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update text dataset

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This commit is contained in:
Yizhou Chi 2024-09-13 11:09:02 +08:00
parent eaf1b62343
commit 562af8c4e4
3 changed files with 96 additions and 43 deletions
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5
expo/data/dataset.py
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@ -268,8 +268,9 @@ class ExpDataset:
print(f"Dataset info for {self.name} already exists")
def save_datasetinfo(self, dataset_info):
with open(Path(self.dataset_dir, self.name, "dataset_info.json"), "w") as file:
json.dump(dataset_info, file, indent=4)
with open(Path(self.dataset_dir, self.name, "dataset_info.json"), "w", encoding="utf-8") as file:
# utf-8 encoding is required
json.dump(dataset_info, file, indent=4, ensure_ascii=False)
def save_split_datasets(self, df, split, target_col=None):
path = Path(self.dataset_dir, self.name)

61
expo/data/hf_data.py
View file

@ -9,12 +9,22 @@ from expo.data.dataset import ExpDataset, process_dataset, save_datasets_dict_to
from expo.insights.solution_designer import SolutionDesigner
HFDATSETS = [
{"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label"},
{"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label"},
{"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label"},
{"name": "oxford-iiit-pet", "dataset_name": "timm/oxford-iiit-pet", "target_col": "label"},
{"name": "stanford_cars", "dataset_name": "tanganke/stanford_cars", "target_col": "label"},
{"name": "fashion_mnist", "dataset_name": "zalando-datasets/fashion_mnist", "target_col": "label"},
# {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"},
# {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"},
# {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"},
{
"name": "oxford-iiit-pet",
"dataset_name": "timm/oxford-iiit-pet",
"target_col": "label_cat_dog",
"modality": "image",
},
{"name": "stanford_cars", "dataset_name": "tanganke/stanford_cars", "target_col": "label", "modality": "image"},
{
"name": "fashion_mnist",
"dataset_name": "zalando-datasets/fashion_mnist",
"target_col": "label",
"modality": "image",
},
]
@ -27,41 +37,60 @@ class HFExpDataset(ExpDataset):
self.name = name
self.dataset_dir = dataset_dir
self.dataset_name = dataset_name
self.modality = kwargs.get("modality", "")
self.target_col = kwargs.get("target_col", "label")
self.dataset = load_dataset(dataset_name)
self.dataset = load_dataset(self.dataset_name, trust_remote_code=True)
super().__init__(self.name, dataset_dir, **kwargs)
def get_raw_dataset(self):
raw_dir = Path(self.dataset_dir, self.name, "raw")
raw_dir.mkdir(parents=True, exist_ok=True)
if os.path.exists(Path(raw_dir, "train.csv")):
df = pd.read_csv(Path(raw_dir, "train.csv"))
df = pd.read_csv(Path(raw_dir, "train.csv"), encoding="utf-8")
else:
df = self.dataset["train"].to_pandas()
df.to_csv(Path(raw_dir, "train.csv"), index=False)
df.to_csv(Path(raw_dir, "train.csv"), index=False, encoding="utf-8")
if os.path.exists(Path(raw_dir, "test.csv")):
test_df = pd.read_csv(Path(raw_dir, "test.csv"), index=False)
test_df = pd.read_csv(Path(raw_dir, "test.csv"), encoding="utf-8")
else:
if "test" in self.dataset:
if self.dataset and "test" in self.dataset:
test_df = self.dataset["test"].to_pandas()
test_df.to_csv(Path(raw_dir, "test.csv"), index=False)
test_df.to_csv(Path(raw_dir, "test.csv"), index=False, encoding="utf-8")
else:
test_df = None
return df, test_df
# def get_df_head(self, raw_df):
# return raw_df.head()
def get_df_head(self, raw_df):
if self.modality == "text":
examples = []
for i in range(5):
examples.append(raw_df.iloc[i].to_dict())
return examples
elif self.modality == "image":
return ""
def get_dataset_info(self):
dataset_info = super().get_dataset_info()
dataset = self.dataset
dataset_info["description"] = dataset["train"].info.description
return dataset_info
if __name__ == "__main__":
dataset_dir = "D:/work/automl/datasets"
save_analysis_pool = True
save_analysis_pool = False
force_update = False
datasets_dict = {"datasets": {}}
solution_designer = SolutionDesigner()
for dataset_meta in HFDATSETS:
hf_dataset = HFExpDataset(
dataset_meta["name"], dataset_dir, dataset_meta["dataset_name"], target_col=dataset_meta["target_col"]
dataset_meta["name"],
dataset_dir,
dataset_meta["dataset_name"],
target_col=dataset_meta["target_col"],
force_update=force_update,
modality=dataset_meta["modality"],
)
asyncio.run(process_dataset(hf_dataset, solution_designer, save_analysis_pool, datasets_dict))
save_datasets_dict_to_yaml(datasets_dict, "hf_datasets.yaml")