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_speedup_runner():
class MockAlgo:
def __init__(self, t):
self.t = t
def fit(self, X, y):
time.sleep(self.t)
return
def predict(self, X):
nr = X.shape[0]
res = np.zeros(nr)
res[0:int(nr / 5.0)] = 1.0
return res
class FastMockAlgo(MockAlgo):
def __init__(self):
MockAlgo.__init__(self, 0.1)
class SlowMockAlgo(MockAlgo):
def __init__(self):
MockAlgo.__init__(self, 2)
pair = algorithms.AlgorithmPair(
SlowMockAlgo,
FastMockAlgo,
shared_args={},
name="Mock",
bench_func=fit_predict,
accuracy_function=metrics.accuracy_score,
)
runner = SpeedupComparisonRunner(
[20], [5], dataset_name='zeros'
)
results = runner.run(pair)[0]
expected_speedup = SlowMockAlgo().t / FastMockAlgo().t
assert results["speedup"] == pytest.approx(expected_speedup, 0.4)
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