Finding outliers in the eCommerce sample data

Verify that your environment is set up properly to use machine learning features. If the Elasticsearch security features are enabled, you need a user that has authority to create and manage data frame analytics jobs. See Setup and security.

Since we’ll be creating transforms, you also need manage_data_frame_transforms cluster privileges.

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 use the preview transform API and the create transform API.

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.

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 > Data Frame Analytics page in Kibana:

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

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 > Data Frame Analytics page in Kibana. Alternatively, you can use the start data frame analytics jobs and stop data frame analytics jobs APIs.

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 Kibana, you can optionally enable histogram charts to get a better understanding of the distribution of values for each column in the result.

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.

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

...
"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
  }
...