Column Transformer with fit_transform error

Question

I'm getting error while using make_column_transformer with LabelEncoder

def train_or_load_model(data,learn=True):
to_categorical = None
to_OH = None
to_drop = None

with open('to_categorical.pickle','rb') as f:
    to_categorical=pickle.load(f)
with open('to_OH.pickle','rb') as f:
    to_OH=pickle.load(f)
with open('to_drop.pickle','rb') as f:
    to_drop=pickle.load(f)

# print(to_drop)

ID = data.drop(['id'],axis=1,inplace=True)


if learn:
    target = np.array(data[['target']])
    target.reshape((300000,-1))
    print(type(target))
    to_drop.append('target')
    data.drop(to_drop,axis=1,inplace=True)
else:
    data.drop(to_drop,axis=1,inplace=True)
if learn:
    transformer = make_column_transformer(
            (LabelEncoder(),to_categorical),
            (OneHotEncoder(),to_OH)
    )


if learn:
    model = Pipeline(
        steps=[('preprocess_data', transformer),
         ('model',KNeighborsClassifier(2,n_jobs=-1))
        ]
    )
    X_train,X_test, y_train, y_test = train_test_split(data,target,test_size=0.2)
    model.fit(X_train,y_train)

I'm using data from https://www.kaggle.com/c/cat-in-the-dat/data and I get error like

    Traceback (most recent call last):
  File "c:\Users\barte\.vscode\extensions\ms-python.python-2019.11.50794\pythonFiles\ptvsd_launcher.py", line 43, in <module>
    main(ptvsdArgs)
  File "c:\Users\barte\.vscode\extensions\ms-python.python-2019.11.50794\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 432, in main
    run()
  File "c:\Users\barte\.vscode\extensions\ms-python.python-2019.11.50794\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 316, in run_file
    runpy.run_path(target, run_name='__main__')
  File "C:\Users\barte\AppData\Local\Programs\Python\Python36\Lib\runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "C:\Users\barte\AppData\Local\Programs\Python\Python36\Lib\runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "C:\Users\barte\AppData\Local\Programs\Python\Python36\Lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "c:\Users\barte\Desktop\Projects\tf\kaggle categorical feature\main.py", line 102, in <module>
    print(train_or_load_model(raw_data))
  File "c:\Users\barte\Desktop\Projects\tf\kaggle categorical feature\main.py", line 97, in train_or_load_model
    model.fit(X_train,y_train)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\sklearn\pipeline.py", line 352, in fit
    Xt, fit_params = self._fit(X, y, **fit_params)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\sklearn\pipeline.py", line 317, in _fit
    **fit_params_steps[name])
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\memory.py", line 355, in __call__
    return self.func(*args, **kwargs)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\sklearn\pipeline.py", line 716, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\sklearn\compose\_column_transformer.py", line 476, in fit_transform
    result = self._fit_transform(X, y, _fit_transform_one)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\sklearn\compose\_column_transformer.py", line 420, in _fit_transform
    self._iter(fitted=fitted, replace_strings=True), 1))
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\parallel.py", line 921, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\parallel.py", line 759, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\parallel.py", line 716, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\_parallel_backends.py", line 182, in apply_async        
    result = ImmediateResult(func)
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\_parallel_backends.py", line 549, in __init__
    self.results = batch()
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\parallel.py", line 225, in __call__
    for func, args, kwargs in self.items]
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\joblib\parallel.py", line 225, in <listcomp>
    for func, args, kwargs in self.items]
  File "C:\Users\barte\Desktop\Projects\tf\env\lib\site-packages\sklearn\pipeline.py", line 716, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)
TypeError: fit_transform() takes 2 positional arguments but 3 were given
enter code here

I don't know why this pipeline gives 3 arguments to LabelEncoder while only X_train should go there I have also tried to make my own class like class MyLabelEncoder(BaseEstimator,TransformerMixin) but that turned out to have bad shape error when fitting.

Ekaba Bisong · Answer 1 · 2019-12-24T11:43:01.790

0

Remove the LabelEncoder() and use the OneHotEncoder(). You no longer need to LabelEncode before performing a OneHot with Scikit-learn.

Also, indeed, LabelEncoder() does have some issues when used with a Pipeline. For this, if you need to encode a particular column, you may devise a way to run this transformation independently without passing through a Pipeline transform.

Edit: To label encode DataFrame columns:

df.apply(LabelEncoder().fit_transform)

This answer on SO will be helpful: https://stackoverflow.com/a/31939145/3353760

edited Dec 24 '19 at 11:43

answered Dec 24 '19 at 11:23

Ekaba Bisong

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But if I have almost 10 columns with less than 10 unique values I should label encode them than use OneHot. Is there a solution like ColumnTransformer to this? – Bartek Sadlej Dec 24 '19 at 11:38

Column Transformer with fit_transform error

1 Answers1