Merge

docs/start/initialization.rst

@@ -63,13 +63,14 @@ Besides `provider_uri` and `region`, `qlib.init` has other parameters. The follo
         If Qlib fails to connect redis via `redis_host` and `redis_port`, cache mechanism will not be used! Please refer to `Cache <../component/data.html#cache>`_ for details.
 - `exp_manager`
     Type: dict, optional parameter, the setting of `experiment manager` to be used in qlib. Users can specify an experiment manager class, as well as the tracking URI for all the experiments. However, please be aware that we only support input of a dictionary in the following style for `exp_manager`. For more information about `exp_manager`, users can refer to `Recorder: Experiment Management <../component/recorder.html>`_.
-    ::
+    .. code-block:: Python
 
-        {
+        # For example, if you want to set your tracking_uri to a <specific folder>, you can initialize qlib below
+        qlib.init(provider_uri=provider_uri, region=REG_CN, exp_manager= {
             "class": "MLflowExpManager",
             "module_path": "qlib.workflow.expm",
             "kwargs": {
                 "uri": "python_execution_path/mlruns",
                 "default_exp_name": "Experiment",
             }
-        }
+        })

docs/start/integration.rst

@@ -5,7 +5,7 @@ Custom Model Integration
 Introduction
 ===================
 
-``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. In addition to the default models ``Qlib`` provide, users can integrate their own custom models into ``Qlib``.
+``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are examples of ``Interday Model``. In addition to the default models ``Qlib`` provide, users can integrate their own custom models into ``Qlib``.
 
 Users can integrate their own custom models according to the following steps.
 
@@ -87,6 +87,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
     .. code-block:: Python
 
         def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
+            # Based on existing model and finetune by train more rounds
             dtrain, _ = self._prepare_data(dataset)
             self.model = lgb.train(
                 self.params,
@@ -101,7 +102,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
 Configuration File
 =======================
 
-The configuration file is described in detail in the `Workflow <../component/workflow.html#complete-example>`_ document. In order to integrate the custom model into ``Qlib``, users need to modify the "model" field in the configuration file.
+The configuration file is described in detail in the `Workflow <../component/workflow.html#complete-example>`_ document. In order to integrate the custom model into ``Qlib``, users need to modify the "model" field in the configuration file. The configuration describes which models to use and how we can initialize it.
 
 - Example: The following example describes the `model` field of configuration file about the custom lightgbm model mentioned above, where `module_path` is the module path, `class` is the class name, and `args` is the hyperparameter passed into the __init__ method. All parameters in the field is passed to `self._params` by `\*\*kwargs` in `__init__` except `loss = mse`.