remove formula parameters from model base

alphamind/model/linearmodel.py

@@ -32,9 +32,8 @@ class ConstLinearModel(ModelBase):
 
     def __init__(self,
                  features: list = None,
-                 formulas: dict = None,
                  weights: np.ndarray = None):
-        super().__init__(features, formulas=formulas)
+        super().__init__(features)
         if features is not None and weights is not None:
             pyFinAssert(len(features) == len(weights),
                         ValueError,
@@ -57,8 +56,8 @@ class ConstLinearModel(ModelBase):
 
 class LinearRegression(ModelBase):
 
-    def __init__(self, features: list = None, formulas: dict = None, fit_intercept: bool = False, **kwargs):
-        super().__init__(features, formulas=formulas)
+    def __init__(self, features: list = None, fit_intercept: bool = False, **kwargs):
+        super().__init__(features)
         self.impl = LinearRegressionImpl(fit_intercept=fit_intercept, **kwargs)
         self.trained_time = None
 
@@ -85,8 +84,8 @@ class LinearRegression(ModelBase):
 
 class LassoRegression(ModelBase):
 
-    def __init__(self, alpha=0.01, features: list = None, formulas: dict = None, fit_intercept: bool = False, **kwargs):
-        super().__init__(features, formulas=formulas)
+    def __init__(self, alpha=0.01, features: list = None, fit_intercept: bool = False, **kwargs):
+        super().__init__(features)
         self.impl = Lasso(alpha=alpha, fit_intercept=fit_intercept, **kwargs)
         self.trained_time = None
 
@@ -113,8 +112,8 @@ class LassoRegression(ModelBase):
 
 class LogisticRegression(ModelBase):
 
-    def __init__(self, features: list = None, formulas: dict = None, fit_intercept: bool = False, **kwargs):
-        super().__init__(features, formulas=formulas)
+    def __init__(self, features: list = None, fit_intercept: bool = False, **kwargs):
+        super().__init__(features)
         self.impl = LogisticRegressionImpl(fit_intercept=fit_intercept, **kwargs)
 
     def save(self) -> dict:

alphamind/model/modelbase.py

@@ -6,7 +6,6 @@ Created on 2017-9-4
 """
 
 import abc
-import copy
 import arrow
 import numpy as np
 from alphamind.utilities import alpha_logger
@@ -17,13 +16,13 @@ from alphamind.data.transformer import Transformer
 
 class ModelBase(metaclass=abc.ABCMeta):
 
-    def __init__(self, features: list=None, formulas: dict=None):
+    def __init__(self, features: list=None):
         if features is not None:
-            self.features = Transformer(features).names
+            self.formulas = Transformer(features)
+            self.features = self.formulas.names
         else:
             self.features = None
         self.impl = None
-        self.formulas = copy.deepcopy(formulas)
         self.trained_time = None
 
     def fit(self, x: np.ndarray, y: np.ndarray):

alphamind/model/treemodel.py

@@ -28,7 +28,7 @@ class RandomForestRegressor(ModelBase):
                  max_features: str='auto',
                  features: List=None,
                  **kwargs):
-        super().__init__(features, **kwargs)
+        super().__init__(features)
         self.impl = RandomForestRegressorImpl(n_estimators=n_estimators,
                                               max_features=max_features,
                                               **kwargs)
@@ -61,9 +61,8 @@ class RandomForestClassifier(ModelBase):
                  n_estimators: int=100,
                  max_features: str='auto',
                  features: List = None,
-                 formulas: dict = None,
                  **kwargs):
-        super().__init__(features, formulas=formulas)
+        super().__init__(features)
         self.impl = RandomForestClassifierImpl(n_estimators=n_estimators,
                                                max_features=max_features,
                                                **kwargs)
@@ -97,10 +96,9 @@ class XGBRegressor(ModelBase):
                  learning_rate: float=0.1,
                  max_depth: int=3,
                  features: List=None,
-                 formulas: dict = None,
                  n_jobs: int=1,
                  **kwargs):
-        super().__init__(features, formulas=formulas)
+        super().__init__(features)
         self.impl = XGBRegressorImpl(n_estimators=n_estimators,
                                      learning_rate=learning_rate,
                                      max_depth=max_depth,
@@ -135,10 +133,9 @@ class XGBClassifier(ModelBase):
                  learning_rate: float=0.1,
                  max_depth: int=3,
                  features: List = None,
-                 formulas: dict = None,
                  n_jobs: int=1,
                  **kwargs):
-        super().__init__(features, formulas=formulas)
+        super().__init__(features)
         self.impl = XGBClassifierImpl(n_estimators=n_estimators,
                                       learning_rate=learning_rate,
                                       max_depth=max_depth,
@@ -180,11 +177,10 @@ class XGBTrainer(ModelBase):
                  subsample=1.,
                  colsample_bytree=1.,
                  features: List = None,
-                 formulas: dict = None,
                  random_state: int=0,
                  n_jobs: int=1,
                  **kwargs):
-        super().__init__(features, formulas=formulas)
+        super().__init__(features)
         self.params = {
             'silent': 1,
             'objective': objective,