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[ML] Outlier detection should only fetch docs that have the analyzed … #44944
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dimitris-athanasiou
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dimitris-athanasiou:outlier-detection-should-query-docs-that-have-all-analyzed-fields
Jul 29, 2019
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103 changes: 103 additions & 0 deletions
103
...test/java/org/elasticsearch/xpack/ml/integration/OutlierDetectionWithMissingFieldsIT.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| /* | ||
| * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one | ||
| * or more contributor license agreements. Licensed under the Elastic License; | ||
| * you may not use this file except in compliance with the Elastic License. | ||
| */ | ||
| package org.elasticsearch.xpack.ml.integration; | ||
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| import org.elasticsearch.action.bulk.BulkRequestBuilder; | ||
| import org.elasticsearch.action.bulk.BulkResponse; | ||
| import org.elasticsearch.action.get.GetResponse; | ||
| import org.elasticsearch.action.index.IndexRequest; | ||
| import org.elasticsearch.action.search.SearchResponse; | ||
| import org.elasticsearch.action.support.WriteRequest; | ||
| import org.elasticsearch.search.SearchHit; | ||
| import org.elasticsearch.xpack.core.ml.dataframe.DataFrameAnalyticsConfig; | ||
| import org.elasticsearch.xpack.core.ml.dataframe.DataFrameAnalyticsState; | ||
| import org.junit.After; | ||
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| import java.util.Map; | ||
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| import static org.hamcrest.Matchers.allOf; | ||
| import static org.hamcrest.Matchers.equalTo; | ||
| import static org.hamcrest.Matchers.greaterThanOrEqualTo; | ||
| import static org.hamcrest.Matchers.is; | ||
| import static org.hamcrest.Matchers.lessThanOrEqualTo; | ||
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| public class OutlierDetectionWithMissingFieldsIT extends MlNativeDataFrameAnalyticsIntegTestCase { | ||
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| @After | ||
| public void cleanup() { | ||
| cleanUp(); | ||
| } | ||
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| public void testMissingFields() throws Exception { | ||
| String sourceIndex = "test-outlier-detection-with-missing-fields"; | ||
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| client().admin().indices().prepareCreate(sourceIndex) | ||
| .addMapping("_doc", "numeric", "type=double", "categorical", "type=keyword") | ||
| .get(); | ||
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| BulkRequestBuilder bulkRequestBuilder = client().prepareBulk(); | ||
| bulkRequestBuilder.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE); | ||
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| // 5 docs with valid value | ||
| for (int i = 0; i < 5; i++) { | ||
| IndexRequest indexRequest = new IndexRequest(sourceIndex); | ||
| indexRequest.source("numeric", 42.0, "categorical", "foo"); | ||
| bulkRequestBuilder.add(indexRequest); | ||
| } | ||
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| // Add a doc with missing field | ||
| { | ||
| IndexRequest missingIndexRequest = new IndexRequest(sourceIndex); | ||
| missingIndexRequest.source("categorical", "foo"); | ||
| bulkRequestBuilder.add(missingIndexRequest); | ||
| } | ||
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| // Add a doc with numeric being array which is also treated as missing | ||
| { | ||
| IndexRequest arrayIndexRequest = new IndexRequest(sourceIndex); | ||
| arrayIndexRequest.source("numeric", new double[]{1.0, 2.0}, "categorical", "foo"); | ||
| bulkRequestBuilder.add(arrayIndexRequest); | ||
| } | ||
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| BulkResponse bulkResponse = bulkRequestBuilder.get(); | ||
| if (bulkResponse.hasFailures()) { | ||
| fail("Failed to index data: " + bulkResponse.buildFailureMessage()); | ||
| } | ||
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| String id = "test_outlier_detection_with_missing_fields"; | ||
| DataFrameAnalyticsConfig config = buildOutlierDetectionAnalytics(id, new String[] {sourceIndex}, sourceIndex + "-results", null); | ||
| registerAnalytics(config); | ||
| putAnalytics(config); | ||
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| assertState(id, DataFrameAnalyticsState.STOPPED); | ||
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| startAnalytics(id); | ||
| waitUntilAnalyticsIsStopped(id); | ||
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| SearchResponse sourceData = client().prepareSearch(sourceIndex).get(); | ||
| for (SearchHit hit : sourceData.getHits()) { | ||
| GetResponse destDocGetResponse = client().prepareGet().setIndex(config.getDest().getIndex()).setId(hit.getId()).get(); | ||
| assertThat(destDocGetResponse.isExists(), is(true)); | ||
| Map<String, Object> sourceDoc = hit.getSourceAsMap(); | ||
| Map<String, Object> destDoc = destDocGetResponse.getSource(); | ||
| for (String field : sourceDoc.keySet()) { | ||
| assertThat(destDoc.containsKey(field), is(true)); | ||
| assertThat(destDoc.get(field), equalTo(sourceDoc.get(field))); | ||
| } | ||
| if (destDoc.containsKey("numeric") && destDoc.get("numeric") instanceof Double) { | ||
| assertThat(destDoc.containsKey("ml"), is(true)); | ||
| @SuppressWarnings("unchecked") | ||
| Map<String, Object> resultsObject = (Map<String, Object>) destDoc.get("ml"); | ||
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| assertThat(resultsObject.containsKey("outlier_score"), is(true)); | ||
| double outlierScore = (double) resultsObject.get("outlier_score"); | ||
| assertThat(outlierScore, allOf(greaterThanOrEqualTo(0.0), lessThanOrEqualTo(1.0))); | ||
| } else { | ||
| assertThat(destDoc.containsKey("ml"), is(false)); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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Just for certainty, you don't need to explicitly set analyzed_fields, because it defaults to all numeric fields?
You could index some docs that have "numeric" but are missing "categorical" to show that missing categorical field doesn't matter and "ml" object is still generated for such docs.
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Yes, the categorical field in this case is not included in the analyzed fields. Good idea, I'll do so.