From 2ca2071d959a007625b2879a57e84efb1507ac59 Mon Sep 17 00:00:00 2001
From: lzh222333 <lzh222333@163.com>
Date: Wed, 10 Mar 2021 17:06:08 +0000
Subject: [PATCH] format code

---
 examples/taskmanager/task_manager_rolling.py | 75 ++++++++------------
 qlib/workflow/task/collect.py                |  4 +-
 2 files changed, 34 insertions(+), 45 deletions(-)

diff --git a/examples/taskmanager/task_manager_rolling.py b/examples/taskmanager/task_manager_rolling.py
index db5d1817f..36ec81962 100644
--- a/examples/taskmanager/task_manager_rolling.py
+++ b/examples/taskmanager/task_manager_rolling.py
@@ -9,53 +9,37 @@ data_handler_config = {
     "end_time": "2020-08-01",
     "fit_start_time": "2008-01-01",
     "fit_end_time": "2014-12-31",
-    "instruments": 'csi100',
+    "instruments": "csi100",
 }
 
 dataset_config = {
-        "class": "DatasetH",
-        "module_path": "qlib.data.dataset",
-        "kwargs": {
-            "handler": {
-                "class": "Alpha158",
-                "module_path": "qlib.contrib.data.handler",
-                "kwargs": data_handler_config,
-            },
-            "segments": {
-                "train": ("2008-01-01", "2014-12-31"),
-                "valid": ("2015-01-01", "2016-12-31"),
-                "test": ("2017-01-01", "2020-08-01"),
-            },
+    "class": "DatasetH",
+    "module_path": "qlib.data.dataset",
+    "kwargs": {
+        "handler": {"class": "Alpha158", "module_path": "qlib.contrib.data.handler", "kwargs": data_handler_config,},
+        "segments": {
+            "train": ("2008-01-01", "2014-12-31"),
+            "valid": ("2015-01-01", "2016-12-31"),
+            "test": ("2017-01-01", "2020-08-01"),
         },
-    }
+    },
+}
 
 record_config = [
-    {
-        "class": "SignalRecord",
-        "module_path": "qlib.workflow.record_temp",
-    },
-    {
-        "class": "SigAnaRecord",
-        "module_path": "qlib.workflow.record_temp",
-    }
+    {"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
+    {"class": "SigAnaRecord", "module_path": "qlib.workflow.record_temp",},
 ]
 
 # use lgb
 task_lgb_config = {
-    "model": {
-        "class": "LGBModel",
-        "module_path": "qlib.contrib.model.gbdt",
-    },
+    "model": {"class": "LGBModel", "module_path": "qlib.contrib.model.gbdt",},
     "dataset": dataset_config,
     "record": record_config,
 }
 
 # use xgboost
 task_xgboost_config = {
-    "model": {
-        "class": "XGBModel",
-        "module_path": "qlib.contrib.model.xgboost",
-    },
+    "model": {"class": "XGBModel", "module_path": "qlib.contrib.model.xgboost",},
     "dataset": dataset_config,
     "record": record_config,
 }
@@ -64,17 +48,17 @@ provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
 qlib.init(provider_uri=provider_uri, region=REG_CN)
 
 C["mongo"] = {
-    "task_url" : "mongodb://localhost:27017/", # maybe you need to change it to your url
-    "task_db_name" : "rolling_db"
+    "task_url": "mongodb://localhost:27017/",  # maybe you need to change it to your url
+    "task_db_name": "rolling_db",
 }
 
-exp_name = 'rolling_exp' # experiment name, will be used as the experiment in MLflow
-task_pool = 'rolling_task' # task pool name, will be used as the document in MongoDB
+exp_name = "rolling_exp"  # experiment name, will be used as the experiment in MLflow
+task_pool = "rolling_task"  # task pool name, will be used as the document in MongoDB
 
 tasks = task_generator(
-    task_xgboost_config, # default task name
-    RollingGen(step=550,rtype=RollingGen.ROLL_SD), # generate different date segment
-    task_lgb=task_lgb_config # use "task_lgb" as the task name
+    task_xgboost_config,  # default task name
+    RollingGen(step=550, rtype=RollingGen.ROLL_SD),  # generate different date segment
+    task_lgb=task_lgb_config,  # use "task_lgb" as the task name
 )
 
 # Uncomment next two lines to see the generated tasks
@@ -82,26 +66,29 @@ tasks = task_generator(
 # pprint(tasks)
 
 tm = TaskManager(task_pool=task_pool)
-tm.create_task(tasks) # all tasks will be saved to MongoDB
+tm.create_task(tasks)  # all tasks will be saved to MongoDB
 
 from qlib.workflow.task.manage import run_task
 from qlib.workflow.task.collect import TaskCollector
 from qlib.model.trainer import task_train
 
-run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
+run_task(task_train, task_pool, experiment_name=exp_name)  # all tasks will be trained using "task_train" method
+
 
 def get_task_key(task_config):
     task_key = task_config["task_key"]
     rolling_end_timestamp = task_config["dataset"]["kwargs"]["segments"]["test"][1]
-    return task_key, rolling_end_timestamp.strftime('%Y-%m-%d')
+    return task_key, rolling_end_timestamp.strftime("%Y-%m-%d")
+
 
 def my_filter(task_config):
     # only choose the results of "task_lgb" and test in 2019 from all tasks
     task_key, rolling_end = get_task_key(task_config)
-    if task_key=="task_lgb" and rolling_end.startswith('2019'):
+    if task_key == "task_lgb" and rolling_end.startswith("2019"):
         return True
     return False
 
+
 # name tasks by "get_task_key" and filter tasks by "my_filter"
-pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, my_filter) 
-pred_rolling
\ No newline at end of file
+pred_rolling = TaskCollector.collect_predictions(exp_name, get_task_key, my_filter)
+pred_rolling
diff --git a/qlib/workflow/task/collect.py b/qlib/workflow/task/collect.py
index 834189561..ccd6ce169 100644
--- a/qlib/workflow/task/collect.py
+++ b/qlib/workflow/task/collect.py
@@ -11,7 +11,9 @@ class TaskCollector:
 
     @staticmethod
     def collect_predictions(
-        experiment_name: str, get_key_func, filter_func=None,
+        experiment_name: str, 
+        get_key_func, 
+        filter_func=None,
     ):
         """