Vectorizers

Please first read the vectorizers section first.

The set of vectorizers included with this package are listed below:

• IdentityEncodableFeatureVectorizer: An identity vectorizer, which encodes tensors verbatim, or concatenates a list of tensors in to one tensor of the same dimension.

• CategoryEncodableFeatureVectorizer: A base class that vectorizies nominal categories in to integer indexes.

• NominalEncodedEncodableFeatureVectorizer: Map each label to a nominal, which is useful for class labels.

• OneHotEncodedEncodableFeatureVectorizer: Vectorize from a list of nominals. This is useful for encoding labels for the categorization machine learning task.

• AggregateEncodableFeatureVectorizer: Use another vectorizer to vectorize each instance in an iterable. Each iterable is then concatenated in to a single tensor on decode.

• MaskTokenContainerFeatureVectorizer: Creates masks where the first N elements of a vector are 1’s with the rest 0’s.

• SeriesEncodableFeatureVectorizer: Vectorize a Pandas series, such as a list of rows. This vectorizer has an undefined shape since both the number of columns and rows are not specified at runtime.

• AttributeEncodableFeatureVectorizer: Vectorize a iterable of floats. This vectorizer has an undefined shape since both the number of columns and rows are not specified at runtime.