fix workflow bug (#882)

* fix workflow bug

* Fix output of pytorch NN

* Fix parameter bug

qlib/contrib/model/pytorch_nn.py

@@ -240,7 +240,7 @@ class DNNModelPytorch(Model):
                 R.log_metrics(val_loss=loss_val.val, step=step)
                 if verbose:
                     self.logger.info(
-                        "[Epoch {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val)
+                        "[Step {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val)
                     )
                 evals_result["train"].append(train_loss)
                 evals_result["valid"].append(loss_val.val)

qlib/model/trainer.py

@@ -37,8 +37,8 @@ def _log_task_info(task_config: dict):
 def _exe_task(task_config: dict):
     rec = R.get_recorder()
     # model & dataset initiation
-    model: Model = init_instance_by_config(task_config["model"])
-    dataset: Dataset = init_instance_by_config(task_config["dataset"])
+    model: Model = init_instance_by_config(task_config["model"], accept_types=Model)
+    dataset: Dataset = init_instance_by_config(task_config["dataset"], accept_types=Dataset)
     reweighter: Reweighter = task_config.get("reweighter", None)
     # model training
     auto_filter_kwargs(model.fit)(dataset, reweighter=reweighter)

qlib/workflow/__init__.py

@@ -226,7 +226,9 @@ class QlibRecorder:
         """
         return self.get_exp(experiment_id=experiment_id, experiment_name=experiment_name).list_recorders()
 
-    def get_exp(self, *, experiment_id=None, experiment_name=None, create: bool = True) -> Experiment:
+    def get_exp(
+        self, *, experiment_id=None, experiment_name=None, create: bool = True, start: bool = False
+    ) -> Experiment:
         """
         Method for retrieving an experiment with given id or name. Once the `create` argument is set to
         True, if no valid experiment is found, this method will create one for you. Otherwise, it will
@@ -291,6 +293,10 @@ class QlibRecorder:
         create : boolean
             an argument determines whether the method will automatically create a new experiment
             according to user's specification if the experiment hasn't been created before.
+        start : bool
+            when start is True,
+                if the experiment has not started(not activated), it will start
+            It is designed for R.log_params to auto start experiments
 
         Returns
         -------
@@ -300,7 +306,7 @@ class QlibRecorder:
             experiment_id=experiment_id,
             experiment_name=experiment_name,
             create=create,
-            start=False,
+            start=start,
         )
 
     def delete_exp(self, experiment_id=None, experiment_name=None):
@@ -542,7 +548,7 @@ class QlibRecorder:
         keyword argument:
             name1=value1, name2=value2, ...
         """
-        self.get_exp().get_recorder(start=True).log_params(**kwargs)
+        self.get_exp(start=True).get_recorder(start=True).log_params(**kwargs)
 
     def log_metrics(self, step=None, **kwargs):
         """
@@ -567,7 +573,7 @@ class QlibRecorder:
         keyword argument:
             name1=value1, name2=value2, ...
         """
-        self.get_exp().get_recorder(start=True).log_metrics(step, **kwargs)
+        self.get_exp(start=True).get_recorder(start=True).log_metrics(step, **kwargs)
 
     def set_tags(self, **kwargs):
         """
@@ -592,7 +598,7 @@ class QlibRecorder:
         keyword argument:
             name1=value1, name2=value2, ...
         """
-        self.get_exp().get_recorder(start=True).set_tags(**kwargs)
+        self.get_exp(start=True).get_recorder(start=True).set_tags(**kwargs)
 
 
 class RecorderWrapper(Wrapper):

qlib/workflow/expm.py

@@ -178,7 +178,7 @@ class ExpManager:
                 self._get_exp(experiment_id=experiment_id, experiment_name=experiment_name),
                 False,
             )
-        if is_new and start:
+        if self.active_experiment is None and start:
             self.active_experiment = exp
             # start the recorder
             self.active_experiment.start()