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alpha-mind
Commit 1fe4765e authored by wegamekinglc's avatar wegamekinglc
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update models

parent 1fbc1bf1
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Showing with 69 additions and 123 deletions
alphamind/model/linearmodel.py
View file @ 1fe4765e
......@@ -6,14 +6,11 @@ Created on 2017-5-10
"""
import numpy as np
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 sklearn.linear_model import LogisticRegression as LogisticRegressionImpl
from PyFin.api import pyFinAssert
from alphamind.model.modelbase import ModelBase
from alphamind.utilities import alpha_logger
from alphamind.model.modelbase import create_model_base
class ConstLinearModelImpl(object):
......@@ -35,7 +32,7 @@ class ConstLinearModelImpl(object):
return 1. - sse / ssto
class ConstLinearModel(ModelBase):
class ConstLinearModel(create_model_base()):
def __init__(self,
features=None,
......@@ -63,7 +60,7 @@ class ConstLinearModel(ModelBase):
return self.impl.weights.tolist()
class LinearRegression(ModelBase):
class LinearRegression(create_model_base('sklearn')):
def __init__(self, features=None, fit_intercept: bool = False, fit_target=None, **kwargs):
super().__init__(features=features, fit_target=fit_target)
......@@ -71,26 +68,15 @@ class LinearRegression(ModelBase):
def save(self) -> dict:
model_desc = super().save()
model_desc['sklearn_version'] = sklearn_version
model_desc['weight'] = self.impl.coef_.tolist()
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
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']))
return obj_layout
@property
def weights(self):
return self.impl.coef_.tolist()
class LassoRegression(ModelBase):
class LassoRegression(create_model_base('sklearn')):
def __init__(self, alpha=0.01, features=None, fit_intercept: bool = False, fit_target=None, **kwargs):
super().__init__(features=features, fit_target=fit_target)
......@@ -98,26 +84,15 @@ class LassoRegression(ModelBase):
def save(self) -> dict:
model_desc = super().save()
model_desc['sklearn_version'] = sklearn_version
model_desc['weight'] = self.impl.coef_.tolist()
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
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']))
return obj_layout
@property
def weights(self):
return self.impl.coef_.tolist()
class LogisticRegression(ModelBase):
class LogisticRegression(create_model_base('sklearn')):
def __init__(self, features=None, fit_intercept: bool = False, fit_target=None, **kwargs):
super().__init__(features=features, fit_target=fit_target)
......@@ -125,20 +100,9 @@ class LogisticRegression(ModelBase):
def save(self) -> dict:
model_desc = super().save()
model_desc['sklearn_version'] = sklearn_version
model_desc['weight'] = self.impl.coef_.tolist()
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
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']))
return obj_layout
@property
def weights(self):
return self.impl.coef_.tolist()
......
alphamind/model/modelbase.py
View file @ 1fe4765e
......@@ -6,10 +6,13 @@ Created on 2017-9-4
"""
import abc
from distutils.version import LooseVersion
import arrow
import numpy as np
import pandas as pd
from simpleutils.miscellaneous import list_eq
from sklearn import __version__ as sklearn_version
from xgboost import __version__ as xgbboot_version
from alphamind.utilities import alpha_logger
from alphamind.utilities import encode
from alphamind.utilities import decode
......@@ -84,3 +87,39 @@ class ModelBase(metaclass=abc.ABCMeta):
obj_layout.fit_target = None
return obj_layout
def create_model_base(party_name=None):
if not party_name:
return ModelBase
else:
class ExternalLibBase(ModelBase):
_lib_name = party_name
def save(self) -> dict:
model_desc = super().save()
if self._lib_name == 'sklearn':
model_desc[self._lib_name] = sklearn_version
elif self._lib_name == 'xgboost':
model_desc[self._lib_name] = xgbboot_version
else:
raise ValueError("3rd party lib name ({0}) is not recognized".format(self._lib_name))
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
if cls._lib_name == 'sklearn':
current_version = sklearn_version
elif cls._lib_name == 'xgboost':
current_version = xgbboot_version
else:
raise ValueError("3rd party lib name ({0}) is not recognized".format(cls._lib_name))
if LooseVersion(current_version) < LooseVersion(model_desc[cls._lib_name]):
alpha_logger.warning('Current {2} version {0} is lower than the model version {1}. '
'Loaded model may work incorrectly.'.format(sklearn_version,
model_desc[cls._lib_name],
cls._lib_name))
return obj_layout
return ExternalLibBase
alphamind/model/svm.py 0 → 100644
View file @ 1fe4765e
# -*- coding: utf-8 -*-
"""
Created on 2018-7-9
@author: cheng.li
"""
from sklearn.svm import NuSVR
from alphamind.model.modelbase import create_model_base
class NvSVRModel(create_model_base('sklearn')):
def __init__(self,
features=None,
fit_target=None,
**kwargs):
super().__init__(features=features, fit_target=fit_target)
self.impl = NuSVR(**kwargs)
alphamind/model/treemodel.py
View file @ 1fe4765e
......@@ -9,7 +9,6 @@ from distutils.version import LooseVersion
import arrow
import numpy as np
import pandas as pd
from sklearn import __version__ as sklearn_version
from sklearn.ensemble import RandomForestRegressor as RandomForestRegressorImpl
from sklearn.ensemble import RandomForestClassifier as RandomForestClassifierImpl
from sklearn.model_selection import train_test_split
......@@ -17,11 +16,11 @@ 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 ModelBase
from alphamind.model.modelbase import create_model_base
from alphamind.utilities import alpha_logger
class RandomForestRegressor(ModelBase):
class RandomForestRegressor(create_model_base('sklearn')):
def __init__(self,
n_estimators: int=100,
......@@ -34,27 +33,12 @@ class RandomForestRegressor(ModelBase):
max_features=max_features,
**kwargs)
def save(self) -> dict:
model_desc = super().save()
model_desc['sklearn_version'] = sklearn_version
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
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']))
return obj_layout
@property
def importances(self):
return self.impl.feature_importances_.tolist()
class RandomForestClassifier(ModelBase):
class RandomForestClassifier(create_model_base('sklearn')):
def __init__(self,
n_estimators: int=100,
......@@ -67,27 +51,12 @@ class RandomForestClassifier(ModelBase):
max_features=max_features,
**kwargs)
def save(self) -> dict:
model_desc = super().save()
model_desc['sklearn_version'] = sklearn_version
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
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']))
return obj_layout
@property
def importances(self):
return self.impl.feature_importances_.tolist()
class XGBRegressor(ModelBase):
class XGBRegressor(create_model_base('xgboost')):
def __init__(self,
n_estimators: int=100,
......@@ -104,27 +73,12 @@ class XGBRegressor(ModelBase):
n_jobs=n_jobs,
**kwargs)
def save(self) -> dict:
model_desc = super().save()
model_desc['xgbboot_version'] = xgbboot_version
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
if LooseVersion(xgbboot_version) < LooseVersion(model_desc['xgbboot_version']):
alpha_logger.warning('Current xgboost version {0} is lower than the model version {1}. '
'Loaded model may work incorrectly.'.format(xgbboot_version,
model_desc['xgbboot_version']))
return obj_layout
@property
def importances(self):
return self.impl.feature_importances_.tolist()
class XGBClassifier(ModelBase):
class XGBClassifier(create_model_base('xgboost')):
def __init__(self,
n_estimators: int=100,
......@@ -141,27 +95,12 @@ class XGBClassifier(ModelBase):
n_jobs=n_jobs,
**kwargs)
def save(self) -> dict:
model_desc = super().save()
model_desc['xgbboot_version'] = xgbboot_version
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
if LooseVersion(xgbboot_version) < LooseVersion(model_desc['xgbboot_version']):
alpha_logger.warning('Current xgboost version {0} is lower than the model version {1}. '
'Loaded model may work incorrectly.'.format(xgbboot_version,
model_desc['xgbboot_version']))
return obj_layout
@property
def importances(self):
return self.impl.feature_importances_.tolist()
class XGBTrainer(ModelBase):
class XGBTrainer(create_model_base('xgboost')):
def __init__(self,
objective='binary:logistic',
......@@ -226,21 +165,6 @@ class XGBTrainer(ModelBase):
d_predict = xgb.DMatrix(x[self.features].values)
return self.impl.predict(d_predict)
def save(self) -> dict:
model_desc = super().save()
model_desc['xgbboot_version'] = xgbboot_version
return model_desc
@classmethod
def load(cls, model_desc: dict):
obj_layout = super().load(model_desc)
if LooseVersion(xgbboot_version) < LooseVersion(model_desc['xgbboot_version']):
alpha_logger.warning('Current xgboost version {0} is lower than the model version {1}. '
'Loaded model may work incorrectly.'.format(xgbboot_version,
model_desc['xgbboot_version']))
return obj_layout
@property
def importances(self):
imps = self.impl.get_fscore().items()
......
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