def test_accuracy_runner():
# Set up data that should deliver accuracy of 0.20 if all goes right
class MockAlgo:
def fit(self, X, y):
return
def predict(self, X):
nr = X.shape[0]
res = np.zeros(nr)
res[0:int(nr / 5.0)] = 1.0
return res
pair = algorithms.AlgorithmPair(
MockAlgo,
MockAlgo,
shared_args={},
name="Mock",
accuracy_function=metrics.accuracy_score,
)
runner = AccuracyComparisonRunner([20], [5],
dataset_name='zeros',
test_fraction=0.20)
results = runner.run(pair)[0]
assert results["cuml_acc"] == pytest.approx(0.80)
def test_fil_input_types(input_type):
pair = algorithms.algorithm_by_name('FIL')
if not has_xgboost():
pytest.xfail()
runner = AccuracyComparisonRunner(
[20], [5], dataset_name='classification', test_fraction=0.5,
input_type=input_type)
results = runner.run(pair, run_cpu=False)[0]
assert results["cuml_acc"] is not None
def test_real_algos_runner(algo_name):
pair = algorithms.algorithm_by_name(algo_name)
if (algo_name == 'UMAP' and not has_umap()) or \
(algo_name == 'FIL' and not has_xgboost()):
pytest.xfail()
runner = AccuracyComparisonRunner([20], [5],
dataset_name='classification',
test_fraction=0.20)
results = runner.run(pair)[0]
print(results)
assert results["cuml_acc"] is not None
def test_multi_reps():
class CountingAlgo:
tot_reps = 0
def fit(self, X, y):
CountingAlgo.tot_reps += 1
pair = algorithms.AlgorithmPair(
CountingAlgo,
CountingAlgo,
shared_args={},
name="Counting",
)
runner = AccuracyComparisonRunner([20], [5],
dataset_name='zeros',
test_fraction=0.20,
n_reps=4)
runner.run(pair)
# Double the n_reps since it is used in cpu and cuml versions
assert CountingAlgo.tot_reps == 8
