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Merged
sdpython merged 2 commits into from
Nov 3, 2025
Merged

Improves aggregation when key changed over time #284

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00c6d07
fix aggregration
xadupre Nov 3, 2025
3ee9d8d
tentative
xadupre Nov 3, 2025
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1 change: 1 addition & 0 deletions CHANGELOGS.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ Change Logs
0.8.0
+++++

* :pr:`283`: fix historical aggregation when multiple input sets are used
* :pr:`282`: add tools to understand better which functions were patched
* :pr:`280`: fixes patches for sdpa_attention_forward for different version of transformers
* :pr:`278`: implements ``onnx_generate_with_genai``
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145 changes: 145 additions & 0 deletions _unittests/ut_helpers/test_log_helper.py
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Expand Up @@ -614,6 +614,151 @@ def test_cube_sbs_with_time(self):
self.assertEqual(sbs_agg.index.names, ["date", "METRICS"])
self.assertEqual(sorted(sbs_agg.columns.names), ["CONF", "exporter"])

def test_fix_non_consistent_historical_data_no_change(self):
# no change
df = pandas.DataFrame(
[
dict(date="2025/01/01", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E1", model_s="O", model="M"),
]
)
cube = CubeLogs(
df, keys=["^model*", "exporter", "opt"], values=["time_p"], time="date"
).load()
view, _view_def = cube.view(
CubeViewDef(["^model.*"], ["^time_.*"]), return_view_def=True
)
expected = {
("time_p", pandas.Timestamp("2025-01-01 00:00:00")): {"ALL": 0.51},
("time_p", pandas.Timestamp("2025-01-02 00:00:00")): {"ALL": 0.51},
("time_p", pandas.Timestamp("2025-01-03 00:00:00")): {"ALL": 0.53},
}
self.assertEqual(expected, view.to_dict())

# no change
df = pandas.DataFrame(
[
dict(date="2025/01/01", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E1", model_s="O", model="M"),
]
)
cube = CubeLogs(
df, keys=["^model*", "exporter", "opt"], values=["time_p"], time="date"
).load()
view, _view_def = cube.view(
CubeViewDef(["^model.*"], ["^time_.*"], fix_aggregation_change=["model_s"]),
return_view_def=True,
)
self.assertEqual(expected, view.to_dict())

def test_fix_non_consistent_historical_data_mixed_values1(self):
df = pandas.DataFrame(
[
dict(date="2025/01/01", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E1", model_s="A", model="M"),
]
)
cube = CubeLogs(
df, keys=["^model*", "exporter", "opt"], values=["time_p"], time="date"
).load()
view, _view_def = cube.view(
CubeViewDef(["^model.*"], ["^time_.*"], fix_aggregation_change=["model_s"]),
return_view_def=True,
)
raw = view.to_dict()
self.assertEqual(
{
("time_p", pandas.Timestamp("2025-01-01 00:00:00")): {"A-O": 0.51},
("time_p", pandas.Timestamp("2025-01-02 00:00:00")): {"A-O": 0.51},
("time_p", pandas.Timestamp("2025-01-03 00:00:00")): {"A-O": 0.53},
},
raw,
)

def test_fix_non_consistent_historical_data_mixed_values2(self):
df = pandas.DataFrame(
[
dict(date="2025/01/01", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E1", model_s="A", model="M"),
]
)
cube = CubeLogs(
df, keys=["^model*", "exporter", "opt"], values=["time_p"], time="date"
).load()
view, _view_def = cube.view(
CubeViewDef(["^model.*"], ["^time_.*"], fix_aggregation_change=["model_s"]),
return_view_def=True,
)
raw = view.to_dict()
self.assertEqual(
{
("time_p", pandas.Timestamp("2025-01-01 00:00:00")): {"A-O": 0.51},
("time_p", pandas.Timestamp("2025-01-02 00:00:00")): {"A-O": 0.51},
("time_p", pandas.Timestamp("2025-01-03 00:00:00")): {"A-O": 0.53},
},
raw,
)

def test_fix_non_consistent_historical_data_mixed_nan(self):
df = pandas.DataFrame(
[
dict(date="2025/01/01", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E1", model="M"),
]
)
cube = CubeLogs(
df, keys=["^model*", "exporter", "opt"], values=["time_p"], time="date"
).load()
view, _view_def = cube.view(
CubeViewDef(["^model.*"], ["^time_.*"], fix_aggregation_change=["model_s"]),
return_view_def=True,
)
raw = view.to_dict()
self.assertEqual(
{
("time_p", pandas.Timestamp("2025-01-01 00:00:00")): {"O": 0.51},
("time_p", pandas.Timestamp("2025-01-02 00:00:00")): {"O": 0.51},
("time_p", pandas.Timestamp("2025-01-03 00:00:00")): {"O": 0.53},
},
raw,
)

def test_fix_non_consistent_historical_data_nan(self):
df = pandas.DataFrame(
[
dict(date="2025/01/01", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E1", model_s="O", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E1", model_s="A", model="M"),
dict(date="2025/01/01", time_p=0.51, exporter="E2", model="M"),
dict(date="2025/01/02", time_p=0.51, exporter="E2", model="M"),
dict(date="2025/01/03", time_p=0.53, exporter="E2", model="M"),
]
)
cube = CubeLogs(
df, keys=["^model*", "exporter", "opt"], values=["time_p"], time="date"
).load()
view, _view_def = cube.view(
CubeViewDef(["^model.*"], ["^time_.*"], fix_aggregation_change=["model_s"]),
return_view_def=True,
)
raw = view.reset_index(drop=True).fillna("NAN").to_dict(orient="list")
self.assertEqual(
{
("time_p", "E1", pandas.Timestamp("2025-01-01 00:00:00")): ["NAN", 0.51],
("time_p", "E1", pandas.Timestamp("2025-01-02 00:00:00")): ["NAN", 0.51],
("time_p", "E1", pandas.Timestamp("2025-01-03 00:00:00")): ["NAN", 0.53],
("time_p", "E2", pandas.Timestamp("2025-01-01 00:00:00")): [0.51, "NAN"],
("time_p", "E2", pandas.Timestamp("2025-01-02 00:00:00")): [0.51, "NAN"],
("time_p", "E2", pandas.Timestamp("2025-01-03 00:00:00")): [0.53, "NAN"],
},
raw,
)


if __name__ == "__main__":
unittest.main(verbosity=2)
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