This API provides explanations for a data frame analytics config that either exists already or one that has not been created yet. The following explanations are provided:

which fields are included or not in the analysis and why,
how much memory is estimated to be required. The estimate can be used when deciding the appropriate value for model_memory_limit setting later on.

If you have object fields or fields that are excluded via source filtering, they are not included in the explanation.

Path parameters

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<data_frame_analytics_id>: (Optional, string) Identifier for the data frame analytics job.

Request body

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A data frame analytics config as described in Create data frame analytics jobs. Note that id and dest don’t need to be provided in the context of this API.

Response body

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The API returns a response that contains the following:

field_selection

(array) An array of objects that explain selection for each field, sorted by the field names.

Properties of field_selection objects

is_included: (Boolean) Whether the field is selected to be included in the analysis.
is_required: (Boolean) Whether the field is required.
feature_type: (string) The feature type of this field for the analysis. May be categorical or numerical.
mapping_types: (string) The mapping types of the field.
name: (string) The field name.
reason: (string) The reason a field is not selected to be included in the analysis.

memory_estimation

(object) An object containing the memory estimates.

Properties of memory_estimation

expected_memory_with_disk: (string) Estimated memory usage under the assumption that overflowing to disk is allowed during data frame analytics. expected_memory_with_disk is usually smaller than expected_memory_without_disk as using disk allows to limit the main memory needed to perform data frame analytics.
expected_memory_without_disk: (string) Estimated memory usage under the assumption that the whole data frame analytics should happen in memory (i.e. without overflowing to disk).

Examples

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resp = client.ml.explain_data_frame_analytics(
    source={
        "index": "houses_sold_last_10_yrs"
    },
    analysis={
        "regression": {
            "dependent_variable": "price"
        }
    },
)
print(resp)

const response = await client.ml.explainDataFrameAnalytics({
  source: {
    index: "houses_sold_last_10_yrs",
  },
  analysis: {
    regression: {
      dependent_variable: "price",
    },
  },
});
console.log(response);

POST _ml/data_frame/analytics/_explain
{
  "source": {
    "index": "houses_sold_last_10_yrs"
  },
  "analysis": {
    "regression": {
      "dependent_variable": "price"
    }
  }
}

The API returns the following results:

{
  "field_selection": [
    {
      "field": "number_of_bedrooms",
      "mappings_types": ["integer"],
      "is_included": true,
      "is_required": false,
      "feature_type": "numerical"
    },
    {
      "field": "postcode",
      "mappings_types": ["text"],
      "is_included": false,
      "is_required": false,
      "reason": "[postcode.keyword] is preferred because it is aggregatable"
    },
    {
      "field": "postcode.keyword",
      "mappings_types": ["keyword"],
      "is_included": true,
      "is_required": false,
      "feature_type": "categorical"
    },
    {
      "field": "price",
      "mappings_types": ["float"],
      "is_included": true,
      "is_required": true,
      "feature_type": "numerical"
    }
  ],
  "memory_estimation": {
    "expected_memory_without_disk": "128MB",
    "expected_memory_with_disk": "32MB"
  }
}

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