_transform() takes 2 positional arguments but 3 were given

Question

I try to build a pipeline with variable transformation And i do as below

import numpy as np
import pandas as pd
import sklearn
from sklearn import linear_mod         


        

Answer 1

So the problem is that transform expects an argument of array of shape [n_samples, n_features]

See the Examples section in the documentation of sklearn.pipeline.Pipeline, it uses sklearn.feature_selection.SelectKBest as a transform, and you can see its source that it expects X to be an array instead of separate variables like X1 and X2.

In short, your code can be fixed like this:

---

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)