我可以使用类别编码和顺序编码将目标列转换为所需的有序数字值。但是,由于出现了以下错误,我无法执行inverse_transform
。
import pandas as pd
import category_encoders as ce
from sklearn.preprocessing import OrdinalEncoder
lst = [ 'BRANCHING/ELONGATION', 'EARLY', 'EARLY', 'EARLY', 'EARLY', 'MID', 'MID', 'ADVANCED/TILLERING',
'FLOWERING', 'FLOWERING', 'FLOWERING', 'SEEDLING/EMERGED']
filtered_df = pd.DataFrame(lst, columns =['growth_state'])
filtered_df['growth_state'].value_counts()
EARLY 4
FLOWERING 3
MID 2
ADVANCED/TILLERING 1
SEEDLING/EMERGED 1
BRANCHING/ELONGATION 1
Name: growth_state, dtype: int64
dictionary = [{'col': 'growth_state',
'mapping':{'SEEDLING/EMERGED':0, 'EARLY':1, 'MID':2,
'ADVANCED/TILLERING':3, 'BRANCHING/ELONGATION':4, 'FLOWERING':5 }}]
# instiating encoder
encoder = ce.OrdinalEncoder(cols = 'growth_state', mapping= dictionary)
filtered_df['growth_state'] = encoder.fit_transform(filtered_df['growth_state'])
filtered_df
growth_state
0 4
1 1
2 1
3 1
4 1
5 2
6 2
7 3
8 5
9 5
10 5
11 0
但是当我执行 inverse_transform 时:
newCol = encoder.inverse_transform(filtered_df['growth_state'])
AttributeError Traceback (most recent call last)
<ipython-input-26-b6505b4be1e1> in <module>
----> 1 newCol = encoder.inverse_transform(filtered_df['growth_state'])
d:\users\tiwariam\appdata\local\programs\python\python36\lib\site-packages\category_encoders\ordinal.py in inverse_transform(self, X_in)
266 for switch in self.mapping:
267 column_mapping = switch.get('mapping')
--> 268 inverse = pd.Series(data=column_mapping.index, index=column_mapping.values)
269 X[switch.get('col')] = X[switch.get('col')].map(inverse).astype(switch.get('data_type'))
270
AttributeError: 'dict' object has no attribute 'index'
注意:上述列是目标列,我本可以使用标签编码器,因为这是一个与分类相关的问题。但我选择了以上分类和序数编码的组合,因为变量本质上是有序的。
enc.inverse_transform(df_transformed.iloc[i: i + 1, :])
,其中i
是您想要反向转换的行的索引(例如,i = 2
)。 - Flavia Giammarino