One-Hot Encoder¶
Encode categorical integer features using a one-hot aka one-of-K scheme.
For each categorical input feature, a number of output features will be given of which exactly one is marked as true and the rest as false. This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels. Note: a one-hot encoding of y labels should use a LabelBinarizer instead.
Configuration:
n_values
Number of values per feature.
‘auto’ : determine value range from training data.
- int : number of categorical values per feature.
Each feature value should be in
range(n_values)
- array :
n_values[i]
is the number of categorical values in
X[:, i]
. Each feature value should be inrange(n_values[i])
Deprecated since version 0.20: The n_values keyword was deprecated in version 0.20 and will be removed in 0.22. Use categories instead.
categorical_features
Specify what features are treated as categorical.
- ‘all’: 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.
Deprecated since version 0.20: The categorical_features keyword was deprecated in version 0.20 and will be removed in 0.22. You can use the
ColumnTransformer
instead.handle_unknown
How to handle unknown categories during (non-fit) transform
sparse
Will generate sparse matrix if true. Warning: sparse matrices are not handled by all Sympathy nodes and may be silently converted to non-sparse arrays
Attributes:
active_features_
Indices for active features, meaning values that actually occur in the training set. Only available when n_values is
'auto'
.Deprecated since version 0.20: The
active_features_
attribute was deprecated in version 0.20 and will be removed in 0.22.feature_indices_
Indices to feature ranges. Feature
i
in the original data is mapped to features fromfeature_indices_[i]
tofeature_indices_[i+1]
(and then potentially masked byactive_features_
afterwards)Deprecated since version 0.20: The
feature_indices_
attribute was deprecated in version 0.20 and will be removed in 0.22.n_values_
Maximum number of values per feature.
Deprecated since version 0.20: The
n_values_
attribute was deprecated in version 0.20 and will be removed in 0.22.categories_
The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of
transform
).
Input ports:
- Output ports:
- model : model
- Model
- n_values (n_values)
Number of values per feature.
‘auto’ : determine value range from training data.
- int : number of categorical values per feature.
Each feature value should be in
range(n_values)
- array :
n_values[i]
is the number of categorical values in X[:, i]
. Each feature value should be inrange(n_values[i])
- array :
Deprecated since version 0.20: The n_values keyword was deprecated in version 0.20 and will be removed in 0.22. Use categories instead.
- categorical_features (categorical_features)
Specify what features are treated as categorical.
- ‘all’: 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.
Deprecated since version 0.20: The categorical_features keyword was deprecated in version 0.20 and will be removed in 0.22. You can use the
ColumnTransformer
instead.- handle_unknown (handle_unknown)
- How to handle unknown categories during (non-fit) transform
- sparse (sparse)
- Will generate sparse matrix if true. Warning: sparse matrices are not handled by all Sympathy nodes and may be silently converted to non-sparse arrays
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.