deeppavlov.models.vectorizers¶
-
class
deeppavlov.models.vectorizers.hashing_tfidf_vectorizer.
HashingTfIdfVectorizer
(tokenizer: deeppavlov.core.models.component.Component, hash_size=16777216, doc_index: Optional[dict] = None, save_path: Optional[str] = None, load_path: Optional[str] = None, **kwargs)[source]¶ Create a tfidf matrix from collection of documents of size [n_documents X n_features(hash_size)].
- Parameters
tokenizer – a tokenizer class
hash_size – a hash size, power of two
doc_index – a dictionary 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
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rows
¶ tfidf matrix rows corresponding to terms
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cols
¶ tfidf matrix cols corresponding to docs
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data
¶ tfidf matrix data corresponding to tfidf values
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__call__
(questions: List[str]) → scipy.sparse.csr.csr_matrix[source]¶ Transform input list of documents to tfidf vectors.
- Parameters
questions – a list of input strings
- Returns
transformed documents as a csr_matrix with shape [n_documents X
hash_size
]
-
fit
(docs: List[str], doc_ids: List[Any], doc_nums: List[int]) → None[source]¶ Fit the vectorizer.
- Parameters
docs – a list of input documents
doc_ids – a list of document ids corresponding to input documents
doc_nums – a list of document integer ids as they appear in a database
- Returns
None
-
get_count_matrix
(row: List[int], col: List[int], data: List[int], size: int) → scipy.sparse.csr.csr_matrix[source]¶ 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, None][source]¶ 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, numpy.array][source]¶ 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][source]¶ Load a tfidf matrix as csr_matrix.
- Returns
a tuple of tfidf matrix and csr data.
:raises FileNotFoundError if
load_path
doesn’t exist.:
-
partial_fit
(docs: List[str], doc_ids: List[Any], doc_nums: List[int]) → None[source]¶ Partially fit on one batch.
- Parameters
docs – a list of input documents
doc_ids – a list of document ids corresponding to input documents
doc_nums – a list of document integer ids as they appear in a database
- Returns
None