MachineLearningClient Class

Member Function Documentation

MachineLearningClient::MachineLearningClient(const QtAws::Core::AwsRegion::Region region = QtAws::Core::AwsRegion::InvalidRegion, QtAws::Core::AwsAbstractCredentials *credentials = NULL, QNetworkAccessManager * const manager = NULL, QObject * const parent = 0)

Constructs a MachineLearningClient object.

The new client object will region, credentials, and manager for network operations.

The new object will be owned by parent, if set.

MachineLearningClient::MachineLearningClient(const QUrl &endpoint, QtAws::Core::AwsAbstractCredentials *credentials = NULL, QNetworkAccessManager * const manager = NULL, QObject * const parent = 0)

This function overloads MachineLearningClient().

This overload allows the caller to specify the specific endpoint to send requests to. Typically, it is easier to use the alternative constructor, which allows the caller to specify an AWS region instead, in which case this client will determine the correct endpoint for the given region automatically (via AwsEndpoint::getEndpoint).

See also QtAws::Core::AwsEndpoint::getEndpoint.

[slot] AddTagsResponse *MachineLearningClient::addTags(const AddTagsRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an AddTagsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, <code>AddTags</code> updates the tag's

[slot] CreateBatchPredictionResponse *MachineLearningClient::createBatchPrediction(const CreateBatchPredictionRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateBatchPredictionResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a <code>DataSource</code>. This operation creates a new <code>BatchPrediction</code>, and uses an <code>MLModel</code> and the data files referenced by the <code>DataSource</code> as information sources.

</p

<code>CreateBatchPrediction</code> is an asynchronous operation. In response to <code>CreateBatchPrediction</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>BatchPrediction</code> status to <code>PENDING</code>. After the <code>BatchPrediction</code> completes, Amazon ML sets the status to <code>COMPLETED</code>.

</p

You can poll for status updates by using the <a>GetBatchPrediction</a> operation and checking the <code>Status</code> parameter of the result. After the <code>COMPLETED</code> status appears, the results are available in the location specified by the <code>OutputUri</code>

[slot] CreateDataSourceFromS3Response *MachineLearningClient::createDataSourceFromS3(const CreateDataSourceFromS3Request &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateDataSourceFromS3Response object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a <code>DataSource</code> object. A <code>DataSource</code> references data that can be used to perform <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code>

operations>

<code>CreateDataSourceFromS3</code> is an asynchronous operation. In response to <code>CreateDataSourceFromS3</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> has been created and is ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can be used to perform only <code>CreateMLModel</code>, <code>CreateEvaluation</code> or <code>CreateBatchPrediction</code> operations.

</p

If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response.

</p

The observation data used in a <code>DataSource</code> should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the <code>DataSource</code>.

</p

After the <code>DataSource</code> has been created, it's ready to use in evaluations and batch predictions. If you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also needs a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these

[slot] CreateDataSourceFromRDSResponse *MachineLearningClient::createDataSourceFromRDS(const CreateDataSourceFromRDSRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateDataSourceFromRDSResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a <code>DataSource</code> object from an <a href="http://aws.amazon.com/rds/"> Amazon Relational Database Service</a> (Amazon RDS). A <code>DataSource</code> references data that can be used to perform <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code>

operations>

<code>CreateDataSourceFromRDS</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRDS</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can be used only to perform <code>&gt;CreateMLModel</code>&gt;, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.

</p

If Amazon ML cannot accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response.

[slot] CreateDataSourceFromRedshiftResponse *MachineLearningClient::createDataSourceFromRedshift(const CreateDataSourceFromRedshiftRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateDataSourceFromRedshiftResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a <code>DataSource</code> from a database hosted on an Amazon Redshift cluster. A <code>DataSource</code> references data that can be used to perform either <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code>

operations>

<code>CreateDataSourceFromRedshift</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRedshift</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in <code>COMPLETED</code> or <code>PENDING</code> states can be used to perform only <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.

</p

If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response.

</p

The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a <code>SelectSqlQuery</code> query. Amazon ML executes an <code>Unload</code> command in Amazon Redshift to transfer the result set of the <code>SelectSqlQuery</code> query to

<code>S3StagingLocation</code>>

After the <code>DataSource</code> has been created, it's ready for use in evaluations and batch predictions. If you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also requires a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these

questions> <?oxy_insert_start author="laurama" timestamp="20160406T153842-0700"><p>You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call <code>GetDataSource</code> for an existing datasource and copy the values to a <code>CreateDataSource</code> call. Change the settings that you want to change and make sure that all required fields have the appropriate

[slot] CreateEvaluationResponse *MachineLearningClient::createEvaluation(const CreateEvaluationRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateEvaluationResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a new <code>Evaluation</code> of an <code>MLModel</code>. An <code>MLModel</code> is evaluated on a set of observations associated to a <code>DataSource</code>. Like a <code>DataSource</code> for an <code>MLModel</code>, the <code>DataSource</code> for an <code>Evaluation</code> contains values for the <code>Target Variable</code>. The <code>Evaluation</code> compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the <code>MLModel</code> functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding <code>MLModelType</code>: <code>BINARY</code>, <code>REGRESSION</code> or <code>MULTICLASS</code>.

</p

<code>CreateEvaluation</code> is an asynchronous operation. In response to <code>CreateEvaluation</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to <code>PENDING</code>. After the <code>Evaluation</code> is created and ready for use, Amazon ML sets the status to <code>COMPLETED</code>.

</p

You can use the <code>GetEvaluation</code> operation to check progress of the evaluation during the creation

[slot] CreateMLModelResponse *MachineLearningClient::createMLModel(const CreateMLModelRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateMLModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe as information sources.

</p

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>.

</p

<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>.

</p

You can use the <code>GetMLModel</code> operation to check the progress of the <code>MLModel</code> during the creation

operation>

<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.

[slot] CreateRealtimeEndpointResponse *MachineLearningClient::createRealtimeEndpoint(const CreateRealtimeEndpointRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an CreateRealtimeEndpointResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a real-time endpoint for the <code>MLModel</code>. The endpoint contains the URI of the <code>MLModel</code>; that is, the location to send real-time prediction requests for the specified

[slot] DeleteBatchPredictionResponse *MachineLearningClient::deleteBatchPrediction(const DeleteBatchPredictionRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DeleteBatchPredictionResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Assigns the DELETED status to a <code>BatchPrediction</code>, rendering it

unusable>

After using the <code>DeleteBatchPrediction</code> operation, you can use the <a>GetBatchPrediction</a> operation to verify that the status of the <code>BatchPrediction</code> changed to

DELETED>

<b>Caution:</b> The result of the <code>DeleteBatchPrediction</code> operation is

[slot] DeleteDataSourceResponse *MachineLearningClient::deleteDataSource(const DeleteDataSourceRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DeleteDataSourceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Assigns the DELETED status to a <code>DataSource</code>, rendering it

unusable>

After using the <code>DeleteDataSource</code> operation, you can use the <a>GetDataSource</a> operation to verify that the status of the <code>DataSource</code> changed to

DELETED>

<b>Caution:</b> The results of the <code>DeleteDataSource</code> operation are

[slot] DeleteEvaluationResponse *MachineLearningClient::deleteEvaluation(const DeleteEvaluationRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DeleteEvaluationResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Assigns the <code>DELETED</code> status to an <code>Evaluation</code>, rendering it

unusable>

After invoking the <code>DeleteEvaluation</code> operation, you can use the <code>GetEvaluation</code> operation to verify that the status of the <code>Evaluation</code> changed to

<code>DELETED</code>> <caution><title>Caution</title>

[slot] DeleteMLModelResponse *MachineLearningClient::deleteMLModel(const DeleteMLModelRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DeleteMLModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Assigns the <code>DELETED</code> status to an <code>MLModel</code>, rendering it

unusable>

After using the <code>DeleteMLModel</code> operation, you can use the <code>GetMLModel</code> operation to verify that the status of the <code>MLModel</code> changed to

DELETED>

<b>Caution:</b> The result of the <code>DeleteMLModel</code> operation is

[slot] DeleteRealtimeEndpointResponse *MachineLearningClient::deleteRealtimeEndpoint(const DeleteRealtimeEndpointRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DeleteRealtimeEndpointResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes a real time endpoint of an

[slot] DeleteTagsResponse *MachineLearningClient::deleteTags(const DeleteTagsRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DeleteTagsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted

tags>

If you specify a tag that doesn't exist, Amazon ML ignores

[slot] DescribeBatchPredictionsResponse *MachineLearningClient::describeBatchPredictions(const DescribeBatchPredictionsRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DescribeBatchPredictionsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a list of <code>BatchPrediction</code> operations that match the search criteria in the

[slot] DescribeDataSourcesResponse *MachineLearningClient::describeDataSources(const DescribeDataSourcesRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DescribeDataSourcesResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a list of <code>DataSource</code> that match the search criteria in the

[slot] DescribeEvaluationsResponse *MachineLearningClient::describeEvaluations(const DescribeEvaluationsRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DescribeEvaluationsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a list of <code>DescribeEvaluations</code> that match the search criteria in the

[slot] DescribeMLModelsResponse *MachineLearningClient::describeMLModels(const DescribeMLModelsRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DescribeMLModelsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a list of <code>MLModel</code> that match the search criteria in the

[slot] DescribeTagsResponse *MachineLearningClient::describeTags(const DescribeTagsRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an DescribeTagsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Describes one or more of the tags for your Amazon ML

[slot] GetBatchPredictionResponse *MachineLearningClient::getBatchPrediction(const GetBatchPredictionRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an GetBatchPredictionResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a <code>BatchPrediction</code> that includes detailed metadata, status, and data file information for a <code>Batch Prediction</code>

[slot] GetDataSourceResponse *MachineLearningClient::getDataSource(const GetDataSourceRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an GetDataSourceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a <code>DataSource</code> that includes metadata and data file information, as well as the current status of the

<code>DataSource</code>>

<code>GetDataSource</code> provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal

[slot] GetEvaluationResponse *MachineLearningClient::getEvaluation(const GetEvaluationRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an GetEvaluationResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns an <code>Evaluation</code> that includes metadata as well as the current status of the

[slot] GetMLModelResponse *MachineLearningClient::getMLModel(const GetMLModelRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an GetMLModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns an <code>MLModel</code> that includes detailed metadata, data source information, and the current status of the

<code>MLModel</code>>

<code>GetMLModel</code> provides results in normal or verbose format.

[slot] PredictResponse *MachineLearningClient::predict(const PredictRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an PredictResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Generates a prediction for the observation using the specified <code>ML

Model</code>> <note><title>Note</title>

Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model

[slot] UpdateBatchPredictionResponse *MachineLearningClient::updateBatchPrediction(const UpdateBatchPredictionRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an UpdateBatchPredictionResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Updates the <code>BatchPredictionName</code> of a

<code>BatchPrediction</code>>

You can use the <code>GetBatchPrediction</code> operation to view the contents of the updated data

[slot] UpdateDataSourceResponse *MachineLearningClient::updateDataSource(const UpdateDataSourceRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an UpdateDataSourceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Updates the <code>DataSourceName</code> of a

<code>DataSource</code>>

You can use the <code>GetDataSource</code> operation to view the contents of the updated data

[slot] UpdateEvaluationResponse *MachineLearningClient::updateEvaluation(const UpdateEvaluationRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an UpdateEvaluationResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Updates the <code>EvaluationName</code> of an

<code>Evaluation</code>>

You can use the <code>GetEvaluation</code> operation to view the contents of the updated data

[slot] UpdateMLModelResponse *MachineLearningClient::updateMLModel(const UpdateMLModelRequest &request)

Sends request to the MachineLearningClient service, and returns a pointer to an UpdateMLModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an

<code>MLModel</code>>

You can use the <code>GetMLModel</code> operation to view the contents of the updated data