Sourcery refactored master branch #15
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| # 统计结果写入result.txt(字典的遍历) | ||
| for (k, v) in num_dict.items(): | ||
| open('data/result.txt', 'a+').write(str(k) + ' ' + str(v) + '\n') # 将k，v转换为str类型 | ||
| open('data/result.txt', 'a+').write(f'{str(k)} {str(v)}' + '\n') |
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Function com_tf refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
This removes the following comments ( why? ):
# 将k，v转换为str类型
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| path1 = path + 'data/title_and_abs/' | ||
| newpath = path + "data/pro_keyword/" | ||
| path1 = f'{path}data/title_and_abs/' | ||
| newpath = f"{path}data/pro_keyword/" |
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Lines 20-21 refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| data_source = open(file_import_url, 'r') | ||
| data = data_source.readline() | ||
| word_in_afile_stat = {} | ||
| word_in_allfiles_stat = {} | ||
| files_num = 0 | ||
| while data != "": # 对文件pro_res.txt进行处理 | ||
| data_temp_1 = data.strip("\n").split("\t") # file name and key words of a file | ||
| data_temp_2 = data_temp_1[1].split(",") # key words of a file | ||
| file_name = data_temp_1[0] | ||
| data_temp_len = len(data_temp_2) | ||
| files_num += 1 | ||
| data_dict = {} | ||
| data_dict.clear() | ||
| for word in data_temp_2: | ||
| if word not in word_in_allfiles_stat: | ||
| word_in_allfiles_stat[word] = 1 | ||
| data_dict[word] = 1 | ||
| else: | ||
| if word not in data_dict: # 如果这个单词在这个文件中之前没有出现过 | ||
| with open(file_import_url, 'r') as data_source: | ||
| data = data_source.readline() | ||
| word_in_afile_stat = {} | ||
| word_in_allfiles_stat = {} | ||
| files_num = 0 | ||
| while data != "": # 对文件pro_res.txt进行处理 | ||
| data_temp_1 = data.strip("\n").split("\t") # file name and key words of a file | ||
| data_temp_2 = data_temp_1[1].split(",") # key words of a file | ||
| file_name = data_temp_1[0] | ||
| data_temp_len = len(data_temp_2) | ||
| files_num += 1 | ||
| data_dict = {} | ||
| data_dict.clear() | ||
| for word in data_temp_2: | ||
| if word not in word_in_allfiles_stat: | ||
| word_in_allfiles_stat[word] = 1 | ||
| data_dict[word] = 1 | ||
| elif word not in data_dict: # 如果这个单词在这个文件中之前没有出现过 | ||
| word_in_allfiles_stat[word] += 1 | ||
| data_dict[word] = 1 | ||
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| if not word_in_afile_stat.has_key(file_name): | ||
| word_in_afile_stat[file_name] = {} | ||
| if not word_in_afile_stat[file_name].has_key(word): | ||
| word_in_afile_stat[file_name][word] = [] | ||
| word_in_afile_stat[file_name][word].append(data_temp_2.count(word)) | ||
| word_in_afile_stat[file_name][word].append(data_temp_len) | ||
| data = data_source.readline() | ||
| data_source.close() | ||
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| if not word_in_afile_stat.has_key(file_name): | ||
| word_in_afile_stat[file_name] = {} | ||
| if not word_in_afile_stat[file_name].has_key(word): | ||
| word_in_afile_stat[file_name][word] = [data_temp_2.count(word), data_temp_len] | ||
| data = data_source.readline() | ||
| # filelist = os.listdir(newpath2) # 取得当前路径下的所有文件 | ||
| TF_IDF_last_result = [] | ||
| if (word_in_afile_stat) and (word_in_allfiles_stat) and (files_num != 0): | ||
| for filename in word_in_afile_stat.keys(): | ||
| for filename, value in word_in_afile_stat.items(): | ||
| TF_IDF_result = {} | ||
| TF_IDF_result.clear() | ||
| for word in word_in_afile_stat[filename].keys(): | ||
| for word in value.keys(): |
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Function TF_IDF_Compute refactored with the following changes:
- Use
withwhen opening file to ensure closure [×2] (ensure-file-closed) - Merge else clause's nested if statement into elif (
merge-else-if-into-elif) - Merge append into list declaration [×2] (
merge-list-append) - Use items() to directly unpack dictionary values (
use-dict-items) - Remove unnecessary call to keys() (
remove-dict-keys) - Replace a[0:x] with a[:x] and a[x:len(a)] with a[x:] (
remove-redundant-slice-index)
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| path = base_path + 'data/computer/'# 原始数据 | ||
| path1 = base_path + 'data/title_and_abs/' # 处理后的标题和摘要 | ||
| newpath = base_path + 'data/pro_keyword/' | ||
| newpath2 = base_path + 'data/keyword/' | ||
| path = f'{base_path}data/computer/' | ||
| path1 = f'{base_path}data/title_and_abs/' | ||
| newpath = f'{base_path}data/pro_keyword/' | ||
| newpath2 = f'{base_path}data/keyword/' |
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Lines 17-20 refactored with the following changes:
- Use f-string instead of string concatenation [×4] (
use-fstring-for-concatenation)
This removes the following comments ( why? ):
# 处理后的标题和摘要
# 原始数据
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| # print b | ||
| if b is None or b.string is None: | ||
| continue | ||
| else: | ||
| abstracts.extend(soup.title.stripped_strings) | ||
| s = b.string | ||
| abstracts.extend(s.encode('utf-8')) | ||
| f = open(path1 + filename + ".txt", "w+") # 写入txt文件 | ||
| abstracts.extend(soup.title.stripped_strings) | ||
| s = b.string | ||
| abstracts.extend(s.encode('utf-8')) | ||
| with open(path1 + filename + ".txt", "w+") as f: | ||
| for i in abstracts: | ||
| f.write(i) | ||
| f.close() | ||
| abstracts = [] | ||
| abstracts = [] |
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Function get_text refactored with the following changes:
- Remove unnecessary else after guard condition (
remove-unnecessary-else) - Use
withwhen opening file to ensure closure [×2] (ensure-file-closed)
This removes the following comments ( why? ):
# 写入txt文件
# 将得到的未处理的文字放在pro_keyword文件夹中
# print b
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| features = [text_len, isHasSH] | ||
| return features | ||
| return [text_len, isHasSH] |
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Function get_feature refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| print(X[0:10]) | ||
| print(Y[0:10]) | ||
| print(X[:10]) | ||
| print(Y[:10]) |
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Function load_data refactored with the following changes:
- Replace a[0:x] with a[:x] and a[x:len(a)] with a[x:] [×2] (
remove-redundant-slice-index)
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| if __name__ == '__main__': | ||
| pass | ||
| pass |
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Lines 48-49 refactored with the following changes:
- Remove redundant conditional (
remove-redundant-if)
| data_size = len(data) | ||
| num_batches_per_epoch = int(len(data)/batch_size) + 1 | ||
| for epoch in range(num_epochs): | ||
| for _ in range(num_epochs): |
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Function batch_iter refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore)
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| raise ValueError("Linear is expecting 2D arguments: %s" % str(shape)) | ||
| raise ValueError(f"Linear is expecting 2D arguments: {str(shape)}") | ||
| if not shape[1]: | ||
| raise ValueError("Linear expects shape[1] of arguments: %s" % str(shape)) | ||
| raise ValueError(f"Linear expects shape[1] of arguments: {str(shape)}") |
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Function linear refactored with the following changes:
- Replace interpolated string formatting with f-string [×2] (
replace-interpolation-with-fstring)
| pooled_outputs = [] | ||
| for filter_size, num_filter in zip(filter_sizes, num_filters): | ||
| with tf.name_scope("conv-maxpool-%s" % filter_size): | ||
| with tf.name_scope(f"conv-maxpool-{filter_size}"): |
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Function TextCNN.__init__ refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
| print("\nParameters:") | ||
| for attr, value in sorted(FLAGS.__flags.iteritems()): | ||
| print("{}={}".format(attr.upper(), value)) | ||
| print(f"{attr.upper()}={value}") |
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Lines 36-46 refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting)
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| f2 = open('%s.txt' % item, 'a+') | ||
| for (k, v) in data_dict.items(): | ||
| f2.write(v + ',' + k + ' ' + '\n') | ||
| f2.close() | ||
| with open(f'{item}.txt', 'a+') as f2: | ||
| for (k, v) in data_dict.items(): | ||
| f2.write(v + ',' + k + ' ' + '\n') |
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Function get_text refactored with the following changes:
- Use
withwhen opening file to ensure closure (ensure-file-closed) - Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
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| # print (files) | ||
| f = open(base_path + files, 'r') | ||
| text = (f.read().decode('GB2312', 'ignore').encode('utf-8')) | ||
| salt = ''.join(random.sample(string.ascii_letters + string.digits, 8)) # 产生随机数 | ||
| f2 = open("C:/Users/kaifun/Desktop/ass_TIP/TextInfoExp/Part2_Text_Classify/test3/" + salt + '.txt', 'w') | ||
| f2.write(text) | ||
| f3.write(salt + ' ' + 'e' + '\n') | ||
| f.close() | ||
| with open(base_path + files, 'r') as f: | ||
| text = (f.read().decode('GB2312', 'ignore').encode('utf-8')) | ||
| salt = ''.join(random.sample(string.ascii_letters + string.digits, 8)) # 产生随机数 | ||
| f2 = open( | ||
| f"C:/Users/kaifun/Desktop/ass_TIP/TextInfoExp/Part2_Text_Classify/test3/{salt}.txt", | ||
| 'w', | ||
| ) | ||
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| f2.write(text) | ||
| f3.write(f'{salt} e' + '\n') |
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Function trans_text refactored with the following changes:
- Use
withwhen opening file to ensure closure (ensure-file-closed) - Use f-string instead of string concatenation [×4] (
use-fstring-for-concatenation)
This removes the following comments ( why? ):
# print (files)
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| f.write(str(test_name[i]) + ' ' + str(result[i]) + '\n') | ||
| f.write(f'{str(test_name[i])} {str(result[i])}' + '\n') |
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Function get_classify refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| if judgement != "": | ||
| return 4, judgement | ||
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| return 0, "" | ||
| return (4, judgement) if judgement != "" else (0, "") |
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Function DictClassifier.__analyse_word refactored with the following changes:
- Lift code into else after jump in control flow (
reintroduce-else) - Replace if statement with if expression (
assign-if-exp)
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| if match is not None: | ||
| pattern = {"key": "要的是…给的是…", "value": 1} | ||
| return pattern | ||
| return "" | ||
| return {"key": "要的是…给的是…", "value": 1} if match is not None else "" |
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Function DictClassifier.__is_clause_pattern1 refactored with the following changes:
- Lift code into else after jump in control flow (
reintroduce-else) - Replace if statement with if expression (
assign-if-exp) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| conjunction = {"key": the_word, "value": self.__conjunction_dict[the_word]} | ||
| return conjunction | ||
| return {"key": the_word, "value": self.__conjunction_dict[the_word]} |
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Function DictClassifier.__is_word_conjunction refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| punctuation = {"key": the_word, "value": self.__punctuation_dict[the_word]} | ||
| return punctuation | ||
| return {"key": the_word, "value": self.__punctuation_dict[the_word]} |
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Function DictClassifier.__is_word_punctuation refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| output += "Sub-clause" + str(i) + ": " | ||
| clause = comment_analysis["su-clause" + str(i)] | ||
| output += f"Sub-clause{str(i)}: " | ||
| clause = comment_analysis[f"su-clause{str(i)}"] |
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Function DictClassifier.__output_analysis refactored with the following changes:
- Use f-string instead of string concatenation [×3] (
use-fstring-for-concatenation)
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| if match is not None and len(self.__split_sentence(match.group(2))) <= 2: | ||
| to_delete = [] | ||
| for i in range(len(the_clauses)): | ||
| if the_clauses[i] in match.group(2): | ||
| to_delete.append(i) | ||
| if len(to_delete) > 0: | ||
| for i in range(len(to_delete)): | ||
| if match is not None and len(self.__split_sentence(match[2])) <= 2: | ||
| if to_delete := [ | ||
| i for i in range(len(the_clauses)) if the_clauses[i] in match[2] | ||
| ]: | ||
| for item in to_delete: | ||
| the_clauses.remove(the_clauses[to_delete[0]]) | ||
| the_clauses.insert(to_delete[0], match.group(2)) | ||
| the_clauses.insert(to_delete[0], match[2]) | ||
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| # 识别“要是|如果……就好了”的假设句式 | ||
| pattern = re.compile(r"([，%。、！；？?,!～~.… ]*)([\u4e00-\u9fa5]*?(如果|要是|" | ||
| r"希望).+就[\u4e00-\u9fa5]+(好|完美)了[，。；！%、？?,!～~.… ]+)") | ||
| match = re.search(pattern, the_sentence.strip()) | ||
| if match is not None and len(self.__split_sentence(match.group(2))) <= 3: | ||
| if match is not None and len(self.__split_sentence(match[2])) <= 3: | ||
| to_delete = [] | ||
| for i in range(len(the_clauses)): | ||
| if the_clauses[i] in match.group(2): | ||
| if the_clauses[i] in match[2]: | ||
| to_delete.append(i) | ||
| if len(to_delete) > 0: | ||
| for i in range(len(to_delete)): | ||
| if to_delete: | ||
| for item_ in to_delete: | ||
| the_clauses.remove(the_clauses[to_delete[0]]) | ||
| the_clauses.insert(to_delete[0], match.group(2)) | ||
| the_clauses.insert(to_delete[0], match[2]) |
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Function DictClassifier.__divide_sentence_into_clauses refactored with the following changes:
- Replace index in for loop with direct reference [×2] (
for-index-replacement) - Replace m.group(x) with m[x] for re.Match objects [×6] (
use-getitem-for-re-match-groups) - Use named expression to simplify assignment and conditional (
use-named-expression) - Convert for loop into list comprehension (
list-comprehension) - Simplify sequence length comparison [×2] (
simplify-len-comparison) - Replace unused for index with underscore [×2] (
for-index-underscore)
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| clauses = [''.join(x) for x in zip(split_clauses, punctuations)] | ||
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| return clauses | ||
| return [''.join(x) for x in zip(split_clauses, punctuations)] |
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Function DictClassifier.__split_sentence refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| with open(self.__root_filepath + "phrase_dict.txt", "r", encoding="utf-8") as f: | ||
| with open(f"{self.__root_filepath}phrase_dict.txt", "r", encoding="utf-8") as f: |
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Function DictClassifier.__get_phrase_dict refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
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| f.write("%s" % info) | ||
| f.write(f"{info}") |
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Function DictClassifier.__write_runout_file refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
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| sorted_distances = distances.argsort() | ||
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| return sorted_distances | ||
| return distances.argsort() |
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Function KNNClassifier.__get_sorted_distances refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| a = WaimaiCorpus() | ||
| a = Waimai2Corpus() | ||
| a = HotelCorpus() | ||
| pass |
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Function test_corpus refactored with the following changes:
- Remove redundant pass statement (
remove-redundant-pass)
| pass | ||
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| if __name__ == "__main__": | ||
| pass |
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Lines 177-177 refactored with the following changes:
- Remove redundant pass statement (
remove-redundant-pass)
| return [word for word in words[:num]] | ||
| else: | ||
| return [word[0] for word in words[:num]] | ||
| return list(words[:num]) if need_score else [word[0] for word in words[:num]] |
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Function ChiSquare.best_words refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp) - Replace identity comprehension with call to collection constructor (
identity-comprehension)
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| if type(self.k) == int: | ||
| k = "%s" % self.k | ||
| else: | ||
| k = "-".join([str(i) for i in self.k]) | ||
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| k = f"{self.k}" if type(self.k) == int else "-".join([str(i) for i in self.k]) | ||
| print("KNNClassifier") | ||
| print("---" * 45) | ||
| print("Train num = %s" % self.train_num) | ||
| print("Test num = %s" % self.test_num) | ||
| print("K = %s" % k) | ||
| print(f"Train num = {self.train_num}") | ||
| print(f"Test num = {self.test_num}") | ||
| print(f"K = {k}") |
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Function Test.test_knn refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp) - Replace interpolated string formatting with f-string [×4] (
replace-interpolation-with-fstring) - Move assignment closer to its usage within a block (
move-assign-in-block) - Convert for loop into list comprehension (
list-comprehension)
| print("BayesClassifier is testing ...") | ||
| for data in self.test_data: | ||
| classify_labels.append(bayes.classify(data)) | ||
| classify_labels = [bayes.classify(data) for data in self.test_data] |
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Function Test.test_bayes refactored with the following changes:
- Replace interpolated string formatting with f-string [×2] (
replace-interpolation-with-fstring) - Move assignment closer to its usage within a block (
move-assign-in-block) - Convert for loop into list comprehension (
list-comprehension)
|
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Sourcery Code Quality Report✅ Merging this PR will increase code quality in the affected files by 0.57%.
Here are some functions in these files that still need a tune-up:
Legend and ExplanationThe emojis denote the absolute quality of the code:
The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request. Please see our documentation here for details on how these metrics are calculated. We are actively working on this report - lots more documentation and extra metrics to come! Help us improve this quality report! |
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