Evaluate data frame analytics APIedit
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 requestedit
Evaluationedit
Evaluation to be performed.
Currently, supported evaluations include: BinarySoftClassification
, Classification
, Regression
.
Binary soft classificationedit
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 |
Classificationedit
Evaluation evaluation = new org.elasticsearch.client.ml.dataframe.evaluation.classification.Classification( "actual_class", "predicted_class", // Evaluation metrics 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 |
|
Multiclass confusion matrix of size 3 |
Regressionedit
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 |
|
Synchronous executionedit
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 executionedit
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:
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:
Responseedit
The returned EvaluateDataFrameResponse
contains the requested evaluation metrics.
Resultsedit
Binary soft classificationedit
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");
Classificationedit
Regressionedit
MeanSquaredErrorMetric.Result meanSquaredErrorResult = response.getMetricByName(MeanSquaredErrorMetric.NAME); double meanSquaredError = meanSquaredErrorResult.getError(); RSquaredMetric.Result rSquaredResult = response.getMetricByName(RSquaredMetric.NAME); double rSquared = rSquaredResult.getValue();