def test_digits_cosine_greedi_ln():
model = GraphCutSelection(100, 'cosine', optimizer='greedi',
optimizer_kwds={'optimizer1': 'lazy', 'optimizer2': 'naive'},
random_state=0)
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_greedi_ranking)
assert_array_almost_equal(model.gains, digits_cosine_greedi_gains, 4)
def test_digits_cosine_greedi_nl_sparse():
model = GraphCutSelection(100, 'precomputed', optimizer='greedi',
optimizer_kwds={'optimizer1': 'naive', 'optimizer2': 'lazy'},
random_state=0)
model.fit(X_digits_cosine_sparse)
assert_array_equal(model.ranking[:30], digits_cosine_greedi_ranking[:30])
assert_array_almost_equal(model.gains[:30], digits_cosine_greedi_gains[:30], 4)
def test_digits_precomputed_two_stage_init():
model = GraphCutSelection(100,
'precomputed',
optimizer='two-stage',
initial_subset=digits_cosine_ranking[:5])
model.fit(X_digits_cosine_cupy)
assert_array_equal(model.ranking[:-5], digits_cosine_ranking[5:])
assert_array_almost_equal(model.gains[:-5], digits_cosine_gains[5:], 4)
def test_digits_sqrt_modular_object():
model = GraphCutSelection(100,
'cosine',
optimizer=ModularGreedy(random_state=0))
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_modular_ranking)
assert_array_almost_equal(model.gains, digits_cosine_modular_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_sqrt_modular_sparse():
model = GraphCutSelection(100,
'precomputed',
optimizer='modular',
random_state=0)
model.fit(X_digits_cosine_sparse)
assert_array_equal(model.ranking, digits_cosine_modular_ranking)
assert_array_almost_equal(model.gains, digits_cosine_modular_gains, 4)
def test_digits_corr_two_stage_init():
model = GraphCutSelection(100,
'corr',
optimizer='two-stage',
initial_subset=digits_corr_ranking[:5])
model.fit(X_digits)
assert_array_equal(model.ranking[:-5], digits_corr_ranking[5:])
assert_array_almost_equal(model.gains[:-5], digits_corr_gains[5:], 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_lazy_init():
model = GraphCutSelection(100,
'cosine',
optimizer='lazy',
initial_subset=digits_cosine_ranking[:5])
model.fit(X_digits)
assert_array_equal(model.ranking[:-5], digits_cosine_ranking[5:])
assert_array_almost_equal(model.gains[:-5], digits_cosine_gains[5:], 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_sample():
model = GraphCutSelection(100,
'cosine',
optimizer='sample',
random_state=0)
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_sample_ranking)
assert_array_almost_equal(model.gains, digits_cosine_sample_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_euclidean_naive_init():
model = GraphCutSelection(100,
'euclidean',
optimizer='naive',
initial_subset=digits_euclidean_ranking[:5])
model.fit(X_digits)
assert_array_equal(model.ranking[:20], digits_euclidean_ranking[5:25])
assert_array_almost_equal(model.gains[:20], digits_euclidean_gains[5:25],
4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_greedi_ln_object():
model = GraphCutSelection(100,
'cosine',
optimizer=GreeDi(optimizer1='lazy',
optimizer2='naive',
random_state=0))
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_greedi_ranking)
assert_array_almost_equal(model.gains, digits_cosine_greedi_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_greedi_ll():
model = GraphCutSelection(100,
'cosine',
optimizer='greedi',
optimizer_kwds={
'optimizer1': 'lazy',
'optimizer2': 'lazy'
},
random_state=0)
model.fit(X_digits)
assert_array_equal(model.ranking[:30], digits_cosine_greedi_ranking[:30])
assert_array_almost_equal(model.gains[:30],
digits_cosine_greedi_gains[:30], 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_two_stage_sparse():
model = GraphCutSelection(100, 'precomputed', optimizer='two-stage')
model.fit(X_digits_cosine_sparse)
assert_array_equal(model.ranking, digits_cosine_ranking)
assert_array_almost_equal(model.gains, digits_cosine_gains, 4)
def test_digits_euclidean_lazy_init(): model = GraphCutSelection(100, 'euclidean', optimizer='lazy', initial_subset=digits_euclidean_ranking[:5]) model.fit(X_digits) assert_array_equal(model.ranking[:-5], digits_euclidean_ranking[5:]) assert_array_almost_equal(model.gains[:-5], digits_euclidean_gains[5:], 4)
def test_digits_cosine_approximate_object():
model = GraphCutSelection(100, 'cosine', optimizer=ApproximateLazyGreedy())
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_approx_ranking)
assert_array_almost_equal(model.gains, digits_cosine_approx_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_two_stage_object():
model = GraphCutSelection(100, 'cosine', optimizer=TwoStageGreedy())
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_ranking)
assert_array_almost_equal(model.gains, digits_cosine_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_stochastic(): model = GraphCutSelection(100, 'cosine', optimizer='stochastic', random_state=0) model.fit(X_digits) assert_array_equal(model.ranking, digits_cosine_stochastic_ranking) assert_array_almost_equal(model.gains, digits_cosine_stochastic_gains, 4)
def test_digits_corr_two_stage():
model = GraphCutSelection(100, 'corr', optimizer='two-stage')
model.fit(X_digits)
assert_array_equal(model.ranking, digits_corr_ranking)
assert_array_almost_equal(model.gains, digits_corr_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_lazy_object(): model = GraphCutSelection(100, 'cosine', optimizer=LazyGreedy()) model.fit(X_digits) assert_array_equal(model.ranking, digits_cosine_ranking) assert_array_almost_equal(model.gains, digits_cosine_gains, 4)
def test_digits_cosine_greedi_ll_object(): model = GraphCutSelection(100, 'cosine', optimizer=GreeDi( optimizer1='lazy', optimizer2='lazy', random_state=0)) model.fit(X_digits) assert_array_equal(model.ranking[:30], digits_cosine_greedi_ranking[:30]) assert_array_almost_equal(model.gains[:30], digits_cosine_greedi_gains[:30], 4)
def test_digits_cosine_sample_object(): model = GraphCutSelection(100, 'cosine', optimizer=SampleGreedy(random_state=0)) model.fit(X_digits) assert_array_equal(model.ranking, digits_cosine_sample_ranking) assert_array_almost_equal(model.gains, digits_cosine_sample_gains, 4)
def test_digits_cosine_naive(): model = GraphCutSelection(100, 'cosine', optimizer='naive') model.fit(X_digits) assert_array_equal(model.ranking, digits_cosine_ranking) assert_array_almost_equal(model.gains, digits_cosine_gains, 4)
def test_digits_precomputed_lazy():
model = GraphCutSelection(100, 'precomputed', optimizer='lazy')
model.fit(X_digits_corr_cupy)
assert_array_equal(model.ranking, digits_corr_ranking)
assert_array_almost_equal(model.gains, digits_corr_gains, 4)
def test_digits_cosine_lazy():
model = GraphCutSelection(100, 'cosine', optimizer='lazy')
model.fit(X_digits)
assert_array_equal(model.ranking, digits_cosine_ranking)
assert_array_almost_equal(model.gains, digits_cosine_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_cosine_approximate_sparse():
model = GraphCutSelection(100, 'precomputed', optimizer='approximate-lazy')
model.fit(X_digits_cosine_sparse)
assert_array_equal(model.ranking, digits_cosine_approx_ranking)
assert_array_almost_equal(model.gains, digits_cosine_approx_gains, 4)
def test_digits_euclidean_two_stage(): model = GraphCutSelection(100, 'euclidean', optimizer='two-stage') model.fit(X_digits) assert_array_equal(model.ranking, digits_euclidean_ranking) assert_array_almost_equal(model.gains, digits_euclidean_gains, 4)
def test_digits_euclidean_naive():
model = GraphCutSelection(100, 'euclidean', optimizer='naive')
model.fit(X_digits)
assert_array_equal(model.ranking, digits_euclidean_ranking)
assert_array_almost_equal(model.gains, digits_euclidean_gains, 4)
assert_array_almost_equal(model.subset, X_digits[model.ranking])
