def test_call(self):
f = FeatureFunc("a + 1", [("a", "a")])
self.assertEqual(f({"a": 2}), 3)
iris = Table("iris")
f = FeatureFunc("sepal_width + 10",
[("sepal_width", iris.domain["sepal width"])])
r = f(iris)
np.testing.assert_array_equal(r, iris.X[:, 1] + 10)
def test_call(self):
iris = Table("iris")
f = FeatureFunc("sepal_width + 10",
[("sepal_width", iris.domain["sepal width"])])
r = f(iris)
np.testing.assert_array_equal(r, iris.X[:, 1] + 10)
self.assertEqual(f(iris[0]), iris[0]["sepal width"] + 10)
def test_reconstruct(self):
f = FeatureFunc("a * x + c", [("x", "x")], {"a": 2, "c": 10})
self.assertEqual(f({"x": 2}), 14)
f1 = pickle.loads(pickle.dumps(f))
self.assertEqual(f1({"x": 2}), 14)
fc = copy.copy(f)
self.assertEqual(fc({"x": 3}), 16)
def test_string_casting(self):
zoo = Table("zoo")
f = FeatureFunc("name[0]",
[("name", zoo.domain["name"])])
r = f(zoo)
self.assertEqual(r, [x[0] for x in zoo.metas[:, 0]])
self.assertEqual(f(zoo[0]), str(zoo[0, "name"])[0])
def test_hash_eq(self):
iris = Table("iris")
f = FeatureFunc("1 / petal_length",
[("petal_length", iris.domain["petal length"])])
g = copy.deepcopy(f)
self.assertEqual(f, g)
self.assertEqual(hash(f), hash(g))
def test_invalid_expression_variable(self):
iris = Table("iris")
f = FeatureFunc("1 / petal_length",
[("petal_length", iris.domain["petal length"])])
iris[0]["petal length"] = 0
f.mask_exceptions = False
self.assertRaises(Exception, f, iris)
self.assertRaises(Exception, f, iris[0])
_ = f(iris[1])
f.mask_exceptions = True
r = f(iris)
self.assertTrue(np.isnan(r[0]))
self.assertFalse(np.isnan(r[1]))
self.assertTrue(np.isnan(f(iris[0])))
self.assertFalse(np.isnan(f(iris[1])))
def test_missing_variable(self):
zoo = Table("zoo")
assert zoo.domain.class_var.name == "type"
f = FeatureFunc("type[0]", [("type", zoo.domain["type"])])
no_class = Domain(zoo.domain.attributes, None, zoo.domain.metas)
data2 = zoo.transform(no_class)
r = f(data2)
self.assertTrue(np.all(np.isnan(r)))
self.assertTrue(np.isnan(f(data2[0])))
def test_reconstruct(self):
iris = Table("iris")
inst1 = iris[0]
val1 = 2 * inst1["sepal width"] + 10
inst2 = iris[100]
val2 = 2 * inst2["sepal width"] + 10
f = FeatureFunc("a * sepal_width + c",
[("sepal_width", iris.domain["sepal width"])],
{"a": 2, "c": 10})
self.assertAlmostEqual(f(inst1), val1)
f1 = pickle.loads(pickle.dumps(f))
self.assertAlmostEqual(f1(inst1), val1)
fc = copy.copy(f)
self.assertEqual(fc(inst2), val2)
def test_repr(self):
self.assertEqual(repr(FeatureFunc("a + 1", [("a", 2)])),
"FeatureFunc('a + 1', [('a', 2)], {}, None)")
def test_time_str(self):
data = Table.from_numpy(Domain([TimeVariable("T", have_date=True)]), [[0], [0]])
f = FeatureFunc("str(T)", [("T", data.domain[0])])
c = f(data)
self.assertEqual(c, ["1970-01-01", "1970-01-01"])