Global White Box Explainers¶
ProfWeight Explainer¶

class
aix360.algorithms.profwt.profwt.
ProfweightExplainer
¶ The main explainer class that implements functions for computation of samplewise weights from probe confidences of different layers from the complex model and then retrains the simple model with those weights. Implements the technique in the following reference: [1].
References
[1] Dhurandhar, Shanmugam, Luss, Olsen. Improving Simple Models with Confidence Profiles. NeurIPS 2018 Initialize ProfweightExplainer.

explain
(x_train, y_train, x_test, y_test, simple_model, hps, list_probe_filenames, start_layer, end_layer, model_type='neural_keras')¶ Obtains (train) samplewise weights from stored probe confidences for different layers of the complex moddel. Retrains a simple model using the corresponding weighted training set.
Parameters:  x_train (numpy array) – Dataset of features to retrain the simple model using weights derived from probe confidences. Dimensions (num of samples x feature dimensions)
 y_train (numpy array) – Labels for the dataset that is used to retrain simple model using weights. Dimensions (num of samples x num of classes)
 x_test (numpy array) – Test dataset to evaluate the simple model trained using weights. Dimensions (num of samples x feature dimensions)
 y_test (numpy array) – Test labels to evaulate the simple model trained using weights. Dimensions (num of samples x num of classes)
 hps (namedtuple) – Hyperparameters (usually a named tuple with entries) to train the simple model. Please see fit function to find out the required set of parameters expected by the fit function.
 list_probe_filenames (list of strings) – List of strings indicated path to files where different probe confidences are stored.
 start_layer (int) – Index corresponding to the starting layer whose probe confidences are going to be averaged to obtain weights. This is an index of list_probe_filenames.
 end_layer (int) – Index corresponding to the last layer whose probe confidences are going to be averaged to obtain weights. This is an index of list_probe_filenames.
 model_type (string) – This specifies the type of simple model to be trained. Default is ‘neural_keras’. Only this option is implemented now.
 simple_model (function object for a Keras model) – This is a function object that would initialize a keras model to specify the architecture of the simple model.
Returns: None

fit
(x_train, y_train, x_test, y_test, simple_model, hps, model_type='neural_keras', sample_weight=None)¶ Fits the training data by initializing a simple model with hyper parameters and returns the test accuracy on the test dataset. This can be trained with or without sample weights. The first 500 samples of the test dataset is used as validation data.
Parameters:  x_train (numpy array) – Training dataset features for training the simple model. Dimensions (num of samples x feature dimensions)
 y_train (numpy array) – Labels for the training dataset to train on. Dimensions (num of samples x num of classes)
 x_test (numpy array) – Test dataset features. Dimensions (num of samples x feature dimensions)
 y_test (numpy array) – Test dataset labels. Dimensions (num of samples x num of classes)
 hps (namedtuple) –
A namedtuple that is expected to have the following named tuple elements:
 optimizer  specified the optimizer in keras.
 complexity_param  Used for Resenet based simple model to specify number of Resunits. Used by simple model function object to intialize a simple model of appropriate complexity.
 num_classes  scalar specifying number of classes used by the simple model function.
 checkpoint_path  specifies the path for saving a checkpoint of the trained model. This is expected.
 lr_scheduler  a function object that takes in a scalar (epochs) and specified a learning rate (scalar). This is a learning rate Scheduler. Expected.
 lr_reducer  a function object that specifies how learning rates must be reduced if validation accuracy does not improve  Optional.
 simple_model (function object for a Keras model) – A function object that constructs a keras model for the simple model and returns the model object. It is expected to take in input_shape, hps.complexity_param and num_classes. It is expected to implement a keras model fit function. It is also expected to implement a keras model evaulate function.
Returns:  model_d (Keras model object) – Returns the trained model that is initialized by simple_model functions.
 scores[1] (float) – Returns the test accuracy of the trained model on (x_test,y_test.)
Return type: tuple

set_params
(*argv, **kwargs)¶ Set parameters for the explainer.
