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Evaluate data frame analytics API

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Evaluate data frame analytics API

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Evaluates the machine learning algorithm that ran on a data frame. The API accepts an EvaluateDataFrameRequest object and returns an EvaluateDataFrameResponse.

Evaluate data frame analytics request

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EvaluateDataFrameRequest request =
    new EvaluateDataFrameRequest( 
        indexName, 
        new QueryConfig(QueryBuilders.termQuery("dataset", "blue")), 
        evaluation);

	Constructing a new evaluation request
	Reference to an existing index
	The query with which to select data from indices
	Evaluation to be performed

Evaluation

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Evaluation to be performed. Currently, supported evaluations include: BinarySoftClassification, Classification, Regression.

Binary soft classification

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Evaluation evaluation =
    new BinarySoftClassification( 
        "label", 
        "p", 
        // Evaluation metrics 
        PrecisionMetric.at(0.4, 0.5, 0.6), 
        RecallMetric.at(0.5, 0.7), 
        ConfusionMatrixMetric.at(0.5), 
        AucRocMetric.withCurve());

	Constructing a new evaluation
	Name of the field in the index. Its value denotes the actual (i.e. ground truth) label for an example. Must be either true or false.
	Name of the field in the index. Its value denotes the probability (as per some ML algorithm) of the example being classified as positive.
	The remaining parameters are the metrics to be calculated based on the two fields described above
	Precision calculated at thresholds: 0.4, 0.5 and 0.6
	Recall calculated at thresholds: 0.5 and 0.7
	Confusion matrix calculated at threshold 0.5
	AuC ROC calculated and the curve points returned

Classification

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Evaluation evaluation =
    new org.elasticsearch.client.ml.dataframe.evaluation.classification.Classification( 
        "actual_class", 
        "predicted_class", 
        // Evaluation metrics 
        new AccuracyMetric(), 
        new org.elasticsearch.client.ml.dataframe.evaluation.classification.PrecisionMetric(), 
        new org.elasticsearch.client.ml.dataframe.evaluation.classification.RecallMetric(), 
        new MulticlassConfusionMatrixMetric(3));

	Constructing a new evaluation
	Name of the field in the index. Its value denotes the actual (i.e. ground truth) class the example belongs to.
	Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) class of the example.
	The remaining parameters are the metrics to be calculated based on the two fields described above
	Accuracy
	Precision
	Recall
	Multiclass confusion matrix of size 3

Regression

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Evaluation evaluation =
    new org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression( 
        "actual_value", 
        "predicted_value", 
        // Evaluation metrics 
        new MeanSquaredErrorMetric(), 
        new RSquaredMetric());

	Constructing a new evaluation
	Name of the field in the index. Its value denotes the actual (i.e. ground truth) value for an example.
	Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) value for the example.
	The remaining parameters are the metrics to be calculated based on the two fields described above
	Mean squared error
	R squared

Synchronous execution

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When executing a EvaluateDataFrameRequest in the following manner, the client waits for the EvaluateDataFrameResponse to be returned before continuing with code execution:

EvaluateDataFrameResponse response = client.machineLearning().evaluateDataFrame(request, RequestOptions.DEFAULT);

Synchronous calls may throw an IOException in case of either failing to parse the REST response in the high-level REST client, the request times out or similar cases where there is no response coming back from the server.

In cases where the server returns a 4xx or 5xx error code, the high-level client tries to parse the response body error details instead and then throws a generic ElasticsearchException and adds the original ResponseException as a suppressed exception to it.

Asynchronous execution

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Executing a EvaluateDataFrameRequest can also be done in an asynchronous fashion so that the client can return directly. Users need to specify how the response or potential failures will be handled by passing the request and a listener to the asynchronous evaluate-data-frame method:

client.machineLearning().evaluateDataFrameAsync(request, RequestOptions.DEFAULT, listener);

The EvaluateDataFrameRequest to execute and the ActionListener to use when the execution completes

The asynchronous method does not block and returns immediately. Once it is completed the ActionListener is called back using the onResponse method if the execution successfully completed or using the onFailure method if it failed. Failure scenarios and expected exceptions are the same as in the synchronous execution case.

A typical listener for evaluate-data-frame looks like:

ActionListener<EvaluateDataFrameResponse> listener = new ActionListener<EvaluateDataFrameResponse>() {
    @Override
    public void onResponse(EvaluateDataFrameResponse response) {
        
    }

    @Override
    public void onFailure(Exception e) {
        
    }
};

	Called when the execution is successfully completed.
	Called when the whole `EvaluateDataFrameRequest` fails.

Response

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The returned EvaluateDataFrameResponse contains the requested evaluation metrics.

List<EvaluationMetric.Result> metrics = response.getMetrics();

Fetching all the calculated metrics results

Results

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Binary soft classification

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PrecisionMetric.Result precisionResult = response.getMetricByName(PrecisionMetric.NAME); 
double precision = precisionResult.getScoreByThreshold("0.4"); 

ConfusionMatrixMetric.Result confusionMatrixResult = response.getMetricByName(ConfusionMatrixMetric.NAME); 
ConfusionMatrix confusionMatrix = confusionMatrixResult.getScoreByThreshold("0.5");

	Fetching precision metric by name
	Fetching precision at a given (0.4) threshold
	Fetching confusion matrix metric by name
	Fetching confusion matrix at a given (0.5) threshold

Classification

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AccuracyMetric.Result accuracyResult = response.getMetricByName(AccuracyMetric.NAME); 
double accuracy = accuracyResult.getOverallAccuracy(); 

org.elasticsearch.client.ml.dataframe.evaluation.classification.PrecisionMetric.Result precisionResult =
    response.getMetricByName(org.elasticsearch.client.ml.dataframe.evaluation.classification.PrecisionMetric.NAME); 
double precision = precisionResult.getAvgPrecision(); 

org.elasticsearch.client.ml.dataframe.evaluation.classification.RecallMetric.Result recallResult =
    response.getMetricByName(org.elasticsearch.client.ml.dataframe.evaluation.classification.RecallMetric.NAME); 
double recall = recallResult.getAvgRecall(); 

MulticlassConfusionMatrixMetric.Result multiclassConfusionMatrix =
    response.getMetricByName(MulticlassConfusionMatrixMetric.NAME); 

List<ActualClass> confusionMatrix = multiclassConfusionMatrix.getConfusionMatrix(); 
long otherClassesCount = multiclassConfusionMatrix.getOtherActualClassCount();

	Fetching accuracy metric by name
	Fetching the actual accuracy value
	Fetching precision metric by name
	Fetching the actual precision value
	Fetching recall metric by name
	Fetching the actual recall value
	Fetching multiclass confusion matrix metric by name
	Fetching the contents of the confusion matrix
	Fetching the number of classes that were not included in the matrix

Regression

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MeanSquaredErrorMetric.Result meanSquaredErrorResult = response.getMetricByName(MeanSquaredErrorMetric.NAME); 
double meanSquaredError = meanSquaredErrorResult.getError(); 

RSquaredMetric.Result rSquaredResult = response.getMetricByName(RSquaredMetric.NAME); 
double rSquared = rSquaredResult.getValue();

	Fetching mean squared error metric by name
	Fetching the actual mean squared error value
	Fetching R squared metric by name
	Fetching the actual R squared value

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