Question

我有一个复杂的方法，在它们之上使用单独的模型和Stacker。像：

# GLMNET
glmnet_pipe = Pipeline([
    ("DATA_CLEANER", DataCleaner(demo='HH_F', mode='strict')),
    ("DATA_ENCODING", Encoder(encoder_name='code')),
    ("MODELLING", glm)
])
# XGBoost 
xgb_1_pipe = Pipeline([
    ("DATA_CLEANER", DataCleaner(demo='HH_F', mode='strict')),
    ("DATA_ENCODING", Encoder(encoder_name='code')),
    ("SCALE", Normalizer(normalizer=NORMALIZE)),
    ("FEATURE_SELECTION", huber_feature_selector),
    ("MODELLING", xgb_1)
])
# set of our models
base_models = [glmnet_pipe, xgb1_pipe]
# using Stacker on top of those ones
StackingRegressor(
  regressors=base_models,
  meta_regressor=SVR()
)

但是，我还在R中使用forecast包实现了一个管道。就Python的实现/重写速度而言，我的所有R结果都有点“独特”。

是否有将R代码合并到sklearn的方法？

到目前为止，我看到了以下可能性：

import subprocess

CustomRModel = class():

  def __init__(self, path, args):
    self.path = path
    self.args = args
    self.cmd = ['RScript', self.path] + self.args

  def fit(self, X, Y):
    # call fit in R
    subprocess.check_output(self.cmd, universal_newlines=True)
    # read ouput of R.csv to Python dataframe
    # pd.read_csv
    return self

  def predict(X):
    # call predict in R
    subprocess.check_output(self.cmd, universal_newlines=True)
    # read ouput of R.csv to Python dataframe
    # pd.read_csv
    # calculate predict
    return predict

以后使用该类作为Pipeline中的常规步骤。

或者你知道更酷的方法吗？

如何在sklearn管道中集成R代码？

0 个答案: