* A user-supplied ID that uniquely identifies the MLModel.
*
* A user-supplied name or description of the MLModel.
*
* 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.
*
* The DataSource that points to the training data.
*
* 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.
*
* 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.
*
* A user-supplied ID that uniquely identifies the MLModel.
*
MLModel.
*/
public void setMLModelId(String mLModelId) {
this.mLModelId = mLModelId;
}
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
MLModel.
*/
public String getMLModelId() {
return this.mLModelId;
}
/**
*
* A user-supplied ID that uniquely identifies the MLModel.
*
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.
*
MLModel.
*/
public void setMLModelName(String mLModelName) {
this.mLModelName = mLModelName;
}
/**
*
* A user-supplied name or description of the MLModel.
*
MLModel.
*/
public String getMLModelName() {
return this.mLModelName;
}
/**
*
* A user-supplied name or description of the MLModel.
*
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 thisMLModel 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 thisMLModel 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 thisMLModel 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 thisMLModel 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) { withMLModelType(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 thisMLModel 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) { this.mLModelType = mLModelType.toString(); 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.
*
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.
*
* 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.
*
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.
*
* 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.
*
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.
*
* The DataSource that points to the training data.
*
DataSource that points to the training data.
*/
public void setTrainingDataSourceId(String trainingDataSourceId) {
this.trainingDataSourceId = trainingDataSourceId;
}
/**
*
* The DataSource that points to the training data.
*
DataSource that points to the training data.
*/
public String getTrainingDataSourceId() {
return this.trainingDataSourceId;
}
/**
*
* The DataSource that points to the training data.
*
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.
*
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.
*
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.
*
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.
*
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.
*
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.
*
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. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @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: ").append(getMLModelId()).append(",");
if (getMLModelName() != null)
sb.append("MLModelName: ").append(getMLModelName()).append(",");
if (getMLModelType() != null)
sb.append("MLModelType: ").append(getMLModelType()).append(",");
if (getParameters() != null)
sb.append("Parameters: ").append(getParameters()).append(",");
if (getTrainingDataSourceId() != null)
sb.append("TrainingDataSourceId: ").append(getTrainingDataSourceId()).append(",");
if (getRecipe() != null)
sb.append("Recipe: ").append(getRecipe()).append(",");
if (getRecipeUri() != null)
sb.append("RecipeUri: ").append(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();
}
}