add input param for autogluon

expo/README.md

@@ -216,9 +216,19 @@ #### Setup
 pip install -U setuptools wheel
 pip install autogluon
 
-python run_expriment.py --exp_mode autogluon --task fashion_mnist
 ```
 
+For Tabular data:
+```
+python run_expriment.py --exp_mode autogluon --task {task_name}
+```
+For Multimodal data:
+```
+python run_expriment.py --exp_mode autogluon --task {task_name} --is_multimodal
+```
+Replace {task_name} with the specific task you want to run.
+
+
 提供github链接，并说明使用的命令以及参数设置
 ### AutoSklearn
 #### System requirements

expo/experimenter/autogluon.py

@@ -1,12 +1,10 @@
 from datetime import datetime
-
 from expo.experimenter.custom import CustomExperimenter
+import os
+import pandas as pd
 
 
 class AGRunner:
-    preset = "best_quality"
-    time_limit = 1000  # 1000s
-
     def __init__(self, state=None):
         self.state = state
         self.datasets = self.state["datasets_dir"]
@@ -32,7 +30,7 @@ class AGRunner:
         test_preds = predictor.predict(test_data)
         return {"test_preds": test_preds, "dev_preds": dev_preds}
 
-    def run_images(self):
+    def run_multimodal(self):
         from autogluon.multimodal import MultiModalPredictor
         target_col = self.state["dataset_config"]["target_col"]
         train_path = self.datasets["train"]
@@ -51,7 +49,7 @@ class AGRunner:
             label=target_col,
             eval_metric=eval_metric,
             path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")),
-        ).fit(train_data=train_data, tuning_data=dev_data, time_limit=self.time_limit)
+        ).fit(train_data=train_data, tuning_data=dev_data)
 
         # Make predictions on dev and test datasets
         dev_preds = predictor.predict(dev_wo_target_data)
@@ -64,8 +62,6 @@ class AGRunner:
         }
 
     def load_split_dataset(self, train_path, dev_path, dev_wo_target_path, test_wo_target_path):
-        import os
-        import pandas as pd
         """
         Loads training, dev, and test datasets from given file paths
 
@@ -91,16 +87,16 @@ class AGRunner:
         dev_wo_target_data = pd.read_csv(dev_wo_target_path)  # Load dev dataset without target labels
         test_data = pd.read_csv(test_wo_target_path)
 
-
         # Get the name of the first column (assuming it's the image path column)
-
         image_column = train_data.columns[0]
+
         # Append root folder path to the image column in each dataset
         train_data[image_column] = train_data[image_column].apply(lambda x: os.path.join(root_folder, x))
         dev_data[image_column] = dev_data[image_column].apply(lambda x: os.path.join(root_folder, x))
         dev_wo_target_data[image_column] = dev_wo_target_data[image_column].apply(
             lambda x: os.path.join(root_folder, x))
         test_data[image_column] = test_data[image_column].apply(lambda x: os.path.join(root_folder, x))
+
         return train_data, dev_data, dev_wo_target_data, test_data
 
 
@@ -110,10 +106,15 @@ class GluonExperimenter(CustomExperimenter):
     def __init__(self, args, **kwargs):
         super().__init__(args, **kwargs)
         self.framework = AGRunner(self.state)
+        self.is_multimodal = args.is_multimodal if hasattr(args, 'is_multimodal') else False
 
     async def run_experiment(self):
-        # result = self.framework.run()
-        result = self.framework.run_images()
+        if not self.is_multimodal:
+            result = self.framework.run()
+        else:
+            result = self.framework.run_multimodal()
+
+        assert result is not None
         user_requirement = self.state["requirement"]
         dev_preds = result["dev_preds"]
         test_preds = result["test_preds"]

expo/run_experiment.py

@@ -34,6 +34,7 @@ def get_mcts_args(parser):
 
 def get_aug_exp_args(parser):
     parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"])
+    parser.add_argument("--is_multimodal", action="store_true", help="Specify if the model is multi-modal")
 
 
 def get_di_args(parser):