Use BigQuery ML to predict penguin weight

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.

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

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
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
Verify that billing is enabled for your Google Cloud project.
Enable the BigQuery API.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.
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.

Console

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).
- Leave the remaining default settings as they are, and click Create dataset.

bq

To create a new dataset, use the bq mk command with the --location flag. For a full list of possible parameters, see the bq mk --dataset command reference.

Create a dataset named bqml_tutorial with the data location set to US and a description of BigQuery ML tutorial dataset:
```
bq --location=US mk -d \
 --description "BigQuery ML tutorial dataset." \
 bqml_tutorial
```
Instead of using the --dataset flag, the command uses the -d shortcut. If you omit -d and --dataset, the command defaults to creating a dataset.
Confirm that the dataset was created:
```
bqls
```

API

Call the datasets.insert method with a defined dataset resource.

{
"datasetReference":{
"datasetId":"bqml_tutorial"
}
}

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 ADC for a local development environment.

importgoogle.cloud.bigquery
bqclient = google.cloud.bigquery .Client ()
bqclient.create_dataset ("bqml_tutorial", exists_ok=True)

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:

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

Go to BigQuery

In the query editor, run the following query:

CREATEORREPLACEMODEL`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_gISNOTNULL;

It takes about 30 seconds to create the penguins_model model.

To see the model, follow these steps:
1. In the left pane, click Explorer:
  
  Highlighted button for the Explorer pane.
  
  If you don't see the left pane, click Expand left pane to open the pane.
2. In the Explorer pane, expand your project and click Datasets.
3. Click the bqml_tutorial dataset.
4. Click the Models tab.

BigQuery DataFrames

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

frombigframes.ml.linear_modelimport LinearRegression
importbigframes.pandasasbpd
# 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, follow these steps:

In the left pane, click Explorer:

Highlighted button for the Explorer pane.
In the Explorer pane, expand your project and click Datasets.
Click the bqml_tutorial dataset.
Click the Models tab.

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:

In the left pane, click Explorer:

Highlighted button for the Explorer pane.
In the Explorer pane, expand your project and click Datasets.
Click the bqml_tutorial dataset.
Click the Models tab.
To open the model information pane, click penguins_model.
Click the Training tab, and then click Table. The results should look similar to the following:

ML.TRAINING_INFO output

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(MODELbqml_tutorial.penguins_model);

Run the ML.EVALUATE query:

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

Go to BigQuery

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_gISNOTNULL));

BigQuery DataFrames

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

importbigframes.pandasasbpd
# 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:

ML.EVALUATE output

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:

ML.EVALUATE output

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:

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

Go to BigQuery

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`
WHEREisland='Biscoe'));

BigQuery DataFrames

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC 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:

ML.PREDICT output

Explain the prediction results

SQL

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:

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

Go to BigQuery

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`
WHEREisland='Biscoe'),
STRUCT(3astop_k_features));

The results should look similar to the following:

ML.EXPLAIN_PREDICT output

BigQuery DataFrames

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

# Use 'predict_explain' function to understand why the model is generating these prediction results.
# 'predict_explain'is an extended version of the 'predict' function that not only outputs prediction results, but also outputs additional columns to explain the prediction results.
# Using the trained model and utilizing data specific to Biscoe Island, explain the predictions of the top 3 features
explained = model.predict_explain(biscoe_data, top_k_features=3)
# Expected results:
# predicted_body_mass_g top_feature_attributions	 baseline_prediction_value	prediction_value	approximation_error	 species	 island	culmen_length_mm	culmen_depth_mm	flipper_length_mm	body_mass_g	 sex
# 0	 5413.510134	 [{'feature': 'island', 'attribution': 7348.877...	-5320.222128	 5413.510134	 0.0	 Gentoo penguin (Pygoscelis papua)	Biscoe	 45.2	 16.4	 223.0	 5950.0	 MALE
# 1	 4768.351092 [{'feature': 'island', 'attribution': 7348.877...	-5320.222128	 4768.351092	 0.0	 Gentoo penguin (Pygoscelis papua)	Biscoe	 46.5	 14.5	 213.0	 4400.0	 FEMALE
# 2	 3235.896372	 [{'feature': 'island', 'attribution': 7348.877...	-5320.222128	 3235.896372	 0.0	 Adelie Penguin (Pygoscelis adeliae)	Biscoe	 37.7	 16.0 183.0	 3075.0	 FEMALE
# 3	 5349.603734	 [{'feature': 'island', 'attribution': 7348.877...	-5320.222128	 5349.603734	 0.0	 Gentoo penguin (Pygoscelis papua)	Biscoe	 46.4	 15.6	 221.0	 5000.0	 MALE
# 4	 4637.165037	 [{'feature': 'island', 'attribution': 7348.877...	-5320.222128	 4637.165037	 0.0	 Gentoo penguin (Pygoscelis papua)	Biscoe	 46.1	 13.2	 211.0	 4500.0	 FEMALE

For linear regression models, Shapley values are used to generate feature attribution values for each feature in the model. The output includes the top three feature attributions per row of the penguins table because top_k_features was set to 3. 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

SQL

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:

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

Go to BigQuery

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

#standardSQL
CREATEORREPLACEMODEL`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_gISNOTNULL;

In the query editor, run the following query to get global explanations:
```
SELECT
*
FROM
ML.GLOBAL_EXPLAIN(MODEL`bqml_tutorial.penguins_model`)
```
The results should look similar to the following:

ML.GLOBAL_EXPLAIN output

BigQuery DataFrames

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

# To use the `global_explain()` function, the model must be recreated with `enable_global_explain` set to `True`.
model = LinearRegression(enable_global_explain=True)
# The model must the be fitted before it can be saved to BigQuery and then explained.
training_data = bq_df.dropna(subset=["body_mass_g"])
X = training_data.drop(columns=["body_mass_g"])
y = training_data[["body_mass_g"]]
model.fit(X, y)
model.to_gbq("bqml_tutorial.penguins_model", replace=True)
# Explain the model
explain_model = model.global_explain()
# Expected results:
# attribution
# feature
# island	 5737.315921
# species	 4073.280549
# sex	 622.070896
# flipper_length_mm	 193.612051
# culmen_depth_mm	 117.084944
# culmen_length_mm	 94.366793

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:

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

Go to the BigQuery page
In the navigation, click the bqml_tutorial dataset you created.
Click Delete dataset on the right side of the window. This action deletes the dataset, the table, and all the data.
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:

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.

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.