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External File

You have a model file on disk - a pickle, joblib, or CatBoost .cbm file - and you want to score your data with it. The External File node loads the file and gives you a code editor to apply it.

When to use

  • Your model is a standalone file not tracked in MLflow (e.g. a .pkl from a colleague or a vendor model).
  • You need to load a JSON lookup file and apply it with custom logic.
  • If your models are managed in MLflow with versioning, use Model Score instead.

This node accepts a single input.

Config Description
path Required. Path to the file (.pkl, .json, .joblib, .cbm)
fileType Required. "pickle", "json", "joblib", or "catboost"
modelClass "classifier" or "regressor" (CatBoost only)
code Required. Code that uses the loaded object (available as obj) and the input data (available as df) 
feature_columns = ["driver_age", "vehicle_age", "area"]  # columns your model was trained on
predictions = obj.predict(df.select(feature_columns).to_pandas())
df = df.with_columns(pl.Series("prediction", predictions))
return df

Reading the code:

Expression What it does
obj The loaded file (your model, lookup table, etc.)
df The input data as a table (dataframe)
obj.predict(...) Asks the model to produce predictions
feature_columns = [...] A list of column names your model was trained on - replace with your own
df.select(feature_columns) Picks those columns from the table
.to_pandas() Converts the data to the format most models expect - you'll see this in most scoring code
pl.Series("prediction", predictions) Wraps the results as a new column called "prediction"
df.with_columns(...) Adds the new column to the table

Always return the result

Your code must end with return df to pass the result to the next node.

JSON lookup example

If your external file is a JSON dictionary (e.g. area factors), you can use it as a lookup table:

# obj is a dict loaded from a JSON file, e.g. {"London": 1.25, "Rural": 0.85}
df = df.with_columns(
    pl.col("area").replace(obj).alias("area_factor")
)
return df

See also: