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/*
* Copyright 2011-2016 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 com.amazonaws.services.machinelearning.model;
import java.io.Serializable;
import com.amazonaws.AmazonWebServiceRequest;
/**
*
*/
public class CreateMLModelRequest extends AmazonWebServiceRequest implements
Serializable, Cloneable {
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
*/
private String mLModelId;
/**
*
* A user-supplied name or description of the MLModel.
*
*/
private 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.
*
*/
private String 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.
*
*
*
*/
private com.amazonaws.internal.SdkInternalMap parameters;
/**
*
* The DataSource that points to the training data.
*
*/
private 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.
*
*/
private 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.
*
*/
private String recipeUri;
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
*
* @param mLModelId
* A user-supplied ID that uniquely identifies the
* MLModel.
*/
public void setMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
}
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
*
* @return A user-supplied ID that uniquely identifies the
* MLModel.
*/
public String getMLModelId() {
return this.mLModelId;
}
/**
*
* 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.
*/
public CreateMLModelRequest withMLModelId(String mLModelId) {
setMLModelId(mLModelId);
return this;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @param mLModelName
* A user-supplied name or description of the MLModel.
*/
public void setMLModelName(String mLModelName) {
this.mLModelName = mLModelName;
}
/**
*
* A user-supplied name or description of the MLModel.
*
*
* @return A user-supplied name or description of the MLModel.
*/
public String getMLModelName() {
return this.mLModelName;
}
/**
*
* 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.
*/
public CreateMLModelRequest withMLModelName(String mLModelName) {
setMLModelName(mLModelName);
return this;
}
/**
*
* 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
*/
public void setMLModelType(String mLModelType) {
this.mLModelType = 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.
*
*
* @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 getMLModelType() {
return this.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.
* @return Returns a reference to this object so that method calls can be
* chained together.
* @see MLModelType
*/
public CreateMLModelRequest withMLModelType(String mLModelType) {
setMLModelType(mLModelType);
return this;
}
/**
*
* 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
*/
public void setMLModelType(MLModelType mLModelType) {
this.mLModelType = mLModelType.toString();
}
/**
*
* 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.
* @return Returns a reference to this object so that method calls can be
* chained together.
* @see MLModelType
*/
public CreateMLModelRequest withMLModelType(MLModelType mLModelType) {
setMLModelType(mLModelType);
return this;
}
/**
*
* 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 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 java.util.Map getParameters() {
if (parameters == null) {
parameters = new com.amazonaws.internal.SdkInternalMap();
}
return 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.
*
*
*
*
* @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.
*
*
*/
public void setParameters(java.util.Map parameters) {
this.parameters = parameters == null ? null
: new com.amazonaws.internal.SdkInternalMap(
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.
*
*
*
*
* @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.
*/
public CreateMLModelRequest withParameters(
java.util.Map parameters) {
setParameters(parameters);
return this;
}
public CreateMLModelRequest addParametersEntry(String key, String value) {
if (null == this.parameters) {
this.parameters = new com.amazonaws.internal.SdkInternalMap();
}
if (this.parameters.containsKey(key))
throw new IllegalArgumentException("Duplicated keys ("
+ key.toString() + ") are provided.");
this.parameters.put(key, value);
return this;
}
/**
* Removes all the entries added into Parameters. <p> Returns a reference
* to this object so that method calls can be chained together.
*/
public CreateMLModelRequest clearParametersEntries() {
this.parameters = null;
return this;
}
/**
*
* The DataSource that points to the training data.
*
*
* @param trainingDataSourceId
* The DataSource that points to the training data.
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
*
* The DataSource that points to the training data.
*
*
* @return The DataSource that points to the training data.
*/
public String getTrainingDataSourceId() {
return this.trainingDataSourceId;
}
/**
*
* 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.
*/
public CreateMLModelRequest withTrainingDataSourceId(
String trainingDataSourceId) {
setTrainingDataSourceId(trainingDataSourceId);
return this;
}
/**
*
* 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.
*/
public void setRecipe(String recipe) {
this.recipe = 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 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 getRecipe() {
return this.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.
*
*
* @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.
*/
public CreateMLModelRequest withRecipe(String recipe) {
setRecipe(recipe);
return this;
}
/**
*
* 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.
*/
public void setRecipeUri(String recipeUri) {
this.recipeUri = 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 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 getRecipeUri() {
return this.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.
*
*
* @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.
*/
public CreateMLModelRequest withRecipeUri(String recipeUri) {
setRecipeUri(recipeUri);
return this;
}
/**
* Returns a string representation of this object; useful for testing and
* debugging.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getMLModelId() != null)
sb.append("MLModelId: " + getMLModelId() + ",");
if (getMLModelName() != null)
sb.append("MLModelName: " + getMLModelName() + ",");
if (getMLModelType() != null)
sb.append("MLModelType: " + getMLModelType() + ",");
if (getParameters() != null)
sb.append("Parameters: " + getParameters() + ",");
if (getTrainingDataSourceId() != null)
sb.append("TrainingDataSourceId: " + getTrainingDataSourceId()
+ ",");
if (getRecipe() != null)
sb.append("Recipe: " + getRecipe() + ",");
if (getRecipeUri() != null)
sb.append("RecipeUri: " + getRecipeUri());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof CreateMLModelRequest == false)
return false;
CreateMLModelRequest other = (CreateMLModelRequest) obj;
if (other.getMLModelId() == null ^ this.getMLModelId() == null)
return false;
if (other.getMLModelId() != null
&& other.getMLModelId().equals(this.getMLModelId()) == false)
return false;
if (other.getMLModelName() == null ^ this.getMLModelName() == null)
return false;
if (other.getMLModelName() != null
&& other.getMLModelName().equals(this.getMLModelName()) == false)
return false;
if (other.getMLModelType() == null ^ this.getMLModelType() == null)
return false;
if (other.getMLModelType() != null
&& other.getMLModelType().equals(this.getMLModelType()) == false)
return false;
if (other.getParameters() == null ^ this.getParameters() == null)
return false;
if (other.getParameters() != null
&& other.getParameters().equals(this.getParameters()) == false)
return false;
if (other.getTrainingDataSourceId() == null
^ this.getTrainingDataSourceId() == null)
return false;
if (other.getTrainingDataSourceId() != null
&& other.getTrainingDataSourceId().equals(
this.getTrainingDataSourceId()) == false)
return false;
if (other.getRecipe() == null ^ this.getRecipe() == null)
return false;
if (other.getRecipe() != null
&& other.getRecipe().equals(this.getRecipe()) == false)
return false;
if (other.getRecipeUri() == null ^ this.getRecipeUri() == null)
return false;
if (other.getRecipeUri() != null
&& other.getRecipeUri().equals(this.getRecipeUri()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode
+ ((getMLModelId() == null) ? 0 : getMLModelId().hashCode());
hashCode = prime
* hashCode
+ ((getMLModelName() == null) ? 0 : getMLModelName().hashCode());
hashCode = prime
* hashCode
+ ((getMLModelType() == null) ? 0 : getMLModelType().hashCode());
hashCode = prime * hashCode
+ ((getParameters() == null) ? 0 : getParameters().hashCode());
hashCode = prime
* hashCode
+ ((getTrainingDataSourceId() == null) ? 0
: getTrainingDataSourceId().hashCode());
hashCode = prime * hashCode
+ ((getRecipe() == null) ? 0 : getRecipe().hashCode());
hashCode = prime * hashCode
+ ((getRecipeUri() == null) ? 0 : getRecipeUri().hashCode());
return hashCode;
}
@Override
public CreateMLModelRequest clone() {
return (CreateMLModelRequest) super.clone();
}
}