sklearn.preprocessing.OneHotEncoder¶
- class sklearn.preprocessing.OneHotEncoder(n_values='auto', categorical_features='all', dtype=<type 'float'>)¶
Encode categorical integer features using a one-hot aka one-of-K scheme.
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 were 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.
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.
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.
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]]) OneHotEncoder(categorical_features='all', dtype=<... 'float'>, n_values='auto') >>> 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.]])
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. Methods
fit(X[, y]) Fit OneHotEncoder to X. fit_transform(X[, y]) Fit OneHotEncoder to X, then transform X. get_params([deep]) Get parameters for this estimator. set_params(**params) Set the parameters of this estimator. transform(X) Transform X using one-hot encoding. - __init__(n_values='auto', categorical_features='all', dtype=<type 'float'>)¶
- fit(X, y=None)¶
Fit OneHotEncoder to X.
Parameters : X : array-like, shape=(n_samples, n_feature)
Input array of type int.
Returns : self :
- fit_transform(X, y=None)¶
Fit OneHotEncoder to X, then transform X.
Equivalent to self.fit(X).transform(X), but more convenient and more efficient. See fit for the parameters, transform for the return value.
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns : params : mapping of string to any
Parameter names mapped to their values.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :
- transform(X)¶
Transform X using one-hot encoding.
Parameters : X : array-like, shape=(n_samples, n_features)
Input array of type int.
Returns : X_out : sparse matrix, dtype=int
Transformed input.