adjust data and model interface

qlib/model/base.py

@@ -1,22 +1,26 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-
-
-from __future__ import division
-from __future__ import print_function
-
 import abc
-import six
+from ..utils.serial import Serializable
 
 
-@six.add_metaclass(abc.ABCMeta)
-class Model(object):
-    """Model base class"""
+class BaseModel(Serializable, metaclass=abc.ABCMeta):
+    '''Modeling things'''
 
-    @property
-    def name(self):
-        return type(self).__name__
+    @abc.abstractmethod
+    def predict(self, *args, **kwargs) -> object:
+        """ Make predictions after modeling things """
+        pass
 
+    def __call__(self, *args, **kwargs) -> object:
+        """ levarge Python syntactic sugar to make the models' behaviors like functions """
+        return self.predict(*args, **kwargs)
+
+
+class Model(BaseModel):
+    '''Learnable Models'''
+
+    # TODO: Make the model easier.
     def fit(self, x_train, y_train, x_valid, y_valid, w_train=None, w_valid=None, **kwargs):
         """fix train with cross-validation
         Fit model when ex_config.finetune is False
@@ -43,25 +47,7 @@ class Model(object):
         """
         raise NotImplementedError()
 
-    def score(self, x_test, y_test, w_test=None, **kwargs):
-        """evaluate model with test data/label
-
-        Parameters
-        ----------
-        x_test : pd.dataframe
-            test data
-        y_test : pd.dataframe
-            test label
-        w_test : pd.dataframe
-            test weight
-
-        Returns
-        ----------
-        float
-            evaluation score
-        """
-        raise NotImplementedError()
-
+    @abc.abstractmethod
     def predict(self, x_test, **kwargs):
         """predict given test data
 
@@ -76,80 +62,3 @@ class Model(object):
             test predict label
         """
         raise NotImplementedError()
-
-    def save(self, fname, **kwargs):
-        """save model
-
-        Parameters
-        ----------
-        fname : str
-            model filename
-        """
-        # TODO: Currently need to save the model as a single file, otherwise the estimator may not be compatible
-        raise NotImplementedError()
-
-    def load(self, buffer, **kwargs):
-        """load model
-
-        Parameters
-        ----------
-        buffer : bytes
-            binary data of model parameters
-
-        Returns
-        ----------
-        Model
-            loaded model
-        """
-        raise NotImplementedError()
-
-    def get_data_with_date(self, date, **kwargs):
-        """
-        Will be called in online module
-        need to return the data that used to predict the label (score) of stocks at date.
-
-        :param
-            date: pd.Timestamp
-                predict date
-        :return:
-            data: the input data that used to predict the label (score) of stocks at predict date.
-        """
-        raise NotImplementedError("get_data_with_date for this model is not implemented.")
-
-    def finetune(self, x_train, y_train, x_valid, y_valid, w_train=None, w_valid=None, **kwargs):
-        """Finetune model
-        In `RollingTrainer`:
-            if loader.model_index is None:
-                If provide 'Static Model', based on the provided 'Static' model update.
-                If provide 'Rolling Model', skip the model of load, based on the last 'provided model' update.
-
-            if loader.model_index is not None:
-                Based on the provided model(loader.model_index) update.
-
-        In `StaticTrainer`:
-            If the load is 'static model':
-                Based on the 'static model' update
-            If the load is 'rolling model':
-                Based on the provided model(`loader.model_index`) update. If `loader.model_index` is None, use the last model.
-
-        Parameters
-        ----------
-        x_train : pd.dataframe
-            train data
-        y_train : pd.dataframe
-            train label
-        x_valid : pd.dataframe
-            valid data
-        y_valid : pd.dataframe
-            valid label
-        w_train : pd.dataframe
-            train weight
-        w_valid : pd.dataframe
-            valid weight
-
-        Returns
-        ----------
-        Model
-            finetune model
-        """
-        raise NotImplementedError("Finetune for this model is not implemented.")