> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-explain/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-explain/api-reference/confidence/model_linearity.md). # alibi.confidence.model\_linearity ## Constants ### `logger` ```python logger: logging.Logger = ``` 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. ## `LinearityMeasure` ### Constructor ```python LinearityMeasure(self, method: str = 'grid', epsilon: float = 0.04, nb_samples: int = 10, res: int = 100, alphas: Optional[numpy.ndarray] = None, model_type: str = 'classifier', agg: str = 'pairwise', verbose: bool = False) -> None ``` | Name | Type | Default | Description | | ------------ | ------------------------- | -------------- | ---------------------------------------------------------------------------------------------- | | `method` | `str` | `'grid'` | Method for sampling. Supported methods: `'knn'` | | `epsilon` | `float` | `0.04` | Size of the sampling region around the central instance as a percentage of the features range. | | `nb_samples` | `int` | `10` | Number of samples to generate. | | `res` | `int` | `100` | Resolution of the grid. Number of intervals in which the feature range is discretized. | | `alphas` | `Optional[numpy.ndarray]` | `None` | Coefficients in the superposition. | | `model_type` | `str` | `'classifier'` | Type of task. Supported values: `'regressor'` | | `agg` | `str` | `'pairwise'` | Aggregation method. Supported values: `'global'` | | `verbose` | `bool` | `False` | | ### Methods #### `fit` ```python fit(X_train: numpy.ndarray) -> None ``` | Name | Type | Default | Description | | --------- | --------------- | ------- | ------------- | | `X_train` | `numpy.ndarray` | | Training set. | **Returns** * Type: `None` #### `score` ```python score(predict_fn: Callable, x: numpy.ndarray) -> numpy.ndarray ``` | Name | Type | Default | Description | | ------------ | --------------- | ------- | --------------------- | | `predict_fn` | `Callable` | | Prediction function. | | `x` | `numpy.ndarray` | | Instance of interest. | **Returns** * Type: `numpy.ndarray` ## Functions ### `infer_feature_range` ```python infer_feature_range(X_train: numpy.ndarray) -> numpy.ndarray ``` Infers the feature range from the training set. | Name | Type | Default | Description | | --------- | --------------- | ------- | ------------- | | `X_train` | `numpy.ndarray` | | Training set. | **Returns** * Type: `numpy.ndarray` ### `linearity_measure` ```python linearity_measure(predict_fn: Callable, x: numpy.ndarray, feature_range: Union[List[Any], numpy.ndarray, None] = None, method: str = 'grid', X_train: Optional[numpy.ndarray] = None, epsilon: float = 0.04, nb_samples: int = 10, res: int = 100, alphas: Optional[numpy.ndarray] = None, agg: str = 'global', model_type: str = 'classifier') -> numpy.ndarray ``` Calculate the linearity measure of the model around an instance of interest x. | Name | Type | Default | Description | | --------------- | --------------------------------------- | -------------- | --------------------------------------------------------------------------------------- | | `predict_fn` | `Callable` | | Predict function. | | `x` | `numpy.ndarray` | | Instance of interest. | | `feature_range` | `Union[List[Any], numpy.ndarray, None]` | `None` | Array with min and max values for each feature. | | `method` | `str` | `'grid'` | Method for sampling. Supported values: `'knn'` | | `X_train` | `Optional[numpy.ndarray]` | `None` | Training set. | | `epsilon` | `float` | `0.04` | Size of the sampling region as a percentage of the feature range. | | `nb_samples` | `int` | `10` | Number of samples to generate. | | `res` | `int` | `100` | Resolution of the grid. Number of intervals in which the features range is discretized. | | `alphas` | `Optional[numpy.ndarray]` | `None` | Coefficients in the superposition. | | `agg` | `str` | `'global'` | Aggregation method. Supported values: `'global'` | | `model_type` | `str` | `'classifier'` | Type of task. Supported values: `'regressor'` | **Returns** * Type: `numpy.ndarray` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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