Pass hyper params from client

tabpfn_client/client.py

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+import traceback
 from pathlib import Path
 import httpx
 import logging
@@ -96,7 +99,7 @@ def upload_train_set(self, X, y) -> str:
         train_set_uid = response.json()["train_set_uid"]
         return train_set_uid
 
-    def predict(self, train_set_uid: str, x_test):
+    def predict(self, train_set_uid: str, x_test, tabpfn_config: dict | None=None):
         """
         Predict the class labels for the provided data (test set).
 
@@ -115,9 +118,14 @@ def predict(self, train_set_uid: str, x_test):
 
         x_test = common_utils.serialize_to_csv_formatted_bytes(x_test)
 
+        params = {"train_set_uid": train_set_uid}
+
+        if tabpfn_config is not None:
+            params["tabpfn_config"] = json.dumps(tabpfn_config, default=lambda x: x.to_dict())
+
         response = self.httpx_client.post(
             url=self.server_endpoints.predict.path,
-            params={"train_set_uid": train_set_uid},
+            params=params,
             files=common_utils.to_httpx_post_file_format([
                 ("x_file", "x_test_filename", x_test)
             ])
@@ -198,7 +206,6 @@ def try_connection(self) -> bool:
 
         except httpx.ConnectError as e:
             logger.error(f"Failed to connect to the server with error: {e}")
-            import traceback
             traceback.print_exc()
             found_valid_connection = False
 

tabpfn_client/service_wrapper.py

@@ -177,16 +177,9 @@ def fit(self, X, y) -> None:
 
         self.last_train_set_uid = self.service_client.upload_train_set(X, y)
 
-    def predict(self, X):
+    def predict(self, X, config=None):
         return self.service_client.predict(
             train_set_uid=self.last_train_set_uid,
-            x_test=X
+            x_test=X,
+            tabpfn_config=config
         )
-
-    def predict_proba(self, X):
-        return self.service_client.predict_proba(
-            train_set_uid=self.last_train_set_uid,
-            x_test=X
-        )
-
-