wefe.get_embeddings_from_query(model: wefe.word_embedding_model.WordEmbeddingModel, query: wefe.query.Query, lost_vocabulary_threshold: float = 0.2, preprocessors: List[Dict[str, Union[str, bool, Callable]]] = [{}], strategy: str = 'first', normalize: bool = False, warn_not_found_words: bool = False, verbose: bool = False) Optional[Tuple[Dict[str, Dict[str, numpy.ndarray]], Dict[str, Dict[str, numpy.ndarray]]]][source]

Obtain the word vectors associated with the provided Query.

The words that does not appears in the word embedding pretrained model vocabulary under the specified pre-processing are discarded. If the remaining words percentage in any query set is lower than the specified threshold, the function will return None.


The query to be processed.

lost_vocabulary_thresholdfloat, optional, by default 0.2

Indicates the proportional limit of words that any set of the query is allowed to lose when transforming its words into embeddings. In the case that any set of the query loses proportionally more words than this limit, this method will return None.

preprocessorsList[Dict[str, Union[str, bool, Callable]]]

A list with preprocessor options.

A preprocessor is a dictionary that specifies what processing(s) are performed on each word before it is looked up in the model vocabulary. For example, the preprocessor {'lowecase': True, 'strip_accents': True} allows you to lowercase and remove the accent from each word before searching for them in the model vocabulary. Note that an empty dictionary {} indicates that no preprocessing is done.

The possible options for a preprocessor are:

  • lowercase: bool. Indicates that the words are transformed to lowercase.

  • uppercase: bool. Indicates that the words are transformed to uppercase.

  • titlecase: bool. Indicates that the words are transformed to titlecase.

  • strip_accents: bool, {'ascii', 'unicode'}: Specifies that the accents of the words are eliminated. The stripping type can be specified. True uses ‘unicode’ by default.

  • preprocessor: Callable. It receives a function that operates on each word. In the case of specifying a function, it overrides the default preprocessor (i.e., the previous options stop working).

A list of preprocessor options allows you to search for several variants of the words into the model. For example, the preprocessors [{}, {"lowercase": True, "strip_accents": True}] {} allows first to search for the original words in the vocabulary of the model. In case some of them are not found, {"lowercase": True, "strip_accents": True} is executed on these words and then they are searched in the model vocabulary. by default [{}]

strategystr, optional

The strategy indicates how it will use the preprocessed words: ‘first’ will include only the first transformed word found. all’ will include all transformed words found, by default “first”.

normalizebool, optional

True indicates that embeddings will be normalized, by default False

warn_not_found_wordsbool, optional

A flag that indicates if the function will warn (in the logger) the words that were not found in the model’s vocabulary, by default False.

verbosebool, optional

Indicates whether the execution status of this function is printed, by default False

Union[Tuple[EmbeddingSets, EmbeddingSets], None]

A tuple of dictionaries containing the targets and attribute sets or None in case there is a set that has proportionally less embeddings than it was allowed to lose.