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Fix missing argument in StratifiedKFold.split() #1304

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Fix missing argument in StratifiedKFold.split() #1304

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18 changes: 8 additions & 10 deletions flaml/automl/task/generic_task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -414,9 +414,7 @@ def prepare_data(
sample_weight_full,
random_state=RANDOM_SEED,
)
state.fit_kwargs[
"sample_weight"
] = (
state.fit_kwargs["sample_weight"] = (
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lets reduce the change, keep it focus

state.sample_weight_all
) # NOTE: _prepare_data is before kwargs is updated to fit_kwargs_by_estimator
if isinstance(state.sample_weight_all, pd.Series):
Expand Down Expand Up @@ -501,9 +499,7 @@ def prepare_data(
y_rest = (
y_train_all[rest]
if isinstance(y_train_all, np.ndarray)
else iloc_pandas_on_spark(y_train_all, rest)
if is_spark_dataframe
else y_train_all.iloc[rest]
else iloc_pandas_on_spark(y_train_all, rest) if is_spark_dataframe else y_train_all.iloc[rest]
)
stratify = y_rest if split_type == "stratified" else None
X_train, X_val, y_train, y_val = self._train_test_split(
Expand Down Expand Up @@ -619,9 +615,11 @@ def preprocess(self, X, transformer=None):
X = pd.DataFrame(
dict(
[
(transformer._str_columns[idx], X[idx])
if isinstance(X[0], List)
else (transformer._str_columns[idx], [X[idx]])
(
(transformer._str_columns[idx], X[idx])
if isinstance(X[0], List)
else (transformer._str_columns[idx], [X[idx]])
)
for idx in range(len(X))
]
)
Expand Down Expand Up @@ -701,7 +699,7 @@ def evaluate_model_CV(
elif isinstance(kf, TimeSeriesSplit):
kf = kf.split(X_train_split, y_train_split)
else:
kf = kf.split(X_train_split)
kf = kf.split(X_train_split, y_train_split)

for train_index, val_index in kf:
if shuffle:
Expand Down