The ML.WEIGHTS function

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ML.WEIGHTS function

The ML.WEIGHTS function allows you to see the underlying weights used by a model during prediction. This function applies to linear & logistic regression models and matrix factorization models.

For information about model weights support in BigQuery ML, see Model weights overview.

For information about supported model types of each SQL statement and function, and all supported SQL statements and functions for each model type, read End-to-end user journey for each model.

ML.WEIGHTS syntax

In the following example syntax, standardize is an optional parameter that determines whether the model weights should be standardized to assume that all features have a mean of zero and a standard deviation of one. Standardizing the weights allows the absolute magnitude of the weights to be compared to each other. The default value is false. The value that is supplied must be the only field in a STRUCT.

ML.WEIGHTS(MODEL `project-id.dataset.model`
          [, STRUCT(<T> as standardize)])

Replace the following:

  • project-id: Your project ID.
  • dataset: The BigQuery dataset that contains the model.
  • model: The name of the model.

ML.WEIGHTS output

ML.WEIGHTS has different output columns for different models.

Linear & logistic regression models

For linear & logistic regression models, ML.WEIGHTS returns the following columns:

  • processed_input — The name of the model feature input. The value of this column matches the name of the column in the SELECT statement used during training.
  • weight — The weight of each feature. For numerical columns, weight contains a value and the category_weights column is NULL. For non-numeric columns that are converted to one-hot encoding, the weight column is NULL and the category_weights column is an ARRAY of category names and weights for each category.
  • category_weights.category — The category name if the input column is non-numeric.
  • category_weights.weight — The category's weight if the input column is non-numeric.
  • class_label — For multiclass models, class_label is the label for a given weight. The output includes one row per <class_label, processed_input> combination.

If the TRANSFORM clause was present in the CREATE MODEL statement that created model, ML.WEIGHTS outputs the weights of TRANSFORM output features. The weights are denormalized by default, with the option to get normalized weights, exactly like models that are created without TRANSFORM.

Matrix factorization models

For matrix factorization models, ML.WEIGHTS returns the following columns:

  • processed_input: The name of the user or item column. The value of this column matches the name of the column in the SELECT statement used during training.
  • feature: The names of the specific users or items used during training.
  • factor_weights: An ARRAY of factors and weights for each factor.
  • factor_weights.factor: A latent factor from training. An INT64 from 1 to NUM_FACTORS.
  • factor_weights.weight: The weight of the respective factor and feature.
  • intercept: The intercept or bias term for a feature.

Finally, there is an additional row in the table that contains the global__intercept__ calculated from the input data. This row has a NULL value for processed_input and factor_weights. For implicit feedback models, global__intercept__ is always 0.

ML.WEIGHTS examples

ML.WEIGHTS without standardization

The following example retrieves weight information from mymodel in mydataset. The dataset is in your default project.

The query returns the weights associated with each one-hot encoded category for the input column input_col.

SELECT
  category,
  weight
FROM
  UNNEST((
    SELECT
      category_weights
    FROM
      ML.WEIGHTS(MODEL `mydataset.mymodel`)
    WHERE
      processed_input = 'input_col'))

This command uses the UNNEST function because the category_weights column is a nested repeated column.

ML.WEIGHTS with standardization

The following example retrieves weight information from mymodel in mydataset. The dataset is in your default project.

The query retrieves standardized weights, which assume all features have a mean of zero and a standard deviation of one.

SELECT
  *
FROM
  ML.WEIGHTS(MODEL `mydataset.mymodel`,
    STRUCT(true AS standardize))