matchzoo.embedding
¶
Submodules¶
Package Contents¶
-
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], initializer=lambda: np.random.uniform(-0.2, 0.2))¶ 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¶ Load embedding from file_path.
Parameters: - file_path – Path to file.
- mode – Embedding file format mode, one of ‘word2vec’, ‘fasttext’ or ‘glove’.(default: ‘word2vec’)
Returns: An
matchzoo.embedding.Embedding
instance.