我尝试构建一个带有变量转换的流水线,步骤如下:
import numpy as np
import pandas as pd
import sklearn
from sklearn import linear_model
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
数据框
df = pd.DataFrame({'y': [4,5,6], 'a':[3,2,3], 'b' : [2,3,4]})
我试图为预测获取一个新的变量
class Complex():
def __init__(self, X1, X2):
self.a = X1
self.b = X2
def transform(self, X1, X2):
age = pd.DataFrame(self.a - self.b)
return age
def fit_transform(self, X1, X2):
self.fit( X1, X2)
return self.transform(X1, X2)
def fit(self, X1, X2):
return self
然后我创建了一个管道。
X = df[['a', 'b']]
y = df['y']
regressor = linear_model.SGDRegressor()
pipeline = Pipeline([
('transform', Complex(X['a'], X['b'])) ,
('model_fitting', regressor)
])
pipeline.fit(X, y)
并且我遇到了错误
pred = pipeline.predict(X)
pred
TypeError Traceback (most recent call last)
<ipython-input-555-7a07ccb0c38a> in <module>()
----> 1 pred = pipeline.predict(X)
2 pred
C:\Program Files\Anaconda3\lib\site-packages\sklearn\utils\metaestimators.py in <lambda>(*args, **kwargs)
52
53 # lambda, but not partial, allows help() to work with update_wrapper
---> 54 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
55 # update the docstring of the returned function
56 update_wrapper(out, self.fn)
C:\Program Files\Anaconda3\lib\site-packages\sklearn\pipeline.py in predict(self, X)
324 for name, transform in self.steps[:-1]:
325 if transform is not None:
--> 326 Xt = transform.transform(Xt)
327 return self.steps[-1][-1].predict(Xt)
328
TypeError: transform() missing 1 required positional argument: 'X2'
我做错了什么?我看到错误在类Complex()中。如何修复它?