Use BigQuery ML to predict penguin weight

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In this tutorial, you use a linear regression model in BigQuery ML to predict the weight of a penguin based on the penguin's demographic information. A linear regression is a type of regression model that generates a continuous value from a linear combination of input features.

This tutorial uses the bigquery-public-data.ml_datasets.penguins dataset.

Objectives

In this tutorial, you will perform the following tasks:

• Create a linear regression model.
• Evaluate the model.
• Make predictions by using the model.

Note: This tutorial covers linear regression in pure SQL. For a linear regression tutorial using Python and BigQuery DataFrames on the same dataset, see Create a regression model with BigQuery DataFrames.

Costs

This tutorial uses billable components of Google Cloud, including the following:

• BigQuery
• BigQuery ML

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

For more information on BigQuery ML costs, see BigQuery ML pricing.

Before you begin

1. 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

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

3. Enable the BigQuery API.

   Enable the API

Required permissions

To create the model using BigQuery ML, you need the following IAM permissions:

• bigquery.jobs.create
• bigquery.models.create
• bigquery.models.getData
• bigquery.models.updateData
• bigquery.models.updateMetadata

To run inference, you need the following permissions:

• bigquery.models.getData on the model
• bigquery.jobs.create

Create a 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.

     

Create the model

Create a linear regression model using the Analytics sample dataset for BigQuery.

SQL

You can create a linear regression model by using the CREATE MODEL statement and specifying LINEAR_REG for the model type. Creating the model includes training the model.

The following are useful things to know about the CREATE MODEL statement:

• The input_label_cols option specifies which column in the SELECT statement to use as the label column. Here, the label column is body_mass_g. For linear regression models, the label column must be real-valued, that is, the column values must be real numbers.

• This query's SELECT statement uses the following columns in the bigquery-public-data.ml_datasets.penguins table to predict a penguin's weight:

  • species: the species of penguin.
  • island: the island that the penguin resides on.
  • culmen_length_mm: the length of the penguin's culmen in millimeters.
  • culmen_depth_mm: the depth of the penguin's culmen in millimeters.
  • flipper_length_mm: the length of the penguin's flippers in millimeters.
  • sex: the sex of the penguin.

• The WHERE clause in this query's SELECT statement, WHERE body_mass_g IS NOT NULL, excludes rows where the body_mass_g column is NULL.

Run the query that creates your linear regression model:

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

   Go to BigQuery

2. In the query editor, run the following query:

   CREATE OR REPLACE MODEL `bqml_tutorial.penguins_model`
   OPTIONS
     (model_type='linear_reg',
     input_label_cols=['body_mass_g']) AS
   SELECT
     *
   FROM
     `bigquery-public-data.ml_datasets.penguins`
   WHERE
     body_mass_g IS NOT NULL;
   

3. It takes about 30 seconds to create the penguins_model model. To see the model, go to the Explorer pane, expand the bqml_tutorial dataset, and then expand the Models folder.

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames. For more information, see the BigQuery DataFrames reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from bigframes.ml.linear_model import LinearRegression
import bigframes.pandas as bpd

# Load data from BigQuery
bq_df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")

# Drop rows with nulls to get training data
training_data = bq_df.dropna(subset=["body_mass_g"])

# Specify your feature (or input) columns and the label (or output) column:
feature_columns = training_data.drop(columns=["body_mass_g"])
label_columns = training_data[["body_mass_g"]]

# Create the linear model
model = LinearRegression()
model.fit(feature_columns, label_columns)
model.to_gbq(
    your_model_id,  # For example: "bqml_tutorial.penguins_model"
    replace=True,
)

It takes about 30 seconds to create the model. To see the model, go to the Explorer pane, expand the bqml_tutorial dataset, and then expand the Models folder.

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. It is 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.

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

1. In the Explorer pane, expand the bqml_tutorial dataset and then the Models folder. Click penguins_model to open the model information pane.

2. Click the Training tab, and then click Table. The results should look similar to the following:

   

   The Training Data Loss column represents the loss metric calculated after the model is trained on the training dataset. Since you performed a linear regression, this column shows the mean squared error value. A normal_equation optimization strategy is automatically used for this training, so only one iteration is required to converge to the final model. For more information on setting the model optimization strategy, see optimize_strategy.

Evaluate the model

After creating the model, evaluate the model's performance by using the ML.EVALUATE function or the score BigQuery DataFrames function to evaluate the predicted values generated by the model against the actual data.

SQL

For input, the ML.EVALUATE function takes the trained model and a dataset that matches the schema of the data that you used to train the model. In a production environment, you should evaluate the model on different data than the data you used to train the model. If you run ML.EVALUATE without providing input data, the function retrieves the evaluation metrics calculated during training. These metrics are calculated by using the automatically reserved evaluation dataset:

    SELECT
      *
    FROM
      ML.EVALUATE(MODEL bqml_tutorial.penguins_model);
    

Run the ML.EVALUATE query:

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

   Go to BigQuery

2. In the query editor, run the following query:

     SELECT
       *
     FROM
       ML.EVALUATE(MODEL `bqml_tutorial.penguins_model`,
         (
         SELECT
           *
         FROM
           `bigquery-public-data.ml_datasets.penguins`
         WHERE
           body_mass_g IS NOT NULL));
     

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames. For more information, see the BigQuery DataFrames reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import bigframes.pandas as bpd

# Select the model you will be evaluating. `read_gbq_model` loads model data from
# BigQuery, but you could also use the `model` object from the previous steps.
model = bpd.read_gbq_model(
    your_model_id,  # For example: "bqml_tutorial.penguins_model"
)

# Score the model with input data defined in an earlier step to compare
# model predictions on feature_columns to true labels in label_columns.
score = model.score(feature_columns, label_columns)
# Expected output results:
# index  mean_absolute_error  mean_squared_error  mean_squared_log_error  median_absolute_error  r2_score  explained_variance
#   0        227.012237         81838.159892            0.00507                173.080816        0.872377    0.872377
#   1 rows x 6 columns

The results should look similar to the following:

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 if the linear regression predictions approximate the actual data. A value of 0 indicates that the model explains none of the variability of the response data around the mean. A value of 1 indicates that the model explains all the variability of the response data around the mean.

You can also look at the model's information pane in the Google Cloud console to view the evaluation metrics:

Use the model to predict outcomes

Now that you have evaluated your model, the next step is to use it to predict an outcome. You can run the ML.PREDICT function or the predict BigQuery DataFrames function on the model to predict the body mass in grams of all penguins that reside on the Biscoe Islands.

SQL

For input, the ML.PREDICT function takes the trained model and a dataset that matches the schema of the data that you used to train the model, excluding the label column.

Run the ML.PREDICT query:

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

   Go to BigQuery

2. In the query editor, run the following query:

   SELECT
   *
   FROM
   ML.PREDICT(MODEL `bqml_tutorial.penguins_model`,
     (
     SELECT
       *
     FROM
       `bigquery-public-data.ml_datasets.penguins`
     WHERE island = 'Biscoe'));
   

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames. For more information, see the BigQuery DataFrames reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# Select the model you'll use for predictions. `read_gbq_model` loads
# model data from BigQuery, but you could also use the `model` object
# object from previous steps.
model = bpd.read_gbq_model(
    your_model_id,
    # For example: "bqml_tutorial.penguins_model",
)

# Load data from BigQuery
bq_df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")

# Use 'contains' function to filter by island containing the string
# "Biscoe".
biscoe_data = bq_df.loc[bq_df["island"].str.contains("Biscoe")]

result = model.predict(biscoe_data)

# Expected output results:
#     predicted_body_mass_g  	      species	                island	 culmen_length_mm  culmen_depth_mm   body_mass_g 	flipper_length_mm	sex
# 23	  4681.782896	   Gentoo penguin (Pygoscelis papua)	Biscoe	      <NA>	            <NA>	        <NA>	          <NA>	        <NA>
# 332	  4740.7907	       Gentoo penguin (Pygoscelis papua)	Biscoe	      46.2	            14.4	        214.0	          4650.0	    <NA>
# 160	  4731.310452	   Gentoo penguin (Pygoscelis papua)	Biscoe	      44.5	            14.3	        216.0	          4100.0	    <NA>

The results should look similar to the following:

Explain the prediction results

To understand why the model is generating these prediction results, you can use the ML.EXPLAIN_PREDICT function.

ML.EXPLAIN_PREDICT is an extended version of the ML.PREDICT function. ML.EXPLAIN_PREDICT not only outputs prediction results, but also outputs additional columns to explain the prediction results. In practice, you can run ML.EXPLAIN_PREDICT instead of ML.PREDICT. For more information, see BigQuery ML explainable AI overview.

Run the ML.EXPLAIN_PREDICT query:

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

   Go to BigQuery

2. In the query editor, run the following query:

   SELECT
   *
   FROM
   ML.EXPLAIN_PREDICT(MODEL `bqml_tutorial.penguins_model`,
     (
     SELECT
       *
     FROM
       `bigquery-public-data.ml_datasets.penguins`
     WHERE island = 'Biscoe'),
     STRUCT(3 as top_k_features));
   

3. The results should look similar to the following:

   

Note: The ML.EXPLAIN_PREDICT query outputs all the input feature columns, similar to what ML.PREDICT does. For readability purposes, only one feature column, species, is shown in the preceding figure.

For linear regression models, Shapley values are used to generate feature attribution values for each feature in the model. ML.EXPLAIN_PREDICT outputs the top three feature attributions per row of the penguins table because top_k_features was set to 3 in the query. These attributions are sorted by the absolute value of the attribution in descending order. In all examples, the feature sex contributed the most to the overall prediction.

Globally explain the model

To know which features are generally the most important to determine penguin weight, you can use the ML.GLOBAL_EXPLAIN function. In order to use ML.GLOBAL_EXPLAIN, you must retrain the model with the ENABLE_GLOBAL_EXPLAIN option set to TRUE.

Retrain and get global explanations for the model:

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

   Go to BigQuery

2. In the query editor, run the following query to retrain the model:

   #standardSQL
   CREATE OR REPLACE MODEL `bqml_tutorial.penguins_model`
     OPTIONS (
       model_type = 'linear_reg',
       input_label_cols = ['body_mass_g'],
       enable_global_explain = TRUE)
   AS
   SELECT
     *
   FROM
     `bigquery-public-data.ml_datasets.penguins`
   WHERE
     body_mass_g IS NOT NULL;
   

3. In the query editor, run the following query to get global explanations:

   SELECT
     *
   FROM
     ML.GLOBAL_EXPLAIN(MODEL `bqml_tutorial.penguins_model`)
   

4. The results should look similar to the following:

   

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, click the bqml_tutorial dataset you created.

3. Click Delete dataset on the right side of the window. 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

• For an overview of BigQuery ML, see Introduction to BigQuery ML.
• For information on creating models, see the CREATE MODEL syntax page.