alibi.explainers.partial_dependence

Constants

DEFAULT_DATA_PD

DEFAULT_DATA_PD: dict = {'feature_deciles': None, 'pd_values': None, 'ice_values': None, 'feature_val...

DEFAULT_META_PD

DEFAULT_META_PD: dict = {'name': None, 'type': ['blackbox'], 'explanations': ['global'], 'params': {}...

logger

logger: logging.Logger = <Logger alibi.explainers.partial_dependence (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

Kind

Inherits from: str, Enum

Enumeration of supported kind.

PartialDependence

Inherits from: PartialDependenceBase, Explainer, ABC, Base

Black-box implementation of partial dependence for tabular datasets. Supports multiple feature interactions.

Constructor

PartialDependence(self, predictor: Callable[[numpy.ndarray], numpy.ndarray], feature_names: Optional[List[str]] = None, categorical_names: Optional[Dict[int, List[str]]] = None, target_names: Optional[List[str]] = None, verbose: bool = False)

Name	Type	Default	Description
predictor	Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]		A prediction function which receives as input a numpy array of size N x F and outputs a numpy array of size N (i.e. (N, )) or N x T, where N is the number of input instances, F is the number of features and T is the number of targets.
feature_names	Optional[List[str]]	None	A list of feature names used for displaying results.
categorical_names	Optional[Dict[int, List[str]]]	None	Dictionary where keys are feature columns and values are the categories for the feature. Necessary to identify the categorical features in the dataset. An example for categorical_names would be:: category_map = {0: ["married", "divorced"], 3: ["high school diploma", "master's degree"]}
target_names	Optional[List[str]]	None	A list of target/output names used for displaying results.
verbose	bool	False	Whether to print the progress of the explainer.

Methods

explain

explain(X: numpy.ndarray, features: Optional[List[Union[int, Tuple[int, int]]]] = None, kind: Literal[average, individual, both] = 'average', percentiles: Tuple[float, float] = (0.0, 1.0), grid_resolution: int = 100, grid_points: Optional[Dict[int, Union[List[Any], numpy.ndarray]]] = None) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	numpy.ndarray
features	Optional[List[Union[int, Tuple[int, int]]]]	None
kind	Literal[average, individual, both]	'average'
percentiles	Tuple[float, float]	(0.0, 1.0)
grid_resolution	int	100
grid_points	Optional[Dict[int, Union[List[Any], numpy.ndarray]]]	None

Returns

• Type: alibi.api.interfaces.Explanation

PartialDependenceBase

Inherits from: Explainer, ABC, Base

Constructor

PartialDependenceBase(self, predictor: Union[sklearn.base.BaseEstimator, Callable[[numpy.ndarray], numpy.ndarray]], feature_names: Optional[List[str]] = None, categorical_names: Optional[Dict[int, List[str]]] = None, target_names: Optional[List[str]] = None, verbose: bool = False)

Name	Type	Default	Description
predictor	Union[sklearn.base.BaseEstimator, Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray]]		A sklearn estimator or a prediction function which receives as input a numpy array of size N x F and outputs a numpy array of size N (i.e. (N, )) or N x T, where N is the number of input instances, F is the number of features and T is the number of targets.
feature_names	Optional[List[str]]	None	A list of feature names used for displaying results.
categorical_names	Optional[Dict[int, List[str]]]	None	Dictionary where keys are feature columns and values are the categories for the feature. Necessary to identify the categorical features in the dataset. An example for categorical_names would be:: category_map = {0: ["married", "divorced"], 3: ["high school diploma", "master's degree"]}
target_names	Optional[List[str]]	None	A list of target/output names used for displaying results.
verbose	bool	False	Whether to print the progress of the explainer.

Methods

explain

explain(X: numpy.ndarray, features: Optional[List[Union[int, Tuple[int, int]]]] = None, kind: Literal[average, individual, both] = 'average', percentiles: Tuple[float, float] = (0.0, 1.0), grid_resolution: int = 100, grid_points: Optional[Dict[int, Union[List[Any], numpy.ndarray]]] = None) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	numpy.ndarray		A N x F tabular dataset used to calculate partial dependence curves. This is typically the training dataset or a representative sample.
features	Optional[List[Union[int, Tuple[int, int]]]]	None	An optional list of features or tuples of features for which to calculate the partial dependence. If not provided, the partial dependence will be computed for every single features in the dataset. Some example for features would be: [0, 2], [0, 2, (0, 2)], [(0, 2)], where 0 and 2 correspond to column 0 and 2 in X, respectively.
kind	Literal[average, individual, both]	'average'	If set to 'average', then only the partial dependence (PD) averaged across all samples from the dataset is returned. If set to 'individual', then only the individual conditional expectation (ICE) is returned for each data point from the dataset. Otherwise, if set to 'both', then both the PD and the ICE are returned.
percentiles	Tuple[float, float]	(0.0, 1.0)	Lower and upper percentiles used to limit the feature values to potentially remove outliers from low-density regions. Note that for features with not many data points with large/low values, the PD estimates are less reliable in those extreme regions. The values must be in [0, 1]. Only used with grid_resolution.
grid_resolution	int	100	Number of equidistant points to split the range of each target feature. Only applies if the number of unique values of a target feature in the reference dataset X is greater than the grid_resolution value. For example, consider a case where a feature can take the following values: [0.1, 0.3, 0.35, 0.351, 0.4, 0.41, 0.44, ..., 0.5, 0.54, 0.56, 0.6, 0.65, 0.7, 0.9], and we are not interested in evaluating the marginal effect at every single point as it can become computationally costly (assume hundreds/thousands of points) without providing any additional information for nearby points (e.g., 0.35 and 351). By setting grid_resolution=5, the marginal effect is computed for the values [0.1, 0.3, 0.5, 0.7, 0.9] instead, which is less computationally demanding and can provide similar insights regarding the model's behaviour. Note that the extreme values of the grid can be controlled using the percentiles argument.
grid_points	Optional[Dict[int, Union[List[Any], numpy.ndarray]]]	None	Custom grid points. Must be a dict where the keys are the target features indices and the values are monotonically increasing arrays defining the grid points for a numerical feature, and a subset of categorical feature values for a categorical feature. If the grid_points are not specified, then the grid will be constructed based on the unique target feature values available in the dataset X, or based on the grid_resolution and percentiles (check grid_resolution to see when it applies). For categorical features, the corresponding value in the grid_points can be specified either as array of strings or array of integers corresponding the label encodings. Note that the label encoding must match the ordering of the values provided in the categorical_names.

Returns

• Type: alibi.api.interfaces.Explanation

reset_predictor

reset_predictor(predictor: Union[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray], sklearn.base.BaseEstimator]) -> None

Name	Type	Default	Description
predictor	Union[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray], sklearn.base.BaseEstimator]		New predictor function or tree-based sklearn estimator.

Returns

• Type: None

TreePartialDependence

Inherits from: PartialDependenceBase, Explainer, ABC, Base

Tree-based model sklearn implementation of the partial dependence for tabular datasets. Supports multiple feature interactions. This method is faster than the general black-box implementation but is only supported by some tree-based estimators. The computation is based on a weighted tree traversal. For more details on the computation, check the sklearn documentation page_. The supported sklearn models are: GradientBoostingClassifier, GradientBoostingRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor, HistGradientBoostingRegressor, DecisionTreeRegressor, RandomForestRegressor.

.. _sklearn documentation page: https://scikit-learn.org/stable/modules/partial_dependence.html#computation-methods

Constructor

TreePartialDependence(self, predictor: sklearn.base.BaseEstimator, feature_names: Optional[List[str]] = None, categorical_names: Optional[Dict[int, List[str]]] = None, target_names: Optional[List[str]] = None, verbose: bool = False)

Name	Type	Default	Description
predictor	sklearn.base.BaseEstimator		A tree-based sklearn estimator.
feature_names	Optional[List[str]]	None	A list of feature names used for displaying results.
categorical_names	Optional[Dict[int, List[str]]]	None	Dictionary where keys are feature columns and values are the categories for the feature. Necessary to identify the categorical features in the dataset. An example for categorical_names would be:: category_map = {0: ["married", "divorced"], 3: ["high school diploma", "master's degree"]}
target_names	Optional[List[str]]	None	A list of target/output names used for displaying results.
verbose	bool	False	Whether to print the progress of the explainer.

Methods

explain

explain(X: numpy.ndarray, features: Optional[List[Union[int, Tuple[int, int]]]] = None, percentiles: Tuple[float, float] = (0.0, 1.0), grid_resolution: int = 100, grid_points: Optional[Dict[int, Union[List[Any], numpy.ndarray]]] = None) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	numpy.ndarray		A N x F tabular dataset used to calculate partial dependence curves. This is typically the training dataset or a representative sample.
features	Optional[List[Union[int, Tuple[int, int]]]]	None	An optional list of features or tuples of features for which to calculate the partial dependence. If not provided, the partial dependence will be computed for every single features in the dataset. Some example for features would be: [0, 2], [0, 2, (0, 2)], [(0, 2)], where 0 and 2 correspond to column 0 and 2 in X, respectively.
percentiles	Tuple[float, float]	(0.0, 1.0)	Lower and upper percentiles used to limit the feature values to potentially remove outliers from low-density regions. Note that for features with not many data points with large/low values, the PD estimates are less reliable in those extreme regions. The values must be in [0, 1]. Only used with grid_resolution.
grid_resolution	int	100	Number of equidistant points to split the range of each target feature. Only applies if the number of unique values of a target feature in the reference dataset X is greater than the grid_resolution value. For example, consider a case where a feature can take the following values: [0.1, 0.3, 0.35, 0.351, 0.4, 0.41, 0.44, ..., 0.5, 0.54, 0.56, 0.6, 0.65, 0.7, 0.9], and we are not interested in evaluating the marginal effect at every single point as it can become computationally costly (assume hundreds/thousands of points) without providing any additional information for nearby points (e.g., 0.35 and 351). By setting grid_resolution=5, the marginal effect is computed for the values [0.1, 0.3, 0.5, 0.7, 0.9] instead, which is less computationally demanding and can provide similar insights regarding the model's behaviour. Note that the extreme values of the grid can be controlled using the percentiles argument.
grid_points	Optional[Dict[int, Union[List[Any], numpy.ndarray]]]	None	Custom grid points. Must be a dict where the keys are the target features indices and the values are monotonically increasing arrays defining the grid points for a numerical feature, and a subset of categorical feature values for a categorical feature. If the grid_points are not specified, then the grid will be constructed based on the unique target feature values available in the dataset X, or based on the grid_resolution and percentiles (check grid_resolution to see when it applies). For categorical features, the corresponding value in the grid_points can be specified either as array of strings or array of integers corresponding the label encodings. Note that the label encoding must match the ordering of the values provided in the categorical_names.

Returns

• Type: alibi.api.interfaces.Explanation

Functions

plot_pd

plot_pd(exp: alibi.api.interfaces.Explanation, features: Union[List[int], Literal[all]] = 'all', target: Union[str, int] = 0, n_cols: int = 3, n_ice: Union[Literal[all], int, List[int]] = 100, center: bool = False, pd_limits: Optional[Tuple[float, float]] = None, levels: int = 8, ax: Union[ForwardRef('plt.Axes'), numpy.ndarray, None] = None, sharey: Optional[Literal[all, row]] = 'all', pd_num_kw: Optional[dict] = None, ice_num_kw: Optional[dict] = None, pd_cat_kw: Optional[dict] = None, ice_cat_kw: Optional[dict] = None, pd_num_num_kw: Optional[dict] = None, pd_num_cat_kw: Optional[dict] = None, pd_cat_cat_kw: Optional[dict] = None, fig_kw: Optional[dict] = None) -> np.ndarray

Plot partial dependence curves on matplotlib axes.

Name	Type	Default	Description
exp	alibi.api.interfaces.Explanation		An Explanation object produced by a call to the :py:meth:alibi.explainers.partial_dependence.PartialDependence.explain method.
features	Union[List[int], Literal[all]]	'all'	A list of features entries in the exp.data['feature_names'] to plot the partial dependence curves for, or 'all' to plot all the explained feature or tuples of features. This includes tuples of features. For example, if exp.data['feature_names'] = ['temp', 'hum', ('temp', 'windspeed')] and we want to plot the partial dependence only for the 'temp' and ('temp', 'windspeed'), then we would set features=[0, 2]. Defaults to 'all'.
target	Union[str, int]	0	The target name or index for which to plot the partial dependence (PD) curves. Can be a mix of integers denoting target index or strings denoting entries in exp.meta['params']['target_names'].
n_cols	int	3	Number of columns to organize the resulting plot into.
n_ice	Union[Literal[all], int, List[int]]	100	Number of ICE plots to be displayed. Can be - a string taking the value 'all' to display the ICE curves for every instance in the reference dataset. - an integer for which n_ice instances from the reference dataset will be sampled uniformly at random to display their ICE curves. - a list of integers, where each integer represents an index of an instance in the reference dataset to display their ICE curves.
center	bool	False	Boolean flag to center the individual conditional expectation (ICE) curves. As mentioned in Goldstein et al. (2014)_, the heterogeneity in the model can be difficult to discern when the intercepts of the ICE curves cover a wide range. Centering the ICE curves removes the level effects and helps to visualise the heterogeneous effect. .. _Goldstein et al. (2014): https://arxiv.org/abs/1309.6392
pd_limits	Optional[Tuple[float, float]]	None	Minimum and maximum y-limits for all the one-way PD plots. If None will be automatically inferred.
levels	int	8	Number of levels in the contour plot.
ax	Union[ForwardRef('plt.Axes'), numpy.ndarray, None]	None	A matplotlib axes object or a numpy array of matplotlib axes to plot on.
sharey	Optional[Literal[all, row]]	'all'	A parameter specifying whether the y-axis of the PD and ICE curves should be on the same scale for several features. Possible values are: 'all'
pd_num_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.pyplot.plot_ function when plotting the PD for a numerical feature.
ice_num_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.pyplot.plot_ function when plotting the ICE for a numerical feature.
pd_cat_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.pyplot.plot_ function when plotting the PD for a categorical feature.
ice_cat_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.pyplot.plot_ function when plotting the ICE for a categorical feature.
pd_num_num_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.pyplot.contourf_ function when plotting the PD for two numerical features.
pd_num_cat_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.pyplot.plot_ function when plotting the PD for a numerical and a categorical feature.
pd_cat_cat_kw	Optional[dict]	None	Keyword arguments passed to the :py:meth:alibi.utils.visualization.heatmap functon when plotting the PD for two categorical features.
fig_kw	Optional[dict]	None	Keyword arguments passed to the matplotlib.figure.set_ function. .. _matplotlib.pyplot.plot: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html .. _matplotlib.pyplot.contourf: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html .. _matplotlib.figure.set: https://matplotlib.org/stable/api/figure_api.html

Returns

• Type: np.ndarray