def test_venn_diagram(self):
sources = ["SVM Learner", "Naive Bayes", "Random Forest"]
item_id_var = StringVariable("item_id")
source_var = StringVariable("source")
table = Table("zoo")
class_var = table.domain.class_var
cv = np.random.randint(len(class_var.values), size=(3, len(sources)))
tables = []
# pylint: disable=consider-using-enumerate
for i in range(len(sources)):
temp_table = Table.from_table(table.domain, table,
[0 + i, 1 + i, 2 + i])
temp_d = (DiscreteVariable("%s(%s)" % (class_var.name,
sources[0 + i]),
class_var.values),
source_var, item_id_var)
temp_m = np.array([[cv[0, i], sources[i], table.metas[0 + i, 0]],
[cv[1, i], sources[i], table.metas[1 + i, 0]],
[cv[2, i], sources[i], table.metas[2 + i, 0]]],
dtype=object)
temp_table = self.add_metas(temp_table, temp_d, temp_m)
tables.append(temp_table)
data = table_concat(tables)
varying = varying_between(data, item_id_var)
if source_var in varying:
varying.remove(source_var)
data = reshape_wide(data, varying, [item_id_var], [source_var])
data = drop_columns(data, [item_id_var])
result = np.array([[table.metas[0, 0], cv[0, 0], np.nan, np.nan],
[table.metas[1, 0], cv[1, 0], cv[0, 1], np.nan],
[table.metas[2, 0], cv[2, 0], cv[1, 1], cv[0, 2]],
[table.metas[3, 0], np.nan, cv[2, 1], cv[1, 2]],
[table.metas[4, 0], np.nan, np.nan, cv[2, 2]]],
dtype=object)
for i in range(len(result)):
for j in range(len(result[0])):
val = result[i][j]
if isinstance(val, float) and np.isnan(val):
self.assertTrue(np.isnan(data.metas[i][j]))
else:
np.testing.assert_equal(data.metas[i][j], result[i][j])
def test_reshape_wide(self):
class_var = DiscreteVariable("c", values=["x"])
item_id_var = StringVariable("item_id")
source_var = StringVariable("source")
c1, c, item_id, ca, cb = np.random.randint(10, size=5)
data = Table(Domain([ContinuousVariable("c1")], [class_var],
[DiscreteVariable("c(a)", class_var.values),
DiscreteVariable("c(b)", class_var.values),
source_var, item_id_var]),
np.array([[c1], [c1]], dtype=object),
np.array([[c], [c]], dtype=object),
np.array([[ca, np.nan, "a", item_id],
[np.nan, cb, "b", item_id]], dtype=object))
data = reshape_wide(data, [], [item_id_var], [source_var])
self.assertFalse(any(np.isnan(data.metas.astype(np.float32)[0])))
self.assertEqual(len(data), 1)
np.testing.assert_equal(data.metas, np.array([[ca, cb, item_id]],
dtype=object))
def test_reshape_wide(self):
class_var = DiscreteVariable("c", values=["x"])
item_id_var = StringVariable("item_id")
source_var = StringVariable("source")
c1, c, item_id, ca, cb = np.random.randint(10, size=5)
data = Table(
Domain([ContinuousVariable("c1")], [class_var], [
DiscreteVariable("c(a)", class_var.values),
DiscreteVariable("c(b)", class_var.values), source_var,
item_id_var
]), np.array([[c1], [c1]], dtype=object),
np.array([[c], [c]], dtype=object),
np.array([[ca, np.nan, "a", item_id], [np.nan, cb, "b", item_id]],
dtype=object))
data = reshape_wide(data, [], [item_id_var], [source_var])
self.assertFalse(any(np.isnan(data.metas.astype(np.float32)[0])))
self.assertEqual(len(data), 1)
np.testing.assert_equal(data.metas,
np.array([[ca, cb, item_id]], dtype=object))
def test_reshape_wide_missing_vals(self):
data = Table(test_filename("test9.tab"))
reshaped_data = reshape_wide(data, [], [data.domain[0]],
[data.domain[0]])
self.assertEqual(2, len(reshaped_data))
def test_reshape_wide_missing_vals(self):
data = Table(test_filename("test9.tab"))
reshaped_data = reshape_wide(data, [], [data.domain[0]],
[data.domain[0]])
self.assertEqual(2, len(reshaped_data))