def test_digits_naive(): return model = MaxCoverageSelection(100, optimizer='naive') model.fit(X_digits_cupy) assert_array_equal(model.ranking, digits_ranking) assert_array_equal(model.ranking, digits_ranking) assert_array_almost_equal(model.gains, digits_gains, 4)
def test_digits_approximate_sparse(): model = MaxCoverageSelection(100, optimizer='approximate-lazy') model.fit(X_digits_sparse) assert_array_equal(model.ranking, digits_approx_ranking) assert_array_almost_equal(model.gains, digits_approx_gains, 4) assert_array_almost_equal(model.subset, X_digits_sparse[model.ranking].toarray())
def test_digits_modular_sparse(): model = MaxCoverageSelection(100, optimizer='modular', random_state=0) model.fit(X_digits_sparse) assert_array_equal(model.ranking, digits_modular_ranking) assert_array_almost_equal(model.gains, digits_modular_gains, 4) assert_array_almost_equal(model.subset, X_digits_sparse[model.ranking].toarray())
def test_digits_two_stage_sparse(): model = MaxCoverageSelection(100, optimizer='two-stage') model.fit(X_digits_sparse) assert_array_equal(model.ranking[:4], digits_ranking[:4]) assert_array_almost_equal(model.gains[:4], digits_gains[:4], 4) assert_array_almost_equal(model.subset, X_digits_sparse[model.ranking].toarray())
def test_digits_stochastic_object(): model = MaxCoverageSelection(100, optimizer=StochasticGreedy(random_state=0)) model.fit(X_digits) assert_array_equal(model.ranking, digits_stochastic_ranking) assert_array_almost_equal(model.gains, digits_stochastic_gains, 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_two_stage_init(): model = MaxCoverageSelection(100, optimizer='two-stage', initial_subset=digits_ranking[:5]) model.fit(X_digits_cupy) assert_array_equal(model.ranking[:10], digits_ranking[5:15]) assert_array_almost_equal(model.gains[:10], digits_gains[5:15], 4)
def test_digits_naive(): model = MaxCoverageSelection(100, optimizer='naive') model.fit(X_digits) assert_array_equal(model.ranking[:15], digits_ranking[:15]) assert_array_equal(model.ranking[:15], digits_ranking[:15]) assert_array_almost_equal(model.gains[:15], digits_gains[:15], 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_lazy_init(): model = MaxCoverageSelection(100, optimizer='lazy', initial_subset=digits_ranking[:5]) model.fit(X_digits) assert_array_equal(model.ranking[:5], digits_ranking[5:10]) assert_array_almost_equal(model.gains[:5], digits_gains[5:10], 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_greedi_nl_object(): model = MaxCoverageSelection(100, optimizer=GreeDi(optimizer1='naive', optimizer2='lazy', random_state=0)) model.fit(X_digits_cupy) assert_array_equal(model.ranking[:2], digits_ranking[:2]) assert_array_almost_equal(model.gains[:2], digits_gains[:2], 4)
def test_digits_greedi_ll(): model = MaxCoverageSelection(100, optimizer='greedi', optimizer_kwds={ 'optimizer1': 'lazy', 'optimizer2': 'lazy' }, random_state=0) model.fit(X_digits_cupy) assert_array_equal(model.ranking[:30], digits_greedi_ranking[:30]) assert_array_almost_equal(model.gains[:30], digits_greedi_gains[:30], 4)
def test_digits_greedi_nl_sparse(): model = MaxCoverageSelection(100, optimizer='greedi', optimizer_kwds={ 'optimizer1': 'naive', 'optimizer2': 'lazy' }, random_state=0) model.fit(X_digits_sparse) assert_array_equal(model.ranking[:2], digits_ranking[:2]) assert_array_almost_equal(model.gains[:2], digits_gains[:2], 4)
def test_digits_sample(): model = MaxCoverageSelection(100, optimizer='sample', random_state=0) model.fit(X_digits_sparse) assert_array_equal(model.ranking, digits_sample_ranking) assert_array_almost_equal(model.gains, digits_sample_gains, 4)
def test_digits_modular_object(): model = MaxCoverageSelection(100, optimizer=ModularGreedy(random_state=0)) model.fit(X_digits_cupy) assert_array_equal(model.ranking, digits_modular_ranking) assert_array_almost_equal(model.gains, digits_modular_gains, 4)
def test_digits_lazy_sparse(): model = MaxCoverageSelection(100, optimizer='lazy') model.fit(X_digits_sparse) assert_array_equal(model.ranking[:3], digits_ranking[:3]) assert_array_almost_equal(model.gains[:3], digits_gains[:3], 4)
def test_digits_naive_object(): model = MaxCoverageSelection(100, optimizer=NaiveGreedy()) model.fit(X_digits) assert_array_equal(model.ranking[:4], digits_ranking[:4]) assert_array_almost_equal(model.gains[:4], digits_gains[:4], 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_approximate_object(): model = MaxCoverageSelection(100, optimizer=ApproximateLazyGreedy()) model.fit(X_digits_cupy) assert_array_equal(model.ranking, digits_approx_ranking) assert_array_almost_equal(model.gains, digits_approx_gains, 4)
def test_digits_two_stage_object(): model = MaxCoverageSelection(100, optimizer=TwoStageGreedy()) model.fit(X_digits_cupy) assert_array_equal(model.ranking[:4], digits_ranking[:4]) assert_array_almost_equal(model.gains[:4], digits_gains[:4], 4)
def test_digits_lazy_object(): model = MaxCoverageSelection(100, optimizer=LazyGreedy()) model.fit(X_digits_cupy) assert_array_equal(model.ranking[:3], digits_ranking[:3]) assert_array_almost_equal(model.gains[:3], digits_gains[:3], 4)
def test_digits_stochastic(): model = MaxCoverageSelection(100, optimizer='stochastic', random_state=0) model.fit(X_digits_cupy) assert_array_equal(model.ranking, digits_stochastic_ranking) assert_array_almost_equal(model.gains, digits_stochastic_gains, 4)
def test_digits_approximate(): model = MaxCoverageSelection(100, optimizer='approximate-lazy') model.fit(X_digits_cupy) assert_array_equal(model.ranking, digits_approx_ranking) assert_array_almost_equal(model.gains, digits_approx_gains, 4)
def test_digits_two_stage(): model = MaxCoverageSelection(100, optimizer='two-stage') model.fit(X_digits_cupy) assert_array_equal(model.ranking[:3], digits_ranking[:3]) assert_array_almost_equal(model.gains[:3], digits_gains[:3], 4)