`matchzoo.embedding`¶

Submodules¶

matchzoo.embedding.embedding

Package Contents¶

Classes¶

Embedding

Embedding class.

Functions¶

load_from_file(file_path: str, mode: str = ‘word2vec’) → Embedding

Load embedding from file_path.

class matchzoo.embedding.Embedding(data: dict, output_dim: int)¶

Bases: object

Embedding class.

Examples::

>>> import matchzoo as mz
>>> train_raw = mz.datasets.toy.load_data()
>>> pp = mz.preprocessors.NaivePreprocessor()
>>> train = pp.fit_transform(train_raw, verbose=0)
>>> vocab_unit = mz.build_vocab_unit(train, verbose=0)
>>> term_index = vocab_unit.state['term_index']
>>> embed_path = mz.datasets.embeddings.EMBED_RANK

To load from a file:

>>> embedding = mz.embedding.load_from_file(embed_path)
>>> matrix = embedding.build_matrix(term_index)
>>> matrix.shape[0] == len(term_index)
True

To build your own:

>>> data = {'A':[0, 1], 'B':[2, 3]}
>>> embedding = mz.Embedding(data, 2)
>>> matrix = embedding.build_matrix({'A': 2, 'B': 1, '_PAD': 0})
>>> matrix.shape == (3, 2)
True

build_matrix(self, term_index: typing.Union[dict, mz.preprocessors.units.Vocabulary.TermIndex]) → np.ndarray¶

Build a matrix using term_index.

Parameters

term_index – A dict or TermIndex to build with.
initializer – A callable that returns a default value for missing terms in data. (default: a random uniform distribution in range) (-0.2, 0.2)).

Returns

A matrix.

matchzoo.embedding.load_from_file(file_path: str, mode: str = 'word2vec') → Embedding ¶