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Merged
merged 4 commits into from
May 24, 2023
Merged

fix_DDG-DA_workflow_bug #1516

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bc8a9a2
1.specify group_keys=False to avoid FutureWarning;
Fivele-Li May 19, 2023
2042b37
fix black
Fivele-Li May 19, 2023
625f6c3
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you-n-g May 24, 2023
b4c0780
Add make file
you-n-g May 24, 2023
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4 changes: 4 additions & 0 deletions examples/benchmarks_dynamic/DDG-DA/Makefile
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Original file line number Diff line number Diff line change
@@ -0,0 +1,4 @@
.PHONY: clean

clean:
-rm -r *.pkl mlruns || true
7 changes: 5 additions & 2 deletions examples/benchmarks_dynamic/DDG-DA/workflow.py
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Expand Up @@ -116,7 +116,9 @@ def dump_data_for_proxy_model(self):

feature_selected = feature_df.loc[:, col_selected.index]

feature_selected = feature_selected.groupby("datetime").apply(lambda df: (df - df.mean()).div(df.std()))
feature_selected = feature_selected.groupby("datetime", group_keys=False).apply(
lambda df: (df - df.mean()).div(df.std())
)
feature_selected = feature_selected.fillna(0.0)

df_all = {
Expand Down Expand Up @@ -168,7 +170,8 @@ def train_meta_model(self, fill_method="max"):
# - Only the dataset part is important, in current version of meta model will integrate the
rb = RollingBenchmark(model_type=self.sim_task_model, **self.rb_kwargs)
sim_task = rb.basic_task()
train_start = self.rb_kwargs.get("train_start", "2008-01-01")
# the train_start for training meta model does not necessarily align with final rolling
train_start = "2008-01-01" if self.rb_kwargs.get("train_start") is None else self.rb_kwargs.get("train_start")
train_end = "2010-12-31" if self.meta_1st_train_end is None else self.meta_1st_train_end
test_start = (pd.Timestamp(train_end) + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
proxy_forecast_model_task = {
Expand Down