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

typeerror __init__ got an unexpected keyword argument year
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typeerror: __init__() got an unexpected keyword argument drop

Spyder(python 3.7)

I am facing following errors here. I have already update all library from anaconda prompt. But can't findout the solution of the problem.

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()

X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
Traceback (most recent call last):

File "<ipython-input-4-05deb1f02719>", line 2, in <module>
onehotencoder = OneHotEncoder(categorical_features = [1])

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

So based on your code, you'd have to:

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer

# Country column
ct = ColumnTransformer([("Country", OneHotEncoder(), [1])], remainder = 'passthrough')
X = ct.fit_transform(X)

# Male/Female
labelencoder_X = LabelEncoder()
X[:, 2] = labelencoder_X.fit_transform(X[:, 2])

Noticed how the first LabelEncoder was removed, you do not need to apply both the label encoded and the one hot encoder on the column anymore.

(I've kinda assumed your example came from the ML Udemy course, and the first column was a list of countries, while the second one a male/female binary choice)

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According to the documentation this is the __init__ line:

class sklearn.preprocessing.OneHotEncoder(categories='auto', drop=None, sparse=True, dtype=<class 'numpy.float64'>, handle_unknown='error')

As you can see the init does not get the variable categorical_features

You have an categories flag:

categories‘auto’ or a list of array-like, default=’auto’ Categories (unique values) per feature:

‘auto’ : Determine categories automatically from the training data.

list : categories[i] holds the categories expected in the ith column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values.

The used categories can be found in the categories_ attribute.

Attributes: categories_list of arrays The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of transform). This includes the category specified in drop (if any).

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    from sklearn.preprocessing import OneHotEncoder
    from sklearn.compose import ColumnTransformer
    columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [0])],     remainder='passthrough')
    X=np.array(columnTransformer.fit_transform(X),dtype=np.str)

Since the latest build of sklearn library removed categorical_features parameter for onehotencoder class. It is advised to use ColumnTransformer class for categorical datasets. Refer the sklearn's official documentation for futher clarifications.

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from sklearn.preprocessing import OneHotEncoder, LabelEncoder

from sklearn.compose import ColumnTransformer

label_encoder_x_1 = LabelEncoder()
X[: , 2] = label_encoder_x_1.fit_transform(X[:,2])
transformer = ColumnTransformer(
    transformers=[
        ("OneHot",        # Just a name
         OneHotEncoder(), # The transformer class
         [1]              # The column(s) to be applied on.
         )
    ],
    remainder='passthrough' # donot apply anything to the remaining columns
)
X = transformer.fit_transform(X.tolist())
X = X.astype('float64')

working like charm :)

Bummer! __init__() got an unexpected keyword argument 'first_name', Not sure I understand the error here. Gives me: Bummer! __init__() got an unexpected keyword argument 'first_name'. I think it would be helpful� TypeError: init() got an unexpected keyword argument 'pipeline' Process finished with exit code 1 it seems pipeline are no need in this suitation? how can i deal with it?

Assuming this is problem from ML course from Udemy complete code I did replaced label encoder 1 with column transformer as suggested by Antoine Jaussoin in above comment.

Categorical Data

from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("Geography", OneHotEncoder(), [1])], remainder = 'passthrough')
X = ct.fit_transform(X)

Your Gender column will have index 4 now

 labelencoder_x_2=LabelEncoder()
 X[:,4]=labelencoder_x_2.fit_transform(X[:,4])

to avoid dummy variable trap

 X=X[:, 1:]

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TypeError: __init__() got an unexpected keyword argument , TypeError: __init__() got an unexpected keyword argument 'alignText' so I'm guessing the alignText parameter won't be available for use� Discord.py Glitch or random error: TypeError: __new__() got an unexpected keyword argument 'deny_new' – lpd11 Jul 23 at 13:36 1 No, that did not help at all, I tried the update via command python3 -m pip install -U discord.py but still the same issue!

Issue #934: [CLI] TypeError: __init__() got an unexpected keyword , #934 [CLI] TypeError: __init__() got an unexpected keyword argument 'strict' with marshmallow-3.0.0. Closed: Fixed 11 months ago by praiskup. Opened a year� I got the following error: Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: __init__() got an unexpected keyword argument 'num_classes' Does the new version not supporting num_classes intake anymore??

Comments
  • This should be marked as the answer. Thanks, Antoine! Stay safe!
  • i'm working thru the ML Udemy course and the column transformer pulls the geography column and inserts 3 columns at the beginning of the ndarray. this leads to the labelencoder needing to be passed and index of 4 instead of 2. Is there a cleaner way to manage indexes, maybe using DataFrame instead of a ndarray?
  • How can I use categorical features in OneHotEncoder ? Please explain with an example
  • Same issue here, seems the API changed and I don't understand how to translate the old categorical_features to the new API.
  • Yes, but: What does this add to other answers, including Antoine Jaussoin's top-voted one?