-Ph %dZddlZddlZddlmZddlmZddlmZm Z m Z ddl m Z ddl mZdd lmZGd d Zd ZdS) zkRecommendation system generator. Generate recommendations based on TF-IDF representation and a KNN model. N) defaultdict)Path)THUMBNAIL_PARENT_DIVTHUMBNAIL_PARENT_DIV_CLOSE_thumbnail_div)extract_intro_and_title)split_code_and_text_blocks) _replace_md5c^eZdZdZdddddZdZedZd Zd Z dd Z d Z dZ d S)ExampleRecommenderacCompute content-based KNN-TF-IFD recommendation system. Parameters ---------- n_examples : int, default=5 Number of most relevant examples to display. min_df : float in range [0.0, 1.0] or int, default=1 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. If float, the parameter represents a proportion of documents, integer represents absolute counts. This value is also called cut-off in the literature. max_df : float in range [0.0, 1.0] or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold. If float, the parameter represents a proportion of documents, integer represents absolute counts. Attributes ---------- file_names_ : list of str The list of file names used for computing the similarity matrix. The recommended examples are chosen among this list. similarity_matrix_ : dense matrix Fitted matrix of pairwise cosine similarities. g? n_examplesmin_dfmax_dfc0||_||_||_dSNr)selfrrrs Z/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sphinx_gallery/recommender.py__init__zExampleRecommender.__init__4s$  cjdtjd|D}||S)z@Extract a dict mapping raw tokens from doc to their occurrences.c3>K|]}|VdSr)lower).0toks r z1ExampleRecommender.token_freqs..;s*JJ3399;;JJJJJJrz\w+)refindall dict_freqs)rdoctoken_generators r token_freqszExampleRecommender.token_freqs9s3JJ"*VS2I2IJJJ///rcXtt}|D]}||xxdz cc<|S)z;Extract a dict mapping list of tokens to their occurrences.r)rint)r#freqrs rr"zExampleRecommender.dict_freqs>s<3  C IIINIIII rcddl}g}tt}|D]L}|D]5\}}|||||6Mdt t |D}|t|t|f} t |D],\} }|D]\}}|| | ||f<-| S)aConvert a dictionary of feature occurrence frequencies into a matrix. Parameters ---------- data : list of dict Each dictionary represents a document where tokens are keys and values are their occurrence frequencies. Returns ------- X : ndarray of shape (n_samples, n_features) A matrix of occurrences where n_samples is the number of samples in the dataset and n_features is the total number of features across all samples. rNci|]\}}|| Sr+)r token_idxtokens r z6ExampleRecommender.dict_vectorizer.._s+   !1EE9   r) numpyrlistitemsappend enumeratesortedzeroslen) rdatanptokens all_values dict_of_freqsr-r( token_dictXdict_of_freqs_idxs rdict_vectorizerz"ExampleRecommender.dict_vectorizerFs1  && ! / /M,2244 / / t e$$$5!((.... /  5>vj?Q?Q5R5R    HHc$iiZ1 2 209$ ? ? , },2244 ? ? t:>#Z%6677 ?rcddl}|jddz}||ddz}|||z dz}||z}|j|j|dz j}|S)aQTransform a term frequency matrix into a TF-IDF matrix. Parameters ---------- X : ndarray of shape (n_samples, n_features) A term frequency matrix. Returns ------- X_tfidf : ndarray of shape (n_samples, n_features) A tf-idf matrix of the same shape as X. rNraxis)r/shape count_nonzerologTlinalgnorm)rr=r8 n_samplesdfidfX_tfidfs rcompute_tf_idfz!ExampleRecommender.compute_tf_idfhs GAJN   aa  ( (1 ,ffY^$$q(c'9ry~~gA~>>>ArNcddl}||us||}||j|z }||ur|}n||j|z }||jz}|S)aCompute the cosine similarity between two vectors X and Y. Parameters ---------- X : ndarray of shape (n_samples_X, n_features) Input data. Y : ndarray of shape (n_samples_Y, n_features), default=None Input data. If `None`, the output will be the pairwise similarities between all samples in `X`. Returns ------- cosine_similarity : ndarray of shape (n_samples_X, n_samples_Y) Cosine similarity matrix. rN)r/rGrHrF)rr=Yr8 X_normalized Y_normalized similaritys rcosine_similarityz$ExampleRecommender.cosine_similarityst"  66QYA29>>!,,, 66'LLry~~a000L!LN2 rc* ddl}|j}|j}|j}t |t jstd|dkrtdt |t jr|dks3t |trd|cxkrdksntdt |t jr|dks3t |trd|cxkrdksntd |j | fd |D}t |tr0t| ||j dz}t |tr0t|||j dz}||d }||k||kz}|||dd|f|_||_|S) zCompute the similarity matrix of a group of documents. Parameters ---------- file_names : list or generator of file names. Returns ------- self : object Fitted recommender. rNzn_examples must be an integerrz$n_examples must be strictly positivegg?z/min_df must be float in range [0.0, 1.0] or intz/max_df must be float in range [0.0, 1.0] or intchg|].}t|d/S)utf-8encoding)r read_text)rfname freq_funcs r z*ExampleRecommender.fit..s: X X XEYYtE{{,,g,>> ? ? X X XrrA)r/rrr isinstancenumbersIntegral ValueErrorfloatr%r?r'ceilrCfloorsumrSrMsimilarity_matrix_ file_names_) r file_namesr8rrr counts_matrixdoc_appearancesmaskr[s @rfitzExampleRecommender.fits9 _ *g&677 E<== = !^^CDD D  0 1 1 P6<kk65))7B.1V.B.B.B.Bs.B.B.B.BNOO O  0 1 1 P6q+A"ABBCCF&&Q&776)o.GH"&"8"8    aaag 6 7 7# # & rcj|}ttj|}t |dd}d|djdzD}fd|D}|S)aNCompute the `n_examples` most similar documents to the query. Parameters ---------- file_name : str Name of the file corresponding to the query index `item_id`. Returns ------- recommendations : list of str Name of the files most similar to the query. c|dS)Nrr+)xs rz,ExampleRecommender.predict..s 1Q4rT)keyreversecg|]\}}|Sr+r+)ridx_s rr\z.ExampleRecommender.predict..sOOOvsAsOOOrrc*g|]}j|Sr+)rf)rrsrs rr\z.ExampleRecommender.predict..s!HHHS4+C0HHHr)rfindexr0r3rer4r)r file_nameitem_id similar_items sorted_items top_k_itemsrecommendationss` rpredictzExampleRecommender.predicts"((33Yt'>w'GHHII mNNN POa$/A:M6M)NOOO HHHHKHHHrr) __name__ __module__ __qualname____doc__rr% staticmethodr"r?rMrSrkr}r+rrr r s6&'q 000 \   D0>...`rr c t|}|j|jz d}||}d}|dd|}t |dd5}|d|d |t|D]{} t| } t| d \} } t|| d j \} }|t| j|d | j | |d||tdddn #1swxYwYt|ddS)aCGenerate `.recommendations` reST file for a given example. Parameters ---------- recommender : ExampleRecommender Instance of a fitted ExampleRecommender. fname : str Path to the example file. gallery_conf : dict Configuration dictionary for the sphinx-gallery extension. z.recommendations.newzRelated examples recommender rubric_headerwrVrWz .. rubric::  F) return_nodersrc_dirT) is_backrefNt)mode)rparentstemr}getopenwriterr r contentrnamerr )rrZ gallery_conf path_fnamerecommend_fnamerecommended_examplesdefault_rubric_headerrex_file example_fname example_pathrt script_blocksintrotitles r_write_recommendationsrseJ#*Z_<RRRO&..u55. /33.M osW 5 5 52 ::::;;; *+++1  M ..L95    A}35-:J:RSSLE5 MM ' + %#       0111'222222222222222(s++++++s'C D??EE)rr^r collectionsrpathlibrbackreferencesrrrgen_rstr py_source_parserr utilsr r rr+rrrs ###### -,,,,,888888MMMMMMMM`+,+,+,+,+,r