @see MLModelType
*/
public String mlModelTypeAsString() {
return mlModelType;
}
/**
* Returns true if the Parameters 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 hasParameters() {
return parameters != null && !(parameters 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. We strongly recommend that you shuffle your data.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a 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. It 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 #hasParameters()} 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. We strongly recommend that you shuffle your
* data.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting
* the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a
* 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. It 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 parameters() {
return parameters;
}
/**
*
* The DataSource that points to the training data.
*
*
* @return The DataSource that points to the training data.
*/
public String trainingDataSourceId() {
return trainingDataSourceId;
}
/**
*
* The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you
* don't specify a recipe or its URI, Amazon ML creates a default.
*
*
* @return The data recipe for creating the MLModel. You must specify either the recipe or its URI. If
* you don't specify a recipe or its URI, Amazon ML creates a default.
*/
public String recipe() {
return recipe;
}
/**
*
* The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
* recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
* creates a default.
*
*
* @return The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
* MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe
* or its URI, Amazon ML creates a default.
*/
public String recipeUri() {
return recipeUri;
}
@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 + super.hashCode();
hashCode = 31 * hashCode + Objects.hashCode(mlModelId());
hashCode = 31 * hashCode + Objects.hashCode(mlModelName());
hashCode = 31 * hashCode + Objects.hashCode(mlModelTypeAsString());
hashCode = 31 * hashCode + Objects.hashCode(hasParameters() ? parameters() : null);
hashCode = 31 * hashCode + Objects.hashCode(trainingDataSourceId());
hashCode = 31 * hashCode + Objects.hashCode(recipe());
hashCode = 31 * hashCode + Objects.hashCode(recipeUri());
return hashCode;
}
@Override
public boolean equals(Object obj) {
return super.equals(obj) && equalsBySdkFields(obj);
}
@Override
public boolean equalsBySdkFields(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (!(obj instanceof CreateMlModelRequest)) {
return false;
}
CreateMlModelRequest other = (CreateMlModelRequest) obj;
return Objects.equals(mlModelId(), other.mlModelId()) && Objects.equals(mlModelName(), other.mlModelName())
&& Objects.equals(mlModelTypeAsString(), other.mlModelTypeAsString()) && hasParameters() == other.hasParameters()
&& Objects.equals(parameters(), other.parameters())
&& Objects.equals(trainingDataSourceId(), other.trainingDataSourceId())
&& Objects.equals(recipe(), other.recipe()) && Objects.equals(recipeUri(), other.recipeUri());
}
/**
* 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("CreateMlModelRequest").add("MLModelId", mlModelId()).add("MLModelName", mlModelName())
.add("MLModelType", mlModelTypeAsString()).add("Parameters", hasParameters() ? parameters() : null)
.add("TrainingDataSourceId", trainingDataSourceId()).add("Recipe", recipe()).add("RecipeUri", recipeUri())
.build();
}
public Optional getValueForField(String fieldName, Class clazz) {
switch (fieldName) {
case "MLModelId":
return Optional.ofNullable(clazz.cast(mlModelId()));
case "MLModelName":
return Optional.ofNullable(clazz.cast(mlModelName()));
case "MLModelType":
return Optional.ofNullable(clazz.cast(mlModelTypeAsString()));
case "Parameters":
return Optional.ofNullable(clazz.cast(parameters()));
case "TrainingDataSourceId":
return Optional.ofNullable(clazz.cast(trainingDataSourceId()));
case "Recipe":
return Optional.ofNullable(clazz.cast(recipe()));
case "RecipeUri":
return Optional.ofNullable(clazz.cast(recipeUri()));
default:
return Optional.empty();
}
}
@Override
public List> sdkFields() {
return SDK_FIELDS;
}
private static Function getter(Function g) {
return obj -> g.apply((CreateMlModelRequest) obj);
}
private static BiConsumer setter(BiConsumer s) {
return (obj, val) -> s.accept((Builder) obj, val);
}
public interface Builder extends MachineLearningRequest.Builder, SdkPojo, CopyableBuilder {
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
*
* @param mlModelId
* A user-supplied ID that uniquely identifies the MLModel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder mlModelId(String mlModelId);
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @param mlModelName
* 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 mlModelName(String mlModelName);
/**
*
* The category of supervised learning that this MLModel will address. Choose from the following
* types:
*
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon Machine
* Learning Developer Guide.
*
*
* @param mlModelType
* The category of supervised learning that this MLModel will address. Choose from the
* following types:
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric
* value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon
* Machine Learning Developer Guide.
* @see MLModelType
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
Builder mlModelType(String mlModelType);
/**
*
* The category of supervised learning that this MLModel will address. Choose from the following
* types:
*
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon Machine
* Learning Developer Guide.
*
*
* @param mlModelType
* The category of supervised learning that this MLModel will address. Choose from the
* following types:
*
* - Choose
REGRESSION if the MLModel will be used to predict a numeric
* value.
* - Choose
BINARY if the MLModel result has two possible values.
* - Choose
MULTICLASS if the MLModel result has a limited number of values.
*
*
* For more information, see the Amazon
* Machine Learning Developer Guide.
* @see MLModelType
* @return Returns a reference to this object so that method calls can be chained together.
* @see MLModelType
*/
Builder mlModelType(MLModelType mlModelType);
/**
*
* 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. We strongly recommend that you shuffle your
* data.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the
* data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a 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. It 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 parameters
* 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. We
* strongly recommend that you
* shuffle your data.
*
*
* -
*
* sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls
* overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero,
* resulting in a 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. It 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 parameters(Map parameters);
/**
*
* The DataSource that points to the training data.
*
*
* @param trainingDataSourceId
* The DataSource that points to the training data.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder trainingDataSourceId(String trainingDataSourceId);
/**
*
* The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you
* don't specify a recipe or its URI, Amazon ML creates a default.
*
*
* @param recipe
* The data recipe for creating the MLModel. You must specify either the recipe or its URI.
* If you don't specify a recipe or its URI, Amazon ML creates a default.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder recipe(String recipe);
/**
*
* The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
* recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
* creates a default.
*
*
* @param recipeUri
* The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
* MLModel recipe. You must specify either the recipe or its URI. If you don't specify a
* recipe or its URI, Amazon ML creates a default.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder recipeUri(String recipeUri);
@Override
Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration);
@Override
Builder overrideConfiguration(Consumer builderConsumer);
}
static final class BuilderImpl extends MachineLearningRequest.BuilderImpl implements Builder {
private String mlModelId;
private String mlModelName;
private String mlModelType;
private Map parameters = DefaultSdkAutoConstructMap.getInstance();
private String trainingDataSourceId;
private String recipe;
private String recipeUri;
private BuilderImpl() {
}
private BuilderImpl(CreateMlModelRequest model) {
super(model);
mlModelId(model.mlModelId);
mlModelName(model.mlModelName);
mlModelType(model.mlModelType);
parameters(model.parameters);
trainingDataSourceId(model.trainingDataSourceId);
recipe(model.recipe);
recipeUri(model.recipeUri);
}
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 getMlModelName() {
return mlModelName;
}
@Override
public final Builder mlModelName(String mlModelName) {
this.mlModelName = mlModelName;
return this;
}
public final void setMlModelName(String mlModelName) {
this.mlModelName = mlModelName;
}
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 Map getParameters() {
if (parameters instanceof SdkAutoConstructMap) {
return null;
}
return parameters;
}
@Override
public final Builder parameters(Map parameters) {
this.parameters = TrainingParametersCopier.copy(parameters);
return this;
}
public final void setParameters(Map parameters) {
this.parameters = TrainingParametersCopier.copy(parameters);
}
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 getRecipe() {
return recipe;
}
@Override
public final Builder recipe(String recipe) {
this.recipe = recipe;
return this;
}
public final void setRecipe(String recipe) {
this.recipe = recipe;
}
public final String getRecipeUri() {
return recipeUri;
}
@Override
public final Builder recipeUri(String recipeUri) {
this.recipeUri = recipeUri;
return this;
}
public final void setRecipeUri(String recipeUri) {
this.recipeUri = recipeUri;
}
@Override
public Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) {
super.overrideConfiguration(overrideConfiguration);
return this;
}
@Override
public Builder overrideConfiguration(Consumer builderConsumer) {
super.overrideConfiguration(builderConsumer);
return this;
}
@Override
public CreateMlModelRequest build() {
return new CreateMlModelRequest(this);
}
@Override
public List> sdkFields() {
return SDK_FIELDS;
}
}
}