semvec/pypi-packages huggingface dataset:
mlflow-xgboost-proba 0.3.1
MLflow XGBoost flavour with probabilities
License: Apache-2.0
Requires Python: <4.0,>=3.11

# mlflow-xgboost-proba

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MLflow XGBoost flavour with probabilities

This package implements `mlflow_xgboost_proba` MLflow flavour, which allows to run `predict_proba` method of `xgboost` models during inference with MLflow `mlflow models serve` CLI command.

Implementation is based on [mlflow.xgboost](https://github.com/mlflow/mlflow/blob/master/mlflow/xgboost/__init__.py) module, which is copied and modified to have the wrapper with `predict_proba` method and `predict` method calling `predict_proba` by default.

The API of the module is identical to [mlflow.xgboost](https://mlflow.org/docs/latest/python_api/mlflow.xgboost.html), only without support of autologging.

- **Github repository**: <https://github.com/sergray/mlflow-xgboost-proba/>

## Usage

Install package with `pip install mlflow-xgboost-proba`.

Prepare XGBoost model and save as MLflow model:

```python
from xgboost import XGBClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

import mlflow_xgboost_proba

# Prepare training dataset
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data["data"], data["target"], test_size=0.2)
# Prepare XGBoost model
xgb_model = XGBClassifier(n_estimators=2, max_depth=2, learning_rate=1, objective="binary:logistic")
xgb_model.fit(X_train, y_train)
# Save XGBoost model as MLflow model
mlflow_xgboost_proba.save_model(xgb_model, "mlflow_xgb_iris_classifier")
```

Run model inference with the probabilities using MLflow:

```shell
mlflow models serve --model-uri mlflow_xgb_iris_classifier --env-manager local
```

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Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).