QuickStartΒΆ

  1. Install the package

(venv) $ pip install xailens

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

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)

  1. Start the dashboard

(venv) $ xailens-dash