Saravanan666/pypi-org huggingface dataset:
Package: mlmr
MLMRThis library will help you easily parallelize your python code for all kind of data transformations.
Core functions are built on Map-Reduce paradigm. In this library Map part is parallelized using native
pythonmultiprocessingmodule.InstallationpipinstallmlmrUsageIn order to find out library API specification and advanced usage I recommend you to start with these short tutorials:Functional API tutorialSklearn integration tutorialHere I'll post several real worldmlmrAPI applications.Sum of squares in MapReduce fashion exampleimportnumpyasnpfrommlmr.functionimportmap_reducearr=[1,2,3,4,5]defsquares_of_slice(arr_slice):# our map function, with partial reductionreturnsum(map(lambdax:x**2,arr_slice))defget_split_data_func(n_slices):# wrapper function of split data functiondefsplit_data(data):returnnp.array_split(data,n_slices)returnsplit_datan_jobs=2result=map_reduce(data=arr,data_split_func=get_split_data_func(n_jobs),# split data into n_jobs slicesmap_func=squares_of_slice,reduce_func=sum,n_jobs=n_jobs)Pandas apply parallelization in MapReduce fashion exampleIn this example function performs parallel data transformations ondf(pd.DataFrame, pd.Series).
Fromn_jobsargument, number of processes to run in parallel is calculated. Data is evenly divided into number
of processes slices. Thenour_transform_funcis applied on each slice in parallel (every process has its own slice).
After calculation is complete all transformation results are flattened. Flattened result is returned.frommlmr.functionimporttransform_concatdefcomutation_costly_transformation(*_):passdefour_transform_func(df):returndf.apply(cosly_computation_func)df_transformed=transform_concat(df,transform_func=our_transform_func,n_jobs=-1)Sklearn MapReduce transformer integration into Pipelinefromsklearn.pipelineimportPipelinefromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.ensembleimportRandomForestClassifierfrommlmr.transformersimportBaseMapReduceTransformerdefcomutation_costly_text_transformation(df):passclassTextPreprocessor(BaseMapReduceTransformer):deftransform_part(self,X):returncomutation_costly_text_transformation(X)n_jobs=4text_classification_pipeline=Pipeline([('text_preprocessor',TextPreprocessor(n_jobs=n_jobs)),('vectorizer',TfidfVectorizer(analyzer="word",max_features=10000)),('classifier',RandomForestClassifier(n_estimators=100,n_jobs=n_jobs))])Alternative implementation:importpandasaspdfromsklearn.pipelineimportPipelinefromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.ensembleimportRandomForestClassifierfrommlmr.transformersimportFunctionMapReduceTransformerdefget_split_data_func(n_slices):# wrapper function of split data functiondefsplit_data(data):returnnp.array_split(data,n_slices)returnsplit_datadefcomutation_costly_text_transformation(df):passn_jobs=4text_classification_pipeline=Pipeline([('text_preprocessor',FunctionMapReduceTransformer(map_func=comutation_costly_text_transformation,reduce_func=pd.concat,data_split_func=get_split_data_func(n_jobs),n_jobs=n_jobs)),('vectorizer',TfidfVectorizer(analyzer="word",max_features=10000)),('classifier',RandomForestClassifier(n_estimators=100,n_jobs=n_jobs))])
Package: mlms
Machine Learning Model SelectionThis package aims to facilitate model selection in Machine Learning. It is a common issue that ML practitioners often struggle to decide on the most appropriate model prior to optimization, as tuning hyperparameters can be time-consuming and computationally demanding. To simplify the process, this package enables users to train several machine learning models using their default hyperparameters and compare their performance, helping them determine the most suitable model to select.Usagepip install mlms -Upypi packageThen instantiate and use it like this:from mlms.ModelSelection import Select_Regressor, Select_ClassifierSelect some models to tune, this list should be the abbreviation of models as below, for exampleMODELS = ['LGR', 'AB', 'CART', 'GBC', 'XGBC', 'RFC', 'ETC', 'KNN', 'NB', 'SVC', 'MLP', 'SGDC', 'GPC', 'PAC']df_performance, fitted_classifiers = Select_Classifier('accuracy', 10, X_train, X_test, y_train, y_test, MODELS)df_performance, fitted_regressors = Select_Classifier('neg_mean_squared_erro', 10, X_train, X_test, y_train, y_test)For classifiers, the performance can set asaccuracy,'f1_score,precision,recall,roc_auc,balanced_accuracy_scoreand so on. Available classifiers are below('LGR', LogisticRegression(n_jobs=-1)),('AB', AdaBoostClassifier()),('CART', DecisionTreeClassifier()),('GBC', GradientBoostingClassifier()),('XGBC', XGBClassifier()),('RFC', RandomForestClassifier()),('ETC', ExtraTreeClassifier()),('KNN', KNeighborsClassifier(n_jobs=-1)),('NB', GaussianNB()),('SVC', SVC()),('MLP', MLPClassifier()),('SGDC', SGDClassifier(n_jobs=-1)),('GPC', GaussianProcessClassifier(n_jobs=-1)),('PAC', PassiveAggressiveClassifier(n_jobs=-1))(The charts is an classifier selection example using Iris dataset)For regressors, the performance can set asr2_score,neg_mean_squared_errorand so on. Available regressors are below:('KNN', KNeighborsRegressor()),('CART', DecisionTreeRegressor()),('SVR', SVR()),('MLP', MLPRegressor()),('ABR', AdaBoostRegressor()),('GBR', GradientBoostingRegressor()),('XGB', XGBRegressor()),('RFR', RandomForestRegressor()),('ETR', ExtraTreesRegressor())Additonally, this package also alow users to plot ROC_Curvefrom mlms.plot_roc_curve import Multiclass_ROC_Curve, Binary_ROC_CurveMulticlass_ROC_Curve(X_test, y_test, fitted_model, chart_title:str)Binary_ROC_Curve(y_true, y_pred,chart_name:str)GitHub
Package: ml-ms4alg
ml_ms4algElectrophysiology tools
MountainLab processor libraryInstallation from PyPI:pip3 install --upgrade ml_ms4algThen add it as a plugin to mountainlab:cd ~/.mountainlab/packages
ml-link-python-module ml_ms4alg ml_ms4algOr installation from source:clone this repository into .mountainlab/packages/cd ml_ms4alg
pip3 install --upgrade .
Package: mln-converter
Minuteman Library Network (MLN) Record ConverterHistorical loan records exported via theMLN websiteare in a semi-human
readable format, making them difficult to use in analyses. This utility converts each record into aRecordobject that can be inspected in multiple ways.Dev installPrerequisitesPython 3.10+Poetry for PythonInstallationClone the git repository locally and runpoetry installfrom the main directory. On Ubuntu:gitclonegit@gitlab.com:woodforsheep/mln-to-csv.gitcdmln-to-csv
poetryinstallCommand Line Usepoetryrunmln-to-csv--help
Usingpython3(3.11.4)usage:mln-to-csv[-h]files[files...]ConvertsexportedMLNrecordstoCSV

positionalarguments:filesThelistoffilestoconvert

options:-h,--helpshowthishelpmessageandexitLibrary useThe main utility in the library islines_to_recordsand can be used as follows:frommln_converterimportlines_to_recordswithopen(records_file)asfp:lines=fp.readlines()records=lines_to_records(lines)forrecordinrecords:print(f"{record.titles[0]}({record.authors[0]})")Convert icons created by iconsax - Flaticon
Package: mlnd-pkg-test
mlnd-pkgSmall distribution building and PyPi packaging experimentUsagegorimboptim module implements DichotomousLineSearch class, which is a line search
optimizer.from gorimboptim import DichotomousLineSearchConstructor arguments include cost function, optimization interval, f(x) tolerance,
and iteration limit.Methodoptimize()returns optimum point x for a given cost function and
interval.
Package: mlnext-framework
MLnext FrameworkMLnext Frameworkis an open source framework for hardware independent execution of machine learning usingPythonandDocker.
It provides machine learning utilities.
The correspondingPythonpackage is calledmlnext-framework.
MLnext Framework belongs to a solution portfolio for the Digital Factory now to realize Data collection, storage, and evaluation.Digitalization is posing numerous challenges for production — but above all, it provides countless opportunities for increasing productivity and system availability.
To ensure that you benefit from the advantages of digitalization as quickly as possible, we will provide you with needs-based support — from installing simple stand-alone solutions to comprehensive digitalization concepts.The Digital Factory now is based on the following four fields of activity:Data collection, storage, and evaluationData transportationData securityData usageThe four fields of action provide you with various solutions, from data acquisition to data utilization. Each individual solution will not only be tailored to your particular requirements; the fields of action can also be combined in any way or considered individually.
Regardless of which path you are taking toward the Digital Factory, we will be happy to support you during the next steps.To help you to meet today’s digitalization challenges and implement opportunities profitably, our solutions provide the following added values:Scalability — tailored to your requirementsTested and validated — in our own in-house production facilitiesReady-to-use — benefit from the digital transformation todayWith target-oriented consultation, we will find the right solution for your Digital Factory together. Let us take on the challenges of digitalization and leverage its opportunities together.InstallationInstall this package usingpip:pipinstallmlnext-frameworkModulesTheMLnext Frameworkconsists of 7 modules:importmlnext.dataasdata# for data loading and manipulationimportmlnext.ioasio# for loading and saving filesimportmlnext.pipelineaspipeline# for data preprocessingimportmlnext.plotasplot# for data visualizationimportmlnext.scoreasscore# for model evaluationimportmlnext.anomalyasanomaly# for anomaly interpretationimportmlnext.utilsasutils# for utility functions# hint: all functions can also be accessed from the root moduleimportmlnextRelease Information0.4.1 (2023-05-12)Featuresfixedmlnext.find_anomaliesfor scalar inputs (every dimension 1)Copyright (c) 2022 Phoenix Contact Electronics GmbH
Package: mlni
MLNIMachine Learning in NeuroImagingDocumentationMLNIMLNI is a python package that performs various tasks using neuroimaging data: i) binary classification for disease diagnosis, following good practice proposed inAD-ML; ii) regression prediction, such as age prediction; and iii) semi-supervised clustering withHYDRA.:warning:The documentation of this software is currently under developmentCiting this workIf you use this software for clustering:Varol, E., Sotiras, A., Davatzikos, C., 2017.HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage, 145, pp.346-364.doi:10.1016/j.neuroimage.2016.02.041-Paper in PDFIf you use this software for classification or regression:Wen, J., Samper-González, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Epelbaum, S., Bertrand, A. and Colliot, O., 2020.Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease. Neuroinformatics, pp.1-22.doi:10.1007/s12021-020-09469-5-Paper in PDFJ. Samper-Gonzalez, N. Burgos, S. Bottani, S. Fontanella, P. Lu, A. Marcoux, A. Routier, J. Guillon, M. Bacci, J. Wen, A. Bertrand, H. Bertin, M.-O. Habert, S. Durrleman, T. Evgeniou and O. Colliot,Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. NeuroImage, 183:504–521, 2018doi:10.1016/j.neuroimage.2018.08.042-Paper in PDF-Supplementary materialPublication using MLNIWen, J., Varol, E., Davatzikos, C., 2020.Multi-scale feature reduction and semi-supervised learning for parsing neuroanatomical heterogeneity. Organization for Human Brain Mapping. -LinkWen, J., Varol, E., Davatzikos, C., 2021.Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. MedIA. -LinkWen, J., Fu, C.H., Tosun, Davatzikos, C. 2022.Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA Psychiatry -LinkLalousis, P.A., Schmaal, L., Wood, S.J., Reniers, R.L., Barnes, N.M., Chisholm, K., Griffiths, S.L., Stainton, A., Wen, J., Hwang, G. and Davatzikos, C., 2022.Neurobiologically Based Stratification of Recent Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes. Biological Psychiatry. -Link
Package: mlnotes
*** Save information of your ml projects ***---- Save eda information and access any time---- Save ml project information and access any time---- It saves your project information in ai_info.json file on your own computer---- No one will able to see your data execpt you---- Your project data is saved on your pc not in any database*** All commandas ***1. mlnotes.add_notes(note_id, your note goes here)2. mlnotes.show_notes(note_id)
Package: mloader
Mangaplus Downloadermloader- download manga from mangaplus.shueisha.co.jp🚩 Table of ContentsInstallationUsageCommand line interface💾 InstallationThe recommended installation method is usingpip:pipinstallmloaderAfter installation, themloadercommand will be available. Check thecommand linesection for supported commands.📙 UsageCopy the url of the chapter or title you want to download and pass it tomloader.You can use--titleand--chaptercommand line argument to download by title and chapter id.You can download individual chapters or full title (but only available chapters).Chapters can be saved asCBZarchives (default) or separate images by passing the--rawparameter.🖥️ Command line interfaceCurrentlymloadersupports these commandsUsage: mloader [OPTIONS] [URLS]...

  Command-line tool to download manga from mangaplus

Options:
  --version                       Show the version and exit.
  -o, --out <directory>           Save directory (not a file)  [default:
                                  mloader_downloads]
  -r, --raw                       Save raw images  [default: False]
  -q, --quality [super_high|high|low]
                                  Image quality  [default: super_high]
  -s, --split                     Split combined images  [default: False]
  -c, --chapter INTEGER           Chapter id
  -t, --title INTEGER             Title id
  -b, --begin INTEGER RANGE       Minimal chapter to try to download
                                  [default: 0;x>=0]
  -e, --end INTEGER RANGE         Maximal chapter to try to download  [x>=1]
  -l, --last                      Download only the last chapter for title
                                  [default: False]
  --chapter-title                 Include chapter titles in filenames
                                  [default: False]
  --chapter-subdir                Save raw images in sub directory by chapter
                                  [default: False]
  --help                          Show this message and exit.
Package: ml-ocean-bl
Installationml-olcean-bl can be installed from PyPI with pip:pipinstallml-ocean-blDownload and Preprocess DataPrerequisite data must be downloaded and preprocessed into a specific format.
Users can either download the original satellite sea surface data and argo-based mixed layer depth data and preprocess themselves, or
users can download already preprocessed data corresponding to the paper ‘Probabilistic Machine Learning Estimation of Ocean Mixed Layer
Depth from Dense Satellite and Sparse In-Situ Observations’ Foster et al. (ex. 2021).For the former, see the jupyter notebook./notebooks/download_preprocess_data.ipynband run the corresponding lines. Users can also runshwget_unprocessed_surface_argo_data.shand see the python files./mloceanbl/preprocess_sss_sst_ssh.pyand./mloceanbl/preprocess_mld.py. Note that in order to download the
satellite sea surface data, you will need to register athttps://urs.earthdata.nasa.gov/and get a corresponding password from the podaac
drive.To simply download the already preprocessed data used in the paper mentioned above, simply runshwget_preprocessed_surface_argo_data.shTraining and UsageFor details on the training and testing procedures, see the notebook ./notebooks/model_train_Example.ipynb. For information about how the
data should be organized, see./mloceanbl/data.py.For detail on the model class, see./mloceanbl/models.py. Individual NN models: Linear,
ANN, ANN with Dropout, ANN with parameterized output distribution, and VAE can be found atlinear.py,ann.py,ann_dropout.py,vann.py,vae.py.Details on the training proceedure can be found inmloceanbl/train.py.
Package: mlocs
# mlocs - sensible location storage…
#
# mlocs provides a simple, yet effective location hashing algorithm.
# it’s not super precise (> 750m.) but it provides perfect results.
#
# There are 5 levels available.
# Lvl 1 : World Scope
# Lvl 2 : Region Scope
# Lvl 3 : Metro Scope
# Lvl 4 : City Scope
# Lvl 5 : I See You
#
# toMaiden([lat, lon], level) returns a char (len = lvl*2)
# toLoc(mloc) takes any string and returns topleft [lat,lon] within mlocGithub repository :git@github.com:zdevops/mlocs.gitVersion 1.0.4 :Just prepped for github too. No other changes….Addedd genNonSense(loc) for other map :)
Version 1.0.3 :
===============
Addedd genNonSense(loc) for other map :)Version 1.0.2 :Added genGooleMap() for generating static google-map images out of MLOCS :)–> for large scale (>25k req/day) thesemustbe cached somehow….
Package: mlog-arithmetic-runner
MlogArithmeticRunnerA Mindustry Logic emulator built for automated testing of compilers (like MlogEvo).Installpip install https://github.com/UMRnInside/MlogArithmeticRunnerorpip install mlog_arithmetic_runnerUsage$ python3 -m mlog_arithmetic_runner --help
usage: mlog_arithmetic_runner [-h] [--limit LIMIT] [--continue-if-past-the-end] [--ipt IPT]
                              [--memory-banks MEMORY_BANKS] [--memory-cells MEMORY_CELLS]
                              [--json-indent JSON_INDENT] [--json-dump-memory-blocks]

Reads some mlog code from stdin, runs it, and generates JSON report

optional arguments:
  -h, --help            show this help message and exit
  --limit LIMIT         Max instructions/cycles allowed, something like TimeLimit
  --continue-if-past-the-end
  --ipt IPT             Instructions per tick. 2 for micro-processor, 8 for logic-processor,
                        25 for hyper-processor
  --memory-banks MEMORY_BANKS
                        Memory bank count
  --memory-cells MEMORY_CELLS
                        Memory cell count
  --json-indent JSON_INDENT
                        JSON indent, set 0 to disable
  --json-dump-memory-blocks
                        dump all memory content in JSON reportSupported InstructionssetjumpendAllops since Mindustry V7 Beta (beta 140), exceptop noisereadandwritememory cells and banksgetlinkto get memory blocks (cells/banks)set @counterorop @counteras unconditional jumpsFeatures@tickand@timeincreases with processor runsStop emulation once some instruction jumps to itself(Optional, Default) Stop emulation once@counterpast the end(Optional) Dump memory cells and banksDump variables into JSON reportLimitationsDoes NOT have@thisyet, so do@thisxand@thisyNo comment in mlog code(not sure)Python Usagefrommlog_arithmetic_runnerimportMlogProcessorprocessor=MlogProcessor(ipt=2,memory_cells=0,memory_banks=0)code="""\set a 90op sin b a 0"""processor.assemble_code(code)processor.run_with_limit(1000)# When comparing 2 float-point numbers, remember there could be float precision errors.# Use abs(a-b) < 1e-6 instead of a == bprint("a =",processor.get_variable("a"))print("b =",processor.get_variable("b"))JSON Report Sampleset a 1
set a 2
set b @tick
set c @timeThis generates:{"cycles":4,"success":true,"reason":"","variables":{"a":2.0,"b":1,"c":0.016666666666666666},"memory_blocks":{}}
Package: mlogconfig
mlogconfig.pyThis module provides a configurable logging setup for Python applications. It supports logging to console, file, syslog (for Linux and macOS), and Windows Event Log. The user can enable or disable each logging method as needed.FeaturesLogging to console, file, syslog, and Windows Event LogEnable or disable each logging method as neededEasy setup and configurationRequirementsPython 3.9 or higherFor Windows Event Log support, the following packages are required:pywin32Install the required packages by running:pipinstallpywin32UsageHere is a basic example of using MLogConfig to set up logging for your application:frommlogconfigimportsetup_loggingimportlogging# Set up loggingsetup_logging(log_file_path="logs/app.log",error_log_file_path="logs/app_error.log",console_logging=True,syslog_logging=True,windows_event_logging=True,log_level=logging.DEBUG,)# Use logging in your applicationlogging.info("This is an info log message.")logging.error("This is an error log message.")CustomizationTo customize the logging configuration, modify the arguments passed to thesetup_loggingfunction. For example:log_file_path: Path to the log fileerror_log_file_path: Path to the error log fileconsole_logging: Set toTrueto enable console logging,Falseotherwisesyslog_logging: Set toTrueto enable syslog logging (Linux and macOS only),Falseotherwisewindows_event_logging: Set toTrueto enable Windows Event Log logging,Falseotherwiselog_level: Set the logging level (e.g.,logging.DEBUG,logging.INFO,logging.WARNING,logging.ERROR,logging.CRITICAL)ContributingContributions are welcome! Please read thecontributing guidelinesbefore submitting pull requests or opening issues.LicenseMLogConfig is licensed under theMIT License.
Package: mlogs
简介loguru 基础上封装的 开箱即用的 python 日志 工具包loguruhttps://loguru.readthedocs.io/en/stable/overview.html#installationnotifiershttps://notifiers.readthedocs.io/en/latest/usage.html#provider-schema目前为 开发版本，未经过严格测试，请勿使用在生产环境。使用方法pipinstallmlogsfrommlogsimportMLoggerL=MLogger()L.info("nice")# trace_id 可以用来 链路追踪， topic 用于分类L.warning("nice",trace_id="12312321",topic="model:task")L.warning({"msg":"nice","gg_monkey":"bugs"},trace_id="12312321",topic="model:task")# MLogger 会同时输出日志到 terminal 日志文件# FileLogger   仅输出日志到 日志文件，按照日志级别生成 不同的日志文件# StdoutLogger 仅输出日志到 terminal# AdaptHistoryLogger 为了兼容历史项目日志# 配置邮件接收 （release 0.1.3）"""email 发送邮件，使用的是python 原生库 smtplib.SMTP"""frommlogsimportStdoutLoggeralerts={"alerts_type":"email","params":{"username":"username","password":"password","host":"smtp.exmail.qq.com",# 邮箱服务器地址"port":465,# 邮箱服务器端口"from":"yourname@cc.com",# 邮件发送人"to":["yourname@cc.com",# 邮件接收人],"login":True,"ssl":True,# 绝大多数邮箱，均开启了ssl服务，所以需要配置},}L=StdoutLogger(alerts=alerts)L.error('nice')# 添加 webhook，此时 会同时发送 邮件和 webhookalerts=[{"alerts_type":"email","params":{"username":"username","password":"password","host":"smtp.exmail.qq.com",# 邮箱服务器地址"port":465,# 邮箱服务器端口"from":"yourname@cc.com",# 邮件发送人"to":["yourname@cc.com",# 邮件接收人],"login":True,"ssl":True,# 绝大多数邮箱，均开启了ssl服务，所以需要配置},},{{"alerts_type":"feishu","params":{"webhook_url":"https://open.feishu.cn/open-apis/bot/v2/hook/your_id",'msg_type':'text','content':{'title':"from mlogs feishu",'text':"this is log feishu test"}}}}]L=StdoutLogger(alerts=alerts)L.error('nice! this is a test')# default topic （release 0.2.0）"""当一个项目里面包含多个服务时，需要使用topic来区分，那么可以在日志初始化时，统一设置default topic。"""frommlogsimportStdoutLoggerL1=StdoutLogger(default_topic="1")L2=StdoutLogger(default_topic="2")L3=StdoutLogger(default_topic="3")L1.info('nice')L2.info('nice')L3.info('nice',topic="333")一些介绍FileLogger 默认按照 50 MB分割文件，最多储存 10 个文件，默认不压缩日志。日志级别默认为DEBUG，可以自定义设置，也可以使用环境变量 export DEPLOYMENT=PRODUCTION，设置日志级别为 INFO遗留问题暂时使用自己修改过的 notifiers，支持飞书webhook。如果有幸 通过原始库合并，则会转到 原始库如果 原本环境中 包含 notifiers，需要先卸载，否则会出现找不到 feishu。原本的 notifiers 库，可以做一个钩子函数，支持添加外部 组件，这样就能很快捷的添加适合的组件了。注意⚠️打包时，会把 tests 文件夹下内容拷贝到资源包。所以包含敏感信息的测试文件，请放入 secret_tests 下。目前测试范仅仅 覆盖 mac ubuntu版本更新内容0.3.5 新增 AdaptHistoryLogger 用来支持历史日志格式。
0.3.4 移除 默认设置 日志导出 支持 numpy 格式数据，现在是可选，通过 json_encoder参数 支持自定义json 解析类 传入。
0.3.3 修复 移除 默认设置 opt(color=True)，防止输入的 msg（不可预料）内出现无法识别的 color tag 导致日志报错。 
0.3.2 新增 部分功能测试
0.3.0 新增 飞书 webhook 支持
0.2.x 修复 部分已知问题
0.2.0 新增 default topic，在类初始化时设置，则后续所有日志会添加此 topic  
0.1.3 新增 邮件发送配置  
0.0.10 工程能够使用