[Solved] fit_transform() takes 2 positional arguments but 3 were given with LabelBinarizer

Solution #1:

The Problem:

The pipeline is assuming LabelBinarizer’s fit_transform method is defined to take three positional arguments:

def fit_transform(self, x, y)
    ...rest of the code

while it is defined to take only two:

def fit_transform(self, x):
    ...rest of the code

Possible Solution:

This can be solved by making a custom transformer that can handle 3 positional arguments:

1. Import and make a new class:

   from sklearn.base import TransformerMixin #gives fit_transform method for free
   class MyLabelBinarizer(TransformerMixin):
       def __init__(self, *args, **kwargs):
           self.encoder = LabelBinarizer(*args, **kwargs)
       def fit(self, x, y=0):
           self.encoder.fit(x)
           return self
       def transform(self, x, y=0):
           return self.encoder.transform(x)
   

2. Keep your code the same only instead of using LabelBinarizer(), use the class we created : MyLabelBinarizer().

Note: If you want access to LabelBinarizer Attributes (e.g. classes_), add the following line to the fit method:

    self.classes_, self.y_type_, self.sparse_input_ = self.encoder.classes_, self.encoder.y_type_, self.encoder.sparse_input_

Solution #2:

I believe your example is from the book Hands-On Machine Learning with Scikit-Learn & TensorFlow. Unfortunately, I ran into this problem, as well. A recent change in scikit-learn (0.19.0) changed LabelBinarizer‘s fit_transform method. Unfortunately, LabelBinarizer was never intended to work how that example uses it. You can see information about the change here and here.

Until they come up with a solution for this, you can install the previous version (0.18.0) as follows:

$ pip install scikit-learn==0.18.0

After running that, your code should run without issue.

In the future, it looks like the correct solution may be to use a CategoricalEncoder class or something similar to that. They have been trying to solve this problem for years apparently. You can see the new class here and further discussion of the problem here.

Solution #3:

I think you are going through the examples from the book: Hands on Machine Learning with Scikit Learn and Tensorflow. I ran into the same problem when going through the example in Chapter 2.

As mentioned by other people, the problem is to do with sklearn’s LabelBinarizer. It takes less args in its fit_transform method compared to other transformers in the pipeline. (only y when other transformers normally take both X and y, see here for details). That’s why when we run pipeline.fit_transform, we fed more args into this transformer than required.

An easy fix I used is to just use OneHotEncoder and set the “sparse” to False to ensure the output is a numpy array same as the num_pipeline output. (this way you don’t need to code up your own custom encoder)

your original cat_pipeline:

cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('label_binarizer', LabelBinarizer())
])

you can simply change this part to:

cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('one_hot_encoder', OneHotEncoder(sparse=False))
])

You can go from here and everything should work.

Solution #4:

Since LabelBinarizer doesn’t allow more than 2 positional arguments you should create your custom binarizer like

class CustomLabelBinarizer(BaseEstimator, TransformerMixin):
    def __init__(self, sparse_output=False):
        self.sparse_output = sparse_output
    def fit(self, X, y=None):
        return self
    def transform(self, X, y=None):
        enc = LabelBinarizer(sparse_output=self.sparse_output)
        return enc.fit_transform(X)

num_attribs = list(housing_num)
cat_attribs = ['ocean_proximity']

num_pipeline = Pipeline([
    ('selector', DataFrameSelector(num_attribs)),
    ('imputer', Imputer(strategy='median')),
    ('attribs_adder', CombinedAttributesAdder()),
    ('std_scalar', StandardScaler())
])

cat_pipeline = Pipeline([
    ('selector', DataFrameSelector(cat_attribs)),
    ('label_binarizer', CustomLabelBinarizer())
])

full_pipeline = FeatureUnion(transformer_list=[
    ('num_pipeline', num_pipeline),
    ('cat_pipeline', cat_pipeline)
])

housing_prepared = full_pipeline.fit_transform(new_housing)

Solution #5:

I ran into the same problem and got it working by applying the workaround specified in the book’s Github repo.

Warning: earlier versions of the book used the LabelBinarizer class at
this point. Again, this was incorrect: just like the LabelEncoder
class, the LabelBinarizer class was designed to preprocess labels, not
input features. A better solution is to use Scikit-Learn’s upcoming
CategoricalEncoder class: it will soon be added to Scikit-Learn, and
in the meantime you can use the code below (copied from Pull Request
#9151).

To save you some grepping here’s the workaround, just paste and run it in a previous cell:

# Definition of the CategoricalEncoder class, copied from PR #9151.
# Just run this cell, or copy it to your code, do not try to understand it (yet).

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array
from sklearn.preprocessing import LabelEncoder
from scipy import sparse

class CategoricalEncoder(BaseEstimator, TransformerMixin):
    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,
                 handle_unknown='error'):
        self.encoding = encoding
        self.categories = categories
        self.dtype = dtype
        self.handle_unknown = handle_unknown

    def fit(self, X, y=None):
        """Fit the CategoricalEncoder to X.
        Parameters
        ----------
        X : array-like, shape [n_samples, n_feature]
            The data to determine the categories of each feature.
        Returns
        -------
        self
        """

        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:
            template = ("encoding should be either 'onehot', 'onehot-dense' "
                        "or 'ordinal', got %s")
            raise ValueError(template % self.handle_unknown)

        if self.handle_unknown not in ['error', 'ignore']:
            template = ("handle_unknown should be either 'error' or "
                        "'ignore', got %s")
            raise ValueError(template % self.handle_unknown)

        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':
            raise ValueError("handle_unknown='ignore' is not supported for"
                             " encoding='ordinal'")

        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)
        n_samples, n_features = X.shape

        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]

        for i in range(n_features):
            le = self._label_encoders_[i]
            Xi = X[:, i]
            if self.categories == 'auto':
                le.fit(Xi)
            else:
                valid_mask = np.in1d(Xi, self.categories[i])
                if not np.all(valid_mask):
                    if self.handle_unknown == 'error':
                        diff = np.unique(Xi[~valid_mask])
                        msg = ("Found unknown categories {0} in column {1}"
                               " during fit".format(diff, i))
                        raise ValueError(msg)
                le.classes_ = np.array(np.sort(self.categories[i]))

        self.categories_ = [le.classes_ for le in self._label_encoders_]

        return self

    def transform(self, X):
        """Transform X using one-hot encoding.
        Parameters
        ----------
        X : array-like, shape [n_samples, n_features]
            The data to encode.
        Returns
        -------
        X_out : sparse matrix or a 2-d array
            Transformed input.
        """
        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)
        n_samples, n_features = X.shape
        X_int = np.zeros_like(X, dtype=np.int)
        X_mask = np.ones_like(X, dtype=np.bool)

        for i in range(n_features):
            valid_mask = np.in1d(X[:, i], self.categories_[i])

            if not np.all(valid_mask):
                if self.handle_unknown == 'error':
                    diff = np.unique(X[~valid_mask, i])
                    msg = ("Found unknown categories {0} in column {1}"
                           " during transform".format(diff, i))
                    raise ValueError(msg)
                else:
                    # Set the problematic rows to an acceptable value and
                    # continue `The rows are marked `X_mask` and will be
                    # removed later.
                    X_mask[:, i] = valid_mask
                    X[:, i][~valid_mask] = self.categories_[i][0]
            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])

        if self.encoding == 'ordinal':
            return X_int.astype(self.dtype, copy=False)

        mask = X_mask.ravel()
        n_values = [cats.shape[0] for cats in self.categories_]
        n_values = np.array([0] + n_values)
        indices = np.cumsum(n_values)

        column_indices = (X_int + indices[:-1]).ravel()[mask]
        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
                                n_features)[mask]
        data = np.ones(n_samples * n_features)[mask]

        out = sparse.csc_matrix((data, (row_indices, column_indices)),
                                shape=(n_samples, indices[-1]),
                                dtype=self.dtype).tocsr()
        if self.encoding == 'onehot-dense':
            return out.toarray()
        else:
            return out

Solution #6:

Simply, what you can do is define following class just before your pipeline:

class NewLabelBinarizer(LabelBinarizer):
    def fit(self, X, y=None):
        return super(NewLabelBinarizer, self).fit(X)
    def transform(self, X, y=None):
        return super(NewLabelBinarizer, self).transform(X)
    def fit_transform(self, X, y=None):
        return super(NewLabelBinarizer, self).fit(X).transform(X)

Then the rest of the code is like the one has mentioned in the book with a tiny modification in cat_pipeline before pipeline concatenation – follow as:

cat_pipeline = Pipeline([
    ("selector", DataFrameSelector(cat_attribs)),
    ("label_binarizer", NewLabelBinarizer())])

You DONE!

Solution #7:

Forget LaberBinarizer and use OneHotEncoder instead.

In case you use a LabelEncoder before OneHotEncoder to convert categories to integers, you can now use the OneHotEncoder directly.

Solution #8:

I have also faced the same issue. Following link helped me in fixing this issue.
https://github.com/ageron/handson-ml/issues/75

Summarizing changes to be made

1) Define following class in your notebook

class SupervisionFriendlyLabelBinarizer(LabelBinarizer):
    def fit_transform(self, X, y=None):
        return super(SupervisionFriendlyLabelBinarizer,self).fit_transform(X)

2) Modify following piece of code

cat_pipeline = Pipeline([('selector', DataFrameSelector(cat_attribs)),
                         ('label_binarizer', SupervisionFriendlyLabelBinarizer()),])

3) Re-run the notebook. You will be able to run now

Solution #9:

I got the same issue, and got resolved by using DataFrameMapper (need to install sklearn_pandas):

from sklearn_pandas import DataFrameMapper
cat_pipeline = Pipeline([
    ('label_binarizer', DataFrameMapper([(cat_attribs, LabelBinarizer())])),
])

Solution #10:

You can create one more Custom Transformer which does the encoding for you.

class CustomLabelEncode(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return LabelEncoder().fit_transform(X);

In this example, we have done LabelEncoding but you can use LabelBinarizer as well

Solution #11:

I ended up rolling my own

class LabelBinarizer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        X = self.prep(X)
        unique_vals = []
        for column in X.T:
            unique_vals.append(np.unique(column))
        self.unique_vals = unique_vals
    def transform(self, X, y=None):
        X = self.prep(X)
        unique_vals = self.unique_vals
        new_columns = []
        for i, column in enumerate(X.T):
            num_uniq_vals = len(unique_vals[i])
            encoder_ring = dict(zip(unique_vals[i], range(len(unique_vals[i]))))
            f = lambda val: encoder_ring[val]
            f = np.vectorize(f, otypes=[np.int])
            new_column = np.array([f(column)])
            if num_uniq_vals <= 2:
                new_columns.append(new_column)
            else:
                one_hots = np.zeros([num_uniq_vals, len(column)], np.int)
                one_hots[new_column, range(len(column))]=1
                new_columns.append(one_hots)
        new_columns = np.concatenate(new_columns, axis=0).T        
        return new_columns

    def fit_transform(self, X, y=None):
        self.fit(X)
        return self.transform(X)

    @staticmethod
    def prep(X):
        shape = X.shape
        if len(shape) == 1:
            X = X.values.reshape(shape[0], 1)
        return X

Seems to work

lbn = LabelBinarizer()
thingy = np.array([['male','male','female', 'male'], ['A', 'B', 'A', 'C']]).T
lbn.fit(thingy)
lbn.transform(thingy)

returns

array([[1, 1, 0, 0],
       [1, 0, 1, 0],
       [0, 1, 0, 0],
       [1, 0, 0, 1]])

Solution #12:

To perform one-hot encoding for multiple categorical features, we can create a new class which customizes our own multiple categorical features binarizer and plug it into categorical pipeline as follows.

Suppose CAT_FEATURES = ['cat_feature1', 'cat_feature2'] is a list of categorical features. The following scripts shall resolve the issue and produce what we want.

import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin

class CustomLabelBinarizer(BaseEstimator, TransformerMixin):
    """Perform one-hot encoding to categorical features."""
    def __init__(self, cat_features):
        self.cat_features = cat_features

    def fit(self, X_cat, y=None):
        return self

    def transform(self, X_cat):
        X_cat_df = pd.DataFrame(X_cat, columns=self.cat_features)
        X_onehot_df = pd.get_dummies(X_cat_df, columns=self.cat_features)
        return X_onehot_df.values

# Pipeline for categorical features.
cat_pipeline = Pipeline([
    ('selector', DataFrameSelector(CAT_FEATURES)),
    ('onehot_encoder', CustomLabelBinarizer(CAT_FEATURES))
])

Solution #13:

We can just add attribute sparce_output=False

cat_pipeline = Pipeline([
  ('selector', DataFrameSelector(cat_attribs)),
  ('label_binarizer', LabelBinarizer(sparse_output=False)),   
])