我在使用 dask.dataframe.core.DataFrame 规范 Dask.dask_ml.preprocessing.MinMaxScaler 时遇到了问题，我可以使用 sklearn.preprocessing.MinMaxScaler ，但是我希望使用dask进行扩展。

最小的、可复制的例子：

# Get data
ddf = dd.read_csv('test.csv') # See below
ddf = ddf.set_index('index')
# Pivot
ddf = ddf.categorize(columns=['item', 'name'])
ddf_p = ddf.pivot_table(index='item', columns='name', values='value', aggfunc='mean')
col = ddf_p.columns.to_list()
# sklearn verison
from sklearn.preprocessing import MinMaxScaler
scaler_s = MinMaxScaler()
scaled_ddf_s = scaler_s.fit_transform(ddf_p[col]) # Works!
# dask verison
from dask_ml.preprocessing import MinMaxScaler
scaler_d = MinMaxScaler()
scaled_values_d = scaler_d.fit_transform(ddf_p[col]) # Doesn't work

错误消息：

TypeError: Categorical is not ordered for operation min
you can use .as_ordered() to change the Categorical to an ordered one

不确定旋转表中的“分类”是什么，但我尝试过 .as_ordered() 索引：

from dask_ml.preprocessing import MinMaxScaler
scaler_d = MinMaxScaler()
ddf_p = ddf_p.index.cat.as_ordered()
scaled_values_d = scaler_d.fit_transform(ddf_p[col])

但我得到了错误信息：

NotImplementedError: Series getitem in only supported for other series objects with matching partition structure

附加信息

test.csv

index,item,name,value
2015-01-01,item_1,A,1
2015-01-01,item_1,B,2
2015-01-01,item_1,C,3
2015-01-01,item_1,D,4
2015-01-01,item_1,E,5
2015-01-02,item_2,A,10
2015-01-02,item_2,B,20
2015-01-02,item_2,C,30
2015-01-02,item_2,D,40