Module dalex.wrappers

Classes

class ShapWrapper (type)

Explanation wrapper for the shap package

This object uses the shap package to create the model explanation. See ttps://github.com/slundberg/shap

Parameters

type : {'predict_parts', 'model_parts'}

Method type for calculations.

Attributes

result : list or numpy.ndarray

Calculated shap values for new_observation data.

shap_explainer : {shap.TreeExplainer, shap.DeepExplainer, shap.GradientExplainer, shap.LinearExplainer, shap.KernelExplainer}

Explainer object from the shap package.

shap_explainer_type : {'TreeExplainer', 'DeepExplainer',

'GradientExplainer', 'LinearExplainer', 'KernelExplainer'} String name of the Explainer class.

new_observation : pandas.Series or pandas.DataFrame

Observations for which the shap values will be calculated (later stored in result).

type : {'predict_parts', 'model_parts'}

Method type for calculations.

Notes

• https://github.com/slundberg/shap

Expand source code Browse git

class ShapWrapper(Explanation):
    """Explanation wrapper for the `shap` package

    This object uses the shap package to create the model explanation.
    See ttps://github.com/slundberg/shap

    Parameters
    ----------
    type : {'predict_parts', 'model_parts'}
        Method type for calculations.

    Attributes
    ----------
    result : list or numpy.ndarray
        Calculated shap values for `new_observation` data.
    shap_explainer : {shap.TreeExplainer, shap.DeepExplainer, shap.GradientExplainer, shap.LinearExplainer, shap.KernelExplainer}
        Explainer object from the `shap` package.
    shap_explainer_type : {'TreeExplainer', 'DeepExplainer',
        'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
        String name of the Explainer class.
    new_observation : pandas.Series or pandas.DataFrame
        Observations for which the shap values will be calculated
        (later stored in `result`).
    type : {'predict_parts', 'model_parts'}
        Method type for calculations.

    Notes
    ----------
    - https://github.com/slundberg/shap
    """

    def __init__(self, type):
        self.shap_explainer = None
        self.type = type
        self.result = None
        self.new_observation = None
        self.shap_explainer_type = None

    def fit(self,
            explainer,
            new_observation,
            shap_explainer_type=None,
            **kwargs):
        """Calculate the result of explanation

        Fit method makes calculations in place and changes the attributes.

        Parameters
        -----------
        explainer : Explainer object
            Model wrapper created using the Explainer class.
        new_observation : pd.Series or np.ndarray
            An observation for which a prediction needs to be explained.
        shap_explainer_type : {'TreeExplainer', 'DeepExplainer', 'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
            String name of the Explainer class (default is `None`, which automatically
            chooses an Explainer to use).
        kwargs: dict
            Keyword parameters passed to the `shapley_values` method.

        Returns
        -----------
        None
        """
        from shap import TreeExplainer, DeepExplainer, GradientExplainer, LinearExplainer, KernelExplainer

        checks.check_compatibility(explainer)
        shap_explainer_type = checks.check_shap_explainer_type(shap_explainer_type, explainer.model)

        if self.type == 'predict_parts':
            new_observation = checks.check_new_observation_predict_parts(new_observation, explainer)

        if shap_explainer_type == "TreeExplainer":
            try:
                self.shap_explainer = TreeExplainer(explainer.model, explainer.data.values)
            except: # https://github.com/ModelOriented/DALEX/issues/371
                self.shap_explainer = TreeExplainer(explainer.model)
        elif shap_explainer_type == "DeepExplainer":
            self.shap_explainer = DeepExplainer(explainer.model, explainer.data.values)
        elif shap_explainer_type == "GradientExplainer":
            self.shap_explainer = GradientExplainer(explainer.model, explainer.data.values)
        elif shap_explainer_type == "LinearExplainer":
            self.shap_explainer = LinearExplainer(explainer.model, explainer.data.values)
        elif shap_explainer_type == "KernelExplainer":
            self.shap_explainer = KernelExplainer(
                lambda x: explainer.predict(x), explainer.data.values
            )

        self.result = self.shap_explainer.shap_values(new_observation.values, **kwargs)
        self.new_observation = new_observation
        self.shap_explainer_type = shap_explainer_type

    def plot(self, **kwargs):
        """Plot the Shap Wrapper

        Parameters
        ----------
        kwargs : dict
            Keyword arguments passed to one of the:
                - shap.force_plot when type is `'predict_parts'`
                - shap.summary_plot when type is `'model_parts'`
            Exceptions are: `base_value`, `shap_values`,
            `features` and `feature_names`.

        Returns
        -----------
        None

        Notes
        --------
        - https://github.com/slundberg/shap
        """
        from shap import force_plot, summary_plot
        
        if self.type == 'predict_parts':
            if isinstance(self.shap_explainer.expected_value, (np.ndarray, list)):
                base_value = self.shap_explainer.expected_value[1]
            else:
                base_value = self.shap_explainer.expected_value

            shap_values = self.result[1] if isinstance(self.result, list) else self.result
            force_plot(base_value=base_value,
                       shap_values=shap_values,
                       features=self.new_observation.values,
                       feature_names=self.new_observation.columns,
                       matplotlib=True,
                       **kwargs)
        elif self.type == 'model_parts':
            summary_plot(shap_values=self.result,
                         features=self.new_observation,
                         **kwargs)

Ancestors

• dalex._explanation.Explanation
• abc.ABC

Methods

def fit(self, explainer, new_observation, shap_explainer_type=None, **kwargs)

Calculate the result of explanation

Fit method makes calculations in place and changes the attributes.

Parameters

explainer : Explainer object

Model wrapper created using the Explainer class.

new_observation : pd.Series or np.ndarray

An observation for which a prediction needs to be explained.

shap_explainer_type : {'TreeExplainer', 'DeepExplainer', 'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}

String name of the Explainer class (default is None, which automatically chooses an Explainer to use).

kwargs : dict

Keyword parameters passed to the shapley_values method.

Returns

None

 

Expand source code Browse git

def fit(self,
        explainer,
        new_observation,
        shap_explainer_type=None,
        **kwargs):
    """Calculate the result of explanation

    Fit method makes calculations in place and changes the attributes.

    Parameters
    -----------
    explainer : Explainer object
        Model wrapper created using the Explainer class.
    new_observation : pd.Series or np.ndarray
        An observation for which a prediction needs to be explained.
    shap_explainer_type : {'TreeExplainer', 'DeepExplainer', 'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
        String name of the Explainer class (default is `None`, which automatically
        chooses an Explainer to use).
    kwargs: dict
        Keyword parameters passed to the `shapley_values` method.

    Returns
    -----------
    None
    """
    from shap import TreeExplainer, DeepExplainer, GradientExplainer, LinearExplainer, KernelExplainer

    checks.check_compatibility(explainer)
    shap_explainer_type = checks.check_shap_explainer_type(shap_explainer_type, explainer.model)

    if self.type == 'predict_parts':
        new_observation = checks.check_new_observation_predict_parts(new_observation, explainer)

    if shap_explainer_type == "TreeExplainer":
        try:
            self.shap_explainer = TreeExplainer(explainer.model, explainer.data.values)
        except: # https://github.com/ModelOriented/DALEX/issues/371
            self.shap_explainer = TreeExplainer(explainer.model)
    elif shap_explainer_type == "DeepExplainer":
        self.shap_explainer = DeepExplainer(explainer.model, explainer.data.values)
    elif shap_explainer_type == "GradientExplainer":
        self.shap_explainer = GradientExplainer(explainer.model, explainer.data.values)
    elif shap_explainer_type == "LinearExplainer":
        self.shap_explainer = LinearExplainer(explainer.model, explainer.data.values)
    elif shap_explainer_type == "KernelExplainer":
        self.shap_explainer = KernelExplainer(
            lambda x: explainer.predict(x), explainer.data.values
        )

    self.result = self.shap_explainer.shap_values(new_observation.values, **kwargs)
    self.new_observation = new_observation
    self.shap_explainer_type = shap_explainer_type

def plot(self, **kwargs)

Plot the Shap Wrapper

Parameters

kwargs : dict

Keyword arguments passed to one of the: - shap.force_plot when type is 'predict_parts' - shap.summary_plot when type is 'model_parts' Exceptions are: base_value, shap_values, features and feature_names.

Returns

None

 

Notes

• https://github.com/slundberg/shap

Expand source code Browse git

def plot(self, **kwargs):
    """Plot the Shap Wrapper

    Parameters
    ----------
    kwargs : dict
        Keyword arguments passed to one of the:
            - shap.force_plot when type is `'predict_parts'`
            - shap.summary_plot when type is `'model_parts'`
        Exceptions are: `base_value`, `shap_values`,
        `features` and `feature_names`.

    Returns
    -----------
    None

    Notes
    --------
    - https://github.com/slundberg/shap
    """
    from shap import force_plot, summary_plot
    
    if self.type == 'predict_parts':
        if isinstance(self.shap_explainer.expected_value, (np.ndarray, list)):
            base_value = self.shap_explainer.expected_value[1]
        else:
            base_value = self.shap_explainer.expected_value

        shap_values = self.result[1] if isinstance(self.result, list) else self.result
        force_plot(base_value=base_value,
                   shap_values=shap_values,
                   features=self.new_observation.values,
                   feature_names=self.new_observation.columns,
                   matplotlib=True,
                   **kwargs)
    elif self.type == 'model_parts':
        summary_plot(shap_values=self.result,
                     features=self.new_observation,
                     **kwargs)