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| """Generate a model (textacy.representations.Vectorizer). | |
| vectorizer = Vectorizer( | |
| tf_type="linear", idf_type="smooth", norm="l2", | |
| min_df=3, max_df=0.95) | |
| doc_term_matrix = vectorizer.fit_transform(tokenized_docs) | |
| doc_term_matrix | |
| tokenized_docs = [insert_spaces(elm).split() for elm in textzh] | |
| """ | |
| # pylint: disable=too-many-arguments, invalid-name, unused-import | |
| from typing import Dict, Iterable, List, Optional, Union # noqa | |
| from textacy.representations import Vectorizer | |
| from logzero import logger | |
| # fmt: off | |
| def gen_model( | |
| tokenized_docs: Iterable[Iterable[str]], # List[List[str]], | |
| tf_type: str = 'linear', | |
| idf_type: Optional[str] = "smooth", | |
| dl_type: str = None, # Optional[str] = "sqrt" “lucene-style tfidf” | |
| norm: Optional[str] = "l2", # + "l2" | |
| min_df: Union[int, float] = 1, | |
| max_df: Union[int, float] = 1.0, | |
| max_n_terms: Optional[int] = None, | |
| vocabulary_terms: Optional[Union[Dict[str, int], Iterable[str]]] = None | |
| ) -> Vectorizer: | |
| # fmt: on | |
| """Generate a model (textacy.representations.Vectorizer). | |
| Args: | |
| tokenized_docs: tokenized docs | |
| (refer to textacy.representation.Vectorizer) | |
| tf_type: Type of term frequency (tf) to use for weights' local component: | |
| - "linear": tf (tfs are already linear, so left as-is) | |
| - "sqrt": tf => sqrt(tf) | |
| - "log": tf => log(tf) + 1 | |
| - "binary": tf => 1 | |
| idf_type: Type of inverse document frequency (idf) to use for weights' | |
| global component: | |
| - "standard": idf = log(n_docs / df) + 1.0 | |
| - "smooth": idf = log(n_docs + 1 / df + 1) + 1.0, i.e. 1 is added | |
| to all document frequencies, as if a single document containing | |
| every unique term was added to the corpus. | |
| - "bm25": idf = log((n_docs - df + 0.5) / (df + 0.5)), which is | |
| a form commonly used in information retrieval that allows for | |
| very common terms to receive negative weights. | |
| - None: no global weighting is applied to local term weights. | |
| dl_type: Type of document-length scaling to use for weights' | |
| normalization component: | |
| - "linear": dl (dls are already linear, so left as-is) | |
| - "sqrt": dl => sqrt(dl) | |
| - "log": dl => log(dl) | |
| - None: no normalization is applied to local(*global?) weights | |
| norm: If "l1" or "l2", normalize weights by the L1 or L2 norms, respectively, | |
| of row-wise vectors; otherwise, don't. | |
| min_df: Minimum number of documents in which a term must appear for it to be | |
| included in the vocabulary and as a column in a transformed doc-term matrix. | |
| If float, value is the fractional proportion of the total number of docs, | |
| which must be in [0.0, 1.0]; if int, value is the absolute number. | |
| max_df: Maximum number of documents in which a term may appear for it to be | |
| included in the vocabulary and as a column in a transformed doc-term matrix. | |
| If float, value is the fractional proportion of the total number of docs, | |
| which must be in [0.0, 1.0]; if int, value is the absolute number. | |
| max_n_terms: If specified, only include terms whose document frequency is within | |
| the top ``max_n_terms``. | |
| vocabulary_terms: Mapping of unique term string to unique term id, or | |
| an iterable of term strings that gets converted into such a mapping. | |
| Note that, if specified, vectorized outputs will include *only* these terms. | |
| “lucene-style tfidf”: Adds a doc-length normalization to the usual local and global components. | |
| Params: tf_type="linear", apply_idf=True, idf_type="smooth", apply_dl=True, dl_type="sqrt" | |
| “lucene-style bm25”: Uses a smoothed idf instead of the classic bm25 variant to prevent weights on terms from going negative. | |
| Params: tf_type="bm25", apply_idf=True, idf_type="smooth", apply_dl=True, dl_type="linear" | |
| Attributes: | |
| doc_term_matrix | |
| Returns: | |
| transform_fit'ted vectorizer | |
| """ | |
| # make sure tokenized_docs is the right typing | |
| try: | |
| for xelm in iter(tokenized_docs): | |
| for elm in iter(xelm): | |
| assert isinstance(elm, str) | |
| except AssertionError as e: | |
| raise AssertionError(" tokenized_docs is not of the typing Iterable[Iterable[str]] ") from e | |
| except Exception as e: | |
| logger.error(e) | |
| raise | |
| vectorizer = Vectorizer( | |
| # tf_type="linear", idf_type="smooth", norm="l2", min_df=3, max_df=0.95) | |
| tf_type=tf_type, | |
| idf_type=idf_type, | |
| dl_type=dl_type, | |
| norm=norm, | |
| min_df=min_df, | |
| max_df=max_df, | |
| max_n_terms=max_n_terms, | |
| vocabulary_terms=vocabulary_terms | |
| ) | |
| doc_term_matrix = vectorizer.fit_transform(tokenized_docs) | |
| gen_model.doc_term_matrix = doc_term_matrix | |
| return vectorizer | |