mlflow.sklearn

The mlflow.sklearn module provides an API for logging and loading scikit-learn models. This module exports scikit-learn models with the following flavors:

Python (native) pickle format: This is the main flavor that can be loaded back into scikit-learn.
mlflow.pyfunc: Produced for use by generic pyfunc-based deployment tools and batch inference.

mlflow.sklearn.get_default_conda_env(include_cloudpickle=False)

Returns:	The default Conda environment for MLflow Models produced by calls to `save_model()` and `log_model()`.

mlflow.sklearn.load_model(model_uri)

Load a scikit-learn model from a local file or a run.

Parameters:

model_uri –

The location, in URI format, of the MLflow model, for example:

/Users/me/path/to/local/model
relative/path/to/local/model
s3://my_bucket/path/to/model
runs:/<mlflow_run_id>/run-relative/path/to/model

For more information about supported URI schemes, see Referencing Artifacts.

Returns: A scikit-learn model.

>>> import mlflow.sklearn
>>> sk_model = mlflow.sklearn.load_model("runs:/96771d893a5e46159d9f3b49bf9013e2/"sk_models")
>>> #use Pandas DataFrame to make predictions
>>> pandas_df = ...
>>> predictions = sk_model.predict(pandas_df)

mlflow.sklearn.log_model(sk_model, artifact_path, conda_env=None, serialization_format='cloudpickle')

Log a scikit-learn model as an MLflow artifact for the current run.

Parameters:

sk_model – scikit-learn model to be saved.
artifact_path – Run-relative artifact path.
conda_env –
Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_default_conda_env() environment is added to the model. The following is an example dictionary representation of a Conda environment:
```
{
    'name': 'mlflow-env',
    'channels': ['defaults'],
    'dependencies': [
        'python=3.7.0',
        'scikit-learn=0.19.2'
    ]
}
```
serialization_format – The format in which to serialize the model. This should be one of the formats listed in mlflow.sklearn.SUPPORTED_SERIALIZATION_FORMATS. The Cloudpickle format, mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE, provides better cross-system compatibility by identifying and packaging code dependencies with the serialized model.

>>> import mlflow
>>> import mlflow.sklearn
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> iris = load_iris()
>>> sk_model = tree.DecisionTreeClassifier()
>>> sk_model = sk_model.fit(iris.data, iris.target)
>>> #set the artifact_path to location where experiment artifacts will be saved
>>> #log model params
>>> mlflow.log_param("criterion", sk_model.criterion)
>>> mlflow.log_param("splitter", sk_model.splitter)
>>> #log model
>>> mlflow.sklearn.log_model(sk_model, "sk_models")

mlflow.sklearn.save_model(sk_model, path, conda_env=None, mlflow_model=<mlflow.models.Model object>, serialization_format='cloudpickle')

Save a scikit-learn model to a path on the local file system.

Parameters:

sk_model – scikit-learn model to be saved.
path – Local path where the model is to be saved.
conda_env –
Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_default_conda_env()`() environment is added to the model. The following is an example dictionary representation of a Conda environment:
```
{
    'name': 'mlflow-env',
    'channels': ['defaults'],
    'dependencies': [
        'python=3.7.0',
        'scikit-learn=0.19.2'
    ]
}
```
mlflow_model – mlflow.models.Model this flavor is being added to.
serialization_format – The format in which to serialize the model. This should be one of the formats listed in mlflow.sklearn.SUPPORTED_SERIALIZATION_FORMATS. The Cloudpickle format, mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE, provides better cross-system compatibility by identifying and packaging code dependencies with the serialized model.

>>> import mlflow.sklearn
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> iris = load_iris()
>>> sk_model = tree.DecisionTreeClassifier()
>>> sk_model = sk_model.fit(iris.data, iris.target)
>>> #Save the model in cloudpickle format
>>> #set path to location for persistence
>>> sk_path_dir_1 = ...
>>> mlflow.sklearn.save_model(
>>>         sk_model, sk_path_dir_1,
>>>         serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE)
>>>
>>> #Save the model in pickle format
>>> #set path to location for persistence
>>> sk_path_dir_2 = ...
>>> mlflow.sklearn.save_model(sk_model, sk_path_dir_2,
>>>                           serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_PICKLE)