Perform feature engineering with the TRANSFORM clause

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This tutorial introduces data analysts to BigQuery ML. BigQuery ML enables users to create and execute machine learning models in BigQuery using SQL queries. This tutorial introduces feature engineering by using the TRANSFORM clause. Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning.

In this tutorial, you use the natality sample table to create a model that predicts the birth weight of a child based on the baby's gender, the length of the pregnancy, and bucketized demographic information about the mother. The natality sample table contains information about every birth in the United States over a 40 year period.

Objectives

In this tutorial, you use:

• BigQuery ML to create a linear regression model using the CREATE MODEL statement with the TRANSFORM clause
• The ML.FEATURE_CROSS and ML.QUANTILE_BUCKETIZE preprocessing functions
• The ML.EVALUATE function to evaluate the ML model
• The ML.PREDICT function to make predictions using the ML model

Costs

This tutorial uses billable components of Google Cloud, including:

• BigQuery
• BigQuery ML

For more information about BigQuery costs, see the BigQuery pricing page.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. 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

3. Make sure that billing is enabled for your Google Cloud project.

4. 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

5. Make sure that billing is enabled for your Google Cloud project.

6. BigQuery is automatically enabled in new projects. To activate BigQuery in a pre-existing project, go to

   Enable the BigQuery API.

   Enable the API

Step one: Create your dataset

Create a BigQuery dataset to store your ML model:

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

   Go to the BigQuery page

2. In the Explorer pane, click your project name.

3. Click View actions > Create dataset.

   

4. 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.

     

Step two: Create your model

Next, create a linear regression model using the natality sample table for BigQuery. The following GoogleSQL query is used to create the model you use to predict the birth weight of a child.

#standardSQL
CREATE MODEL `bqml_tutorial.natality_model`
TRANSFORM(weight_pounds,
    is_male,
    gestation_weeks,
    ML.QUANTILE_BUCKETIZE(mother_age,
      5) OVER() AS bucketized_mother_age,
    CAST(mother_race AS string) AS mother_race,
    ML.FEATURE_CROSS(STRUCT(is_male,
        CAST(mother_race AS STRING) AS mother_race)) is_male_mother_race)
OPTIONS
  (model_type='linear_reg',
    input_label_cols=['weight_pounds']) AS
SELECT
  *
FROM
  `bigquery-public-data.samples.natality`
WHERE
  weight_pounds IS NOT NULL
  AND RAND() < 0.001

In addition to creating the model, running the CREATE MODEL command trains the model you create.

Query details

The CREATE MODEL clause is used to create and train the model named bqml_tutorial.natality_model.

The OPTIONS(model_type='linear_reg', input_label_cols=['weight_pounds']) clause indicates that you are creating a linear regression model. A linear regression is a type of regression model that generates a continuous value from a linear combination of input features. The weight_pounds column is the input label column. For linear regression models, the label column must be real valued (that is, the column values must be real numbers).

This query's TRANSFORM clause uses the following columns from the SELECT statement:

• weight_pounds: The weight, in pounds, of the child (FLOAT64).
• is_male: The sex of the child. TRUE if the child is male, FALSE if female (BOOL).
• gestation_weeks: The number of weeks of the pregnancy (INT64).
• mother_age: The age of the mother when giving birth (INT64).
• mother_race: The race of the mother (INT64). This integer value is the same as the child_race value in the table schema. To force BigQuery ML to treat mother_race as a non-numeric feature, with each distinct value representing a different category, the query casts mother_race to a STRING. This is important because race is more likely to have more meaning as a category than an integer, which has ordering and scale.

Through the TRANSFORM clause, the original features are preprocessed to feed in training. The generated columns are:

• weight_pounds: Passed as is, without any change.
• is_male: Passed through to feed in training.
• gestation_weeks: Passed through to feed in training.
• bucketized_mother_age: Generated from mother_age by bucketizing mother_age based on quantiles using the ML.QUANTILE_BUCKETIZE() analytic function.
• mother_race: String format of the original mother_race.
• is_male_mother_race: Generated from crossing is_male and mother_race using the ML.FEATURE_CROSS function.

The query's SELECT statement provides the columns that you can use in the TRANSFORM clause. However, you do not need to use all columns in the TRANSFORM clause. As a result, you can do both feature selection and preprocessing inside the TRANSFORM clause.

The FROM clause—bigquery-public-data.samples.natality—indicates that you are querying the natality sample table in the samples dataset. This dataset is in the bigquery-public-data project.

The WHERE clause—WHERE weight_pounds IS NOT NULL AND RAND() < 0.001—excludes rows where weight is NULL and uses the RAND function to draw a random sample of the data.

Run the CREATE MODEL query

To run the CREATE MODEL query to create and train your model:

1. In the Google Cloud console, click the Compose new query button.

2. Enter the following GoogleSQL query in the Query editor text area.

   #standardSQL
   CREATE MODEL `bqml_tutorial.natality_model`
   TRANSFORM(weight_pounds,
       is_male,
       gestation_weeks,
       ML.QUANTILE_BUCKETIZE(mother_age,
         5) OVER() AS bucketized_mother_age,
       CAST(mother_race AS string) AS mother_race,
       ML.FEATURE_CROSS(STRUCT(is_male,
           CAST(mother_race AS STRING) AS mother_race)) is_male_mother_race)
   OPTIONS
     (model_type='linear_reg',
       input_label_cols=['weight_pounds']) AS
   SELECT
     *
   FROM
     `bigquery-public-data.samples.natality`
   WHERE
     weight_pounds IS NOT NULL
     AND RAND() < 0.001

3. Click Run.

   The query takes about 30 seconds to complete, after which your model (natality_model) appears in the navigation panel. Because the query uses a CREATE MODEL statement to create a table, you do not see query results.

Step three (optional): Get training statistics

To see the results of the model training, you can use the ML.TRAINING_INFO function, or you can view the statistics in the Google Cloud console. In this tutorial, you use the Google Cloud console.

A machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss. This process is called empirical risk minimization.

Loss is the penalty for a bad prediction—a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples.

To see the model training statistics that were generated when you ran the CREATE MODEL query:

1. In the Google Cloud console navigation panel, in the Resources section, expand project-name > bqml_tutorial and then click natality_model.

2. Click the Training tab and for View as, select the Table option. The results should look like the following:

   +-----------+--------------------+----------------------+--------------------+
   | Iteration | Training data loss | Evaluation data loss | Duration (seconds) |
   +-----------+--------------------+----------------------+--------------------+
   | 0         | 1.6640             | 1.7352               | 6.27               |
   +-----------+--------------------+----------------------+--------------------+
   

   The Training Data Loss column represents the loss metric calculated after the model is trained on the training dataset. Because you performed a linear regression, this column is the mean squared error.

   The Evaluation Data Loss column is the same loss metric calculated on the holdout dataset (data that is held back from training to validate the model). The default optimize strategy used for the training is "normal_equation", so only one iteration is required to converge to the final model.

   For more information about the optimize_strategy option, see the CREATE MODEL statement.

   For more information about the ML.TRAINING_INFO function and the "optimize_strategy" training option, see the BigQuery ML syntax reference.

Step four: Evaluate your model

After creating your model, you evaluate the performance of the classifier by using the ML.EVALUATE function. The ML.EVALUATE function evaluates the predicted values against the actual data.

The query used to evaluate the model is as follows:

#standardSQL
SELECT
  *
FROM
  ML.EVALUATE(MODEL `bqml_tutorial.natality_model`,
    (
    SELECT
      *
    FROM
      `bigquery-public-data.samples.natality`
    WHERE
      weight_pounds IS NOT NULL))

Query details

The upper SELECT statement retrieves the columns from your model.

The FROM clause uses the ML.EVALUATE function against your model: bqml_tutorial.natality_model.

This query's nested SELECT statement and FROM clause are the same as those in the CREATE MODEL query. Because the TRANSFORM clause is used in training, you don't need to specify the specific columns and transformations. They are automatically restored.

The WHERE clause—WHERE weight_pounds IS NOT NULL—excludes rows where weight is NULL.

Note: You can also call the ML.EVALUATE function without providing the input data. The function uses the evaluation metrics calculated during training:

#standardSQL
SELECT
  *
FROM
  ML.EVALUATE(MODEL `bqml_tutorial.natality_model`)

Run the ML.EVALUATE query

To run the ML.EVALUATE query that evaluates the model, complete the following steps:

1. In the Google Cloud console, click the Compose new query button.

2. Enter the following GoogleSQL query in the Query editor text area.

   #standardSQL
   SELECT
     *
   FROM
     ML.EVALUATE(MODEL `bqml_tutorial.natality_model`,
       (
       SELECT
         *
       FROM
         `bigquery-public-data.samples.natality`
       WHERE
         weight_pounds IS NOT NULL))

3. (Optional) To set the processing location, on the More drop-down list, click Query settings. For Processing location, choose United States (US). This step is optional because the processing location is automatically detected based on the dataset's location.

   

4. Click Run.

5. When the query is complete, click the Results tab below the query text area. The results should look like the following:

   +---------------------+--------------------+------------------------+---------------------+---------------------+----------------------+
   | mean_absolute_error | mean_squared_error | mean_squared_log_error | mean_absolute_error | r2_score            | explained_variance   |
   +---------------------+--------------------+------------------------+---------------------+---------------------+----------------------+
   | 0.9566580179970666  | 1.6756289722442677 | 0.034241471462096516   | 0.7385590721661188  | 0.04650972930257946 | 0.046516832131241026 |
   +---------------------+--------------------+------------------------+---------------------+---------------------+----------------------+
   

   Because you performed a linear regression, the results include the following columns:

   • mean_absolute_error
   • mean_squared_error
   • mean_squared_log_error
   • median_absolute_error
   • r2_score
   • explained_variance

An important metric in the evaluation results is the R² score. The R² score is a statistical measure that determines whether the linear regression predictions approximate the actual data. A 0 value indicates that the model explains none of the variability of the response data around the mean. A 1 value indicates that the model explains all the variability of the response data around the mean.

Step five: Use your model to predict outcomes

Now that you have evaluated your model, the next step is to use it to predict an outcome. You can use your model to predict the birth weight of all babies born in Wyoming.

The query used to predict the outcome is as follows:

#standardSQL
SELECT
  predicted_weight_pounds
FROM
  ML.PREDICT(MODEL `bqml_tutorial.natality_model`,
    (
    SELECT
      *
    FROM
      `bigquery-public-data.samples.natality`
    WHERE
      state = "WY"))

Query details

The top-most SELECT statement retrieves the predicted_weight_pounds column. This column is generated by the ML.PREDICT function. When you use the ML.PREDICT function, the output column name for the model is predicted_label_column_name. For linear regression models, predicted_label is the estimated value of label. For logistic regression models, predicted_label is one of the two input labels, depending on which label has the higher predicted probability.

The ML.PREDICT function is used to predict results using your model: bqml_tutorial.natality_model.

This query's nested SELECT statement and FROM clause are the same as those in the CREATE MODEL query. Note that you don't necessarily need to pass in all columns as in training, and only the ones used in the TRANSFORM clause are required. Similar to ML.EVALUATE, the transformations inside the TRANSFORM are automatically restored.

The WHERE clause—WHERE state = "WY"—indicates that you are limiting the prediction to the state of Wyoming.

Run the ML.PREDICT query

To run the query that uses the model to predict an outcome:

1. In the Google Cloud console, click the Compose new query button.

2. Enter the following GoogleSQL query in the Query editor text area.

   #standardSQL
   SELECT
     predicted_weight_pounds
   FROM
     ML.PREDICT(MODEL `bqml_tutorial.natality_model`,
       (
       SELECT
         *
       FROM
         `bigquery-public-data.samples.natality`
       WHERE
         state = "WY"))

3. (Optional) To set the processing location, on the More drop-down list, click Query settings. For Processing location, choose United States (US). This step is optional because the processing location is automatically detected based on the dataset's location.

   

4. Click Run.

5. When the query is complete, click the Results tab below the query text area. The results should look like the following:

   +----------------------------+
   | predicted_weight_pounds    |
   +----------------------------+
   |  7.735962399307027         |
   +----------------------------+
   |  7.728855793480761         |
   +----------------------------+
   |  7.383850250400428         |
   +----------------------------+
   | 7.4132677633242565         |
   +----------------------------+
   |  7.734971309702814         |
   +----------------------------+
   

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

• You can delete the project you created.
• Or you can keep the project and delete the dataset.

Delete your dataset

Deleting your project removes all datasets and all tables in the project. If you prefer to reuse the project, you can delete the dataset you created in this tutorial:

1. If necessary, open the BigQuery page in the Google Cloud console.

   Go to the BigQuery page

2. In the navigation panel, click the bqml_tutorial dataset you created.

3. On the right side of the window, click Delete dataset. This action deletes the dataset, the table, and all the data.

4. In the Delete dataset dialog box, confirm the delete command by typing the name of your dataset (bqml_tutorial) and then click Delete.

Delete your project

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

What's next

• To learn more about machine learning, see the Machine learning crash course.
• For an overview of BigQuery ML, see Introduction to BigQuery ML.
• To learn more about the Google Cloud console, see Using the Google Cloud console.