def test_ratio(self):
'''
Compare several competing methods changing the ratio of the positive
class in the dataset. We use binary class dataset for the easy of
interpretation.
'''
dataset = rcv1_binary_reader.toNumpy()
set_size = 100
X_train_full, y_train_full, X_test, y_test = dataset
X_train, y_train = self.get_sub_set_with_size([X_train_full, y_train_full], set_size)
assert(len(y_train) == set_size)
train_set = (X_train, y_train)
test_set_original = (X_test, y_test)
clf = LogisticRegression()
clf.fit(X_train, y_train)
p = Prior(clf)
for r in np.arange(0.05, 1.0, 0.05):
# Generate a new test set with desired positive proportions.
X_test_new, y_test_new = SetGen.with_pos_ratio(test_set_original, r, pos_label=1)
test_set = [X_test_new, y_test_new]
true_pos = DE.arrayToDist(y_test_new)[1]
p.fit(X_train, y_train, {-1:1-true_pos, 1:true_pos})
y_pred = p.predict(X_test_new)
cm = confusion_matrix(y_test_new, y_pred)
acc = self.accuracy(cm)
print r, acc
