我正在尝试在Python中创建预测模型，通过交叉验证比较几种不同的回归模型。为了适应序数逻辑模型（MASS.polr），我必须通过rpy2与R进行交互，如下所示：
from rpy2.robjects.packages import importr
import rpy2.robjects as ro
df = pd.DataFrame()
df = df.append(pd.DataFrame({"y":25,"X":7},index=[0]))
df = df.append(pd.DataFrame({"y":50,"X":22},index=[0]))
df = df.append(pd.DataFrame({"y":25,"X":15},index=[0]))
df = df.append(pd.DataFrame({"y":75,"X":27},index=[0]))
df = df.append(pd.DataFrame({"y":25,"X":12},index=[0]))
df = df.append(pd.DataFrame({"y":25,"X":13},index=[0]))
# Loads R packages.
base = importr('base')
mass = importr('MASS')
# Converts df to an R dataframe.
from rpy2.robjects import pandas2ri
pandas2ri.activate()
ro.globalenv["rdf"] = pandas2ri.py2ri(df)
# Makes R recognise y as a factor.
ro.r("""rdf$y <- as.factor(rdf$y)""")
# Fits regression.
formula = "y ~ X"
ordlog = mass.polr(formula, data=base.as_symbol("rdf"))
ro.globalenv["ordlog"] = ordlog
print(base.summary(ordlog))
到目前为止，我主要使用sklearn.cross_validation.test_train_split和sklearn.metrics.accuracy_score比较我的模型，得到0到1之间的数字，表示训练集模型在预测测试集值时的准确性
我如何使用rpy2和MASS.polr复制此测试？