update text dataset

expo/data/dataset.py

@@ -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)

expo/data/hf_data.py

@@ -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")

expo/datasets.yaml

@@ -79,6 +79,14 @@ datasets:
       \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\
       \ engineering, and modeling to predict the target. \nReport rmse on the eval\
       \ data. Do not plot or make any visualizations.\n"
+  concrete-strength:
+    dataset: concrete-strength
+    metric: rmse
+    target_col: Strength
+    user_requirement: "This is a concrete-strength dataset. Your goal is to predict\
+      \ the target column `Strength`.\nPerform data analysis, data preprocessing,\
+      \ feature engineering, and modeling to predict the target. \nReport rmse on\
+      \ the eval data. Do not plot or make any visualizations.\n"
   credit-g:
     dataset: credit-g
     metric: f1
@@ -135,30 +143,6 @@ datasets:
       \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\
       \ and modeling to predict the target. \nReport f1 weighted on the eval data.\
       \ Do not plot or make any visualizations.\n"
-  steel-plates-fault:
-    dataset: steel-plates-fault
-    metric: f1 weighted
-    target_col: target
-    user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\
-      \ the target column `target`.\nPerform data analysis, data preprocessing, feature\
-      \ engineering, and modeling to predict the target. \nReport f1 weighted on the\
-      \ eval data. Do not plot or make any visualizations.\n"
-  wine-quality-white:
-    dataset: wine-quality-white
-    metric: f1 weighted
-    target_col: Class
-    user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\
-      \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\
-      \ engineering, and modeling to predict the target. \nReport f1 weighted on the\
-      \ eval data. Do not plot or make any visualizations.\n"
-  concrete-strength:
-    dataset: concrete-strength
-    metric: rmse
-    target_col: Strength
-    user_requirement: "This is a concrete-strength dataset. Your goal is to predict\
-      \ the target column `Strength`.\nPerform data analysis, data preprocessing,\
-      \ feature engineering, and modeling to predict the target. \nReport rmse on\
-      \ the eval data. Do not plot or make any visualizations.\n"
   smoker-status:
     dataset: smoker-status
     metric: f1
@@ -175,4 +159,43 @@ datasets:
       \ the target column `defects`.\nPerform data analysis, data preprocessing, feature\
       \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\
       \ Do not plot or make any visualizations.\n"
-
+  steel-plates-fault:
+    dataset: steel-plates-fault
+    metric: f1 weighted
+    target_col: target
+    user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\
+      \ the target column `target`.\nPerform data analysis, data preprocessing, feature\
+      \ engineering, and modeling to predict the target. \nReport f1 weighted on the\
+      \ eval data. Do not plot or make any visualizations.\n"
+  wine-quality-white:
+    dataset: wine-quality-white
+    metric: f1 weighted
+    target_col: Class
+    user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\
+      \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\
+      \ engineering, and modeling to predict the target. \nReport f1 weighted on the\
+      \ eval data. Do not plot or make any visualizations.\n"
+  banking77:
+    dataset: banking77
+    metric: f1 weighted
+    target_col: label
+    user_requirement: "This is a banking77 dataset. Your goal is to predict the target\
+      \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\
+      \ and modeling to predict the target. \nReport f1 weighted on the eval data.\
+      \ Do not plot or make any visualizations.\n"
+  gnad10:
+    dataset: gnad10
+    metric: f1 weighted
+    target_col: label
+    user_requirement: "This is a gnad10 dataset. Your goal is to predict the target\
+      \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\
+      \ and modeling to predict the target. \nReport f1 weighted on the eval data.\
+      \ Do not plot or make any visualizations.\n"
+  sms_spam:
+    dataset: sms_spam
+    metric: f1
+    target_col: label
+    user_requirement: "This is a sms_spam dataset. Your goal is to predict the target\
+      \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\
+      \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\
+      \ or make any visualizations.\n"