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fix(automl): fix data leakage in classification holdout validation #1418

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fix(automl): fix data leakage in classification holdout validation #1418

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fix(automl): fix data leakage in classification holdout validation
commint-tian Apr 8, 2025
7ca0b85
Merge branch 'main' into main
thinkall Apr 9, 2025
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65 changes: 30 additions & 35 deletions flaml/automl/task/generic_task.py
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Original file line number Diff line number Diff line change
Expand Up @@ -514,45 +514,40 @@ def prepare_data(
last = first[i] + 1
rest.extend(range(last, len(y_train_all)))
X_first = X_train_all.iloc[first] if data_is_df else X_train_all[first]
if len(first) < len(y_train_all) / 2:
# Get X_rest and y_rest with drop, sparse matrix can't apply np.delete
X_rest = (
np.delete(X_train_all, first, axis=0)
if isinstance(X_train_all, np.ndarray)
else X_train_all.drop(first.tolist())
if data_is_df
else X_train_all[rest]
)
y_rest = (
np.delete(y_train_all, first, axis=0)
if isinstance(y_train_all, np.ndarray)
else y_train_all.drop(first.tolist())
if data_is_df
else y_train_all[rest]
)
else:
X_rest = (
iloc_pandas_on_spark(X_train_all, rest)
if is_spark_dataframe
else X_train_all.iloc[rest]
if data_is_df
else X_train_all[rest]
)
y_rest = (
iloc_pandas_on_spark(y_train_all, rest)
if is_spark_dataframe
else y_train_all.iloc[rest]
if data_is_df
else y_train_all[rest]
)
Comment on lines -517 to -547
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Why remove the second way of getting X_rest?

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Oh，I made a mistake about it. The second part should be kept here.

X_rest = (
np.delete(X_train_all, first, axis=0)
if isinstance(X_train_all, np.ndarray)
else X_train_all.drop(first.tolist())
if data_is_df
else X_train_all[rest]
)
y_rest = (
np.delete(y_train_all, first, axis=0)
if isinstance(y_train_all, np.ndarray)
else y_train_all.drop(first.tolist())
if data_is_df
else y_train_all[rest]
)
stratify = y_rest if split_type == "stratified" else None
X_train, X_val, y_train, y_val = self._train_test_split(
state, X_rest, y_rest, first, rest, split_ratio, stratify
)
X_train = concat(X_first, X_train)
y_train = concat(label_set, y_train) if data_is_df else np.concatenate([label_set, y_train])
X_val = concat(X_first, X_val)
y_val = concat(label_set, y_val) if data_is_df else np.concatenate([label_set, y_val])
#Check whether the training set and validation set cover all categories.
train_labels = np.unique(y_train)
val_labels = np.unique(y_val)
Comment on lines +536 to +537
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np.unique doesn't work for psSeries or psDataFrame. Check an example here.

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it can be modified like this：
if isinstance(y_train, (ps.Series, ps.DataFrame)):
train_labels = y_train.unique() if isinstance(y_train, ps.Series) else y_train.iloc[:, 0].unique()
else:
train_labels = np.unique(y_train)

if isinstance(y_val, (ps.Series, ps.DataFrame)):
val_labels = y_val.unique() if isinstance(y_val, ps.Series) else y_val.iloc[:, 0].unique()
else:
val_labels = np.unique(y_val)

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try reusing existing functions

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this is a pretty big project, and I'm not very familiar with it yet. I'm not sure if there are already existing functions that I can use.

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@thinkall thinkall Apr 9, 2025 •
edited
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Check an example here.

You can reuse the function len_labels.

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Yes, I noticed this function after seeing your correction. I hadn't paid attention to it before，thank you！

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This is the modified code：
train_labels= len_labels(y_train)
val_labels= len_labels(y_val)

missing_in_train = set(label_set) - set(train_labels)
missing_in_val = set(label_set) - set(val_labels)

#Add X_first only to the validation set (remove the merge of the training set).
if missing_in_val:
X_val = concat(X_first, X_val)
y_val = concat(label_set, y_val) if data_is_df else np.concatenate([label_set, y_val])
#The training set supplements the missing categories with the remaining data.
if missing_in_train:
Comment on lines +542 to +546
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What if missing_in_val only has 1 value missed?

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Indeed, the code can be optimized.

if missing_in_val:
if len(label_set) == 1:
X_val = concat(X_first.iloc[[0]], X_val)
y_val = concat(label_set, y_val) if data_is_df else np.concatenate([label_set, y_val])
else:
X_val = concat(X_first, X_val)
y_val = concat(label_set, y_val) if data_is_df else np.concatenate([label_set, y_val])

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a better way is to fill the missing labels in both train and val with first. For those not missing in either train or val, put them in train.

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Indeed, I've learned it.

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@commint-tian commint-tian Apr 14, 2025 •
edited
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if missing_in_val:
if data_is_df:
X_val = concat([X_first, X_val])
y_val = concat([label_set, y_val])
else:
X_val = np.concatenate([X_first, X_val], axis=0)
y_val = np.concatenate([label_set, y_val])

if missing_in_train:
if data_is_df:
X_train = concat([X_first, X_train])
y_train = concat([label_set, y_train])
else:
X_train = np.concatenate([X_first, X_train], axis=0)
y_train = np.concatenate([label_set, y_train])

common_labels = set(train_labels) & set(val_labels)
only_train_labels = set(train_labels) - set(val_labels)
only_val_labels = set(val_labels) - set(train_labels)

#将不在train或val中缺失的标签放到train中
for label in only_val_labels:
#从val中移除这些标签
mask = y_val != label
X_val = X_val[mask]
y_val = y_val[mask]
#将这些标签添加到train中
if data_is_df:
X_train = concat([X_train, X_first.loc[X_first[label_col] == label]])
y_train = concat([y_train, label_set.loc[label_set[label_col] == label]])
else:
X_train = np.concatenate([X_train, X_first[y_first == label]], axis=0)
y_train = np.concatenate([y_train, label_set[y_first == label]])

for label in missing_in_train:
mask = (y_rest == label)
X_train = concat(X_rest[mask], X_train)
y_train = concat(y_rest[mask], y_train)
Comment on lines +548 to +550
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X_rest[mask] may not work for dataframe.

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Perhaps this could work：
mask = (y_rest == label)
filtered_X_rest = X_rest.filter(mask)
filtered_y_rest = y_rest.filter(mask)

X_train = X_train.union(filtered_X_rest)
y_train = y_train.union(filtered_y_rest)


if isinstance(y_train, (psDataFrame, pd.DataFrame)) and y_train.shape[1] == 1:
y_train = y_train[y_train.columns[0]]
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