modified format and added TaskCollector

qlib/model/trainer.py

@@ -6,7 +6,7 @@ from qlib.workflow import R
 from qlib.workflow.record_temp import SignalRecord
 
 
-def task_train(task_config: dict, experiment_name):
+def task_train(task_config: dict, experiment_name: str):
     """
     task based training
 
@@ -14,6 +14,8 @@ def task_train(task_config: dict, experiment_name):
     ----------
     task_config : dict
         A dict describes a task setting.
+    experiment_name: str
+        The name of experiment
     """
 
     # model initiaiton
@@ -30,7 +32,7 @@ def task_train(task_config: dict, experiment_name):
         R.save_objects(param=task_config)  # keep the original format and datatype
 
         # generate records: prediction, backtest, and analysis
-        records = task_config.get('record', [])
+        records = task_config.get("record", [])
         if isinstance(records, dict):  # prevent only one dict
             records = [records]
         for record in records:

qlib/workflow/task/collect.py

@@ -4,6 +4,62 @@ from typing import Union
 from tqdm.auto import tqdm
 
 
+class TaskCollector:
+    """
+    Collect the record results of the finished tasks with key and filter
+    """
+
+    @staticmethod
+    def collect(
+        experiment_name: str,
+        get_key_func,
+        filter_func=None,
+    ):
+        """
+
+        Parameters
+        ----------
+        experiment_name : str
+        get_key_func : function(task: dict) -> Union[Number, str, tuple]
+            get the key of a task when collect it
+        filter_func : function(task: dict) -> bool
+            to judge a task will be collected or not
+
+        Returns
+        -------
+
+        """
+        exp = R.get_exp(experiment_name=experiment_name)
+        # filter records
+        recs = exp.list_recorders()
+
+        recs_flt = {}
+        for rid, rec in tqdm(recs.items(), desc="Loading data"):
+            params = rec.load_object("param")
+            if rec.status == rec.STATUS_FI:
+                if filter_func is None or filter_func(params):
+                    rec.params = params
+                    recs_flt[rid] = rec
+
+        # group
+        recs_group = {}
+        for _, rec in recs_flt.items():
+            params = rec.params
+            group_key = get_key_func(params)
+            recs_group.setdefault(group_key, []).append(rec)
+
+        # reduce group
+        reduce_group = {}
+        for k, rec_l in recs_group.items():
+            pred_l = []
+            for rec in rec_l:
+                pred_l.append(rec.load_object("pred.pkl").iloc[:, 0])
+            pred = pd.concat(pred_l).sort_index()
+            reduce_group[k] = pred
+
+        return reduce_group
+
+
 class RollingCollector:
     """
     Rolling Models Ensemble based on (R)ecord
@@ -13,7 +69,7 @@ class RollingCollector:
 
     # TODO: speed up this class
     def __init__(self, get_key_func, flt_func=None):
-        self.get_key_func = get_key_func  # user need to implement this method to get the key of a task based on task config
+        self.get_key_func = get_key_func  # get the key of a task based on task config
         self.flt_func = flt_func  # determine whether a task can be retained based on task config
 
     def __call__(self, exp_name) -> Union[pd.Series, dict]:

qlib/workflow/task/gen.py

@@ -79,6 +79,7 @@ class TaskGen(metaclass=abc.ABCMeta):
         output: a set of tasks with different losses
 
     """
+
     @abc.abstractmethod
     def generate(self, task: dict) -> typing.List[dict]:
         """

qlib/workflow/task/utils.py

@@ -8,6 +8,7 @@ from qlib.log import get_module_logger
 from pymongo import MongoClient
 from typing import Union
 
+
 def get_mongodb():
     """