Class OneHotEncoderScikitsLearnNode

Encode categorical integer features using a one-hot aka one-of-K scheme.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.OneHotEncoder`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

The input to this transformer should be a matrix of integers, denoting
the values taken on by categorical (discrete) features. The output will be
a sparse matrix where each column corresponds to one possible value of one
feature. It is assumed that input features take on values in the range
[0, n_values).

This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.

Read more in the :ref:`User Guide <preprocessing_categorical_features>`.

**Parameters**

n_values : 'auto', int or array of ints
    Number of values per feature.

    - 'auto' : determine value range from training data.
    - int : maximum value for all features.
    - array : maximum value per feature.

categorical_features: "all" or array of indices or mask
    Specify what features are treated as categorical.

    - 'all' (default): All features are treated as categorical.
    - array of indices: Array of categorical feature indices.
    - mask: Array of length n_features and with dtype=bool.

    Non-categorical features are always stacked to the right of the matrix.

dtype : number type, default=np.float
    Desired dtype of output.

sparse : boolean, default=True
    Will return sparse matrix if set True else will return an array.

handle_unknown : str, 'error' or 'ignore'
    Whether to raise an error or ignore if a unknown categorical feature is
    present during transform.

**Attributes**

``active_features_`` : array
    Indices for active features, meaning values that actually occur
    in the training set. Only available when n_values is ``'auto'``.

``feature_indices_`` : array of shape (n_features,)
    Indices to feature ranges.
    Feature ``i`` in the original data is mapped to features
    from ``feature_indices_[i]`` to ``feature_indices_[i+1]``
    (and then potentially masked by `active_features_` afterwards)

``n_values_`` : array of shape (n_features,)
    Maximum number of values per feature.

**Examples**

Given a dataset with three features and two samples, we let the encoder
find the maximum value per feature and transform the data to a binary
one-hot encoding.

>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])  # doctest: +ELLIPSIS
OneHotEncoder(categorical_features='all', dtype=<... 'float'>,
       handle_unknown='error', n_values='auto', sparse=True)
>>> enc.n_values_
array([2, 3, 4])
>>> enc.feature_indices_
array([0, 2, 5, 9])
>>> enc.transform([[0, 1, 1]]).toarray()
array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.]])

See also

sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of
  dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
  encoding of dictionary items or strings.

Overrides: object.__init__

Transform X using one-hot encoding.

This node has been automatically generated by wrapping the sklearn.preprocessing.data.OneHotEncoder class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape [n_samples, n_features]

Input array of type int.

Returns

X_out : sparse matrix if sparse=True else a 2-d array, dtype=int

Transformed input.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit OneHotEncoder to X.

This node has been automatically generated by wrapping the sklearn.preprocessing.data.OneHotEncoder class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape [n_samples, n_feature]

Input array of type int.

Returns

self

Overrides: Node.stop_training