.. _model:

============================================
Interday Model: Model Training & Prediction
============================================

Introduction
===================

``Interday Model`` is designed to make the `prediction score` about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``qrun``, please refer to `Workflow: Workflow Management <workflow.html>`_.  

Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Model`` can be used as an independent module also.

Base Class & Interface
======================

``Qlib`` provides a base class `qlib.model.base.Model <../reference/api.html#module-qlib.model.base>`_ from which all models should inherit.

The base class provides the following interfaces:

- `__init__(**kwargs)`
    - Initialization.

- `fit(self, dataset, **kwargs)`
    - Train model.
    - Parameter:
        - `dataset`, ``Qlib``'s ``DatasetH`` type. For more information about ``DatasetH``, users can refer to the related document: `Qlib Dataset <../component/data.html#dataset>`_.
            The `dataset` is passed into the `model`'s method because there are some unique data preprocessing procedures for each, we want to give each model maximum flexibility to handle the data that is suitable for their own.
            The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:

            .. code-block:: Python

                # get features and labels
                df_train, df_valid = dataset.prepare(
                    ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
                )
                x_train, y_train = df_train["feature"], df_train["label"]
                x_valid, y_valid = df_valid["feature"], df_valid["label"]

                # get weights
                try:
                    wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], data_key=DataHandlerLP.DK_L)
                    w_train, w_valid = wdf_train["weight"], wdf_valid["weight"]
                except KeyError as e:
                    w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
                    w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
        
- `predict(self, dataset, **kwargs)`
    - Predict test data.
    - Parameter:
        - `dataset`, ``Qlib``'s ``DatasetH`` type. The usage is similar to the example above.
    - Returns:
        - Predic results with type: `pandas.Series`.

- `finetune(self, dataset, **kwargs)`
    - Finetune the model.
    - Parameter:
        - `dataset`, ``Qlib``'s ``DatasetH`` type. The usage is similar to the example above.

    
For other interfaces such as `finetune`, please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.

Example
==================

``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. The following steps show how to run`` LightGBM`` as an independent module.

- Initialize ``Qlib`` with `qlib.init` first, please refer to `Initialization <../start/initialization.html>`_.
- Run the following code to get the `prediction score` `pred_score`
    .. code-block:: Python

        from qlib.contrib.model.gbdt import LGBModel
        from qlib.contrib.data.handler import Alpha158
        from qlib.utils import init_instance_by_config, flatten_dict
        from qlib.workflow import R
        from qlib.workflow.record_temp import SignalRecord, PortAnaRecord

        market = "csi300"
        benchmark = "SH000300"

        data_handler_config = {
            "start_time": "2008-01-01",
            "end_time": "2020-08-01",
            "fit_start_time": "2008-01-01",
            "fit_end_time": "2014-12-31",
            "instruments": market,
        }

        task = {
            "model": {
                "class": "LGBModel",
                "module_path": "qlib.contrib.model.gbdt",
                "kwargs": {
                    "loss": "mse",
                    "colsample_bytree": 0.8879,
                    "learning_rate": 0.0421,
                    "subsample": 0.8789,
                    "lambda_l1": 205.6999,
                    "lambda_l2": 580.9768,
                    "max_depth": 8,
                    "num_leaves": 210,
                    "num_threads": 20,
                },
            },
            "dataset": {
                "class": "DatasetH",
                "module_path": "qlib.data.dataset",
                "kwargs": {
                    "handler": {
                        "class": "Alpha158",
                        "module_path": "qlib.contrib.data.handler",
                        "kwargs": data_handler_config,
                    },
                    "segments": {
                        "train": ("2008-01-01", "2014-12-31"),
                        "valid": ("2015-01-01", "2016-12-31"),
                        "test": ("2017-01-01", "2020-08-01"),
                    },
                },
            },
        }
        
        # model initiaiton
        model = init_instance_by_config(task["model"])
        dataset = init_instance_by_config(task["dataset"])

        # start exp
        with R.start(experiment_name="workflow"):
            # train
            R.log_params(**flatten_dict(task))
            model.fit(dataset)

            # prediction
            recorder = R.get_recorder()
            sr = SignalRecord(model, dataset, recorder)
            sr.generate()

    .. note:: 
        
        `Alpha158` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
        `SignalRecord` is the `Record Template` in ``Qlib``, please refer to `Workflow <recorder.html#record-template>`_.

Also, the above example has been given in ``examples/train_backtest_analyze.ipynb``.

Custom Model
===================

Qlib supports custom models. If users are interested in customizing their own models and integrating the models into ``Qlib``, please refer to `Custom Model Integration <../start/integration.html>`_.


API
===================
Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.