model models

alphamind/api.py

@@ -26,7 +26,9 @@ from alphamind.data.neutralize import neutralize
 from alphamind.data.engines.sqlengine import factor_tables
 
 from alphamind.model.linearmodel import LinearRegression
+from alphamind.model.linearmodel import LassoRegression
 from alphamind.model.linearmodel import ConstLinearModel
+from alphamind.model.treemodel import RandomForestRegressor
 from alphamind.model.loader import load_model
 from alphamind.model.data_preparing import fetch_data_package
 from alphamind.model.data_preparing import fetch_train_phase
@@ -81,7 +83,9 @@ __all__ = [
     'fetch_data_package',
     'fetch_train_phase',
     'LinearRegression',
+    'LassoRegression',
     'ConstLinearModel',
+    'RandomForestRegressor',
     'load_model',
     'NaiveExecutor',
     'ThresholdExecutor',

alphamind/data/dbmodel/models.py

@@ -923,6 +923,7 @@ class PortfolioSettings(Base):
     trade_date = Column(DateTime, primary_key=True, nullable=False)
     portfolio_name = Column(String(50), primary_key=True, nullable=False)
     model_id = Column(BigInteger, primary_key=True, nullable=False)
+    weight = Column(Float(53), nullable=False)
 
 
 class Positions(Base):

alphamind/model/__init__.py

@@ -6,8 +6,13 @@ Created on 2017-5-2
 """
 
 from alphamind.model.linearmodel import LinearRegression
+from alphamind.model.linearmodel import LassoRegression
 from alphamind.model.linearmodel import ConstLinearModel
 
+from alphamind.model.treemodel import RandomForestRegressor
+
 
 __all__ = ['LinearRegression',
-           'ConstLinearModel']
+           'LassoRegression',
\ No newline at end of file
+           'ConstLinearModel',
+           'RandomForestRegressor']
\ No newline at end of file

alphamind/model/linearmodel.py

@@ -10,6 +10,7 @@ import arrow
 from distutils.version import LooseVersion
 from sklearn import __version__ as sklearn_version
 from sklearn.linear_model import LinearRegression as LinearRegressionImpl
+from sklearn.linear_model import Lasso
 from PyFin.api import pyFinAssert
 from alphamind.model.modelbase import ModelBase
 from alphamind.utilities import alpha_logger
@@ -93,6 +94,51 @@ class LinearRegression(ModelBase):
         return self.impl.coef_.tolist()
 
 
+class LassoRegression(ModelBase):
+
+    def __init__(self, alpha, features: list=None, fit_intercept: bool=False):
+        super().__init__(features)
+        self.impl = Lasso(alpha=alpha, fit_intercept=fit_intercept)
+        self.trained_time = None
+
+    def fit(self, x: np.ndarray, y: np.ndarray):
+        self.impl.fit(x, y)
+        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 save(self) -> dict:
+        model_desc = super().save()
+        model_desc['internal_model'] = self.impl.__class__.__module__ + "." + self.impl.__class__.__name__
+        model_desc['desc'] = encode(self.impl)
+        model_desc['sklearn_version'] = sklearn_version
+        model_desc['trained_time'] = self.trained_time
+        model_desc['weight'] = self.impl.coef_.tolist()
+        return model_desc
+
+    def score(self) -> float:
+        return self.impl.score()
+
+    @classmethod
+    def load(cls, model_desc: dict):
+        obj_layout = cls()
+        obj_layout.features = model_desc['features']
+        obj_layout.trained_time = model_desc['trained_time']
+
+        if LooseVersion(sklearn_version) < LooseVersion(model_desc['sklearn_version']):
+            alpha_logger.warning('Current sklearn version {0} is lower than the model version {1}. '
+                                 'Loaded model may work incorrectly.'.format(
+                sklearn_version, model_desc['sklearn_version']))
+
+        obj_layout.impl = decode(model_desc['desc'])
+        return obj_layout
+
+    @property
+    def weights(self):
+        return self.impl.coef_.tolist()
+
+
 if __name__ == '__main__':
 
     import pprint

alphamind/model/treemodel.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-12-4
+
+@author: cheng.li
+"""
+
+import arrow
+import numpy as np
+from distutils.version import LooseVersion
+from sklearn import __version__ as sklearn_version
+from sklearn.ensemble import RandomForestRegressor as RandomForestRegressorImpl
+from alphamind.model.modelbase import ModelBase
+from alphamind.utilities import alpha_logger
+from alphamind.utilities import encode
+from alphamind.utilities import decode
+
+
+class RandomForestRegressor(ModelBase):
+
+    def __init__(self, n_estimators, features=None, *args, **kwargs):
+        super().__init__(features)
+        self.impl = RandomForestRegressorImpl(n_estimators, *args, **kwargs)
+
+    def fit(self, x: np.ndarray, y: np.ndarray):
+        self.impl.fit(x, y)
+        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 save(self) -> dict:
+        model_desc = super().save()
+        model_desc['internal_model'] = self.impl.__class__.__module__ + "." + self.impl.__class__.__name__
+        model_desc['desc'] = encode(self.impl)
+        model_desc['sklearn_version'] = sklearn_version
+        model_desc['trained_time'] = self.trained_time
+    @classmethod
+    def load(cls, model_desc: dict):
+        obj_layout = cls()
+        obj_layout.features = model_desc['features']
+        obj_layout.trained_time = model_desc['trained_time']
+
+        if LooseVersion(sklearn_version) < LooseVersion(model_desc['sklearn_version']):
+            alpha_logger.warning('Current sklearn version {0} is lower than the model version {1}. '
+                                 'Loaded model may work incorrectly.'.format(
+                sklearn_version, model_desc['sklearn_version']))
+
+        obj_layout.impl = decode(model_desc['desc'])
+        return obj_layout
+