Update repr for Experiment & Recorder

qlib/workflow/__init__.py

@@ -38,8 +38,10 @@ class QlibRecorder:
         recorder_name : str
             name of the recorder under the experiment one wants to start.
         uri : str
-            the tracking uri of the experiment, where all the artifacts/metrics etc. will be stored.
-            The default uri are set in the qlib.config.
+            The tracking uri of the experiment, where all the artifacts/metrics etc. will be stored.
+            The default uri are set in the qlib.config. Note that this uri argument will not change the one defined in the config file.
+            Therefore, the next time when user call this function in the same experiment,
+            they have to also specify this argument with the same value. Otherwise, inconsistent uri may occur.
         """
         run = self.start_exp(experiment_name, recorder_name, uri)
         try:

qlib/workflow/exp.py

@@ -11,7 +11,7 @@ from ..log import get_module_logger
 logger = get_module_logger("workflow", "INFO")
 
 
-class Experiment:
+class Experiment(object):
     """
     This is the `Experiment` class for each experiment being run. The API is designed similar to mlflow.
     (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
@@ -23,7 +23,7 @@ class Experiment:
         self.active_recorder = None  # only one recorder can running each time
 
     def __repr__(self):
-        return str(self.info)
+        return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info)
 
     def __str__(self):
         return str(self.info)

qlib/workflow/expm.py

@@ -14,7 +14,7 @@ from ..log import get_module_logger
 logger = get_module_logger("workflow", "INFO")
 
 
-class ExpManager:
+class ExpManager(object):
     """
     This is the `ExpManager` class for managing experiments. The API is designed similar to mlflow.
     (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

qlib/workflow/recorder.py

@@ -11,7 +11,7 @@ from ..log import get_module_logger
 logger = get_module_logger("workflow", "INFO")
 
 
-class Recorder:
+class Recorder(object):
     """
     This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow.
     (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
@@ -34,7 +34,7 @@ class Recorder:
         self.status = Recorder.STATUS_S
 
     def __repr__(self):
-        return str(self.info)
+        return "{name}(info={info})".format(name=self.__class__.__name__, info=self.info)
 
     def __str__(self):
         return str(self.info)

tests/test_all_pipeline.py

@@ -110,7 +110,10 @@ def train():
 
     # model initiaiton
     model = init_instance_by_config(task["model"])
+    print(model)
     dataset = init_instance_by_config(task["dataset"])
+    print(dataset)
+    print(R)
 
     # start exp
     with R.start(experiment_name="workflow"):
@@ -119,6 +122,7 @@ def train():
 
         # prediction
         recorder = R.get_recorder()
+        print(recorder)
         rid = recorder.id
         sr = SignalRecord(model, dataset, recorder)
         sr.generate()