Get data insights from a contribution analysis model

In this tutorial, you use a contribution analysis model to analyze taxi fare changes between 2011 and 2012 in New York City. This tutorial guides you through performing the following tasks:

Creating an input table based on publicly available taxi data.
Creating a contribution analysis model that uses a summable metric. This type of model summarizes a given metric for a combination of one or more dimensions in the data, to determine how those dimensions contribute to the metric value.
Get the metric insights from the model by using the ML.GET_INSIGHTS function.

Before starting this tutorial, you should be familiar with the contribution analysis use case.

Required permissions

To create the dataset, you need the bigquery.datasets.create Identity and Access Management (IAM) permission.
To create the model, you need the following permissions:
- bigquery.jobs.create
- bigquery.models.create
- bigquery.models.getData
- bigquery.models.updateData
To run inference, you need the following permissions:
- bigquery.models.getData
- bigquery.jobs.create

Costs

In this document, you use the following billable components of Google Cloud:

BigQuery ML: You incur costs for the data that you process in BigQuery.

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

For more information about BigQuery pricing, see BigQuery pricing in the BigQuery documentation.

Before you begin

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector
Make sure that billing is enabled for your Google Cloud project.
Enable the BigQuery API.
Enable the API

Create a dataset

Create a BigQuery dataset to store your ML model:

In the Google Cloud console, go to the BigQuery page.

Go to the BigQuery page
In the Explorer pane, click your project name.
Click View actions > Create dataset.
On the Create dataset page, do the following:
- For Dataset ID, enter bqml_tutorial.
- For Location type, select Multi-region, and then select US (multiple regions in United States).
  
  The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.
- Leave the remaining default settings as they are, and click Create dataset.

Create a table of input data

Create a table that contains test and control data to analyze. The following query creates two intermediate tables, a test table with taxi data from 2012 and a control table with taxi data from 2011, and then performs a union of the intermediate tables to create a table with both test and control rows and the same set of columns.

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement:

CREATE OR REPLACE TABLE bqml_tutorial.taxi_control_and_test
AS (
  SELECT
    vendor_id,
    passenger_count,
    payment_type,
    pickup_location_id,
    EXTRACT(MONTH FROM pickup_datetime) AS month,
    AVG(total_amount) AS avg_total_fare,
    FALSE AS is_test
  FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2011`
  WHERE total_amount > 0
  GROUP BY vendor_id, passenger_count, payment_type, pickup_location_id, month, is_test
)
UNION ALL
(
  SELECT
    vendor_id,
    passenger_count,
    payment_type,
    pickup_location_id,
    EXTRACT(MONTH FROM pickup_datetime) AS month,
    AVG(total_amount) AS avg_total_fare,
    TRUE AS is_test
  FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2012`
  WHERE total_amount > 0
  GROUP BY vendor_id, passenger_count, payment_type, pickup_location_id, month, is_test
);

Create the model

Create a contribution analysis model:

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement:

CREATE OR REPLACE MODEL `bqml_tutorial.taxi_contribution_analysis_model`
  OPTIONS (
    MODEL_TYPE = 'CONTRIBUTION_ANALYSIS',
    CONTRIBUTION_METRIC = 'SUM(avg_total_fare)',
    DIMENSION_ID_COLS =
      ['vendor_id', 'passenger_count', 'pickup_location_id', 'payment_type', 'month'],
    IS_TEST_COL = 'is_test',
    MIN_APRIORI_SUPPORT = 0.05)
AS
SELECT * FROM bqml_tutorial.taxi_control_and_test;

The query takes approximately 20 seconds to complete, after which the model taxi_contribution_analysis_model appears in the bqml_tutorial dataset in the Explorer pane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

Get insights from the model

Get insights generated by the contribution analysis model by using the ML.GET_INSIGHTS function.

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement:

SELECT
  contributors,
  metric_test,
  metric_control,
  difference,
  relative_difference,
  unexpected_difference,
  relative_unexpected_difference,
  apriori_support
FROM
  ML.GET_INSIGHTS(
    MODEL `bqml_tutorial.taxi_contribution_analysis_model`)
WHERE relative_difference IS NOT NULL
ORDER BY unexpected_difference DESC;

The WHERE clause excludes the results for contributors that are present in the test set, but not in the control set.

If you had used SELECT * rather than specifying columns in the SELECT statement for the function, the output would also include the dimension columns specified in the DIMENSION_ID_COLS option of the CREATE MODEL statement.

The first several rows of the output should look similar to the following:

+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+
|                        contributors                         |   metric_test    |  metric_control  |    difference    | relative_difference | unexpected_difference | relative_unexpected_difference | apriori_support |
+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+
| ["payment_type=5"]                                          |   82996.99307095 |           138.26 |   82858.73307095 |       599.296492629 |       82825.246757081 |                  482.253417818 |     0.063593301 |
+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+
| ["vendor_id=1"]                                             | 651674.026105812 | 475749.798118647 | 175924.227987165 |         0.369783085 |        39985.82041025 |                    0.065369611 |     0.499320531 |
+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+
| ["passenger_count=1"]                                       | 304615.252142054 | 214839.058249037 |  89776.193893017 |         0.417876501 |       25149.907437652 |                     0.08999294 |     0.233399895 |
+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+
| ["passenger_count=6"]                                       | 106980.236314059 | 64751.247894565  |  42228.988419494 |          0.65217258 |       22582.018639759 |                    0.267565108 |     0.081969553 |
+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+
| ["vendor_id=1","passenger_count=1"]                         |     169184.64374 | 114583.997774386 |  54600.645965614 |         0.476511965 |       19471.037967023 |                    0.130055237 |     0.129631323 |
+-------------------------------------------------------------+------------------+------------------+------------------+---------------------+-----------------------+--------------------------------+-----------------+

Because you performed contribution analysis on a summable metric, the results contain the summable metric output columns.

Clean up

In the Google Cloud console, go to the Manage resources page.
Go to Manage resources
In the project list, select the project that you want to delete, and then click Delete.
In the dialog, type the project ID, and then click Shut down to delete the project.