Sourcery refactored master branch#1
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| body = self.es.get(index=self.es_index, id=word) | ||
| data = body["_source"] | ||
| return data | ||
| return body["_source"] |
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Function Dictionary.get_word refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| search_text_box = placeholder.text_input('Word', value=st.session_state['current_word'], key='sidebar_text_input') | ||
| if search_text_box: | ||
| if search_text_box := placeholder.text_input( | ||
| 'Word', | ||
| value=st.session_state['current_word'], | ||
| key='sidebar_text_input', | ||
| ): |
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Lines 43-59 refactored with the following changes:
- Use named expression to simplify assignment and conditional [×2] (
use-named-expression)
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| f = open(DICTIONARY_FILE, "a") | ||
| i = 0 | ||
| for res in query_doc(es, index_name): | ||
| docs = res["hits"]["hits"] | ||
| print(len(docs)) | ||
| doc_content = extract_dictionary_from_elasticsearch(docs) | ||
| if len(doc_content) > 1: | ||
| dict_content = yaml.dump(doc_content, allow_unicode=True, sort_keys=True) | ||
| f.write(dict_content) | ||
| i += 1 | ||
| f.close() | ||
| with open(DICTIONARY_FILE, "a") as f: | ||
| i = 0 | ||
| for res in query_doc(es, index_name): | ||
| docs = res["hits"]["hits"] | ||
| print(len(docs)) | ||
| doc_content = extract_dictionary_from_elasticsearch(docs) | ||
| if len(doc_content) > 1: | ||
| dict_content = yaml.dump(doc_content, allow_unicode=True, sort_keys=True) | ||
| f.write(dict_content) | ||
| i += 1 |
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Lines 40-50 refactored with the following changes:
- Use
withwhen opening file to ensure closure (ensure-file-closed)
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| dict = joblib.load(dict_file) | ||
| return dict | ||
| return joblib.load(dict_file) |
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Function load_dictionary refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| "ADJ", "ADV", "INTJ", "NOUN", "PROPN", "PRON", "SYM", "X", "N:G", "VERB:G", "NY", | ||
| "N", "NB", "NNPy", | ||
| "NNP", "NNPy", | ||
| "V", "VERB", | ||
| "Num", "NUMx", "NUM", "NUMX" | ||
| "ADJ", | ||
| "ADV", | ||
| "INTJ", | ||
| "NOUN", | ||
| "PROPN", | ||
| "PRON", | ||
| "SYM", | ||
| "X", | ||
| "N:G", | ||
| "VERB:G", | ||
| "NY", | ||
| "N", | ||
| "NB", | ||
| "NNP", | ||
| "NNPy", | ||
| "V", | ||
| "VERB", | ||
| "Num", | ||
| "NUMx", | ||
| "NUM", | ||
| "NUMX", | ||
| } | ||
|
|
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Lines 63-67 refactored with the following changes:
- Remove duplicate keys when instantiating sets (
remove-duplicate-set-key)
| result.append(new_row) | ||
| new_sentence = "\n".join(result) | ||
| return new_sentence | ||
| return "\n".join(result) |
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Function add_lemma_column refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| if r is None: | ||
| return None | ||
| return r.json() | ||
| return None if r is None else r.json() |
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Function ChatUser.send 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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| max_len = 0 | ||
| for file in ["train.txt", "test.txt", "dev.txt"]: | ||
| print(file) | ||
| max_len = 0 |
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Lines 31-31 refactored with the following changes:
- Hoist statements out of for/while loops (
hoist-statement-from-loop)
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| if label not in self.label2index: | ||
| index = self.vocab_size | ||
| self.label2index[label] = index | ||
| self.index2label[index] = label | ||
| self.vocab_size += 1 | ||
| return index | ||
| else: | ||
| if label in self.label2index: | ||
| return self.label2index[label] | ||
| index = self.vocab_size | ||
| self.label2index[label] = index | ||
| self.index2label[index] = label | ||
| self.vocab_size += 1 | ||
| return index |
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Function LabelEncoder.encode refactored with the following changes:
- Swap if/else branches (
swap-if-else-branches) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
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| optimizer = AdamW(self.parameters(), lr=2e-5) | ||
| return optimizer | ||
| return AdamW(self.parameters(), lr=2e-5) |
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Function BertForTokenClassification.configure_optimizers refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| example_id += 1 | ||
| elif preds_list[example_id]: | ||
| output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n" | ||
| output_line = f"{line.split()[0]} {preds_list[example_id].pop(0)}" + "\n" |
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Function NER.write_predictions_to_file refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| if path: | ||
| with open(path, "r") as f: | ||
| labels = f.read().splitlines() | ||
| if "O" not in labels: | ||
| labels = ["O"] + labels | ||
| return labels | ||
| else: | ||
| if not path: | ||
| return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] | ||
| with open(path, "r") as f: | ||
| labels = f.read().splitlines() | ||
| if "O" not in labels: | ||
| labels = ["O"] + labels | ||
| return labels |
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Function NER.get_labels refactored with the following changes:
- Swap if/else branches (
swap-if-else-branches) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
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| if path: | ||
| with open(path, "r") as f: | ||
| labels = f.read().splitlines() | ||
| if "O" not in labels: | ||
| labels = ["O"] + labels | ||
| return labels | ||
| else: | ||
| if not path: |
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Function Chunk.get_labels refactored with the following changes:
- Swap if/else branches (
swap-if-else-branches) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
| def configure_optimizers(self): | ||
| optimizer = AdamW(self.parameters(), lr=2e-5) | ||
| return optimizer | ||
| return AdamW(self.parameters(), lr=2e-5) |
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Function BertForMultilabelClassification.configure_optimizers refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| loss = 0 | ||
| gpt2_outputs = self.gpt2(input_ids) | ||
| hidden_states = gpt2_outputs[0].squeeze() | ||
| logits = self.logit(self.linear(hidden_states)) | ||
| batch_size, sequence_length = input_ids.shape[:2] | ||
| logits = logits[range(batch_size), sequence_length] | ||
| if labels is not None: | ||
| loss = self.criterion(logits, labels) | ||
| loss = self.criterion(logits, labels) if labels is not None else 0 |
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Function GPT2TextClassification.forward refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp) - Move assignment closer to its usage within a block (
move-assign-in-block) - Move setting of default value for variable into
elsebranch (introduce-default-else)
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| optimizer = SGD(self.parameters(), lr=1e-6) | ||
| return optimizer | ||
| return SGD(self.parameters(), lr=1e-6) |
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Function GPT2TextClassification.configure_optimizers refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| for i, line in enumerate(f): | ||
| for line in f: | ||
| word, freq = line.split("\t\t") | ||
| other_words = Normalizer.normalize(word) | ||
| uts_words = text_normalize(word) | ||
| if word != "nghiêng" and len(word) > 6: | ||
| continue | ||
| if other_words != word and other_words != uts_words: | ||
| if other_words not in [word, uts_words]: |
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Function compare_two_tools refactored with the following changes:
- Remove unnecessary calls to
enumeratewhen the index is not used (remove-unused-enumerate) - Replace multiple comparisons of same variable with
inoperator (merge-comparisons)
| result.append(new_row) | ||
| new_s = "\n".join(result) | ||
| return new_s | ||
| return "\n".join(result) |
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Function predict_sentence refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| for key in syllable_map_r: | ||
| items = syllable_map_r[key] | ||
| for key, items in syllable_map_r.items(): | ||
| for item in items: | ||
| syllable_map[item] = key | ||
| NONE_DIACRITIC_SINGLE_VOWELS = set(["a", "e", "i", "o", "u", "y"]) | ||
| NONE_DIACRITIC_DOUBLE_VOWELS = set([ | ||
| "ai", "ao", "au", "ay", | ||
| "eo", "eu", "ia", "ie", "iu", "oa", "oe", "oi", "oo", | ||
| "ua", "ue", "ui", "uo", "uu", "uy", "ye" | ||
| ]) | ||
| NONE_DIACRITIC_TRIPLE_VOWELS = set([ | ||
| "iai", "ieu", "iua", "oai", "oao", "oay", "oeo", | ||
| "uao", "uai", "uay", "uoi", "uou", "uya", "uye", "uyu", | ||
| "yeu" | ||
| ]) | ||
| NONE_DIACRITIC_SINGLE_VOWELS = {"a", "e", "i", "o", "u", "y"} | ||
| NONE_DIACRITIC_DOUBLE_VOWELS = { | ||
| "ai", | ||
| "ao", | ||
| "au", | ||
| "ay", | ||
| "eo", | ||
| "eu", | ||
| "ia", | ||
| "ie", | ||
| "iu", | ||
| "oa", | ||
| "oe", | ||
| "oi", | ||
| "oo", | ||
| "ua", | ||
| "ue", | ||
| "ui", | ||
| "uo", | ||
| "uu", | ||
| "uy", | ||
| "ye", | ||
| } | ||
|
|
||
| NONE_DIACRITIC_TRIPLE_VOWELS = { | ||
| "iai", | ||
| "ieu", | ||
| "iua", | ||
| "oai", | ||
| "oao", | ||
| "oay", | ||
| "oeo", | ||
| "uao", | ||
| "uai", | ||
| "uay", | ||
| "uoi", | ||
| "uou", | ||
| "uya", | ||
| "uye", | ||
| "uyu", | ||
| "yeu", | ||
| } | ||
|
|
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Lines 81-95 refactored with the following changes:
- Use items() to directly unpack dictionary values (
use-dict-items) - Unwrap a constant iterable constructor [×3] (
unwrap-iterable-construction)
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| if group in NONE_DIACRITIC_VOWELS: | ||
| miss_spell = False | ||
| else: | ||
| miss_spell = True | ||
| miss_spell = group not in NONE_DIACRITIC_VOWELS |
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Function AnalysableWord.__init__ refactored with the following changes:
- Simplify boolean if expression (
boolean-if-exp-identity) - Remove unnecessary casts to int, str, float or bool (
remove-unnecessary-cast) - Replace if statement with if expression (
assign-if-exp)
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| dic = {} | ||
| char1252 = 'à|á|ả|ã|ạ|ầ|ấ|ẩ|ẫ|ậ|ằ|ắ|ẳ|ẵ|ặ|è|é|ẻ|ẽ|ẹ|ề|ế|ể|ễ|ệ|ì|í|ỉ|ĩ|ị|ò|ó|ỏ|õ|ọ|ồ|ố|ổ|ỗ|ộ|ờ|ớ|ở|ỡ|ợ|ù|ú|ủ|ũ|ụ|ừ|ứ|ử|ữ|ự|ỳ|ý|ỷ|ỹ|ỵ|À|Á|Ả|Ã|Ạ|Ầ|Ấ|Ẩ|Ẫ|Ậ|Ằ|Ắ|Ẳ|Ẵ|Ặ|È|É|Ẻ|Ẽ|Ẹ|Ề|Ế|Ể|Ễ|Ệ|Ì|Í|Ỉ|Ĩ|Ị|Ò|Ó|Ỏ|Õ|Ọ|Ồ|Ố|Ổ|Ỗ|Ộ|Ờ|Ớ|Ở|Ỡ|Ợ|Ù|Ú|Ủ|Ũ|Ụ|Ừ|Ứ|Ử|Ữ|Ự|Ỳ|Ý|Ỷ|Ỹ|Ỵ'.split( | ||
| '|') | ||
| charutf8 = "à|á|ả|ã|ạ|ầ|ấ|ẩ|ẫ|ậ|ằ|ắ|ẳ|ẵ|ặ|è|é|ẻ|ẽ|ẹ|ề|ế|ể|ễ|ệ|ì|í|ỉ|ĩ|ị|ò|ó|ỏ|õ|ọ|ồ|ố|ổ|ỗ|ộ|ờ|ớ|ở|ỡ|ợ|ù|ú|ủ|ũ|ụ|ừ|ứ|ử|ữ|ự|ỳ|ý|ỷ|ỹ|ỵ|À|Á|Ả|Ã|Ạ|Ầ|Ấ|Ẩ|Ẫ|Ậ|Ằ|Ắ|Ẳ|Ẵ|Ặ|È|É|Ẻ|Ẽ|Ẹ|Ề|Ế|Ể|Ễ|Ệ|Ì|Í|Ỉ|Ĩ|Ị|Ò|Ó|Ỏ|Õ|Ọ|Ồ|Ố|Ổ|Ỗ|Ộ|Ờ|Ớ|Ở|Ỡ|Ợ|Ù|Ú|Ủ|Ũ|Ụ|Ừ|Ứ|Ử|Ữ|Ự|Ỳ|Ý|Ỷ|Ỹ|Ỵ".split( | ||
| '|') | ||
| for i in range(len(char1252)): | ||
| dic[char1252[i]] = charutf8[i] | ||
| return dic | ||
| return {char1252[i]: charutf8[i] for i in range(len(char1252))} |
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Function loaddicchar refactored with the following changes:
- Move assignment closer to its usage within a block (
move-assign-in-block) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Convert for loop into dictionary comprehension (
dict-comprehension)
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| if x == 4 or x == 8: # ê, ơ | ||
| if x in [4, 8]: # ê, ơ |
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Function chuan_hoa_dau_tu_tieng_viet refactored with the following changes:
- Replace multiple comparisons of same variable with
inoperator (merge-comparisons)
| if index - nguyen_am_index != 1: | ||
| return False | ||
| nguyen_am_index = index | ||
| return False |
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Function is_valid_vietnam_word refactored with the following changes:
- Merge nested if conditions (
merge-nested-ifs) - Lift code into else after jump in control flow (
reintroduce-else) - Hoist nested repeated code outside conditional statements [×2] (
hoist-similar-statement-from-if) - Swap positions of nested conditionals [×2] (
swap-nested-ifs) - Swap if/else to remove empty if body (
remove-pass-body) - Hoist repeated code outside conditional statement (
hoist-statement-from-if) - Swap if/else branches (
swap-if-else-branches)
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| if response.status_code not in [200, 302]: | ||
| if "www.dropbox.com" in url: | ||
| # dropbox return code 301, so we ignore this error | ||
| pass | ||
| else: | ||
| raise IOError("HEAD request failed for url {}".format(url)) | ||
| if response.status_code not in [200, 302] and "www.dropbox.com" not in url: | ||
| raise IOError(f"HEAD request failed for url {url}") |
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Function get_from_cache refactored with the following changes:
- Merge nested if conditions (
merge-nested-ifs) - Swap if/else to remove empty if body (
remove-pass-body) - Replace call to format with f-string (
use-fstring-for-formatting)
This removes the following comments ( why? ):
# dropbox return code 301, so we ignore this error
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| if use_slower_interval: | ||
| Tqdm.default_mininterval = 10.0 | ||
| else: | ||
| Tqdm.default_mininterval = 0.1 | ||
| Tqdm.default_mininterval = 10.0 if use_slower_interval else 0.1 |
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Function Tqdm.set_slower_interval refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
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| if not all: | ||
| if license == "Close": | ||
| continue | ||
| if not all and license == "Close": | ||
| continue |
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Function ModelFetcher.list refactored with the following changes:
- Merge nested if conditions (
merge-nested-ifs)
| self._score = score | ||
| else: | ||
| self._score = 1.0 | ||
| self._score = score if 0.0 <= score <= 1.0 else 1.0 |
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Function Label.score refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
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| return "{} ({})".format(self._value, self._score) | ||
| return f"{self._value} ({self._score})" |
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Function Label.__str__ refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting)
Author
Sourcery Code Quality Report✅ Merging this PR will increase code quality in the affected files by 0.13%.
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! |
* Open file with encoding='utf-8'
undertheseanlpGH-560: Add encoding='utf-8' to fix "UnicodeDecodeError"
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