重写lore estimator

import  inspect
import  logging
import  warnings
import  threading
import  lore.env
from lore.util import timed

lore.env.require(

)

from lore.estimators.xgboost import Base
from    sklearn.ensemble import  RandomForestRegressor

logger=logging.getLogger(__name__)

class RFRegression(Base,RandomForestRegressor):
    def __init__(self,
                 base_estimator,
                 n_estimators=10,
                 estimator_params=tuple(),
                 bootstrap=False,
                 oob_score=False,
                 n_jobs=1,
                 random_state=None,
                 verbose=0,
                 warm_start=False,
                 criterion = 'gini',
                 max_depth = None,
                 min_samples_split = 2,
                 min_sample_leaf = 1,
                 min_weight_fraction_leaf = 0,
                 max_features = "auto",
                 max_leaf_nodes = None,
                 min_impurity_decrease = 0.,
                 min_impurity_split = None,
                 class_weight = None,
                **kwargs

                 ):

        kwargs=locals()
        kwargs.pop('self')
        kwargs.pop('__class__',None)
        kwargs=dict(kwargs,**(kwargs.pop('kwargs',{})))

        if 'random_state' not in kwargs and 'seed' in kwargs:
            kwargs['random_state']=kwargs.pop('seed')

        if 'n_jobs' not in kwargs and 'nthread'  in kwargs:
            kwargs['n_jobs']=kwargs.pop('nthread')
        super(RFRegression,self).__init__(
            base_estimator,
            n_estimators=n_estimators,
            estimator_params=estimator_params,
            bootstrap=bootstrap,
            oob_score=oob_score,
            n_jobs=n_jobs,
            random_state=random_state,
            verbose=verbose,
            warm_start=warm_start,
            criterion = criterion,
            max_depth = max_depth,
            min_samples_split = min_samples_split,
            min_sample_leaf = min_sample_leaf,
            min_weight_fraction_leaf = min_weight_fraction_leaf,
            max_features = max_features,
            max_leaf_nodes = max_leaf_nodes,
            min_impurity_decrease = min_impurity_decrease,
            min_impurity_split = min_impurity_split,
            class_weight = class_weight
        )