Source code for

from functools import partial
import numpy as np
import tensorflow as tf
from tensorflow.keras.losses import BinaryCrossentropy
from scipy.special import softmax
from typing import Callable, Dict, Optional, Tuple, Union
from import BaseClassifierDrift
from alibi_detect.models.tensorflow.trainer import trainer
from import TFDataset
from alibi_detect.utils.tensorflow.misc import clone_model
from alibi_detect.utils.tensorflow.prediction import predict_batch
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.utils.frameworks import Framework
from alibi_detect.utils._types import OptimizerTF

[docs]class ClassifierDriftTF(BaseClassifierDrift):
[docs] @deprecated_alias(preprocess_x_ref='preprocess_at_init') def __init__( self, x_ref: np.ndarray, model: tf.keras.Model, p_val: float = .05, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_x_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, preds_type: str = 'probs', binarize_preds: bool = False, reg_loss_fn: Callable = (lambda model: 0), train_size: Optional[float] = .75, n_folds: Optional[int] = None, retrain_from_scratch: bool = True, seed: int = 0, optimizer: OptimizerTF = tf.keras.optimizers.Adam, learning_rate: float = 1e-3, batch_size: int = 32, preprocess_batch_fn: Optional[Callable] = None, epochs: int = 3, verbose: int = 0, train_kwargs: Optional[dict] = None, dataset: Callable = TFDataset, input_shape: Optional[tuple] = None, data_type: Optional[str] = None ) -> None: """ Classifier-based drift detector. The classifier is trained on a fraction of the combined reference and test data and drift is detected on the remaining data. To use all the data to detect drift, a stratified cross-validation scheme can be chosen. Parameters ---------- x_ref Data used as reference distribution. model TensorFlow classification model used for drift detection. p_val p-value used for the significance of the test. x_ref_preprocessed Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference data will also be preprocessed. preprocess_at_init Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`. update_x_ref 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. preprocess_fn Function to preprocess the data before computing the data drift metrics. preds_type Whether the model outputs 'probs' or 'logits'. binarize_preds Whether to test for discrepency on soft (e.g. prob/log-prob) model predictions directly with a K-S test or binarise to 0-1 prediction errors and apply a binomial test. reg_loss_fn The regularisation term reg_loss_fn(model) is added to the loss function being optimized. train_size Optional fraction (float between 0 and 1) of the dataset used to train the classifier. The drift is detected on `1 - train_size`. Cannot be used in combination with `n_folds`. n_folds Optional number of stratified folds used for training. The model preds are then calculated on all the out-of-fold predictions. This allows to leverage all the reference and test data for drift detection at the expense of longer computation. If both `train_size` and `n_folds` are specified, `n_folds` is prioritized. retrain_from_scratch Whether the classifier should be retrained from scratch for each set of test data or whether it should instead continue training from where it left off on the previous set. seed Optional random seed for fold selection. optimizer Optimizer used during training of the classifier. learning_rate Learning rate used by optimizer. batch_size Batch size used during training of the classifier. preprocess_batch_fn Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the model. epochs Number of training epochs for the classifier for each (optional) fold. verbose Verbosity level during the training of the classifier. 0 is silent, 1 a progress bar and 2 prints the statistics after each epoch. train_kwargs Optional additional kwargs when fitting the classifier. dataset Dataset object used during training. input_shape Shape of input data. data_type Optionally specify the data type (tabular, image or time-series). Added to metadata. """ super().__init__( x_ref=x_ref, p_val=p_val, x_ref_preprocessed=x_ref_preprocessed, preprocess_at_init=preprocess_at_init, update_x_ref=update_x_ref, preprocess_fn=preprocess_fn, preds_type=preds_type, binarize_preds=binarize_preds, train_size=train_size, n_folds=n_folds, retrain_from_scratch=retrain_from_scratch, seed=seed, input_shape=input_shape, data_type=data_type ) if preds_type not in ['probs', 'logits']: raise ValueError("'preds_type' should be 'probs' or 'logits'") self.meta.update({'backend': Framework.TENSORFLOW.value}) # define and compile classifier model self.original_model = model self.model = clone_model(model) self.loss_fn = BinaryCrossentropy(from_logits=(self.preds_type == 'logits')) self.dataset = partial(dataset, batch_size=batch_size, shuffle=True) self.predict_fn = partial(predict_batch, preprocess_fn=preprocess_batch_fn, batch_size=batch_size) optimizer = optimizer(learning_rate=learning_rate) if isinstance(optimizer, type) else optimizer self.train_kwargs = {'optimizer': optimizer, 'epochs': epochs, 'reg_loss_fn': reg_loss_fn, 'preprocess_fn': preprocess_batch_fn, 'verbose': verbose} if isinstance(train_kwargs, dict): self.train_kwargs.update(train_kwargs)
[docs] def score(self, x: np.ndarray) -> Tuple[float, float, np.ndarray, np.ndarray, # type: ignore[override] Union[np.ndarray, list], Union[np.ndarray, list]]: """ Compute the out-of-fold drift metric such as the accuracy from a classifier trained to distinguish the reference data from the data to be tested. Parameters ---------- x Batch of instances. Returns ------- p-value, a notion of distance between the trained classifier's out-of-fold performance \ and that which we'd expect under the null assumption of no drift, \ and the out-of-fold classifier model prediction probabilities on the reference and test data \ as well as the associated reference and test instances of the out-of-fold predictions. """ x_ref, x = self.preprocess(x) # type: ignore[assignment] x, y, splits = self.get_splits(x_ref, x) # type: ignore # iterate over folds: train a new model for each fold and make out-of-fold (oof) predictions preds_oof_list, idx_oof_list = [], [] for idx_tr, idx_te in splits: y_tr = np.eye(2)[y[idx_tr]] if isinstance(x, np.ndarray): x_tr, x_te = x[idx_tr], x[idx_te] elif isinstance(x, list): x_tr, x_te = [x[_] for _ in idx_tr], [x[_] for _ in idx_te] else: raise TypeError(f'x needs to be of type np.ndarray or list and not {type(x)}.') ds_tr = self.dataset(x_tr, y_tr) if self.retrain_from_scratch: # clone model to re-initialise self.model = clone_model(self.original_model) # Clone optimizer to prevent error due to cloned model (with new tf>=2.11 optimizers) optimizer = self.train_kwargs['optimizer'] self.train_kwargs['optimizer'] = optimizer.__class__.from_config(optimizer.get_config()) train_args = [self.model, self.loss_fn, None] self.train_kwargs.update({'dataset': ds_tr}) trainer(*train_args, **self.train_kwargs) preds = self.predict_fn(x_te, self.model) preds_oof_list.append(preds) idx_oof_list.append(idx_te) preds_oof = np.concatenate(preds_oof_list, axis=0) probs_oof = softmax(preds_oof, axis=-1) if self.preds_type == 'logits' else preds_oof idx_oof = np.concatenate(idx_oof_list, axis=0) y_oof = y[idx_oof] n_cur = y_oof.sum() n_ref = len(y_oof) - n_cur p_val, dist = self.test_probs(y_oof, probs_oof, n_ref, n_cur) idx_sort = np.argsort(idx_oof) probs_sort = probs_oof[idx_sort] if isinstance(x, np.ndarray): x_oof = x[idx_oof] x_sort = x_oof[idx_sort] else: x_oof = [x[_] for _ in idx_oof] x_sort = [x_oof[_] for _ in idx_sort] return p_val, dist, probs_sort[:n_ref, 1], probs_sort[n_ref:, 1], x_sort[:n_ref], x_sort[n_ref:]