rename modules

qlib/model/base.py

@@ -0,0 +1,155 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import six
+
+
+@six.add_metaclass(abc.ABCMeta)
+class Model(object):
+    """Model base class"""
+
+    @property
+    def name(self):
+        return type(self).__name__
+
+    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
+
+        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
+            trained model
+        """
+        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()
+
+    def predict(self, x_test, **kwargs):
+        """predict given test data
+
+        Parameters
+        ----------
+        x_test : pd.dataframe
+            test data
+
+        Returns
+        ----------
+        np.ndarray
+            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.")