You've already forked DataMate
109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
#!/usr/bin/python3.9
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# -*- coding: utf-8 -*-
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import math
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from multiprocessing import Pool, cpu_count
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from six import iteritems
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from six.moves import range
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from loguru import logger
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PARAM_K1 = 1.5
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PARAM_B = 0.75
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EPSILON = 0.25
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def effective_n_jobs(n_jobs):
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if n_jobs == 0:
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raise ValueError('n_jobs == 0 in Parallel has no meaning')
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elif n_jobs is None:
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return 1
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elif n_jobs < 0:
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n_jobs = max(cpu_count() + 1 + n_jobs, 1)
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return n_jobs
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class SimilarityAlgBM25(object):
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def __init__(self, corpus_docs):
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self.corpus_files_size = 0
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self.avg_dl = 0
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self.doc_file_freqs = []
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self.idf_dict = {}
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self.doc_len = []
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self._initialize(corpus_docs)
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def get_sim_score(self, document, index):
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score = 0
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doc_freqs = self.doc_file_freqs[index]
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for word in document:
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if word not in doc_freqs:
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continue
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try:
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score += (self.idf_dict[word] * doc_freqs[word] * (PARAM_K1 + 1)
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/ (doc_freqs[word] + PARAM_K1 * (1 - PARAM_B + PARAM_B * self.doc_len[index] / self.avg_dl)))
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except KeyError as ke:
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logger.warning('key not found in doc_freqs dict: ', word)
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return score
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def get_sim_scores(self, document):
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scores = []
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for index in range(self.corpus_files_size):
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cur_score = self.get_sim_score(document, index)
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scores.append(cur_score)
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return scores
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def get_scores_bow(self, document):
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scores = []
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for index in range(self.corpus_files_size):
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score = self.get_sim_score(document, index)
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if score > 0:
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scores.append((index, score))
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return scores
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def _initialize(self, corpus_files):
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"""
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Calculates frequencies of terms in documents and in corpus_files.
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Also computes inverse document frequencies.
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"""
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nd = {} # word -> number of documents with word
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num_doc = 0
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for document_file in corpus_files:
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self.corpus_files_size += 1
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self.doc_len.append(len(document_file))
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num_doc += len(document_file)
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frequencies_dict = {}
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for word in document_file:
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if word not in frequencies_dict:
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frequencies_dict[word] = 0
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frequencies_dict[word] += 1
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self.doc_file_freqs.append(frequencies_dict)
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for word, _ in iteritems(frequencies_dict):
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if word not in nd:
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nd[word] = 0
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nd[word] += 1
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self.avg_dl = float(num_doc) / self.corpus_files_size
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# collect idf sum to calculate an average idf for epsilon value
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idf_sum = 0
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negative_idfs_list = []
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for word, freq in iteritems(nd):
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idf = math.log(self.corpus_files_size - freq + 0.5) - math.log(freq + 0.5)
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self.idf_dict[word] = idf
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idf_sum += idf
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if idf < 0:
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negative_idfs_list.append(word)
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self.average_idf = float(idf_sum) / len(self.idf_dict)
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eps = EPSILON * self.average_idf
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for word in negative_idfs_list:
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self.idf_dict[word] = eps
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