Sourcery refactored main branch #2
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| self.extras: dict = {} | ||
| self.extras['autofe'] = list_requirements("pyrecdp/autofe/requirements.txt") | ||
| self.extras: dict = { | ||
| 'autofe': list_requirements("pyrecdp/autofe/requirements.txt") | ||
| } | ||
| self.extras['LLM'] = list_requirements("pyrecdp/LLM/requirements.txt") | ||
| self.extras["all"] = list(set(chain.from_iterable(self.extras.values())) | ||
| ) | ||
| self.extras["all"] = list(set(chain.from_iterable(self.extras.values())) | ||
| ) |
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Function SetupSpec.__init__ refactored with the following changes:
- Merge dictionary assignment with declaration (
merge-dict-assign)
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| file_names = dict((key, path_prefix + os.path.join(current_path, data_folder, filename)) for key, filename in SO_FILE.items()) | ||
| file_names = { | ||
| key: path_prefix + os.path.join(current_path, data_folder, filename) | ||
| for key, filename in SO_FILE.items() | ||
| } |
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Function main refactored with the following changes:
- Replace list(), dict() or set() with comprehension (
collection-builtin-to-comprehension)
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| str_fields1 = [StructField('%s' % i, StringType()) | ||
| for i in self.string_cols1] | ||
| long_fields1 = [StructField('%s' % i, LongType()) | ||
| for i in self.long_cols1] | ||
| str_fields2 = [StructField('%s' % i, StringType()) | ||
| for i in self.string_cols2] | ||
| long_fields2 = [StructField('%s' % i, LongType()) | ||
| for i in self.long_cols2] | ||
| bool_fields1 = [StructField('%s' % i, BooleanType()) | ||
| for i in self.bool_cols1] | ||
| long_fields3 = [StructField('%s' % i, LongType()) | ||
| for i in self.long_cols3] | ||
| str_fields3 = [StructField('%s' % i, StringType()) | ||
| for i in self.string_cols3] | ||
| long_fields4 = [StructField('%s' % i, LongType()) | ||
| for i in self.long_cols4] | ||
| bool_fields2 = [StructField('%s' % i, BooleanType()) | ||
| for i in self.bool_cols2] | ||
| long_fields5 = [StructField('%s' % i, LongType()) | ||
| for i in self.long_cols5] | ||
| bool_fields3 = [StructField('%s' % i, BooleanType()) | ||
| for i in self.bool_cols3] | ||
| double_fields = [StructField('%s' % i, DoubleType()) | ||
| for i in self.double_cols] | ||
| str_fields1 = [StructField(f'{i}', StringType()) for i in self.string_cols1] | ||
| long_fields1 = [StructField(f'{i}', LongType()) for i in self.long_cols1] | ||
| str_fields2 = [StructField(f'{i}', StringType()) for i in self.string_cols2] | ||
| long_fields2 = [StructField(f'{i}', LongType()) for i in self.long_cols2] | ||
| bool_fields1 = [StructField(f'{i}', BooleanType()) for i in self.bool_cols1] | ||
| long_fields3 = [StructField(f'{i}', LongType()) for i in self.long_cols3] | ||
| str_fields3 = [StructField(f'{i}', StringType()) for i in self.string_cols3] | ||
| long_fields4 = [StructField(f'{i}', LongType()) for i in self.long_cols4] | ||
| bool_fields2 = [StructField(f'{i}', BooleanType()) for i in self.bool_cols2] | ||
| long_fields5 = [StructField(f'{i}', LongType()) for i in self.long_cols5] | ||
| bool_fields3 = [StructField(f'{i}', BooleanType()) for i in self.bool_cols3] | ||
| double_fields = [StructField(f'{i}', DoubleType()) for i in self.double_cols] |
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Function RecsysSchema.toStructType refactored with the following changes:
- Replace interpolated string formatting with f-string [×12] (
replace-interpolation-with-fstring)
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| if len(x)>rw: | ||
| return hashit(x[rw]) | ||
| elif rw<0: | ||
| if len(x)>0: | ||
| return hashit(x[-1]) | ||
| else: | ||
| return 0 | ||
| return hashit(x[-1]) if len(x)>0 else 0 |
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Function ret_word refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
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| if len(text_split)>1: | ||
| if text_split[1] in ['_']: | ||
| uhash += clean_text(text_split[1]) + clean_text(text_split[2]) | ||
| text_split = text_split[2:] | ||
| else: | ||
| cl_loop = False | ||
| if len(text_split) > 1 and text_split[1] in ['_']: | ||
| uhash += clean_text(text_split[1]) + clean_text(text_split[2]) | ||
| text_split = text_split[2:] | ||
| else: | ||
| cl_loop = False | ||
|
|
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Function extract_hash refactored with the following changes:
- Merge duplicate blocks in conditional (
merge-duplicate-blocks)
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| df = spark.read.parquet(path_prefix+"/recsys2021/datapre_stage1/stage1_valid_all") | ||
| df = spark.read.parquet( | ||
| f"{path_prefix}/recsys2021/datapre_stage1/stage1_valid_all" | ||
| ) |
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Function valid_stage2 refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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| ############# load decoder data | ||
| df = spark.read.parquet(path_prefix+current_path+"test1_decode") | ||
| print("data decoded!") | ||
|
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| ############# load dict from stage 1 | ||
| dict_names = ['tweet', 'mention'] | ||
| dict_dfs = [{'col_name': name, 'dict': spark.read.parquet( | ||
| "%s/%s/%s/%s" % (proc.path_prefix, proc.current_path, proc.dicts_path, name))} for name in dict_names] | ||
| dict_dfs = [ | ||
| { | ||
| 'col_name': name, | ||
| 'dict': spark.read.parquet( | ||
| f"{proc.path_prefix}/{proc.current_path}/{proc.dicts_path}/{name}" | ||
| ), | ||
| } | ||
| for name in dict_names | ||
| ] | ||
| _, te_test_dfs, y_mean_all_df = getTargetEncodingFeaturesDicts(proc, mode='stage1', train_dict_load=False) | ||
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| ############# set up to stage 2 | ||
| current_path = "/recsys2021/datapre_stage2/" | ||
| proc = DataProcessor(spark, path_prefix, | ||
| current_path=current_path, dicts_path=dicts_folder, shuffle_disk_capacity="1500GB",spark_mode='local') | ||
|
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| ############# count encoding | ||
| ce_test_dfs = CountEncodingFeatures(df, proc, gen_dict=True,mode="inference",train_generate=False) | ||
|
|
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Function inference_join refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
| ctr = positive/float(len(gt)) | ||
| return ctr | ||
| return positive/float(len(gt)) |
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Function calculate_ctr refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| feature_list = [] | ||
| feature_list.append(stage1_reply_features) | ||
| feature_list.append(stage1_retweet_features) | ||
| feature_list.append(stage1_comment_features) | ||
| feature_list.append(stage1_like_features) | ||
| feature_list = [ | ||
| stage1_reply_features, | ||
| stage1_retweet_features, | ||
| stage1_comment_features, | ||
| stage1_like_features, | ||
| ] |
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Lines 56-174 refactored with the following changes:
- Merge append into list declaration [×4] (
merge-list-append)
| ctr = positive/float(len(gt)) | ||
| return ctr | ||
| return positive/float(len(gt)) |
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Function calculate_ctr refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| feature_list = [] | ||
| feature_list.append(stage2_reply_features) | ||
| feature_list.append(stage2_retweet_features) | ||
| feature_list.append(stage2_comment_features) | ||
| feature_list.append(stage2_like_features) | ||
| feature_list = [ | ||
| stage2_reply_features, | ||
| stage2_retweet_features, | ||
| stage2_comment_features, | ||
| stage2_like_features, | ||
| ] |
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Lines 51-165 refactored with the following changes:
- Merge append into list declaration [×4] (
merge-list-append)
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| test = pd.read_parquet(f'{data_path}/stage12_test') | ||
| test = pd.read_parquet(f'{data_path}/stage12_test') | ||
| print(test.shape) | ||
| print(f"load data took {time.time() - t1} s") | ||
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| ######## split data | ||
| t1 = time.time() | ||
| indexs = [i for i in range(distributed_nodes)] | ||
| step = int(len(test)/distributed_nodes) | ||
| indexs = list(range(distributed_nodes)) | ||
| step = len(test) // distributed_nodes | ||
| tests = [] | ||
| for i in range(distributed_nodes): | ||
| if i<distributed_nodes-1: | ||
| tests.append(test[i*step:(i+1)*step]) | ||
| else: | ||
| tests.append(test[i*step:]) | ||
|
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Lines 16-30 refactored with the following changes:
- Replace identity comprehension with call to collection constructor (
identity-comprehension) - Simplify division expressions (
simplify-division)
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| if len(x)>rw: | ||
| return hashit(x[rw]) | ||
| elif rw<0: | ||
| if len(x)>0: | ||
| return hashit(x[-1]) | ||
| else: | ||
| return 0 | ||
| return hashit(x[-1]) if len(x)>0 else 0 |
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Function ret_word refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
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| if len(text_split)>1: | ||
| if text_split[1] in ['_']: | ||
| uhash += clean_text(text_split[1]) + clean_text(text_split[2]) | ||
| text_split = text_split[2:] | ||
| else: | ||
| cl_loop = False | ||
| if len(text_split) > 1 and text_split[1] in ['_']: | ||
| uhash += clean_text(text_split[1]) + clean_text(text_split[2]) | ||
| text_split = text_split[2:] | ||
| else: | ||
| cl_loop = False | ||
|
|
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Function extract_hash refactored with the following changes:
- Merge duplicate blocks in conditional (
merge-duplicate-blocks)
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| elif x[-1]=='?' and x[-2]=='!': | ||
| elif x[-1] == '?': | ||
| return(2) | ||
| elif x[-1]=='!' and x[-2]=='?': | ||
| return(3) | ||
| elif x[-1]=='!' and x[-2]!='?': | ||
| elif x[-1] == '!': |
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Function check_last_char_quest refactored with the following changes:
- Remove redundant conditional [×2] (
remove-redundant-if)
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| ############# load decoder data | ||
| df = spark.read.parquet(path_prefix+current_path+'test1_decode.parquet') | ||
| print("data decoded!") | ||
|
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| ############# load dict from stage 1 | ||
| dict_names = ['tweet', 'mention'] | ||
| dict_dfs = [{'col_name': name, 'dict': pd.read_parquet( | ||
| "%s/%s/%s/%s" % (path_prefix, current_path, dicts_folder, name+'.parquet'))} for name in dict_names] | ||
| dict_dfs = [ | ||
| { | ||
| 'col_name': name, | ||
| 'dict': pd.read_parquet( | ||
| f"{path_prefix}/{current_path}/{dicts_folder}/{name + '.parquet'}" | ||
| ), | ||
| } | ||
| for name in dict_names | ||
| ] | ||
| _, te_test_dfs, y_mean_all_df = getTargetEncodingFeaturesDicts(mode='stage1', train_dict_load=False) | ||
|
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| ############# set up to stage 2 | ||
| current_path = "/recsys2021/datapre_stage2/" | ||
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| ############# count encoding | ||
| ce_test_dfs = CountEncodingFeatures(df, gen_dict=True,mode="inference",train_generate=False) | ||
|
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Function inference_join refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
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| dict_dfs = [{'col_name': name, 'dict': df.select(spk_func.col(name).alias('dict_col'))} for name in dict_names] | ||
| return dict_dfs | ||
| return [ | ||
| { | ||
| 'col_name': name, | ||
| 'dict': df.select(spk_func.col(name).alias('dict_col')), | ||
| } | ||
| for name in dict_names | ||
| ] |
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Function get_dict_for_asin refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| dict_dfs = [] | ||
| dict_dfs.append({'col_name': 'reviewer_id', 'dict': user_df}) | ||
| dict_dfs.append({'col_name': 'asin', 'dict': asin_df}) | ||
| dict_dfs.append({'col_name': 'category', 'dict': cat_df}) | ||
| dict_dfs.append({'col_name': 'hist_asin', 'dict': asin_df}) | ||
| dict_dfs.append({'col_name': 'hist_category', 'dict': cat_df}) | ||
| dict_dfs.append({'col_name': 'noclk_hist_asin', 'dict': asin_df}) | ||
| dict_dfs.append({'col_name': 'noclk_hist_category', 'dict': asin_cat_df}) | ||
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| dict_dfs = [ | ||
| {'col_name': 'reviewer_id', 'dict': user_df}, | ||
| {'col_name': 'asin', 'dict': asin_df}, | ||
| {'col_name': 'category', 'dict': cat_df}, | ||
| {'col_name': 'hist_asin', 'dict': asin_df}, | ||
| {'col_name': 'hist_category', 'dict': cat_df}, | ||
| {'col_name': 'noclk_hist_asin', 'dict': asin_df}, | ||
| {'col_name': 'noclk_hist_category', 'dict': asin_cat_df}, | ||
| ] |
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Function categorify_dien_data refactored with the following changes:
- Merge append into list declaration [×7] (
merge-list-append)
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| with open("/home/xxx/dien/" + f'/{output_name}.pkl', "rb") as f: | ||
| voc = dict((key, value) for (key,value) in pkl.load(f).items()) #nosec | ||
| dict_df = convert_to_spark_df(voc, proc.spark) | ||
| return dict_df | ||
| with open(f'/home/xxx/dien//{output_name}.pkl', "rb") as f: | ||
| voc = dict(pkl.load(f).items()) | ||
| return convert_to_spark_df(voc, proc.spark) |
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Function load_voc refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation) - Replace list(), dict() or set() with comprehension (
collection-builtin-to-comprehension) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Replace identity comprehension with call to collection constructor (
identity-comprehension)
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| for user, r in user_map.items(): | ||
| for r in user_map.values(): |
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Function save_to_local_splitByUser refactored with the following changes:
- Replace calls to
dict.itemswithdict.valueswhen the keys are not used (replace-dict-items-with-values)
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| idx = 0 | ||
| if len(source_path_dict[output_name + fix]) == 1: | ||
| file_name = source_path_dict[output_name + fix][0] | ||
| shutil.copy(file_name, f"{tgt_path}") | ||
| else: | ||
| for file_name in source_path_dict[output_name + fix]: | ||
| for idx, file_name in enumerate(source_path_dict[output_name + fix]): | ||
| #print(f"result renamed from {file_name} to {tgt_path}_{idx}") | ||
| shutil.copy(file_name, f"{tgt_path}_{idx}") | ||
| shutil.rmtree(file_name, ignore_errors=True) | ||
| idx += 1 |
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Function result_rename_or_convert refactored with the following changes:
- Move assignments closer to their usage (
move-assign) - Replace manual loop counter with call to enumerate (
convert-to-enumerate)
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| reviews_info_df = process_reviews(spark, "%s/%s/raw_data/reviews_Books.json" % (path_prefix, original_folder), proc, "reviews-info") | ||
| reviews_info_df = process_reviews( | ||
| spark, | ||
| f"{path_prefix}/{original_folder}/raw_data/reviews_Books.json", | ||
| proc, | ||
| "reviews-info", | ||
| ) | ||
| #reviews_info_df.repartition(1).write.format("csv").option('sep', '\t').mode("overwrite").save("%s/%s/j2c_test/reviews-info-spark" % (path_prefix, original_folder)) | ||
| t1 = timer() | ||
| print(f"parse reviews-info with spark took {(t1 - t0)} secs") | ||
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| t0 = timer() | ||
| item_info_df = process_meta(spark, '%s/%s/raw_data/meta_Books.json' % (path_prefix, original_folder), proc, "item-info") | ||
| item_info_df = process_meta( | ||
| spark, | ||
| f'{path_prefix}/{original_folder}/raw_data/meta_Books.json', | ||
| proc, | ||
| "item-info", | ||
| ) |
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Function main refactored with the following changes:
- Replace interpolated string formatting with f-string [×2] (
replace-interpolation-with-fstring)
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| reviews_info_df = spark.read.schema(reviews_info_schema).option('sep', '\t').csv(path + "/reviews-info") | ||
| item_info_df = spark.read.schema(item_info_schema).option('sep', '\t').csv(path + "/item-info") | ||
| reviews_info_df = ( | ||
| spark.read.schema(reviews_info_schema) | ||
| .option('sep', '\t') | ||
| .csv(f"{path}/reviews-info") | ||
| ) | ||
| item_info_df = ( | ||
| spark.read.schema(item_info_schema) | ||
| .option('sep', '\t') | ||
| .csv(f"{path}/item-info") | ||
| ) |
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Function load_csv refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| dict_dfs = [{'col_name': name, 'dict': df.select(spk_func.col(name).alias('dict_col'))} for name in dict_names] | ||
| return dict_dfs | ||
| return [ | ||
| { | ||
| 'col_name': name, | ||
| 'dict': df.select(spk_func.col(name).alias('dict_col')), | ||
| } | ||
| for name in dict_names | ||
| ] |
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Function get_dict_for_asin refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| dict_dfs = [] | ||
| dict_dfs.append({'col_name': 'reviewer_id', 'dict': user_df}) | ||
| dict_dfs.append({'col_name': 'asin', 'dict': asin_df}) | ||
| dict_dfs.append({'col_name': 'category', 'dict': cat_df}) | ||
|
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| dict_dfs = [ | ||
| {'col_name': 'reviewer_id', 'dict': user_df}, | ||
| {'col_name': 'asin', 'dict': asin_df}, | ||
| {'col_name': 'category', 'dict': cat_df}, | ||
| ] |
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Function categorify_dien_data refactored with the following changes:
- Merge append into list declaration [×3] (
merge-list-append)
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| reviews_info_df = process_reviews(spark, "%s/%s/raw_data/reviews_Books.json" % (path_prefix, original_folder), proc, "reviews-info") | ||
| reviews_info_df = process_reviews( | ||
| spark, | ||
| f"{path_prefix}/{original_folder}/raw_data/reviews_Books.json", | ||
| proc, | ||
| "reviews-info", | ||
| ) | ||
| #reviews_info_df.repartition(1).write.format("csv").option('sep', '\t').mode("overwrite").save("%s/%s/j2c_test/reviews-info-spark" % (path_prefix, original_folder)) | ||
| t1 = timer() | ||
| print(f"parse reviews-info with spark took {(t1 - t0)} secs") | ||
|
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| t0 = timer() | ||
| item_info_df = process_meta(spark, '%s/%s/raw_data/meta_Books.json' % (path_prefix, original_folder), proc, "item-info") | ||
| item_info_df = process_meta( | ||
| spark, | ||
| f'{path_prefix}/{original_folder}/raw_data/meta_Books.json', | ||
| proc, | ||
| "item-info", | ||
| ) |
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Function main refactored with the following changes:
- Replace interpolated string formatting with f-string [×2] (
replace-interpolation-with-fstring) - Merge append into list declaration (
merge-list-append)
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| dict_dfs = [{'col_name': name, 'dict': proc.spark.read.parquet( | ||
| "%s/%s/%s/%s" % (proc.path_prefix, proc.current_path, proc.dicts_path, name))} for name in to_categorify_cols] | ||
| dict_dfs = [ | ||
| { | ||
| 'col_name': name, | ||
| 'dict': proc.spark.read.parquet( | ||
| f"{proc.path_prefix}/{proc.current_path}/{proc.dicts_path}/{name}" | ||
| ), | ||
| } | ||
| for name in to_categorify_cols | ||
| ] |
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Function categorifyAllFeatures refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
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| dict_dfs = [{'col_name': name, 'dict': proc.spark.read.format("arrow").load( | ||
| "%s/%s/%s/%s" % (proc.path_prefix, proc.current_path, proc.dicts_path, name))} for name in to_categorify_cols] | ||
| dict_dfs = [ | ||
| { | ||
| 'col_name': name, | ||
| 'dict': proc.spark.read.format("arrow").load( | ||
| f"{proc.path_prefix}/{proc.current_path}/{proc.dicts_path}/{name}" | ||
| ), | ||
| } | ||
| for name in to_categorify_cols | ||
| ] |
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Function categorifyAllFeatures refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
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| print(f"start launcu_milvus") | ||
| print("start launcu_milvus") | ||
| launch_milvus() | ||
| document_store = MilvusDocumentStore(host="localhost", username="", password="", | ||
| index="flat") | ||
| print(f"start create retriever") | ||
| print("start create retriever") |
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Function main refactored with the following changes:
- Replace f-string with no interpolated values with string [×3] (
remove-redundant-fstring)
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| file_names = dict((key, path_prefix + os.path.join(current_path, data_folder, filename)) for key, filename in SO_FILE.items()) | ||
| file_names = { | ||
| key: path_prefix + os.path.join(current_path, data_folder, filename) | ||
| for key, filename in SO_FILE.items() | ||
| } |
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Function main refactored with the following changes:
- Replace list(), dict() or set() with comprehension (
collection-builtin-to-comprehension)
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