def test_sparse_basket(self):
current_dir = os.path.dirname(__file__)
dataset = os.path.join(current_dir, "iris_basket.basket")
table = BasketReader().read_file(dataset)
test = Orange.data.Table.from_table_rows(table,
range(0, len(table), 2))
train = Orange.data.Table.from_table_rows(table,
range(1, len(table), 2))
learn = DummyMulticlassLearner()
clf = learn(train)
p = clf(test)
self.assertEqual(p.shape, test.Y.shape)
p = clf(test.X)
self.assertEqual(p.shape, test.Y.shape)
def test_probs_from_value(self):
nrows = 100
ncols = 5
x = np.random.randint(0, 2, (nrows, ncols))
# single class variable
y = np.random.randint(0, 2, (nrows, 1))
d = Domain(
[
DiscreteVariable("v" + str(i),
values=[str(v) for v in np.unique(x[:, i])])
for i in range(ncols)
],
DiscreteVariable("c", values="12"),
)
t = Table(d, x, y)
learn = DummyLearner()
clf = learn(t)
clf.ret = Model.Value
y2 = clf(x, ret=Model.Probs)
self.assertEqual(y2.shape, (nrows, 2))
y2, probs = clf(x, ret=Model.ValueProbs)
self.assertEqual(y2.shape, (nrows, ))
self.assertEqual(probs.shape, (nrows, 2))
# multitarget
y = np.random.randint(1, 6, (nrows, 2))
y[:, 0] = y[:, 0] // 3 # majority = 1
y[:, 1] = (y[:, 1] + 4) // 3 - 1 # majority = 1
domain = Domain(
[ContinuousVariable("i" + str(i)) for i in range(ncols)],
[
DiscreteVariable("c" + str(i), values="0123")
for i in range(y.shape[1])
],
)
t = Table(domain, x, y)
learn = DummyMulticlassLearner()
clf = learn(t)
clf.ret = Model.Value
probs = clf(x, ret=Model.Probs)
self.assertEqual(probs.shape, (nrows, 2, 4))
y2, probs = clf(x, ret=Model.ValueProbs)
self.assertEqual(y2.shape, y.shape)
self.assertEqual(probs.shape, (nrows, 2, 4))
def test_probs_from_value(self):
nrows = 100
ncols = 5
x = np.random.random_integers(0, 1, (nrows, ncols))
# single class variable
y = np.random.random_integers(0, 1, (nrows, 1))
t = Table(
Domain([
DiscreteVariable('v' + str(i), values=np.unique(x[:, i]))
for i in range(ncols)
], DiscreteVariable('c', values=[1, 2])), x, y)
learn = DummyLearner()
clf = learn(t)
clf.ret = Model.Value
y2 = clf(x, ret=Model.Probs)
self.assertEqual(y2.shape, (nrows, 2))
y2, probs = clf(x, ret=Model.ValueProbs)
self.assertEqual(y2.shape, (nrows, ))
self.assertEqual(probs.shape, (nrows, 2))
# multitarget
y = np.random.random_integers(1, 5, (nrows, 2))
y[:, 0] = y[:, 0] // 3 # majority = 1
y[:, 1] = (y[:, 1] + 4) // 3 - 1 # majority = 1
domain = Domain(
[ContinuousVariable('i' + str(i)) for i in range(ncols)], [
DiscreteVariable('c' + str(i), values=range(4))
for i in range(y.shape[1])
])
t = Table(domain, x, y)
learn = DummyMulticlassLearner()
clf = learn(t)
clf.ret = Model.Value
probs = clf(x, ret=Model.Probs)
self.assertEqual(probs.shape, (nrows, 2, 4))
y2, probs = clf(x, ret=Model.ValueProbs)
self.assertEqual(y2.shape, y.shape)
self.assertEqual(probs.shape, (nrows, 2, 4))
