diff --git a/expo/README.md b/expo/README.md index 011322897..e5da96708 100644 --- a/expo/README.md +++ b/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 diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index 93dfdb4bc..dabf0c138 100644 --- a/expo/experimenter/autogluon.py +++ b/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"] diff --git a/expo/run_experiment.py b/expo/run_experiment.py index be028c47e..038b57ad2 100644 --- a/expo/run_experiment.py +++ b/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):