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Advanced BQML Features

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In this section, we will delve into the advanced features of BigQuery Machine Learning (BQML). These features allow you to perform more sophisticated machine learning tasks and optimize your models for better performance and accuracy.

Key Concepts

  1. Hyperparameter Tuning
  2. Model Export and Import
  3. Feature Engineering
  4. Model Explainability
  5. Advanced Model Types

  1. Hyperparameter Tuning

Hyperparameter tuning is the process of optimizing the parameters that govern the training process of a machine learning model. BQML provides built-in support for hyperparameter tuning to help you find the best set of parameters for your model.

Example

CREATE MODEL `my_dataset.my_model`
OPTIONS(
  model_type='linear_reg',
  max_iterations=50,
  learn_rate=0.1,
  l1_reg=0.01,
  l2_reg=0.01
) AS
SELECT
  feature1,
  feature2,
  label
FROM
  `my_dataset.my_table`;

Explanation

  • max_iterations: The maximum number of iterations for training.
  • learn_rate: The learning rate for the model.
  • l1_reg and l2_reg: Regularization parameters to prevent overfitting.

  1. Model Export and Import

BQML allows you to export trained models for use in other environments and import pre-trained models into BigQuery.

Exporting a Model

EXPORT MODEL `my_dataset.my_model`
OPTIONS(
  uri='gs://my_bucket/my_model',
  format='tensorflow_saved_model'
);

Importing a Model

CREATE MODEL `my_dataset.imported_model`
OPTIONS(
  model_type='tensorflow',
  model_path='gs://my_bucket/my_model'
);

Explanation

  • uri: The Google Cloud Storage URI where the model will be exported.
  • format: The format of the exported model, such as tensorflow_saved_model.
  • model_path: The path to the pre-trained model in Google Cloud Storage.

  1. Feature Engineering

Feature engineering involves creating new features or modifying existing ones to improve the performance of your machine learning model.

Example

CREATE OR REPLACE MODEL `my_dataset.my_model`
OPTIONS(
  model_type='linear_reg'
) AS
SELECT
  feature1,
  feature2,
  feature1 * feature2 AS interaction_feature,
  label
FROM
  `my_dataset.my_table`;

Explanation

  • interaction_feature: A new feature created by multiplying feature1 and feature2.

  1. Model Explainability

Model explainability helps you understand how your model makes predictions. BQML provides tools to interpret the importance of different features.

Example

SELECT
  *
FROM
  ML.EXPLAIN_PREDICT(MODEL `my_dataset.my_model`,
    (
      SELECT
        feature1,
        feature2
      FROM
        `my_dataset.my_table`
    ));

Explanation

  • ML.EXPLAIN_PREDICT: A function that provides explanations for the predictions made by the model.

  1. Advanced Model Types

BQML supports various advanced model types, including time series models and deep neural networks.

Time Series Model Example

CREATE MODEL `my_dataset.time_series_model`
OPTIONS(
  model_type='ARIMA',
  time_series_timestamp_col='timestamp',
  time_series_data_col='value'
) AS
SELECT
  timestamp,
  value
FROM
  `my_dataset.time_series_table`;

Deep Neural Network Example

CREATE MODEL `my_dataset.dnn_model`
OPTIONS(
  model_type='dnn_classifier',
  hidden_units=[128, 64, 32],
  activation_fn='relu'
) AS
SELECT
  feature1,
  feature2,
  label
FROM
  `my_dataset.my_table`;

Explanation

  • ARIMA: A model type for time series forecasting.
  • dnn_classifier: A deep neural network classifier.
  • hidden_units: The number of units in each hidden layer.
  • activation_fn: The activation function used in the neural network.

Practical Exercise

Exercise

  1. Create a linear regression model with hyperparameter tuning.
  2. Export the trained model to Google Cloud Storage.
  3. Create a new feature by combining two existing features.
  4. Use ML.EXPLAIN_PREDICT to interpret the model's predictions.
  5. Create a time series model using ARIMA.

Solution

-- Step 1: Create a linear regression model with hyperparameter tuning
CREATE MODEL `my_dataset.my_model`
OPTIONS(
  model_type='linear_reg',
  max_iterations=50,
  learn_rate=0.1,
  l1_reg=0.01,
  l2_reg=0.01
) AS
SELECT
  feature1,
  feature2,
  label
FROM
  `my_dataset.my_table`;

-- Step 2: Export the trained model to Google Cloud Storage
EXPORT MODEL `my_dataset.my_model`
OPTIONS(
  uri='gs://my_bucket/my_model',
  format='tensorflow_saved_model'
);

-- Step 3: Create a new feature by combining two existing features
CREATE OR REPLACE MODEL `my_dataset.my_model`
OPTIONS(
  model_type='linear_reg'
) AS
SELECT
  feature1,
  feature2,
  feature1 * feature2 AS interaction_feature,
  label
FROM
  `my_dataset.my_table`;

-- Step 4: Use ML.EXPLAIN_PREDICT to interpret the model's predictions
SELECT
  *
FROM
  ML.EXPLAIN_PREDICT(MODEL `my_dataset.my_model`,
    (
      SELECT
        feature1,
        feature2
      FROM
        `my_dataset.my_table`
    ));

-- Step 5: Create a time series model using ARIMA
CREATE MODEL `my_dataset.time_series_model`
OPTIONS(
  model_type='ARIMA',
  time_series_timestamp_col='timestamp',
  time_series_data_col='value'
) AS
SELECT
  timestamp,
  value
FROM
  `my_dataset.time_series_table`;

Conclusion

In this section, we explored advanced features of BQML, including hyperparameter tuning, model export and import, feature engineering, model explainability, and advanced model types. These features enable you to build more sophisticated and accurate machine learning models in BigQuery. In the next module, we will look at real-world use cases of BigQuery to see how these concepts are applied in practice.

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