# alibi_detect.cd.model_uncertainty module¶

class alibi_detect.cd.model_uncertainty.ClassifierUncertaintyDrift(x_ref, model, p_val=0.05, backend=None, update_x_ref=None, preds_type='probs', uncertainty_type='entropy', margin_width=0.1, batch_size=32, preprocess_batch_fn=None, device=None, tokenizer=None, max_len=None, data_type=None)[source]

Bases: object

__init__(x_ref, model, p_val=0.05, backend=None, update_x_ref=None, preds_type='probs', uncertainty_type='entropy', margin_width=0.1, batch_size=32, preprocess_batch_fn=None, device=None, tokenizer=None, max_len=None, data_type=None)[source]

Test for a change in the number of instances falling into regions on which the model is uncertain. Performs either a K-S test on prediction entropies or Chi-squared test on 0-1 indicators of predictions falling into a margin of uncertainty (e.g. probs falling into [0.45, 0.55] in binary case).

Parameters
• x_ref (Union[ndarray, list]) – Data used as reference distribution. Should be disjoint from the data the model was trained on for accurate p-values.

• model (Callable) – Classification model outputting class probabilities (or logits)

• backend (Optional[str]) – Backend to use if model requires batch prediction. Options are ‘tensorflow’ or ‘pytorch’.

• p_val (float) – p-value used for the significance of the test.

• update_x_ref (Optional[Dict[str, int]]) – Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {‘last’: n} while for reservoir sampling {‘reservoir_sampling’: n} is passed.

• preds_type (str) – Type of prediction output by the model. Options are ‘probs’ (in [0,1]) or ‘logits’ (in [-inf,inf]).

• uncertainty_type (str) – Method for determining the model’s uncertainty for a given instance. Options are ‘entropy’ or ‘margin’.

• margin_width (float) – Width of the margin if uncertainty_type = ‘margin’. The model is considered uncertain on an instance if the highest two class probabilities it assigns to the instance differ by less than margin_width.

• batch_size (int) – Batch size used to evaluate model. Only relevant when backend has been specified for batch prediction.

• preprocess_batch_fn (Optional[Callable]) – Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the model.

• device (Optional[str]) – Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either ‘cuda’, ‘gpu’ or ‘cpu’. Only relevant for ‘pytorch’ backend.

• tokenizer (Optional[Callable]) – Optional tokenizer for NLP models.

• max_len (Optional[int]) – Optional max token length for NLP models.

• data_type (Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.

Return type

None

predict(x, return_p_val=True, return_distance=True)[source]

Predict whether a batch of data has drifted from the reference data.

Parameters
Return type
Returns

• Dictionary containing ‘meta’ and ‘data’ dictionaries.

• ’meta’ has the model’s metadata.

• ’data’ contains the drift prediction and optionally the p-value, threshold and test statistic.

class alibi_detect.cd.model_uncertainty.RegressorUncertaintyDrift(x_ref, model, p_val=0.05, backend=None, update_x_ref=None, uncertainty_type='mc_dropout', n_evals=25, batch_size=32, preprocess_batch_fn=None, device=None, tokenizer=None, max_len=None, data_type=None)[source]

Bases: object

__init__(x_ref, model, p_val=0.05, backend=None, update_x_ref=None, uncertainty_type='mc_dropout', n_evals=25, batch_size=32, preprocess_batch_fn=None, device=None, tokenizer=None, max_len=None, data_type=None)[source]

Test for a change in the number of instances falling into regions on which the model is uncertain. Performs either a K-S test on uncertainties estimated from an preditive ensemble given either explicitly or implicitly as a model with dropout layers.

Parameters
• x_ref (Union[ndarray, list]) – Data used as reference distribution. Should be disjoint from the data the model was trained on for accurate p-values.

• model (Callable) – Regression model outputting class probabilities (or logits)

• backend (Optional[str]) – Backend to use if model requires batch prediction. Options are ‘tensorflow’ or ‘pytorch’.

• p_val (float) – p-value used for the significance of the test.

• update_x_ref (Optional[Dict[str, int]]) – Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {‘last’: n} while for reservoir sampling {‘reservoir_sampling’: n} is passed.

• uncertainty_type (str) – Method for determining the model’s uncertainty for a given instance. Options are ‘mc_dropout’ or ‘ensemble’. The former should output a scalar per instance. The latter should output a vector of predictions per instance.

• n_evals (int) – The number of times to evaluate the model under different dropout configurations. Only relevant when using the ‘mc_dropout’ uncertainty type.

• batch_size (int) – Batch size used to evaluate model. Only relevant when backend has been specified for batch prediction.

• preprocess_batch_fn (Optional[Callable]) – Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the model.

• device (Optional[str]) – Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either ‘cuda’, ‘gpu’ or ‘cpu’. Only relevant for ‘pytorch’ backend.

• tokenizer (Optional[Callable]) – Optional tokenizer for NLP models.

• max_len (Optional[int]) – Optional max token length for NLP models.

• data_type (Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.

Return type

None

predict(x, return_p_val=True, return_distance=True)[source]

Predict whether a batch of data has drifted from the reference data.

Parameters
Return type
Returns

• Dictionary containing ‘meta’ and ‘data’ dictionaries.

• ’meta’ has the model’s metadata.

• ’data’ contains the drift prediction and optionally the p-value, threshold and test statistic.