Class TestServiceClientPredictionNormalization

tests/unit/test_client.py:567–642

                "fitted_train_set_id": fitted_train_set_id,
                "status": "failed",
                "error": "boom",
            },
        )

        ServiceClient.authorize("dummy_access_token")

        with (
            patch.object(ServiceClient, "_upload_to_gcs"),
            patch.object(
                ServiceClient,
                "_resolve_async_settings",
                return_value=_fast_poll_settings(),
            ),
        ):
            with self.assertRaises(RuntimeError) as cm:
                ServiceClient.fit(
                    self.X_train,
                    self.y_train,
                    tabpfn_systems=["preprocessing", "text"],
                    task_config=ClassifierFitTaskConfig(),
                )

        self.assertIn("boom", str(cm.exception))

    @with_mock_server()
    def test_predict_with_same_test_set_calls_prepare_and_predict_each_time(
        self, mock_server
    ):
        prepare_route = mock_server.router.post("/tabpfn/prepare_test_set_upload")
        prepare_route.respond(
            200,
            json=self._prepare_test_set_upload_response(
                "00000000-0000-0000-0000-000000000003"
            ),
        )
        predict_route = mock_server.router.post("/tabpfn/predict")
        predict_route.respond(
            200,
            json=self._predict_response([1, 0, 1]),
        )

        fitted_train_set_id = UUID("00000000-0000-0000-0000-000000000002")

        with patch.object(ServiceClient, "_upload_to_gcs"):
            pred_1 = ServiceClient.predict(
                fitted_train_set_id=fitted_train_set_id,
                x_test=self.X_test,
                task_config=ClassifierConfig(),
            )
            pred_2 = ServiceClient.predict(
                fitted_train_set_id=fitted_train_set_id,
                x_test=self.X_test,
                task_config=ClassifierConfig(),
            )

        assert isinstance(pred_1.y_pred, np.ndarray)
        assert isinstance(pred_2.y_pred, np.ndarray)
        self.assertTrue(np.array_equal(pred_1.y_pred, pred_2.y_pred))