fix: pylint error

Signed-off-by: Yu Fan <fany@buaa.edu.cn>

core/common/constant.py

@@ -51,7 +51,7 @@ class ModuleType(Enum):
     CLOUDMODEL = "cloudmodel"
 
     # Dataset Preprocessor
-    DATA_PROCESSOR = "dataset_processor" 
+    DATA_PROCESSOR = "dataset_processor"
 
     # HEM
     HARD_EXAMPLE_MINING = "hard_example_mining"

core/testcasecontroller/algorithm/paradigm/base.py

@@ -101,8 +101,9 @@ def build_paradigm_job(self, paradigm_type):
 
         if paradigm_type == ParadigmType.LIFELONG_LEARNING.value:
             return LifelongLearning(
-                estimator=self.module_instances.get(
+                seen_estimator=self.module_instances.get(
                     ModuleType.BASEMODEL.value),
+                unseen_estimator=None,
                 task_definition=self.module_instances.get(
                     ModuleType.TASK_DEFINITION.value),
                 task_relationship_discovery=self.module_instances.get(

core/testcasecontroller/algorithm/paradigm/joint_inference/joint_inference.py

@@ -50,13 +50,19 @@ class JointInference(ParadigmBase):
 
     def __init__(self, workspace, **kwargs):
         ParadigmBase.__init__(self, workspace, **kwargs)
+        self.inference_dataset = None
         self.kwargs = kwargs
         self.hard_example_mining_mode = kwargs.get(
             "hard_example_mining_mode",
             "mining-then-inference"
         )
 
     def set_config(self):
+        """Configure output_dir, dataset, modules
+
+        Raises:
+            KeyError: Required Modules are not fully loaded.
+        """
 
         inference_output_dir = os.path.dirname(self.workspace)
         os.environ["RESULT_SAVED_URL"] = inference_output_dir
@@ -65,7 +71,7 @@ def set_config(self):
         LOGGER.info("Loading dataset")
 
         self.inference_dataset = self.dataset.load_data(
-            self.dataset.test_data_info, 
+            self.dataset.test_data_info,
             "inference"
         )
 
@@ -77,12 +83,13 @@ def set_config(self):
         required_modules = {"edgemodel", "cloudmodel", "hard_example_mining"}
 
         if not required_modules.issubset(set(self.module_instances.keys())):
-            raise ValueError(
+            raise KeyError(
                 f"Required modules: {required_modules}, "
                 f"but got: {self.module_instances.keys()}"
             )
-
-        # if hard example mining is OracleRouter, add the edgemodel and cloudmodel object to its kwargs so that it can use them.
+
+        # if hard example mining is OracleRouter,
+        # add the edgemodel and cloudmodel object to its kwargs so that it can use them.
         mining = self.module_instances["hard_example_mining"]
         param = mining.get("param")
         if mining.get("method", None) == "OracleRouter":
@@ -116,12 +123,13 @@ def _cleanup(self, job):
         for module in self.module_instances.values():
             if hasattr(module, "cleanup"):
                 module.cleanup()
-
-        # Since the hard example mining module is instantiated within the job, special handling is required.
+
+        # Since the hard example mining module is instantiated within the job,
+        # special call is required.
         mining_instance = job.hard_example_mining_algorithm
         if hasattr(mining_instance, "cleanup"):
             mining_instance.cleanup()
-        
+
         del job
 
     def _inference(self, job):
@@ -132,7 +140,7 @@ def _inference(self, job):
         LOGGER.info("Inference Start")
 
         pbar = tqdm(
-            self.inference_dataset.x, 
+            self.inference_dataset.x,
             total=len(self.inference_dataset.x),
             ncols=100
         )

core/testenvmanager/dataset/dataset.py

@@ -29,7 +29,7 @@
 from core.common import utils
 from core.common.constant import DatasetFormat
 
-
+# pylint: disable=too-many-instance-attributes
 class Dataset:
     """
     Data:
@@ -420,7 +420,8 @@ def _hard_example_splitting(self, data_file, data_format, ratio,
         return data_files
 
     @classmethod
-    def load_data(cls, file: str, data_type: str, label=None, use_raw=False, feature_process=None, **kwargs):
+    def load_data(cls, file: str, data_type: str, label=None,
+                  use_raw=False, feature_process=None, **kwargs):
         """
         load data