update models

alphamind/model/linearmodel.py

@@ -66,9 +66,6 @@ class LinearRegression(ModelBase):
         model_desc['weight'] = self.impl.coef_.tolist()
         return model_desc
 
-    def score(self,  x: np.ndarray, y: np.ndarray) -> float:
-        return self.impl.score(x, y)
-
     @classmethod
     def load(cls, model_desc: dict):
         obj_layout = super().load(model_desc)
@@ -97,9 +94,6 @@ class LassoRegression(ModelBase):
         model_desc['weight'] = self.impl.coef_.tolist()
         return model_desc
 
-    def score(self, x: np.ndarray, y: np.ndarray) -> float:
-        return self.impl.score(x, y)
-
     @classmethod
     def load(cls, model_desc: dict):
         obj_layout = super().load(model_desc)

alphamind/model/modelbase.py

@@ -22,12 +22,15 @@ class ModelBase(metaclass=abc.ABCMeta):
         self.trained_time = None
 
     def fit(self, x, y):
-        self.impl.fit(x, y)
+        self.impl.fit(x, x.flatten())
         self.trained_time = arrow.now().format("YYYY-MM-DD HH:mm:ss")
 
     def predict(self, x: np.ndarray) -> np.ndarray:
         return self.impl.predict(x)
 
+    def score(self, x: np.ndarray, y: np.ndarray) -> float:
+        return self.impl.score(x, y)
+
     @abc.abstractmethod
     def save(self) -> dict:
 

alphamind/model/treemodel.py

@@ -6,7 +6,6 @@ Created on 2017-12-4
 """
 
 from typing import List
-import numpy as np
 from distutils.version import LooseVersion
 from sklearn import __version__ as sklearn_version
 from sklearn.ensemble import RandomForestRegressor as RandomForestRegressorImpl
@@ -23,9 +22,6 @@ class RandomForestRegressor(ModelBase):
         self.impl = RandomForestRegressorImpl(n_estimators, **kwargs)
         self.trained_time = None
 
-    def score(self, x: np.ndarray, y: np.ndarray) -> float:
-        return self.impl.score(x, y)
-
     def save(self) -> dict:
         model_desc = super().save()
         model_desc['sklearn_version'] = sklearn_version