def test_example_group_algorithm_same_size_best_approximation(
data_example, dataprops_example
):
assert Grouper(
data_example, dataprops_example
).group_algorithm_same_size_best_approximation(2) == {
0: ["id1"],
1: ["id2"],
}
def test_b_unique_group_algorithm_same_size_best_approximation(
data_b_unique, dataprops_b_unique
):
assert Grouper(
data_b_unique, dataprops_b_unique
).group_algorithm_same_size_best_approximation(2) == {
0: ["a", "b"],
1: ["c"],
}
def test_random_group_algorithm_same_size_best_approximation(
data_random, dataprops_random
):
assert Grouper(
data_random, dataprops_random[0]
).group_algorithm_same_size_best_approximation(3) in [
{0: ["c", "g", "j"], 1: ["f", "a", "d", "e"], 2: ["i", "b", "h"]},
{0: ["c", "g", "e"], 1: ["f", "a", "d", "j"], 2: ["i", "b", "h"]},
]
def test_a_split_group_algorithm_same_size_best_approximation(
data_a_split, dataprops_a_split
):
assert Grouper(
data_a_split, dataprops_a_split
).group_algorithm_same_size_best_approximation(2) == {
0: ["a"],
1: ["b", "c"],
}
def test_ten_percent_difference_matrix(
data_ten_percent_difference, dataprops_ten_percent_difference
):
assert Grouper(
data_ten_percent_difference, dataprops_ten_percent_difference
).difference_matrix() == {
"a": {"a": 0.0, "b": 0.5900000000000001, "c": 0.1},
"b": {"a": 0.5900000000000001, "b": 0.0, "c": 0.49000000000000005},
"c": {"a": 0.1, "b": 0.49000000000000005, "c": 0.0},
}
def test_random_scaled_grid(data_random, dataprops_random):
assert Grouper(data_random, dataprops_random[0]).scaled_grid() == {
"a": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 1.0 # type: ignore
},
"b": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.8523199056630058 # type: ignore
},
"c": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.2760946577539797 # type: ignore
},
"d": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.0 # type: ignore
},
"e": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.4310090049507776 # type: ignore
},
"f": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.24938704521990493 # type: ignore
},
"g": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.8964598901654225 # type: ignore
},
"h": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.07582508793269464 # type: ignore
},
"i": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.37178190830673485 # type: ignore
},
"j": {
RandomizerProperty(
Key("rand"), random=dataprops_random[1]
): 0.5541630439756922 # type: ignore
},
}
def test_ten_percent_scaled_grid(
data_ten_percent_difference, dataprops_ten_percent_difference
):
assert Grouper(
data_ten_percent_difference, dataprops_ten_percent_difference
).scaled_grid() == {
"a": {
CategoricalProperty(Key("c1"), Weight(1.0)): "left",
CategoricalProperty(
Key("c2"),
Weight(1.0),
connections=[Connection("ye", "yee", 0.5)], # type: ignore
): "ye",
CategoricalProperty(Key("c3"), Weight(0.25)): "re",
NumericProperty(
Key("n1"),
Weight(1.0),
measurement_bounds=ValuePair(0.0, 10.0),
scale_bounds=ValuePair(0.0, 1.0),
): 0.1,
NumericProperty(Key("n2"), Weight(1.0)): 0.0,
},
"b": {
CategoricalProperty(Key("c1"), Weight(1.0)): "right",
CategoricalProperty(
Key("c2"),
Weight(1.0),
connections=[Connection("ye", "yee", 0.5)], # type: ignore
): "yee",
CategoricalProperty(Key("c3"), Weight(0.25)): "ree",
NumericProperty(
Key("n1"), Weight(1.0), measurement_bounds=ValuePair(0.0, 10.0)
): 0.3,
NumericProperty(Key("n2"), Weight(1.0)): 1.0,
},
"c": {
CategoricalProperty(Key("c1"), Weight(1.0)): "left",
CategoricalProperty(
Key("c2"),
Weight(1.0),
connections=[Connection("ye", "yee", 0.5)], # type: ignore
): "yee",
CategoricalProperty(Key("c3"), Weight(0.25)): "re",
NumericProperty(
Key("n1"), Weight(1.0), measurement_bounds=ValuePair(0.0, 10.0)
): 0.1,
NumericProperty(Key("n2"), Weight(1.0)): 0.0,
},
}
def test_a_split_scaled_grid(data_a_split, dataprops_a_split):
assert Grouper(data_a_split, dataprops_a_split).scaled_grid() == {
"a": {
CategoricalProperty(Key("c1"), Weight(1.0)): "left",
CategoricalProperty(Key("c2"), Weight(1.0)): "up",
},
"b": {
CategoricalProperty(Key("c1"), Weight(1.0)): "right",
CategoricalProperty(Key("c2"), Weight(1.0)): "up",
},
"c": {
CategoricalProperty(Key("c1"), Weight(1.0)): "left",
CategoricalProperty(Key("c2"), Weight(1.0)): "down",
},
}
def test_example_scaled_grid(data_example, dataprops_example):
assert Grouper(data_example, dataprops_example).scaled_grid() == {
"id1": {
CategoricalProperty(Key("c1"), Weight(1.0)): "me",
CategoricalProperty(
Key("c2"),
Weight(1.0),
connections=[
Connection( # type: ignore
"val1",
"val2",
Similarity(0.25),
)
],
): "val1",
NumericProperty(
Key("n"), Weight(0.1), measurement_bounds=ValuePair(0.0, 10.0)
): 0.2,
},
"id2": {
CategoricalProperty(Key("c1"), Weight(1.0)): "you",
CategoricalProperty(
Key("c2"),
Weight(1.0),
connections=[
Connection( # type: ignore
"val1",
"val2",
Similarity(0.25),
)
],
): "val2",
NumericProperty(
Key("n"), Weight(0.1), measurement_bounds=ValuePair(0.0, 10.0)
): 0.7,
},
}
def test_ten_percent_group_algorithm_number(
data_ten_percent_difference, dataprops_ten_percent_difference
):
assert Grouper(
data_ten_percent_difference, dataprops_ten_percent_difference
).group_algorithm_number(2) == {0: ["a", "b"], 2: ["c"]}
def test_b_unique_scaled_grid(data_b_unique, dataprops_b_unique):
assert Grouper(data_b_unique, dataprops_b_unique).scaled_grid() == {
"a": {CategoricalProperty(Key("c1"), Weight(1.0)): "left"},
"b": {CategoricalProperty(Key("c1"), Weight(1.0)): "right"},
"c": {CategoricalProperty(Key("c1"), Weight(1.0)): "left"},
}
def test_random_matrix(data_random, dataprops_random):
assert Grouper(data_random, dataprops_random[0]).difference_matrix() == {
"a": {
"a": 0.0,
"b": 0.14768009433699425,
"c": 0.7239053422460203,
"d": 1.0,
"e": 0.5689909950492225,
"f": 0.750612954780095,
"g": 0.10354010983457751,
"h": 0.9241749120673054,
"i": 0.6282180916932651,
"j": 0.44583695602430784,
},
"b": {
"a": 0.14768009433699425,
"b": 0.0,
"c": 0.5762252479090261,
"d": 0.8523199056630058,
"e": 0.42131090071222815,
"f": 0.6029328604431008,
"g": 0.04413998450241674,
"h": 0.7764948177303111,
"i": 0.4805379973562709,
"j": 0.2981568616873136,
},
"c": {
"a": 0.7239053422460203,
"b": 0.5762252479090261,
"c": 0.0,
"d": 0.2760946577539797,
"e": 0.15491434719679792,
"f": 0.02670761253407475,
"g": 0.6203652324114428,
"h": 0.20026956982128502,
"i": 0.09568725055275518,
"j": 0.2780683862217125,
},
"d": {
"a": 1.0,
"b": 0.8523199056630058,
"c": 0.2760946577539797,
"d": 0.0,
"e": 0.4310090049507776,
"f": 0.24938704521990493,
"g": 0.8964598901654225,
"h": 0.07582508793269464,
"i": 0.37178190830673485,
"j": 0.5541630439756922,
},
"e": {
"a": 0.5689909950492225,
"b": 0.42131090071222815,
"c": 0.15491434719679792,
"d": 0.4310090049507776,
"e": 0.0,
"f": 0.18162195973087267,
"g": 0.4654508852146449,
"h": 0.35518391701808294,
"i": 0.05922709664404274,
"j": 0.12315403902491456,
},
"f": {
"a": 0.750612954780095,
"b": 0.6029328604431008,
"c": 0.02670761253407475,
"d": 0.24938704521990493,
"e": 0.18162195973087267,
"f": 0.0,
"g": 0.6470728449455175,
"h": 0.1735619572872103,
"i": 0.12239486308682992,
"j": 0.3047759987557872,
},
"g": {
"a": 0.10354010983457751,
"b": 0.04413998450241674,
"c": 0.6203652324114428,
"d": 0.8964598901654225,
"e": 0.4654508852146449,
"f": 0.6470728449455175,
"g": 0.0,
"h": 0.8206348022327279,
"i": 0.5246779818586876,
"j": 0.34229684618973033,
},
"h": {
"a": 0.9241749120673054,
"b": 0.7764948177303111,
"c": 0.20026956982128502,
"d": 0.07582508793269464,
"e": 0.35518391701808294,
"f": 0.1735619572872103,
"g": 0.8206348022327279,
"h": 0.0,
"i": 0.2959568203740402,
"j": 0.4783379560429975,
},
"i": {
"a": 0.6282180916932651,
"b": 0.4805379973562709,
"c": 0.09568725055275518,
"d": 0.37178190830673485,
"e": 0.05922709664404274,
"f": 0.12239486308682992,
"g": 0.5246779818586876,
"h": 0.2959568203740402,
"i": 0.0,
"j": 0.1823811356689573,
},
"j": {
"a": 0.44583695602430784,
"b": 0.2981568616873136,
"c": 0.2780683862217125,
"d": 0.5541630439756922,
"e": 0.12315403902491456,
"f": 0.3047759987557872,
"g": 0.34229684618973033,
"h": 0.4783379560429975,
"i": 0.1823811356689573,
"j": 0.0,
},
}
def test_b_unique_matrix(data_b_unique, dataprops_b_unique):
assert Grouper(data_b_unique, dataprops_b_unique).difference_matrix() == {
"a": {"a": 0.0, "b": 1.0, "c": 0.0},
"b": {"a": 1.0, "b": 0.0, "c": 1.0},
"c": {"a": 0.0, "b": 1.0, "c": 0.0},
}
def test_example_group_algorithm_number(data_example, dataprops_example):
assert Grouper(data_example, dataprops_example).group_algorithm_number(2) == {
0: ["id1"],
1: ["id2"],
}
def test_example_matrix(data_example, dataprops_example):
assert Grouper(data_example, dataprops_example).difference_matrix() == {
"id1": {"id1": 0.0, "id2": 0.6},
"id2": {"id1": 0.6, "id2": 0.0},
}
def test_ten_percent_group_algorithm_same_size_best_approximation(
data_ten_percent_difference, dataprops_ten_percent_difference
):
assert Grouper(
data_ten_percent_difference, dataprops_ten_percent_difference
).group_algorithm_same_size_best_approximation(2) == {0: ["a", "b"], 1: ["c"]}
def test_a_split_group_algorithm_number(data_a_split, dataprops_a_split):
assert Grouper(data_a_split, dataprops_a_split).group_algorithm_number(2) == {
0: ["a"],
1: ["b", "c"],
}
def test_a_split_matrix(data_a_split, dataprops_a_split):
assert Grouper(data_a_split, dataprops_a_split).difference_matrix() == {
"a": {"a": 0.0, "b": 0.5, "c": 0.5},
"b": {"a": 0.5, "b": 0.0, "c": 1.0},
"c": {"a": 0.5, "b": 1.0, "c": 0.0},
}
def test_b_unique_group_algorithm_number(data_b_unique, dataprops_b_unique):
assert Grouper(data_b_unique, dataprops_b_unique).group_algorithm_number(2) == {
0: ["a", "b"],
2: ["c"],
}