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Custom Model Integration
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Introduction
===================

``Qlib`` provides ``lightGBM`` and ``Dnn`` model as the baseline of ``Interday Model``. In addition to the default model, users can integrate their own custom models into ``Qlib``.

Users can integrate their own custom models according to the following steps.

- Define a custom model class, which should be a subclass of the `qlib.contrib.model.base.Model <../reference/api.html#module-qlib.contrib.model.base>`_
- Write a configuration file that describes the path and parameters of the custom model
- Test the custom model

Custom Model Class
===========================
The Custom models need to inherit `qlib.contrib.model.base.Model <../reference/api.html#module-qlib.contrib.model.base>`_ and override the methods in it.

- Override the `__init__` method
    - ``Qlib`` passes the initialized parameters to the \_\_init\_\_ method
    - The parameter must be consistent with the hyperparameters in the configuration file.
    - Code Example: In the following example, the hyperparameter filed of the configuration file should contain parameters such as ‘loss:mse’.
    .. code-block:: Python

        def __init__(self, loss='mse', **kwargs):
            if loss not in {'mse', 'binary'}:
                raise NotImplementedError
            self._scorer = mean_squared_error if loss == 'mse' else roc_auc_score
            self._params.update(objective=loss, **kwargs)
            self._model = None

- Override the `fit` method
    - ``Qlib`` calls the fit method to train the model
    - The parameters must include training feature 'x_train', training label 'y_train', test feature 'x_valid', test label 'y_valid'at least.
    - The parameters could include some optional parameters with default values, such as train weight 'w_train', test weight 'w_valid' and 'num_boost_round = 1000'.
    - Code Example: In the following example, 'num_boost_round = 1000' is an optional parameter.
    .. code-block:: Python
    
        def fit(self, x_train:pd.DataFrame, y_train:pd.DataFrame, x_valid:pd.DataFrame, y_valid:pd.DataFrame,
            w_train:pd.DataFrame = None, w_valid:pd.DataFrame = None, num_boost_round = 1000, **kwargs):

            # Lightgbm need 1D array as its label
            if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
                y_train_1d, y_valid_1d = np.squeeze(y_train.values), np.squeeze(y_valid.values)
            else:
                raise ValueError('LightGBM doesn\'t support multi-label training')

            w_train_weight = None if w_train is None else w_train.values
            w_valid_weight = None if w_valid is None else w_valid.values

            dtrain = lgb.Dataset(x_train.values, label=y_train_1d, weight=w_train_weight)
            dvalid = lgb.Dataset(x_valid.values, label=y_valid_1d, weight=w_valid_weight)
            self._model = lgb.train(
                self._params, 
                dtrain, 
                num_boost_round=num_boost_round,
                valid_sets=[dtrain, dvalid],
                valid_names=['train', 'valid'],
                **kwargs
            )

- Override the `predict` method
    - The parameters include the test features
    - Return the prediction score
    - Please refer to `qlib.contrib.model.base.Model <../reference/api.html#module-qlib.contrib.model.base>`_ for the parameter types of the fit method
    - Code Example:In the following example, user need to user dnn to predict the label(such as 'preds') of test data 'x_test' and return it.
    .. code-block:: Python

        def predict(self, x_test:pd.DataFrame, **kwargs)-> numpy.ndarray:
            if self._model is None:
                raise ValueError('model is not fitted yet!')
            return self._model.predict(x_test.values)

- Override the `score` method
    - The parameters include the test features and test labels
    - Return the evaluation score of model. It's recommended to adopt the loss between labels and prediction score.
    - Code Example:In the following example, user need to calculate the weighted loss with test data 'x_test',  test label 'y_test' and the weight 'w_test'.
    .. code-block:: Python

        def score(self, x_test:pd.Dataframe, y_test:pd.Dataframe, w_test:pd.DataFrame = None) -> float:
            # Remove rows from x, y and w, which contain Nan in any columns in y_test.
            x_test, y_test, w_test = drop_nan_by_y_index(x_test, y_test, w_test)
            preds = self.predict(x_test)
            w_test_weight = None if w_test is None else w_test.values
            scorer = mean_squared_error if self.loss_type == 'mse' else roc_auc_score
            return scorer(y_test.values, preds, sample_weight=w_test_weight)

- Override the `save` method & `load` method
    - The `save` method parameter include the a `filename` that represents an absolute path, user need to save model into the path.
    - The `load` method parameter include the a `buffer` read from the `filename` passed in `save` method , user need to load model from the `buffer`.
    - Code Example:
    .. code-block:: Python

        def save(self, filename):
            if self._model is None:
                raise ValueError('model is not fitted yet!')
            self._model.save_model(filename)

        def load(self, buffer):
            self._model = lgb.Booster(params={'model_str': buffer.decode('utf-8')})


Configuration File
=======================

The configuration file is described in detail in the `estimator <../advanced/estimator.html#Example>`_ document. In order to integrate the custom model into ``Qlib``, you need to modify the "model" field in the configuration file.

- 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'. 

.. code-block:: YAML
    
    model:
        class: LGBModel
        module_path: qlib.contrib.model.gbdt
        args:
            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

Users could find configuration file of the baseline of the ``Model`` in ``qlib/examples/estimator/estimator_config.yaml`` and ``qlib/examples/estimator/estimator_config_dnn.yaml``

Model Testing
=====================
Assuming that the configuration file is ``examples/estimator/estimator_config.yaml``, user can run the following command to test the custom model:

.. code-block:: bash

    cd examples  # Avoid running program under the directory contains `qlib`
    estimator -c estimator/estimator_config.yaml

.. note:: ``estimator`` is a built-in command of ``Qlib``.

Also, ``Model`` can also be tested as a single module. An example has been given in ``examples.estimator.train_backtest_analyze.ipynb``. 


Reference
=====================

To know more about ``Model``, please refer to `Interday Model: Model Training & Prediction <../advanced/model.rst>`_ and `Model API <../reference/api.html#module-qlib.contrib.model.base>`_.