[ML] Outlier detection should only fetch docs that have the analyzed … #44944
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…fields As data frame rows with missing values for analyzed fields are skipped, we can be more efficient by including a query that only picks documents that have values for all analyzed fields. Besides improving the number of documents we go through, we also provide a more accurate measurement of how many rows we need which reduces the memory requirements. This also adds an integration test that runs outlier detection on data with missing fields.
dimitris-athanasiou
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Pinging @elastic/ml-core |
przemekwitek
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Jul 29, 2019
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| String id = "test_outlier_detection_with_missing_fields"; | ||
| DataFrameAnalyticsConfig config = buildOutlierDetectionAnalytics(id, new String[] {sourceIndex}, sourceIndex + "-results", null); |
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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.
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run elasticsearch-ci/packaging-sample |
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elastic#44944) As data frame rows with missing values for analyzed fields are skipped, we can be more efficient by including a query that only picks documents that have values for all analyzed fields. Besides improving the number of documents we go through, we also provide a more accurate measurement of how many rows we need which reduces the memory requirements. This also adds an integration test that runs outlier detection on data with missing fields.
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elastic#44944) As data frame rows with missing values for analyzed fields are skipped, we can be more efficient by including a query that only picks documents that have values for all analyzed fields. Besides improving the number of documents we go through, we also provide a more accurate measurement of how many rows we need which reduces the memory requirements. This also adds an integration test that runs outlier detection on data with missing fields.
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#44944) (#44959) As data frame rows with missing values for analyzed fields are skipped, we can be more efficient by including a query that only picks documents that have values for all analyzed fields. Besides improving the number of documents we go through, we also provide a more accurate measurement of how many rows we need which reduces the memory requirements. This also adds an integration test that runs outlier detection on data with missing fields.
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#44944) As data frame rows with missing values for analyzed fields are skipped, we can be more efficient by including a query that only picks documents that have values for all analyzed fields. Besides improving the number of documents we go through, we also provide a more accurate measurement of how many rows we need which reduces the memory requirements. This also adds an integration test that runs outlier detection on data with missing fields.
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#44944) (#44960) As data frame rows with missing values for analyzed fields are skipped, we can be more efficient by including a query that only picks documents that have values for all analyzed fields. Besides improving the number of documents we go through, we also provide a more accurate measurement of how many rows we need which reduces the memory requirements. This also adds an integration test that runs outlier detection on data with missing fields.
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…fields
As data frame rows with missing values for analyzed fields are skipped,
we can be more efficient by including a query that only picks documents
that have values for all analyzed fields. Besides improving the number
of documents we go through, we also provide a more accurate measurement
of how many rows we need which reduces the memory requirements.
This also adds an integration test that runs outlier detection on data
with missing fields.