fixed bug for xgb encode

alphamind/model/modelbase.py

@@ -35,8 +35,15 @@ class ModelBase(metaclass=abc.ABCMeta):
         self.impl = None
         self.trained_time = None
 
+    def model_encode(self):
+        return encode(self.impl)
+
+    @classmethod
+    def model_decode(cls, model_desc):
+        return decode(model_desc)
+
     def __eq__(self, rhs):
-        return encode(self.impl) == encode(rhs.impl) \
+        return self.model_encode() == rhs.model_encode() \
                and self.trained_time == rhs.trained_time \
                and list_eq(self.features, rhs.features) \
                and encode(self.formulas) == encode(rhs.formulas) \
@@ -67,7 +74,7 @@ class ModelBase(metaclass=abc.ABCMeta):
                           saved_time=arrow.now().format("YYYY-MM-DD HH:mm:ss"),
                           features=list(self.features),
                           trained_time=self.trained_time,
-                          desc=encode(self.impl),
+                          desc=self.model_encode(),
                           formulas=encode(self.formulas),
                           fit_target=encode(self.fit_target),
                           internal_model=self.impl.__class__.__module__ + "." + self.impl.__class__.__name__)
@@ -80,7 +87,7 @@ class ModelBase(metaclass=abc.ABCMeta):
         obj_layout.features = model_desc['features']
         obj_layout.formulas = decode(model_desc['formulas'])
         obj_layout.trained_time = model_desc['trained_time']
-        obj_layout.impl = decode(model_desc['desc'])
+        obj_layout.impl = cls.model_decode(model_desc['desc'])
         if 'fit_target' in model_desc:
             obj_layout.fit_target = decode(model_desc['fit_target'])
         else:

alphamind/model/treemodel.py

@@ -5,7 +5,6 @@ Created on 2017-12-4
 @author: cheng.li
 """
 
-from distutils.version import LooseVersion
 import arrow
 import numpy as np
 import pandas as pd
@@ -13,11 +12,9 @@ from sklearn.ensemble import RandomForestRegressor as RandomForestRegressorImpl
 from sklearn.ensemble import RandomForestClassifier as RandomForestClassifierImpl
 from sklearn.model_selection import train_test_split
 import xgboost as xgb
-from xgboost import __version__ as xgbboot_version
 from xgboost import XGBRegressor as XGBRegressorImpl
 from xgboost import XGBClassifier as XGBClassifierImpl
 from alphamind.model.modelbase import create_model_base
-from alphamind.utilities import alpha_logger
 
 
 class RandomForestRegressor(create_model_base('sklearn')):
@@ -65,12 +62,14 @@ class XGBRegressor(create_model_base('xgboost')):
                  features=None,
                  fit_target=None,
                  n_jobs: int=1,
+                 missing: float=np.nan,
                  **kwargs):
         super().__init__(features=features, fit_target=fit_target)
         self.impl = XGBRegressorImpl(n_estimators=n_estimators,
                                      learning_rate=learning_rate,
                                      max_depth=max_depth,
                                      n_jobs=n_jobs,
+                                     missing=missing,
                                      **kwargs)
 
     @property
@@ -87,13 +86,16 @@ class XGBClassifier(create_model_base('xgboost')):
                  features=None,
                  fit_target=None,
                  n_jobs: int=1,
+                 missing: float=np.nan,
                  **kwargs):
         super().__init__(features=features, fit_target=fit_target)
         self.impl = XGBClassifierImpl(n_estimators=n_estimators,
                                       learning_rate=learning_rate,
                                       max_depth=max_depth,
                                       n_jobs=n_jobs,
+                                      missing=missing,
                                       **kwargs)
+        self.impl = XGBClassifier.model_decode(self.model_encode())
 
     @property
     def importances(self):