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 in range(n_values[i])

• categorical_features

  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.

• 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'.

• feature_indices_

  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_

  Maximum number of values per feature.

Inputs: Outputs:

model : model

Model

Ports:

Outputs:

model:

model

Model

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 in range(n_values[i])

categorical_features

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.

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

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_preprocessing.OneHotEncoder