Merge branch 'ray_multilmodal_0925' into 'expo'

add autogluon multimodal support

See merge request agents/exp_optimizer!23

expo/README.md

@@ -215,8 +215,20 @@ #### Setup
 pip install -U pip
 pip install -U setuptools wheel
 pip install autogluon
+
 ```
 
+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

@@ -1,37 +1,104 @@
 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"]
 
     def run(self):
         from autogluon.tabular import TabularDataset, TabularPredictor
-
         train_path = self.datasets["train"]
+        dev_path = self.datasets["dev"]
         dev_wo_target_path = self.datasets["dev_wo_target"]
         test_wo_target_path = self.datasets["test_wo_target"]
         target_col = self.state["dataset_config"]["target_col"]
         train_data = TabularDataset(train_path)
-        dev_data = TabularDataset(dev_wo_target_path)
+        dev_data = TabularDataset(dev_path)
+        dev_wo_target_data = TabularDataset(dev_wo_target_path)
         test_data = TabularDataset(test_wo_target_path)
         eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_")
-        # predictor = TabularPredictor(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, presets=self.preset, time_limit=self.time_limit, fit_weighted_ensemble=False, num_gpus=1)
         predictor = TabularPredictor(
             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, num_gpus=1)
-        dev_preds = predictor.predict(dev_data)
+        ).fit(train_data=train_data, tuning_data=dev_data, num_gpus=1)
+        dev_preds = predictor.predict(dev_wo_target_data)
         test_preds = predictor.predict(test_data)
         return {"test_preds": test_preds, "dev_preds": dev_preds}
 
+    def run_multimodal(self):
+        from autogluon.multimodal import MultiModalPredictor
+        target_col = self.state["dataset_config"]["target_col"]
+        train_path = self.datasets["train"]
+        dev_path = self.datasets["dev"]
+        dev_wo_target_path = self.datasets["dev_wo_target"]  # Updated variable name
+        test_wo_target_path = self.datasets["test_wo_target"]
+        eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_")
+
+        # Load the datasets
+        train_data, dev_data, dev_wo_target_data, test_data = self.load_split_dataset(
+            train_path, dev_path, dev_wo_target_path, test_wo_target_path
+        )
+
+        # Create and fit the predictor
+        predictor = MultiModalPredictor(
+            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)
+
+        # Make predictions on dev and test datasets
+        dev_preds = predictor.predict(dev_wo_target_data)
+        test_preds = predictor.predict(test_data)
+
+        # Return predictions for dev and test datasets
+        return {
+            "dev_preds": dev_preds,
+            "test_preds": test_preds
+        }
+
+    def load_split_dataset(self, train_path, dev_path, dev_wo_target_path, test_wo_target_path):
+        """
+        Loads training, dev, and test datasets from given file paths
+
+        Args:
+            train_path (str): Path to the training dataset.
+            dev_path (str): Path to the dev dataset with target labels.
+            dev_wo_target_path (str): Path to the dev dataset without target labels.
+            test_wo_target_path (str): Path to the test dataset without target labels.
+
+        Returns:
+            train_data (pd.DataFrame): Loaded training dataset with updated image paths.
+            dev_data (pd.DataFrame): Loaded dev dataset with updated image paths.
+            dev_wo_target_data (pd.DataFrame): Loaded dev dataset without target labels and updated image paths.
+            test_data (pd.DataFrame): Loaded test dataset with updated image paths.
+        """
+
+        # Define the root path to append
+        root_folder = os.path.join("F:/Download/Dataset/", self.state["task"])
+
+        # Load the datasets
+        train_data = pd.read_csv(train_path)
+        dev_data = pd.read_csv(dev_path)  # Load dev dataset with target labels
+        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
+
 
 class GluonExperimenter(CustomExperimenter):
     result_path: str = "results/autogluon"
@@ -39,9 +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()
+        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):