update models

alphamind/data/transformer.py

@@ -5,6 +5,7 @@ Created on 2017-8-23
 @author: cheng.li
 """
 
+import copy
 import pandas as pd
 from PyFin.api import pyFinAssert
 from PyFin.Analysis.SecurityValueHolders import SecurityValueHolder
@@ -58,7 +59,7 @@ class Transformer(object):
     def __init__(self,
                  expressions):
         expression_dict, expression_dependency = \
-            factor_translator(expressions)
+            factor_translator(copy.deepcopy(expressions))
 
         if expression_dict:
             self.names = sorted(expression_dict.keys())

alphamind/model/linearmodel.py

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

alphamind/model/modelbase.py

@@ -6,6 +6,7 @@ Created on 2017-9-4
 """
 
 import abc
+import copy
 import arrow
 import numpy as np
 from alphamind.utilities import alpha_logger
@@ -15,13 +16,13 @@ from alphamind.utilities import decode
 
 class ModelBase(metaclass=abc.ABCMeta):
 
-    def __init__(self, features: list=None, formulas: dict=None, **kwargs):
+    def __init__(self, features: list=None, formulas: dict=None):
         if features is not None:
             self.features = list(features)
         else:
-            self.features = formulas
+            self.features = None
         self.impl = None
-        self.formulas = formulas
+        self.formulas = copy.deepcopy(formulas)
         self.trained_time = None
 
     def fit(self, x: np.ndarray, y: np.ndarray):

alphamind/model/treemodel.py

@@ -61,8 +61,9 @@ class RandomForestClassifier(ModelBase):
                  n_estimators: int=100,
                  max_features: str='auto',
                  features: List = None,
+                 formulas: dict = None,
                  **kwargs):
-        super().__init__(features, **kwargs)
+        super().__init__(features, formulas=formulas)
         self.impl = RandomForestClassifierImpl(n_estimators=n_estimators,
                                                max_features=max_features,
                                                **kwargs)
@@ -96,8 +97,9 @@ class XGBRegressor(ModelBase):
                  learning_rate: float=0.1,
                  max_depth: int=3,
                  features: List=None,
+                 formulas: dict = None,
                  **kwargs):
-        super().__init__(features, **kwargs)
+        super().__init__(features, formulas=formulas)
         self.impl = XGBRegressorImpl(n_estimators=n_estimators,
                                      learning_rate=learning_rate,
                                      max_depth=max_depth,
@@ -131,8 +133,9 @@ class XGBClassifier(ModelBase):
                  learning_rate: float=0.1,
                  max_depth: int=3,
                  features: List = None,
+                 formulas: dict = None,
                  **kwargs):
-        super().__init__(features, **kwargs)
+        super().__init__(features, formulas=formulas)
         self.impl = XGBClassifierImpl(n_estimators=n_estimators,
                                      learning_rate=learning_rate,
                                      max_depth=max_depth,
@@ -173,9 +176,10 @@ class XGBTrainer(ModelBase):
                  subsample=1.,
                  colsample_bytree=1.,
                  features: List = None,
+                 formulas: dict = None,
                  random_state=0,
                  **kwargs):
-        super().__init__(features)
+        super().__init__(features, formulas=formulas)
         self.params = {
             'silent': 1,
             'objective': objective,