Add inst_processors to D.features

examples/benchmarks/LightGBM/features_sample.py

@@ -1,9 +1,16 @@
 import datetime
 import pandas as pd
 
+from qlib.data.inst_processor import InstProcessor
 
-def resample_feature(df: pd.DataFrame) -> pd.DataFrame:
-    df = df.droplevel(level="instrument")
-    df = df.loc[df.index.time == datetime.time(13, 1)]
-    df.index = df.index.normalize()
-    return df
+
+class ResampleProcessor(InstProcessor):
+    def __init__(self, freq: str, hour: int, minute: int):
+        self.freq = freq
+        self.hour = hour
+        self.minute = minute
+
+    def __call__(self, df: pd.DataFrame, *args, **kwargs):
+        df = df.loc[df.index.time == datetime.time(self.hour, self.minute)]
+        df.index = df.index.normalize()
+        return df

examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_multi_freq.yaml

@@ -1,6 +1,5 @@
 qlib_init:
-    provider_uri: "~/.qlib/qlib_data/cn_data"
-    backend_freq_config:
+    provider_uri:
         day: "~/.qlib/qlib_data/cn_data"
         1min: "~/.qlib/qlib_data/cn_data_1min"
     region: cn
@@ -19,13 +18,14 @@ data_handler_config: &data_handler_config
     # with label as reference
     sample_benchmark: label
     sample_config:
-        # using pandas.DataFrame.resample
-        feature: resample("1d", level="datetime").last()
-        # or
-        # using custom function, df.groupby(level="instrument").apply(<user func>)
-#        feature:
-#            module_path: features_sample.py
-#            func: resample_feature
+        feature:
+            - class: ResampleProcessor
+              moudle_path: features_sample.py
+              kwargs:
+                  freq: 1d
+                  hour: 13
+                  minute: 1
+
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy