mlflow--mlflow

The examples in this directory demonstrate how to use the mlflow.evaluate() API. Specifically, they show how to evaluate a PyFunc model on a specified dataset using the builtin default evaluator and specified extra metrics, where the resulting metrics & artifacts are logged to MLflow Tracking. They also show how to specify validation thresholds for the resulting metrics to validate the quality of your model. See full list of examples below:

Example evaluate_on_binary_classifier.py evaluates an xgboost XGBClassifier model on dataset loaded by shap.datasets.adult.
Example evaluate_on_multiclass_classifier.py evaluates a scikit-learn LogisticRegression model on dataset generated by sklearn.datasets.make_classification.
Example evaluate_on_regressor.py evaluate as scikit-learn LinearRegression model on dataset loaded by sklearn.datasets.load_diabetes
Example evaluate_with_custom_metrics.py evaluates a scikit-learn LinearRegression model with a custom metric function on dataset loaded by sklearn.datasets.load_diabetes
Example evaluate_with_custom_metrics_comprehensive.py evaluates a scikit-learn LinearRegression model with a comprehensive list of custom metric functions on dataset loaded by sklearn.datasets.load_diabetes
Example evaluate_with_model_validation.py trains both a candidate xgboost XGBClassifier model and a baseline DummyClassifier model on dataset loaded by shap.datasets.adult. Then, it validates the candidate model against specified thresholds on both builtin and extra metrics and the dummy model.

Prerequisites

pip install scikit-learn xgboost shap>=0.40 matplotlib

How to run the examples

Run in this directory with Python.

python evaluate_on_binary_classifier.py
python evaluate_on_multiclass_classifier.py
python evaluate_on_regressor.py
python evaluate_with_custom_metrics.py
python evaluate_with_custom_metrics_comprehensive.py
python evaluate_with_model_validation.py