Robust Bias-Aware Classifier¶

class libtlda.rba.RobustBiasAwareClassifier(l2=0.0, order='first', gamma=1.0, tau=1e-05, learning_rate=1.0, rate_decay='linear', max_iter=100, clip=1000, verbose=True)¶

Class of robust bias-aware classifiers.

Reference: Liu & Ziebart (20140. Robust Classification under Sample Selection Bias. NIPS.

Methods contain training and prediction functions.

Methods

`feature_stats`(X, y[, order])	Compute first-order moment feature statistics.
`fit`(X, y, Z)	Fit/train a robust bias-aware classifier.
`get_params`()	Get classifier parameters.
`is_trained`()	Check whether classifier is trained.
`iwe_kernel_densities`(X, Z[, clip])	Estimate importance weights based on kernel density estimation.
`learning_rate_t`(t)	Compute current learning rate after decay.
`posterior`(psi)	Class-posterior estimation.
`predict`(Z)	Make predictions on new dataset.
`predict_proba`(Z)	Compute posteriors on new dataset.
`psi`(X, theta, w[, K])	Compute psi function.

feature_stats(X, y, order='first')¶

Compute first-order moment feature statistics.

Parameters:	X : array dataset (N samples by D features) y : array label vector (N samples by 1)
Returns:	array array containing label vector, feature moments and 1-augmentation.

fit(X, y, Z)¶

Fit/train a robust bias-aware classifier.

Parameters:	X : array source data (N samples by D features) y : array source labels (N samples by 1) Z : array target data (M samples by D features)
Returns:	None

get_params()¶: Get classifier parameters.

is_trained()¶: Check whether classifier is trained.

iwe_kernel_densities(X, Z, clip=1000)¶

Estimate importance weights based on kernel density estimation.

Parameters:	X : array source data (N samples by D features) Z : array target data (M samples by D features) clip : float maximum allowed value for individual weights (def: 1000)
Returns:	array importance weights (N samples by 1)

learning_rate_t(t)¶

Compute current learning rate after decay.

Parameters:	t : int current iteration
Returns:	alpha : float current learning rate

posterior(psi)¶

Class-posterior estimation.

Parameters:	psi : array weighted data-classifier output (N samples by K classes)
Returns:	pyx : array class-posterior estimation (N samples by K classes)

predict(Z)¶

Make predictions on new dataset.

Parameters:	Z : array new data set (M samples by D features)
Returns:	preds : array label predictions (M samples by 1)

predict_proba(Z)¶

Compute posteriors on new dataset.

Parameters:	Z : array new data set (M samples by D features)
Returns:	preds : array label predictions (M samples by 1)

psi(X, theta, w, K=2)¶

Compute psi function.

Parameters:	X : array data set (N samples by D features) theta : array classifier parameters (D features by 1) w : array importance-weights (N samples by 1) K : int number of classes (def: 2)
Returns:	psi : array array with psi function values (N samples by K classes)