OneHotEncoder not behaving?

小能豆

OneHotEncoder not behaving?

I am given a code chunk to run in Jupyter to learn about One Hot Encoding and when I run the code an error shows up.

    from sklearn.preprocessing import OneHotEncoder as ohc

    enc = ohc(drop='if_binary', sparse_output=False).set_output(transform='pandas')

    df = enc.fit_transform(default[["student"]])

    default_enc = default.assign(student = df['student_Yes'])

then I get the error code:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-f958840e2f7e> in <module>
      1 from sklearn.preprocessing import OneHotEncoder as ohc
      2 default = pd.read_csv("default.csv", index_col=[0])
----> 3 enc = ohc(drop = 'if_binary',sparse_output=False).set_output(transform='pandas')
      4 df = enc.fit_transform(default[["student"]])
      5 default_enc = default.assign(student = df['student_Yes'])

/usr/local/lib64/python3.6/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

TypeError: __init__() got an unexpected keyword argument 'sparse_output'

I have tried updating anaconda, and sklearn. The code is supposed to work the next few problems rely on editing it to see how different parts affect it. your text

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2023-12-23

共1个答案

小能豆

The error you’re encountering suggests that the OneHotEncoder class in scikit-learn does not have a sparse_output parameter in its __init__ method. This could be due to version differences.

In scikit-learn version 0.22.0 and later, the OneHotEncoder class does not have a sparse_output parameter in its constructor. Instead, the sparse_output parameter is part of the fit_transform method.

Here’s how you can modify your code:

from sklearn.preprocessing import OneHotEncoder
import pandas as pd

enc = OneHotEncoder(drop='if_binary', sparse_output=False)
df = pd.DataFrame(enc.fit_transform(default[["student"]]).toarray(), columns=enc.get_feature_names_out(["student"]))
default_enc = default.join(df)

In this code:

fit_transform is used directly on the OneHotEncoder instance.
toarray() is called on the result to convert the sparse matrix to a dense array.
get_feature_names_out is used to get the column names for the one-hot encoded features.
pd.DataFrame is used to create a DataFrame from the one-hot encoded array.
Finally, default.join(df) is used to concatenate the original DataFrame (default) with the one-hot encoded DataFrame (df).

Make sure to adjust the code according to your specific requirements and the structure of your dataset.

2023-12-23