update comments

examples/highfreq/highfreq_processor.py

@@ -70,10 +70,3 @@ class HighFreqNorm(Processor):
             columns=["FEATURE_%d" % i for i in range(12 * 240)],
         ).sort_index()
         return df_new_features
-
-    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
-        if fit_start_time:
-            self.fit_start_time = fit_start_time
-        if fit_end_time:
-            self.fit_end_time = fit_end_time
-        super().config(**kwargs)

examples/highfreq/workflow.py

@@ -27,7 +27,7 @@ from qlib.tests.data import GetData
 from highfreq_ops import get_calendar_day, DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut
 
 
-class HighfreqWorkflow(object):
+class HighfreqWorkflow:
 
     SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut], "expression_cache": None}
 
@@ -177,8 +177,8 @@ class HighfreqWorkflow(object):
         dataset_backtest.setup_data(handler_kwargs={})
 
         ##=============get data=============
-        (xtest,) = dataset.prepare(["test"])
+        xtest = dataset.prepare("test")
-        (backtest_test,) = dataset_backtest.prepare(["test"])
+        backtest_test = dataset_backtest.prepare("test")
 
         print(xtest, backtest_test)
         return

examples/rolling_process_data/workflow.py

@@ -14,7 +14,7 @@ from qlib.utils import exists_qlib_data, init_instance_by_config
 from qlib.tests.data import GetData
 
 
-class RollingDataWorkflow(object):
+class RollingDataWorkflow:
 
     MARKET = "csi300"
     start_time = "2010-01-01"

qlib/data/dataset/__init__.py

@@ -3,6 +3,7 @@ from typing import Union, List, Tuple, Dict, Text, Optional
 from ...utils import init_instance_by_config, np_ffill
 from ...log import get_module_logger
 from .handler import DataHandler, DataHandlerLP
+from copy import deepcopy
 from inspect import getfullargspec
 import pandas as pd
 import numpy as np
@@ -16,7 +17,7 @@ class Dataset(Serializable):
     Preparing data for model training and inferencing.
     """
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, **kwargs):
         """
         init is designed to finish following steps:
 
@@ -28,16 +29,16 @@ class Dataset(Serializable):
 
         The data could specify the info to caculate the essential data for preparation
         """
-        self.setup_data(*args, **kwargs)
+        self.setup_data(**kwargs)
         super().__init__()
 
-    def config(self, *arg, **kwargs):
+    def config(self, **kwargs):
         """
         config is designed to configure and parameters that cannot be learned from the data
         """
-        super().config(*arg, **kwargs)
+        super().config(**kwargs)
 
-    def setup_data(self, *args, **kwargs):
+    def setup_data(self, **kwargs):
         """
         Setup the data.
 
@@ -53,7 +54,7 @@ class Dataset(Serializable):
         """
         pass
 
-    def prepare(self, *args, **kwargs) -> object:
+    def prepare(self, **kwargs) -> object:
         """
         The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
         The parameters should specify the scope for the prepared data
@@ -115,7 +116,7 @@ class DatasetH(Dataset):
         self.segments = segments.copy()
         super().__init__(**kwargs)
 
-    def config(self, handler_kwargs: dict = None, segments: dict = None, **kwargs):
+    def config(self, handler_kwargs: dict = None, **kwargs):
         """
         Initialize the DatasetH
 
@@ -133,11 +134,11 @@ class DatasetH(Dataset):
                 Config of segments which is same as 'segments' in self.__init__
 
         """
-        super().config(**kwargs)
         if handler_kwargs is not None:
             self.handler.config(**handler_kwargs)
-        if segments is not None:
+        if "segments" in kwargs:
-            self.segments = segments.copy()
+            self.segments = deepcopy(kwargs.pop("segments"))
+        super().config(**kwargs)
 
     def setup_data(self, handler_kwargs: dict = None, **kwargs):
         """
@@ -449,10 +450,10 @@ class TSDatasetH(DatasetH):
         self.step_len = step_len
         super().__init__(**kwargs)
 
-    def config(self, step_len=None, **kwargs):
+    def config(self, **kwargs):
+        if "step_len" in kwargs:
+            self.step_len = kwargs.pop("step_len")
         super().config(**kwargs)
-        if step_len:
-            self.step_len = step_len
 
     def setup_data(self, **kwargs):
         super().setup_data(**kwargs)

qlib/data/dataset/handler.py

@@ -103,7 +103,7 @@ class DataHandler(Serializable):
                 self.setup_data()
         super().__init__()
 
-    def config(self, instruments=None, start_time=None, end_time=None, **kwargs):
+    def config(self, **kwargs):
         """
         configuration of data.
         # what data to be loaded from data source
@@ -112,13 +112,16 @@ class DataHandler(Serializable):
         The data will be initialized with different time range.
 
         """
+        attr_list = {"instruments", "start_time", "end_time"}
+        for k, v in kwargs.items():
+            if k in attr_list:
+                setattr(self, k, v)
+
+        for attr in attr_list:
+            if attr in kwargs:
+                kwargs.pop(attr)
+
         super().config(**kwargs)
-        if instruments:
-            self.instruments = instruments
-        if start_time:
-            self.start_time = start_time
-        if end_time:
-            self.end_time = end_time
 
     def setup_data(self, enable_cache: bool = False):
         """

qlib/data/dataset/loader.py

@@ -261,7 +261,7 @@ class DataLoaderDH(DataLoader):
 
         self.is_group = is_group
         self.fetch_kwargs = {"col_set": DataHandler.CS_RAW}
-        self.fetch_kwargs = {**self.fetch_kwargs, **fetch_kwargs}
+        self.fetch_kwargs.update(fetch_kwargs)
 
     def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
         if instruments is not None:

qlib/data/dataset/processor.py

@@ -73,7 +73,15 @@ class Processor(Serializable):
         return True
 
     def config(self, **kwargs):
-        super().config(kwargs.get("dump_all", None), kwargs.get("exclude", None))
+        attr_list = {"fit_start_time", "fit_end_time"}
+        for k, v in kwargs.items():
+            if k in attr_list and getattr(self, k, None) is not None:
+                setattr(self, k, v)
+
+        for attr in attr_list:
+            if attr in kwargs:
+                kwargs.pop(attr)
+        super().config(**kwargs)
 
 
 class DropnaProcessor(Processor):
@@ -195,13 +203,6 @@ class MinMaxNorm(Processor):
         df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
         return df
 
-    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
-        if fit_start_time:
-            self.fit_start_time = fit_start_time
-        if fit_end_time:
-            self.fit_end_time = fit_end_time
-        super().config(**kwargs)
-
 
 class ZScoreNorm(Processor):
     """ZScore Normalization"""
@@ -231,13 +232,6 @@ class ZScoreNorm(Processor):
         df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
         return df
 
-    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
-        if fit_start_time:
-            self.fit_start_time = fit_start_time
-        if fit_end_time:
-            self.fit_end_time = fit_end_time
-        super().config(**kwargs)
-
 
 class RobustZScoreNorm(Processor):
     """Robust ZScore Normalization
@@ -274,13 +268,6 @@ class RobustZScoreNorm(Processor):
             df.clip(-3, 3, inplace=True)
         return df
 
-    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
-        if fit_start_time:
-            self.fit_start_time = fit_start_time
-        if fit_end_time:
-            self.fit_end_time = fit_end_time
-        super().config(**kwargs)
-
 
 class CSZScoreNorm(Processor):
     """Cross Sectional ZScore Normalization"""