Question

我尝试构建一个具有变量转换的管道我这样做

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

数据帧

df = pd.DataFrame({'y': [4,5,6], 'a':[3,2,3], 'b' : [2,3,4]})

我尝试为预测

获取一个新变量

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

然后我做了一个管道

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)

我收到错误

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？我看到错误是在类Complex（）中。如何解决？

Answer 1

所以问题是transform期望形状数组的参数[n_samples, n_features]

请参阅documentation of sklearn.pipeline.Pipeline中的示例部分，它使用sklearn.feature_selection.SelectKBest作为转换，您可以看到它所期望的source X }是一个数组而不是像X1和X2这样的单独变量。

简而言之，您的代码可以像这样修复：

import pandas as pd
import sklearn
from sklearn import linear_model
from sklearn.pipeline import Pipeline

df = pd.DataFrame({'y': [4,5,6], 'a':[3,2,3], 'b' : [2,3,4]})

class Complex():
    def transform(self, Xt):
        return pd.DataFrame(Xt['a'] - Xt['b'])

    def fit_transform(self, X1, X2):
        return self.transform(X1)

X = df[['a', 'b']]
y = df['y']
regressor = linear_model.SGDRegressor()
pipeline = Pipeline([
        ('transform', Complex()) ,
        ('model_fitting', regressor)
    ])
pipeline.fit(X, y)

pred = pipeline.predict(X)
print(pred)

_transform（）需要2个位置参数，但是给出了3个

1 个答案: