/*******************************************************************************
* Copyright 2012-2019 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.
* *****************************************************************************
*
* AWS Tools for Windows (TM) PowerShell (TM)
*
*/
using System;
using System.Collections.Generic;
using System.Linq;
using System.Management.Automation;
using System.Text;
using Amazon.PowerShell.Common;
using Amazon.Runtime;
using Amazon.MachineLearning;
using Amazon.MachineLearning.Model;
namespace Amazon.PowerShell.Cmdlets.ML
{
/// <summary>
/// Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe
/// as information sources.
///
///
/// <para>
/// An <code>MLModel</code> is nearly immutable. Users can update only the <code>MLModelName</code>
/// and the <code>ScoreThreshold</code> in an <code>MLModel</code> without creating a
/// new <code>MLModel</code>.
/// </para><para><code>CreateMLModel</code> is an asynchronous operation. In response to <code>CreateMLModel</code>,
/// Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>MLModel</code>
/// status to <code>PENDING</code>. After the <code>MLModel</code> has been created and
/// ready is for use, Amazon ML sets the status to <code>COMPLETED</code>.
/// </para><para>
/// You can use the <code>GetMLModel</code> operation to check the progress of the <code>MLModel</code>
/// during the creation operation.
/// </para><para><code>CreateMLModel</code> requires a <code>DataSource</code> with computed statistics,
/// which can be created by setting <code>ComputeStatistics</code> to <code>true</code>
/// in <code>CreateDataSourceFromRDS</code>, <code>CreateDataSourceFromS3</code>, or <code>CreateDataSourceFromRedshift</code>
/// operations.
/// </para>
/// </summary>
[Cmdlet("New", "MLModel", SupportsShouldProcess = true, ConfirmImpact = ConfirmImpact.Medium)]
[OutputType("System.String")]
[AWSCmdlet("Calls the Amazon Machine Learning CreateMLModel API operation.", Operation = new[] {"CreateMLModel"}, SelectReturnType = typeof(Amazon.MachineLearning.Model.CreateMLModelResponse))]
[AWSCmdletOutput("System.String or Amazon.MachineLearning.Model.CreateMLModelResponse",
"This cmdlet returns a System.String object.",
"The service call response (type Amazon.MachineLearning.Model.CreateMLModelResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack."
)]
public partial class NewMLModelCmdlet : AmazonMachineLearningClientCmdlet, IExecutor
{
#region Parameter MLModelId
/// <summary>
/// <para>
/// <para>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</para>
/// </para>
/// </summary>
#if !MODULAR
[System.Management.Automation.Parameter(Position = 0, ValueFromPipelineByPropertyName = true, ValueFromPipeline = true)]
#else
[System.Management.Automation.Parameter(Position = 0, ValueFromPipelineByPropertyName = true, ValueFromPipeline = true, Mandatory = true)]
[System.Management.Automation.AllowEmptyString]
[System.Management.Automation.AllowNull]
#endif
[Amazon.PowerShell.Common.AWSRequiredParameter]
[Alias("ModelId")]
public System.String MLModelId { get; set; }
#endregion
#region Parameter MLModelName
/// <summary>
/// <para>
/// <para>A user-supplied name or description of the <code>MLModel</code>.</para>
/// </para>
/// </summary>
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
[Alias("Name")]
public System.String MLModelName { get; set; }
#endregion
#region Parameter MLModelType
/// <summary>
/// <para>
/// <para>The category of supervised learning that this <code>MLModel</code> will address. Choose
/// from the following types:</para><ul><li><para>Choose <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict
/// a numeric value.</para></li><li><para>Choose <code>BINARY</code> if the <code>MLModel</code> result has two possible values.</para></li><li><para>Choose <code>MULTICLASS</code> if the <code>MLModel</code> result has a limited number
/// of values.</para></li></ul><para> For more information, see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon
/// Machine Learning Developer Guide</a>.</para>
/// </para>
/// </summary>
#if !MODULAR
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
#else
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true, Mandatory = true)]
[System.Management.Automation.AllowNull]
#endif
[Amazon.PowerShell.Common.AWSRequiredParameter]
[Alias("ModelType")]
[AWSConstantClassSource("Amazon.MachineLearning.MLModelType")]
public Amazon.MachineLearning.MLModelType MLModelType { get; set; }
#endregion
#region Parameter Parameter
/// <summary>
/// <para>
/// <para>A list of the training parameters in the <code>MLModel</code>. The list is implemented
/// as a map of key-value pairs.</para><para>The following is the current set of training parameters:</para><ul><li><para><code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending
/// on the input data, the size of the model might affect its performance.</para><para> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>.
/// The default value is <code>33554432</code>.</para></li><li><para><code>sgd.maxPasses</code> - The number of times that the training process traverses
/// the observations to build the <code>MLModel</code>. The value is an integer that ranges
/// from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</para></li><li><para><code>sgd.shuffleType</code> - 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 <code>auto</code> and <code>none</code>. The default
/// value is <code>none</code>. We strongly recommend that you shuffle your data.</para></li><li><para><code>sgd.l1RegularizationAmount</code> - 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 <code>1.0E-08</code>.</para><para>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
/// The default is to not use L1 normalization. This parameter can't be used when <code>L2</code>
/// is specified. Use this parameter sparingly.</para></li><li><para><code>sgd.l2RegularizationAmount</code> - 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 <code>1.0E-08</code>.</para><para>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
/// The default is to not use L2 normalization. This parameter can't be used when <code>L1</code>
/// is specified. Use this parameter sparingly.</para></li></ul>
/// </para>
/// </summary>
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
[Alias("Parameters")]
public System.Collections.Hashtable Parameter { get; set; }
#endregion
#region Parameter Recipe
/// <summary>
/// <para>
/// <para>The data recipe for creating the <code>MLModel</code>. You must specify either the
/// recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</para>
/// </para>
/// </summary>
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
public System.String Recipe { get; set; }
#endregion
#region Parameter RecipeUri
/// <summary>
/// <para>
/// <para>The Amazon Simple Storage Service (Amazon S3) location and file name that contains
/// the <code>MLModel</code> 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.</para>
/// </para>
/// </summary>
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
public System.String RecipeUri { get; set; }
#endregion
#region Parameter TrainingDataSourceId
/// <summary>
/// <para>
/// <para>The <code>DataSource</code> that points to the training data.</para>
/// </para>
/// </summary>
#if !MODULAR
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
#else
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true, Mandatory = true)]
[System.Management.Automation.AllowEmptyString]
[System.Management.Automation.AllowNull]
#endif
[Amazon.PowerShell.Common.AWSRequiredParameter]
public System.String TrainingDataSourceId { get; set; }
#endregion
#region Parameter Select
/// <summary>
/// Use the -Select parameter to control the cmdlet output. The default value is 'MLModelId'.
/// Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.MachineLearning.Model.CreateMLModelResponse).
/// Specifying the name of a property of type Amazon.MachineLearning.Model.CreateMLModelResponse will result in that property being returned.
/// Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
/// </summary>
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
public string Select { get; set; } = "MLModelId";
#endregion
#region Parameter PassThru
/// <summary>
/// Changes the cmdlet behavior to return the value passed to the MLModelId parameter.
/// The -PassThru parameter is deprecated, use -Select '^MLModelId' instead. This parameter will be removed in a future version.
/// </summary>
[System.Obsolete("The -PassThru parameter is deprecated, use -Select '^MLModelId' instead. This parameter will be removed in a future version.")]
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
public SwitchParameter PassThru { get; set; }
#endregion
#region Parameter Force
/// <summary>
/// This parameter overrides confirmation prompts to force
/// the cmdlet to continue its operation. This parameter should always
/// be used with caution.
/// </summary>
[System.Management.Automation.Parameter(ValueFromPipelineByPropertyName = true)]
public SwitchParameter Force { get; set; }
#endregion
protected override void ProcessRecord()
{
this._AWSSignerType = "v4";
base.ProcessRecord();
var resourceIdentifiersText = FormatParameterValuesForConfirmationMsg(nameof(this.MLModelId), MyInvocation.BoundParameters);
if (!ConfirmShouldProceed(this.Force.IsPresent, resourceIdentifiersText, "New-MLModel (CreateMLModel)"))
{
return;
}
var context = new CmdletContext();
// allow for manipulation of parameters prior to loading into context
PreExecutionContextLoad(context);
#pragma warning disable CS0618, CS0612 //A class member was marked with the Obsolete attribute
if (ParameterWasBound(nameof(this.Select)))
{
context.Select = CreateSelectDelegate<Amazon.MachineLearning.Model.CreateMLModelResponse, NewMLModelCmdlet>(Select) ??
throw new System.ArgumentException("Invalid value for -Select parameter.", nameof(this.Select));
if (this.PassThru.IsPresent)
{
throw new System.ArgumentException("-PassThru cannot be used when -Select is specified.", nameof(this.Select));
}
}
else if (this.PassThru.IsPresent)
{
context.Select = (response, cmdlet) => this.MLModelId;
}
#pragma warning restore CS0618, CS0612 //A class member was marked with the Obsolete attribute
context.MLModelId = this.MLModelId;
#if MODULAR
if (this.MLModelId == null && ParameterWasBound(nameof(this.MLModelId)))
{
WriteWarning("You are passing $null as a value for parameter MLModelId which is marked as required. In case you believe this parameter was incorrectly marked as required, report this by opening an issue at https://github.com/aws/aws-tools-for-powershell/issues.");
}
#endif
context.MLModelName = this.MLModelName;
context.MLModelType = this.MLModelType;
#if MODULAR
if (this.MLModelType == null && ParameterWasBound(nameof(this.MLModelType)))
{
WriteWarning("You are passing $null as a value for parameter MLModelType which is marked as required. In case you believe this parameter was incorrectly marked as required, report this by opening an issue at https://github.com/aws/aws-tools-for-powershell/issues.");
}
#endif
if (this.Parameter != null)
{
context.Parameter = new Dictionary<System.String, System.String>(StringComparer.Ordinal);
foreach (var hashKey in this.Parameter.Keys)
{
context.Parameter.Add((String)hashKey, (String)(this.Parameter[hashKey]));
}
}
context.Recipe = this.Recipe;
context.RecipeUri = this.RecipeUri;
context.TrainingDataSourceId = this.TrainingDataSourceId;
#if MODULAR
if (this.TrainingDataSourceId == null && ParameterWasBound(nameof(this.TrainingDataSourceId)))
{
WriteWarning("You are passing $null as a value for parameter TrainingDataSourceId which is marked as required. In case you believe this parameter was incorrectly marked as required, report this by opening an issue at https://github.com/aws/aws-tools-for-powershell/issues.");
}
#endif
// allow further manipulation of loaded context prior to processing
PostExecutionContextLoad(context);
var output = Execute(context) as CmdletOutput;
ProcessOutput(output);
}
#region IExecutor Members
public object Execute(ExecutorContext context)
{
var cmdletContext = context as CmdletContext;
// create request
var request = new Amazon.MachineLearning.Model.CreateMLModelRequest();
if (cmdletContext.MLModelId != null)
{
request.MLModelId = cmdletContext.MLModelId;
}
if (cmdletContext.MLModelName != null)
{
request.MLModelName = cmdletContext.MLModelName;
}
if (cmdletContext.MLModelType != null)
{
request.MLModelType = cmdletContext.MLModelType;
}
if (cmdletContext.Parameter != null)
{
request.Parameters = cmdletContext.Parameter;
}
if (cmdletContext.Recipe != null)
{
request.Recipe = cmdletContext.Recipe;
}
if (cmdletContext.RecipeUri != null)
{
request.RecipeUri = cmdletContext.RecipeUri;
}
if (cmdletContext.TrainingDataSourceId != null)
{
request.TrainingDataSourceId = cmdletContext.TrainingDataSourceId;
}
CmdletOutput output;
// issue call
var client = Client ?? CreateClient(_CurrentCredentials, _RegionEndpoint);
try
{
var response = CallAWSServiceOperation(client, request);
object pipelineOutput = null;
pipelineOutput = cmdletContext.Select(response, this);
output = new CmdletOutput
{
PipelineOutput = pipelineOutput,
ServiceResponse = response
};
}
catch (Exception e)
{
output = new CmdletOutput { ErrorResponse = e };
}
return output;
}
public ExecutorContext CreateContext()
{
return new CmdletContext();
}
#endregion
#region AWS Service Operation Call
private Amazon.MachineLearning.Model.CreateMLModelResponse CallAWSServiceOperation(IAmazonMachineLearning client, Amazon.MachineLearning.Model.CreateMLModelRequest request)
{
Utils.Common.WriteVerboseEndpointMessage(this, client.Config, "Amazon Machine Learning", "CreateMLModel");
try
{
#if DESKTOP
return client.CreateMLModel(request);
#elif CORECLR
return client.CreateMLModelAsync(request).GetAwaiter().GetResult();
#else
#error "Unknown build edition"
#endif
}
catch (AmazonServiceException exc)
{
var webException = exc.InnerException as System.Net.WebException;
if (webException != null)
{
throw new Exception(Utils.Common.FormatNameResolutionFailureMessage(client.Config, webException.Message), webException);
}
throw;
}
}
#endregion
internal partial class CmdletContext : ExecutorContext
{
public System.String MLModelId { get; set; }
public System.String MLModelName { get; set; }
public Amazon.MachineLearning.MLModelType MLModelType { get; set; }
public Dictionary<System.String, System.String> Parameter { get; set; }
public System.String Recipe { get; set; }
public System.String RecipeUri { get; set; }
public System.String TrainingDataSourceId { get; set; }
public System.Func<Amazon.MachineLearning.Model.CreateMLModelResponse, NewMLModelCmdlet, object> Select { get; set; } =
(response, cmdlet) => response.MLModelId;
}
}
}