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228 lines
7.3 KiB
Python
228 lines
7.3 KiB
Python
from itertools import combinations as _combinations
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from queue import Queue
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from .pagerank_weighted import pagerank_weighted_scipy as _pagerank
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from .preprocessing.textcleaner import clean_text_by_word as _clean_text_by_word
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from .preprocessing.textcleaner import tokenize_by_word as _tokenize_by_word
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from .commons import build_graph as _build_graph
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from .commons import remove_unreachable_nodes as _remove_unreachable_nodes
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WINDOW_SIZE = 2
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"""Check tags in http://www.clips.ua.ac.be/pages/mbsp-tags and use only first two letters
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Example: filter for nouns and adjectives:
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INCLUDING_FILTER = ['NN', 'JJ']"""
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INCLUDING_FILTER = ['NN', 'JJ']
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EXCLUDING_FILTER = []
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def _get_pos_filters():
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return frozenset(INCLUDING_FILTER), frozenset(EXCLUDING_FILTER)
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def _get_words_for_graph(tokens):
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include_filters, exclude_filters = _get_pos_filters()
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if include_filters and exclude_filters:
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raise ValueError("Can't use both include and exclude filters, should use only one")
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result = []
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for word, unit in tokens.items():
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if exclude_filters and unit.tag in exclude_filters:
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continue
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if (include_filters and unit.tag in include_filters) or not include_filters or not unit.tag:
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result.append(unit.token)
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return result
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def _get_first_window(split_text):
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return split_text[:WINDOW_SIZE]
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def _set_graph_edge(graph, tokens, word_a, word_b):
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if word_a in tokens and word_b in tokens:
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lemma_a = tokens[word_a].token
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lemma_b = tokens[word_b].token
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edge = (lemma_a, lemma_b)
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if graph.has_node(lemma_a) and graph.has_node(lemma_b) and not graph.has_edge(edge):
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graph.add_edge(edge)
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def _process_first_window(graph, tokens, split_text):
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first_window = _get_first_window(split_text)
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for word_a, word_b in _combinations(first_window, 2):
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_set_graph_edge(graph, tokens, word_a, word_b)
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def _init_queue(split_text):
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queue = Queue()
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first_window = _get_first_window(split_text)
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for word in first_window[1:]:
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queue.put(word)
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return queue
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def _process_word(graph, tokens, queue, word):
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for word_to_compare in _queue_iterator(queue):
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_set_graph_edge(graph, tokens, word, word_to_compare)
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def _update_queue(queue, word):
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queue.get()
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queue.put(word)
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assert queue.qsize() == (WINDOW_SIZE - 1)
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def _process_text(graph, tokens, split_text):
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queue = _init_queue(split_text)
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for i in range(WINDOW_SIZE, len(split_text)):
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word = split_text[i]
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_process_word(graph, tokens, queue, word)
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_update_queue(queue, word)
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def _queue_iterator(queue):
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iterations = queue.qsize()
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for i in range(iterations):
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var = queue.get()
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yield var
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queue.put(var)
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def _set_graph_edges(graph, tokens, split_text):
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_process_first_window(graph, tokens, split_text)
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_process_text(graph, tokens, split_text)
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def _extract_tokens(lemmas, scores, ratio, words):
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lemmas.sort(key=lambda s: scores[s], reverse=True)
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# If no "words" option is selected, the number of sentences is
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# reduced by the provided ratio, else, the ratio is ignored.
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length = len(lemmas) * ratio if words is None else words
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return [(scores[lemmas[i]], lemmas[i],) for i in range(int(length))]
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def _lemmas_to_words(tokens):
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lemma_to_word = {}
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for word, unit in tokens.items():
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lemma = unit.token
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if lemma in lemma_to_word:
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lemma_to_word[lemma].append(word)
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else:
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lemma_to_word[lemma] = [word]
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return lemma_to_word
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def _get_keywords_with_score(extracted_lemmas, lemma_to_word):
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"""
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:param extracted_lemmas:list of tuples
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:param lemma_to_word: dict of {lemma:list of words}
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:return: dict of {keyword:score}
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"""
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keywords = {}
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for score, lemma in extracted_lemmas:
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keyword_list = lemma_to_word[lemma]
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for keyword in keyword_list:
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keywords[keyword] = score
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return keywords
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def _strip_word(word):
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stripped_word_list = list(_tokenize_by_word(word))
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return stripped_word_list[0] if stripped_word_list else ""
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def _get_combined_keywords(_keywords, split_text):
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"""
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:param keywords:dict of keywords:scores
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:param split_text: list of strings
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:return: combined_keywords:list
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"""
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result = []
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_keywords = _keywords.copy()
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len_text = len(split_text)
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for i in range(len_text):
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word = _strip_word(split_text[i])
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if word in _keywords:
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combined_word = [word]
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if i + 1 == len_text:
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result.append(word) # appends last word if keyword and doesn't iterate
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for j in range(i + 1, len_text):
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other_word = _strip_word(split_text[j])
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if other_word in _keywords and other_word == split_text[j] \
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and other_word not in combined_word:
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combined_word.append(other_word)
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else:
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for keyword in combined_word:
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_keywords.pop(keyword)
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result.append(" ".join(combined_word))
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break
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return result
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def _get_average_score(concept, _keywords):
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word_list = concept.split()
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word_counter = 0
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total = 0
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for word in word_list:
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total += _keywords[word]
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word_counter += 1
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return total / word_counter
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def _format_results(_keywords, combined_keywords, split, scores):
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"""
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:param keywords:dict of keywords:scores
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:param combined_keywords:list of word/s
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"""
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combined_keywords.sort(key=lambda w: _get_average_score(w, _keywords), reverse=True)
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if scores:
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return [(word, _get_average_score(word, _keywords)) for word in combined_keywords]
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if split:
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return combined_keywords
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return "\n".join(combined_keywords)
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def keywords(text, ratio=0.2, words=None, language="english", split=False, scores=False, deaccent=False, additional_stopwords=None):
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if not isinstance(text, str):
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raise ValueError("Text parameter must be a Unicode object (str)!")
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# Gets a dict of word -> lemma
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tokens = _clean_text_by_word(text, language, deacc=deaccent, additional_stopwords=additional_stopwords)
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split_text = list(_tokenize_by_word(text))
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# Creates the graph and adds the edges
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graph = _build_graph(_get_words_for_graph(tokens))
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_set_graph_edges(graph, tokens, split_text)
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del split_text # It's no longer used
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_remove_unreachable_nodes(graph)
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# PageRank cannot be run in an empty graph.
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if len(graph.nodes()) == 0:
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return [] if split else ""
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# Ranks the tokens using the PageRank algorithm. Returns dict of lemma -> score
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pagerank_scores = _pagerank(graph)
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extracted_lemmas = _extract_tokens(graph.nodes(), pagerank_scores, ratio, words)
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lemmas_to_word = _lemmas_to_words(tokens)
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keywords = _get_keywords_with_score(extracted_lemmas, lemmas_to_word)
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# text.split() to keep numbers and punctuation marks, so separeted concepts are not combined
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combined_keywords = _get_combined_keywords(keywords, text.split())
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return _format_results(keywords, combined_keywords, split, scores)
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def get_graph(text, language="english", deaccent=False):
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tokens = _clean_text_by_word(text, language, deacc=deaccent)
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split_text = list(_tokenize_by_word(text, deacc=deaccent))
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graph = _build_graph(_get_words_for_graph(tokens))
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_set_graph_edges(graph, tokens, split_text)
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return graph
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