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fixed a problem with multi index caused by the default value of groupkey (#1917)

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* fixed a problem with multi index caused by the default value of groupkey

* modify group_key default value

* limit pandas verion

* format with black

* fix docs error

* fix docs error

* fixed bugs caused by pandas upgrade

* remove needless code

* reformat with black

* limit version & add docs
This commit is contained in:
Linlang
2025-05-13 16:02:49 +08:00
committed by GitHub
parent df557d29d5
commit fbba768006
43 changed files with 153 additions and 98 deletions
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4
examples/benchmarks/TFT/libs/tft_model.py
View File

@@ -599,7 +599,7 @@ class TemporalFusionTransformer:
print("Getting valid sampling locations.")
valid_sampling_locations = []
split_data_map = {}
for identifier, df in data.groupby(id_col):
for identifier, df in data.groupby(id_col, group_key=False):
print("Getting locations for {}".format(identifier))
num_entries = len(df)
if num_entries >= self.time_steps:
@@ -678,7 +678,7 @@ class TemporalFusionTransformer:
input_cols = [tup[0] for tup in self.column_definition if tup[2] not in {InputTypes.ID, InputTypes.TIME}]
data_map = {}
for _, sliced in data.groupby(id_col):
for _, sliced in data.groupby(id_col, group_keys=False):
col_mappings = {"identifier": [id_col], "time": [time_col], "outputs": [target_col], "inputs": input_cols}
for k in col_mappings:

6
examples/benchmarks/TFT/tft.py
View File

@@ -78,13 +78,15 @@ DATASET_SETTING = {
def get_shifted_label(data_df, shifts=5, col_shift="LABEL0"):
return data_df[[col_shift]].groupby("instrument").apply(lambda df: df.shift(shifts))
return data_df[[col_shift]].groupby("instrument", group_keys=False).apply(lambda df: df.shift(shifts))
def fill_test_na(test_df):
test_df_res = test_df.copy()
feature_cols = ~test_df_res.columns.str.contains("label", case=False)
test_feature_fna = test_df_res.loc[:, feature_cols].groupby("datetime").apply(lambda df: df.fillna(df.mean()))
test_feature_fna = (
test_df_res.loc[:, feature_cols].groupby("datetime", group_keys=False).apply(lambda df: df.fillna(df.mean()))
)
test_df_res.loc[:, feature_cols] = test_feature_fna
return test_df_res