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import unittest
from unittest.mock import Mock
import numpy as np
import pandas as pd
from Orange.data import (
DiscreteVariable,
ContinuousVariable,
Domain,
StringVariable,
Table,
table_to_frame,
)
def create_sample_data():
domain = Domain(
[
ContinuousVariable("a"),
ContinuousVariable("b"),
ContinuousVariable("cvar"),
DiscreteVariable("dvar", values=["val1", "val2"]),
],
metas=[StringVariable("svar")],
)
return Table.from_numpy(
domain,
np.array(
[
[1, 1, 0.1, 0],
[1, 1, 0.2, 1],
[1, 2, np.nan, np.nan],
[1, 2, 0.3, 1],
[1, 3, 0.3, 0],
[1, 3, 0.4, 1],
[1, 3, 0.6, 0],
[2, 1, 1.0, 1],
[2, 1, 2.0, 0],
[2, 2, 3.0, 1],
[2, 2, -4.0, 0],
[2, 3, 5.0, 1],
[2, 3, 5.0, 0],
]
),
metas=np.array(
[
["sval1"],
["sval2"],
[""],
["sval2"],
["sval1"],
["sval2"],
["sval1"],
["sval2"],
["sval1"],
["sval2"],
["sval1"],
["sval2"],
["sval1"],
]
),
)
# pylint: disable=abstract-method
class AlternativeTable(Table):
pass
class DomainTest(unittest.TestCase):
def setUp(self) -> None:
self.data = create_sample_data()
def test_simple_aggregation(self):
"""Test aggregation results"""
d = self.data.domain
gb = self.data.groupby([d["a"]])
output = gb.aggregate({d["a"]: ["mean"], d["b"]: ["mean"]})
np.testing.assert_array_almost_equal(output.X, [[1, 2.143], [2, 2]], decimal=3)
np.testing.assert_array_almost_equal(output.metas, [[1], [2]], decimal=3)
self.assertListEqual(
["a - mean", "b - mean"], [d.name for d in output.domain.attributes]
)
self.assertListEqual(["a"], [d.name for d in output.domain.metas])
def test_aggregation(self):
d = self.data.domain
gb = self.data.groupby([self.data.domain["a"], self.data.domain["b"]])
output = gb.aggregate(
{
d["cvar"]: [("Mean", "mean"), ("Median", "median"), ("Mean1", np.mean)],
d["dvar"]: [("Count defined", "count"), ("Count", "size")],
d["svar"]: [("Concatenate", "".join)],
}
)
expected_columns = [
"cvar - Mean",
"cvar - Median",
"cvar - Mean1",
"dvar - Count defined",
"dvar - Count",
"svar - Concatenate",
"a", # groupby variables are last two in metas
"b",
]
exp_df = pd.DataFrame(
[
[0.15, 0.15, 0.15, 2, 2, "sval1sval2", 1, 1],
[0.3, 0.3, 0.3, 1, 2, "sval2", 1, 2],
[0.433, 0.4, 0.433, 3, 3, "sval1sval2sval1", 1, 3],
[1.5, 1.5, 1.5, 2, 2, "sval2sval1", 2, 1],
[-0.5, -0.5, -0.5, 2, 2, "sval2sval1", 2, 2],
[5, 5, 5, 2, 2, "sval2sval1", 2, 3],
],
columns=expected_columns,
)
out_df = table_to_frame(output, include_metas=True)
pd.testing.assert_frame_equal(
out_df,
exp_df,
check_dtype=False,
check_column_type=False,
check_categorical=False,
atol=1e-3,
)
def test_preserve_table_class(self):
"""
Test whether result table has the same type than the imnput table,
e.g. if input table corpus the resulting table must be corpus too.
"""
data = AlternativeTable.from_table(self.data.domain, self.data)
gb = data.groupby([data.domain["a"]])
output = gb.aggregate({data.domain["a"]: ["mean"]})
self.assertIsInstance(output, AlternativeTable)
def test_preserve_variables(self):
a, _, _, dvar = self.data.domain.attributes
gb = self.data.groupby([a])
a.attributes = {"foo": "bar"}
dvar.attributes = {"foo": "baz"}
a.copy = Mock(side_effect=a.copy)
a.make = Mock(side_effect=a.make)
def f(*_):
return 0
output = gb.aggregate(
{a: [("copy", f, True),
("make", f, False),
("auto", f, None),
("string", f, StringVariable),
("number", f, ContinuousVariable)],
dvar: [("copy", f, True),
("make", f, False),
("auto", f, None),
("string", f, StringVariable),
("discrete", f, DiscreteVariable)]}
)
self.assertIsInstance(output.domain["a - copy"], ContinuousVariable)
a.copy.assert_called_once()
self.assertEqual(output.domain["a - copy"].attributes, {"foo": "bar"})
self.assertIsInstance(output.domain["a - make"], ContinuousVariable)
a.make.assert_called_once()
self.assertNotEqual(output.domain["a - make"].attributes, {"foo": "bar"})
self.assertIsInstance(output.domain["a - auto"], ContinuousVariable)
self.assertNotEqual(output.domain["a - auto"].attributes, {"foo": "bar"})
self.assertIsInstance(output.domain["a - string"], StringVariable)
self.assertIsInstance(output.domain["a - number"], ContinuousVariable)
self.assertNotEqual(output.domain["a - number"].attributes, {"foo": "bar"})
self.assertIsInstance(output.domain["dvar - copy"], DiscreteVariable)
self.assertEqual(output.domain["dvar - copy"].attributes, {"foo": "baz"})
self.assertIsInstance(output.domain["dvar - make"], DiscreteVariable)
self.assertNotEqual(output.domain["dvar - make"].attributes, {"foo": "baz"})
# f returns 0, so the column looks numeric! Let's test that it is
# converted to numeric.
self.assertIsInstance(output.domain["dvar - auto"], ContinuousVariable)
self.assertIsInstance(output.domain["dvar - string"], StringVariable)
self.assertIsInstance(output.domain["dvar - discrete"], DiscreteVariable)
self.assertNotEqual(output.domain["dvar - discrete"].attributes, {"foo": "baz"})
if __name__ == "__main__":
unittest.main()
|
