Finding outliers in the eCommerce sample data

Obtain a license that includes the machine learning features.

By default, when you install Elastic Stack products, they apply basic licenses with no expiration dates. To view your license in Kibana, go to Management and click License Management.

For more information about Elastic license levels, see https://www.elastic.co/subscriptions.

You can start a 30-day trial to try out all of the platinum features, including machine learning features. Click Start trial on the License Management page in Kibana.

At the end of the trial period, the platinum features operate in a degraded mode. You can revert to a basic license, extend the trial, or purchase a subscription.

If the Elasticsearch security features are enabled, obtain a user ID with sufficient privileges to complete these steps.

You need manage_data_frame_transforms cluster privileges to preview and create transforms. Members of the built-in data_frame_transforms_admin role have these privileges.

You must also be a member of the machine_learning_admin built-in role to create and manage data frame analytics jobs.

You also need read and view_index_metadata index privileges on the source indices and read, create_index, and index privileges on the destination indices.

For more information, see Security privileges and Built-in roles.

Create a transform that generates an entity-centric index with numeric or boolean data to analyze.

In this example, we’ll use the eCommerce orders sample data and pivot the data such that we get a new index that contains a sales summary for each customer.

In particular, create a transform that calculates the sum of the products (products.quantity) and the sum of prices (products.taxful_price) in all of the orders, grouped by customer (customer_full_name). Also include a value count aggregation, so that we know how many orders (order_id) exist for each customer.

You can preview the transform before you create it in Kibana:

Alternatively, you can preview and create the transform with the following APIs:

POST _data_frame/transforms/_preview
{
  "source": {
    "index": [
      "kibana_sample_data_ecommerce"
    ]
  },
  "pivot": {
    "group_by": {
      "customer_full_name.keyword": {
        "terms": {
          "field": "customer_full_name.keyword"
        }
      }
    },
    "aggregations": {
      "products.quantity.sum": {
        "sum": {
          "field": "products.quantity"
        }
      },
      "products.taxful_price.sum": {
        "sum": {
          "field": "products.taxful_price"
        }
      },
      "order_id.value_count": {
        "value_count": {
          "field": "order_id"
        }
      }
    }
  }
}

PUT _data_frame/transforms/ecommerce-customer-sales
{
  "source": {
    "index": [
      "kibana_sample_data_ecommerce"
    ]
  },
  "pivot": {
    "group_by": {
      "customer_full_name.keyword": {
        "terms": {
          "field": "customer_full_name.keyword"
        }
      }
    },
    "aggregations": {
      "products.quantity.sum": {
        "sum": {
          "field": "products.quantity"
        }
      },
      "products.taxful_price.sum": {
        "sum": {
          "field": "products.taxful_price"
        }
      },
      "order_id.value_count": {
        "value_count": {
          "field": "order_id"
        }
      }
    }
  },
  "description": "E-commerce sales by customer",
  "dest": {
    "index": "ecommerce-customer-sales"
  }
}

For more details about creating transforms, see Transforming the eCommerce sample data.

Start the transform.

You can start, stop, and manage transforms in Kibana. Alternatively, you can use the start transforms API. For example:

POST _data_frame/transforms/ecommerce-customer-sales/_start

Create a data frame analytics job to detect outliers in the new entity-centric index.

There is a wizard for creating data frame analytics jobs on the Machine Learning > Analytics page in Kibana:

Alternatively, you can use the create data frame analytics jobs API. For example:

PUT _ml/data_frame/analytics/ecommerce
{
  "source": {
    "index": "ecommerce-customer-sales"
  },
  "dest": {
    "index": "ecommerce-outliers"
  },
  "analysis": {
    "outlier_detection": {
    }
  },
  "analyzed_fields" : {
    "includes" : ["products.quantity.sum","products.taxful_price.sum","order_id.value_count"]
  }
}

Start the data frame analytics job.

You can start, stop, and manage data frame analytics jobs on the Machine Learning > Analytics page in Kibana. Alternatively, you can use the start data frame analytics jobs and stop data frame analytics jobs APIs. For example:

POST _ml/data_frame/analytics/ecommerce/_start

View the results of the outlier detection analysis.

The data frame analytics job creates an index that contains the original data and outlier scores for each document. The outlier score indicates how different each entity is from other entities.

In Kibana, you can view the results from the data frame analytics job and sort them on the outlier score:

The ml.outlier score is a value between 0 and 1. The larger the value, the more likely they are to be an outlier.

In addition to an overall outlier score, each document is annotated with feature influence values for each field. These values add up to 1 and indicate which fields are the most important in deciding whether an entity is an outlier or inlier. For example, the dark shading on the products.taxful_price.sum field for Wagdi Shaw indicates that the sum of the product prices was the most influential feature in determining that Wagdi is an outlier.

If you want to see the exact feature influence values, you can retrieve them from the index that is associated with your data frame analytics job. For example:

GET ecommerce-outliers/_search?q="Wagdi Shaw"

The search results include the following outlier detection scores:

...
"ml" :{
  "outlier_score" : 0.9653657078742981,
  "feature_influence.products.quantity.sum" : 0.00592468399554491,
  "feature_influence.order_id.value_count" : 0.01975759118795395,
  "feature_influence.products.taxful_price.sum" : 0.974317729473114
}
...