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Python Models

Profiles model for generating predictive features.

2 minute read

Predictive features are generated using a new type of Profiles models called python_model.

Using a Python model

There are two key steps involved in using a Python model - train and predict.

To use a Python model, you need to modify the train and predict blocks in your profiles.yaml file. The following snippet highlights these blocks:

# This is a sample file, for detailed reference see: https://rudderlabs.github.io/pywht/
models:
  - name: shopify_churn
    model_type: python_model
    model_spec:
      occurred_at_col: insert_ts
      entity_key: user
      validity_time: 24h # 1 day
      py_repo_url: https://github.com/rudderlabs/rudderstack-profiles-classifier.git
      predict:
        inputs:
          - packages/feature_table/models/shopify_user_features
        config:
          outputs:
            column_names:
              percentile: &percentile_name percentile_churn_score_7_days
              score: churn_score_7_days
            feature_meta_data: &feature_meta_data
              features:
                - name: *percentile_name
                  description: 'Percentile of churn score. Higher the percentile, higher the probability of churn'
      train:
        file_extension: .json
        file_validity: 60m
        inputs:
          - packages/feature_table/models/shopify_user_features
        config:
          data:
            label_column: is_churned_7_days
            label_value: 1
            prediction_horizon_days: 7
            model_name: 'shopify_user_features'
            
      <<: *feature_meta_data

In a Python model, the actual logic resides in a remote location defined by the key py_repo_url. This need not be modified for setting up a predictive feature.

In the train block, you can define the label columns by pointing to the entity_var defined in the feature table model. You also need to define the following:

Expected label value for users who performed the event.
Horizon days, that is, number of days in advance when the predictions need to be made.
Feature table model name defined for your predictive features project. See Set up a feature table model for more information.
Criteria for eligible users so the model need not be used to predict for all users. You can set this criteria by defining a SQL statement referring to the different entity_vars. For example:

eligible_users: lower(country) = 'us' and amount_spent_overall > 0

The above example ensures that the model is trained only on the paying users from the US. Also, the model makes predictions only on this set of users.

Note that:
The eligible_users key should be added as one more parameter in the data configuration.
To build a model based on all available users, you can leave the eligible_users parameter blank.

Optional: Run Python model locally

If you are using the RudderStack dashboard for running the models, you can skip this step. However, note that RudderStack runs the models locally a few time to get the correct setup.

To run Python models locally, you need to set up a Python environment with the required packages and add the Python path to the siteconfig.yaml file.

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