From 85e2944ef34af8587b1384778f5a5e00f0bf59c6 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Wed, 2 Mar 2022 17:37:14 +0100 Subject: [PATCH 01/24] first blood --- .gitignore | 1 - README.md | 3 +- pke/models/Kea-semeval2010.py2.pickle | Bin 700 -> 0 bytes pke/models/WINGNUS-semeval2010.py2.pickle | Bin 732 -> 0 bytes pke/models/lda-1000-semeval2010.py2.pickle.gz | Bin 3512621 -> 0 bytes pke/supervised/__init__.py | 4 - pke/supervised/api.py | 9 +- pke/supervised/feature_based/__init__.py | 1 - pke/supervised/feature_based/topiccorank.py | 321 ------------------ pke/supervised/neural_based/__init__.py | 2 - pke/supervised/neural_based/seq2seq.py | 44 --- pke/unsupervised/__init__.py | 4 - pke/unsupervised/graph_based/collabrank.py | 163 --------- pke/unsupervised/graph_based/expandrank.py | 161 --------- pke/unsupervised/statistical/embedrank.py | 168 --------- pke/unsupervised/statistical/kpminer.py | 156 --------- requirements.txt | 1 - setup.py | 5 +- 18 files changed, 4 insertions(+), 1039 deletions(-) delete mode 100644 pke/models/Kea-semeval2010.py2.pickle delete mode 100644 pke/models/WINGNUS-semeval2010.py2.pickle delete mode 100644 pke/models/lda-1000-semeval2010.py2.pickle.gz delete mode 100644 pke/supervised/feature_based/topiccorank.py delete mode 100644 pke/supervised/neural_based/__init__.py delete mode 100644 pke/supervised/neural_based/seq2seq.py delete mode 100644 pke/unsupervised/graph_based/collabrank.py delete mode 100644 pke/unsupervised/graph_based/expandrank.py delete mode 100644 pke/unsupervised/statistical/embedrank.py delete mode 100644 pke/unsupervised/statistical/kpminer.py diff --git a/.gitignore b/.gitignore index 45400bcc..9c9644af 100644 --- a/.gitignore +++ b/.gitignore @@ -5,5 +5,4 @@ docs/build/html/_sources/* dev/ .pytest_cache/ /venv/ -/venv2/ \.idea/ diff --git a/README.md b/README.md index 225965f4..ce6a42fb 100644 --- a/README.md +++ b/README.md @@ -76,8 +76,8 @@ Tutorials and code documentation are available at * Unsupervised models * Statistical models + * FirstPhrases * TfIdf [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#tfidf)] - * KPMiner [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#kpminer), [article by (El-Beltagy and Rafea, 2010)](http://www.aclweb.org/anthology/S10-1041.pdf)] * YAKE [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#yake), [article by (Campos et al., 2020)](https://doi.org/10.1016/j.ins.2019.09.013)] * Graph-based models * TextRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#textrank), [article by (Mihalcea and Tarau, 2004)](http://www.aclweb.org/anthology/W04-3252.pdf)] @@ -89,7 +89,6 @@ Tutorials and code documentation are available at * Supervised models * Feature-based models * Kea [[documentation](https://boudinfl.github.io/pke/build/html/supervised.html#kea), [article by (Witten et al., 2005)](https://www.cs.waikato.ac.nz/ml/publications/2005/chap_Witten-et-al_Windows.pdf)] - * WINGNUS [[documentation](https://boudinfl.github.io/pke/build/html/supervised.html#wingnus), [article by (Nguyen and Luong, 2010)](http://www.aclweb.org/anthology/S10-1035.pdf)] ## Citing pke diff --git a/pke/models/Kea-semeval2010.py2.pickle b/pke/models/Kea-semeval2010.py2.pickle deleted file mode 100644 index 3633e2ba6b3a801846477ab54955c4961c0767a9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 700 zcmZXS%Wl&^6o#FIG^rCx(l(_)3$)zQ1`J_U5E2Pg0@bP)l|}^tnvR_bV{mMrv0apf zC0GQy;{jN*LaJE7hE2-t2|xliNGy?9@Bm1yZ$*lF7Bf1F`TqYua~`KP=b=FfHzkwk z>lCY`OC539GhE%YIyy0wYa$49Pax$(x{6AgK^zAg)_rVq-QpNB+5<~9bX79Fj@`w! zt_3_%37aP8q`S(AZBs77z5tT-VW^6-Ew^h^$Z63NG3rFfqjY-#hGjucJp*wC3RO9S zQdgj;D9X}yP|+a^F6>uP)?p+-jL7L896(u%wv5W7v{Vcg7J8rX;b4{JSb+tq za{4JuvJxw^Q8vax(3|?_y4T8W3731XJ}z$dCokTre=7d!-+a4#dsY6^MvU^bcNi9ORisfK}iaB4tUNGg@wXt=Gr@t?!#4OGI(+TeA{9o;hF 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ztlszxnsOR(WLL~CX%sYuJSjve6?Lw z=^wuJQ4)9oYaHRUnO&yec_tlwAfakTIV}_Dj_Vkv{U{?<{*Gg&TS91CbXjWC_#aW` z*x1H5&fSc=1ZGn+o8pj%2q{p9QX*1WWXppX^qsXzvyyA%G^!8W&b0R{4~OBkfLVjp zt|*X#!WgDGS4bRX0A*9jmcL#g71F=6)HnuQcF@R2lCxa&A+_5@20Vw(ODOXw71wMF z9D-kY?euVA;j;74Al(ilJ9e!YoU($t6wc$&$zCS(EJ9Sn|9_V0zZW*+|9RU9$gBm} zNoGReVkMI< 0: - if not self.graph.has_edge(i, j): - self.graph.add_edge(i, j, weight=0, type="in") - self.graph[i][j]['weight'] += weight - - def unify_with_domain_graph(self, - input_file, - excluded_file=None, - prune_unreachable_nodes=True): - """Unify the domain graph, built from a reference file, with the topic - graph, built from a document. - - Args: - input_file (str): path to the reference file. - excluded_file (str): file to exclude (for leave-one-out - cross-validation), defaults to None. - prune_unreachable_nodes (bool): prune nodes from the domain graph - that are not reachable from the document nodes, defaults to - True. - """ - - if input_file.endswith('.json'): - references = load_references(input_file=input_file, - language=self.language, - excluded_file=excluded_file) - else: - logging.warning("{} is not a reference file".format(input_file)) - references = {} - pass - - # initialize the topic_to_integer map - for i, topic in enumerate(self.topics): - for candidate in topic: - self.topic_to_integer[candidate] = i - - offset = len(self.topics) - - # loop through the doc_ids - for doc_id in references: - - # for each pair of gold keyphrases - for gold_1, gold_2 in combinations(references[doc_id], 2): - - # adding nodes to the graph - if gold_1 not in self.domain_to_integer: - self.domain_to_integer[gold_1] = offset - self.graph.add_node(offset, src="domain", candidate=gold_1) - - # checking for out edges with topics - if gold_1 in self.topic_to_integer: - self.graph.add_edge(self.domain_to_integer[gold_1], - self.topic_to_integer[gold_1], - weight=1, type="out") - - offset += 1 - - if gold_2 not in self.domain_to_integer: - self.domain_to_integer[gold_2] = offset - self.graph.add_node(offset, src="domain", candidate=gold_2) - - # checking for out edges with topics - if gold_2 in self.topic_to_integer: - self.graph.add_edge(self.domain_to_integer[gold_2], - self.topic_to_integer[gold_2], - weight=1, type="out") - - offset += 1 - - node_1 = self.domain_to_integer[gold_1] - node_2 = self.domain_to_integer[gold_2] - - # add/update the edge - if not self.graph.has_edge(node_1, node_2): - self.graph.add_edge(node_1, node_2, weight=0, type="in") - self.graph[node_1][node_2]['weight'] += 1 - - # prune not reachable domain nodes - if prune_unreachable_nodes: - - # find all descendants - descendants = set() - for i in range(len(self.topics)): - descendants.update(nx.algorithms.descendants(self.graph, i)) - - # remove unreachable nodes - self.graph.remove_nodes_from(set(self.graph.nodes) - descendants) - - def candidate_weighting(self, - input_file=None, - excluded_file=None, - prune_unreachable_nodes=True, - lambda_t=0.1, - lambda_k=0.5, - nb_iter=100, - convergence_threshold=0.001): - """Weight candidates using the co-ranking formulae. - - Args: - input_file (str): path to the reference file. - excluded_file (str): file to exclude (for leave-one-out - cross-validation), defaults to None. - lambda_t(float): lambda for topics used in the co-ranking formulae, - defaults to 0.1. - lambda_k(float): lambda for keyphrases used in the co-ranking - formulae, defaults to 0.5. - nb_iter (int): maximum number of iterations, defaults to 100. - convergence_threshold (float): early stop threshold, defaults to - 0.001. - """ - - # compute topics - self.topic_clustering() - - # build graph - self.build_topic_graph() - - # unify with domain graph - self.unify_with_domain_graph(input_file=input_file, - excluded_file=excluded_file, - prune_unreachable_nodes=prune_unreachable_nodes) - - nb_nodes = len(self.graph.nodes) - - logging.info("resulting graph is {} nodes".format(nb_nodes)) - - # weights = [1.0] * nb_nodes - weights = defaultdict(lambda:1.0) - - # pre-compute the inner/outer normalizations - # inner_norms = [0.0] * nb_nodes - # outer_norms = [0.0] * nb_nodes - inner_norms = defaultdict(lambda:0.0) - outer_norms = defaultdict(lambda:0.0) - - for j in self.graph.nodes(): - inner_norm = 0 - outer_norm = 0 - for k in self.graph.neighbors(j): - if self.graph[j][k]['type'] == "in": - inner_norm += self.graph[j][k]["weight"] - else: - outer_norm += 1 - inner_norms[j] = inner_norm - outer_norms[j] = outer_norm - - # ranking nodes in the graph using co-ranking - converged = False - while nb_iter > 0 and not converged: - - converged = True - - #logging.info("{} iter left".format(nb_iter)) - - # save the weights - w = weights.copy() - - for i in self.graph.nodes(): - - # compute inner/outer recommendations - r_in = 0.0 - r_out = 0.0 - for j in self.graph.neighbors(i): - - # inner recommendation - if self.graph[i][j]['type'] == "in": - r_in += (self.graph[i][j]["weight"] * w[j]) / \ - inner_norms[j] - - # outer recommendation - else: - r_out += w[j] / outer_norms[j] - - # compute the new weight - if self.graph.nodes[i]["src"] == "topic": - weights[i] = (1 - lambda_t) * r_out - weights[i] += lambda_t * r_in - else: - weights[i] = (1 - lambda_k) * r_out - weights[i] += lambda_k * r_in - - # check for non convergence - if math.fabs(weights[i] - w[i]) > convergence_threshold: - converged = False - - nb_iter -= 1 - - # get the final ranking - for i in self.graph.nodes(): - - # if it is a topic candidate - if self.graph.nodes[i]["src"] == "topic": - - # get the candidates from the topic - topic = self.topics[i] - - # get the offsets of the topic candidates - offsets = [self.candidates[t].offsets[0] for t in topic] - - first = offsets.index(min(offsets)) - self.weights[topic[first]] = weights[i] - - # otherwise it is a keyphrase from the domain - else: - - gold = self.graph.nodes[i]["candidate"] - - # check if it is acceptable, i.e. if it is directly or - # transitively connected to a topic - connected = False - for j in self.graph.neighbors(i): - if self.graph.nodes[j]["src"] == "topic": - connected = True - break - for k in self.graph.neighbors(j): - if self.graph.nodes[k]["src"] == "topic": - connected = True - break - if connected: - break - - if connected: - if gold in self.weights: - self.weights[gold] = max(self.weights[gold], weights[i]) - else: - self.weights[gold] = weights[i] diff --git a/pke/supervised/neural_based/__init__.py b/pke/supervised/neural_based/__init__.py deleted file mode 100644 index cbd8229f..00000000 --- a/pke/supervised/neural_based/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# -*- coding: utf-8 -*- -# Python Keyphrase Extraction toolkit: supervised neural-based ranking models diff --git a/pke/supervised/neural_based/seq2seq.py b/pke/supervised/neural_based/seq2seq.py deleted file mode 100644 index 499bba8c..00000000 --- a/pke/supervised/neural_based/seq2seq.py +++ /dev/null @@ -1,44 +0,0 @@ -# -*- coding: utf-8 -*- -# Author: Florian Boudin -# Date: 11-11-2018 - -""" -Implementation of the Seq2Seq model for automatic keyphrase extraction. -""" - -from __future__ import absolute_import -from __future__ import print_function - -from pke.supervised.api import SupervisedLoadFile - - -class Seq2Seq(SupervisedLoadFile): - - def __init__(self): - """Redefining initializer for Seq2Seq.""" - - super(Seq2Seq, self).__init__() - - self.sequence = [] - """Input sequence.""" - - self.vocabulary = ['', '', ''] - """Vocabulary.""" - - def document_to_ix(self): - """Convert the document to a sequence of ix.""" - - self.sequence.append(self.vocabulary.index('')) - for i, sentence in enumerate(self.sentences): - for word in sentence.stems: - try: - self.sequence.append(self.vocabulary.index(word)) - except ValueError: - self.sequence.append(self.vocabulary.index('')) - self.sequence.append(self.vocabulary.index('')) - - def candidate_selection(self): - pass - - def candidate_weighting(self): - pass diff --git a/pke/unsupervised/__init__.py b/pke/unsupervised/__init__.py index 7032f3c5..115da3fb 100644 --- a/pke/unsupervised/__init__.py +++ b/pke/unsupervised/__init__.py @@ -8,13 +8,9 @@ from pke.unsupervised.graph_based.multipartiterank import MultipartiteRank from pke.unsupervised.graph_based.positionrank import PositionRank from pke.unsupervised.graph_based.single_tpr import TopicalPageRank -from pke.unsupervised.graph_based.expandrank import ExpandRank from pke.unsupervised.graph_based.textrank import TextRank -from pke.unsupervised.graph_based.collabrank import CollabRank from pke.unsupervised.statistical.tfidf import TfIdf -from pke.unsupervised.statistical.kpminer import KPMiner from pke.unsupervised.statistical.yake import YAKE from pke.unsupervised.statistical.firstphrases import FirstPhrases -from pke.unsupervised.statistical.embedrank import EmbedRank diff --git a/pke/unsupervised/graph_based/collabrank.py b/pke/unsupervised/graph_based/collabrank.py deleted file mode 100644 index bcdfe5d3..00000000 --- a/pke/unsupervised/graph_based/collabrank.py +++ /dev/null @@ -1,163 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# Author: Florian Boudin & Timothée Poulain -# Date: 10-02-2018 - -"""CollabRank Towards a Collaborative Approach to Single-Document -Keyphrase model -Graph-based ranking approach to keyphrase extraction described in: -* Xiaojun Wan and Jianguo Xiao -Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 969–976 -""" - - -from __future__ import print_function -from __future__ import division -from __future__ import unicode_literals -from __future__ import absolute_import - -from pke.unsupervised import SingleRank -from pke.base import LoadFile - -import networkx as nx -import logging - - -class CollabRank(SingleRank): - """CollabRank keyphrase extraction model. - - Parameterized example:: - - import pke - import string - from nltk.corpus import stopwords - - # 1. create an CollabRank extractor. - extractor = pke.unsupervised.CollabRank() - - # 2. load the content of the document. - extractor.load_document(input='path/to/input.xml') - - # 3. select the the longest sequences of nouns and adjectives, that do - # not contain punctuation marks or stopwords as candidates. - pos = {'NOUN', 'PROPN', 'ADJ'} - stoplist = list(string.punctuation) - stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] - stoplist += stopwords.words('english') - extractor.candidate_selection(pos=pos, stoplist=stoplist) - - # 4. weight the candidates using the sum of their word's scores that are - # computed using random walk. In the graph, nodes are words (nouns - # and adjectives only) that are connected if they occur in a window - # of 10 words. A set of extra documents should be provided to expand - # the graph. - collab_documents = [('path/to/input1.xml', similarity1), - ('path/to/input2.xml', similarity2)] - extractor.candidate_weighting(window=10, - pos=pos, - collab_documents=collab_documents, - format='corenlp') - - # 5. get the 10-highest scored candidates as keyphrases - keyphrases = extractor.get_n_best(n=10) - - """ - - def __init__(self): - """ Redefining initializer for CollabRank. """ - - super(CollabRank, self).__init__() - - def collab_word_graph(self, - input_file, - similarity, - window=10, - pos=None): - """Expands the word graph using the given document. - - Args: - input_file (str): path to the input file. - similarity (float): similarity for weighting edges. - window (int): the window within the sentence for connecting two - words in the graph, defaults to 10. - pos (set): the set of valid pos for words to be considered as nodes - in the graph, defaults to ('NOUN', 'PROPN', 'ADJ'). - """ - - # define default pos tags set - if pos is None: - pos = {'NOUN', 'PROPN', 'ADJ'} - - # initialize document loader - doc = LoadFile() - print(input_file) - # read document - doc.load_document(input=input_file, - language=self.language, - normalization=self.normalization) - - # flatten document and initialize nodes - sequence = [] - - for sentence in doc.sentences: - for j, node in enumerate(sentence.stems): - if node not in self.graph and sentence.pos[j] in pos: - self.graph.add_node(node) - sequence.append((node, sentence.pos[j])) - - # loop through sequence to build the edges in the graph - for j, node_1 in enumerate(sequence): - for k in range(j + 1, min(j + window, len(sequence))): - node_2 = sequence[k] - if node_1[1] in pos and node_2[1] in pos \ - and node_1[0] != node_2[0]: - if not self.graph.has_edge(node_1[0], node_2[0]): - self.graph.add_edge(node_1[0], node_2[0], weight=0) - self.graph[node_1[0]][node_2[0]]['weight'] += float(similarity) - - def candidate_weighting(self, - window=10, - pos=None, - collab_documents=None, - normalized=False): - """Candidate ranking using random walk. - - Args: - window (int): the window within the sentence for connecting two - words in the graph, defaults to 10. - pos (set): the set of valid pos for words to be considered as nodes - in the graph, defaults to ('NOUN', 'PROPN', 'ADJ'). - collab_documents (list): the set of documents to expand the graph, - should be a list of tuples (input_path, similarity). Defaults to - empty list, i.e. no expansion. - normalized (False): normalize keyphrase score by their length, - defaults to False. - """ - - # define default pos tags set - if pos is None: - pos = {'NOUN', 'PROPN', 'ADJ'} - - if collab_documents is None: - collab_documents = [] - logging.warning('No cluster documents provided for CollabRank.') - - # build the word graph - self.build_word_graph(window=window, pos=pos) - - # expand the word graph - for input_file, similarity in collab_documents: - self.collab_word_graph(input_file=input_file, - similarity=similarity, - window=window, - pos=pos) - - # compute the word scores using random walk - w = nx.pagerank_scipy(self.graph, alpha=0.85, weight='weight') - - # loop through the candidates - for k in self.candidates.keys(): - tokens = self.candidates[k].lexical_form - self.weights[k] = sum([w[t] for t in tokens]) - if normalized: - self.weights[k] /= len(tokens) diff --git a/pke/unsupervised/graph_based/expandrank.py b/pke/unsupervised/graph_based/expandrank.py deleted file mode 100644 index bce80b04..00000000 --- a/pke/unsupervised/graph_based/expandrank.py +++ /dev/null @@ -1,161 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# Author: Florian Boudin -# Date: 10-02-2018 - -"""ExpandRank keyphrase extraction model. - -Graph-based ranking approach to keyphrase extraction described in: - -* Xiaojun Wan and Jianguo Xiao. - Single Document Keyphrase Extraction Using Neighborhood Knowledge. - *In proceedings of AAAI*, pages 855-860, 2008. - -""" - -from __future__ import print_function -from __future__ import division -from __future__ import unicode_literals -from __future__ import absolute_import - -from pke.unsupervised import SingleRank -from pke.base import LoadFile - -import networkx as nx -import logging - - -class ExpandRank(SingleRank): - """ExpandRank keyphrase extraction model. - - Parameterized example:: - - import pke - import string - from nltk.corpus import stopwords - - # 1. create an ExpandRank extractor. - extractor = pke.unsupervised.ExpandRank() - - # 2. load the content of the document. - extractor.load_document(input='path/to/input.xml') - - # 3. select the the longest sequences of nouns and adjectives, that do - # not contain punctuation marks or stopwords as candidates. - pos = {'NOUN', 'PROPN', 'ADJ'} - stoplist = list(string.punctuation) - stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] - stoplist += stopwords.words('english') - extractor.candidate_selection(pos=pos, stoplist=stoplist) - - # 4. weight the candidates using the sum of their word's scores that are - # computed using random walk. In the graph, nodes are words (nouns - # and adjectives only) that are connected if they occur in a window - # of 10 words. A set of extra documents should be provided to expand - # the graph. - expanded_documents = [('path/to/input1.xml', similarity1), - ('path/to/input2.xml', similarity2)] - extractor.candidate_weighting(window=10, - pos=pos, - expanded_documents=expanded_documents, - format='corenlp') - - # 5. get the 10-highest scored candidates as keyphrases - keyphrases = extractor.get_n_best(n=10) - - """ - - def __init__(self): - """ Redefining initializer for ExpandRank. """ - - super(ExpandRank, self).__init__() - - def expand_word_graph(self, - input_file, - similarity, - window=10, - pos=None): - """Expands the word graph using the given document. - - Args: - input_file (str): path to the input file. - similarity (float): similarity for weighting edges. - window (int): the window within the sentence for connecting two - words in the graph, defaults to 10. - pos (set): the set of valid pos for words to be considered as nodes - in the graph, defaults to ('NOUN', 'PROPN', 'ADJ'). - """ - - # define default pos tags set - if pos is None: - pos = {'NOUN', 'PROPN', 'ADJ'} - - # initialize document loader - doc = LoadFile() - - # read document - doc.load_document(input=input_file, language=self.language, normalization=self.normalization) - # flatten document and initialize nodes - sequence = [] - - for sentence in doc.sentences: - for j, node in enumerate(sentence.stems): - if node not in self.graph and sentence.pos[j] in pos: - self.graph.add_node(node) - sequence.append((node, sentence.pos[j])) - - # loop through sequence to build the edges in the graph - for j, node_1 in enumerate(sequence): - for k in range(j + 1, min(j + window, len(sequence))): - node_2 = sequence[k] - if node_1[1] in pos and node_2[1] in pos \ - and node_1[0] != node_2[0]: - if not self.graph.has_edge(node_1[0], node_2[0]): - self.graph.add_edge(node_1[0], node_2[0], weight=0) - self.graph[node_1[0]][node_2[0]]['weight'] += similarity - - def candidate_weighting(self, - window=10, - pos=None, - expanded_documents=None, - normalized=False): - """Candidate ranking using random walk. - - Args: - window (int): the window within the sentence for connecting two - words in the graph, defaults to 10. - pos (set): the set of valid pos for words to be considered as nodes - in the graph, defaults to ('NOUN', 'PROPN', 'ADJ'). - expanded_documents (list): the set of documents to expand the graph, - should be a list of tuples (input_path, similarity). Defaults to - empty list, i.e. no expansion. - normalized (False): normalize keyphrase score by their length, - defaults to False. - """ - - # define default pos tags set - if pos is None: - pos = {'NOUN', 'PROPN', 'ADJ'} - - if expanded_documents is None: - expanded_documents = [] - logging.warning('No neighbor documents provided for ExpandRank.') - - # build the word graph - self.build_word_graph(window=window, pos=pos) - - # expand the word graph - for input_file, similarity in expanded_documents: - self.expand_word_graph(input_file=input_file, - similarity=similarity, - window=window, - pos=pos) - # compute the word scores using random walk - w = nx.pagerank_scipy(self.graph, alpha=0.85, weight='weight') - - # loop through the candidates - for k in self.candidates.keys(): - tokens = self.candidates[k].lexical_form - self.weights[k] = sum([w[t] for t in tokens]) - if normalized: - self.weights[k] /= len(tokens) diff --git a/pke/unsupervised/statistical/embedrank.py b/pke/unsupervised/statistical/embedrank.py deleted file mode 100644 index 9e2480d3..00000000 --- a/pke/unsupervised/statistical/embedrank.py +++ /dev/null @@ -1,168 +0,0 @@ -import os -import logging - -import numpy as np -from sklearn.metrics.pairwise import cosine_similarity - - -from pke import LoadFile - - -class EmbedRank(LoadFile): - """EmbedRank keyphrase extraction model. - - Parameterized example:: - - import string - import pke - - # 1. create an EmbedRank extractor. - extractor = pke.unsupervised.EmbedRank() - - # 2. load the content of the document. - extractor.load_document(input='path/to/input', - language='en', - normalization=None) - - # 3. select sequences of nouns and adjectives as candidates. - extractor.candidate_selection() - - # 4. weight the candidates using EmbedRank method - extractor.candidate_weighting() - - # 5. get the 10-highest scored candidates as keyphrases - keyphrases = extractor.get_n_best(n=10) - """ - - _embedding_path = None - _embedding_model = None - - def __init__(self, embedding_path=None): - try: - import sent2vec # See https://github.com/epfml/sent2vec - except ImportError: - logging.warning('Module sent2vec was not found.') - logging.warning('Please install using `python -m pip install cython;' - 'python -m pip install git+https://github.com/epfml/sent2vec` ' - 'to use EmbedRank') - return - - super(EmbedRank, self).__init__() - - if embedding_path is None: - model_name = 'wiki_bigrams.bin' - self._embedding_path = os.path.join(self._models, model_name) - else: - self._embedding_path = embedding_path - - if not os.path.exists(self._embedding_path): - logging.error('Could not find {}'.format(self._embedding_path)) - logging.error('Please download "sent2vec_wiki_bigrams" model from ' - 'https://github.com/epfml/sent2vec#downloading-sent2vec-pre-trained-models.') - logging.error('And place it in {}.'.format(self._models)) - logging.error('Or provide an embedding path.') - - if EmbedRank._embedding_path is None or EmbedRank._embedding_path != self._embedding_path: - logging.info('Loading sent2vec model') - EmbedRank._embedding_model = sent2vec.Sent2vecModel() - EmbedRank._embedding_model.load_model(self._embedding_path) - self._embedding_model = EmbedRank._embedding_model - EmbedRank._embedding_path = self._embedding_path - logging.info('Done loading sent2vec model') - - # Initialize _pos here, if another selection function is used. - self._pos = {'NOUN', 'PROPN', 'ADJ'} - - def candidate_selection(self, pos=None): - """Candidate selection using longest sequences of PoS. - - Args: - pos (set): set of valid POS tags, defaults to ('NOUN', 'PROPN', - 'ADJ'). - """ - - if pos is not None: - self._pos = pos - - # select sequence of adjectives and nouns - self.longest_pos_sequence_selection(valid_pos=self._pos) - - def mmr_ranking(self, document, candidates, l): - """Rank candidates according to a query - - Args: - document (np.array): dense representation of document (query) - candidates (np.array): dense representation of candidates - l (float): ratio between distance to query or distance between - chosen candidates - Returns: - list of candidates rank - """ - - def norm(sim, **kwargs): - sim -= sim.min(**kwargs) - sim /= sim.max(**kwargs) - sim = 0.5 + (sim - sim.mean(**kwargs)) / sim.std(**kwargs) - return sim - - sim_doc = cosine_similarity(document, candidates) - sim_doc[np.isnan(sim_doc)] = 0. - sim_doc = norm(sim_doc) - sim_doc[np.isnan(sim_doc)] = 0. - - sim_can = cosine_similarity(candidates) - sim_can[np.isnan(sim_can)] = 0. - sim_can = norm(sim_can, axis=1) - sim_can[np.isnan(sim_can)] = 0. - - sel = np.zeros(len(candidates), dtype=bool) - ranks = [None] * len(candidates) - # Compute first candidate, the second part of the calculation is 0 - # as there are no other chosen candidates to maximise distance to - chosen_candidate = (sim_doc * l).argmax() - sel[chosen_candidate] = True - ranks[chosen_candidate] = 0 - - for r in range(1, len(candidates)): - - # Remove already chosen candidates - sim_can[sel] = np.nan - - # Compute MMR score - scores = l * sim_doc - (1 - l) * sim_can[:, sel].max(axis=1) - chosen_candidate = np.nanargmax(scores) - - # Update output and mask with chosen candidate - sel[chosen_candidate] = True - ranks[chosen_candidate] = r - - return ranks - - def candidate_weighting(self, l=1, lower=False): - """Candidate weighting function using distance to document. - - Args: - l (float): Lambda parameter for EmbedRank++ Maximal Marginal - Relevance (MMR) computation. Use 1 to compute EmbedRank and 0 to not - use the document, but only the most diverse set of candidates - (defaults to 1). - """ - # Flatten sentences and remove words with unvalid POS - doc = ' '.join(w.lower() if lower else w for s in self.sentences - for i, w in enumerate(s.words) - if s.pos[i] in self._pos) - - doc_embed = self._embedding_model.embed_sentence(doc) - cand_name = list(self.candidates.keys()) - cand = (self.candidates[k] for k in cand_name) - cand = [' '.join(k.surface_forms[0]) for k in cand] - cand = [k.lower() if lower else k for k in cand] - cand_embed = self._embedding_model.embed_sentences(cand) - rank = self.mmr_ranking(doc_embed, cand_embed, l) - for candidate_id, r in enumerate(rank): - if len(rank) > 1: - # Inverting ranks so the first ranked candidate has the biggest score - score = (len(rank) - 1 - r) / (len(rank) - 1) - else: - score = r - self.weights[cand_name[candidate_id]] = score diff --git a/pke/unsupervised/statistical/kpminer.py b/pke/unsupervised/statistical/kpminer.py deleted file mode 100644 index 53afee35..00000000 --- a/pke/unsupervised/statistical/kpminer.py +++ /dev/null @@ -1,156 +0,0 @@ -# -*- coding: utf-8 -*- -# Author: Florian Boudin -# Date: 09-10-2018 - -"""KP-Miner keyphrase extraction model. - -Statistical approach to keyphrase extraction described in: - -* Samhaa R. El-Beltagy and Ahmed Rafea. - KP-Miner: Participation in SemEval-2. - *Proceedings of SemEval*, pages 190-193, 2010. - -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math -import string -import logging - -from pke.base import LoadFile -from pke.utils import load_document_frequency_file - - -class KPMiner(LoadFile): - """KP-Miner keyphrase extraction model. - - Parameterized example:: - - import pke - - # 1. create a KPMiner extractor. - extractor = pke.unsupervised.KPMiner() - - # 2. load the content of the document. - extractor.load_document(input='path/to/input', - language='en', - normalization=None) - - - # 3. select {1-5}-grams that do not contain punctuation marks or - # stopwords as keyphrase candidates. Set the least allowable seen - # frequency to 5 and the number of words after which candidates are - # filtered out to 200. - lasf = 5 - cutoff = 200 - extractor.candidate_selection(lasf=lasf, cutoff=cutoff) - - # 4. weight the candidates using KPMiner weighting function. - df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz') - alpha = 2.3 - sigma = 3.0 - extractor.candidate_weighting(df=df, alpha=alpha, sigma=sigma) - - # 5. get the 10-highest scored candidates as keyphrases - keyphrases = extractor.get_n_best(n=10) - """ - - def candidate_selection(self, lasf=3, cutoff=400, stoplist=None, **kwargs): - """The candidate selection as described in the KP-Miner paper. - - Args: - lasf (int): least allowable seen frequency, defaults to 3. - cutoff (int): the number of words after which candidates are - filtered out, defaults to 400. - stoplist (list): the stoplist for filtering candidates, defaults - to the nltk stoplist. Words that are punctuation marks from - string.punctuation are not allowed. - """ - - # select ngrams from 1 to 5 grams - self.ngram_selection(n=5) - - # initialize stoplist list if not provided - if stoplist is None: - stoplist = self.stoplist - - # filter candidates containing stopwords or punctuation marks - self.candidate_filtering(stoplist=list(string.punctuation) + stoplist) - - # further filter candidates using lasf and cutoff - # Python 2/3 compatible - for k in list(self.candidates): - - # get the candidate - v = self.candidates[k] - - # delete if first candidate offset is greater than cutoff - if v.offsets[0] > cutoff: - del self.candidates[k] - - # delete if frequency is lower than lasf - elif len(v.surface_forms) < lasf: - del self.candidates[k] - - def candidate_weighting(self, df=None, sigma=3.0, alpha=2.3): - """Candidate weight calculation as described in the KP-Miner paper. - - Note: - w = tf * idf * B * P_f - with - - * B = N_d / (P_d * alpha) and B = min(sigma, B) - * N_d = the number of all candidate terms - * P_d = number of candidates whose length exceeds one - * P_f = 1 - - Args: - df (dict): document frequencies, the number of documents should - be specified using the "--NB_DOC--" key. - sigma (int): parameter for boosting factor, defaults to 3.0. - alpha (int): parameter for boosting factor, defaults to 2.3. - """ - - # initialize default document frequency counts if none provided - if df is None: - logging.warning('LoadFile._df_counts is hard coded to {}'.format( - self._df_counts)) - df = load_document_frequency_file(self._df_counts, delimiter='\t') - - # initialize the number of documents as --NB_DOC-- + 1 (current) - N = 1 + df.get('--NB_DOC--', 0) - - # compute the number of candidates whose length exceeds one - P_d = sum([len(v.surface_forms) for v in self.candidates.values() - if len(v.lexical_form) > 1]) - - # fall back to 1 if all candidates are words - P_d = max(1, P_d) - - # compute the number of all candidate terms - N_d = sum([len(v.surface_forms) for v in self.candidates.values()]) - - # compute the boosting factor - B = min(N_d / (P_d * alpha), sigma) - - # loop throught the candidates - for k, v in self.candidates.items(): - - # get candidate document frequency - candidate_df = 1 - - # get the df for unigram only - if len(v.lexical_form) == 1: - candidate_df += df.get(k, 0) - - # compute the idf score - idf = math.log(N / candidate_df, 2) - - if len(v.lexical_form) == 1: - # If single word candidate do not apply boosting factor - self.weights[k] = len(v.surface_forms) * idf - else: - self.weights[k] = len(v.surface_forms) * B * idf diff --git a/requirements.txt b/requirements.txt index f55f8d26..63f063a1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -six spacy nltk networkx diff --git a/setup.py b/setup.py index 68e6cf2d..f11bc78a 100644 --- a/setup.py +++ b/setup.py @@ -1,14 +1,14 @@ from distutils.core import setup setup(name='pke', - version='1.8.1', + version='2.0.0', description='Python Keyphrase Extraction module', author='pke contributors', author_email='florian.boudin@univ-nantes.fr', license='gnu', packages=['pke', 'pke.unsupervised', 'pke.supervised', 'pke.supervised.feature_based', 'pke.unsupervised.graph_based', - 'pke.unsupervised.statistical', 'pke.supervised.neural_based'], + 'pke.unsupervised.statistical'], url="https://github.com/boudinfl/pke", install_requires=[ 'nltk', @@ -16,7 +16,6 @@ 'numpy', 'scipy', 'spacy', - 'six', 'sklearn', 'unidecode', 'future', From afaf3a3204c6121c29b130bda37cc7b17699aa31 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Wed, 2 Mar 2022 18:07:13 +0100 Subject: [PATCH 02/24] second blood --- pke/base.py | 39 ++++----------------- pke/data_structures.py | 73 ++++++---------------------------------- pke/stopwords.py | 58 +++++++++++++++++++++++++++++++ tests/test_embedrank.py | 40 ---------------------- tests/test_expandrank.py | 65 ----------------------------------- 5 files changed, 76 insertions(+), 199 deletions(-) create mode 100644 pke/stopwords.py delete mode 100644 tests/test_embedrank.py delete mode 100644 tests/test_expandrank.py diff --git a/pke/base.py b/pke/base.py index b8cd1f1c..74e81d65 100644 --- a/pke/base.py +++ b/pke/base.py @@ -8,18 +8,17 @@ from pke.readers import MinimalCoreNLPReader, RawTextReader from nltk import RegexpParser -from nltk.corpus import stopwords +#from nltk.corpus import stopwords from nltk.tag.mapping import map_tag -from nltk.stem.snowball import SnowballStemmer, PorterStemmer +from nltk.stem.snowball import SnowballStemmer +from .stopwords import stopwords from .langcodes import LANGUAGE_CODE_BY_NAME from string import punctuation import os import logging -import codecs -from six import string_types from builtins import str @@ -28,37 +27,10 @@ get_alpha_2 = lambda l: LANGUAGE_CODE_BY_NAME[l] -lang_stopwords = {get_alpha_2(l): l for l in stopwords._fileids} - lang_stem = {get_alpha_2(l): l for l in set(SnowballStemmer.languages) - set(['porter'])} lang_stem.update({'en': 'porter'}) PRINT_NO_STEM_WARNING = defaultdict(lambda: True) -PRINT_NO_STWO_WARNING = defaultdict(lambda: True) - - -def get_stopwords(lang): - """Provide stopwords for the given language, or default value. - - If stopwords are not available for a given language, a default value is - returned and a warning is displayed - :param lang: Alpha-2 language code. - :type lang: str - :returns: A list of stop words or an empty list. - :rtype: {List} - """ - global PRINT_NO_STWO_WARNING - try: - lang = lang_stopwords[lang] - return stopwords.words(lang) - except KeyError: - if PRINT_NO_STWO_WARNING[lang]: - logging.warning('No stopwords for \'{}\' language.'.format(lang)) - logging.warning( - 'Please provide custom stoplist if willing to use stopwords. Or ' - 'update nltk\'s `stopwords` corpora using `nltk.download(\'stopwords\')`') - PRINT_NO_STWO_WARNING[lang] = False - return [] def get_stemmer_func(lang): @@ -184,7 +156,10 @@ def load_document(self, input, **kwargs): self.sentences = doc.sentences # initialize the stoplist - self.stoplist = get_stopwords(self.language) + if self.language in stopwords: + self.stoplist = stopwords[self.language] + else: + logging.warning('No stopwords for \'{}\' language.'.format(self.language)) # word normalization self.normalization = kwargs.get('normalization', 'stemming') diff --git a/pke/data_structures.py b/pke/data_structures.py index 2ad6bc80..868ac1d3 100644 --- a/pke/data_structures.py +++ b/pke/data_structures.py @@ -1,9 +1,12 @@ # -*- coding: utf-8 -*- +from dataclasses import dataclass + """Data structures for the pke module.""" -class Sentence(object): +@dataclass +class Sentence: """The sentence data structure.""" def __init__(self, words): @@ -23,38 +26,9 @@ def __init__(self, words): self.meta = {} """meta-information of the sentence.""" - def __eq__(self, other): - """Compares two sentences for equality.""" - - # test whether they are instances of different classes - if type(self) != type(other): - return False - - # test whether they are of same length - if self.length != other.length: - return False - - # test whether they have the same words - if self.words != other.words: - return False - - # test whether they have the same PoS tags - if self.pos != other.pos: - return False - - # test whether they have the same stem forms - if self.stems != other.stems: - return False - - # test whether they have the same meta-information - if self.meta != other.meta: - return False - - # if everything is ok then they are equal - return True - -class Candidate(object): +@dataclass +class Candidate: """The keyphrase candidate data structure.""" def __init__(self): @@ -75,7 +49,8 @@ def __init__(self): """ the lexical form of the candidate. """ -class Document(object): +@dataclass +class Document: """The Document data structure.""" def __init__(self): @@ -87,20 +62,16 @@ def __init__(self): """ The sentence container (list of Sentence). """ @staticmethod - def from_sentences(sentences, **kwargs): + def from_sentences(sentences): """Populate the sentence list. Args: sentences (Sentence list): content to create the document. - input_file (str): path to the input file. """ # initialize document doc = Document() - # set the input file - doc.input_file = kwargs.get('input_file', None) - # loop through the parsed sentences for i, sentence in enumerate(sentences): @@ -114,33 +85,11 @@ def from_sentences(sentences, **kwargs): s.stems = sentence['lemmas'] # add the meta-information - for (k, infos) in sentence.items(): + for (k, info) in sentence.items(): if k not in {'POS', 'lemmas', 'words'}: - s.meta[k] = infos + s.meta[k] = info # add the sentence to the document doc.sentences.append(s) return doc - - def __eq__(self, other): - """Compares two documents for equality.""" - - # test whether they are instances of different classes - if type(self) != type(other): - return False - - # test whether they have the same language - if self.language != other.language: - return False - - # test whether they have the same input path - if self.input_file != other.input_file: - return False - - # test whether they contain the same lists of sentences - if self.sentences != other.sentences: - return False - - # if everything is ok then they are equal - return True diff --git a/pke/stopwords.py b/pke/stopwords.py new file mode 100644 index 00000000..6ea1520b --- /dev/null +++ b/pke/stopwords.py @@ -0,0 +1,58 @@ +# -*- coding: utf-8 -*- + +"""Stopwords + +These lists were obtained from nltk's stopwords corpus data. +https://github.com/nltk/nltk_data/blob/gh-pages/packages/corpora/stopwords.zip + +""" + +stopwords = { + "en": ["i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "you\'re", "you\'ve", "you\'ll", + "you\'d", "your", "yours", "yourself", "yourselves", "he", "him", "his", "himself", "she", "she\'s", "her", + "hers", "herself", "it", "it\'s", "its", "itself", "they", "them", "their", "theirs", "themselves", "what", + "which", "who", "whom", "this", "that", "that\'ll", "these", "those", "am", "is", "are", "was", "were", "be", + "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", "the", "and", + "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against", + "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", + "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here", "there", "when", + "where", "why", "how", "all", "any", "both", "each", "few", "more", "most", "other", "some", "such", "no", + "nor", "not", "only", "own", "same", "so", "than", "too", "very", "s", "t", "can", "will", "just", "don", + "don\'t", "should", "should\'ve", "now", "d", "ll", "m", "o", "re", "ve", "y", "ain", "aren", "aren\'t", + "couldn", "couldn\'t", "didn", "didn\'t", "doesn", "doesn\'t", "hadn", "hadn\'t", "hasn", "hasn\'t", "haven", + "haven\'t", "isn", "isn\'t", "ma", "mightn", "mightn\'t", "mustn", "mustn\'t", "needn", "needn\'t", "shan", + "shan\'t", "shouldn", "shouldn\'t", "wasn", "wasn\'t", "weren", "weren\'t", "won", "won\'t", "wouldn", + "wouldn\'t"], + "fr": ['au', 'aux', 'avec', 'ce', 'ces', 'dans', 'de', 'des', 'du', 'elle', 'en', 'et', 'eux', 'il', 'ils', 'je', + 'la', 'le', 'les', 'leur', 'lui', 'ma', 'mais', 'me', 'même', 'mes', 'moi', 'mon', 'ne', 'nos', 'notre', + 'nous', 'on', 'ou', 'par', 'pas', 'pour', 'qu', 'que', 'qui', 'sa', 'se', 'ses', 'son', 'sur', 'ta', 'te', + 'tes', 'toi', 'ton', 'tu', 'un', 'une', 'vos', 'votre', 'vous', 'c', 'd', 'j', 'l', 'à', 'm', 'n', 's', 't', + 'y', 'été', 'étée', 'étées', 'étés', 'étant', 'étante', 'étants', 'étantes', 'suis', 'es', 'est', 'sommes', + 'êtes', 'sont', 'serai', 'seras', 'sera', 'serons', 'serez', 'seront', 'serais', 'serait', 'serions', + 'seriez', 'seraient', 'étais', 'était', 'étions', 'étiez', 'étaient', 'fus', 'fut', 'fûmes', 'fûtes', + 'furent', 'sois', 'soit', 'soyons', 'soyez', 'soient', 'fusse', 'fusses', 'fût', 'fussions', 'fussiez', + 'fussent', 'ayant', 'ayante', 'ayantes', 'ayants', 'eu', 'eue', 'eues', 'eus', 'ai', 'as', 'avons', 'avez', + 'ont', 'aurai', 'auras', 'aura', 'aurons', 'aurez', 'auront', 'aurais', 'aurait', 'aurions', 'auriez', + 'auraient', 'avais', 'avait', 'avions', 'aviez', 'avaient', 'eut', 'eûmes', 'eûtes', 'eurent', 'aie', 'aies', + 'ait', 'ayons', 'ayez', 'aient', 'eusse', 'eusses', 'eût', 'eussions', 'eussiez', 'eussent'], + "ge": ['aber', 'alle', 'allem', 'allen', 'aller', 'alles', 'als', 'also', 'am', 'an', 'ander', 'andere', 'anderem', + 'anderen', 'anderer', 'anderes', 'anderm', 'andern', 'anderr', 'anders', 'auch', 'auf', 'aus', 'bei', 'bin', + 'bis', 'bist', 'da', 'damit', 'dann', 'der', 'den', 'des', 'dem', 'die', 'das', 'dass', 'daß', 'derselbe', + 'derselben', 'denselben', 'desselben', 'demselben', 'dieselbe', 'dieselben', 'dasselbe', 'dazu', 'dein', + 'deine', 'deinem', 'deinen', 'deiner', 'deines', 'denn', 'derer', 'dessen', 'dich', 'dir', 'du', 'dies', + 'diese', 'diesem', 'diesen', 'dieser', 'dieses', 'doch', 'dort', 'durch', 'ein', 'eine', 'einem', 'einen', + 'einer', 'eines', 'einig', 'einige', 'einigem', 'einigen', 'einiger', 'einiges', 'einmal', 'er', 'ihn', + 'ihm', 'es', 'etwas', 'euer', 'eure', 'eurem', 'euren', 'eurer', 'eures', 'für', 'gegen', 'gewesen', 'hab', + 'habe', 'haben', 'hat', 'hatte', 'hatten', 'hier', 'hin', 'hinter', 'ich', 'mich', 'mir', 'ihr', 'ihre', + 'ihrem', 'ihren', 'ihrer', 'ihres', 'euch', 'im', 'in', 'indem', 'ins', 'ist', 'jede', 'jedem', 'jeden', + 'jeder', 'jedes', 'jene', 'jenem', 'jenen', 'jener', 'jenes', 'jetzt', 'kann', 'kein', 'keine', 'keinem', + 'keinen', 'keiner', 'keines', 'können', 'könnte', 'machen', 'man', 'manche', 'manchem', 'manchen', 'mancher', + 'manches', 'mein', 'meine', 'meinem', 'meinen', 'meiner', 'meines', 'mit', 'muss', 'musste', 'nach', 'nicht', + 'nichts', 'noch', 'nun', 'nur', 'ob', 'oder', 'ohne', 'sehr', 'sein', 'seine', 'seinem', 'seinen', 'seiner', + 'seines', 'selbst', 'sich', 'sie', 'ihnen', 'sind', 'so', 'solche', 'solchem', 'solchen', 'solcher', + 'solches', 'soll', 'sollte', 'sondern', 'sonst', 'über', 'um', 'und', 'uns', 'unsere', 'unserem', 'unseren', + 'unser', 'unseres', 'unter', 'viel', 'vom', 'von', 'vor', 'während', 'war', 'waren', 'warst', 'was', 'weg', + 'weil', 'weiter', 'welche', 'welchem', 'welchen', 'welcher', 'welches', 'wenn', 'werde', 'werden', 'wie', + 'wieder', 'will', 'wir', 'wird', 'wirst', 'wo', 'wollen', 'wollte', 'würde', 'würden', 'zu', 'zum', 'zur', + 'zwar', 'zwischen'] +} diff --git a/tests/test_embedrank.py b/tests/test_embedrank.py deleted file mode 100644 index 561869b6..00000000 --- a/tests/test_embedrank.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import sys - -import mock -import pke - -text = u"Compatibility of systems of linear constraints over the set of natural\ - numbers. Criteria of compatibility of a system of linear Diophantine equations\ -, strict inequations, and nonstrict inequations are considered. Upper bounds fo\ -r components of a minimal set of solutions and algorithms of construction of mi\ -nimal generating sets of solutions for all types of systems are given. These cr\ -iteria and the corresponding algorithms for constructing a minimal supporting s\ -et of solutions can be used in solving all the considered types systems and sys\ -tems of mixed types." - -pos = {'NOUN', 'PROPN', 'ADJ'} - - -def test_embedrank_candidate_weighting(): - """Test SingleRank candidate weighting method.""" - extractor = pke.unsupervised.EmbedRank( - embedding_path=os.path.join('tests', 'data', 'inspec_sent2vec.bin')) - extractor.load_document(input=text) - extractor.candidate_selection() - extractor.candidate_weighting(l=1) - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['systems', 'types systems', 'algorithms'] - - -def test_import_embedrank_nosent2vec(): - # Without sent2vec this should not thorw an error - - # Make sent2vec unavailable - with mock.patch.dict(sys.modules, {'sent2vec': None}): - pke.unsupervised.EmbedRank() diff --git a/tests/test_expandrank.py b/tests/test_expandrank.py deleted file mode 100644 index 0d5a9725..00000000 --- a/tests/test_expandrank.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import pke -from collections import defaultdict -import gzip -import bisect - -input_file = os.path.join('test', 'data', 'FT923-5089.xml') -pos = {'NOUN', 'PROPN', 'ADJ'} - - -def create_corpus(values, tmp_path, name='corpus.gz'): - # from test_utils.py - corpus_dir = tmp_path / name - corpus_dir.mkdir() - for k, v in values.items(): - (corpus_dir / k).write_text(v) - return corpus_dir - - -def create_df(corpus_dir, tmp_path, name='corpus_df.gz'): - # from test_utils.py - corpus_df_file = tmp_path / name - pke.utils.compute_document_frequency( - str(corpus_dir), str(corpus_df_file), extension='txt', n=1) - corpus_df = pke.utils.load_document_frequency_file(str(corpus_df_file)) - return corpus_df, corpus_df_file - - -def test_expandrank_candidate_selection(): - """Test ExtandRank candidate selection method.""" - - extractor = pke.unsupervised.ExpandRank() - extractor.load_document(input=input_file) - extractor.candidate_selection(pos=pos) - - -def test_expandrank_candidate_weighting(tmp_path): - """Test ExtandRank candidate weighting method.""" - - # Create a corpus, compute the df, compute the pairwise_similarity_matric - input_file = 'a.txt' - corpus = {input_file: 'lorem sit amet', 'b.txt': 'lorem ipsum'} - corpus_dir = create_corpus(corpus, tmp_path) - # compute_df_count - corpus_df, _ = create_df(corpus_dir, tmp_path) - # compute_pairwise_similarity_matrix - pairw_file = tmp_path / 'pairwise.gz' - pke.utils.compute_pairwise_similarity_matrix( - str(corpus_dir), str(pairw_file), extension='txt', - collection_dir=None, df=corpus_df) - pairwise = pke.utils.load_pairwise_similarities(str(pairw_file)) - - n_expanded = 1 - expanded_documents = [(v, u) for u, v in pairwise[os.path.basename(input_file)][-n_expanded:]] - - extractor = pke.unsupervised.ExpandRank() - extractor.load_document(input=input_file) - extractor.candidate_selection(pos=pos) - extractor.candidate_weighting(window=10, pos=pos, expanded_documents=expanded_documents, normalized=False) - keyphrases = [k for k, s in extractor.get_n_best(n=10)] From fa785af18e13867048cfe9e7d95052934fd60fc0 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Thu, 3 Mar 2022 15:19:30 +0100 Subject: [PATCH 03/24] third blood --- pke/__init__.py | 1 - pke/base.py | 133 ++++------------------- pke/data_structures.py | 2 +- pke/{stopwords.py => lang.py} | 24 ++++- pke/readers.py | 198 ++++++++++------------------------ pke/utils.py | 12 ++- requirements.txt | 2 +- tests/sample.py | 30 ++++++ tests/test_reading.py | 38 +++---- 9 files changed, 153 insertions(+), 287 deletions(-) rename pke/{stopwords.py => lang.py} (92%) create mode 100644 tests/sample.py diff --git a/pke/__init__.py b/pke/__init__.py index e4d9eaad..9d737b74 100644 --- a/pke/__init__.py +++ b/pke/__init__.py @@ -1,7 +1,6 @@ from __future__ import absolute_import from pke.data_structures import Candidate, Document, Sentence -from pke.readers import MinimalCoreNLPReader, RawTextReader from pke.base import LoadFile from pke.utils import (load_document_frequency_file, compute_document_frequency, train_supervised_model, load_references, diff --git a/pke/base.py b/pke/base.py index 74e81d65..62f7db59 100644 --- a/pke/base.py +++ b/pke/base.py @@ -4,76 +4,18 @@ from collections import defaultdict -from pke.data_structures import Candidate, Document -from pke.readers import MinimalCoreNLPReader, RawTextReader +from pke.data_structures import Candidate +from pke.readers import RawTextReader, SpacyDocReader, PreprocessedReader from nltk import RegexpParser -#from nltk.corpus import stopwords -from nltk.tag.mapping import map_tag from nltk.stem.snowball import SnowballStemmer -from .stopwords import stopwords -from .langcodes import LANGUAGE_CODE_BY_NAME +from pke.lang import stopwords, langcodes from string import punctuation import os import logging - - -from builtins import str - - -# The language management should be in `pke.utils` but it would create a circular import. - -get_alpha_2 = lambda l: LANGUAGE_CODE_BY_NAME[l] - -lang_stem = {get_alpha_2(l): l for l in set(SnowballStemmer.languages) - set(['porter'])} -lang_stem.update({'en': 'porter'}) - -PRINT_NO_STEM_WARNING = defaultdict(lambda: True) - - -def get_stemmer_func(lang): - """Provide steming function for the given language, or identity function. - - If stemming is not available for a given language, a default value is - returned and a warning is displayed - :param lang: Alpha-2 language code. - :type lang: str - :returns: A function to stem a word (or the identity function). - :rtype: {Callable[[str], str]} - """ - global PRINT_NO_STEM_WARNING - try: - lang = lang_stem[lang] - ignore_sw = lang != 'porter' # PorterStemmer do not use stop_words - stemmer = SnowballStemmer(lang, ignore_stopwords=ignore_sw) - return stemmer.stem - except KeyError: - if PRINT_NO_STEM_WARNING[lang]: - logging.warning('No stemmer for \'{}\' language.'.format(lang)) - logging.warning('Stemming will not be applied.') - PRINT_NO_STEM_WARNING[lang] = False - return lambda x: x - - -escaped_punctuation = {'-lrb-': '(', '-rrb-': ')', '-lsb-': '[', '-rsb-': ']', - '-lcb-': '{', '-rcb-': '}'} - - -def is_file_path(input): - try: - return os.path.isfile(input) - except Exception: - # On some windows version the maximum path length is 255. When calling - # `os.path.isfile` on long string it will raise a ValueError. - # We return false as even is the string is a file_path we won't be able - # to open it - return False - - -def is_corenlp(input): - return is_file_path(input) and input.endswith('.xml') +import spacy class LoadFile(object): @@ -124,26 +66,20 @@ def load_document(self, input, **kwargs): # get the language parameter language = kwargs.get('language', 'en') - # initialize document - doc = Document() - - if is_corenlp(input): - path = input - parser = MinimalCoreNLPReader() - doc = parser.read(path=input, **kwargs) - doc.is_corenlp_file = True - elif is_file_path(input): - path = input - with open(path, encoding=kwargs.get('encoding', 'utf-8')) as f: - input = f.read() - parser = RawTextReader(language=language) - doc = parser.read(text=input, path=path, **kwargs) + # check whether input is a spacy doc object instance + if isinstance(input, spacy.tokens.doc.Doc): + parser = SpacyDocReader() + doc = parser.read(spacy_doc=input, **kwargs) + # check whether input is a string elif isinstance(input, str): parser = RawTextReader(language=language) doc = parser.read(text=input, **kwargs) + # check whether input is processed text + elif isinstance(input, list) and all(isinstance(item, list) for item in input): + parser = PreprocessedReader() + doc = parser.read(list_of_sentence_tuples=input, **kwargs) else: - logging.error('Cannot process input. It is neither a file path ' - 'or a string: {}'.format(type(input))) + logging.error('Cannot process input. It is neither a spacy doc or a string: {}'.format(type(input))) return # set the input file @@ -156,46 +92,23 @@ def load_document(self, input, **kwargs): self.sentences = doc.sentences # initialize the stoplist - if self.language in stopwords: - self.stoplist = stopwords[self.language] - else: - logging.warning('No stopwords for \'{}\' language.'.format(self.language)) + self.stoplist = stopwords.get(self.language) # word normalization self.normalization = kwargs.get('normalization', 'stemming') if self.normalization == 'stemming': - stem = get_stemmer_func(self.language) - get_stem = lambda s: [stem(w).lower() for w in s.words] - else: - get_stem = lambda s: [w.lower() for w in s.words] + # fall back to porter if english language is used + langcode = langcodes.get(self.language.replace('en', 'xx'), 'porter') + stemmer = SnowballStemmer(langcode) - # Populate Sentence.stems according to normalization - for i, sentence in enumerate(self.sentences): - self.sentences[i].stems = get_stem(sentence) - - # POS normalization - if getattr(doc, 'is_corenlp_file', False): - self.normalize_pos_tags() - self.unescape_punctuation_marks() - - def normalize_pos_tags(self): - """Normalizes the PoS tags from udp-penn to UD.""" - - if self.language == 'en': - # iterate throughout the sentences + # populate Sentence.stems for i, sentence in enumerate(self.sentences): - self.sentences[i].pos = [map_tag('en-ptb', 'universal', tag) - for tag in sentence.pos] + self.sentences[i].stems = [stemmer.stem(w).lower() for w in sentence.words] - def unescape_punctuation_marks(self): - """Replaces the special punctuation marks produced by CoreNLP.""" - - for i, sentence in enumerate(self.sentences): - for j, word in enumerate(sentence.words): - l_word = word.lower() - self.sentences[i].words[j] = escaped_punctuation.get(l_word, - word) + elif self.normalization is None: + for i, sentence in enumerate(self.sentences): + self.sentences[i].stems = [w.lower() for w in sentence.words] def is_redundant(self, candidate, prev, minimum_length=1): """Test if one candidate is redundant with respect to a list of already diff --git a/pke/data_structures.py b/pke/data_structures.py index 868ac1d3..64826c19 100644 --- a/pke/data_structures.py +++ b/pke/data_structures.py @@ -62,7 +62,7 @@ def __init__(self): """ The sentence container (list of Sentence). """ @staticmethod - def from_sentences(sentences): + def from_sentences(sentences, **kwargs): """Populate the sentence list. Args: diff --git a/pke/stopwords.py b/pke/lang.py similarity index 92% rename from pke/stopwords.py rename to pke/lang.py index 6ea1520b..0eb81689 100644 --- a/pke/stopwords.py +++ b/pke/lang.py @@ -1,10 +1,14 @@ # -*- coding: utf-8 -*- -"""Stopwords +"""Language resources of pke. + +Lists of stopwords in different languages. These lists were obtained from nltk's stopwords corpus data. https://github.com/nltk/nltk_data/blob/gh-pages/packages/corpora/stopwords.zip +Langcodes. + """ stopwords = { @@ -56,3 +60,21 @@ 'wieder', 'will', 'wir', 'wird', 'wirst', 'wo', 'wollen', 'wollte', 'würde', 'würden', 'zu', 'zum', 'zur', 'zwar', 'zwischen'] } + +langcodes = { + "ar": "arabic", + "da": "danish", + "du": "dutch", + "en": "english", + "fi": "finnish", + "fr": "french", + "ge": "german", + "hu": "hungarian", + "it": "italian", + "no": "norwegian", + "po": "portuguese", + "ro": "romanian", + "ru": "russian", + "sp": "spanish", + "sw": "swedish", +} diff --git a/pke/readers.py b/pke/readers.py index faf41b11..fdb4ae2e 100644 --- a/pke/readers.py +++ b/pke/readers.py @@ -3,130 +3,19 @@ """Readers for the pke module.""" -import os import sys -import json import logging -import xml.etree.ElementTree as etree import spacy from pke.data_structures import Document class Reader(object): + """Reader default class.""" def read(self, path): raise NotImplementedError -class MinimalCoreNLPReader(Reader): - """Minimal CoreNLP XML Parser.""" - - def __init__(self): - self.parser = etree.XMLParser() - - def read(self, path, **kwargs): - sentences = [] - tree = etree.parse(path, self.parser) - for sentence in tree.iterfind('./document/sentences/sentence'): - # get the character offsets - starts = [int(u.text) for u in - sentence.iterfind("tokens/token/CharacterOffsetBegin")] - ends = [int(u.text) for u in - sentence.iterfind("tokens/token/CharacterOffsetEnd")] - sentences.append({ - "words": [u.text for u in - sentence.iterfind("tokens/token/word")], - "lemmas": [u.text for u in - sentence.iterfind("tokens/token/lemma")], - "POS": [u.text for u in sentence.iterfind("tokens/token/POS")], - "char_offsets": [(starts[k], ends[k]) for k in - range(len(starts))] - }) - sentences[-1].update(sentence.attrib) - - doc = Document.from_sentences(sentences, input_file=path, **kwargs) - - return doc - - -# FIX -def fix_spacy_for_french(nlp): - """Fixes https://github.com/boudinfl/pke/issues/115. - For some special tokenisation cases, spacy do not assign a `pos` field. - - Taken from https://github.com/explosion/spaCy/issues/5179. - """ - from spacy.symbols import TAG - if nlp.lang != 'fr': - # Only fix french model - return nlp - if '' not in [t.pos_ for t in nlp('est-ce')]: - # If the bug does not happen do nothing - return nlp - rules = nlp.Defaults.tokenizer_exceptions - - for orth, token_dicts in rules.items(): - for token_dict in token_dicts: - if TAG in token_dict: - del token_dict[TAG] - try: - nlp.tokenizer = nlp.Defaults.create_tokenizer(nlp) # this property assignment flushes the cache - except Exception as e: - # There was a problem fallback on using `pos = token.pos_ or token.tag_` - () - return nlp - - -def list_linked_spacy_models(): - """ Read SPACY/data and return a list of link_name """ - spacy_data = os.path.join(spacy.info(silent=True)['Location'], 'data') - linked = [d for d in os.listdir(spacy_data) if os.path.islink(os.path.join(spacy_data, d))] - # linked = [os.path.join(spacy_data, d) for d in os.listdir(spacy_data)] - # linked = {os.readlink(d): os.path.basename(d) for d in linked if os.path.islink(d)} - return linked - - -def list_downloaded_spacy_models(): - """ Scan PYTHONPATH to find spacy models """ - models = [] - # For each directory in PYTHONPATH - paths = [p for p in sys.path if os.path.isdir(p)] - for site_package_dir in paths: - # For each module - modules = [os.path.join(site_package_dir, m) for m in os.listdir(site_package_dir)] - modules = [m for m in modules if os.path.isdir(m)] - for module_dir in modules: - if 'meta.json' in os.listdir(module_dir): - # Ensure the package we're in is a spacy model - meta_path = os.path.join(module_dir, 'meta.json') - with open(meta_path) as f: - meta = json.load(f) - if meta.get('parent_package', '') == 'spacy': - models.append(module_dir) - return models - - -def str2spacy(model): - if int(spacy.__version__.split('.')[0]) < 3: - downloaded_models = [os.path.basename(m) for m in list_downloaded_spacy_models()] - links = list_linked_spacy_models() - else: - # As of spacy v3, links do not exist anymore and it is simpler to get a list of - # downloaded models - downloaded_models = list(spacy.info()['pipelines']) - links = [] - filtered_downloaded = [m for m in downloaded_models if m[:2] == model] - if model in downloaded_models + links: - # Check whether `model` is the name of a model/link - return model - elif filtered_downloaded: - # Check whether `model` is a lang code and corresponds to a downloaded model - return filtered_downloaded[0] - else: - # Return asked model to have an informative error. - return model - - class RawTextReader(Reader): """Reader for raw text.""" @@ -155,50 +44,71 @@ def read(self, text, **kwargs): Args: text (str): raw text to pre-process. - max_length (int): maximum number of characters in a single text for - spacy (for spacy<3 compatibility, as of spacy v3 long texts - should be splitted in smaller portions), default to - 1,000,000 characters (1mb). spacy_model (model): an already loaded spacy model. """ - spacy_model = kwargs.get('spacy_model', None) - - if spacy_model is None: - try: - spacy_model = spacy.load(str2spacy(self.language), - disable=['ner', 'textcat', 'parser']) - except OSError: - logging.warning('No spacy model for \'{}\' language.'.format(self.language)) - logging.warning('Falling back to using english model. There might ' - 'be tokenization and postagging errors. A list of available ' - 'spacy model is available at https://spacy.io/models.'.format( - self.language)) - spacy_model = spacy.load(str2spacy('en'), - disable=['ner', 'textcat', 'parser']) - if int(spacy.__version__.split('.')[0]) < 3: - sentencizer = spacy_model.create_pipe('sentencizer') + nlp = kwargs.get('spacy_model', None) + + if nlp is None: + + # list installed models + installed_models = [m for m in spacy.util.get_installed_models() if m[:2] == self.language] + + # select first model for the language + if len(installed_models): + nlp = spacy.load(installed_models[0], disable=['ner', 'textcat', 'parser']) + + # stop execution is no model is available else: - sentencizer = 'sentencizer' - spacy_model.add_pipe(sentencizer) - if 'max_length' in kwargs and kwargs['max_length']: - spacy_model.max_length = kwargs['max_length'] + logging.error('No spacy model for \'{}\' language.'.format(self.language)) + logging.error('A list of available spacy models is available at https://spacy.io/models.') + return + + # add the sentence splitter + nlp.add_pipe('sentencizer') + + # process the document + spacy_doc = nlp(text) - spacy_model = fix_spacy_for_french(spacy_model) - spacy_doc = spacy_model(text) + sentences = [] + for sentence_id, sentence in enumerate(spacy_doc.sents): + sentences.append({ + "words": [token.text for token in sentence], + "lemmas": [token.lemma_ for token in sentence], + "POS": [token.pos_ or token.tag_ for token in sentence], + "char_offsets": [(token.idx, token.idx + len(token.text)) for token in sentence] + }) + return Document.from_sentences(sentences, input_file=kwargs.get('input_file', None), **kwargs) + +class SpacyDocReader(Reader): + """Minimal Spacy Doc Reader.""" + + def read(self, spacy_doc, **kwargs): sentences = [] for sentence_id, sentence in enumerate(spacy_doc.sents): sentences.append({ "words": [token.text for token in sentence], "lemmas": [token.lemma_ for token in sentence], - # FIX : This is a fallback if `fix_spacy_for_french` does not work "POS": [token.pos_ or token.tag_ for token in sentence], - "char_offsets": [(token.idx, token.idx + len(token.text)) - for token in sentence] + "char_offsets": [(token.idx, token.idx + len(token.text)) for token in sentence] }) + return Document.from_sentences(sentences, **kwargs) + - doc = Document.from_sentences( - sentences, input_file=kwargs.get('input_file', None), **kwargs) +class PreprocessedReader(Reader): + """Reader for preprocessed text.""" - return doc + def read(self, list_of_sentence_tuples, **kwargs): + sentences = [] + for sentence_id, sentence in enumerate(list_of_sentence_tuples): + words = [word for word, pos_tag in sentence] + pos_tags = [pos_tag for word, pos_tag in sentence] + shift = 0 + sentences.append({ + "words": words, + "lemmas": words, + "POS": pos_tags + }) + shift += len(' '.join(words)) + return Document.from_sentences(sentences, **kwargs) diff --git a/pke/utils.py b/pke/utils.py index f8b65a30..5fa31279 100644 --- a/pke/utils.py +++ b/pke/utils.py @@ -21,11 +21,14 @@ from itertools import combinations, product from collections import defaultdict -from pke.base import LoadFile, get_stopwords, get_stemmer_func +from pke.base import LoadFile +from pke.lang import stopwords, langcodes from sklearn.feature_extraction.text import CountVectorizer from sklearn.decomposition import LatentDirichletAllocation +from nltk.stem.snowball import SnowballStemmer + def load_document_frequency_file(input_file, delimiter='\t'): @@ -316,11 +319,12 @@ def load_references(input_file, if normalize_reference: # initialize stemmer - stem = get_stemmer_func(language) + langcode = langcodes.get(language.replace('en', 'xx'), 'porter') + stemmer = SnowballStemmer(langcode) for doc_id in references: for i, keyphrase in enumerate(references[doc_id]): - stems = [stem(w) for w in keyphrase.split()] + stems = [stemmer.stem(w) for w in keyphrase.split()] references[doc_id][i] = ' '.join(stems) # remove excluded file if needed @@ -413,7 +417,7 @@ def compute_lda_model(input_dir, # get the stoplist from nltk because CountVectorizer only contains english # stopwords atm tf_vectorizer = CountVectorizer( - stop_words=get_stopwords(language)) + stop_words=stopwords.get(language)) tf = tf_vectorizer.fit_transform(texts) # extract vocabulary diff --git a/requirements.txt b/requirements.txt index 63f063a1..5fc3a3cd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -spacy +spacy>3.1.0 nltk networkx numpy diff --git a/tests/sample.py b/tests/sample.py new file mode 100644 index 00000000..7743abe1 --- /dev/null +++ b/tests/sample.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -* + +import spacy + +nlp = spacy.load("en_core_web_sm") + +sample = """Inverse problems for a mathematical model of ion exchange in a compressible ion exchanger. +A mathematical model of ion exchange is considered, allowing for ion exchanger compression in the process +of ion exchange. Two inverse problems are investigated for this model, unique solvability is proved, and +numerical solution methods are proposed. The efficiency of the proposed methods is demonstrated by a +numerical experiment.""".replace("\n", " ") + +sample_doc = nlp(sample) + +sample_list = [[('Inverse', 'NOUN'), ('problems', 'NOUN'), ('for', 'ADP'), ('a', 'DET'), ('mathematical', 'ADJ'), + ('model', 'NOUN'), ('of', 'ADP'), ('ion', 'NOUN'), ('exchange', 'NOUN'), ('in', 'ADP'), ('a', 'DET'), + ('compressible', 'ADJ'), ('ion', 'NOUN'), ('exchanger', 'NOUN'), ('.', 'PUNCT')], + [('A', 'DET'), ('mathematical', 'ADJ'), ('model', 'NOUN'), ('of', 'ADP'), ('ion', 'NOUN'), + ('exchange', 'NOUN'), ('is', 'AUX'), ('considered', 'VERB'), (',', 'PUNCT'), ('allowing', 'VERB'), + ('for', 'ADP'), ('ion', 'NOUN'), ('exchanger', 'NOUN'), ('compression', 'NOUN'), ('in', 'ADP'), + ('the', 'DET'), ('process', 'NOUN'), ('of', 'ADP'), ('ion', 'NOUN'), ('exchange', 'NOUN'), + ('.', 'PUNCT')], + [('Two', 'NUM'), ('inverse', 'NOUN'), ('problems', 'NOUN'), ('are', 'AUX'), ('investigated', 'VERB'), + ('for', 'ADP'), ('this', 'DET'), ('model', 'NOUN'), (',', 'PUNCT'), ('unique', 'ADJ'), + ('solvability', 'NOUN'), ('is', 'AUX'), ('proved', 'VERB'), (',', 'PUNCT'), ('and', 'CCONJ'), + ('numerical', 'ADJ'), ('solution', 'NOUN'), ('methods', 'NOUN'), ('are', 'AUX'), ('proposed', 'VERB'), + ('.', 'PUNCT')], + [('The', 'DET'), ('efficiency', 'NOUN'), ('of', 'ADP'), ('the', 'DET'), ('proposed', 'VERB'), + ('methods', 'NOUN'), ('is', 'AUX'), ('demonstrated', 'VERB'), ('by', 'ADP'), ('a', 'DET'), + ('numerical', 'ADJ'), ('experiment', 'NOUN'), ('.', 'PUNCT')]] diff --git a/tests/test_reading.py b/tests/test_reading.py index 351b2786..d40cbf3f 100644 --- a/tests/test_reading.py +++ b/tests/test_reading.py @@ -1,40 +1,28 @@ -#!/usr/bin/env python # -*- coding: utf-8 -*- -import os import pke -import codecs -model = pke.unsupervised.TopicRank - -data_path = os.path.join('tests', 'data') -xml_test_file = data_path + os.sep + '1939.xml' -raw_test_file = data_path + os.sep + '1939.txt' +from .sample import sample, sample_doc, sample_list def test_reading(): - # loading XML input - extr1 = model() - extr1.load_document(xml_test_file) - - # loading txt input - extr2 = model() - extr2.load_document(raw_test_file) + # loading from string + extractor1 = pke.base.LoadFile() + extractor1.load_document(sample) # loading from string - extr3 = model() - with codecs.open(raw_test_file, 'r', 'utf-8') as f: - text = f.read() - extr3.load_document(text) + extractor2 = pke.base.LoadFile() + extractor2.load_document(sample_doc) - assert len(extr1.sentences) == 4 and extr2.sentences == extr3.sentences + # loading from preprocessed text + extractor3 = pke.base.LoadFile() + extractor3.load_document(sample_list) + assert len(extractor1.sentences) == 4 and extractor1.sentences == extractor2.sentences and \ + extractor2.sentences == extractor3.sentences and extractor1.sentences[0] == extractor2.sentences[0] and \ + extractor2.sentences[0] == extractor3.sentences[0] -def test_french_model(): - extr = model() - extr.load_document('est-ce') - assert '' not in extr.sentences[0].pos if __name__ == '__main__': - test_reading() \ No newline at end of file + test_reading() From f877d46c1af8e26dbcc320ba9a22d29aa41b99e6 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Thu, 3 Mar 2022 15:27:42 +0100 Subject: [PATCH 04/24] clean up tests and validate minimal subset of tests --- tests/test_firstphrases.py | 14 +-- tests/test_positionrank.py | 39 ------- tests/test_running.py | 52 --------- tests/test_singlerank.py | 37 ------ tests/test_singletpr.py | 39 ------- tests/test_textrank.py | 41 ------- tests/test_tfidf.py | 31 ----- tests/test_topicrank.py | 29 ----- tests/test_utils.py | 228 ------------------------------------- 9 files changed, 5 insertions(+), 505 deletions(-) delete mode 100644 tests/test_positionrank.py delete mode 100644 tests/test_running.py delete mode 100644 tests/test_singlerank.py delete mode 100644 tests/test_singletpr.py delete mode 100644 tests/test_textrank.py delete mode 100644 tests/test_tfidf.py delete mode 100644 tests/test_topicrank.py delete mode 100644 tests/test_utils.py diff --git a/tests/test_firstphrases.py b/tests/test_firstphrases.py index 4a0c0d81..0f90307a 100644 --- a/tests/test_firstphrases.py +++ b/tests/test_firstphrases.py @@ -1,29 +1,25 @@ -#!/usr/bin/env python # -*- coding: utf-8 -*- -from __future__ import unicode_literals - -import os import pke +from .sample import sample_list valid_pos = {'NOUN', 'PROPN', 'ADJ'} -test_file = os.path.join('tests', 'data', '1939.xml') def test_firstphrases_candidate_selection(): extractor = pke.unsupervised.FirstPhrases() - extractor.load_document(test_file) + extractor.load_document(input=sample_list) extractor.candidate_selection(pos=valid_pos) - assert len(extractor.candidates) == 20 + assert len(extractor.candidates) == 12 def test_firstphrases_candidate_weighting(): extractor = pke.unsupervised.FirstPhrases() - extractor.load_document(test_file) + extractor.load_document(input=sample_list) extractor.candidate_selection(pos=valid_pos) extractor.candidate_weighting() keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['compatibility', 'systems', 'linear constraints'] + assert keyphrases == ['inverse problems', 'mathematical model', 'ion exchange'] if __name__ == '__main__': diff --git a/tests/test_positionrank.py b/tests/test_positionrank.py deleted file mode 100644 index 1e0c7457..00000000 --- a/tests/test_positionrank.py +++ /dev/null @@ -1,39 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import pke - -test_file = os.path.join('tests', 'data', '1939.xml') - -grammar = "NP: {*+}" -pos = {'NOUN', 'PROPN', 'ADJ'} - - -def test_positionrank_candidate_selection(): - """Test PositionRank candidate selection method.""" - - extractor = pke.unsupervised.PositionRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(grammar=grammar) - assert len(extractor.candidates) == 19 - - -def test_positionrank_candidate_weighting(): - """Test PositionRank candidate weighting method.""" - - extractor = pke.unsupervised.PositionRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(grammar=grammar) - extractor.candidate_weighting(window=10, pos=pos) - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['minimal supporting set', - 'linear diophantine equations', - 'minimal set'] - - -if __name__ == '__main__': - test_positionrank_candidate_selection() - test_positionrank_candidate_weighting() \ No newline at end of file diff --git a/tests/test_running.py b/tests/test_running.py deleted file mode 100644 index ad383b49..00000000 --- a/tests/test_running.py +++ /dev/null @@ -1,52 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import os -from pke.unsupervised import ( - TopicRank, SingleRank, - MultipartiteRank, PositionRank, - TopicalPageRank, ExpandRank, - TextRank, TfIdf, KPMiner, - YAKE, FirstPhrases -) -from pke.supervised import Kea, WINGNUS - -test_file = os.path.join('tests', 'data', '1939.xml') - -def test_unsupervised_run(): - def test(model, file): - extractor = model() - extractor.load_document(file) - extractor.candidate_selection() - extractor.candidate_weighting() - - models = [ - TopicRank, SingleRank, - MultipartiteRank, PositionRank, - TopicalPageRank, ExpandRank, - TextRank, TfIdf, KPMiner, - YAKE, FirstPhrases - ] - for m in models: - print("testing {}".format(m)) - test(m, test_file) - - -def test_supervised_run(): - def test(model, file): - extractor = model() - extractor.load_document(file) - extractor.candidate_selection() - extractor.candidate_weighting() - - models = [ - Kea, WINGNUS - ] - for m in models: - print("testing {}".format(m)) - test(m, test_file) - - -if __name__ == '__main__': - test_unsupervised_run() - test_supervised_run() \ No newline at end of file diff --git a/tests/test_singlerank.py b/tests/test_singlerank.py deleted file mode 100644 index 83d06a9f..00000000 --- a/tests/test_singlerank.py +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import pke - -test_file = os.path.join('tests', 'data', '1939.xml') -pos = {'NOUN', 'PROPN', 'ADJ'} - - -def test_singlerank_candidate_selection(): - """Test SingleRank candidate selection method.""" - - extractor = pke.unsupervised.SingleRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(pos=pos) - assert len(extractor.candidates) == 20 - - -def test_singlerank_candidate_weighting(): - """Test SingleRank candidate weighting method.""" - - extractor = pke.unsupervised.SingleRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(pos=pos) - extractor.candidate_weighting(window=10, pos=pos) - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['minimal supporting set', - 'minimal set', - 'linear diophantine equations'] - - -if __name__ == '__main__': - test_singlerank_candidate_selection() - test_singlerank_candidate_weighting() diff --git a/tests/test_singletpr.py b/tests/test_singletpr.py deleted file mode 100644 index 8882e33a..00000000 --- a/tests/test_singletpr.py +++ /dev/null @@ -1,39 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import pke - -test_file = os.path.join('tests', 'data', '1939.xml') -grammar = "NP: {*+}" -pos = {'NOUN', 'PROPN', 'ADJ'} - - -def test_topicalpagerank_candidate_selection(): - """Test Single Topical PageRank candidate selection method.""" - - extractor = pke.unsupervised.TopicalPageRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(grammar=grammar) - assert len(extractor.candidates) == 19 - - -def test_topicalpagerank_candidate_weighting(): - """Test Single Topical PageRank weighting method.""" - - extractor = pke.unsupervised.TopicalPageRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(grammar=grammar) - extractor.candidate_weighting(window=10, pos=pos) - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['minimal supporting set', - 'minimal set', - - 'linear diophantine equations'] - - -if __name__ == '__main__': - test_topicalpagerank_candidate_selection() - test_topicalpagerank_candidate_weighting() \ No newline at end of file diff --git a/tests/test_textrank.py b/tests/test_textrank.py deleted file mode 100644 index 62e7b612..00000000 --- a/tests/test_textrank.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import pke - -test_file = os.path.join('tests', 'data', '1939.xml') -pos = {'NOUN', 'PROPN', 'ADJ'} - - -def test_textrank(): - """Test TextRank for keyword extraction using original paper's example.""" - - extractor = pke.unsupervised.TextRank() - extractor.load_document(input=test_file) - extractor.candidate_weighting(top_percent=.33, pos=pos) - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['linear diophantine', - 'upper bounds', - 'inequations'] - - -def test_textrank_with_candidate_selection(): - """Test TextRank with longest-POS-sequences candidate selection.""" - - extractor = pke.unsupervised.TextRank() - extractor.load_document(input=test_file) - extractor.candidate_selection(pos=pos) - extractor.candidate_weighting(pos=pos) - keyphrases = [k - for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['linear diophantine equations', - 'minimal supporting set', - 'nonstrict inequations'] - - -if __name__ == '__main__': - test_textrank() - test_textrank_with_candidate_selection() diff --git a/tests/test_tfidf.py b/tests/test_tfidf.py deleted file mode 100644 index b50b5272..00000000 --- a/tests/test_tfidf.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import unicode_literals - -import os -import pke - -test_file = os.path.join('tests', 'data', '1939.xml') - - -def test_tfidf_candidate_selection(): - extractor = pke.unsupervised.TfIdf() - extractor.load_document(test_file) - extractor.candidate_selection() - assert len(extractor.candidates) == 160 - - -def test_tfidf_candidate_weighting(): - extractor = pke.unsupervised.TfIdf() - extractor.load_document(test_file) - extractor.candidate_selection() - extractor.candidate_weighting() - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['set of solutions', 'systems of linear', - 'of solutions'] - - -if __name__ == '__main__': - test_tfidf_candidate_selection() - test_tfidf_candidate_weighting() diff --git a/tests/test_topicrank.py b/tests/test_topicrank.py deleted file mode 100644 index 2c2c199f..00000000 --- a/tests/test_topicrank.py +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import os -import pke - -valid_pos = {'NOUN', 'PROPN', 'ADJ'} -test_file = os.path.join('tests', 'data', '1939.xml') - - -def test_topicrank_candidate_selection(): - extractor = pke.unsupervised.TopicRank() - extractor.load_document(test_file) - extractor.candidate_selection(pos=valid_pos) - assert len(extractor.candidates) == 20 - - -def test_topicrank_candidate_weighting(): - extractor = pke.unsupervised.TopicRank() - extractor.load_document(test_file) - extractor.candidate_selection(pos=valid_pos) - extractor.candidate_weighting(threshold=0.74, method='average') - keyphrases = [k for k, s in extractor.get_n_best(n=3)] - assert keyphrases == ['set', 'systems', 'solutions'] - - -if __name__ == '__main__': - test_topicrank_candidate_selection() - test_topicrank_candidate_weighting() diff --git a/tests/test_utils.py b/tests/test_utils.py deleted file mode 100644 index a58042d8..00000000 --- a/tests/test_utils.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import os -import pke - -data_path = os.path.join('tests', 'data') - - -def create_corpus(values, tmp_path, name='corpus.gz'): - corpus_dir = tmp_path / name - corpus_dir.mkdir() - for k, v in values.items(): - (corpus_dir / k).write_text(v) - return corpus_dir - - -def create_df(corpus_dir, tmp_path, name='corpus_df.gz'): - corpus_df_file = tmp_path / name - pke.utils.compute_document_frequency( - str(corpus_dir), str(corpus_df_file), extension='txt', n=1) - corpus_df = pke.utils.load_document_frequency_file(str(corpus_df_file)) - return corpus_df, corpus_df_file - - -def load_pairwise_similarities(path): - from collections import defaultdict - import gzip - import bisect - """Load the pairwise similarities for ExpandRank.""" - - pairwise_sim = defaultdict(list) - with gzip.open(path, 'rb') as f: - lines = f.readlines() - for line in lines: - cols = line.decode('utf-8').strip().split() - cols[0] = os.path.basename(cols[0]) - cols[1] = os.path.basename(cols[1]) - # Add (score, file1) to pairwise_sim[file0] - # while ensuring that duplicate element are next to eahch other ? - bisect.insort(pairwise_sim[cols[0]], (float(cols[2]), cols[1])) - bisect.insort(pairwise_sim[cols[1]], (float(cols[2]), cols[0])) - return pairwise_sim - - -def test_load_reference(): - """Various tests for loading a reference file.""" - - id = 'C-41' - - g1 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.json', - normalize_reference=True, - language="en", - encoding='utf-8') - - g2 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.stem.json', - normalize_reference=False, - language="en", - encoding='utf-8') - - g3 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.final', - normalize_reference=True, - language="en", - encoding='utf-8') - - g4 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.stem.final', - normalize_reference=False, - language="en", - encoding='utf-8') - - assert set(g1[id]) == set(g2[id]) == set(g3[id]) == set(g4[id]) - - -# TODO: test load_document_frequency_file ? As it is used to test -# compute_document_frequency - -def test_compute_document_frequency(tmp_path): - from collections import Counter - # tmp_path is a Path object provided automatically by pytest - - # Create a corpus - corpus = {'a.txt': 'lorem sit amet', 'b.txt': 'lorem ipsum'} - tmp_corpus = create_corpus(corpus, tmp_path) - - # Create expected value - expected = Counter([t for v in corpus.values() for t in v.split()]) - expected['--NB_DOC--'] = len(corpus) - - # Compute document frequency - tmp_freq = tmp_path / 'tmp_doc_freq.tsv.gz' - pke.utils.compute_document_frequency( - str(tmp_corpus), str(tmp_freq), extension='txt', n=1) - - # Asserting - df = pke.utils.load_document_frequency_file(str(tmp_freq)) - assert df == expected - - -def test_compute_lda(tmp_path): - import gzip - import pickle - - # Create a corpus - corpus = {'a.txt': 'lorem sit amet', 'b.txt': 'lorem ipsum'} - tmp_corpus = create_corpus(corpus, tmp_path) - - # Create expected value - expected_dict = set(t for v in corpus.values() for t in v.split()) - - # Compute LDA topics - tmp_lda = tmp_path / 'lda.pickle.gz' - pke.utils.compute_lda_model( - str(tmp_corpus), str(tmp_lda), n_topics=2, extension='txt') - - # Asserting - with gzip.open(tmp_lda, 'rb') as f: - (dictionary, _, _, _) = pickle.load(f) - assert sorted(dictionary) == sorted(expected_dict) - - -def test_load_document_as_bos(tmp_path): - - # Create file - tmp_file = tmp_path / 'tmp_file.txt' - tmp_file.write_text('lorem ipsum sit amet') - - # Create expected value - expected = {'lorem': 1, 'ipsum': 1, 'sit': 1, 'amet': 1} - - # Compute bag of stem - bos = pke.utils.load_document_as_bos(str(tmp_file)) - - assert bos == expected - - -def test_compute_pairwise_sim_one_corpus(tmp_path): - # TODO: test with/without collection_dir and with/without df - # Create a corpus - corpus = {'a.txt': 'lorem sit amet', 'b.txt': 'lorem ipsum'} - corpus_dir = create_corpus(corpus, tmp_path) - corpus_df, _ = create_df(corpus_dir, tmp_path) - - # Create expected value - expected = {k: sorted([k2 for k2 in corpus if k2 != k]) for k in corpus} - - # Compute pairwise similarity - pairw_file = tmp_path / 'pairwise.gz' - pke.utils.compute_pairwise_similarity_matrix( - str(corpus_dir), str(pairw_file), extension='txt', - collection_dir=None, df=corpus_df) # TODO: remove and fix "df={}" - pairw = load_pairwise_similarities(str(pairw_file)) - - # Asserting - # Removing score from Dict[File, (Score, File)] - pairw = {k: sorted([f for _, f in v]) for k, v in pairw.items()} - - assert pairw == expected - - -def test_compute_pairwise_sim_two_corpus(tmp_path): - # TODO: test with/without collection_dir and with/without df - # Create a corpus - corpus = {'a.txt': 'lorem sit amet', 'b.txt': 'lorem ipsum'} - corpus_dir = create_corpus(corpus, tmp_path) - corpus_df, _ = create_df(corpus_dir, tmp_path) - - # Create a collection - collection = {'1.txt': 'sit ipsum', '2.txt': 'lorem ipsum sit amet'} - collection_dir = create_corpus(collection, tmp_path, name='collection') - - # Create expected value - expected = {k: sorted([k2 for k2 in collection]) for k in corpus} - expected.update({k: sorted([k2 for k2 in corpus]) for k in collection}) - - # Compute pairwise similarity - tmp_pairw = tmp_path / 'pairwise.gz' - pke.utils.compute_pairwise_similarity_matrix( - str(corpus_dir), str(tmp_pairw), extension='txt', - collection_dir=str(collection_dir), df=corpus_df) - pairw = load_pairwise_similarities(str(tmp_pairw)) - - # Asserting - # Removing score from Dict[File, (Score, File)] - pairw = {k: sorted([f for _, f in v]) for k, v in pairw.items()} - - assert pairw == expected - - -def test_train_supervised_model(tmp_path): - # TODO : test with/without leave_one_out - # Create a corpus - corpus = {'a.txt': 'lorem sit amet', 'b.txt': 'lorem ipsum'} - tmp_corpus = create_corpus(corpus, tmp_path) - - # Create a reference - tmp_ref = tmp_path / 'ref.json' - tmp_ref.write_text( - '{"a": [["lorem"]], "b": [["lorem ipsum"]]}' - ) - - tmp_model = tmp_path / 'model.pickle' - pke.utils.train_supervised_model( - str(tmp_corpus), str(tmp_ref), str(tmp_model), - extension='txt', df=None, leave_one_out=False, - model=pke.supervised.Kea()) # TODO: fix doc for model param - - -def test_train_supervised_model_leave_one_out(tmp_path): - # TODO : test with/without leave_one_out - # Create a corpus - corpus = {'a.txt': 'lorem sit amet', 'b.txt': 'lorem ipsum'} - tmp_corpus = create_corpus(corpus, tmp_path) - - # Create a reference - tmp_ref = tmp_path / 'ref.json' - tmp_ref.write_text( - '{"a": [["lorem"]], "b": [["ipsum"]]}' - ) - - tmp_model = tmp_path / 'model.pickle' - pke.utils.train_supervised_model( - str(tmp_corpus), str(tmp_ref), str(tmp_model), - extension='txt', df=None, leave_one_out=True, - model=pke.supervised.Kea()) # TODO: fix doc for model param - - -if __name__ == '__main__': - test_load_reference() From f3a1f736a737550230766690d63be9362d3d9551 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Thu, 3 Mar 2022 16:53:08 +0100 Subject: [PATCH 05/24] Fourth blood --- pke/langcodes.py | 207 ------------------ pke/readers.py | 1 - .../graph_based/multipartiterank.py | 2 +- pke/unsupervised/graph_based/singlerank.py | 2 +- pke/unsupervised/graph_based/textrank.py | 2 +- pke/unsupervised/graph_based/topicrank.py | 2 +- 6 files changed, 4 insertions(+), 212 deletions(-) delete mode 100644 pke/langcodes.py diff --git a/pke/langcodes.py b/pke/langcodes.py deleted file mode 100644 index 246638a7..00000000 --- a/pke/langcodes.py +++ /dev/null @@ -1,207 +0,0 @@ -# Extracted from https://github.com/LuminosoInsight/langcodes/blob/master/langcodes/data/language-subtag-registry.txt -LANGUAGES = [ - {'code':'aa', 'name':'Afar'}, - {'code':'ab', 'name':'Abkhazian'}, - {'code':'ae', 'name':'Avestan'}, - {'code':'af', 'name':'Afrikaans'}, - {'code':'ak', 'name':'Akan'}, - {'code':'am', 'name':'Amharic'}, - {'code':'an', 'name':'Aragonese'}, - {'code':'ar', 'name':'Arabic'}, - {'code':'as', 'name':'Assamese'}, - {'code':'av', 'name':'Avaric'}, - {'code':'ay', 'name':'Aymara'}, - {'code':'az', 'name':'Azerbaijani'}, - {'code':'ba', 'name':'Bashkir'}, - {'code':'be', 'name':'Belarusian'}, - {'code':'bg', 'name':'Bulgarian'}, - {'code':'bh', 'name':'Bihari'}, - {'code':'bi', 'name':'Bislama'}, - {'code':'bm', 'name':'Bambara'}, - {'code':'bn', 'name':'Bengali, Bangla'}, - {'code':'bo', 'name':'Tibetan'}, - {'code':'br', 'name':'Breton'}, - {'code':'bs', 'name':'Bosnian'}, - {'code':'ca', 'name':'Catalan, Valencian'}, - {'code':'ce', 'name':'Chechen'}, - {'code':'ch', 'name':'Chamorro'}, - {'code':'co', 'name':'Corsican'}, - {'code':'cr', 'name':'Cree'}, - {'code':'cs', 'name':'Czech'}, - {'code':'cu', 'name':'Church Slavic, Old Slavonic, Church Slavonic, Old Bulgarian, Old Church Slavonic'}, - {'code':'cv', 'name':'Chuvash'}, - {'code':'cy', 'name':'Welsh'}, - {'code':'da', 'name':'Danish'}, - {'code':'de', 'name':'German'}, - {'code':'dv', 'name':'Divehi, Dhivehi, Maldivian'}, - {'code':'dz', 'name':'Dzongkha'}, - {'code':'ee', 'name':'Ewe'}, - {'code':'el', 'name':'Greek, Modern Greek'}, - {'code':'en', 'name':'English'}, - {'code':'eo', 'name':'Esperanto'}, - {'code':'es', 'name':'Spanish, Castilian'}, - {'code':'et', 'name':'Estonian'}, - {'code':'eu', 'name':'Basque'}, - {'code':'fa', 'name':'Persian'}, - {'code':'ff', 'name':'Fulah'}, - {'code':'fi', 'name':'Finnish'}, - {'code':'fj', 'name':'Fijian'}, - {'code':'fo', 'name':'Faroese'}, - {'code':'fr', 'name':'French'}, - {'code':'fy', 'name':'Western Frisian'}, - {'code':'ga', 'name':'Irish'}, - {'code':'gd', 'name':'Gaelic, Scottish Gaelic'}, - {'code':'gl', 'name':'Galician'}, - {'code':'gn', 'name':'Guarani, Guaraní'}, - {'code':'gu', 'name':'Gujarati'}, - {'code':'gv', 'name':'Manx'}, - {'code':'ha', 'name':'Hausa'}, - {'code':'he', 'name':'Hebrew, Modern Hebrew'}, - {'code':'hi', 'name':'Hindi'}, - {'code':'ho', 'name':'Hiri Motu'}, - {'code':'hr', 'name':'Croatian'}, - {'code':'ht', 'name':'Haitian, Haitian Creole'}, - {'code':'hu', 'name':'Hungarian'}, - {'code':'hy', 'name':'Armenian'}, - {'code':'hz', 'name':'Herero'}, - {'code':'ia', 'name':'Interlingua'}, - {'code':'id', 'name':'Indonesian'}, - {'code':'ie', 'name':'Interlingue, Occidental'}, - {'code':'ig', 'name':'Igbo'}, - {'code':'ii', 'name':'Sichuan Yi, Nuosu'}, - {'code':'ik', 'name':'Inupiaq'}, - {'code':'in', 'name':'Indonesian'}, - {'code':'io', 'name':'Ido'}, - {'code':'is', 'name':'Icelandic'}, - {'code':'it', 'name':'Italian'}, - {'code':'iu', 'name':'Inuktitut'}, - {'code':'iw', 'name':'Hebrew'}, - {'code':'ja', 'name':'Japanese'}, - {'code':'ji', 'name':'Yiddish'}, - {'code':'jv', 'name':'Javanese'}, - {'code':'jw', 'name':'Javanese'}, - {'code':'ka', 'name':'Georgian'}, - {'code':'kg', 'name':'Kongo'}, - {'code':'ki', 'name':'Kikuyu, Gikuyu'}, - {'code':'kj', 'name':'Kwanyama, Kuanyama'}, - {'code':'kk', 'name':'Kazakh'}, - {'code':'kl', 'name':'Kalaallisut, Greenlandic'}, - {'code':'km', 'name':'Central Khmer'}, - {'code':'kn', 'name':'Kannada'}, - {'code':'ko', 'name':'Korean'}, - {'code':'kr', 'name':'Kanuri'}, - {'code':'ks', 'name':'Kashmiri'}, - {'code':'ku', 'name':'Kurdish'}, - {'code':'kv', 'name':'Komi'}, - {'code':'kw', 'name':'Cornish'}, - {'code':'ky', 'name':'Kirghiz, Kyrgyz'}, - {'code':'la', 'name':'Latin'}, - {'code':'lb', 'name':'Luxembourgish, Letzeburgesch'}, - {'code':'lg', 'name':'Luganda, Ganda'}, - {'code':'li', 'name':'Limburgish, Limburgan, Limburger'}, - {'code':'ln', 'name':'Lingala'}, - {'code':'lo', 'name':'Lao'}, - {'code':'lt', 'name':'Lithuanian'}, - {'code':'lu', 'name':'Luba-Katanga'}, - {'code':'lv', 'name':'Latvian'}, - {'code':'mg', 'name':'Malagasy'}, - {'code':'mh', 'name':'Marshallese'}, - {'code':'mi', 'name':'Maori, Māori'}, - {'code':'mk', 'name':'Macedonian'}, - {'code':'ml', 'name':'Malayalam'}, - {'code':'mn', 'name':'Mongolian'}, - {'code':'mo', 'name':'Moldovan'}, - {'code':'mr', 'name':'Marathi'}, - {'code':'ms', 'name':'Malay'}, - {'code':'mt', 'name':'Maltese'}, - {'code':'my', 'name':'Burmese'}, - {'code':'na', 'name':'Nauru'}, - {'code':'nb', 'name':'Norwegian Bokmål'}, - {'code':'nd', 'name':'North Ndebele'}, - {'code':'ne', 'name':'Nepali'}, - {'code':'ng', 'name':'Ndonga'}, - {'code':'nl', 'name':'Dutch, Flemish'}, - {'code':'nn', 'name':'Norwegian Nynorsk'}, - {'code':'no', 'name':'Norwegian'}, - {'code':'nr', 'name':'South Ndebele'}, - {'code':'nv', 'name':'Navaho, Navajo'}, - {'code':'ny', 'name':'Chichewa, Chewa, Nyanja'}, - {'code':'oc', 'name':'Occitan'}, - {'code':'oj', 'name':'Ojibwa'}, - {'code':'om', 'name':'Oromo'}, - {'code':'or', 'name':'Odia, Oriya'}, - {'code':'os', 'name':'Ossetian, Ossetic'}, - {'code':'pa', 'name':'Panjabi, Punjabi'}, - {'code':'pi', 'name':'Pali, Pāli'}, - {'code':'pl', 'name':'Polish'}, - {'code':'ps', 'name':'Pashto, Pushto'}, - {'code':'pt', 'name':'Portuguese'}, - {'code':'qu', 'name':'Quechua'}, - {'code':'rm', 'name':'Romansh'}, - {'code':'rn', 'name':'Rundi'}, - {'code':'ro', 'name':'Romanian, Moldavian, Moldovan'}, - {'code':'ru', 'name':'Russian'}, - {'code':'rw', 'name':'Kinyarwanda'}, - {'code':'sa', 'name':'Sanskrit'}, - {'code':'sc', 'name':'Sardinian'}, - {'code':'sd', 'name':'Sindhi'}, - {'code':'se', 'name':'Northern Sami'}, - {'code':'sg', 'name':'Sango'}, - {'code':'sh', 'name':'Serbo-Croatian'}, - {'code':'si', 'name':'Sinhala, Sinhalese'}, - {'code':'sk', 'name':'Slovak'}, - {'code':'sl', 'name':'Slovenian, Slovene'}, - {'code':'sm', 'name':'Samoan'}, - {'code':'sn', 'name':'Shona'}, - {'code':'so', 'name':'Somali'}, - {'code':'sq', 'name':'Albanian'}, - {'code':'sr', 'name':'Serbian'}, - {'code':'ss', 'name':'Swati'}, - {'code':'st', 'name':'Southern Sotho'}, - {'code':'su', 'name':'Sundanese'}, - {'code':'sv', 'name':'Swedish'}, - {'code':'sw', 'name':'Swahili'}, - {'code':'ta', 'name':'Tamil'}, - {'code':'te', 'name':'Telugu'}, - {'code':'tg', 'name':'Tajik'}, - {'code':'th', 'name':'Thai'}, - {'code':'ti', 'name':'Tigrinya'}, - {'code':'tk', 'name':'Turkmen'}, - {'code':'tl', 'name':'Tagalog'}, - {'code':'tn', 'name':'Tswana'}, - {'code':'to', 'name':'Tonga'}, - {'code':'tr', 'name':'Turkish'}, - {'code':'ts', 'name':'Tsonga'}, - {'code':'tt', 'name':'Tatar'}, - {'code':'tw', 'name':'Twi'}, - {'code':'ty', 'name':'Tahitian'}, - {'code':'ug', 'name':'Uighur, Uyghur'}, - {'code':'uk', 'name':'Ukrainian'}, - {'code':'ur', 'name':'Urdu'}, - {'code':'uz', 'name':'Uzbek'}, - {'code':'ve', 'name':'Venda'}, - {'code':'vi', 'name':'Vietnamese'}, - {'code':'vo', 'name':'Volapük'}, - {'code':'wa', 'name':'Walloon'}, - {'code':'wo', 'name':'Wolof'}, - {'code':'xh', 'name':'Xhosa'}, - {'code':'yi', 'name':'Yiddish'}, - {'code':'yo', 'name':'Yoruba'}, - {'code':'za', 'name':'Zhuang, Chuang'}, - {'code':'zh', 'name':'Chinese'}, - {'code':'zu', 'name':'Zulu'} -] - -def parse_language_names(s): - return [i.strip().lower() for i in s.split(',')] - -LANGUAGE_CODES = { - item['code']: parse_language_names(item['name']) - for item in LANGUAGES -} - -LANGUAGE_CODE_BY_NAME = {} - -for k, v in LANGUAGE_CODES.items(): - for name in v: - LANGUAGE_CODE_BY_NAME[name] = k diff --git a/pke/readers.py b/pke/readers.py index fdb4ae2e..48d30b48 100644 --- a/pke/readers.py +++ b/pke/readers.py @@ -3,7 +3,6 @@ """Readers for the pke module.""" -import sys import logging import spacy diff --git a/pke/unsupervised/graph_based/multipartiterank.py b/pke/unsupervised/graph_based/multipartiterank.py index 5f2c4f9d..b3f59cbe 100644 --- a/pke/unsupervised/graph_based/multipartiterank.py +++ b/pke/unsupervised/graph_based/multipartiterank.py @@ -234,4 +234,4 @@ def candidate_weighting(self, self.weight_adjustment(alpha) # compute the word scores using random walk - self.weights = nx.pagerank_scipy(self.graph) + self.weights = nx.pagerank(self.graph) diff --git a/pke/unsupervised/graph_based/singlerank.py b/pke/unsupervised/graph_based/singlerank.py index 44c45587..87a4e430 100644 --- a/pke/unsupervised/graph_based/singlerank.py +++ b/pke/unsupervised/graph_based/singlerank.py @@ -125,7 +125,7 @@ def candidate_weighting(self, window=10, pos=None, normalized=False): self.build_word_graph(window=window, pos=pos) # compute the word scores using random walk - w = nx.pagerank_scipy(self.graph, + w = nx.pagerank(self.graph, alpha=0.85, tol=0.0001, weight='weight') diff --git a/pke/unsupervised/graph_based/textrank.py b/pke/unsupervised/graph_based/textrank.py index 7aab5f2f..9db89c02 100644 --- a/pke/unsupervised/graph_based/textrank.py +++ b/pke/unsupervised/graph_based/textrank.py @@ -155,7 +155,7 @@ def candidate_weighting(self, self.build_word_graph(window=window, pos=pos) # compute the word scores using the unweighted PageRank formulae - w = nx.pagerank_scipy(self.graph, alpha=0.85, tol=0.0001, weight=None) + w = nx.pagerank(self.graph, alpha=0.85, tol=0.0001, weight=None) # generate the phrases from the T-percent top ranked words if top_percent is not None: diff --git a/pke/unsupervised/graph_based/topicrank.py b/pke/unsupervised/graph_based/topicrank.py index fc90db5d..23606a06 100644 --- a/pke/unsupervised/graph_based/topicrank.py +++ b/pke/unsupervised/graph_based/topicrank.py @@ -223,7 +223,7 @@ def candidate_weighting(self, self.build_topic_graph() # compute the word scores using random walk - w = nx.pagerank_scipy(self.graph, alpha=0.85, weight='weight') + w = nx.pagerank(self.graph, alpha=0.85, weight='weight') # loop through the topics for i, topic in enumerate(self.topics): From 7c5ef0e06d5ddce97696ff0ba46abed14c54c9d7 Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 17:09:03 +0100 Subject: [PATCH 06/24] Removing reminiscence of python2 compat --- pke/unsupervised/graph_based/single_tpr.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/pke/unsupervised/graph_based/single_tpr.py b/pke/unsupervised/graph_based/single_tpr.py index 9b7d08e1..0849c651 100644 --- a/pke/unsupervised/graph_based/single_tpr.py +++ b/pke/unsupervised/graph_based/single_tpr.py @@ -22,7 +22,6 @@ import networkx as nx import numpy as np -import six from scipy.spatial.distance import cosine from sklearn.feature_extraction.text import CountVectorizer @@ -137,12 +136,8 @@ def candidate_weighting(self, # set the default LDA model if none provided if lda_model is None: - if six.PY2: - lda_model = os.path.join(self._models, - "lda-1000-semeval2010.py2.pickle.gz") - else: - lda_model = os.path.join(self._models, - "lda-1000-semeval2010.py3.pickle.gz") + lda_model = os.path.join(self._models, + "lda-1000-semeval2010.py3.pickle.gz") logging.warning('LDA model is hard coded to {}'.format(lda_model)) # load parameters from file From 03942ae2df882a16012bd9984bebc45347071cef Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 18:10:41 +0100 Subject: [PATCH 07/24] Stopword code cleaning Stopword are only loaded when loading the document --- .travis.yml | 1 - README.md | 1 - .../compute-pairwise-similarity-matrix.py | 14 +----- pke/base.py | 19 ++++---- pke/supervised/feature_based/kea.py | 26 ++++------- pke/supervised/feature_based/wingnus.py | 3 +- .../graph_based/multipartiterank.py | 12 ++--- pke/unsupervised/graph_based/single_tpr.py | 10 +---- pke/unsupervised/graph_based/topicrank.py | 32 +++++-------- pke/unsupervised/statistical/tfidf.py | 21 ++++----- pke/unsupervised/statistical/yake.py | 45 +++++++------------ pke/utils.py | 39 ++++++++++------ 12 files changed, 88 insertions(+), 135 deletions(-) diff --git a/.travis.yml b/.travis.yml index 3f4e3e5f..de399250 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,7 +5,6 @@ python: # command to install dependencies install: - pip install -r requirements.txt - - python -m nltk.downloader stopwords - python -m nltk.downloader universal_tagset - python -m spacy download en_core_web_sm - python -m pip install cython diff --git a/README.md b/README.md index ce6a42fb..735f1805 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,6 @@ pip install git+https://github.com/boudinfl/pke.git `pke` also requires external resources that can be obtained using: ```bash -python -m nltk.downloader stopwords python -m nltk.downloader universal_tagset python -m spacy download en_core_web_sm # download the english model ``` diff --git a/examples/compute-pairwise-similarity-matrix.py b/examples/compute-pairwise-similarity-matrix.py index 51a9e10d..e6101a99 100644 --- a/examples/compute-pairwise-similarity-matrix.py +++ b/examples/compute-pairwise-similarity-matrix.py @@ -10,9 +10,8 @@ import string import logging -from nltk.corpus import stopwords - from pke import load_document_frequency_file, compute_pairwise_similarity_matrix +from pke.lang import stopwords # setting info in terminal logging.basicConfig(level=logging.INFO) @@ -34,8 +33,7 @@ # stoplist for terms in document vectors stoplist = list(string.punctuation) -stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] -stoplist += stopwords.words('english') +stoplist += stopwords.get('en') # compute the pairwise similarity measures and write output compute_pairwise_similarity_matrix(input_dir=input_dir, @@ -46,11 +44,3 @@ language="en", normalization="stemming", stoplist=stoplist) - - - - - - - - diff --git a/pke/base.py b/pke/base.py index 62f7db59..74bb5b17 100644 --- a/pke/base.py +++ b/pke/base.py @@ -51,12 +51,14 @@ def __init__(self): self.stoplist = None """List of stopwords.""" - def load_document(self, input, **kwargs): + def load_document(self, input, stoplist=None, **kwargs): """Loads the content of a document/string/stream in a given language. Args: input (str): input. language (str): language of the input, defaults to 'en'. + stoplist (list): custom list of stopwords, defaults to + pke.lang.stopwords[language]. encoding (str): encoding of the raw file. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' @@ -92,7 +94,10 @@ def load_document(self, input, **kwargs): self.sentences = doc.sentences # initialize the stoplist - self.stoplist = stopwords.get(self.language) + if stoplist: + self.stoplist = stoplist + else: + self.stoplist = stopwords.get(self.language) # word normalization self.normalization = kwargs.get('normalization', 'stemming') @@ -356,7 +361,6 @@ def _is_alphanum(word, valid_punctuation_marks='-'): return word.isalnum() def candidate_filtering(self, - stoplist=None, minimum_length=3, minimum_word_size=2, valid_punctuation_marks='-', @@ -367,9 +371,8 @@ def candidate_filtering(self, keep the candidates containing alpha-numeric characters (if the non_latin_filter is set to True) and those length exceeds a given number of characters. - + Args: - stoplist (list): list of strings, defaults to None. minimum_length (int): minimum number of characters for a candidate, defaults to 3. minimum_word_size (int): minimum number of characters for a @@ -384,9 +387,6 @@ def candidate_filtering(self, candidates, defaults to []. """ - if stoplist is None: - stoplist = [] - if pos_blacklist is None: pos_blacklist = [] @@ -400,7 +400,8 @@ def candidate_filtering(self, words = [u.lower() for u in v.surface_forms[0]] # discard if words are in the stoplist - if set(words).intersection(stoplist): + # TODO: shouldn't it be the stems ? + if set(words).intersection(self.stoplist): del self.candidates[k] # discard if tags are in the pos_blacklist diff --git a/pke/supervised/feature_based/kea.py b/pke/supervised/feature_based/kea.py index c0dc81d1..c936403a 100644 --- a/pke/supervised/feature_based/kea.py +++ b/pke/supervised/feature_based/kea.py @@ -36,23 +36,21 @@ class Kea(SupervisedLoadFile): Parameterized example:: import pke - from nltk.corpus import stopwords - - # define a list of stopwords - stoplist = stopwords.words('english') # 1. create a Kea extractor. extractor = pke.supervised.Kea() # 2. load the content of the document. + stoplist = pke.lang.stopwords.get('en') extractor.load_document(input='path/to/input', language='en', + stoplist=stoplist, normalization=None) # 3. select 1-3 grams that do not start or end with a stopword as # candidates. Candidates that contain punctuation marks as words # are discarded. - extractor.candidate_selection(stoplist=stoplist) + extractor.candidate_selection() # 4. classify candidates as keyphrase or not keyphrase. df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz') @@ -69,26 +67,20 @@ def __init__(self): super(Kea, self).__init__() - def candidate_selection(self, stoplist=None, **kwargs): + def candidate_selection(self): """Select 1-3 grams of `normalized` words as keyphrase candidates. Candidates that start or end with a stopword are discarded. Candidates that contain punctuation marks (from `string.punctuation`) as words are filtered out. - - Args: - stoplist (list): the stoplist for filtering candidates, defaults - to the nltk stoplist. """ # select ngrams from 1 to 3 grams self.ngram_selection(n=3) - # filter candidates containing punctuation marks - self.candidate_filtering(list(string.punctuation)) - - # initialize stoplist list if not provided - if stoplist is None: - stoplist = self.stoplist + # filter candidates + self.candidate_filtering() + # TODO: is filtering only candidate with punctuation mandatory ? + #self.candidate_filtering(list(string.punctuation)) # filter candidates that start or end with a stopword for k in list(self.candidates): @@ -98,7 +90,7 @@ def candidate_selection(self, stoplist=None, **kwargs): # delete if candidate contains a stopword in first/last position words = [u.lower() for u in v.surface_forms[0]] - if words[0] in stoplist or words[-1] in stoplist: + if words[0] in self.stoplist or words[-1] in self.stoplist: del self.candidates[k] def feature_extraction(self, df=None, training=False): diff --git a/pke/supervised/feature_based/wingnus.py b/pke/supervised/feature_based/wingnus.py index dfb34c33..fbe3aaeb 100644 --- a/pke/supervised/feature_based/wingnus.py +++ b/pke/supervised/feature_based/wingnus.py @@ -135,7 +135,6 @@ def feature_extraction(self, df=None, training=False, features_set=None): # [F3] -> term frequency of substrings tf_of_substrings = 0 - stoplist = self.stoplist for i in range(len(v.lexical_form)): for j in range(i, min(len(v.lexical_form), i + 3)): sub_words = v.lexical_form[i:j + 1] @@ -146,7 +145,7 @@ def feature_extraction(self, df=None, training=False, features_set=None): continue # skip if substring contains a stopword - if set(sub_words).intersection(stoplist): + if set(sub_words).intersection(self.stoplist): continue # check whether the substring occurs "as it" diff --git a/pke/unsupervised/graph_based/multipartiterank.py b/pke/unsupervised/graph_based/multipartiterank.py index b3f59cbe..3e410fcb 100644 --- a/pke/unsupervised/graph_based/multipartiterank.py +++ b/pke/unsupervised/graph_based/multipartiterank.py @@ -34,21 +34,21 @@ class MultipartiteRank(TopicRank): import pke import string - from nltk.corpus import stopwords # 1. create a MultipartiteRank extractor. extractor = pke.unsupervised.MultipartiteRank() + stoplist = list(string.punctuation) + stoplist += pke.lang.stopwords.get('en') + # 2. load the content of the document. - extractor.load_document(input='path/to/input.xml') + extractor.load_document(input='path/to/input.xml', + stoplist=stoplist) # 3. select the longest sequences of nouns and adjectives, that do # not contain punctuation marks or stopwords as candidates. pos = {'NOUN', 'PROPN', 'ADJ'} - stoplist = list(string.punctuation) - stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] - stoplist += stopwords.words('english') - extractor.candidate_selection(pos=pos, stoplist=stoplist) + extractor.candidate_selection(pos=pos) # 4. build the Multipartite graph and rank candidates using random # walk, alpha controls the weight adjustment mechanism, see diff --git a/pke/unsupervised/graph_based/single_tpr.py b/pke/unsupervised/graph_based/single_tpr.py index 0849c651..5463de0f 100644 --- a/pke/unsupervised/graph_based/single_tpr.py +++ b/pke/unsupervised/graph_based/single_tpr.py @@ -35,7 +35,6 @@ class TopicalPageRank(SingleRank): Parameterized example:: import pke - from nltk.corpus import stopwords # define the valid Part-of-Speeches to occur in the graph pos = {'NOUN', 'PROPN', 'ADJ'} @@ -101,7 +100,6 @@ def candidate_weighting(self, window=10, pos=None, lda_model=None, - stoplist=None, normalized=False): """Candidate weight calculation using a biased PageRank towards LDA topic distributions. @@ -113,8 +111,6 @@ def candidate_weighting(self, nodes in the graph, defaults to ('NOUN', 'PROPN', 'ADJ'). lda_model (pickle.gz): an LDA model produced by sklearn in pickle compressed (.gz) format - stoplist (list): the stoplist for filtering words in LDA, defaults - to the nltk stoplist. normalized (False): normalize keyphrase score by their length, defaults to False. """ @@ -124,10 +120,6 @@ def candidate_weighting(self, if pos is None: pos = {'NOUN', 'PROPN', 'ADJ'} - # initialize stoplist list if not provided - if stoplist is None: - stoplist = self.stoplist - # build the word graph # ``Since keyphrases are usually noun phrases, we only add adjectives # and nouns in word graph.'' -> (Liu et al., 2010) @@ -149,7 +141,7 @@ def candidate_weighting(self, doc.extend([s.stems[i] for i in range(s.length)]) # vectorize document - tf_vectorizer = CountVectorizer(stop_words=stoplist, + tf_vectorizer = CountVectorizer(stop_words=self.stoplist, vocabulary=dictionary) tf = tf_vectorizer.fit_transform([' '.join(doc)]) diff --git a/pke/unsupervised/graph_based/topicrank.py b/pke/unsupervised/graph_based/topicrank.py index 23606a06..b1d908c6 100644 --- a/pke/unsupervised/graph_based/topicrank.py +++ b/pke/unsupervised/graph_based/topicrank.py @@ -16,7 +16,6 @@ from __future__ import division from __future__ import print_function -import string from itertools import combinations import networkx as nx @@ -34,21 +33,20 @@ class TopicRank(LoadFile): import pke import string - from nltk.corpus import stopwords # 1. create a TopicRank extractor. extractor = pke.unsupervised.TopicRank() # 2. load the content of the document. - extractor.load_document(input='path/to/input.xml') + stoplist = list(string.punctuation) + stoplist += pke.lang.stopwords.get('en') + extractor.load_document(input='path/to/input.xml', + stoplist=stoplist) # 3. select the longest sequences of nouns and adjectives, that do # not contain punctuation marks or stopwords as candidates. pos = {'NOUN', 'PROPN', 'ADJ'} - stoplist = list(string.punctuation) - stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] - stoplist += stopwords.words('english') - extractor.candidate_selection(pos=pos, stoplist=stoplist) + extractor.candidate_selection(pos=pos) # 4. build topics by grouping candidates with HAC (average linkage, # threshold of 1/4 of shared stems). Weight the topics using random @@ -72,17 +70,13 @@ def __init__(self): self.topics = [] """ The topic container. """ - def candidate_selection(self, pos=None, stoplist=None): + def candidate_selection(self, pos=None): """Selects longest sequences of nouns and adjectives as keyphrase candidates. Args: pos (set): the set of valid POS tags, defaults to ('NOUN', 'PROPN', 'ADJ'). - stoplist (list): the stoplist for filtering candidates, defaults to - the nltk stoplist. Words that are punctuation marks from - string.punctuation are not allowed. - """ # define default pos tags set @@ -92,16 +86,10 @@ def candidate_selection(self, pos=None, stoplist=None): # select sequence of adjectives and nouns self.longest_pos_sequence_selection(valid_pos=pos) - # initialize stoplist list if not provided - if stoplist is None: - stoplist = self.stoplist - - # filter candidates containing stopwords or punctuation marks - self.candidate_filtering(stoplist=( - list(string.punctuation) - + ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] - + stoplist - )) + # filter candidates containing stopwords + self.candidate_filtering() + # TODO: is filtering only candidate with punctuation mandatory ? + #self.candidate_filtering(list(string.punctuation)) def vectorize_candidates(self): """Vectorize the keyphrase candidates. diff --git a/pke/unsupervised/statistical/tfidf.py b/pke/unsupervised/statistical/tfidf.py index 18b8f4c0..278095f8 100644 --- a/pke/unsupervised/statistical/tfidf.py +++ b/pke/unsupervised/statistical/tfidf.py @@ -9,7 +9,6 @@ from __future__ import print_function import math -import string import logging from pke.base import LoadFile @@ -28,13 +27,15 @@ class TfIdf(LoadFile): extractor = pke.unsupervised.TfIdf() # 2. load the content of the document. + stoplist = list(string.punctuation) + stoplist += pke.lang.stopwords.get('en') extractor.load_document(input='path/to/input', language='en', + stoplist=stoplist, normalization=None) # 3. select {1-3}-grams not containing punctuation marks as candidates. - extractor.candidate_selection(n=3, - stoplist=list(string.punctuation)) + extractor.candidate_selection(n=3) # 4. weight the candidates using a `tf` x `idf` df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz') @@ -44,26 +45,20 @@ class TfIdf(LoadFile): keyphrases = extractor.get_n_best(n=10) """ - def candidate_selection(self, n=3, stoplist=None, **kwargs): + def candidate_selection(self, n=3): """Select 1-3 grams as keyphrase candidates. Args: n (int): the length of the n-grams, defaults to 3. - stoplist (list): the stoplist for filtering candidates, defaults to - `None`. Words that are punctuation marks from - `string.punctuation` are not allowed. - """ # select ngrams from 1 to 3 grams self.ngram_selection(n=n) - # initialize empty list if stoplist is not provided - if stoplist is None: - stoplist = list(string.punctuation) - # filter candidates containing punctuation marks - self.candidate_filtering(stoplist=stoplist) + self.candidate_filtering() + # TODO: is filtering only candidate with punctuation mandatory ? + #self.candidate_filtering(list(string.punctuation)) def candidate_weighting(self, df=None): """Candidate weighting function using document frequencies. diff --git a/pke/unsupervised/statistical/yake.py b/pke/unsupervised/statistical/yake.py index 87c2da46..2a9aa348 100644 --- a/pke/unsupervised/statistical/yake.py +++ b/pke/unsupervised/statistical/yake.py @@ -19,7 +19,6 @@ import math import re -import string from collections import defaultdict import numpy @@ -34,28 +33,28 @@ class YAKE(LoadFile): Parameterized example:: import pke - from nltk.corpus import stopwords + from pke.lang import stopwords # 1. create a YAKE extractor. extractor = pke.unsupervised.YAKE() # 2. load the content of the document. + stoplist = stopwords.get('english') extractor.load_document(input='path/to/input', language='en', + stoplist=stoplist, normalization=None) # 3. select {1-3}-grams not containing punctuation marks and not # beginning/ending with a stopword as candidates. - stoplist = stopwords.words('english') - extractor.candidate_selection(n=3, stoplist=stoplist) + extractor.candidate_selection(n=3) # 4. weight the candidates using YAKE weighting scheme, a window (in # words) for computing left/right contexts can be specified. window = 2 use_stems = False # use stems instead of words for weighting extractor.candidate_weighting(window=window, - stoplist=stoplist, use_stems=use_stems) # 5. get the 10-highest scored candidates as keyphrases. @@ -83,26 +82,22 @@ def __init__(self): self.surface_to_lexical = {} """ Mapping from surface form to lexical form. """ - def candidate_selection(self, n=3, stoplist=None, **kwargs): + def candidate_selection(self, n=3, **kwargs): """Select 1-3 grams as keyphrase candidates. Candidates beginning or ending with a stopword are filtered out. Words that do not contain at least one alpha-numeric character are not allowed. Args: n (int): the n-gram length, defaults to 3. - stoplist (list): the stoplist for filtering candidates, defaults to - the nltk stoplist. """ # select ngrams from 1 to 3 grams self.ngram_selection(n=n) # filter candidates containing punctuation marks - self.candidate_filtering(stoplist=list(string.punctuation)) - - # initialize empty list if stoplist is not provided - if stoplist is None: - stoplist = self.stoplist + self.candidate_filtering() + # TODO: is filtering only candidate with punctuation mandatory ? + #self.candidate_filtering(list(string.punctuation)) # further filter candidates for k in list(self.candidates): @@ -112,8 +107,8 @@ def candidate_selection(self, n=3, stoplist=None, **kwargs): # filter candidates starting/ending with a stopword or containing # a first/last word with less than 3 characters - if v.surface_forms[0][0].lower() in stoplist or v.surface_forms[0][ - -1].lower() in stoplist or len( + if v.surface_forms[0][0].lower() in self.stoplist or v.surface_forms[0][ + -1].lower() in self.stoplist or len( v.surface_forms[0][0]) < 3 or len( v.surface_forms[0][-1]) < 3: del self.candidates[k] @@ -194,7 +189,7 @@ def _contexts_building(self, use_stems=False, window=2): # add word to the current block block.append(word) - def _feature_extraction(self, stoplist=None): + def _feature_extraction(self): """Compute the weight of individual words using the following five features: @@ -233,21 +228,13 @@ def _feature_extraction(self, stoplist=None): DIFFERENT(w) = SF(w) / # sentences with SF(w) being the sentence frequency of word w. - - Args: - stoplist (list): the stoplist for filtering candidates, defaults to - the nltk stoplist. """ - # initialize stoplist list if not provided - if stoplist is None: - stoplist = self.stoplist - # get the Term Frequency of each word TF = [len(self.words[w]) for w in self.words] # get the Term Frequency of non-stop words - TF_nsw = [len(self.words[w]) for w in self.words if w not in stoplist] + TF_nsw = [len(self.words[w]) for w in self.words if w not in self.stoplist] # compute statistics mean_TF = numpy.mean(TF_nsw) @@ -258,7 +245,7 @@ def _feature_extraction(self, stoplist=None): for word in self.words: # Indicating whether the word is a stopword (vitordouzi change) - self.features[word]['isstop'] = word in stoplist or len(word) < 3 + self.features[word]['isstop'] = word in self.stoplist or len(word) < 3 # Term Frequency self.features[word]['TF'] = len(self.words[word]) @@ -321,12 +308,10 @@ def _feature_extraction(self, stoplist=None): E = self.features[word]['DIFFERENT'] self.features[word]['weight'] = (D * B) / (A + (C / D) + (E / D)) - def candidate_weighting(self, window=2, stoplist=None, use_stems=False): + def candidate_weighting(self, window=2, use_stems=False): """Candidate weight calculation as described in the YAKE paper. Args: - stoplist (list): the stoplist for filtering candidates, defaults to - the nltk stoplist. use_stems (bool): whether to use stems instead of lowercase words for weighting, defaults to False. window (int): the size in words of the window used for computing @@ -342,7 +327,7 @@ def candidate_weighting(self, window=2, stoplist=None, use_stems=False): self._contexts_building(use_stems=use_stems, window=window) # compute the word features - self._feature_extraction(stoplist=stoplist) + self._feature_extraction() # compute candidate weights for k, v in self.candidates.items(): diff --git a/pke/utils.py b/pke/utils.py index 5fa31279..862e0218 100644 --- a/pke/utils.py +++ b/pke/utils.py @@ -67,8 +67,8 @@ def compute_document_frequency(input_dir, output_file, extension='xml', language='en', - normalization="stemming", stoplist=None, + normalization="stemming", delimiter='\t', n=3, max_length=None, @@ -84,10 +84,11 @@ def compute_document_frequency(input_dir, extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents (used for computing the n-stem or n-lemma forms), defaults to 'en' (english). + stoplist (list): the stop words for filtering n-grams, default to + pke.lang.stopwords[language]. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. - stoplist (list): the stop words for filtering n-grams, default to None. delimiter (str): the delimiter between n-grams and document frequencies, defaults to tabulation (\t). n (int): the size of the n-grams, defaults to 3. @@ -111,6 +112,7 @@ def compute_document_frequency(input_dir, # read the input file doc.load_document(input=input_file, language=language, + stoplist=stoplist, normalization=normalization, max_length=max_length, encoding=encoding) @@ -119,7 +121,7 @@ def compute_document_frequency(input_dir, doc.ngram_selection(n=n) # filter candidates containing punctuation marks - doc.candidate_filtering(stoplist=stoplist) + doc.candidate_filtering() # loop through candidates for lexical_form in doc.candidates: @@ -152,8 +154,9 @@ def compute_document_frequency(input_dir, def train_supervised_model(input_dir, reference_file, model_file, - extension='xml', + extension='xml', # TODO: still necessary? language='en', + stoplist=None, normalization="stemming", df=None, model=None, @@ -173,6 +176,8 @@ def train_supervised_model(input_dir, extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents (used for computing the n-stem or n-lemma forms), defaults to 'en' (english). + stoplist (list): the stop words for filtering n-grams, default to + pke.lang.stopwords[language]. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. @@ -218,6 +223,7 @@ def train_supervised_model(input_dir, # load the document model.load_document(input=input_file, language=language, + stoplist=stoplist, normalization=normalization, encoding=encoding) @@ -362,6 +368,7 @@ def compute_lda_model(input_dir, n_topics=500, extension="xml", language="en", + stoplist=None, normalization="stemming", max_length=None, encoding=None): @@ -375,6 +382,8 @@ def compute_lda_model(input_dir, extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents, used for stop_words in sklearn CountVectorizer, defaults to 'en'. + stoplist (list): the stop words for filtering words, default to + pke.lang.stopwords[language]. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. @@ -416,8 +425,10 @@ def compute_lda_model(input_dir, # vectorize dataset # get the stoplist from nltk because CountVectorizer only contains english # stopwords atm + if stoplist is None: + stoplist = stopwords.get(language) tf_vectorizer = CountVectorizer( - stop_words=stopwords.get(language)) + stop_words=stoplist) tf = tf_vectorizer.fit_transform(texts) # extract vocabulary @@ -449,8 +460,8 @@ def compute_lda_model(input_dir, def load_document_as_bos(input_file, language="en", - normalization="stemming", stoplist=None, + normalization="stemming", encoding=None): """Load a document as a bag of words/stems/lemmas. @@ -458,16 +469,17 @@ def load_document_as_bos(input_file, input_file (str): path to input file. language (str): language of the input documents, used for stop_words in sklearn CountVectorizer, defaults to 'en'. + stoplist (list): the stop words for filtering tokens, default to + pke.lang.stopwords[language]. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. - stoplist (list): the stop words for filtering tokens, default to []. encoding (str): encoding of `input_file`, default to None. """ # initialize empty stoplist is None provided if stoplist is None: - stoplist = [] + stoplist = stopwords.get('language') # initialize load file object doc = LoadFile() @@ -514,8 +526,8 @@ def compute_pairwise_similarity_matrix(input_dir, df=None, extension="xml", language="en", - normalization="stemming", stoplist=None, + normalization="stemming", encoding=None): """Compute the pairwise similarity between documents in `input_dir` and documents in `collection_dir`. Similarity scores are computed using a cosine @@ -532,10 +544,11 @@ def compute_pairwise_similarity_matrix(input_dir, extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents, used for stop_words in sklearn CountVectorizer, defaults to 'en'. + stoplist (list): the stop words for filtering tokens, default to + pke.lang.stopwords[language]. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. - stoplist (list): the stop words for filtering tokens, default to []. encoding (str): encoding of files in `input_dir`, default to None. """ @@ -548,7 +561,7 @@ def compute_pairwise_similarity_matrix(input_dir, # initialize stoplist as empty if None provided if stoplist is None: - stoplist = [] + stoplist = stopwords.get(language) # build collection tf*idf vectors if collection_dir is not None: @@ -561,7 +574,7 @@ def compute_pairwise_similarity_matrix(input_dir, # initialize document vector collection[input_file] = load_document_as_bos( input_file=input_file, language=language, - normalization=normalization, stoplist=stoplist, + stoplist=stoplist, normalization=normalization, encoding=encoding) # compute TF*IDF weights @@ -579,7 +592,7 @@ def compute_pairwise_similarity_matrix(input_dir, # initialize document vector documents[input_file] = load_document_as_bos( input_file=input_file, language=language, - normalization=normalization, stoplist=stoplist, + stoplist=stoplist, normalization=normalization, encoding=encoding) # compute TF*IDF weights From 4263b41a18a4d421748d1ff4f27671ce6b59aa30 Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 18:29:15 +0100 Subject: [PATCH 08/24] Removing last corenlp entities code -LRB-, -RRB-, ... --- .travis.yml | 1 - README.md | 1 - examples/compute-df-counts.py | 1 - 3 files changed, 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index de399250..6c022028 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,7 +5,6 @@ python: # command to install dependencies install: - pip install -r requirements.txt - - python -m nltk.downloader universal_tagset - python -m spacy download en_core_web_sm - python -m pip install cython - python -m pip install git+https://github.com/epfml/sent2vec diff --git a/README.md b/README.md index 735f1805..4b07ce86 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,6 @@ pip install git+https://github.com/boudinfl/pke.git `pke` also requires external resources that can be obtained using: ```bash -python -m nltk.downloader universal_tagset python -m spacy download en_core_web_sm # download the english model ``` diff --git a/examples/compute-df-counts.py b/examples/compute-df-counts.py index 1cb49b1f..b43a4903 100644 --- a/examples/compute-df-counts.py +++ b/examples/compute-df-counts.py @@ -17,7 +17,6 @@ # stoplist are punctuation marks stoplist = list(punctuation) -stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] # compute idf weights compute_document_frequency(input_dir=input_dir, From f03c1345e5293c164df5b8040631c5c7f7d155bf Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 18:33:07 +0100 Subject: [PATCH 09/24] Removing kwargs It was mainly use to pass `spacy_model` from pke.LoadFIle.load_document to pke.RawTextReader.read --- pke/base.py | 20 ++++++++++++++------ pke/data_structures.py | 2 +- pke/readers.py | 14 +++++++------- pke/unsupervised/graph_based/positionrank.py | 3 +-- pke/unsupervised/graph_based/single_tpr.py | 2 +- pke/unsupervised/statistical/yake.py | 2 +- 6 files changed, 25 insertions(+), 18 deletions(-) diff --git a/pke/base.py b/pke/base.py index 74bb5b17..228d9b6f 100644 --- a/pke/base.py +++ b/pke/base.py @@ -51,7 +51,8 @@ def __init__(self): self.stoplist = None """List of stopwords.""" - def load_document(self, input, stoplist=None, **kwargs): + def load_document(self, input, language=None, stoplist=None, + normalization=None, spacy_model=None): """Loads the content of a document/string/stream in a given language. Args: @@ -63,25 +64,29 @@ def load_document(self, input, stoplist=None, **kwargs): normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. + spacy_model (spacy.lang): preloaded spacy model when input is a + string. """ # get the language parameter - language = kwargs.get('language', 'en') + if language is None: + language = 'en' # check whether input is a spacy doc object instance if isinstance(input, spacy.tokens.doc.Doc): parser = SpacyDocReader() - doc = parser.read(spacy_doc=input, **kwargs) + doc = parser.read(spacy_doc=input) # check whether input is a string elif isinstance(input, str): parser = RawTextReader(language=language) - doc = parser.read(text=input, **kwargs) + doc = parser.read(text=input, spacy_model=spacy_model) # check whether input is processed text elif isinstance(input, list) and all(isinstance(item, list) for item in input): parser = PreprocessedReader() - doc = parser.read(list_of_sentence_tuples=input, **kwargs) + doc = parser.read(list_of_sentence_tuples=input) else: logging.error('Cannot process input. It is neither a spacy doc or a string: {}'.format(type(input))) + # TODO raise TypeError('Cannot process input. It is neither a spacy doc, a string or a list of tuple: {}'.format(type(input)))) ? return # set the input file @@ -100,7 +105,7 @@ def load_document(self, input, stoplist=None, **kwargs): self.stoplist = stopwords.get(self.language) # word normalization - self.normalization = kwargs.get('normalization', 'stemming') + self.normalization = normalization if normalization is not None else 'stemming' if self.normalization == 'stemming': # fall back to porter if english language is used @@ -112,6 +117,9 @@ def load_document(self, input, stoplist=None, **kwargs): self.sentences[i].stems = [stemmer.stem(w).lower() for w in sentence.words] elif self.normalization is None: + # TODO: this code is not accessible (cf. l.104) + # What the possible values for normalization ? + # stemming, lemmatizing, lowering and defaulting to lowering ? for i, sentence in enumerate(self.sentences): self.sentences[i].stems = [w.lower() for w in sentence.words] diff --git a/pke/data_structures.py b/pke/data_structures.py index 64826c19..868ac1d3 100644 --- a/pke/data_structures.py +++ b/pke/data_structures.py @@ -62,7 +62,7 @@ def __init__(self): """ The sentence container (list of Sentence). """ @staticmethod - def from_sentences(sentences, **kwargs): + def from_sentences(sentences): """Populate the sentence list. Args: diff --git a/pke/readers.py b/pke/readers.py index 48d30b48..72fe16d3 100644 --- a/pke/readers.py +++ b/pke/readers.py @@ -30,7 +30,7 @@ def __init__(self, language=None): if language is None: self.language = 'en' - def read(self, text, **kwargs): + def read(self, text, spacy_model=None): """Read the input file and use spacy to pre-process. Spacy model selection: By default this function will load the spacy @@ -46,7 +46,7 @@ def read(self, text, **kwargs): spacy_model (model): an already loaded spacy model. """ - nlp = kwargs.get('spacy_model', None) + nlp = spacy_model if nlp is None: @@ -77,13 +77,13 @@ def read(self, text, **kwargs): "POS": [token.pos_ or token.tag_ for token in sentence], "char_offsets": [(token.idx, token.idx + len(token.text)) for token in sentence] }) - return Document.from_sentences(sentences, input_file=kwargs.get('input_file', None), **kwargs) + return Document.from_sentences(sentences) class SpacyDocReader(Reader): """Minimal Spacy Doc Reader.""" - def read(self, spacy_doc, **kwargs): + def read(self, spacy_doc): sentences = [] for sentence_id, sentence in enumerate(spacy_doc.sents): sentences.append({ @@ -92,13 +92,13 @@ def read(self, spacy_doc, **kwargs): "POS": [token.pos_ or token.tag_ for token in sentence], "char_offsets": [(token.idx, token.idx + len(token.text)) for token in sentence] }) - return Document.from_sentences(sentences, **kwargs) + return Document.from_sentences(sentences) class PreprocessedReader(Reader): """Reader for preprocessed text.""" - def read(self, list_of_sentence_tuples, **kwargs): + def read(self, list_of_sentence_tuples): sentences = [] for sentence_id, sentence in enumerate(list_of_sentence_tuples): words = [word for word, pos_tag in sentence] @@ -110,4 +110,4 @@ def read(self, list_of_sentence_tuples, **kwargs): "POS": pos_tags }) shift += len(' '.join(words)) - return Document.from_sentences(sentences, **kwargs) + return Document.from_sentences(sentences) diff --git a/pke/unsupervised/graph_based/positionrank.py b/pke/unsupervised/graph_based/positionrank.py index ad1ab178..34972f4e 100644 --- a/pke/unsupervised/graph_based/positionrank.py +++ b/pke/unsupervised/graph_based/positionrank.py @@ -72,8 +72,7 @@ def __init__(self): def candidate_selection(self, grammar=None, - maximum_word_number=3, - **kwargs): + maximum_word_number=3): """Candidate selection heuristic using a syntactic PoS pattern for noun phrase extraction. diff --git a/pke/unsupervised/graph_based/single_tpr.py b/pke/unsupervised/graph_based/single_tpr.py index 5463de0f..0fc05793 100644 --- a/pke/unsupervised/graph_based/single_tpr.py +++ b/pke/unsupervised/graph_based/single_tpr.py @@ -70,7 +70,7 @@ def __init__(self): super(TopicalPageRank, self).__init__() - def candidate_selection(self, grammar=None, **kwargs): + def candidate_selection(self, grammar=None): """Candidate selection heuristic. Here we select noun phrases that match the regular expression diff --git a/pke/unsupervised/statistical/yake.py b/pke/unsupervised/statistical/yake.py index 2a9aa348..a44336a5 100644 --- a/pke/unsupervised/statistical/yake.py +++ b/pke/unsupervised/statistical/yake.py @@ -82,7 +82,7 @@ def __init__(self): self.surface_to_lexical = {} """ Mapping from surface form to lexical form. """ - def candidate_selection(self, n=3, **kwargs): + def candidate_selection(self, n=3): """Select 1-3 grams as keyphrase candidates. Candidates beginning or ending with a stopword are filtered out. Words that do not contain at least one alpha-numeric character are not allowed. From 60f0ebcf9fe669e6e382fa0cbe6a2a70372176fd Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 18:39:06 +0100 Subject: [PATCH 10/24] Removing encoding parameters Keeping the one the are used to load the reference files. --- pke/base.py | 1 - pke/utils.py | 36 ++++++++++-------------------------- 2 files changed, 10 insertions(+), 27 deletions(-) diff --git a/pke/base.py b/pke/base.py index 228d9b6f..082eda3d 100644 --- a/pke/base.py +++ b/pke/base.py @@ -60,7 +60,6 @@ def load_document(self, input, language=None, stoplist=None, language (str): language of the input, defaults to 'en'. stoplist (list): custom list of stopwords, defaults to pke.lang.stopwords[language]. - encoding (str): encoding of the raw file. normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. diff --git a/pke/utils.py b/pke/utils.py index 862e0218..045767a5 100644 --- a/pke/utils.py +++ b/pke/utils.py @@ -71,8 +71,7 @@ def compute_document_frequency(input_dir, normalization="stemming", delimiter='\t', n=3, - max_length=None, - encoding=None): + max_length=None): """Compute the n-gram document frequencies from a set of input documents. An extra row is added to the output file for specifying the number of documents from which the document frequencies were computed @@ -92,7 +91,6 @@ def compute_document_frequency(input_dir, delimiter (str): the delimiter between n-grams and document frequencies, defaults to tabulation (\t). n (int): the size of the n-grams, defaults to 3. - encoding (str): encoding of files in input_dir, default to None. """ # document frequency container @@ -114,8 +112,7 @@ def compute_document_frequency(input_dir, language=language, stoplist=stoplist, normalization=normalization, - max_length=max_length, - encoding=encoding) + max_length=max_length) # candidate selection doc.ngram_selection(n=n) @@ -164,7 +161,6 @@ def train_supervised_model(input_dir, sep_ref_keyphrases=',', normalize_reference=False, leave_one_out=False, - encoding=None, ref_encoding=None): """Build a supervised keyphrase extraction model from a set of documents and a reference file. @@ -191,7 +187,6 @@ def train_supervised_model(input_dir, keyphrases, default to False. leave_one_out (bool): whether to use a leave-one-out procedure for training, creating one model per input, defaults to False. - encoding (str): encoding of files in `input_dir`, default to None. ref_encoding (str): encoding of `reference_file`, default to None. """ @@ -224,8 +219,7 @@ def train_supervised_model(input_dir, model.load_document(input=input_file, language=language, stoplist=stoplist, - normalization=normalization, - encoding=encoding) + normalization=normalization) # candidate selection model.candidate_selection() @@ -370,8 +364,7 @@ def compute_lda_model(input_dir, language="en", stoplist=None, normalization="stemming", - max_length=None, - encoding=None): + max_length=None): """Compute a LDA model from a collection of documents. Latent Dirichlet Allocation is computed using sklearn module. @@ -387,7 +380,6 @@ def compute_lda_model(input_dir, normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. - encoding (str): encoding of files in `input_dir`, default to None. """ # texts container @@ -405,8 +397,7 @@ def compute_lda_model(input_dir, doc.load_document(input=input_file, language=language, normalization=normalization, - max_length=max_length, - encoding=encoding) + max_length=max_length) # container for current document text = [] @@ -461,8 +452,7 @@ def compute_lda_model(input_dir, def load_document_as_bos(input_file, language="en", stoplist=None, - normalization="stemming", - encoding=None): + normalization="stemming"): """Load a document as a bag of words/stems/lemmas. Args: @@ -474,7 +464,6 @@ def load_document_as_bos(input_file, normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. - encoding (str): encoding of `input_file`, default to None. """ # initialize empty stoplist is None provided @@ -487,8 +476,7 @@ def load_document_as_bos(input_file, # read the input file doc.load_document(input=input_file, language=language, - normalization=normalization, - encoding=encoding) + normalization=normalization) # initialize document vector vector = defaultdict(int) @@ -527,8 +515,7 @@ def compute_pairwise_similarity_matrix(input_dir, extension="xml", language="en", stoplist=None, - normalization="stemming", - encoding=None): + normalization="stemming"): """Compute the pairwise similarity between documents in `input_dir` and documents in `collection_dir`. Similarity scores are computed using a cosine similarity over TF x IDF term weights. If there is no collection to compute @@ -549,7 +536,6 @@ def compute_pairwise_similarity_matrix(input_dir, normalization (str): word normalization method, defaults to 'stemming'. Other possible values are 'lemmatization' or 'None' for using word surface forms instead of stems/lemmas. - encoding (str): encoding of files in `input_dir`, default to None. """ # containers @@ -574,8 +560,7 @@ def compute_pairwise_similarity_matrix(input_dir, # initialize document vector collection[input_file] = load_document_as_bos( input_file=input_file, language=language, - stoplist=stoplist, normalization=normalization, - encoding=encoding) + stoplist=stoplist, normalization=normalization) # compute TF*IDF weights for stem in collection[input_file]: @@ -592,8 +577,7 @@ def compute_pairwise_similarity_matrix(input_dir, # initialize document vector documents[input_file] = load_document_as_bos( input_file=input_file, language=language, - stoplist=stoplist, normalization=normalization, - encoding=encoding) + stoplist=stoplist, normalization=normalization) # compute TF*IDF weights for stem in documents[input_file]: From 12f89357705464bc39907c8c9bd44a8fae377d3d Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 19:28:02 +0100 Subject: [PATCH 11/24] Removed data_structure.Document It was useless, Sentences are now returned by the readers directly. --- pke/__init__.py | 2 +- pke/base.py | 39 +++++++++++++++++-------------- pke/data_structures.py | 52 +++--------------------------------------- pke/readers.py | 48 +++++++++++++++++++++----------------- 4 files changed, 53 insertions(+), 88 deletions(-) diff --git a/pke/__init__.py b/pke/__init__.py index 9d737b74..6a29eecd 100644 --- a/pke/__init__.py +++ b/pke/__init__.py @@ -1,6 +1,6 @@ from __future__ import absolute_import -from pke.data_structures import Candidate, Document, Sentence +from pke.data_structures import Candidate, Sentence from pke.base import LoadFile from pke.utils import (load_document_frequency_file, compute_document_frequency, train_supervised_model, load_references, diff --git a/pke/base.py b/pke/base.py index 082eda3d..ea91595f 100644 --- a/pke/base.py +++ b/pke/base.py @@ -24,9 +24,6 @@ class LoadFile(object): def __init__(self): """Initializer for LoadFile class.""" - self.input_file = None - """Path to the input file.""" - self.language = None """Language of the input file.""" @@ -52,7 +49,7 @@ def __init__(self): """List of stopwords.""" def load_document(self, input, language=None, stoplist=None, - normalization=None, spacy_model=None): + normalization='stemming', spacy_model=None): """Loads the content of a document/string/stream in a given language. Args: @@ -74,28 +71,25 @@ def load_document(self, input, language=None, stoplist=None, # check whether input is a spacy doc object instance if isinstance(input, spacy.tokens.doc.Doc): parser = SpacyDocReader() - doc = parser.read(spacy_doc=input) + sents = parser.read(spacy_doc=input) # check whether input is a string elif isinstance(input, str): parser = RawTextReader(language=language) - doc = parser.read(text=input, spacy_model=spacy_model) + sents = parser.read(text=input, spacy_model=spacy_model) # check whether input is processed text elif isinstance(input, list) and all(isinstance(item, list) for item in input): parser = PreprocessedReader() - doc = parser.read(list_of_sentence_tuples=input) + sents = parser.read(list_of_sentence_tuples=input) else: logging.error('Cannot process input. It is neither a spacy doc or a string: {}'.format(type(input))) # TODO raise TypeError('Cannot process input. It is neither a spacy doc, a string or a list of tuple: {}'.format(type(input)))) ? return - # set the input file - self.input_file = doc.input_file - # set the language of the document self.language = language # set the sentences - self.sentences = doc.sentences + self.sentences = sents # initialize the stoplist if stoplist: @@ -103,9 +97,11 @@ def load_document(self, input, language=None, stoplist=None, else: self.stoplist = stopwords.get(self.language) - # word normalization - self.normalization = normalization if normalization is not None else 'stemming' + # word normalization (filling self.sentences[].stems) + # TODO: normalization=None should result in 'stemming' not in nothing + # normalization='none' should result in no normalization IMO + # TODO: this code could go into Reader.normalize ? if self.normalization == 'stemming': # fall back to porter if english language is used langcode = langcodes.get(self.language.replace('en', 'xx'), 'porter') @@ -115,13 +111,22 @@ def load_document(self, input, language=None, stoplist=None, for i, sentence in enumerate(self.sentences): self.sentences[i].stems = [stemmer.stem(w).lower() for w in sentence.words] - elif self.normalization is None: - # TODO: this code is not accessible (cf. l.104) - # What the possible values for normalization ? - # stemming, lemmatizing, lowering and defaulting to lowering ? + elif self.normalization == 'lemmatization': + # populate Sentence.stems + for i, sentence in enumerate(self.sentences): + # TODO: change by a warning and fallback to stemming ? + if 'lemmas' not in sentence.meta: + logging.error('Lemmas are not available.' + 'Use `normalization=\'stemming\' or preprocess ' + 'document with a spacy model that computes lemma.') + return + self.sentences[i].stems = [w for w in sentence.meta['lemmas']] + + else: for i, sentence in enumerate(self.sentences): self.sentences[i].stems = [w.lower() for w in sentence.words] + def is_redundant(self, candidate, prev, minimum_length=1): """Test if one candidate is redundant with respect to a list of already selected candidates. A candidate is considered redundant if it is diff --git a/pke/data_structures.py b/pke/data_structures.py index 868ac1d3..65967f51 100644 --- a/pke/data_structures.py +++ b/pke/data_structures.py @@ -9,12 +9,12 @@ class Sentence: """The sentence data structure.""" - def __init__(self, words): + def __init__(self, words, pos=[], meta={}): self.words = words """list of words (tokens) in the sentence.""" - self.pos = [] + self.pos = pos """list of Part-Of-Speeches.""" self.stems = [] @@ -23,7 +23,7 @@ def __init__(self, words): self.length = len(words) """length (number of tokens) of the sentence.""" - self.meta = {} + self.meta = meta """meta-information of the sentence.""" @@ -47,49 +47,3 @@ def __init__(self): self.lexical_form = [] """ the lexical form of the candidate. """ - - -@dataclass -class Document: - """The Document data structure.""" - - def __init__(self): - - self.input_file = None - """ The path of the input file. """ - - self.sentences = [] - """ The sentence container (list of Sentence). """ - - @staticmethod - def from_sentences(sentences): - """Populate the sentence list. - - Args: - sentences (Sentence list): content to create the document. - """ - - # initialize document - doc = Document() - - # loop through the parsed sentences - for i, sentence in enumerate(sentences): - - # add the sentence to the container - s = Sentence(words=sentence['words']) - - # add the POS - s.pos = sentence['POS'] - - # add the lemmas - s.stems = sentence['lemmas'] - - # add the meta-information - for (k, info) in sentence.items(): - if k not in {'POS', 'lemmas', 'words'}: - s.meta[k] = info - - # add the sentence to the document - doc.sentences.append(s) - - return doc diff --git a/pke/readers.py b/pke/readers.py index 72fe16d3..79dc0d72 100644 --- a/pke/readers.py +++ b/pke/readers.py @@ -6,11 +6,12 @@ import logging import spacy -from pke.data_structures import Document +from pke.data_structures import Sentence class Reader(object): """Reader default class.""" + def read(self, path): raise NotImplementedError @@ -71,13 +72,16 @@ def read(self, text, spacy_model=None): sentences = [] for sentence_id, sentence in enumerate(spacy_doc.sents): - sentences.append({ - "words": [token.text for token in sentence], - "lemmas": [token.lemma_ for token in sentence], - "POS": [token.pos_ or token.tag_ for token in sentence], - "char_offsets": [(token.idx, token.idx + len(token.text)) for token in sentence] - }) - return Document.from_sentences(sentences) + sentences.append(Sentence( + words=[token.text for token in sentence], + pos=[token.pos_ or token.tag_ for token in sentence], + meta={ + "lemmas": [token.lemma_ for token in sentence], + "char_offsets": [(token.idx, token.idx + len(token.text)) + for token in sentence] + } + )) + return sentences class SpacyDocReader(Reader): @@ -86,13 +90,16 @@ class SpacyDocReader(Reader): def read(self, spacy_doc): sentences = [] for sentence_id, sentence in enumerate(spacy_doc.sents): - sentences.append({ - "words": [token.text for token in sentence], - "lemmas": [token.lemma_ for token in sentence], - "POS": [token.pos_ or token.tag_ for token in sentence], - "char_offsets": [(token.idx, token.idx + len(token.text)) for token in sentence] - }) - return Document.from_sentences(sentences) + sentences.append(Sentence( + words=[token.text for token in sentence], + pos=[token.pos_ or token.tag_ for token in sentence], + meta={ + "lemmas": [token.lemma_ for token in sentence], + "char_offsets": [(token.idx, token.idx + len(token.text)) + for token in sentence] + } + )) + return sentences class PreprocessedReader(Reader): @@ -104,10 +111,9 @@ def read(self, list_of_sentence_tuples): words = [word for word, pos_tag in sentence] pos_tags = [pos_tag for word, pos_tag in sentence] shift = 0 - sentences.append({ - "words": words, - "lemmas": words, - "POS": pos_tags - }) + sentences.append(Sentence( + words=words, + pos=pos_tags + )) shift += len(' '.join(words)) - return Document.from_sentences(sentences) + return sentences From 11be2fb3b731350023d2f41ee8f02e3f77ad3ca0 Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Thu, 3 Mar 2022 19:46:52 +0100 Subject: [PATCH 12/24] Quickfix --- pke/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pke/utils.py b/pke/utils.py index 045767a5..06a76980 100644 --- a/pke/utils.py +++ b/pke/utils.py @@ -468,7 +468,7 @@ def load_document_as_bos(input_file, # initialize empty stoplist is None provided if stoplist is None: - stoplist = stopwords.get('language') + stoplist = stopwords.get(language) # initialize load file object doc = LoadFile() From 9fda3f3963132e5bbcf1e20d7466867c977e0e72 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 08:48:14 +0100 Subject: [PATCH 13/24] fix in-word hyphens issue with spacy --- pke/readers.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/pke/readers.py b/pke/readers.py index 79dc0d72..e8c05c76 100644 --- a/pke/readers.py +++ b/pke/readers.py @@ -3,10 +3,12 @@ """Readers for the pke module.""" +import re import logging import spacy from pke.data_structures import Sentence +from spacy.tokenizer import _get_regex_pattern class Reader(object): @@ -67,6 +69,16 @@ def read(self, text, spacy_model=None): # add the sentence splitter nlp.add_pipe('sentencizer') + # Fix for non splitting words with hyphens with spacy taken from + # https://stackoverflow.com/questions/43388476/how-could-spacy-tokenize-hashtag-as-a-whole + + # get default pattern for tokens that don't get split + re_token_match = _get_regex_pattern(nlp.Defaults.token_match) + # add your patterns (here: in-word hyphens) + re_token_match = f"({re_token_match}|\w+-\w+)" + # overwrite token_match function of the tokenizer + nlp.tokenizer.token_match = re.compile(re_token_match).match + # process the document spacy_doc = nlp(text) From 953bd5cc4d885ecfc1975107f5ddfeee61c8b817 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 09:02:37 +0100 Subject: [PATCH 14/24] make _topic_weights reachable in TopicRank --- pke/unsupervised/graph_based/topicrank.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/pke/unsupervised/graph_based/topicrank.py b/pke/unsupervised/graph_based/topicrank.py index b1d908c6..07a4c170 100644 --- a/pke/unsupervised/graph_based/topicrank.py +++ b/pke/unsupervised/graph_based/topicrank.py @@ -70,6 +70,9 @@ def __init__(self): self.topics = [] """ The topic container. """ + self._topic_weights = {} + """ Weights computed for each topic. """ + def candidate_selection(self, pos=None): """Selects longest sequences of nouns and adjectives as keyphrase candidates. @@ -211,7 +214,7 @@ def candidate_weighting(self, self.build_topic_graph() # compute the word scores using random walk - w = nx.pagerank(self.graph, alpha=0.85, weight='weight') + self._topic_weights = nx.pagerank(self.graph, alpha=0.85, weight='weight') # loop through the topics for i, topic in enumerate(self.topics): @@ -232,8 +235,8 @@ def candidate_weighting(self, indexes_offsets = [offsets[j] for j in indexes] # Choosing the first occuring most frequent candidate most_frequent = offsets.index(min(indexes_offsets)) - self.weights[topic[most_frequent]] = w[i] + self.weights[topic[most_frequent]] = self._topic_weights[i] else: first = offsets.index(min(offsets)) - self.weights[topic[first]] = w[i] + self.weights[topic[first]] = self._topic_weights[i] From 0e8f7497ad023c1f821715958b13b071a8b52a71 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 09:04:36 +0100 Subject: [PATCH 15/24] Update topicrank.py --- pke/unsupervised/graph_based/topicrank.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pke/unsupervised/graph_based/topicrank.py b/pke/unsupervised/graph_based/topicrank.py index 07a4c170..6c0ffbdd 100644 --- a/pke/unsupervised/graph_based/topicrank.py +++ b/pke/unsupervised/graph_based/topicrank.py @@ -70,7 +70,7 @@ def __init__(self): self.topics = [] """ The topic container. """ - self._topic_weights = {} + self._w = {} """ Weights computed for each topic. """ def candidate_selection(self, pos=None): @@ -214,7 +214,7 @@ def candidate_weighting(self, self.build_topic_graph() # compute the word scores using random walk - self._topic_weights = nx.pagerank(self.graph, alpha=0.85, weight='weight') + self._w = nx.pagerank(self.graph, alpha=0.85, weight='weight') # loop through the topics for i, topic in enumerate(self.topics): @@ -235,8 +235,8 @@ def candidate_weighting(self, indexes_offsets = [offsets[j] for j in indexes] # Choosing the first occuring most frequent candidate most_frequent = offsets.index(min(indexes_offsets)) - self.weights[topic[most_frequent]] = self._topic_weights[i] + self.weights[topic[most_frequent]] = self._w[i] else: first = offsets.index(min(offsets)) - self.weights[topic[first]] = self._topic_weights[i] + self.weights[topic[first]] = self._w[i] From b132f4cfe606f43bd76ace0b69fe3a3221e11bfc Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 09:08:29 +0100 Subject: [PATCH 16/24] Update topicrank.py --- pke/unsupervised/graph_based/topicrank.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pke/unsupervised/graph_based/topicrank.py b/pke/unsupervised/graph_based/topicrank.py index 6c0ffbdd..5433e897 100644 --- a/pke/unsupervised/graph_based/topicrank.py +++ b/pke/unsupervised/graph_based/topicrank.py @@ -70,7 +70,7 @@ def __init__(self): self.topics = [] """ The topic container. """ - self._w = {} + self.w = {} """ Weights computed for each topic. """ def candidate_selection(self, pos=None): @@ -214,7 +214,7 @@ def candidate_weighting(self, self.build_topic_graph() # compute the word scores using random walk - self._w = nx.pagerank(self.graph, alpha=0.85, weight='weight') + self.w = nx.pagerank(self.graph, alpha=0.85, weight='weight') # loop through the topics for i, topic in enumerate(self.topics): @@ -235,8 +235,8 @@ def candidate_weighting(self, indexes_offsets = [offsets[j] for j in indexes] # Choosing the first occuring most frequent candidate most_frequent = offsets.index(min(indexes_offsets)) - self.weights[topic[most_frequent]] = self._w[i] + self.weights[topic[most_frequent]] = self.w[i] else: first = offsets.index(min(offsets)) - self.weights[topic[first]] = self._w[i] + self.weights[topic[first]] = self.w[i] From c62511e731194d00d94b1594ae7af59c64d4a3e7 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 11:35:09 +0100 Subject: [PATCH 17/24] fix private _w in TopicRank --- pke/unsupervised/graph_based/topicrank.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pke/unsupervised/graph_based/topicrank.py b/pke/unsupervised/graph_based/topicrank.py index 5433e897..6c0ffbdd 100644 --- a/pke/unsupervised/graph_based/topicrank.py +++ b/pke/unsupervised/graph_based/topicrank.py @@ -70,7 +70,7 @@ def __init__(self): self.topics = [] """ The topic container. """ - self.w = {} + self._w = {} """ Weights computed for each topic. """ def candidate_selection(self, pos=None): @@ -214,7 +214,7 @@ def candidate_weighting(self, self.build_topic_graph() # compute the word scores using random walk - self.w = nx.pagerank(self.graph, alpha=0.85, weight='weight') + self._w = nx.pagerank(self.graph, alpha=0.85, weight='weight') # loop through the topics for i, topic in enumerate(self.topics): @@ -235,8 +235,8 @@ def candidate_weighting(self, indexes_offsets = [offsets[j] for j in indexes] # Choosing the first occuring most frequent candidate most_frequent = offsets.index(min(indexes_offsets)) - self.weights[topic[most_frequent]] = self.w[i] + self.weights[topic[most_frequent]] = self._w[i] else: first = offsets.index(min(offsets)) - self.weights[topic[first]] = self.w[i] + self.weights[topic[first]] = self._w[i] From 3a1dd89c66da424005757c0cb760be29d3480db5 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 12:08:19 +0100 Subject: [PATCH 18/24] Fix word normalization --- pke/base.py | 44 ++++++++++++++++---------------------------- 1 file changed, 16 insertions(+), 28 deletions(-) diff --git a/pke/base.py b/pke/base.py index ea91595f..46f31c2c 100644 --- a/pke/base.py +++ b/pke/base.py @@ -58,7 +58,7 @@ def load_document(self, input, language=None, stoplist=None, stoplist (list): custom list of stopwords, defaults to pke.lang.stopwords[language]. normalization (str): word normalization method, defaults to - 'stemming'. Other possible values are 'lemmatization' or 'None' + 'stemming'. Other possible value is 'none' for using word surface forms instead of stems/lemmas. spacy_model (spacy.lang): preloaded spacy model when input is a string. @@ -68,13 +68,25 @@ def load_document(self, input, language=None, stoplist=None, if language is None: language = 'en' + # set the language of the document + self.language = language + + # word normalization (filling self.sentences[].stems) + self.normalization = normalization + + # initialize the stoplist + if stoplist: + self.stoplist = stoplist + else: + self.stoplist = stopwords.get(self.language) + # check whether input is a spacy doc object instance if isinstance(input, spacy.tokens.doc.Doc): parser = SpacyDocReader() sents = parser.read(spacy_doc=input) # check whether input is a string elif isinstance(input, str): - parser = RawTextReader(language=language) + parser = RawTextReader(language=self.language) sents = parser.read(text=input, spacy_model=spacy_model) # check whether input is processed text elif isinstance(input, list) and all(isinstance(item, list) for item in input): @@ -85,23 +97,10 @@ def load_document(self, input, language=None, stoplist=None, # TODO raise TypeError('Cannot process input. It is neither a spacy doc, a string or a list of tuple: {}'.format(type(input)))) ? return - # set the language of the document - self.language = language - - # set the sentences + # populate the sentences self.sentences = sents - # initialize the stoplist - if stoplist: - self.stoplist = stoplist - else: - self.stoplist = stopwords.get(self.language) - - - # word normalization (filling self.sentences[].stems) - # TODO: normalization=None should result in 'stemming' not in nothing - # normalization='none' should result in no normalization IMO - # TODO: this code could go into Reader.normalize ? + # TODO: this code could go into Reader.normalize ? Hum, not sure if self.normalization == 'stemming': # fall back to porter if english language is used langcode = langcodes.get(self.language.replace('en', 'xx'), 'porter') @@ -111,17 +110,6 @@ def load_document(self, input, language=None, stoplist=None, for i, sentence in enumerate(self.sentences): self.sentences[i].stems = [stemmer.stem(w).lower() for w in sentence.words] - elif self.normalization == 'lemmatization': - # populate Sentence.stems - for i, sentence in enumerate(self.sentences): - # TODO: change by a warning and fallback to stemming ? - if 'lemmas' not in sentence.meta: - logging.error('Lemmas are not available.' - 'Use `normalization=\'stemming\' or preprocess ' - 'document with a spacy model that computes lemma.') - return - self.sentences[i].stems = [w for w in sentence.meta['lemmas']] - else: for i, sentence in enumerate(self.sentences): self.sentences[i].stems = [w.lower() for w in sentence.words] From d8b0c695f5a50718eab38e2240871fcdd6e08c27 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Fri, 4 Mar 2022 12:15:29 +0100 Subject: [PATCH 19/24] clearing candidate containers when candidate_selection is called --- pke/base.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/pke/base.py b/pke/base.py index 46f31c2c..35527a52 100644 --- a/pke/base.py +++ b/pke/base.py @@ -64,6 +64,9 @@ def load_document(self, input, language=None, stoplist=None, string. """ + # Reset object for new document + self.__init__() + # get the language parameter if language is None: language = 'en' @@ -114,7 +117,6 @@ def load_document(self, input, language=None, stoplist=None, for i, sentence in enumerate(self.sentences): self.sentences[i].stems = [w.lower() for w in sentence.words] - def is_redundant(self, candidate, prev, minimum_length=1): """Test if one candidate is redundant with respect to a list of already selected candidates. A candidate is considered redundant if it is @@ -230,6 +232,9 @@ def ngram_selection(self, n=3): n (int): the n-gram length, defaults to 3. """ + # reset the candidates + self.candidates.clear() + # loop through the sentences for i, sentence in enumerate(self.sentences): @@ -265,6 +270,9 @@ def longest_sequence_selection(self, key, valid_values): valid_values (set): the set of valid values, defaults to None. """ + # reset the candidates + self.candidates.clear() + # loop through the sentences for i, sentence in enumerate(self.sentences): @@ -304,6 +312,9 @@ def grammar_selection(self, grammar=None): grammar (str): grammar defining POS patterns of NPs. """ + # reset the candidates + self.candidates.clear() + # initialize default grammar if none provided if grammar is None: grammar = r""" From 0afe4734a97b6fb54178b989452eaea6a9cb06c8 Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Fri, 4 Mar 2022 19:10:19 +0100 Subject: [PATCH 20/24] Update `utils.py`, removing pairwise_similarities, adding tests Computing LDA and DF now requires a list of document 'pke-readable'. Training a models requires a list of tuple (id, doc) and a dict of reference (cf. `test_utils.py`). The tests are updated and pass. --- pke/__init__.py | 8 +- pke/utils.py | 315 +++++++++----------------------------------- tests/test_utils.py | 112 ++++++++++++++++ 3 files changed, 175 insertions(+), 260 deletions(-) mode change 100644 => 100755 pke/utils.py create mode 100755 tests/test_utils.py diff --git a/pke/__init__.py b/pke/__init__.py index 6a29eecd..11ede42d 100644 --- a/pke/__init__.py +++ b/pke/__init__.py @@ -2,9 +2,9 @@ from pke.data_structures import Candidate, Sentence from pke.base import LoadFile -from pke.utils import (load_document_frequency_file, compute_document_frequency, - train_supervised_model, load_references, - compute_lda_model, load_document_as_bos, - compute_pairwise_similarity_matrix) +from pke.utils import ( + load_document_frequency_file, compute_document_frequency, + train_supervised_model, load_references, + compute_lda_model) import pke.unsupervised import pke.supervised diff --git a/pke/utils.py b/pke/utils.py old mode 100644 new mode 100755 index 06a76980..1dd104a5 --- a/pke/utils.py +++ b/pke/utils.py @@ -9,16 +9,13 @@ import os import sys import csv -import math import glob import pickle import gzip import json -import bisect import codecs import logging -from itertools import combinations, product from collections import defaultdict from pke.base import LoadFile @@ -50,8 +47,9 @@ def load_document_frequency_file(input_file, frequencies = {} # open the input file - with gzip.open(input_file, 'rt', encoding='utf-8') if input_file.endswith('.gz') else \ - codecs.open(input_file, 'rt', encoding='utf-8') as f: + with (gzip.open(input_file, 'rt', encoding='utf-8') + if input_file.endswith('.gz') + else codecs.open(input_file, 'rt', encoding='utf-8')) as f: # read the csv file df_reader = csv.reader(f, delimiter=delimiter) @@ -63,33 +61,31 @@ def load_document_frequency_file(input_file, return frequencies -def compute_document_frequency(input_dir, +def compute_document_frequency(documents, output_file, - extension='xml', language='en', stoplist=None, - normalization="stemming", + normalization='stemming', delimiter='\t', - n=3, - max_length=None): - """Compute the n-gram document frequencies from a set of input documents. An - extra row is added to the output file for specifying the number of + # TODO: What is the use case for changing this ? + n=3): + """Compute the n-gram document frequencies from a set of input documents. + An extra row is added to the output file for specifying the number of documents from which the document frequencies were computed (--NB_DOC-- tab XXX). The output file is compressed using gzip. Args: - input_dir (str): the input directory. + documents (list): list of pke-readable documents. output_file (str): the output file. - extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents (used for computing the n-stem or n-lemma forms), defaults to 'en' (english). stoplist (list): the stop words for filtering n-grams, default to pke.lang.stopwords[language]. - normalization (str): word normalization method, defaults to 'stemming'. - Other possible values are 'lemmatization' or 'None' for using word - surface forms instead of stems/lemmas. - delimiter (str): the delimiter between n-grams and document frequencies, - defaults to tabulation (\t). + normalization (str): word normalization method, defaults to + 'stemming'. Other possible value is 'none' for using word surface + forms instead of stems/lemmas. + delimiter (str): the delimiter between n-grams and document + frequencies, defaults to tabulation (\t). n (int): the size of the n-grams, defaults to 3. """ @@ -100,19 +96,16 @@ def compute_document_frequency(input_dir, nb_documents = 0 # loop through the documents - for input_file in glob.iglob(input_dir + os.sep + '*.' + extension): - - #logging.info('reading file {}'.format(input_file)) + for document in documents: # initialize load file object doc = LoadFile() # read the input file - doc.load_document(input=input_file, + doc.load_document(input=document, language=language, stoplist=stoplist, - normalization=normalization, - max_length=max_length) + normalization=normalization) # candidate selection doc.ngram_selection(n=n) @@ -127,10 +120,9 @@ def compute_document_frequency(input_dir, nb_documents += 1 if nb_documents % 1000 == 0: - logging.info("{} docs, memory used: {} mb".format(nb_documents, - sys.getsizeof( - frequencies) - / 1024 / 1024 )) + logging.info("{} docs, memory used: {} mb".format( + nb_documents, + sys.getsizeof(frequencies) / 1024 / 1024)) # create directories from path if not exists if os.path.dirname(output_file): @@ -148,28 +140,23 @@ def compute_document_frequency(input_dir, f.write(line + '\n') -def train_supervised_model(input_dir, - reference_file, +def train_supervised_model(documents, + references, model_file, - extension='xml', # TODO: still necessary? language='en', stoplist=None, normalization="stemming", df=None, model=None, - sep_doc_id=':', - sep_ref_keyphrases=',', - normalize_reference=False, - leave_one_out=False, - ref_encoding=None): - """Build a supervised keyphrase extraction model from a set of documents and - a reference file. + leave_one_out=False): + """Build a supervised keyphrase extraction model from a set of documents + and reference keywords. Args: - input_dir (str): the input directory. - reference_file (str): the reference file. + documents (list): list of tuple (id, pke-readable documents). `id`s + should match the one in reference. + references (dict): reference keywords. model_file (str): the model output file. - extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents (used for computing the n-stem or n-lemma forms), defaults to 'en' (english). stoplist (list): the stop words for filtering n-grams, default to @@ -179,44 +166,34 @@ def train_supervised_model(input_dir, surface forms instead of stems/lemmas. df (dict): df weights dictionary. model (object): the supervised model to train, defaults to Kea. - sep_doc_id (str): the separator used for doc_id in reference file, - defaults to ':'. - sep_ref_keyphrases (str): the separator used for keyphrases in - reference file, defaults to ','. - normalize_reference (bool): whether to normalize the reference - keyphrases, default to False. leave_one_out (bool): whether to use a leave-one-out procedure for training, creating one model per input, defaults to False. - ref_encoding (str): encoding of `reference_file`, default to None. """ - logging.info('building model {} from {}'.format(model, input_dir)) - - references = load_references(reference_file, + """references = load_references(reference_file, sep_doc_id=sep_doc_id, sep_ref_keyphrases=sep_ref_keyphrases, normalize_reference=normalize_reference, language=language, encoding=ref_encoding) + """ training_instances = [] training_classes = [] masks = {} - offsets = [] - sizes = [] # get the input files from the input directory - for input_file in glob.iglob(input_dir + os.sep + '*.' + extension): + for doc_id, document in documents: - logging.info('reading file {}'.format(input_file)) + # logging.info('reading file {}'.format(input_file)) # get the document id from file name - doc_id = '.'.join(os.path.basename(input_file).split('.')[0:-1]) + # doc_id = '.'.join(os.path.basename(input_file).split('.')[0:-1]) # initialize the input file model.__init__() # load the document - model.load_document(input=input_file, + model.load_document(input=document, language=language, stoplist=stoplist, normalization=normalization) @@ -260,7 +237,7 @@ def train_supervised_model(input_dir, gold = training_classes[:ind[0]] + training_classes[ind[1]:] model.train(training_instances=fold, training_classes=gold, - model_file=model_file+"."+doc_id+".pickle") + model_file='{}.{}.pickle'.format(model_file, doc_id)) def load_references(input_file, @@ -345,59 +322,55 @@ def load_lda_model(input_file): input_file (str): the gzip input file containing lda model. Returns: - dictionnary: a dictionary of the form {term_1: freq}, freq being an integer. - model: an initialized sklearn.decomposition.LatentDirichletAllocation model. + dictionnary: a dictionary of the form {term_1: freq}, freq being an + integer. + model: an initialized sklearn.decomposition.LatentDirichletAllocation + model. """ model = LatentDirichletAllocation() with gzip.open(input_file, 'rb') as f: - (dictionary, - model.components_, - model.exp_dirichlet_component_, - model.doc_topic_prior_) = pickle.load(f) + (dictionary, + model.components_, + model.exp_dirichlet_component_, + model.doc_topic_prior_) = pickle.load(f) return dictionary, model -def compute_lda_model(input_dir, +def compute_lda_model(documents, output_file, n_topics=500, - extension="xml", language="en", stoplist=None, - normalization="stemming", - max_length=None): + normalization="stemming"): """Compute a LDA model from a collection of documents. Latent Dirichlet Allocation is computed using sklearn module. Args: - input_dir (str): the input directory. + documents (str): list fo pke-readable documents. output_file (str): the output file. n_topics (int): number of topics for the LDA model, defaults to 500. - extension (str): file extension for input documents, defaults to xml. language (str): language of the input documents, used for stop_words in sklearn CountVectorizer, defaults to 'en'. stoplist (list): the stop words for filtering words, default to pke.lang.stopwords[language]. - normalization (str): word normalization method, defaults to 'stemming'. - Other possible values are 'lemmatization' or 'None' for using word - surface forms instead of stems/lemmas. + normalization (str): word normalization method, defaults to + 'stemming'. Other possible value is 'none' + for using word surface forms instead of stems/lemmas. """ # texts container texts = [] # loop throught the documents - for input_file in glob.iglob(input_dir + os.sep + '*.' + extension): - - logging.info('reading file {}'.format(input_file)) + for document in documents: # initialize load file object doc = LoadFile() # read the input file - doc.load_document(input=input_file, + doc.load_document(input=document, language=language, - normalization=normalization, - max_length=max_length) + normalization=normalization) # container for current document text = [] @@ -405,17 +378,17 @@ def compute_lda_model(input_dir, # loop through sentences for sentence in doc.sentences: # get the tokens (stems) from the sentence if they are not - # punctuation marks + # punctuation marks text.extend([sentence.stems[i] for i in range(sentence.length) - if sentence.pos[i] != 'PUNCT' and - sentence.pos[i].isalpha()]) + if sentence.pos[i] != 'PUNCT' + and sentence.pos[i].isalpha()]) # add the document to the texts container texts.append(' '.join(text)) # vectorize dataset - # get the stoplist from nltk because CountVectorizer only contains english - # stopwords atm + # get the stoplist from pke.lang because CountVectorizer only contains + # english stopwords atm if stoplist is None: stoplist = stopwords.get(language) tf_vectorizer = CountVectorizer( @@ -424,6 +397,7 @@ def compute_lda_model(input_dir, # extract vocabulary vocabulary = tf_vectorizer.get_feature_names() + # TODO: deprecation warning: use get_feature_names_out # create LDA model and train lda_model = LatentDirichletAllocation(n_components=n_topics, @@ -447,174 +421,3 @@ def compute_lda_model(input_dir, # dump the LDA model with gzip.open(output_file, 'wb') as fp: pickle.dump(saved_model, fp) - - -def load_document_as_bos(input_file, - language="en", - stoplist=None, - normalization="stemming"): - """Load a document as a bag of words/stems/lemmas. - - Args: - input_file (str): path to input file. - language (str): language of the input documents, used for stop_words - in sklearn CountVectorizer, defaults to 'en'. - stoplist (list): the stop words for filtering tokens, default to - pke.lang.stopwords[language]. - normalization (str): word normalization method, defaults to 'stemming'. - Other possible values are 'lemmatization' or 'None' for using word - surface forms instead of stems/lemmas. - """ - - # initialize empty stoplist is None provided - if stoplist is None: - stoplist = stopwords.get(language) - - # initialize load file object - doc = LoadFile() - - # read the input file - doc.load_document(input=input_file, - language=language, - normalization=normalization) - - # initialize document vector - vector = defaultdict(int) - - # loop through the sentences and add the stems to the vector - for i, sentence in enumerate(doc.sentences): - for j, stem in enumerate(sentence.stems): - if stem in stoplist: - continue - vector[stem] += 1 - - return vector - - -def load_pairwise_similarities(path): - """Load the pairwise similarities for ExpandRank.""" - - pairwise_sim = defaultdict(list) - with gzip.open(path, 'rt', encoding='utf-8') as f: - lines = f.readlines() - for line in lines: - cols = line.strip().split() - cols[0] = os.path.basename(cols[0]) - cols[1] = os.path.basename(cols[1]) - # Add (score, file1) to pairwise_sim[file0] - # while ensuring that duplicate element are next to eahch other ? - bisect.insort(pairwise_sim[cols[0]], (float(cols[2]), cols[1])) - bisect.insort(pairwise_sim[cols[1]], (float(cols[2]), cols[0])) - return pairwise_sim - - -def compute_pairwise_similarity_matrix(input_dir, - output_file, - collection_dir=None, - df=None, - extension="xml", - language="en", - stoplist=None, - normalization="stemming"): - """Compute the pairwise similarity between documents in `input_dir` and - documents in `collection_dir`. Similarity scores are computed using a cosine - similarity over TF x IDF term weights. If there is no collection to compute - those scores, the similarities between documents in input_dir are returned - instead. - - Args: - input_dir (str): path to the input directory. - output_file (str): path to the output file. - collection_dir (str): path to the collection of documents, defaults to - None. - df (dict): df weights dictionary. - extension (str): file extension for input documents, defaults to xml. - language (str): language of the input documents, used for stop_words - in sklearn CountVectorizer, defaults to 'en'. - stoplist (list): the stop words for filtering tokens, default to - pke.lang.stopwords[language]. - normalization (str): word normalization method, defaults to 'stemming'. - Other possible values are 'lemmatization' or 'None' for using word - surface forms instead of stems/lemmas. - """ - - # containers - collection = {} - documents = {} - - # initialize the number of documents - N = df.get('--NB_DOC--', 1) - - # initialize stoplist as empty if None provided - if stoplist is None: - stoplist = stopwords.get(language) - - # build collection tf*idf vectors - if collection_dir is not None: - - # loop throught the documents in the collection - for input_file in glob.iglob( - collection_dir + os.sep + '*.' + extension): - logging.info('Reading file from {}'.format(input_file)) - - # initialize document vector - collection[input_file] = load_document_as_bos( - input_file=input_file, language=language, - stoplist=stoplist, normalization=normalization) - - # compute TF*IDF weights - for stem in collection[input_file]: - collection[input_file][stem] *= math.log(N / df.get(stem, 1), 2) - - # update N if a collection of documents is provided - N += 1 - - # loop throught the documents in the input directory - for input_file in glob.iglob(input_dir + os.sep + '*.' + extension): - - logging.info('Reading file from {}'.format(input_file)) - - # initialize document vector - documents[input_file] = load_document_as_bos( - input_file=input_file, language=language, - stoplist=stoplist, normalization=normalization) - - # compute TF*IDF weights - for stem in documents[input_file]: - documents[input_file][stem] *= math.log(N / df.get(stem, 1), 2) - - # consider input documents as collection if None provided - if not collection: - collection = documents - iterator = combinations(documents, 2) - else: - iterator = product(documents, collection) - - # create directories from path if not exists - if os.path.dirname(output_file): - os.makedirs(os.path.dirname(output_file), exist_ok=True) - - # open the output file in gzip mode - with gzip.open(output_file, 'wt', encoding='utf-8') as f: - - # compute pairwise similarity scores - for doc_i, doc_j in iterator: - # inner product - inner = 0.0 - for stem in set(documents[doc_i]) & set(collection[doc_j]): - inner += documents[doc_i][stem] * collection[doc_j][stem] - - # norms - norm_i = sum([math.pow(documents[doc_i][t], 2) for t in - documents[doc_i]]) - norm_i = math.sqrt(norm_i) - norm_j = sum([math.pow(collection[doc_j][t], 2) for t in - collection[doc_j]]) - norm_j = math.sqrt(norm_j) - - # compute cosine - cosine = inner / (norm_i * norm_j) - - # write line to output file - line = doc_i + '\t' + doc_j + '\t' + str(cosine) + '\n' - f.write(line) diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100755 index 00000000..19002e12 --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import os +import pke + +data_path = os.path.join('tests', 'data') + + +def create_df(corpus, tmp_path, name='corpus_df.gz'): + df_file = tmp_path / name + pke.utils.compute_document_frequency( + corpus, str(df_file), n=1) + corpus_df = pke.utils.load_document_frequency_file(str(df_file)) + return corpus_df, df_file + + +def test_load_reference(): + """Various tests for loading a reference file.""" + + id = 'C-41' + + g1 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.json', + normalize_reference=True, + language="en", + encoding='utf-8') + + g2 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.stem.json', + normalize_reference=False, + language="en", + encoding='utf-8') + + g3 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.final', + normalize_reference=True, + language="en", + encoding='utf-8') + + g4 = pke.utils.load_references(input_file=data_path + os.sep + 'reference.stem.final', + normalize_reference=False, + language="en", + encoding='utf-8') + + assert set(g1[id]) == set(g2[id]) == set(g3[id]) == set(g4[id]) + + +# TODO: test load_document_frequency_file ? As it is used to test +# compute_document_frequency + +def test_compute_document_frequency(tmp_path): + from collections import Counter + # tmp_path is a Path object provided automatically by pytest + + # Create a corpus + corpus = ['lorem sit amet', 'lorem ipsum'] + + # Create expected value + expected = Counter([t for v in corpus for t in v.split()]) + expected['--NB_DOC--'] = len(corpus) + + # Compute document frequency + tmp_freq = tmp_path / 'tmp_doc_freq.tsv.gz' + pke.utils.compute_document_frequency( + corpus, str(tmp_freq), n=1) + + # Asserting + df = pke.utils.load_document_frequency_file(str(tmp_freq)) + assert df == expected + + +def test_compute_lda(tmp_path): + import gzip + import pickle + + # Create a corpus + corpus = ['lorem sit amet', 'lorem ipsum'] + + # Create expected value + expected_dict = set(t for v in corpus for t in v.split()) + + # Compute LDA topics + tmp_lda = tmp_path / 'lda.pickle.gz' + pke.utils.compute_lda_model( + corpus, str(tmp_lda), n_topics=2) + + # Asserting + with gzip.open(tmp_lda, 'rb') as f: + (dictionary, _, _, _) = pickle.load(f) + assert sorted(dictionary) == sorted(expected_dict) + + +def test_train_supervised_model(tmp_path): + # Create a corpus + corpus = [('001', 'lorem sit amet'), ('002', 'lorem ipsum')] + reference = {'001': ['ref1', 'ref2'], '002': ['ref1', 'ref2']} + + tmp_model = tmp_path / 'model.pickle' + pke.utils.train_supervised_model( + corpus, reference, str(tmp_model), + df=None, leave_one_out=False, + model=pke.supervised.Kea()) # TODO: fix doc for model param + + +def test_train_supervised_model_leave_one_out(tmp_path): + # Create a corpus + corpus = [('001', 'lorem sit amet'), ('002', 'lorem ipsum')] + reference = {'001': ['ref1', 'ref2'], '002': ['ref1', 'ref2']} + + tmp_model = tmp_path / 'model.pickle' + pke.utils.train_supervised_model( + corpus, reference, str(tmp_model), + df=None, leave_one_out=True, + model=pke.supervised.Kea()) # TODO: fix doc for model param From d41dee75ec15288b69e36a93d0cb57b43650cace Mon Sep 17 00:00:00 2001 From: Ygor Gallina Date: Fri, 4 Mar 2022 19:12:42 +0100 Subject: [PATCH 21/24] Updated examples to work with new `utils.py` Replaced PubMed xml files by Inspec txt ones. Removed pairwise_similarities computation example. LDA and DF still use CoreNLP to be compatible with ake-datasets. --- examples/2.txt | 7 + examples/C-1.txt | 593 - examples/C-1.xml | 37030 ---------------- examples/compute-df-counts.py | 53 +- examples/compute-lda_model.py | 52 +- .../compute-pairwise-similarity-matrix.py | 46 - examples/keyphrase-extraction.py | 10 +- .../training_and_testing_a_kea_model/2.txt | 7 + .../training_and_testing_a_kea_model/C-1.xml | 37030 ---------------- .../gold-annotation.txt | 6 +- .../model.pickle | Bin 750 -> 723 bytes .../training_and_testing_a_kea_model/test.py | 21 +- .../training_and_testing_a_kea_model/train.py | 45 +- .../train/6.txt | 8 + .../train/7.txt | 3 + .../train/C-41.xml | 33411 -------------- .../train/C-42.xml | 26635 ----------- 17 files changed, 164 insertions(+), 134793 deletions(-) create mode 100644 examples/2.txt delete mode 100644 examples/C-1.txt delete mode 100644 examples/C-1.xml mode change 100644 => 100755 examples/compute-lda_model.py delete mode 100644 examples/compute-pairwise-similarity-matrix.py create mode 100644 examples/training_and_testing_a_kea_model/2.txt delete mode 100644 examples/training_and_testing_a_kea_model/C-1.xml create mode 100644 examples/training_and_testing_a_kea_model/train/6.txt create mode 100644 examples/training_and_testing_a_kea_model/train/7.txt delete mode 100644 examples/training_and_testing_a_kea_model/train/C-41.xml delete mode 100644 examples/training_and_testing_a_kea_model/train/C-42.xml diff --git a/examples/2.txt b/examples/2.txt new file mode 100644 index 00000000..950a00d8 --- /dev/null +++ b/examples/2.txt @@ -0,0 +1,7 @@ +Waiting for the wave to crest [wavelength services] +Wavelength services have been hyped ad nauseam for years. But despite their + quick turn-up time and impressive margins, such services have yet to + live up to the industry's expectations. The reasons for this lukewarm + reception are many, not the least of which is the confusion that still + surrounds the technology, but most industry observers are still + convinced that wavelength services with ultimately flourish diff --git a/examples/C-1.txt b/examples/C-1.txt deleted file mode 100644 index 9ec5017b..00000000 --- a/examples/C-1.txt +++ /dev/null @@ -1,593 +0,0 @@ -Scalable Grid Service Discovery Based on UDDI* -* -Authors are listed in alphabetical order. -Sujata Banerjee$ -, Sujoy Basu$ -, Shishir Garg , Sukesh Garg , Sung-Ju Lee$ -, Pramila Mullan , Puneet Sharma$ -$ -HP Labs -1501 Page Mill Road -Palo Alto, CA, 94304 USA -+1-650-857-2137 -{sujata.banerjee,sujoy.basu,sungju.lee,puneet.sharma}@hp.com -France Telecom R&D Division -801 Gateway Blvd, # 500 -South San Francisco, CA, 94080 USA -+1 650 -875-1500 -{shishir.garg,sukesh.garg,pramila.mullan}@francetelecom.com -ABSTRACT -Efficient discovery of grid services is essential for the success of -grid computing. The standardization of grids based on web -services has resulted in the need for scalable web service -discovery mechanisms to be deployed in grids Even though UDDI -has been the de facto industry standard for web-services -discovery, imposed requirements of tight-replication among -registries and lack of autonomous control has severely hindered -its widespread deployment and usage. With the advent of grid -computing the scalability issue of UDDI will become a roadblock -that will prevent its deployment in grids. In this paper we present -our distributed web-service discovery architecture, called DUDE -(Distributed UDDI Deployment Engine). DUDE leverages DHT -(Distributed Hash Tables) as a rendezvous mechanism between -multiple UDDI registries. DUDE enables consumers to query -multiple registries, still at the same time allowing organizations to -have autonomous control over their registries.. Based on -preliminary prototype on PlanetLab, we believe that DUDE -architecture can support effective distribution of UDDI registries -thereby making UDDI more robust and also addressing its scaling -issues. Furthermore, The DUDE architecture for scalable -distribution can be applied beyond UDDI to any Grid Service -Discovery mechanism. -Categories and Subject Descriptors -C2.4 [Distributed Systems] -General Terms -Design, Experimentation, Standardization. -1. INTRODUCTION -Efficient discovery of grid services is essential for the success of -grid computing. The standardization of grids based on web -services has resulted in the need for scalable web service -Permission to make digital or hard copies of all or part of this -work for personal or classroom use is granted without fee -provided that copies are not made or distributed for profit or -commercial advantage and that copies bear this notice and the -full citation on the first page. To copy otherwise,to republish, -to post on servers or to redistribute to lists, requires prior -specific permission and/or a fee. -MGC '05, November 28- December 2, 2005 Grenoble , France -discovery mechanisms to be deployed in grids. Grid discovery -services provide the ability to monitor and discover resources and -services on grids. They provide the ability to query and subscribe -to resource/service information. In addition, threshold traps might -be required to indicate specific change in existing conditions. The -state of the data needs to be maintained in a soft state so that the -most recent information is always available. The information -gathered needs to be provided to variety of systems for the -purpose of either utilizing the grid or proving summary -information. However, the fundamental problem is the need to be -scalable to handle huge amounts of data from multiple sources. -The web services community has addressed the need for service -discovery, before grids were anticipated, via an industry standard -called UDDI. However, even though UDDI has been the de facto -industry standard for web-services discovery, imposed -requirements of tight-replication among registries and lack of -autonomous control, among other things has severely hindered its -widespread deployment and usage [7]. With the advent of grid -computing the scalability issue with UDDI will become a -roadblock that will prevent its deployment in grids. -This paper tackles the scalability issue and a way to find services -across multiple registries in UDDI by developing a distributed -web services discovery architecture. Distributing UDDI -functionality can be achieved in multiple ways and perhaps using -different distributed computing infrastructure/platforms (e.g., -CORBA, DCE, etc.). In this paper we explore how Distributed -Hash Table (DHT) technology can be leveraged to develop a -scalable distributed web services discovery architecture. A DHT is -a peer-to-peer (P2P) distributed system that forms a structured -overlay allowing more efficient routing than the underlying -network. This crucial design choice is motivated by two factors. -The first motivating factor is the inherent simplicity of the put/get -abstraction that DHTs provide, which makes it easy to rapidly -build applications on top of DHTs. We recognize that having just -this abstraction may not suffice for all distributed applications, but -for the objective at hand, works very well as will become clear -later. Other distributed computing platforms/middleware while -providing more functionality have much higher overhead and -complexity. The second motivating factor stems from the fact that -DHTs are relatively new tool for building distributed applications -and we would like to test its potential by applying it to the -problem of distributing UDDI. -In the next section, we provide a brief overview of grid -information services, UDDI and its limitations, which is followed -by an overview of DHTs in Section 3. Section 4 describes our -proposed architecture with details on use cases. In Section 5, we -Article 2 -describe our current implementation, followed by our findings in -Section 6. Section 7 discusses the related work in this area and -Section 8 contains our concluding remarks. -2. BACKGROUND -2.1 Grid Service Discovery -Grid computing is based on standards which use web services -technology. In the architecture presented in [6], the service -discovery function is assigned to a specialized Grid service called -Registry. The implementation of the web service version of the -Monitoring and Discovery Service (WS MDS), also known as the -MDS4 component of the Globus Toolkit version 4 (GT4), -includes such a registry in the form of the Index service Resource -and service properties are collected and indexed by this service. -Its basic function makes it similar to UDDI registry. To attain -scalability, Index services from different Globus containers can -register with each other in a hierarchical fashion to aggregate data. -This approach for attaining scalability works best in hierarchical -Virtual Organizations (VO), and expanding a search to find -sufficient number of matches involves traversing the hierarchy. -Specifically, this approach is not a good match for systems that try -to exploit the convergence of grid and peer-to-peer computing [5]. -2.2 UDDI -Beyond grid computing, the problem of service discovery needs to -be addressed more generally in the web services community. -Again, scalability is a major concern since millions of buyers -looking for specific services need to find all the potential sellers -of the service who can meet their needs. Although there are -different ways of doing this, the web services standards -committees address this requirement through a specification -called UDDI (Universal Description, Discovery, and Integration). -A UDDI registry enables a business to enter three types of -information in a UDDI registry - white pages, yellow pages and -green pages. UDDI"s intent is to function as a registry for services -just as the yellow pages is a registry for businesses. Just like in -Yellow pages, companies register themselves and their services -under different categories. In UDDI, White Pages are a listing of -the business entities. Green pages represent the technical -information that is necessary to invoke a given service. Thus, by -browsing a UDDI registry, a developer should be able to locate a -service and a company and find out how to invoke the service. -When UDDI was initially offered, it provided a lot of potential. -However, today we find that UDDI has not been widely deployed -in the Internet. In fact, the only known uses of UDDI are what are -known as private UDDI registries within an enterprise"s -boundaries. The readers can refer to [7] for a recent article that -discusses the shortcomings of UDDI and the properties of an ideal -service registry. Improvement of the UDDI standard is continuing -in full force and UDDI version 3 (V3) was recently approved as -an OASIS Standard. However, UDDI today has issues that have -not been addressed, such as scalability and autonomy of -individual registries. -UDDI V3 provides larger support for multi-registry environments -based on portability of keys By allowing keys to be re-registered -in multiple registries, the ability to link registries in various -topologies is effectively enabled. However, no normative -description of these topologies is provided in the UDDI -specification at this point. The improvements within UDDI V3 -that allow support for multi-registry environments are significant -and open the possibility for additional research around how -multiregistry environments may be deployed. A recommended -deployment scenario proposed by the UDDI V3.0.2 Specification -is to use the UDDI Business Registries as root registries, and it is -possible to enable this using our solution. -2.3 Distributed Hash Tables -A Distributed Hash Table (DHT) is a peer-to-peer (P2P) -distributed system that forms a structured overlay allowing more -efficient routing than the underlying network. It maintains a -collection of key-value pairs on the nodes participating in this -graph structure. For our deployment, a key is the hash of a -keyword from a service name or description. There will be -multiple values for this key, one for each service containing the -keyword. Just like any other hash table data structure, it provides -a simple interface consisting of put() and get() operations. This -has to be done with robustness because of the transient nature of -nodes in P2P systems. The value stored in the DHT can be any -object or a copy or reference to it. The DHT keys are obtained -from a large identifier space. A hash function, such as MD5 or -SHA-1, is applied to an object name to obtain its DHT key. Nodes -in a DHT are also mapped into the same identifier space by -applying the hash function to their identifier, such as IP address -and port number, or public key. The identifier space is assigned -to the nodes in a distributed and deterministic fashion, so that -routing and lookup can be performed efficiently. The nodes of a -DHT maintain links to some of the other nodes in the DHT. The -pattern of these links is known as the DHT"s geometry. For -example, in the Bamboo DHT [11], and in the Pastry DHT [8] on -which Bamboo is based, nodes maintain links to neighboring -nodes and to other distant nodes found in a routing table. The -routing table entry at row i and column j, denoted Ri[j], is another -node whose identifier matches its own in first i digits, and whose -(i + 1)st digit is j. The routing table allows efficient overlay -routing. Bamboo, like all DHTs, specifies algorithms to be -followed when a node joins the overlay network, or when a node -fails or leaves the network The geometry must be maintained even -when this rate is high. To attain consistent routing or lookup, a -DHT key must be routed to the node with the numerically closest -identifier. For details of how the routing tables are constructed -and maintained, the reader is referred to [8, 11]. -3. PROPOSED ARCHITECTURE OF DHT -BASED UDDI REGISTRY HIERARCHIES -As mentioned earlier, we propose to build a distributed UDDI -system on top of a DHT infrastructure. This choice is primarily -motivated by the simplicity of the put/get abstraction that DHTs -provide, which is powerful enough for the task at hand, especially -since we plan to validate our approach with an implementation -running on PlanetLab [9]. A secondary motivation is to -understand deployment issues with DHT based systems. Several -applications have been built as overlays using DHTs, such as -distributed file storage, databases, publish-subscribe systems and -content distribution networks. In our case, we are building a DHT -based overlay network of UDDI registries, where the DHT acts as -a rendezvous network that connects multiple registries. In the -grid computing scenario, an overlay network of multiple UDDI -registries seems to an interesting alternative to the UDDI public -Article 2 -registries currently maintained by Microsoft, IBM, SAP and NTT. -In addition, our aim is to not change any of the UDDI interfaces -for clients as well as publishers. -Figure 1 highlights the proposed architecture for the DHT based -UDDI Registry framework. UDDI nodes are replicated in a UDDI -registry as per the current UDDI standard. However, each local -registry has a local proxy registry that mediates between the local -UDDI registry and the DHT Service. The DHT service is the glue -that connects the Proxy Registries together and facilitates -searching across registries. -Figure 1: DUDE Architecture -Service information can be dispersed to several UDDI registries to -promote scalability. The proxy registry publishes, performs -queries and deletes information from the dispersed UDDI -registries. However, the scope of the queries is limited to relevant -registries. The DHT provides information about the relevant -registries. The core idea in the architecture is to populate DHT -nodes with the necessary information from the proxies which -enables easy and ubiquitous searching when queries are made. -When a new service is added to a registry, all potential search -terms are hashed by the proxy and used as DHT keys to publish -the service in the DHT. The value stored for this service uniquely -identifies the service, and includes the URL of a registry and the -unique UDDI key of the service in that registry. Similarly when -queries arrive, they are parsed and a set of search terms are -identified. These search terms are hashed and the values stored -with those hash values are retrieved from the DHT. Note that a -proxy does not need to know all DHT nodes; it needs to know just -one DHT node (this is done as part of the bootstrapping process) -and as described in Section 2.3, this DHT node can route the -query as necessary to the other nodes on the DHT overlay. We -describe three usage scenarios later that deal with adding a new -local registry, inserting a new service, and querying for a service. -Furthermore, the DHT optimizes the UDDI query mechanism. -This process becomes a lookup using a UDDI unique key rather -than a query using a set of search parameters. This key and the -URL of the registry are obtained by searching initially in the -DHT. The DHT query can return multiple values for matching -services, and in each of the matching registries, the proxy -performs lookup operations. -The service name is used as a hash for inserting the service -information. The service information contains the query URL and -unique UDDI key for the registry containing the service. There -could be multiple registries associated with a given service. The -service information conforms to the following schema. - - - -Service Information - - - - - - - - - -… - -There can be multiple proxy UDDI registries in this architecture. -The advantage of this is to introduce distributed interactions -between the UDDI clients and registries. Organization can also -decide what information is available from the local registries by -implementing policies at the proxy registry. -3.1 Sequence of Operations -In this section, we demonstrate what the sequence of operations -should be for three crucial scenarios - adding a new local registry, -inserting a new service and querying a service. Other operations -like deleting a registry, deleting a service, etc. are similar and for -the sake of brevity are omitted here. -Figure 2: Sequence Diagram- Add New Local Registry -Add a New Local UDDI Registry -Figure 2 contains a sequence diagram illustrating how a new -UDDI registry is added to the network of UDDI registries. The -new registry registers itself with its proxy registry. The proxy -registry in turn queries the new registry for all services that it has -UDDI Local Registry UDDI Local Registry -UDDI Local Registry -Proxy Registry -DHT Based Distribution -Proxy Registry -Proxy Registry -Article 2 -stored in its databases and in turn registers each of those entries -with the DHT. -Figure 3: Sequence Diagram - Add New Service -Add a New Service -The use case diagram depicted in Error! Reference source not -found. highlights how a client publishes a new service to the -UDDI registry. In order to interact with the registry a client has to -know how to contact its local proxy registry. It then publishes a -service with the proxy registry which in turn publishes the service -with the local UDDI registry and receives the UDDI key of the -registry entry. Then new key-value pairs are published in the -DHT, where each key is obtained by hashing a searchable -keyword of the service and the value consists of the query URL of -the registry and the UDDI key. -Figure 4: Sequence Diagram - Query for a Service -Query a Service -Figure 4 shows how a client queries the UDDI registry for a -service. Once again, the client needs to know how to contact its -local proxy registry and invokes the query service request. The -proxy registry in turn contacts one of the DHT nodes to determine -DHT queries using the search terms. -As explained earlier in the context of Figure 1, multiple values -might be retrieved from the DHT. Each value includes the query -URL of a registry, and the unique UDDI key of a matching -service in that registry. The proxy then contacts the matching -registries and waits for the response of lookup operations using -the corresponding UDDI keys. Upon receiving the responses, the -proxy registry collates all responses and returns the aggregated set -of services to the client. -We will now illustrate these operations using an example. -Consider a client contacting its local proxy to publish a service -called Computer Accessories. The proxy follows the steps in -Figure 3 to add the service to UDDI 1 registry, and also publishes -two entries in the DHT. The keys of these entries are obtained by -hashing the words computer and accessories respectively. -Both entries have the same value consisting of the query URL of -this registry and the unique UDDI key returned by the registry for -this service. Next we consider another client publishing a service -called Computer Repair through its proxy to UDDI 2 registry. A -similar process results in 2 more entries being added to the DHT. -Recall that our DHT deployment can have multiple entries with -the same key. If we follow the steps in Figure 4 for a client -sending a query to its proxy using the word computer, we see -that the DHT is queried with the hash of the word computer as -key. This retrieves the query URL and respective UDDI keys of -both services mentioned before in this example. The proxy can -then do a simple lookup operation at both UDDI 1 and 2 -registries. It is clear that as the number of UDDI registries and -clients increases, this process of lookup at only relevant UDDI -registries is more scalable that doing a full search using the word -computer at all UDDI registries. -4. IMPLEMENTATION -In this section, we describe our implementation which is currently -deployed on PlanetLab [9]. PlanetLab is an open, globally -distributed platform for developing, deploying, and accessing -network services. It currently has 527 machines, hosted by 249 -sites, spanning over 25 countries. PlanetLab machines are hosted -by research/academic institutions as well as industrial companies. -France Telecom and HP are two of the major industry supporters -for PlanetLab. Every PlanetLab host machine is connected to the -Internet and runs a common software package including a Linux -based operating system that supports server virtualization. Thus -the users can develop and experiment with new services under -real-world conditions. The advantage of using PlanetLab is that -we can test the DUDE architecture under real-world conditions -with a large scale geographically dispersed node base. -Due to the availability of jUDDI, an open source UDDI V2 -registry (http://www.juddi.org) and a lack of existing readily -available UDDI V3 registry, a decision to use UDDI V2 was -made. The standardization of UDDI V3 is recent and we intend to -extend this work to support UDDI V3 and subsequent versions in -the future. The proxy registry is implemented by modifying the -jUDDI source to enable publishing, querying and deleting service -information from a DHT. Furthermore, it also allows querying -multiple registries and collating the response using UDDI4j [13]. -For the DHT implementation, we use the Bamboo DHT code -[11]. The Bamboo DHT allows multiple proxy registries to -publish and delete service information from their respective UDDI -registries, as well as to query for services from all the registries. -The proxy uses the service name as input to the DHT"s hash -Article 2 -function to get the DHT key. The value that is stored in the DHT -using this key is the URI of the registry along with the UDDI key -of the service. This ensures that when the proxy registry queries -for services with a certain name, it gets back the URI and UDDI -keys for matching entries. Using these returned results, the proxy -can do fast lookup operations at the respective UDDI registries. -The UDDI keys make it unnecessary to repeat the search at the -UDDI registries with the service name. -We have so far described the process of exact match on service -name. However there are additional types of search that must be -supported. Firstly, the search requested could be case-insensitive. -To support that, the proxy registry has to publish the same service -once using the name exactly as entered in the UDDI registry, and -once with the name converted to all lower-case letters. To do a -case-insensitive search, the proxy registry simply has to convert -the query string into lower-case letters. Secondly, the user could -query based on the prefix of a service name. Indeed, this is the -default behavior of search in UDDI. In other words, a wildcard is -implicit at the end of the service name being searched. To support -this efficiently in the DHT, our proxy registries have to take -prefixes of the service name of varying length and publish the -URI and UDDI key multiple times, once using each prefix. For -example, the prefix sizes chosen in one deployment might be 5, -10, 15 and 20 characters. If a search for the first 12 characters of a -service name is submitted, the proxy registry will query the DHT -with the first 10 characters of the search string, and then refine the -search result to ensure that the match extends to the 12th -character. -If the search string has less than 5 characters, and the search is for -a prefix rather than an exact match, the DHT cannot be of any -help, unless every service is published in the DHT with prefix of -length 0. Using this null prefix will send a copy of every -advertised service to the DHT node to which the hash of the null -prefix maps. Since this can lead to load-imbalance, a better -solution might be to use the DHT only to get a list of all UDDI -registries, and send the search to all of them in the locations to be -searched. Thirdly, the service name being searched can be a -regular expression, such as one with embedded wildcard -characters. For example, a search for Garden%s should match -both Garden Supplies and Gardening Tools. This will be -treated similarly to the previous case as the DHT has to be queried -with the longest available prefix. The results returned have to be -refined to ensure that the regular expression matches. -Figure 5 shows the network diagram for our implementation. -There are two proxy UDDI and juddi registry pairs. Consider a -client which contacts the UDDI proxy on grouse.hpl.hp.com. The -proxy does a lookup of the DHT using the query string or a prefix. -This involves contacting one of the DHT nodes, such as -pli1-br3.hpl.hp.com, which serves as the gateway to the DHT for -grouse.hpl.hp.com, based on the latter"s configuration file. The -DHT node may then route the query to one of the other DHT -nodes which is responsible for the DHT key that the query string -maps to. The results of the DHT lookup return to -pli1-br3.hpl.hp.com, which forwards them to grouse.hpl.hp.com. The -results may include a few services from each of the juddi -registries. So the proxy registry performs the lookup operations at -both planetlab1 and planetlab2.rdfrancetelecom.com for their -respective entries listed in the search results. The responses to -these lookups are collated by the proxy registry and returned to -the client. -Figure 5 Network Diagram -5. RELATED WORK -A framework for QoS-based service discovery in grids has been -proposed in [18]. UDDIe, an extended UDDI registry for -publishing and discovering services based on QoS parameters, is -proposed in [19]. Our work is complementary since we focus on -how to federate the UDDI registries and address the scalability -issue with UDDI. The DUDE proxy can publish the service -properties supported by UDDIe in the DHT and support range -queries using techniques proposed for such queries on DHTs. -Then we can deliver the scalability benefits of our current solution -to both UDDI and UDDIe registries. Discovering services meeting -QoS and price requirements has been studied in the context of a -grid economy, so that grid schedulers can use various market -models such as commodity markets and auctions. The Grid -Market Directory [20] was proposed for this purpose. -In [12], the authors present an ontology-based matchmaker. -Resource and request descriptions are expressed in RDF Schema, -a semantic markup language. Matchmaking rules are expressed in -TRIPLE, a language based on Horn Logic. Although our current -implementation focuses on UDDI version 2, in future we will -consider semantic extensions to UDDI, WS-Discovery [16] and -other Grid computing standards such as Monitoring and -Discovery Service (MDS) [10]. So the simplest extension of our -work could involve using the DHT to do an initial syntax-based -search to identify the local registries that need to be contacted. -Then the Proxy Registry can contact these registries, which do -semantic matchmaking to identify their matches, which are then -merged at the Proxy Registry and returned to the client. -The convergence of grid and P2P computing has been explored in -[5]. GridVine [2] builds a logical semantic overlay on top of a -physical layer consisting of P-Grid [1], a structured overlay based -on distributed search tree that uses prefix-based routing and -changes the overlay paths as part of the network maintenance -protocol to adapt to load in different parts of the keyspace. A -federated UDDI service [4] has been built on top of the PlanetP -[3] publish-subscribe system for unstructured P2P communities. -The focus of this work has been on the manageability of the -federated service. The UDDI service is treated as an application -Article 2 -service to be managed in their framework. So they do not address -the issue of scalability in UDDI, and instead use simple -replication. In [21], the authors describe a UDDI extension (UX) -system that launches a federated query only if locally found -results are not adequate. While the UX Server is positioned as an -intermediary similarly to the UDDI Proxy described in our DUDE -framework, it focuses more on the QoS framework and does not -attempt to implement a seamless federation mechanism such as -our DHT based approach. In [22] D2HT describes a discovery -framework built on top of DHT. However, we have chosen to use -UDDI on top of DHT. D2HT have used (Agent Management -System) AMS/ (Directory Facilitator) DF on top of DHT. -6. CONCLUSIONS AND FUTURE WORK -In this paper, we have described a distributed architecture to -support large scale discovery of web-services. Our architecture -will enable organizations to maintain autonomous control over -their UDDI registries and at the same time allowing clients to -query multiple registries simultaneously. The clients are oblivious -to the transparent proxy approach we have adopted and get richer -and more complete response to their queries. Based on initial -prototype testing, we believe that DUDE architecture can support -effective distribution of UDDI registries thereby making UDDI -more robust and also addressing its scaling issues. The paper has -solved the scalability issues with UDDI but does not preclude the -application of this approach to other service discovery -mechanisms. An example of another service discovery mechanism -that could benefit from such an approach is Globus Toolkit"s -MDS. Furthermore, we plan to investigate other aspects of grid -service discovery that extend this work. Some of these aspects -include the ability to subscribe to resource/service information, -the ability to maintain soft states and the ability to provide a -variety of views for various different purposes. In addition, we -plan to revisit the service APIs for a Grid Service Discovery -solution leveraging the available solutions and specifications as -well as the work presented in this paper. -7. REFERENCES -[1] P-grid: A self-organizing structured p2p system. K. -Aberer, P. Cudr_e-Mauroux, A. Datta, Z. Despotovic, M. -Hauswirth, M. Punceva, and R. Schmidt. ACM SIGMOD -Record, 32(3), 2003. -[2] GridVine: Building Internet-Scale Semantic Overlay -Networks Karl Aberer, Philippe Cudre-Mauroux, Manfred -Hauswirth, and Tim van Pelt. Proceedings, 3rd ISWC 2004, -Hiroshima, Japan. -[3] "PlanetP: Using Gossiping to Build Content Addressable -Peer-to-Peer Information Sharing Communities". F. M. -Cuenca-Acuna, C. Peery, R. P. Martin, and T. D. Nguyen. In -Proceedings of 12th Intl Symposium on HPDC, June 2003. -[4] Self-Managing Federated Services. Francisco Matias -Cuenca-Acuna and Thu D. Nguyen. In Proceedings of 23rd -IEEE International SRDS, 2004, Florianpolis, BRAZIL. -[5] On Death, Taxes, and the Convergence of P2P and Grid -Computing. Ian Foster and Adriana Iamnitchi. In -Proceedings of the 2nd IPTPS 2003. -[6] The Physiology of the Grid: An Open Grid Services -Architecture for Distributed Systems Integration, I. Foster, -C. Kesselman, J. M. Nick and S. Tuecke. Presented to OGSI -WG, Global Grid Forum, June 22, 2002. Available at -http://www.globus.org/alliance/publications/papers.php -[7] Was the Universal Service Registry a Dream?, Fred -Hartman and Harris Reynolds, In the Web Services Journal, -Dec 2, 2004. -[8] Pastry: Scalable, distributed object location and routing for -large scale peer-to-peer systems. A. Rowstron and P. -Druschel. In Proc. of IFIP/ACM Middleware, Nov. 2001 -[9] http://www.planet-lab.org -[10] Grid information services for distributed resource sharing. -K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman. -Proceedings of the IEEE HPDC-10, 2001. -[11] Handling churn in a DHT. S. Rhea, D. Geels, T. Roscoe, -and J. Kubiatowicz. Proceedings of the USENIX Annual -Technical Conference, June 2004. -[12] Ontology-based Resource Matching in the Grid - The Grid -Meets the Semantic Web, Hongsuda Tangmunarunkit, -Stefan Decker, Carl Kesselman. In Proceedings of the -Second ISWC (2003), Miami, Florida. -[13] UDDI4j Java Class Library: -http://www124.ibm.com/developerworks/oss/uddi4j/ -[14] UDDI V2 specification: Available at http://uddi.org/ -[15] UDDI V3.0.2 specification: http://uddi.org/ -[16] Web Services Dynamic Discovery (WS-Discovery) -Specification, February 2004. -http://msdn.microsoft.com/ws/2004/02/discovery -[17] Information Services (MDS): Key Concepts. -http://www.globus.org/toolkit/docs/4.0/info/key/ -[18] G- QoSM: Grid Service Discovery using QoS Properties, -R J. Al-Ali, O.F. Rana, D.W. Walker, S. Jha and S. Sohail. -Journal of Computing and Informatics (Special issue on Grid -Computing), Ed: Domenico LaForenza, Vol. 21, No. 4, pp. -363-382, 2002. -[19] UDDIe: An Extended Registry for Web Services, A. -ShaikhAli, O.F. Rana, R. Al-Ali and D.W. Walker, -Workshop on Service Oriented Computing: Models, -Architectures and Applications at SAINT Conference, -Florida, US, January 2003. IEEE Computer Society Press. -[20] A Market-Oriented Grid Directory Service for Publication -and Discovery of Grid Service Providers and their Services, -J. Yu, S. Venugopal and R. Buyya, Journal of -Supercomputing, Kluwer Academic Publishers, USA, 2005. -[21] Chen Zhou, Liang-Tien Chia, Bilhanan Silverajan, Bu-Sung -Lee: UX - An Architecture Providing QoS-Aware and -Federated Support for UDDI. ICWS 2003: 171-176. -[22] Kee-Hyun Choi, Ho-Jin Shin, Dong-Ryeol Shin, Service -Discovery Supporting Open Scalability Using -FIPACompliant Agent Platform for Ubiquitous Networks, Lecture -Notes in Computer Science, Volume 3482, Jan 2005. -Article 2 diff --git a/examples/C-1.xml b/examples/C-1.xml deleted file mode 100644 index 739cf977..00000000 --- a/examples/C-1.xml +++ /dev/null @@ -1,37030 +0,0 @@ - - - - - - - - Scalable - scalable - 0 - 8 - JJ - - - Grid - Grid - 9 - 13 - NNP - - - Service - Service - 14 - 21 - NNP - - - Discovery - Discovery - 22 - 31 - NNP - - - Based - base - 32 - 37 - VBD - - - on - on - 38 - 40 - IN - - - UDDI - UDDI - 41 - 45 - NNP - - - * - * - 46 - 47 - SYM - - - - - - - ABSTRACT - abstract - 353 - 361 - NN - - - - - - - Efficient - efficient - 362 - 371 - JJ - - - discovery - discovery - 372 - 381 - NN - - - of - of - 382 - 384 - IN - - - grid - grid - 385 - 389 - NN - - - services - service - 390 - 398 - NNS - - - is - be - 399 - 401 - VBZ - - - essential - 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30628 - 30630 - RB - - - well - well - 30631 - 30635 - RB - - - as - as - 30636 - 30638 - IN - - - the - the - 30639 - 30642 - DT - - - work - work - 30643 - 30647 - NN - - - presented - present - 30648 - 30657 - VBN - - - in - in - 30658 - 30660 - IN - - - this - this - 30661 - 30665 - DT - - - paper - paper - 30666 - 30671 - NN - - - . - . - 30672 - 30673 - . - - - - - - \ No newline at end of file diff --git a/examples/compute-df-counts.py b/examples/compute-df-counts.py index b43a4903..d3c88c7e 100644 --- a/examples/compute-df-counts.py +++ b/examples/compute-df-counts.py @@ -1,15 +1,17 @@ # -*- coding: utf-8 -*- -import logging import sys +import logging +from glob import glob from string import punctuation +import xml.etree.ElementTree as etree from pke import compute_document_frequency # setting info in terminal logging.basicConfig(level=logging.INFO) -# path to the collection of documents +# path to the collection of xml documents input_dir = sys.argv[1] # path to the df weights dictionary, saved as a gzipped csv file @@ -18,12 +20,43 @@ # stoplist are punctuation marks stoplist = list(punctuation) + +def read_corenlp_xml(path): + sentences = [] + tree = etree.parse(path, etree.XMLParser()) + for sentence in tree.iterfind('./document/sentences/sentence'): + # get the character offsets + starts = [int(u.text) for u in + sentence.iterfind('tokens/token/CharacterOffsetBegin')] + ends = [int(u.text) for u in + sentence.iterfind('tokens/token/CharacterOffsetEnd')] + doc = { + 'words': [u.text for u in + sentence.iterfind('tokens/token/word')], + 'lemmas': [u.text for u in + sentence.iterfind('tokens/token/lemma')], + 'POS': [u.text for u in sentence.iterfind('tokens/token/POS')], + 'char_offsets': [(starts[k], ends[k]) for k in + range(len(starts))] + } + sentences.append( + [(doc['words'][i], doc['POS'][i]) + for i in range(len(doc['words']))]) + return sentences + + +documents = [] +for fn in glob(input_dir + '*.xml'): + doc = read_corenlp_xml(fn) + documents.append(doc) + + # compute idf weights -compute_document_frequency(input_dir=input_dir, - output_file=output_file, - extension='xml', # input file extension - language='en', # language of the input files - normalization="stemming", # use porter stemmer - stoplist=stoplist, # stoplist - delimiter='\t', # tab separated output - n=5) # compute n-grams up to 5-grams \ No newline at end of file +compute_document_frequency( + documents, + output_file=output_file, + language='en', # language of the input files + normalization='stemming', # use porter stemmer + stoplist=stoplist, # stoplist + n=5 # compute n-grams up to 5-grams +) diff --git a/examples/compute-lda_model.py b/examples/compute-lda_model.py old mode 100644 new mode 100755 index 3f5117c6..25e6d7f1 --- a/examples/compute-lda_model.py +++ b/examples/compute-lda_model.py @@ -1,26 +1,58 @@ # -*- coding: utf-8 -*- -import logging import sys +import logging +from glob import glob +import xml.etree.ElementTree as etree from pke import compute_lda_model # setting info in terminal logging.basicConfig(level=logging.INFO) -# path to the collection of documents +# path to the collection of xml documents input_dir = sys.argv[1] -# path to the df weights dictionary, saved as a gzipped csv file +# path to the lda model, saved as a gzip file output_file = sys.argv[2] # number of topics for the LDA model n_topics = int(sys.argv[3]) -# compute idf weights -compute_lda_model(input_dir=input_dir, - output_file=output_file, - n_topics=n_topics, - extension="xml", - language="en", - normalization="stemming") + +def read_corenlp_xml(path): + sentences = [] + tree = etree.parse(path, etree.XMLParser()) + for sentence in tree.iterfind('./document/sentences/sentence'): + # get the character offsets + starts = [int(u.text) for u in + sentence.iterfind('tokens/token/CharacterOffsetBegin')] + ends = [int(u.text) for u in + sentence.iterfind('tokens/token/CharacterOffsetEnd')] + doc = { + 'words': [u.text for u in + sentence.iterfind('tokens/token/word')], + 'lemmas': [u.text for u in + sentence.iterfind('tokens/token/lemma')], + 'POS': [u.text for u in sentence.iterfind('tokens/token/POS')], + 'char_offsets': [(starts[k], ends[k]) for k in + range(len(starts))] + } + sentences.append( + [(doc['words'][i], doc['POS'][i]) + for i in range(len(doc['words']))]) + return sentences + + +documents = [] +for fn in glob(input_dir + '*.xml'): + doc = read_corenlp_xml(fn) + documents.append(doc) + +compute_lda_model( + documents, + output_file=output_file, + n_topics=n_topics, + language='en', + normalization='stemming' +) diff --git a/examples/compute-pairwise-similarity-matrix.py b/examples/compute-pairwise-similarity-matrix.py deleted file mode 100644 index e6101a99..00000000 --- a/examples/compute-pairwise-similarity-matrix.py +++ /dev/null @@ -1,46 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import print_function -from __future__ import division -from __future__ import unicode_literals -from __future__ import absolute_import - -import sys -import string -import logging - -from pke import load_document_frequency_file, compute_pairwise_similarity_matrix -from pke.lang import stopwords - -# setting info in terminal -logging.basicConfig(level=logging.INFO) - -# path to the input set of documents -input_dir = sys.argv[1] - -# path to the pairwise similarity scores -output_file = sys.argv[2] - -# path to the collection of documents -collection_dir = sys.argv[3] - -# path to the df counts, saved as a gzipped csv file -df_file = sys.argv[4] - -# load the DF counts -df_counts = load_document_frequency_file(input_file=df_file) - -# stoplist for terms in document vectors -stoplist = list(string.punctuation) -stoplist += stopwords.get('en') - -# compute the pairwise similarity measures and write output -compute_pairwise_similarity_matrix(input_dir=input_dir, - output_file=output_file, - collection_dir=collection_dir, - df=df_counts, - extension="xml", - language="en", - normalization="stemming", - stoplist=stoplist) diff --git a/examples/keyphrase-extraction.py b/examples/keyphrase-extraction.py index ebe3f7dc..892be8cf 100644 --- a/examples/keyphrase-extraction.py +++ b/examples/keyphrase-extraction.py @@ -4,15 +4,19 @@ # this example uses TopicRank from pke.unsupervised import TopicRank + # create a TopicRank extractor extractor = TopicRank() # load the content of the document, here in CoreNLP XML format # the input language is set to English (used for the stoplist) # normalization is set to stemming (computed with Porter's stemming algorithm) -extractor.load_document(input='C-1.xml', - language="en", - normalization='stemming') +with open('2.txt') as f: + doc = f.read() +extractor.load_document( + doc, + language='en', + normalization='stemming') # select the keyphrase candidates, for TopicRank the longest sequences of # nouns and adjectives diff --git a/examples/training_and_testing_a_kea_model/2.txt b/examples/training_and_testing_a_kea_model/2.txt new file mode 100644 index 00000000..950a00d8 --- /dev/null +++ b/examples/training_and_testing_a_kea_model/2.txt @@ -0,0 +1,7 @@ +Waiting for the wave to crest [wavelength services] +Wavelength services have been hyped ad nauseam for years. But despite their + quick turn-up time and impressive margins, such services have yet to + live up to the industry's expectations. The reasons for this lukewarm + reception are many, not the least of which is the confusion that still + surrounds the technology, but most industry observers are still + convinced that wavelength services with ultimately flourish diff --git a/examples/training_and_testing_a_kea_model/C-1.xml b/examples/training_and_testing_a_kea_model/C-1.xml deleted file mode 100644 index 739cf977..00000000 --- a/examples/training_and_testing_a_kea_model/C-1.xml +++ /dev/null @@ -1,37030 +0,0 @@ - - - - - - - - Scalable - scalable - 0 - 8 - JJ - - - Grid - Grid - 9 - 13 - NNP - - - Service - Service - 14 - 21 - NNP - - - Discovery - Discovery - 22 - 31 - NNP - - - Based - base - 32 - 37 - VBD - - - on - on - 38 - 40 - IN - - - UDDI - UDDI - 41 - 45 - NNP - - - * - * - 46 - 47 - SYM - - - - - - - ABSTRACT - abstract - 353 - 361 - NN - - - - - - - Efficient - efficient - 362 - 371 - JJ - - - discovery - discovery - 372 - 381 - NN - - - of - of - 382 - 384 - IN - - - grid - grid - 385 - 389 - NN - - - services - 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NNS - - - and - and - 30609 - 30612 - CC - - - specifications - specification - 30613 - 30627 - NNS - - - as - as - 30628 - 30630 - RB - - - well - well - 30631 - 30635 - RB - - - as - as - 30636 - 30638 - IN - - - the - the - 30639 - 30642 - DT - - - work - work - 30643 - 30647 - NN - - - presented - present - 30648 - 30657 - VBN - - - in - in - 30658 - 30660 - IN - - - this - this - 30661 - 30665 - DT - - - paper - paper - 30666 - 30671 - NN - - - . - . - 30672 - 30673 - . - - - - - - \ No newline at end of file diff --git a/examples/training_and_testing_a_kea_model/gold-annotation.txt b/examples/training_and_testing_a_kea_model/gold-annotation.txt index f5355a25..9ed7c236 100644 --- a/examples/training_and_testing_a_kea_model/gold-annotation.txt +++ b/examples/training_and_testing_a_kea_model/gold-annotation.txt @@ -1,3 +1,3 @@ -C-41 : adapt resourc manag,distribut real-time embed system,end-to-end qualiti of servic+servic end-to-end qualiti,hybrid adapt resourcemanag middlewar,hybrid control techniqu,real-time video distribut system,real-time corba specif,video encod/decod,resourc reserv mechan,dynam environ,stream servic,distribut real-time emb system,hybrid system,qualiti of servic+servic qualiti -C-42 : ensembl kalman filter,data assimil methodolog,hydrocarbon reservoir simul,energi explor,tigr grid comput environ,grid comput,cyberinfrastructur develop project,high perform comput,tigr grid middlewar,strateg applic area,gridwai metaschedul,pool licens,grid-enabl,reservoir model,enkf,tigr -C-44 : multi-sequenc posit,wireless sensor network,massiv uva-base deploment,node local,spatiotempor correl,event distribut,rang-base approach,distribut-base locat estim,listen-detect-assembl-report protocol,margin distribut,local,node sequenc process \ No newline at end of file +2 : wavelength services,fiber optic networks,Looking Glass Networks,PointEast Research +6 : SBC Communications,regulatory compliance,telecom carrier +7 : Sprint,telecom service providers,regulations,anti-spam act,lawsuit \ No newline at end of file diff --git a/examples/training_and_testing_a_kea_model/model.pickle b/examples/training_and_testing_a_kea_model/model.pickle index af68b62368c1f8c734d2fab5b13d9a7d91945bc6..95005b16d58d7bbb90a13a6be36b440e5599d118 100644 GIT binary patch literal 723 zcmZ`%L2DC16yBr;tJXF()QCtgR;ZUn3k45C(kes^T~rY9G?`5{&eX}wGP~Q7LqQMK zROTS`?Qifmcv`_9Ab9hpAc)|h9>kkJS_jvF7zW2>MKM^mLtSnS5c*Z;| zxy>=%@{o&%UI6OsM#MtO#eibwtiwKBeE~bLtSXrK1VgKFV8&1NX^4DDg@lnh(`7ga zOzNmvj`|)BBk2W*as=(Nn$9|O4yv{(Tt;1+M+1L|eA?AIK+QX8x+WzaK9<<`JvkQ{a zN|kPc<2d&Yb!11|3c*#UF=JvI4F=eUQRL>UT9&cQitn3>n#mXLiin2@$V~1MnIq>& zE!9C5$azvH7gV|bA1k>u$w?NEh@LbJd^27th-QtE=oqSY`DHWh{Z%zpy9B-Tptxxdior#StpgT@+0DjT-R$n0-K_>$ zP)ju;2!b~~dbT(5Qd9qc;<4gI@aV-~cu)kLoy1`99p3W3y!ZLOnVnln!@p*mggJ^s ztOXP6q+$B<`Opq5$E{n$)}~~T#&1Jk1c@R_Ns>ftj}l0pIsKUzJkTIdH7md#vs?!K zO_bq+VH4l)NGS~s{FO`}L)y6N>b9jTPFVLE*s~12gRI3xnK9D1#E9paOok2Icp!qo zqL}wXT~I+b#Gvd{N!%_&Ml9x2;+c@GB$i;K5L{L@8RSqOH=l;Q(5gX$Hx)VrQOPib z61>HlgiZIG_h9o33>Pa&F?JG0G)+7An}?l&1W~kV7C(Y43EztG~wMp0&udsUtM)?Z#fAc*$?YdoD zR%0hgI-#N*|I|zpgv`VpzrDh5Uj+NRewD;sIM8t%6`;juzoy^6xU5dCyu7iNda6G9 za_ZZY#eVf>Zuk|ukX6ypKVaLv!hfl&V;B?cgAp9+VIS^cKU{nB^UiX5RhpPvdH4N! zODVtp>Yx4C8q0qwHy?axp#o>Ouc%Gt?*~IspvNOP(nT-9(H^i=TL1BQ-fzvnolV#3 fBdY?0J1-e8n9R3a2acgM=a-b@C8Y!tp|1P^E93Bf diff --git a/examples/training_and_testing_a_kea_model/test.py b/examples/training_and_testing_a_kea_model/test.py index 9969f506..81a14fe0 100644 --- a/examples/training_and_testing_a_kea_model/test.py +++ b/examples/training_and_testing_a_kea_model/test.py @@ -1,27 +1,34 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +import os + import pke +base = os.path.dirname(__file__) + # create a Kea extractor and set the input language to English (used for # the stoplist in the candidate selection method) extractor = pke.supervised.Kea() -# load the content of the document, here in CoreNLP XML format -# the use_lemmas parameter allows to choose using CoreNLP lemmas or stems -# computed using nltk -extractor.load_document('C-1.xml') +# load the content of the document, here in corenlp format +with open(base + os.sep + '2.txt') as f: + doc = f.read() +extractor.load_document(doc) # select the keyphrase candidates, for Kea the 1-3 grams that do not start or # end with a stopword. extractor.candidate_selection() # load the df counts -df_counts = pke.load_document_frequency_file(input_file="df.tsv.gz", - delimiter='\t') +df_counts = pke.load_document_frequency_file( + input_file=base + os.sep + 'df.tsv.gz', + delimiter='\t') # weight the candidates using Kea model. -extractor.candidate_weighting(model_file="model.pickle", df=df_counts) +extractor.candidate_weighting( + model_file=base + os.sep + 'model.pickle', + df=df_counts) # print the n-highest (10) scored candidates for (keyphrase, score) in extractor.get_n_best(n=10): diff --git a/examples/training_and_testing_a_kea_model/train.py b/examples/training_and_testing_a_kea_model/train.py index eed46ad1..bd42d9a0 100644 --- a/examples/training_and_testing_a_kea_model/train.py +++ b/examples/training_and_testing_a_kea_model/train.py @@ -1,32 +1,47 @@ # -*- coding: utf-8 -*- +import os import logging +from glob import glob import pke # setting info in terminal logging.basicConfig(level=logging.INFO) +base = os.path.dirname(__file__) # path to the collection of documents -input_dir = 'train' + os.sep +documents = [] +for fn in glob(base + os.sep + 'train/*.txt'): + with open(fn) as f: + doc = f.read() + doc_id = os.path.basename(fn).rsplit('.', 1)[0] + documents.append((doc_id, doc)) + +logging.info('Loaded {} documents'.format(len(documents))) # path to the reference file -reference_file = "gold-annotation.txt" +reference = {} +with open(base + os.sep + 'gold-annotation.txt') as f: + for line in f: + doc_id, keywords = line.split(' : ') + reference[doc_id] = keywords.split(',') # path to the df file -df_file = "df.tsv.gz" +df_file = base + os.sep + 'df.tsv.gz' logging.info('Loading df counts from {}'.format(df_file)) -df_counts = pke.load_document_frequency_file(input_file=df_file, - delimiter='\t') +df_counts = pke.load_document_frequency_file( + input_file=df_file, delimiter='\t' +) # path to the model, saved as a pickle -output_mdl = "model.pickle" - -pke.train_supervised_model(input_dir=input_dir, - reference_file=reference_file, - model_file=output_mdl, - extension='xml', - language='en', - normalization="stemming", - df=df_counts, - model=pke.supervised.Kea()) +output_mdl = base + os.sep + 'model.pickle' +pke.train_supervised_model( + documents, + reference, + model_file=output_mdl, + language='en', + normalization='stemming', + df=df_counts, + model=pke.supervised.Kea() +) diff --git a/examples/training_and_testing_a_kea_model/train/6.txt b/examples/training_and_testing_a_kea_model/train/6.txt new file mode 100644 index 00000000..a204313c --- /dev/null +++ b/examples/training_and_testing_a_kea_model/train/6.txt @@ -0,0 +1,8 @@ +SBC gets more serious on regulatory compliance +With one eye on the past and the other on its future, SBC Communications last + week created a unit it hopes will bring a cohesiveness and efficiency + to its regulatory compliance efforts that previously had been lacking. + The carrier also hopes the new regulatory compliance unit will help it + accomplish its short-term goal of landing FCC approval. to provide + long-distance service throughout its region, and its longer-term, goal + of reducing the regulatory burdens under which it and currently operate diff --git a/examples/training_and_testing_a_kea_model/train/7.txt b/examples/training_and_testing_a_kea_model/train/7.txt new file mode 100644 index 00000000..8c6c1cb4 --- /dev/null +++ b/examples/training_and_testing_a_kea_model/train/7.txt @@ -0,0 +1,3 @@ +Anti-spam suit attempts to hold carriers accountable +A lawsuit alleges that Sprint has violated Utah's new anti-spam act. 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collaborative - 24444 - 24457 - JJ - - - research - research - 24458 - 24466 - NN - - - , - , - 24467 - 24468 - , - - - educational - educational - 24469 - 24480 - JJ - - - opportunities - opportunity - 24481 - 24494 - NNS - - - , - , - 24495 - 24496 - , - - - and - and - 24497 - 24500 - CC - - - workforce - workforce - 24501 - 24510 - NN - - - development - development - 24511 - 24522 - NN - - - through - through - 24523 - 24530 - IN - - - graduate/faculty - graduate/faculty - 24531 - 24547 - NN - - - research - research - 24548 - 24556 - NN - - - programs - program - 24557 - 24565 - NNS - - - across - across - 24566 - 24572 - IN - - - TIGRE - TIGRE - 24573 - 24578 - NNP - - - Institutions - Institutions - 24579 - 24591 - NNP - - - . - . - 24592 - 24593 - . - - - - - - \ No newline at end of file From 7e90d6dbe4ecbc5a1cd90132ba3589052516ba79 Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Mon, 7 Mar 2022 10:43:35 +0100 Subject: [PATCH 22/24] update requirements and README --- README.md | 41 +++++++++++++++++++---------------------- requirements.txt | 4 +++- 2 files changed, 22 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 4b07ce86..894baaf9 100644 --- a/README.md +++ b/README.md @@ -25,14 +25,6 @@ To pip install `pke` from github: pip install git+https://github.com/boudinfl/pke.git ``` -`pke` also requires external resources that can be obtained using: - -```bash -python -m spacy download en_core_web_sm # download the english model -``` - -As of April 2019, `pke` only supports Python 3.6+. - ## Minimal example `pke` provides a standardized API for extracting keyphrases from a document. @@ -45,9 +37,9 @@ import pke # initialize keyphrase extraction model, here TopicRank extractor = pke.unsupervised.TopicRank() -# load the content of the document, here document is expected to be in raw -# format (i.e. a simple text file) and preprocessing is carried out using spacy -extractor.load_document(input='/path/to/input.txt', language='en') +# load the content of the document, here document is expected to be a simple +# test string and preprocessing is carried out using spacy +extractor.load_document(input='text', language='en') # keyphrase candidate selection, in the case of TopicRank: sequences of nouns # and adjectives (i.e. `(Noun|Adj)*`) @@ -65,8 +57,13 @@ A detailed example is provided in the [`examples/`](examples/) directory. ## Getting started -Tutorials and code documentation are available at -[https://boudinfl.github.io/pke/](https://boudinfl.github.io/pke/). +To get your hands dirty with `pke`, we invite you to try our tutorials out. + +| Name | Link | +| ---------------------------------------------- | ---------- | +| Graph-based keyphrase extraction with TopicRank | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-1-graph-based-keyphrase-extraction.ipynb) | +| Model parameterization | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-2-parameterization.ipynb) | +| Benchmarking models | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-3-benchmarking-models.ipynb) | ## Implemented models @@ -75,18 +72,18 @@ Tutorials and code documentation are available at * Unsupervised models * Statistical models * FirstPhrases - * TfIdf [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#tfidf)] - * YAKE [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#yake), [article by (Campos et al., 2020)](https://doi.org/10.1016/j.ins.2019.09.013)] + * TfIdf + * YAKE [(Campos et al., 2020)](https://doi.org/10.1016/j.ins.2019.09.013) * Graph-based models - * TextRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#textrank), [article by (Mihalcea and Tarau, 2004)](http://www.aclweb.org/anthology/W04-3252.pdf)] - * SingleRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#singlerank), [article by (Wan and Xiao, 2008)](http://www.aclweb.org/anthology/C08-1122.pdf)] - * TopicRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#topicrank), [article by (Bougouin et al., 2013)](http://aclweb.org/anthology/I13-1062.pdf)] - * TopicalPageRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#topicalpagerank), [article by (Sterckx et al., 2015)](http://users.intec.ugent.be/cdvelder/papers/2015/sterckx2015wwwb.pdf)] - * PositionRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#positionrank), [article by (Florescu and Caragea, 2017)](http://www.aclweb.org/anthology/P17-1102.pdf)] - * MultipartiteRank [[documentation](https://boudinfl.github.io/pke/build/html/unsupervised.html#multipartiterank), [article by (Boudin, 2018)](https://arxiv.org/abs/1803.08721)] + * TextRank [(Mihalcea and Tarau, 2004)](http://www.aclweb.org/anthology/W04-3252.pdf) + * SingleRank [(Wan and Xiao, 2008)](http://www.aclweb.org/anthology/C08-1122.pdf) + * TopicRank [(Bougouin et al., 2013)](http://aclweb.org/anthology/I13-1062.pdf) + * TopicalPageRank [(Sterckx et al., 2015)](http://users.intec.ugent.be/cdvelder/papers/2015/sterckx2015wwwb.pdf) + * PositionRank [(Florescu and Caragea, 2017)](http://www.aclweb.org/anthology/P17-1102.pdf) + * MultipartiteRank [(Boudin, 2018)](https://arxiv.org/abs/1803.08721) * Supervised models * Feature-based models - * Kea [[documentation](https://boudinfl.github.io/pke/build/html/supervised.html#kea), [article by (Witten et al., 2005)](https://www.cs.waikato.ac.nz/ml/publications/2005/chap_Witten-et-al_Windows.pdf)] + * Kea [(Witten et al., 2005)](https://www.cs.waikato.ac.nz/ml/publications/2005/chap_Witten-et-al_Windows.pdf) ## Citing pke diff --git a/requirements.txt b/requirements.txt index 5fc3a3cd..962f9fbc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -spacy>3.1.0 nltk networkx numpy @@ -7,3 +6,6 @@ scikit-learn unidecode future joblib +spacy>=3.0.0,<4.0.0 +https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0.tar.gz#egg=en_core_web_sm + From 43b9783f5397df3d1741feaadf0ff64f401ce19c Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Mon, 7 Mar 2022 10:51:56 +0100 Subject: [PATCH 23/24] fix/updates --- examples/keyphrase-extraction.py | 3 +-- pke/utils.py | 1 - setup.py | 5 +++-- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/keyphrase-extraction.py b/examples/keyphrase-extraction.py index 892be8cf..d0ac70a1 100644 --- a/examples/keyphrase-extraction.py +++ b/examples/keyphrase-extraction.py @@ -4,11 +4,10 @@ # this example uses TopicRank from pke.unsupervised import TopicRank - # create a TopicRank extractor extractor = TopicRank() -# load the content of the document, here in CoreNLP XML format +# load the content of the document, here in raw text format # the input language is set to English (used for the stoplist) # normalization is set to stemming (computed with Porter's stemming algorithm) with open('2.txt') as f: diff --git a/pke/utils.py b/pke/utils.py index 1dd104a5..3f3a5dfd 100755 --- a/pke/utils.py +++ b/pke/utils.py @@ -9,7 +9,6 @@ import os import sys import csv -import glob import pickle import gzip import json diff --git a/setup.py b/setup.py index f11bc78a..90213901 100644 --- a/setup.py +++ b/setup.py @@ -15,11 +15,12 @@ 'networkx', 'numpy', 'scipy', - 'spacy', 'sklearn', 'unidecode', 'future', - 'joblib' + 'joblib', + 'spacy', + 'en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0.tar.gz', ], package_data={'pke': ['models/*.pickle', 'models/*.gz']} ) From 67b46dc2f61adb8e02fcbe05b32497f80e77d4fa Mon Sep 17 00:00:00 2001 From: Florian Boudin Date: Mon, 7 Mar 2022 11:10:13 +0100 Subject: [PATCH 24/24] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 894baaf9..8e4aad7b 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ To get your hands dirty with `pke`, we invite you to try our tutorials out. | Name | Link | | ---------------------------------------------- | ---------- | -| Graph-based keyphrase extraction with TopicRank | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-1-graph-based-keyphrase-extraction.ipynb) | +| Getting started with `pke` and keyphrase extraction | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-1-graph-based-keyphrase-extraction.ipynb) | | Model parameterization | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-2-parameterization.ipynb) | | Benchmarking models | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-3-benchmarking-models.ipynb) |

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