omnixai.explainers.tabular package

omnixai.explainers.tabular.base module

class omnixai.explainers.tabular.base.TabularExplainerMixin

Bases: object

class omnixai.explainers.tabular.base.TabularExplainer(training_data, predict_function, mode='classification', **kwargs)

Bases: ExplainerBase, TabularExplainerMixin

The base class of model-agnostic explainers for tabular data.

Parameters

  • training_data (Tabular) – The data used to initialize the explainer.

  • predict_function (Callable) – The prediction function corresponding to the model to explain. When the model is for classification, the outputs of the predict_function are the class probabilities. When the model is for regression, the outputs of the predict_function are the estimated values.

  • mode (str) – The task type, e.g., classification or regression.

  • kwargs – Additional parameters.

class omnixai.explainers.tabular.base.SklearnBase(mode='classification', cate_encoder=<omnixai.preprocessing.encode.OneHot object>, cont_encoder=<omnixai.preprocessing.normalize.Standard object>, target_encoder=<omnixai.preprocessing.encode.LabelEncoder object>, **kwargs)

Bases: ExplainerBase, BaseEstimator

The base class of model-specific explainers for scikit-learn models.

Parameters

  • mode (str) – The task type, e.g., classification or regression.

  • cate_encoder (TransformBase) – The encoder for categorical features, e.g., OneHot, Ordinal.

  • cont_encoder (TransformBase) – The encoder for continuous-valued features, e.g., Identity, Standard, MinMax, Scale.

  • target_encoder (TransformBase) – The encoder for targets/labels, e.g., Identity for regression, LabelEncoder for classification.

fit(training_data, train_size=0.8, **kwargs)

Trains the model given the training dataset.

Parameters

  • training_data (Tabular) – The training dataset.

  • train_size (float) – Used in train-test splits, i.e., the proportion of the training samples.

predict(X)

Predicts targets or labels.

Parameters

X (Tabular) – The test dataset.

Returns

The predictions.

Return type

np.ndarray

predict_proba(X)

Predicts class probabilities in classification.

Parameters

X (Tabular) – The test dataset.

Returns

The predicted class probabilities.

Return type

np.ndarray

class_names()

Returns the class names in classification.

Returns

The class names.

Return type

List

omnixai.explainers.tabular.auto module

class omnixai.explainers.tabular.auto.TabularExplainer(explainers, mode, data, model, preprocess=None, postprocess=None, params=None)

Bases: AutoExplainerBase

The class derived from AutoExplainerBase for tabular data, allowing users to choose multiple explainers and generate different explanations at the same time.

explainers = TabularExplainer(
    explainers=["lime", "shap", "mace", "pdp"],
    mode="classification",
    data=data,
    model=model,
    preprocess=preprocess_function,
    postprocess=None,
    params={
        "lime": {"kernel_width": 3},
        "shap": {"nsamples": 100},
        "mace": {"ignored_features": ["Sex", "Race", "Relationship", "Capital Loss"]}
    }
)
local_explanations = explainers.explain(x)
global_explanations = explainers.explain_global()
Parameters

  • explainers (Collection) – The names or alias of the explainers to use.

  • mode (str) – The task type, e.g. classification or regression.

  • data (Tabular) – The training data used to initialize explainers. data can be the training dataset for training the machine learning model. If the training dataset is large, data can be its subset by applying omnixai.sampler.tabular.Sampler.subsample.

  • model (Any) – The machine learning model to explain, which can be a scikit-learn model, a tensorflow model, a torch model, or a black-box prediction function.

  • preprocess (Optional[Callable]) – The preprocessing function that converts the raw input features into the inputs of model.

  • postprocess (Optional[Callable]) – The postprocessing function that transforms the outputs of model to a user-specific form, e.g., the predicted probability for each class.

  • params (Optional[Dict]) – A dict containing the additional parameters for initializing each explainer, e.g., params[“lime”] = {“param_1”: param_1, …}.

static list_explainers()

List the supported explainers.

Subpackages