deeppavlov.models.vectorizers

class deeppavlov.models.vectorizers.hashing_tfidf_vectorizer.HashingTfIdfVectorizer(tokenizer: deeppavlov.core.models.component.Component, hash_size=16777216, doc_index: Union[dict, NoneType] = None, save_path: Union[str, NoneType] = None, load_path: Union[str, NoneType] = None, **kwargs)

Create a tfidf matrix from collection of documents.

Parameters:

• tokenizer – a tokenizer class
• hash_size – a hash size, power of two
• doc_index – a dictinary of document ids and their titles
• save_path – a path to .npz file where tfidf matrix is saved
• load_path – a path to .npz file where tfidf matrix is loaded from

hash_size

a hash size

tokenizer

instance of a tokenizer class

term_freqs

a dictionary with tfidf terms and their frequences

doc_index

provided by a user ids or generated automatically ids

rows

tfidf matrix rows corresponding to terms

cols

tfidf matrix cols corresponding to docs

data

tfidf matrix data corresponding to tfidf values

__call__(questions: List[str]) → scipy.sparse.csr.csr_matrix

Transform input list of documents to a tfidf vectors.

Parameters:questions – a list of input strings Returns:transformed documents as a csr_matrix

fit_batch(docs: List[str], doc_ids: List[Any]) → None

Fit batch of documents.

Parameters:

• docs – a list of input documents
• doc_ids – a list of document ids corresponding to input documents

Returns:

None

get_count_matrix(row: List[int], col: List[int], data: List[int], size: int) → scipy.sparse.csr.csr_matrix

Get count matrix.

Parameters:

• row – tfidf matrix rows corresponding to terms
• col – tfidf matrix cols corresponding to docs
• data – tfidf matrix data corresponding to tfidf values
• size – doc_index size

Returns:

a count csr_matrix

get_counts(docs: List[str], doc_ids: List[Any]) → Generator[[Tuple[KeysView, ValuesView, List[int]], Any], NoneType]

Get term counts for a list of documents.

Parameters:

• docs – a list of input documents
• doc_ids – a list of document ids corresponding to input documents

Yields:

a tuple of term hashes, count values and column ids

Returns:

None

static get_tfidf_matrix(count_matrix: scipy.sparse.csr.csr_matrix) → Tuple[scipy.sparse.csr.csr_matrix, <built-in function array>]

Convert a count matrix into a tfidf matrix.

Parameters:count_matrix – a count matrix Returns:a tuple of tfidf matrix and term frequences

load() → Tuple[scipy.sparse.csr.csr_matrix, Dict]

Load a tfidf matrix as csr_matrix.

Returns:a tuple of tfidf matrix and csr data.

reset() → None

Clear rows, cols and data

Returns:None

save() → None

Save tfidf matrix into .npz format.

Returns:None