mlflow.shap

mlflow.shap.get_default_conda_env()[source]
Returns

The default Conda environment for MLflow Models produced by calls to save_explainer() and log_explainer().

mlflow.shap.get_default_pip_requirements()[source]

A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_explainer() and log_explainer() produce a pip environment that, at minimum, contains these requirements.

mlflow.shap.get_underlying_model_flavor(model)[source]

Find the underlying models flavor.

Parameters

model – underlying model of the explainer.

mlflow.shap.load_explainer(model_uri)[source]

Load a SHAP explainer 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 SHAP explainer.

mlflow.shap.log_explainer(explainer, artifact_path, serialize_model_using_mlflow=True, conda_env=None, code_paths=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=None)[source]

Log an SHAP explainer as an MLflow artifact for the current run.

Parameters

  • explainer – SHAP explainer to be saved.

  • artifact_path – Run-relative artifact path.

  • serialize_model_using_mlflow – When set to True, MLflow will extract the underlying model and serialize it as an MLmodel, otherwise it uses SHAP’s internal serialization. Defaults to True. Currently MLflow serialization is only supported for models of ‘sklearn’ or ‘pytorch’ flavors.

  • conda_env –

    Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:

    {
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "shap==x.y.z"
            ],
        },
    ],
}

  • code_paths –

    A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

    For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.

  • registered_model_name – 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 –

    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 import infer_signature

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

  • input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.

  • await_registration_for – Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["shap", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

  • extra_pip_requirements –

    Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

    Warning

    The following arguments can’t be specified at the same time:

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.

  • metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

mlflow.shap.log_explanation(predict_function, features, artifact_path=None)[source]

Given a predict_function capable of computing ML model output on the provided features, computes and logs explanations of an ML model’s output. Explanations are logged as a directory of artifacts containing the following items generated by SHAP (SHapley Additive exPlanations).

  • Base values

  • SHAP values (computed using shap.KernelExplainer)

  • Summary bar plot (shows the average impact of each feature on model output)

Parameters

  • predict_function –

    A function to compute the output of a model (e.g. predict_proba method of scikit-learn classifiers). Must have the following signature:

    def predict_function(X) -> pred: ...

    • X: An array-like object whose shape should be (# samples, # features).

    • pred: An array-like object whose shape should be (# samples) for a regressor or (# classes, # samples) for a classifier. For a classifier, the values in pred should correspond to the predicted probability of each class.

    Acceptable array-like object types:

    • numpy.array

    • pandas.DataFrame

    • shap.common.DenseData

    • scipy.sparse matrix

  • features –

    A matrix of features to compute SHAP values with. The provided features should have shape (# samples, # features), and can be either of the array-like object types listed above.

    Note

    Background data for shap.KernelExplainer is generated by subsampling features with shap.kmeans. The background data size is limited to 100 rows for performance reasons.

  • artifact_path – The run-relative artifact path to which the explanation is saved. If unspecified, defaults to “model_explanations_shap”.

Returns

Artifact URI of the logged explanations.

Example 
import os

import numpy as np
import pandas as pd
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression

import mlflow
from mlflow import MlflowClient

# prepare training data
X, y = dataset = load_diabetes(return_X_y=True, as_frame=True)
X = pd.DataFrame(dataset.data[:50, :8], columns=dataset.feature_names[:8])
y = dataset.target[:50]

# train a model
model = LinearRegression()
model.fit(X, y)

# log an explanation
with mlflow.start_run() as run:
    mlflow.shap.log_explanation(model.predict, X)

# list artifacts
client = MlflowClient()
artifact_path = "model_explanations_shap"
artifacts = [x.path for x in client.list_artifacts(run.info.run_id, artifact_path)]
print("# artifacts:")
print(artifacts)

# load back the logged explanation
dst_path = client.download_artifacts(run.info.run_id, artifact_path)
base_values = np.load(os.path.join(dst_path, "base_values.npy"))
shap_values = np.load(os.path.join(dst_path, "shap_values.npy"))

print("\n# base_values:")
print(base_values)
print("\n# shap_values:")
print(shap_values[:3])
Output 
# artifacts:
['model_explanations_shap/base_values.npy',
 'model_explanations_shap/shap_values.npy',
 'model_explanations_shap/summary_bar_plot.png']

# base_values:
20.502000000000002

# shap_values:
[[ 2.09975523  0.4746513   7.63759026  0.        ]
 [ 2.00883109 -0.18816665 -0.14419184  0.        ]
 [ 2.00891772 -0.18816665 -0.14419184  0.        ]]
../_images/shap-ui-screenshot.png

Logged artifacts

mlflow.shap.save_explainer(explainer, path, serialize_model_using_mlflow=True, conda_env=None, code_paths=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, metadata=None)[source]

Save a SHAP explainer to a path on the local file system. Produces an MLflow Model containing the following flavors:

Parameters

  • explainer – SHAP explainer to be saved.

  • path – Local path where the explainer is to be saved.

  • serialize_model_using_mlflow – When set to True, MLflow will extract the underlying model and serialize it as an MLmodel, otherwise it uses SHAP’s internal serialization. Defaults to True. Currently MLflow serialization is only supported for models of ‘sklearn’ or ‘pytorch’ flavors.

  • conda_env –

    Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:

    {
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "shap==x.y.z"
            ],
        },
    ],
}

  • code_paths –

    A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

    For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.

  • mlflow_model – mlflow.models.Model this flavor is being added to.

  • signature –

    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 import infer_signature

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

  • input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["shap", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

  • extra_pip_requirements –

    Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

    Warning

    The following arguments can’t be specified at the same time:

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.

  • metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.