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.autolog()[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Enables autologging for scikit-learn estimators.

When is autologging performed?

Autologging is performed when you call:

  • estimator.fit()

  • estimator.fit_predict()

  • estimator.fit_transform()

Logged information
Parameters
  • Parameters obtained by estimator.get_params(deep=True). Note that get_params is called with deep=True. This means when you fit a meta estimator that chains a series of estimators, the parameters of these child estimators are also logged.

Metrics
  • A training score obtained by estimator.score. Note that the training score is computed using parameters given to fit().

Tags
  • An estimator class name (e.g. “LinearRegression”).

  • A fully qualified estimator class name (e.g. “sklearn.linear_model._base.LinearRegression”).

Artifacts
How does autologging work for meta estimators?

When a meta estimator (e.g. Pipeline, GridSearchCV) calls fit(), it internally calls fit() on its child estimators. Autologging does NOT perform logging on these constituent fit() calls.

Parameter search

In addition to recording the information discussed above, autologging for parameter search meta estimators (GridSearchCV and RandomizedSearchCV) records child runs with metrics for each set of explored parameters, as well as artifacts and parameters for the best model (if available).

Supported estimators

Example

from pprint import pprint
import numpy as np
import sklearn.linear_model
import mlflow

# enable autologging
mlflow.sklearn.autolog()

# prepare training data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3

# train a model
with mlflow.start_run() as run:
    reg = sklearn.linear_model.LinearRegression().fit(X, y)

def fetch_logged_data(run_id):
    client = mlflow.tracking.MlflowClient()
    data = client.get_run(run_id).data
    tags = {k: v for k, v in data.tags.items() if not k.startswith("mlflow.")}
    artifacts = [f.path for f in client.list_artifacts(run_id, "model")]

# fetch logged data
params, metrics, tags, artifacts = fetch_logged_data(run._info.run_id)

pprint(params)
# {'copy_X': 'True',
#  'fit_intercept': 'True',
#  'n_jobs': 'None',
#  'normalize': 'False'}

pprint(metrics)
# {'training_score': 1.0}

pprint(tags)
# {'estimator_class': 'sklearn.linear_model._base.LinearRegression',
#  'estimator_name': 'LinearRegression'}

pprint(artifacts)
# ['model/MLmodel', 'model/conda.yaml', 'model/model.pkl']
mlflow.sklearn.get_default_conda_env(include_cloudpickle=False)[source]
Returns

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

mlflow.sklearn.load_model(model_uri)[source]

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

  • models:/<model_name>/<model_version>

  • models:/<model_name>/<stage>

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

Returns

A scikit-learn model.

Example
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', registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None)[source]

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 decsribes 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.

  • registered_model_name – (Experimental) If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.

  • signature

    (Experimental) ModelSignature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:

    from mlflow.models.signature import infer_signature
    train = df.drop_column("target_label")
    predictions = ... # compute model predictions
    signature = infer_signature(train, predictions)
    

  • input_example – (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.

Example
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=None, serialization_format='cloudpickle', signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None)[source]

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 decsribes 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_modelmlflow.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.

  • signature

    (Experimental) ModelSignature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:

    from mlflow.models.signature import infer_signature
    train = df.drop_column("target_label")
    predictions = ... # compute model predictions
    signature = infer_signature(train, predictions)
    

  • input_example – (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.

Example
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)