QuickStart
--------------

1. Install the package

.. code-block:: console

   (venv) $ pip install xailens

2 .Add at the end of your model model code:

.. code-block:: python

    import xailens

    # define the dataschema
    data_schema = xailens.DataSchema(
        instance_id_column="instance_uuid", # name of the unique identifier column for your instances
        target_column="target", # name of your target column, from y_test
        prediction_column="pred_value" # name of your prediction column, from y_pred
        )

    # your data and predictions
    model_data = xailens.ModelData(
        data_schema=data_schema, # DataSchema from above
        raw_instances=raw, # your raw data
        x_test=X_test, # your X_test dataframe
        y_pred=y_pred, # your y_pred dataframe
        y_test=y_test # your y_test dataframe
        )

    # overall context
    ctx = xailens.ModelContext(
        "logistic_regression", # model type, can be "logistic_regression", "xgboost" or "llm"
        ["coefficients"], # XAI methods to apply, options are "coefficients", "tree_shap", "llm_reasoning"
        "cleveland-heart-data", # identifier for your dataset
        model_data, # ModelData from above
        model=model, # your model (joblib file)
        model_dir_name="heart_lr", # directory name to save to (can be blank)
        model_display_name="Logistic Regression" # display name for your model, as it will appear in the dashboard
        )

    xailens.run(ctx)


3. Start the dashboard

.. code-block:: console

   (venv) $ xailens-dash