* [ML] add _cat/ml/trained_models API (#51529) This adds _cat/ml/trained_models.
This commit is contained in:
Benjamin Trent
GitHub
parent
b70cbc97aa
commit
79f143907a
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@ -103,6 +103,14 @@ public class GetTrainedModelsStatsAction extends ActionType<GetTrainedModelsStat
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return modelId;
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return modelId;
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}
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}
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public IngestStats getIngestStats() {
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return ingestStats;
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}
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public int getPipelineCount() {
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return pipelineCount;
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}
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@Override
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@Override
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public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
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public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
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builder.startObject();
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builder.startObject();
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@ -256,6 +256,7 @@ import org.elasticsearch.xpack.ml.rest.calendar.RestPutCalendarAction;
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import org.elasticsearch.xpack.ml.rest.calendar.RestPutCalendarJobAction;
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import org.elasticsearch.xpack.ml.rest.calendar.RestPutCalendarJobAction;
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import org.elasticsearch.xpack.ml.rest.cat.RestCatDatafeedsAction;
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import org.elasticsearch.xpack.ml.rest.cat.RestCatDatafeedsAction;
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import org.elasticsearch.xpack.ml.rest.cat.RestCatJobsAction;
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import org.elasticsearch.xpack.ml.rest.cat.RestCatJobsAction;
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import org.elasticsearch.xpack.ml.rest.cat.RestCatTrainedModelsAction;
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import org.elasticsearch.xpack.ml.rest.datafeeds.RestDeleteDatafeedAction;
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import org.elasticsearch.xpack.ml.rest.datafeeds.RestDeleteDatafeedAction;
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import org.elasticsearch.xpack.ml.rest.datafeeds.RestGetDatafeedStatsAction;
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import org.elasticsearch.xpack.ml.rest.datafeeds.RestGetDatafeedStatsAction;
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import org.elasticsearch.xpack.ml.rest.datafeeds.RestGetDatafeedsAction;
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import org.elasticsearch.xpack.ml.rest.datafeeds.RestGetDatafeedsAction;
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@ -786,6 +787,7 @@ public class MachineLearning extends Plugin implements SystemIndexPlugin, Analys
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new RestPutTrainedModelAction(restController),
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new RestPutTrainedModelAction(restController),
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// CAT Handlers
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// CAT Handlers
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new RestCatJobsAction(restController),
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new RestCatJobsAction(restController),
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new RestCatTrainedModelsAction(restController),
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new RestCatDatafeedsAction(restController)
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new RestCatDatafeedsAction(restController)
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);
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);
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}
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}
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@ -177,6 +177,7 @@ public class AnalyticsResultProcessor {
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.setCreatedBy(XPackUser.NAME)
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.setCreatedBy(XPackUser.NAME)
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.setVersion(Version.CURRENT)
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.setVersion(Version.CURRENT)
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.setCreateTime(createTime)
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.setCreateTime(createTime)
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// NOTE: GET _cat/ml/trained_models relies on the creating analytics ID being in the tags
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.setTags(Collections.singletonList(analytics.getId()))
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.setTags(Collections.singletonList(analytics.getId()))
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.setDescription(analytics.getDescription())
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.setDescription(analytics.getDescription())
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.setMetadata(Collections.singletonMap("analytics_config",
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.setMetadata(Collections.singletonMap("analytics_config",
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@ -0,0 +1,283 @@
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/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the Elastic License;
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* you may not use this file except in compliance with the Elastic License.
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*/
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package org.elasticsearch.xpack.ml.rest.cat;
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import org.elasticsearch.action.ActionListener;
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import org.elasticsearch.action.ActionResponse;
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import org.elasticsearch.action.support.GroupedActionListener;
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import org.elasticsearch.client.node.NodeClient;
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import org.elasticsearch.cluster.metadata.MetaData;
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import org.elasticsearch.common.Strings;
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import org.elasticsearch.common.Table;
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import org.elasticsearch.common.unit.ByteSizeValue;
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import org.elasticsearch.common.unit.TimeValue;
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import org.elasticsearch.rest.RestController;
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import org.elasticsearch.rest.RestRequest;
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import org.elasticsearch.rest.RestResponse;
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import org.elasticsearch.rest.action.RestResponseListener;
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import org.elasticsearch.rest.action.cat.AbstractCatAction;
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import org.elasticsearch.rest.action.cat.RestTable;
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import org.elasticsearch.xpack.core.action.util.PageParams;
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import org.elasticsearch.xpack.core.ml.action.GetDataFrameAnalyticsAction;
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import org.elasticsearch.xpack.core.ml.action.GetTrainedModelsAction;
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import org.elasticsearch.xpack.core.ml.action.GetTrainedModelsStatsAction;
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import org.elasticsearch.xpack.core.ml.dataframe.DataFrameAnalyticsConfig;
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import org.elasticsearch.xpack.core.ml.dataframe.analyses.DataFrameAnalysis;
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import org.elasticsearch.xpack.core.ml.inference.TrainedModelConfig;
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import org.elasticsearch.xpack.core.security.user.XPackUser;
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import java.util.Collection;
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import java.util.Collections;
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import java.util.HashSet;
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import java.util.List;
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import java.util.Map;
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import java.util.Set;
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import java.util.function.Function;
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import java.util.stream.Collectors;
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import static org.elasticsearch.rest.RestRequest.Method.GET;
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import static org.elasticsearch.xpack.core.ml.action.GetTrainedModelsAction.Request.ALLOW_NO_MATCH;
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public class RestCatTrainedModelsAction extends AbstractCatAction {
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public RestCatTrainedModelsAction(RestController controller) {
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controller.registerHandler(GET, "_cat/ml/trained_models/{" + TrainedModelConfig.MODEL_ID.getPreferredName() + "}", this);
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controller.registerHandler(GET, "_cat/ml/trained_models", this);
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}
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@Override
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public String getName() {
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return "cat_ml_get_trained_models_action";
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}
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@Override
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protected RestChannelConsumer doCatRequest(RestRequest restRequest, NodeClient client) {
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String modelId = restRequest.param(TrainedModelConfig.MODEL_ID.getPreferredName());
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if (Strings.isNullOrEmpty(modelId)) {
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modelId = MetaData.ALL;
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}
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GetTrainedModelsStatsAction.Request statsRequest = new GetTrainedModelsStatsAction.Request(modelId);
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GetTrainedModelsAction.Request modelsAction = new GetTrainedModelsAction.Request(modelId, false, null);
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if (restRequest.hasParam(PageParams.FROM.getPreferredName()) || restRequest.hasParam(PageParams.SIZE.getPreferredName())) {
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statsRequest.setPageParams(new PageParams(restRequest.paramAsInt(PageParams.FROM.getPreferredName(), PageParams.DEFAULT_FROM),
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restRequest.paramAsInt(PageParams.SIZE.getPreferredName(), PageParams.DEFAULT_SIZE)));
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modelsAction.setPageParams(new PageParams(restRequest.paramAsInt(PageParams.FROM.getPreferredName(), PageParams.DEFAULT_FROM),
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restRequest.paramAsInt(PageParams.SIZE.getPreferredName(), PageParams.DEFAULT_SIZE)));
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}
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statsRequest.setAllowNoResources(true);
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modelsAction.setAllowNoResources(restRequest.paramAsBoolean(ALLOW_NO_MATCH.getPreferredName(),
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statsRequest.isAllowNoResources()));
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return channel -> {
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final ActionListener<Table> listener = ActionListener.notifyOnce(new RestResponseListener<Table>(channel) {
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@Override
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public RestResponse buildResponse(final Table table) throws Exception {
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return RestTable.buildResponse(table, channel);
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}
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});
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client.execute(GetTrainedModelsAction.INSTANCE, modelsAction, ActionListener.wrap(
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trainedModels -> {
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final List<TrainedModelConfig> trainedModelConfigs = trainedModels.getResources().results();
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Set<String> potentialAnalyticsIds = new HashSet<>();
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// Analytics Configs are created by the XPackUser
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trainedModelConfigs.stream()
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.filter(c -> XPackUser.NAME.equals(c.getCreatedBy()))
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.forEach(c -> potentialAnalyticsIds.addAll(c.getTags()));
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// Find the related DataFrameAnalyticsConfigs
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String requestIdPattern = Strings.collectionToDelimitedString(potentialAnalyticsIds, "*,") + "*";
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final GroupedActionListener<ActionResponse> groupedListener = createGroupedListener(restRequest,
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2,
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trainedModels.getResources().results(),
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listener);
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client.execute(GetTrainedModelsStatsAction.INSTANCE,
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statsRequest,
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ActionListener.wrap(groupedListener::onResponse, groupedListener::onFailure));
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GetDataFrameAnalyticsAction.Request dataFrameAnalyticsRequest =
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new GetDataFrameAnalyticsAction.Request(requestIdPattern);
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dataFrameAnalyticsRequest.setAllowNoResources(true);
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dataFrameAnalyticsRequest.setPageParams(new PageParams(0, potentialAnalyticsIds.size()));
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client.execute(GetDataFrameAnalyticsAction.INSTANCE,
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dataFrameAnalyticsRequest,
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ActionListener.wrap(groupedListener::onResponse, groupedListener::onFailure));
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},
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listener::onFailure
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));
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};
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}
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@Override
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protected void documentation(StringBuilder sb) {
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sb.append("/_cat/ml/trained_models\n");
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sb.append("/_cat/ml/trained_models/{model_id}\n");
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}
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@Override
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protected Table getTableWithHeader(RestRequest request) {
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Table table = new Table();
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table.startHeaders();
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// Trained Model Info
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table.addCell("id", TableColumnAttributeBuilder.builder().setDescription("the trained model id").build());
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table.addCell("created_by", TableColumnAttributeBuilder.builder("who created the model", false)
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.setAliases("c", "createdBy")
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.setTextAlignment(TableColumnAttributeBuilder.TextAlign.RIGHT)
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.build());
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table.addCell("heap_size", TableColumnAttributeBuilder.builder()
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.setDescription("the estimated heap size to keep the model in memory")
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.setAliases("hs","modelHeapSize")
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.build());
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table.addCell("operations", TableColumnAttributeBuilder.builder()
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.setDescription("the estimated number of operations to use the model")
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.setAliases("o", "modelOperations")
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.build());
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table.addCell("license", TableColumnAttributeBuilder.builder("The license level of the model", false)
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.setAliases("l")
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.build());
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table.addCell("create_time", TableColumnAttributeBuilder.builder("The time the model was created")
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.setAliases("ct")
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.build());
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table.addCell("version", TableColumnAttributeBuilder.builder("The version of Elasticsearch when the model was created", false)
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.setAliases("v")
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.build());
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table.addCell("description", TableColumnAttributeBuilder.builder("The model description", false)
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.setAliases("d")
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.build());
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// Trained Model Stats
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table.addCell("ingest.pipelines", TableColumnAttributeBuilder.builder("The number of pipelines referencing the model")
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.setAliases("ip", "ingestPipelines")
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.build());
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table.addCell("ingest.count", TableColumnAttributeBuilder.builder("The total number of docs processed by the model", false)
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.setAliases("ic", "ingestCount")
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.build());
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table.addCell("ingest.time", TableColumnAttributeBuilder.builder(
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"The total time spent processing docs with this model",
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false)
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.setAliases("it", "ingestTime")
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.build());
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table.addCell("ingest.current", TableColumnAttributeBuilder.builder(
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"The total documents currently being handled by the model",
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false)
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.setAliases("icurr", "ingestCurrent")
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.build());
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table.addCell("ingest.failed", TableColumnAttributeBuilder.builder(
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"The total count of failed ingest attempts with this model",
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false)
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.setAliases("if", "ingestFailed")
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.build());
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table.addCell("data_frame.id", TableColumnAttributeBuilder.builder(
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"The data frame analytics config id that created the model (if still available)")
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.setAliases("dfid", "dataFrameAnalytics")
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.build());
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table.addCell("data_frame.create_time", TableColumnAttributeBuilder.builder(
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"The time the data frame analytics config was created",
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false)
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.setAliases("dft", "dataFrameAnalyticsTime")
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.build());
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table.addCell("data_frame.source_index", TableColumnAttributeBuilder.builder(
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"The source index used to train in the data frame analysis",
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false)
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.setAliases("dfsi", "dataFrameAnalyticsSrcIndex")
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.build());
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table.addCell("data_frame.analysis", TableColumnAttributeBuilder.builder(
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"The analysis used by the data frame to build the model",
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false)
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.setAliases("dfa", "dataFrameAnalyticsAnalysis")
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.build());
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table.endHeaders();
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return table;
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}
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private GroupedActionListener<ActionResponse> createGroupedListener(final RestRequest request,
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final int size,
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final List<TrainedModelConfig> configs,
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final ActionListener<Table> listener) {
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return new GroupedActionListener<>(new ActionListener<Collection<ActionResponse>>() {
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@Override
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public void onResponse(final Collection<ActionResponse> responses) {
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GetTrainedModelsStatsAction.Response statsResponse = extractResponse(responses, GetTrainedModelsStatsAction.Response.class);
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GetDataFrameAnalyticsAction.Response analytics = extractResponse(responses, GetDataFrameAnalyticsAction.Response.class);
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listener.onResponse(buildTable(request,
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statsResponse.getResources().results(),
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configs,
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analytics == null ? Collections.emptyList() : analytics.getResources().results()));
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}
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@Override
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public void onFailure(final Exception e) {
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listener.onFailure(e);
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}
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}, size);
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}
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private Table buildTable(RestRequest request,
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List<GetTrainedModelsStatsAction.Response.TrainedModelStats> stats,
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List<TrainedModelConfig> configs,
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List<DataFrameAnalyticsConfig> analyticsConfigs) {
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Table table = getTableWithHeader(request);
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assert configs.size() == stats.size();
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Map<String, DataFrameAnalyticsConfig> analyticsMap = analyticsConfigs.stream()
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.collect(Collectors.toMap(DataFrameAnalyticsConfig::getId, Function.identity()));
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Map<String, GetTrainedModelsStatsAction.Response.TrainedModelStats> statsMap = stats.stream()
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.collect(Collectors.toMap(GetTrainedModelsStatsAction.Response.TrainedModelStats::getModelId, Function.identity()));
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configs.forEach(config -> {
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table.startRow();
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// Trained Model Info
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table.addCell(config.getModelId());
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table.addCell(config.getCreatedBy());
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table.addCell(new ByteSizeValue(config.getEstimatedHeapMemory()));
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table.addCell(config.getEstimatedOperations());
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table.addCell(config.getLicenseLevel());
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table.addCell(config.getCreateTime());
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table.addCell(config.getVersion().toString());
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table.addCell(config.getDescription());
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GetTrainedModelsStatsAction.Response.TrainedModelStats modelStats = statsMap.get(config.getModelId());
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table.addCell(modelStats.getPipelineCount());
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boolean hasIngestStats = modelStats != null && modelStats.getIngestStats() != null;
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table.addCell(hasIngestStats ? modelStats.getIngestStats().getTotalStats().getIngestCount() : 0);
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table.addCell(hasIngestStats ?
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TimeValue.timeValueMillis(modelStats.getIngestStats().getTotalStats().getIngestTimeInMillis()) :
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TimeValue.timeValueMillis(0));
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table.addCell(hasIngestStats ? modelStats.getIngestStats().getTotalStats().getIngestCurrent() : 0);
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table.addCell(hasIngestStats ? modelStats.getIngestStats().getTotalStats().getIngestFailedCount() : 0);
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DataFrameAnalyticsConfig dataFrameAnalyticsConfig = config.getTags()
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.stream()
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.filter(analyticsMap::containsKey)
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.map(analyticsMap::get)
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.findFirst()
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.orElse(null);
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table.addCell(dataFrameAnalyticsConfig == null ? "__none__" : dataFrameAnalyticsConfig.getId());
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table.addCell(dataFrameAnalyticsConfig == null ? null : dataFrameAnalyticsConfig.getCreateTime());
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table.addCell(dataFrameAnalyticsConfig == null ?
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null :
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Strings.arrayToCommaDelimitedString(dataFrameAnalyticsConfig.getSource().getIndex()));
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DataFrameAnalysis analysis = dataFrameAnalyticsConfig == null ? null : dataFrameAnalyticsConfig.getAnalysis();
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table.addCell(analysis == null ? null : analysis.getWriteableName());
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table.endRow();
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});
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return table;
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}
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@SuppressWarnings("unchecked")
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private static <A extends ActionResponse> A extractResponse(final Collection<? extends ActionResponse> responses, Class<A> c) {
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return (A) responses.stream().filter(c::isInstance).findFirst().get();
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}
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}
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@ -0,0 +1,100 @@
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{
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|
"cat.ml.trained_models":{
|
||||||
|
"documentation":{
|
||||||
|
"url":"https://www.elastic.co/guide/en/elasticsearch/reference/current/get-inference-stats.html"
|
||||||
|
},
|
||||||
|
"stability":"stable",
|
||||||
|
"url":{
|
||||||
|
"paths":[
|
||||||
|
{
|
||||||
|
"path":"/_cat/ml/trained_models",
|
||||||
|
"methods":[
|
||||||
|
"GET"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"path":"/_cat/ml/trained_models/{model_id}",
|
||||||
|
"methods":[
|
||||||
|
"GET"
|
||||||
|
],
|
||||||
|
"parts":{
|
||||||
|
"model_id":{
|
||||||
|
"type":"string",
|
||||||
|
"description":"The ID of the trained models stats to fetch"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"params":{
|
||||||
|
"allow_no_match":{
|
||||||
|
"type":"boolean",
|
||||||
|
"required":false,
|
||||||
|
"description":"Whether to ignore if a wildcard expression matches no trained models. (This includes `_all` string or when no trained models have been specified)",
|
||||||
|
"default":true
|
||||||
|
},
|
||||||
|
"from":{
|
||||||
|
"type":"int",
|
||||||
|
"description":"skips a number of trained models",
|
||||||
|
"default":0
|
||||||
|
},
|
||||||
|
"size":{
|
||||||
|
"type":"int",
|
||||||
|
"description":"specifies a max number of trained models to get",
|
||||||
|
"default":100
|
||||||
|
},
|
||||||
|
"bytes":{
|
||||||
|
"type":"enum",
|
||||||
|
"description":"The unit in which to display byte values",
|
||||||
|
"options":[
|
||||||
|
"b",
|
||||||
|
"k",
|
||||||
|
"kb",
|
||||||
|
"m",
|
||||||
|
"mb",
|
||||||
|
"g",
|
||||||
|
"gb",
|
||||||
|
"t",
|
||||||
|
"tb",
|
||||||
|
"p",
|
||||||
|
"pb"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"format":{
|
||||||
|
"type":"string",
|
||||||
|
"description":"a short version of the Accept header, e.g. json, yaml"
|
||||||
|
},
|
||||||
|
"h":{
|
||||||
|
"type":"list",
|
||||||
|
"description":"Comma-separated list of column names to display"
|
||||||
|
},
|
||||||
|
"help":{
|
||||||
|
"type":"boolean",
|
||||||
|
"description":"Return help information",
|
||||||
|
"default":false
|
||||||
|
},
|
||||||
|
"s":{
|
||||||
|
"type":"list",
|
||||||
|
"description":"Comma-separated list of column names or column aliases to sort by"
|
||||||
|
},
|
||||||
|
"time":{
|
||||||
|
"type":"enum",
|
||||||
|
"description":"The unit in which to display time values",
|
||||||
|
"options":[
|
||||||
|
"d (Days)",
|
||||||
|
"h (Hours)",
|
||||||
|
"m (Minutes)",
|
||||||
|
"s (Seconds)",
|
||||||
|
"ms (Milliseconds)",
|
||||||
|
"micros (Microseconds)",
|
||||||
|
"nanos (Nanoseconds)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"v":{
|
||||||
|
"type":"boolean",
|
||||||
|
"description":"Verbose mode. Display column headers",
|
||||||
|
"default":false
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,110 @@
|
||||||
|
setup:
|
||||||
|
- skip:
|
||||||
|
features: headers
|
||||||
|
- do:
|
||||||
|
indices.create:
|
||||||
|
index: index-source
|
||||||
|
- do:
|
||||||
|
headers:
|
||||||
|
Authorization: "Basic eF9wYWNrX3Jlc3RfdXNlcjp4LXBhY2stdGVzdC1wYXNzd29yZA==" # run as x_pack_rest_user, i.e. the test setup superuser
|
||||||
|
ml.put_trained_model:
|
||||||
|
model_id: a-regression-model-0
|
||||||
|
body: >
|
||||||
|
{
|
||||||
|
"description": "empty model for tests",
|
||||||
|
"tags": ["regression", "tag1"],
|
||||||
|
"input": {"field_names": ["field1", "field2"]},
|
||||||
|
"definition": {
|
||||||
|
"preprocessors": [],
|
||||||
|
"trained_model": {
|
||||||
|
"tree": {
|
||||||
|
"feature_names": ["field1", "field2"],
|
||||||
|
"tree_structure": [
|
||||||
|
{"node_index": 0, "leaf_value": 1}
|
||||||
|
],
|
||||||
|
"target_type": "regression"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
- do:
|
||||||
|
headers:
|
||||||
|
Authorization: "Basic eF9wYWNrX3Jlc3RfdXNlcjp4LXBhY2stdGVzdC1wYXNzd29yZA==" # run as x_pack_rest_user, i.e. the test setup superuser
|
||||||
|
ml.put_trained_model:
|
||||||
|
model_id: a-regression-model-1
|
||||||
|
body: >
|
||||||
|
{
|
||||||
|
"description": "empty model for tests",
|
||||||
|
"input": {"field_names": ["field1", "field2"]},
|
||||||
|
"definition": {
|
||||||
|
"preprocessors": [],
|
||||||
|
"trained_model": {
|
||||||
|
"tree": {
|
||||||
|
"feature_names": ["field1", "field2"],
|
||||||
|
"tree_structure": [
|
||||||
|
{"node_index": 0, "leaf_value": 1}
|
||||||
|
],
|
||||||
|
"target_type": "regression"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
- do:
|
||||||
|
headers:
|
||||||
|
Authorization: "Basic eF9wYWNrX3Jlc3RfdXNlcjp4LXBhY2stdGVzdC1wYXNzd29yZA==" # run as x_pack_rest_user, i.e. the test setup superuser
|
||||||
|
ml.put_data_frame_analytics:
|
||||||
|
id: "prepackaged"
|
||||||
|
body: >
|
||||||
|
{
|
||||||
|
"source": {
|
||||||
|
"index": "index-source"
|
||||||
|
},
|
||||||
|
"dest": {
|
||||||
|
"index": "index-dest"
|
||||||
|
},
|
||||||
|
"analysis": {"regression":{
|
||||||
|
"dependent_variable": "to_predict"
|
||||||
|
}}
|
||||||
|
}
|
||||||
|
|
||||||
|
---
|
||||||
|
"Test cat trained models":
|
||||||
|
|
||||||
|
- do:
|
||||||
|
cat.ml.trained_models:
|
||||||
|
model_id: a-regression-model-0
|
||||||
|
- match:
|
||||||
|
$body: |
|
||||||
|
/ #id heap_size operations create_time ingest.pipelines data_frame.id
|
||||||
|
^ (a\-regression\-model\-0 \s+ \w+ \s+ \d+ \s+ .*? \s+ \d+ .*? \n)+ $/
|
||||||
|
|
||||||
|
- do:
|
||||||
|
cat.ml.trained_models:
|
||||||
|
v: true
|
||||||
|
model_id: a-regression-model-0
|
||||||
|
- match:
|
||||||
|
$body: |
|
||||||
|
/^ id \s+ heap_size \s+ operations \s+ create_time \s+ ingest\.pipelines \s+ data_frame\.id \n
|
||||||
|
(a\-regression\-model\-0 \s+ \w+ \s+ \d+ \s+ .*? \s+ \d+ \s+ .*? \n)+ $/
|
||||||
|
|
||||||
|
- do:
|
||||||
|
cat.ml.trained_models:
|
||||||
|
h: id,license,dfid,ip
|
||||||
|
v: true
|
||||||
|
- match:
|
||||||
|
$body: |
|
||||||
|
/^ id \s+ license \s+ dfid \s+ ip \n
|
||||||
|
(a\-regression\-model\-0 \s+ \w+ \s+ __none__ \s+ \d+ \n)+
|
||||||
|
(a\-regression\-model\-1 \s+ \w+ \s+ __none__ \s+ \d+ \n)+
|
||||||
|
(lang_ident_model_1 \s+ \w+ \s+ prepackaged \s+ \d+ \n)+ $/
|
||||||
|
|
||||||
|
- do:
|
||||||
|
cat.ml.trained_models:
|
||||||
|
model_id: a-regression-model-1
|
||||||
|
h: id,license,dfid,ip
|
||||||
|
v: true
|
||||||
|
- match:
|
||||||
|
$body: |
|
||||||
|
/^ id \s+ license \s+ dfid \s+ ip \n
|
||||||
|
(a\-regression\-model\-1 \s+ \w+ \s+ __none__ \s+ \d+ \n)+ $/
|
||||||
Loading…
Reference in New Issue