Refactored and reformatted all the files according to the pre-commit …

…configuration.

.pre-commit-config.yaml

@@ -5,10 +5,8 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: check-yaml
-
+      - id: check-added-large-files
   - repo: https://github.com/psf/black
     rev: 23.7.0
     hooks:
       - id: black
-        args: # arguments to configure black
-          - --line-length=100

README_ospp.md

@@ -1,3 +1,3 @@
-# OSPP 
-I changed the sedna source code to implement my algorithm.   
-Please turn to https://github.com/kubeedge/sedna/pull/378 and https://github.com/nailtu30/sedna/blob/ospp-final/README_ospp.md for more information.
+# OSPP
+I changed the sedna source code to implement my algorithm.
+Please turn to https://github.com/kubeedge/sedna/pull/378 and https://github.com/nailtu30/sedna/blob/ospp-final/README_ospp.md for more information.

core/cmd/benchmarking.py

@@ -14,13 +14,13 @@
 
 """main"""
 
-import sys
 import argparse
+import sys
 
-from core.common.log import LOGGER
-from core.common import utils
-from core.cmd.obj import BenchmarkingJob
 from core.__version__ import __version__
+from core.cmd.obj import BenchmarkingJob
+from core.common import utils
+from core.common.log import LOGGER
 
 
 def main():
@@ -30,7 +30,9 @@ def main():
         args = parser.parse_args()
         config_file = args.benchmarking_config_file
         if not utils.is_local_file(config_file):
-            raise SystemExit(f"not found benchmarking config({config_file}) file in local")
+            raise SystemExit(
+                f"not found benchmarking config({config_file}) file in local"
+            )
 
         config = utils.yaml2dict(args.benchmarking_config_file)
         job = BenchmarkingJob(config[str.lower(BenchmarkingJob.__name__)])
@@ -50,7 +52,8 @@ def _generate_parser():
         "--benchmarking_config_file",
         nargs="?",
         type=str,
-        help="run a benchmarking job, " "and the benchmarking config file must be yaml/yml file.",
+        help="run a benchmarking job, "
+        "and the benchmarking config file must be yaml/yml file.",
     )
 
     parser.add_argument(

core/cmd/obj/benchmarkingjob.py

@@ -18,11 +18,11 @@
 
 from core.common import utils
 from core.common.constant import TestObjectType
-from core.testenvmanager.testenv import TestEnv
 from core.storymanager.rank import Rank
 from core.testcasecontroller.simulation import Simulation
 from core.testcasecontroller.simulation_system_admin import build_simulation_enviroment
 from core.testcasecontroller.testcasecontroller import TestCaseController
+from core.testenvmanager.testenv import TestEnv
 
 
 # pylint: disable=too-few-public-methods
@@ -52,15 +52,19 @@ def __init__(self, config):
     def _check_fields(self):
         if not self.name and not isinstance(self.name, str):
             raise ValueError(
-                f"benchmarkingjob's name({self.name}) must be provided" f" and be string type."
+                f"benchmarkingjob's name({self.name}) must be provided"
+                f" and be string type."
             )
 
         if not isinstance(self.workspace, str):
-            raise ValueError(f"benchmarkingjob's workspace({self.workspace}) must be string type.")
+            raise ValueError(
+                f"benchmarkingjob's workspace({self.workspace}) must be string type."
+            )
 
         if not self.test_object and not isinstance(self.test_object, dict):
             raise ValueError(
-                f"benchmarkingjob's test_object({self.test_object})" f" must be dict type."
+                f"benchmarkingjob's test_object({self.test_object})"
+                f" must be dict type."
             )
 
         test_object_types = [e.value for e in TestObjectType.__members__.values()]
@@ -73,7 +77,8 @@ def _check_fields(self):
 
         if not self.test_object.get(test_object_type):
             raise ValueError(
-                f"benchmarkingjob' test_object doesn't find" f" the field({test_object_type})."
+                f"benchmarkingjob' test_object doesn't find"
+                f" the field({test_object_type})."
             )
 
     def run(self):
@@ -95,7 +100,9 @@ def run(self):
             test_env=self.test_env, test_object=self.test_object
         )
 
-        succeed_testcases, test_results = self.testcase_controller.run_testcases(self.workspace)
+        succeed_testcases, test_results = self.testcase_controller.run_testcases(
+            self.workspace
+        )
 
         if test_results:
             self.rank.save(succeed_testcases, test_results, output_dir=self.workspace)

core/common/constant.py

@@ -71,6 +71,7 @@ class SystemMetricType(Enum):
     """
     System metric type of ianvs.
     """
+
     # pylint: disable=C0103
     SAMPLES_TRANSFER_RATIO = "samples_transfer_ratio"
     FWT = "FWT"

core/common/log.py

@@ -15,6 +15,7 @@
 """Base logger"""
 
 import logging
+
 import colorlog
 
 

core/common/utils.py

@@ -18,9 +18,9 @@
 import os
 import sys
 import time
-
 from importlib import import_module
 from inspect import getfullargspec
+
 import yaml
 
 
@@ -63,7 +63,9 @@ def py2dict(url):
         mod = import_module(module_name)
         sys.path.pop(0)
         raw_dict = {
-            name: value for name, value in mod.__dict__.items() if not name.startswith("__")
+            name: value
+            for name, value in mod.__dict__.items()
+            if not name.startswith("__")
         }
         sys.modules.pop(module_name)
 

core/storymanager/rank/rank.py

@@ -21,7 +21,7 @@
 import pandas as pd
 
 from core.common import utils
-from core.storymanager.visualization import get_visualization_func, draw_heatmap_picture
+from core.storymanager.visualization import draw_heatmap_picture, get_visualization_func
 
 
 # pylint: disable=R0902
@@ -59,11 +59,14 @@ def _parse_config(self, config):
 
     def _check_fields(self):
         if not self.sort_by and not isinstance(self.sort_by, list):
-            raise ValueError(f"rank's sort_by({self.sort_by}) must be provided and be list type.")
+            raise ValueError(
+                f"rank's sort_by({self.sort_by}) must be provided and be list type."
+            )
 
         if not self.visualization and not isinstance(self.visualization, dict):
             raise ValueError(
-                f"rank's visualization({self.visualization}) " f"must be provided and be dict type."
+                f"rank's visualization({self.visualization}) "
+                f"must be provided and be dict type."
             )
 
         if not self.selected_dataitem and not isinstance(self.selected_dataitem, dict):
@@ -83,7 +86,8 @@ def _check_fields(self):
 
         if not self.save_mode and not isinstance(self.save_mode, list):
             raise ValueError(
-                f"rank's save_mode({self.save_mode}) " f"must be provided and be list type."
+                f"rank's save_mode({self.save_mode}) "
+                f"must be provided and be list type."
             )
 
     @classmethod
@@ -149,7 +153,9 @@ def _get_all(self, test_cases, test_results) -> pd.DataFrame:
             all_df.loc[i][0] = algorithm.name
             # fill metric columns of algorithm
             for metric_name in test_results[test_case.id][0]:
-                all_df.loc[i][metric_name] = test_results[test_case.id][0].get(metric_name)
+                all_df.loc[i][metric_name] = test_results[test_case.id][0].get(
+                    metric_name
+                )
 
             # file paradigm column of algorithm
             all_df.loc[i]["paradigm"] = algorithm.paradigm_type
@@ -192,7 +198,15 @@ def _get_selected(self, test_cases, test_results) -> pd.DataFrame:
         if metric_names == ["all"]:
             metric_names = self._get_all_metric_names(test_results)
 
-        header = ["algorithm", *metric_names, "paradigm", *module_types, *hps_names, "time", "url"]
+        header = [
+            "algorithm",
+            *metric_names,
+            "paradigm",
+            *module_types,
+            *hps_names,
+            "time",
+            "url",
+        ]
 
         all_df = copy.deepcopy(self.all_df)
         selected_df = pd.DataFrame(all_df, columns=header)
@@ -207,15 +221,17 @@ def _save_selected(self, test_cases, test_results):
         # pylint: disable=E1101
         selected_df = self._get_selected(test_cases, test_results)
         selected_df.index = pd.np.arange(1, len(selected_df) + 1)
-        selected_df.to_csv(self.selected_rank_file, index_label="rank", encoding="utf-8", sep=" ")
+        selected_df.to_csv(
+            self.selected_rank_file, index_label="rank", encoding="utf-8", sep=" "
+        )
 
     def _draw_pictures(self, test_cases, test_results):
         # pylint: disable=E1101
         for test_case in test_cases:
             out_put = test_case.output_dir
             test_result = test_results[test_case.id][0]
-            matrix = test_result.get('Matrix')
-            #print(out_put)
+            matrix = test_result.get("Matrix")
+            # print(out_put)
             for key in matrix.keys():
                 draw_heatmap_picture(out_put, key, matrix[key])
 

core/storymanager/visualization/__init__.py

@@ -13,4 +13,4 @@
 # limitations under the License.
 
 # pylint: disable=missing-module-docstring
-from .visualization import get_visualization_func, draw_heatmap_picture
+from .visualization import draw_heatmap_picture, get_visualization_func

core/storymanager/visualization/visualization.py

@@ -14,8 +14,9 @@
 
 """Visualization"""
 
-import sys
 import os
+import sys
+
 import matplotlib.pyplot as plt
 from prettytable import from_csv
 
@@ -26,24 +27,30 @@ def print_table(rank_file):
         table = from_csv(file)
         print(table)
 
+
 def draw_heatmap_picture(output, title, matrix):
     """
     draw heatmap for results
     """
     plt.figure(figsize=(10, 8), dpi=80)
-    plt.imshow(matrix, cmap='bwr', extent=(0.5, len(matrix)+0.5, 0.5, len(matrix)+0.5),
-                    origin='lower')
+    plt.imshow(
+        matrix,
+        cmap="bwr",
+        extent=(0.5, len(matrix) + 0.5, 0.5, len(matrix) + 0.5),
+        origin="lower",
+    )
     plt.xticks(fontsize=15)
     plt.yticks(fontsize=15)
-    plt.xlabel('task round', fontsize=15)
-    plt.ylabel('task', fontsize=15)
+    plt.xlabel("task round", fontsize=15)
+    plt.ylabel("task", fontsize=15)
     plt.title(title, fontsize=15)
-    plt.colorbar(format='%.2f')
+    plt.colorbar(format="%.2f")
     output_dir = os.path.join(output, f"output/{title}-heatmap.png")
-    #print(output_dir)
+    # print(output_dir)
     plt.savefig(output_dir)
     plt.show()
 
+
 def get_visualization_func(mode):
     """get visualization func"""
     return getattr(sys.modules[__name__], mode)

core/testcasecontroller/algorithm/algorithm.py

@@ -20,13 +20,14 @@
 from core.common.utils import load_module
 from core.testcasecontroller.algorithm.module import Module
 from core.testcasecontroller.algorithm.paradigm import (
-    SingleTaskLearning,
     IncrementalLearning,
-    MultiedgeInference,
     LifelongLearning,
+    MultiedgeInference,
+    SingleTaskLearning,
 )
 from core.testcasecontroller.generation_assistant import get_full_combinations
 
+
 class Algorithm:
     # pylint: disable=too-many-instance-attributes
     # pylint: disable=too-few-public-methods
@@ -62,7 +63,7 @@ def __init__(self, name, config):
         }
         self.lifelong_learning_data_setting: dict = {
             "train_ratio": 0.8,
-            "splitting_method": "default"
+            "splitting_method": "default",
         }
         self.initial_model_url: str = ""
         self.modules: list = []
@@ -108,7 +109,9 @@ def paradigm(self, workspace: str, **kwargs):
 
     def _check_fields(self):
         if not self.name and not isinstance(self.name, str):
-            raise ValueError(f"algorithm name({self.name}) must be provided and be string type.")
+            raise ValueError(
+                f"algorithm name({self.name}) must be provided and be string type."
+            )
 
         if not self.paradigm_type and not isinstance(self.paradigm_type, str):
             raise ValueError(
@@ -131,7 +134,8 @@ def _check_fields(self):
         if not isinstance(self.lifelong_learning_data_setting, dict):
             raise ValueError(
                 f"algorithm lifelong_learning_data_setting"
-                f"({self.lifelong_learning_data_setting} must be dictionary type.")
+                f"({self.lifelong_learning_data_setting} must be dictionary type."
+            )
 
         if not isinstance(self.initial_model_url, str):
             raise ValueError(
@@ -184,5 +188,7 @@ def _load_third_party_packages(self):
             try:
                 load_module(url)
             except Exception as err:
-                raise RuntimeError(f"load third party packages(name={name}, url={url}) failed,"
-                                f" error: {err}.") from err
+                raise RuntimeError(
+                    f"load third party packages(name={name}, url={url}) failed,"
+                    f" error: {err}."
+                ) from err