Examples

In the /demos folder, there are a number of example scripts. These show a potential use case on synthetic data.

Here we walk through a simple version.

First, we import a number of modules and generate a synthetic data set:

import numpy as np
import numpy.random as rnd

from sklearn.linear_model import LogisticRegression
from libtlda.iw import ImportanceWeightedClassifier

"""Generate synthetic data set"""

# Sample sizes
N = 100
M = 50

# Class properties
labels = [0, 1]
nK = 2

# Dimensionality
D = 2

# Source domain
pi_S = [1./2, 1./2]
si_S = 1.0
N0 = int(np.round(N*pi_S[0]))
N1 = N - N0
X0 = rnd.randn(N0, D)*si_S + (-2, 0)
X1 = rnd.randn(N1, D)*si_S + (+2, 0)
X = np.concatenate((X0, X1), axis=0)
y = np.concatenate((labels[0]*np.ones((N0,), dtype='int'),
                    labels[1]*np.ones((N1,), dtype='int')), axis=0)

# Target domain
pi_T = [1./2, 1./2]
si_T = 3.0
M0 = int(np.round(M*pi_T[0]))
M1 = M - M0
Z0 = rnd.randn(M0, D)*si_T + (-2, -2)
Z1 = rnd.randn(M1, D)*si_T + (+2, +2)
Z = np.concatenate((Z0, Z1), axis=0)
u = np.concatenate((labels[0]*np.ones((M0,), dtype='int'),
                    labels[1]*np.ones((M1,), dtype='int')), axis=0)

Next, we create an adaptive classifier:

# Call an importance-weighted classifier
clf = ImportanceWeightedClassifier(iwe='lr', loss='logistic')

# Train classifier
clf.fit(X, y, Z)

# Make predictions
pred_adapt = clf.predict(Z)

We can compare this with a non-adaptive classifier:

# Train a naive logistic regressor
lr = LogisticRegression().fit(X, y)

# Make predictions
pred_naive = lr.predict(Z)

And compute error rates:

# Compute error rates
print('Error naive: ' + str(np.mean(pred_naive != u, axis=0)))
print('Error adapt: ' + str(np.mean(pred_adapt != u, axis=0)))