def explain_prediction_lightgbm(
        lgb, doc,
        vec=None,
        top=None,
        top_targets=None,
        target_names=None,
        targets=None,
        feature_names=None,
        feature_re=None,
        feature_filter=None,
        vectorized=False,
        ):
    """ Return an explanation of LightGBM prediction (via scikit-learn wrapper
    LGBMClassifier or LGBMRegressor) as feature weights.

    See :func:`eli5.explain_prediction` for description of
    ``top``, ``top_targets``, ``target_names``, ``targets``,
    ``feature_names``, ``feature_re`` and ``feature_filter`` parameters.

    ``vec`` is a vectorizer instance used to transform
    raw features to the input of the estimator ``xgb``
    (e.g. a fitted CountVectorizer instance); you can pass it
    instead of ``feature_names``.

    ``vectorized`` is a flag which tells eli5 if ``doc`` should be
    passed through ``vec`` or not. By default it is False, meaning that
    if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
    estimator. Set it to True if you're passing ``vec``,
    but ``doc`` is already vectorized.

    Method for determining feature importances follows an idea from
    http://blog.datadive.net/interpreting-random-forests/.
    Feature weights are calculated by following decision paths in trees
    of an ensemble.
    Each leaf has an output score, and expected scores can also be assigned
    to parent nodes.
    Contribution of one feature on the decision path is how much expected score
    changes from parent to child.
    Weights of all features sum to the output score of the estimator.
    """

    vec, feature_names = handle_vec(lgb, doc, vec, vectorized, feature_names)
    if feature_names.bias_name is None:
        # LightGBM estimators do not have an intercept, but here we interpret
        # them as having an intercept
        feature_names.bias_name = '<BIAS>'
    X = get_X(doc, vec, vectorized=vectorized)

    proba = predict_proba(lgb, X)
    weight_dicts = _get_prediction_feature_weights(lgb, X, _lgb_n_targets(lgb))
    x = get_X0(add_intercept(X))

    is_regression = isinstance(lgb, lightgbm.LGBMRegressor)
    is_multiclass = _lgb_n_targets(lgb) > 2
    names = lgb.classes_ if not is_regression else ['y']

    def get_score_weights(_label_id):
        _weights = _target_feature_weights(
            weight_dicts[_label_id],
            num_features=len(feature_names),
            bias_idx=feature_names.bias_idx,
        )
        _score = _get_score(weight_dicts[_label_id])
        return _score, _weights

    return get_decision_path_explanation(
        lgb, doc, vec,
        x=x,
        feature_names=feature_names,
        feature_filter=feature_filter,
        feature_re=feature_re,
        top=top,
        vectorized=vectorized,
        original_display_names=names,
        target_names=target_names,
        targets=targets,
        top_targets=top_targets,
        is_regression=is_regression,
        is_multiclass=is_multiclass,
        proba=proba,
        get_score_weights=get_score_weights,
     )
def explain_prediction_lightgbm ( lgb , doc , vec = None , top = None , top_targets = None , target_names = None , targets = None , feature_names = None , feature_re = None , feature_filter = None , vectorized = False , ) : vec , feature_names = handle_vec ( lgb , doc , vec , vectorized , feature_names ) if feature_names . bias_name is None : feature_names . bias_name = '<BIAS>' X = get_X ( doc , vec , vectorized = vectorized ) proba = predict_proba ( lgb , X ) weight_dicts = _get_prediction_feature_weights ( lgb , X , _lgb_n_targets ( lgb ) ) x = get_X0 ( add_intercept ( X ) ) is_regression = isinstance ( lgb , lightgbm . LGBMRegressor ) is_multiclass = _lgb_n_targets ( lgb ) > 2 names = lgb . classes_ if not is_regression else [ 'y' ] def get_score_weights ( _label_id ) : _weights = _target_feature_weights ( weight_dicts [ _label_id ] , num_features = len ( feature_names ) , bias_idx = feature_names . bias_idx , ) _score = _get_score ( weight_dicts [ _label_id ] ) return _score , _weights return get_decision_path_explanation ( lgb , doc , vec , x = x , feature_names = feature_names , feature_filter = feature_filter , feature_re = feature_re , top = top , vectorized = vectorized , original_display_names = names , target_names = target_names , targets = targets , top_targets = top_targets , is_regression = is_regression , is_multiclass = is_multiclass , proba = proba , get_score_weights = get_score_weights , )
Return an explanation of LightGBM prediction (via scikit-learn wrapper
    LGBMClassifier or LGBMRegressor) as feature weights.

    See :func:`eli5.explain_prediction` for description of
    ``top``, ``top_targets``, ``target_names``, ``targets``,
    ``feature_names``, ``feature_re`` and ``feature_filter`` parameters.

    ``vec`` is a vectorizer instance used to transform
    raw features to the input of the estimator ``xgb``
    (e.g. a fitted CountVectorizer instance); you can pass it
    instead of ``feature_names``.

    ``vectorized`` is a flag which tells eli5 if ``doc`` should be
    passed through ``vec`` or not. By default it is False, meaning that
    if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
    estimator. Set it to True if you're passing ``vec``,
    but ``doc`` is already vectorized.

    Method for determining feature importances follows an idea from
    http://blog.datadive.net/interpreting-random-forests/.
    Feature weights are calculated by following decision paths in trees
    of an ensemble.
    Each leaf has an output score, and expected scores can also be assigned
    to parent nodes.
    Contribution of one feature on the decision path is how much expected score
    changes from parent to child.
    Weights of all features sum to the output score of the estimator.