add autosklearn setup run

expo/experimenter/autosklearn.py

@@ -0,0 +1,110 @@
+from datetime import datetime
+import autosklearn.classification
+import autosklearn.regression
+import pandas as pd
+from expo.experimenter.custom import CustomExperimenter
+from expo.evaluation.evaluation import evaluate_score
+from autosklearn.metrics import make_scorer
+from functools import partial
+
+
+def custom_scorer(y_true, y_pred, metric_name):
+    return evaluate_score(y_pred, y_true, metric_name)
+
+
+def create_autosklearn_scorer(metric_name):
+    return make_scorer(
+        name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name)
+    )
+
+
+class ASRunner:
+    time_limit = 300
+
+    def __init__(self, state=None):
+        self.state = state
+        self.datasets = self.state["datasets_dir"]
+
+    def run(self):
+        train_path = self.datasets["train"]
+        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 = pd.read_csv(train_path)
+        dev_data = pd.read_csv(dev_wo_target_path)
+        test_data = pd.read_csv(test_wo_target_path)
+        eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_")
+        X_train = train_data.drop(columns=[target_col])
+        y_train = train_data[target_col]
+
+        if eval_metric == "rmse":
+            automl = autosklearn.regression.AutoSklearnRegressor(
+                time_left_for_this_task=self.time_limit,
+                per_run_time_limit=60,
+                metric=create_autosklearn_scorer("rmse"),  # 使用新的函数创建评分器
+                memory_limit=8192,
+                tmp_folder="AutosklearnModels/as-{}-{}".format(
+                    self.state["task"], datetime.now().strftime("%y%m%d_%H%M")
+                ),
+                n_jobs=-1,
+            )
+        elif eval_metric == "f1":
+            automl = autosklearn.classification.AutoSklearnClassifier(
+                time_left_for_this_task=self.time_limit,
+                per_run_time_limit=60,
+                metric=create_autosklearn_scorer("f1"),  # 使用新的函数创建评分器
+                memory_limit=8192,
+                tmp_folder="AutosklearnModels/as-{}-{}".format(
+                    self.state["task"], datetime.now().strftime("%y%m%d_%H%M")
+                ),
+                n_jobs=-1,
+            )
+        elif eval_metric == "f1_weighted":
+            automl = autosklearn.classification.AutoSklearnClassifier(
+                time_left_for_this_task=self.time_limit,
+                per_run_time_limit=60,
+                metric=create_autosklearn_scorer(
+                    "f1 weighted"
+                ),  # 使用新的函数创建评分器
+                memory_limit=8192,
+                tmp_folder="AutosklearnModels/as-{}-{}".format(
+                    self.state["task"], datetime.now().strftime("%y%m%d_%H%M")
+                ),
+                n_jobs=-1,
+            )
+        else:
+            raise ValueError(f"Unsupported metric: {eval_metric}")
+        automl.fit(X_train, y_train)
+
+        dev_preds = automl.predict(dev_data)
+        test_preds = automl.predict(test_data)
+
+        return {"test_preds": test_preds, "dev_preds": dev_preds}
+
+
+class AutoSklearnExperimenter(CustomExperimenter):
+    result_path: str = "results/autosklearn"
+
+    def __init__(self, args, **kwargs):
+        super().__init__(args, **kwargs)
+        self.framework = ASRunner(self.state)
+
+    async def run_experiment(self):
+        result = self.framework.run()
+        user_requirement = self.state["requirement"]
+        dev_preds = result["dev_preds"]
+        test_preds = result["test_preds"]
+        score_dict = {
+            "dev_score": self.evaluate_predictions(dev_preds, "dev"),
+            "test_score": self.evaluate_predictions(test_preds, "test"),
+        }
+        results = [
+            0,
+            {
+                "score_dict": score_dict,
+                "user_requirement": user_requirement,
+                "args": vars(self.args),
+            },
+        ]
+        self.save_result(results)