def test_digits_greedi_ln_sparse():
model1 = FeatureBasedSelection(100, 'sqrt')
model2 = FeatureBasedSelection(100, 'log')
model = MixtureSelection(100, [model1, model2], [1.0, 0.3],
optimizer='greedi',
optimizer_kwds={
'optimizer1': 'lazy',
'optimizer2': 'naive'
},
random_state=0)
model.fit(X_digits_sparse)
assert_array_equal(model.ranking[:85], digits_greedi_ranking[:85])
assert_array_almost_equal(model.gains[:85], digits_greedi_gains[:85], 4)
assert_array_almost_equal(model.subset,
X_digits_sparse[model.ranking].toarray())
def test_digits_sqrt_sieve_minibatch():
model1 = FeatureBasedSelection(100, 'sqrt')
model2 = FeatureBasedSelection(100, 'log')
model = MixtureSelection(100, [model1, model2], [1.0, 0.3], random_state=0)
model.partial_fit(X_digits[:300])
model.partial_fit(X_digits[300:500])
model.partial_fit(X_digits[500:])
assert_array_equal(model.ranking, digits_sieve_ranking)
assert_array_almost_equal(model.gains, digits_sieve_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
