def test_save_summary(self):
with self.assertRaises(ValueError):
grid = {
"sigma": np.arange(2, 3, 2),
"prototypes_per_class": np.arange(2, 3, 2)
}
gs = GridSearch(clf=RRSLVQ(), grid=grid, max_samples=300)
gs.save_summary()
def test_parameter_grid_search_arslvq():
grid = {
"sigma": np.append(1, np.arange(2, 11, 2)),
"prototypes_per_class": np.append(1, np.arange(2, 11, 2)),
"gamma": np.array([0.7, 0.9, 0.999]),
"confidence": np.array([0.01, 0.001]),
"window_size": np.array([100, 200, 300, 800])
}
clf = ARSLVQ()
gs = GridSearch(clf=clf, grid=grid, max_samples=50000)
gs.streams = gs.init_real_world() + gs.init_standard_streams(
) + gs.init_reoccuring_standard_streams()
gs.search()
gs.save_summary()
def test_parameter_grid_search_arslvq():
grid = {
"sigma": np.arange(2, 11, 2),
"prototypes_per_class": np.arange(2, 11, 2)
}
clf = ARSLVQ(gradient_descent="Adadelta")
gs = GridSearch(clf=clf, grid=grid, max_samples=50000)
gs.search()
gs.save_summary()
def test_parameter_grid_search_rslvq():
grid = {
"sigma": np.arange(2, 11, 2),
"prototypes_per_class": np.arange(2, 11, 2)
}
clf = RSLVQ()
gs = GridSearch(clf=clf, grid=grid, max_samples=50000)
gs.search()
gs.save_summary()
def test_grid_search(self):
grid = {
"sigma": np.arange(2, 3, 2),
"prototypes_per_class": np.arange(2, 3, 2)
}
gs = GridSearch(clf=RRSLVQ(), grid=grid, max_samples=300)
gs.search()
gs.save_summary()
self.assertIsNotNone(gs.best_runs)
def test_gridsearch_via_cv():
grid = {
"sigma": np.append(1, np.arange(2, 11, 2)),
"prototypes_per_class": np.append(1, np.arange(2, 11, 2)),
"gamma": np.array([0.7, 0.9, 0.999]),
"confidence": np.array([0.01, 0.001]),
"window_size": np.array([100, 200, 300, 800])
}
grid = {
"sigma": np.append(1, np.arange(2, 3, 2)),
"prototypes_per_class": np.append(1, np.arange(2, 3, 2))
}
clf = ARSLVQ()
cv = GridSearch([clf], max_samples=500)
cv.streams = cv.init_standard_streams()
cv.parameter_grid_search(grid)
def test_missing_streams():
grid = {
"sigma": np.append(1, np.arange(2, 11, 2)),
"prototypes_per_class": np.append(1, np.arange(2, 11, 2)),
"gamma": np.array([0.7, 0.9, 0.999]),
"confidence": np.array([0.01, 0.001]),
"window_size": np.array([800])
}
clf = ARSLVQ()
led_a = ConceptDriftStream(
stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=3),
drift_stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=7),
random_state=None,
alpha=90.0, # angle of change grade 0 - 90
position=250000,
width=1)
led_a.name = "led_a"
led_g = ConceptDriftStream(stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=3),
drift_stream=LEDGeneratorDrift(
has_noise=False,
noise_percentage=0.0,
n_drift_features=7),
random_state=None,
position=250000,
width=50000)
led_g.name = "led_g"
led_fa = ReoccuringDriftStream(
stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=3),
drift_stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=7),
random_state=None,
alpha=90.0, # angle of change grade 0 - 90
position=2000,
width=1)
led_fg = ReoccuringDriftStream(
stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=3),
drift_stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=7),
random_state=None,
position=2000,
width=1000)
covertype = FileStream(os.path.realpath('covtype.csv')) # Label failure
covertype.name = "covertype"
poker = FileStream(os.path.realpath('poker.csv')) # label failure
poker.name = "poker"
airlines = FileStream(os.path.realpath('airlines.csv'))
airlines.name = "airport"
gs = GridSearch(clf=clf, grid=grid, max_samples=50000)
gs.streams = [led_a, led_g, led_fa, led_fg, covertype, poker, airlines]
gs.search()
gs.save_summary()