Saravanan666/pypi-org huggingface dataset:
Package: classic-messaging
Classic MessagingThis package provides abstract classes for message queues.Usage:fromclassic.componentsimportcomponentfromclassic.messagingimportMessage,Publisher@componentclassSomeService:publisher:Publisherdefdo_some_work(self):message=Message('target','Some very useful info')self.publisher.publish(message)Publisher have inner buffer. Messages can be planned for publishing, and
will be published laterUsage with deferred publishing:fromclassic.componentsimportcomponentfromclassic.messagingimportMessage,Publisher@componentclassSomeService:publisher:Publisherdefcreate_message(self):message=Message('target','Some very useful info')self.publisher.plan(message)defdo_some_work(self):try:self.create_message()exceptException:self.publisher.reset()raiseelse:self.publisher.flush()Usage with join_points:fromclassic.componentsimportcomponentfromclassic.messagingimportMessage,Publisherfromclassic.aspectsimportjoin_poin@componentclassSomeService:publisher:Publisher@join_poindefdo_some_work(self):message=Message('target','Some very useful info')self.publisher.plan(message)classRealPublisher(Publisher):defpublish(self,message):print(message)publisher=RealPublisher()SomeService.do_some_work.join(publisher)
Package: classic-messaging-kombu
Classic Messaging KombuThis package provides implementation of interfaces in classic-messaging and
base for consumers and messages handling.Usage with publishing:fromclassic.componentsimportcomponentfromclassic.messagingimportMessage,Publisherfromclassic.messaging_kombuimportBrokerScheme,KombuPublisherfromkombuimportExchange,Queue,Connection@componentclassSomeService:publisher:Publisherdefdo_some_work(self):message=Message('some','Some very useful info')self.publisher.publish(message)broker_scheme=BrokerScheme(Queue('queue1',Exchange('some')),)connection=Connection('amqp://localhost:5672/')publisher=KombuPublisher(connection=connection,scheme=broker_scheme)service=SomeService(publisher=publisher)service.do_some_work()Message have 2 arguments - target and body. Target is a str with destination.
In simple case it is an exchange name, in complex case - entry in mapping.Body can be any serializable object.Customization of target:fromclassic.messagingimportMessagefromclassic.messaging_kombuimportBrokerScheme,KombuPublisherfromkombuimportExchange,Queue,Connectiontargets={'FIRST':{'exchange':'exchange1',},'SECOND':{'exchange':'exchange2','timeout':10,}}broker_scheme=BrokerScheme(Queue('queue1',Exchange('exchange1')),Queue('queue2',Exchange('exchange2')),)connection=Connection('amqp://localhost:5672/')publisher=KombuPublisher(connection=connection,scheme=broker_scheme,messages_params=targets,)publisher=KombuPublisher(connection=connection,scheme=broker_scheme)publisher.publish(Message('FIRST','Hello'),# Will be published to exchange1 and queue1Message('SECOND','By'),# Will be published to exchange2, queue2 and timeout=10)Usage with consuming:fromclassic.messaging_kombuimportBrokerScheme,KombuConsumer,MessageHandlerfromkombuimportExchange,Queue,ConnectionclassSomeSerice:defhandle_message(self,message):print(message)classCustomHandler(MessageHandler):defhandle(self,message,body):print(body)message.ack()broker_scheme=BrokerScheme(Queue('queue1',Exchange('exchange1')),Queue('queue2',Exchange('exchange2')),)connection=Connection('amqp://localhost:5672/')consumer=KombuConsumer(connection=connection,scheme=broker_scheme,)service=SomeSerice()handler=CustomHandler()consumer.register_function(service.handle_message,'queue1')consumer.register_handler(handler,'queue2')
Package: classicML
classicML: 简单易用的经典机器学习框架classicML 是一个用 Python 和 C++ 混编的机器学习项目，您既可以使用纯 Python 版本进行学习，也可以使用CC标准版进行实验和探索自定义功能。它既实现了Python的简单易用快速上手，又实现了C++的高效性能。classicML的设计目标是简单易用，快速入门，高扩展性和编程风格简洁。更多信息请访问文档网站。多后端支持classicML 本身是一个Python项目，但是机器学习中涉及到的复杂的矩阵运算对于Python有点儿捉襟见肘，因此我们提供了使用C++后端的加速版本。为了保证兼容性，classicML默认使用Python后端，现在全部算法支持了使用C++作为后端进行加速，如果您需要使用标准版的classicML，只需在开头使用这条语句切换后端。importosos.environ['CLASSICML_ENGINE']='CC'精度控制目前，classicML 正在对全部算法支持32位和64位切换精度，使用32位的精度可以获得更快的运行速度和更小固化模型。importosos.environ['CLASSICML_PRECISION']='32-bit'第一个机器学习程序使用线性判别分析进行二分类下载示例数据集wgethttps://github.com/sun1638650145/classicML/blob/master/datasets/西瓜数据集alpha.csv运行下面的代码importclassicMLascmlDATASET_PATH='/path/to/西瓜数据集alpha.csv'# 读取数据ds=cml.data.Dataset()ds.from_csv(DATASET_PATH)# 生成模型model=cml.models.LDA()# 训练模型model.fit(ds.x,ds.y)# 可视化模型cml.plots.plot_lda(model,ds.x,ds.y,'密度','含糖率')更多示例代码点击感谢Jetbrains Open Source对项目的支持v0.9 预览这个版本将增加cml.models.cluster模块, 并添加几种聚类算法将在v1.0之前添加100%的类型注释
Package: classicML-lite
classicML: 简单易用的经典机器学习框架classicML 是一个用 Python 和 C++ 混编的机器学习项目，您既可以使用纯 Python 版本进行学习，也可以使用CC标准版进行实验和探索自定义功能。它既实现了Python的简单易用快速上手，又实现了C++的高效性能。classicML的设计目标是简单易用，快速入门，高扩展性和编程风格简洁。更多信息请访问文档网站。多后端支持classicML 本身是一个Python项目，但是机器学习中涉及到的复杂的矩阵运算对于Python有点儿捉襟见肘，因此我们提供了使用C++后端的加速版本。为了保证兼容性，classicML默认使用Python后端，现在全部算法支持了使用C++作为后端进行加速，如果您需要使用标准版的classicML，只需在开头使用这条语句切换后端。importosos.environ['CLASSICML_ENGINE']='CC'精度控制目前，classicML 正在对全部算法支持32位和64位切换精度，使用32位的精度可以获得更快的运行速度和更小固化模型。importosos.environ['CLASSICML_PRECISION']='32-bit'第一个机器学习程序使用线性判别分析进行二分类下载示例数据集wgethttps://github.com/sun1638650145/classicML/blob/master/datasets/西瓜数据集alpha.csv运行下面的代码importclassicMLascmlDATASET_PATH='/path/to/西瓜数据集alpha.csv'# 读取数据ds=cml.data.Dataset()ds.from_csv(DATASET_PATH)# 生成模型model=cml.models.LDA()# 训练模型model.fit(ds.x,ds.y)# 可视化模型cml.plots.plot_lda(model,ds.x,ds.y,'密度','含糖率')更多示例代码点击v0.7 预览在之前的版本classicML全局精度是float64的，现在我们引入CLASSICML_PRECISION，这样你就可以控制全局精度，使用32位的精度时，可以获得更快的运行速度和更小固化模型。
Package: classicML-python
classicML: 简单易用的经典机器学习框架classicML 是一个用 Python 和 C++ 混编的机器学习项目，您既可以使用纯 Python 版本进行学习，也可以使用CC标准版进行实验和探索自定义功能。它既实现了Python的简单易用快速上手，又实现了C++的高效性能。classicML的设计目标是简单易用，快速入门，高扩展性和编程风格简洁。更多信息请访问文档网站。多后端支持classicML 本身是一个Python项目，但是机器学习中涉及到的复杂的矩阵运算对于Python有点儿捉襟见肘，因此我们提供了使用C++后端的加速版本。为了保证兼容性，classicML默认使用Python后端，现在全部算法支持了使用C++作为后端进行加速，如果您需要使用标准版的classicML，只需在开头使用这条语句切换后端。importosos.environ['CLASSICML_ENGINE']='CC'精度控制目前，classicML 正在对全部算法支持32位和64位切换精度，使用32位的精度可以获得更快的运行速度和更小固化模型。importosos.environ['CLASSICML_PRECISION']='32-bit'第一个机器学习程序使用线性判别分析进行二分类下载示例数据集wgethttps://github.com/sun1638650145/classicML/blob/master/datasets/西瓜数据集alpha.csv运行下面的代码importclassicMLascmlDATASET_PATH='/path/to/西瓜数据集alpha.csv'# 读取数据ds=cml.data.Dataset()ds.from_csv(DATASET_PATH)# 生成模型model=cml.models.LDA()# 训练模型model.fit(ds.x,ds.y)# 可视化模型cml.plots.plot_lda(model,ds.x,ds.y,'密度','含糖率')更多示例代码点击感谢Jetbrains Open Source对项目的支持v0.9 预览这个版本将增加cml.models.cluster模块, 并添加几种聚类算法将在v1.0之前添加100%的类型注释
Package: classic-signals
Classic SignalsДанный пакет предоставляет возможность использовать сигналы и реакции в проекте.Они нужны для инверсии вызова. По сути, это паттерн Observer и немного сахара
сверху. В одном месте программы мы объявляем сигналы, пользуясь декоратором
signal, в других местах приложения пишем реакции на этих сигналов.
Реакции - это любой вызываемый объект, принимающий один аргумент - инстанс
сигнала. Это может быть и просто функция или метод, для удобства завернутый
в декоратор reaction. Последнее, что нужно, это инстанс класса Hub. В Hub
перед началом работы можно зарегистрировать реакции, потом можно вызвать у него
метод notify, передав в него инстанс сигнала, и Hub вызовет все реакции,
относящиеся к сигналу.Пример:fromclassic.componentsimportcomponentfromclassic.signalsimportreaction,Hub,signal@signalclassSomethingHappened:"""Под капотом сигналы - на самом деле датаклассы"""some_field:int@signalclassSomethingAnotherHappened:some_field:int@componentclassSomeHandlers:@reactiondefon_something_happened(self,signal:SomethingHappened):print(f'Что-то произошло:{signal}')@reactiondefon_something_another_happened(self,signal:SomethingAnotherHappened):print(f'Что-то еще произошло:{signal}')hub=Hub()SomeHandlers(signals=hub)signal_1=SomethingHappened(some_field=0)signal_2=SomethingAnotherHappened(some_field=0)hub.notify(signal_1,signal_2)
Package: classic-sql-storage
Classic SQL StorageThis package provides contextual transactions processing for SQLAlchemy and
base for pattern "Repository".Part of project "Classic".Usage:fromclassic.sql_storageimportTransactionContextfromsqlalchemyimportcreate_engine,textengine=create_engine('sqlite:///')transaction=TransactionContext(bind=engine)# As context manager:withtransaction:transaction.current_session.execute(text('SELECT 1'))# As decorator:@transactiondefsome_work():transaction.current_session.execute(text('SELECT 1'))# Propagation:@transactiondefcomplex_function():"""Doing complex work with db.Session will be commited only after finish of complex_function call.TransactionContext will count all calls, and will commit or rollback sessiononly in last call."""some_work()some_work()some_work()withtransaction:transaction.current_session.execute(text('SELECT 1'))# Automatic rollback@transactiondeffunction_with_error():"""Changes, made by some_work, will be cancelled after raising error"""some_work()raiseValueError()
Package: classification-algorithm
All Classifier Algorithm At One PlaceThis is a package that contains all the best classifier algorithms likeLogisticRegressionSVMDecision TreeRandom ForestNaiv BayesSGDClassifierXgboostAdaboostKNNTo access all these classifier you have to implement a simple code rather that importing all these classifier from different packageProvide the x_train,y_train,x_test,y_test value to the Classifier objectNow call the classifier functionNote: In case of KNN also provide the n_neighbour valuefromclassification_algorithmimportClassifierclf=Classifier(x_train,y_train,x_test,y_test)clf.logisticregression()clf.svm()clf.sgdclassifier()clf.decisiontree()clf.adaboost()clf.randomforest()clf.xgboost()clf.knn(3)clf.gaussain_naiv_bayes()clf.multinomial_naiv_bayes()Output would look like:********************==>LogisticRegression<==********************Accuracyscore:0.8072916666666666F1_Score:0.8614232209737829AUC_Score:0.76680015016894********************==>SVM<==********************Accuracyscore:0.796875F1_Score:0.8592057761732852AUC_Score:0.7328869978726066********************==>SGDClassifier<==********************Accuracyscore:0.4375F1_Score:0.30769230769230765AUC_Score:0.5834063321236391********************==>DecisionTreeClassifier<==********************Accuracyscore:0.7395833333333334F1_Score:0.8134328358208955AUC_Score:0.6865223376298335********************==>AdaBoostClassifier<==********************Accuracyscore:0.7239583333333334F1_Score:0.7984790874524715AUC_Score:0.6794518833687899********************==>RandomForestClassifier<==********************Accuracyscore:0.7760416666666666F1_Score:0.8377358490566038AUC_Score:0.7351395319734702********************==>XGBoostClassifier<==********************Accuracyscore:0.7395833333333334F1_Score:0.8076923076923077AUC_Score:0.7040420473032162********************==>KNeighborsClassifier<==********************Accuracyscore:0.71875F1_Score:0.798507462686567AUC_Score:0.6624953072206232********************==>GaussianNaivBayes<==********************Accuracyscore:0.765625F1_Score:0.8351648351648351AUC_Score:0.7056063070954824********************==>MultinomialNaivBayes<==********************Accuracyscore:0.6510416666666666F1_Score:0.7545787545787546AUC_Score:0.5734576398448255If you want's to test you dataset for all classifier algorithm at one call then you have to do thisfromclassification_algorithmimportClassifierclf=Classifier(x_train,y_train,x_test,y_test)clf.pipeline()Output would look like this:Accuracyscore:0.6510416666666666F1_Score:0.7545787545787546AUC_Score:0.5734576398448255LogisticRegressionscore:0.8072916666666666auc_score:0.76680015016894SVMscore:0.796875auc_score:0.7328869978726066DEcisionTreescore:0.734375auc_score:0.6870854711550494RandomForestscore:0.7708333333333334auc_score:0.722562883243649Xgboostscore:0.7395833333333334auc_score:0.7040420473032162Gaussian_Naiv_Bayesscore:0.765625auc_score:0.7056063070954824Multinomial_Naiv_Bayesscore:0.6510416666666666auc_score:0.5734576398448255BestModelforthisdatasetaccordingtoaccuracyscoreis:LogisticRegressionBestModelforthisdatasetaccordingtoauc_scoreis:LogisticRegressionBy. Manikant Kumar
Package: classification-library
Classification LibraryA classification library using anovel audio-inspired technique.InstallationTry:pip install classification_libraryExcept:pip install git+https://github.com/lol-cubes/classification-libraryUsagefromclassification_libraryimportAudioClassifierimportnumpyasnpX_train=np.array([1,2,3,4])y_train=np.array([1,4,9,16])X_test=np.array([5,6])y_test=np.array([25,36])model=AudioClassifier(alpha=10)model.fit(X_train,y_train)predictions=model.predict(X_test)correct_predictions=0fory_true,y_predinzip(y_test,predictions):ify_true==y_pred:correct_predictions+=1print(f"Accuracy:{correct_predictions/len(y_test)}")
Package: classification-report
Classification-ReportClassification Report is a high-level library built on top of Pytorch which utilizes Tensorboard and scikit-learn and can be used for any  classification problem. It tracks models Weight, Biases and Gradients  during training and generates a detailed evaluation report for the  model, all of this can be visualized on Tensorboard giving comphrensive  insights. It can also be used for HyperParameter tracking which then can be utilized to compare different experiments.FeaturesModel Weights, Biases and Gradients Tracking and plotting on histogram.Visualizing the distribution of above described Model parameters.Generating an interactive graph of the entire Model.Graph of Precision, Recall and F1 Score for all the classes for each epoch.Graph of Macro Avg and Weighted Avg of Precision, Recall and F1-score for each epoch.Training and Validation Loss tracking for each epoch.Accuracy and MCC metric tracking at each epoch.Generating Confusion Matrix after certain number of epochs.Bar Graph for False Positive and False Negative count for each class.Scatter Plot for the predicited probabilities.HyperParameter Tracking for comparing experiments.For More Detail look in FeaturesInstallationpipinstallclassification-reportDemoGoole Colab (Recommended)​	Just open this notebook on colab and view the entire tensorboard visualization. -Simple Mnist Simple Reporting Visualize on colabUsageUnderstand The UsageSay Thanks, By connecting me over Linkedin.
Package: classification-reportzr
Classification ReportzrAutomate machine learning classification task report for Pak ZuhermanInstallpipinstall-Uclassification-reportzrTestpytest-vUsageSetting-up the experimentfromsklearnimportdatasetsfromsklearn.svmimportSVCfromreporterzrimportReporterzriris=datasets.load_iris()samples,labels=iris.data[:-1],iris.target[:-1]param_grid={'C':[10,50,100],'gamma':[0.005,0.05,0.5],'kernel':['poly','rbf','linear']}svc_reporter=Reporterzr(SVC,param_grid)Run The Experiment# `test_sizes` defaults to [0.1, ..., 0.9]# `repetition` defaults to 10report=svc_reporter.run_experiment(samples,labels,test_sizes=[0.1,0.2],repetition=5)print(report)printsTest Size   C  gamma  kernel                     Train Accuracies  \
0        0.1  10  0.005    poly  [0.881, 0.888, 0.873, 0.888, 0.881]
1        0.1  10  0.005     rbf   [0.978, 0.955, 0.955, 0.955, 0.97]
2        0.1  10  0.005  linear    [0.978, 0.97, 0.985, 0.978, 0.97]
3        0.1  10  0.050    poly  [0.985, 0.978, 0.978, 0.978, 0.985]
4        0.1  10  0.050     rbf  [0.985, 0.993, 0.993, 0.993, 0.993]

   Max Train Acc  Mean Train Acc  Stdev Train Acc  \
0          0.888           0.882            0.006
1          0.978           0.963            0.010
2          0.985           0.976            0.006
3          0.985           0.981            0.003
4          0.993           0.991            0.003

                       Test Accuracies  Max Test Acc  Mean Test Acc  \
0      [0.867, 0.867, 1.0, 0.8, 0.933]         1.000          0.893
1  [0.933, 0.933, 0.933, 0.867, 0.933]         0.933          0.920
2            [1.0, 1.0, 1.0, 1.0, 1.0]         1.000          1.000
3          [1.0, 1.0, 1.0, 1.0, 0.933]         1.000          0.987
4        [0.933, 1.0, 1.0, 0.867, 1.0]         1.000          0.960

   Stdev Test Acc                         Experiment Times (sec)
0           0.068  [0.00095, 0.00077, 0.00072, 0.00077, 0.00074]
1           0.026   [0.00079, 0.0008, 0.00082, 0.00082, 0.00081]
2           0.000   [0.0005, 0.00052, 0.00045, 0.00049, 0.00049]
3           0.027  [0.00052, 0.00055, 0.00052, 0.00054, 0.00053]
4           0.053  [0.00062, 0.00062, 0.00064, 0.00061, 0.00065]
Package: classifiction-f-20
classification_f_20The human oral bioavailability. For any drug administrated by the oral route, oral bioavailability is undoubtedly one of the most important pharmacokinetic parameters because it is the indicator of the eﬃciency of the drug delivery to the systemic circulation.  The range of bioavailability value is 0-100. Two thresholds (20% and 30%) were applied to split all the compounds into positive and negative compounds.[25] If the threshold is 20%.
Package: classifier-agent
Machine Learning Classification Python PackageFunctionality of the PackagePerforms Classification using the following algorithms with the default parameters:LogisticRegressionKNeighborsClassifierDecisionTreeClassifierRandomForestClassifierGradientBoostingClassifierSVCGaussianNBBernoulliNBMultinomialNBReturns a results dataframe that has information of the model name, accuracy and F1-score on the test data.The package takes the following parameters as input:dataset_path: Path to the csv or excel dataset.output_column: Name of the output column which contains the target variable.train_test_ratio: Ratio in which the dataset is to be divided in train and test splits.UsageMake sure you have Python installed in your system.Run Following command in the CMD.pip install classifier_agentExamplefrom classifier_agent import classifier_agent

dataset_path = "diabetes.csv"
output_column = "Outcome"
train_test_ratio = 0.25

results = classifier_agent(dataset_path, output_column, train_test_ratio)
print(results)NoteThe package is currently in a very elementary stage and work is in progress.The whole project is developed with python versionPython 3.7.7and pip versionpip 19.2.3.In case of error, feel free to contact me over Linkedin atAdnan.
Package: classifier-checker
Classifier-checkerClassifier-checker validates configured classifiers for you!Install$pipinstallclassifier-checkerUsage$classifier-checker--helpusage: classifier-checker [-h] [--strict] [--ignore-private] [--trove] [-a ACCEPT] [-d DENY] [-af ACCEPT_FILE] [-df DENY_FILE] [--verbose] packageClassifier-checker v0.1.2.Classifier-checker validates configured classifiers for you!positional arguments:package               Installed package to validateoptional arguments:-h, --help            show this help message and exit--strict              Enables strict mode, classifiers should be exactly equal to accepted classifiers--ignore-private      Ignore all classifiers that start with "Private ::"--trove               Ensure that classifiers are in line with trove-classifiers, the canonical source for classifiers on PyPI-a ACCEPT, --accept ACCEPTAccepted classifier(s) (comma-separated)-d DENY, --deny DENY  Denied classifier(s) (comma-separated)-af ACCEPT_FILE, --accept-file ACCEPT_FILEPath to file containing accepted classifier(s) (new line-separated)-df DENY_FILE, --deny-file DENY_FILEPath to file containing denied classifier(s) (new line-separated)--verbose             Enable verbose loggingLicensed under MIT, 2022 Thomas Rooijakkers
Package: classifier-pipeline
PI classifierThis pacakge will install the cacophony pipeline tracking onto a RPIYou will also need to install these system packages in order to run the packagesudo apt install python3-opencv libglib2.0-dev libgirepository1.0-dev libcairo2-dev libcairo2  python3-dbus build-essential libdbus-glib-1-dev libgirepository1.0-devOnce installed the tracking can be run usingpi_classifierThe default config is installed with this package if you would like to override the config you can create a config file at/etc/cacophony/classifier.yaml
Package: classifyawsetsjsonbyregion
Classify AWSets JSON by RegionClassifies AWSets JSON by AWS region.AdvantageOutput the list of AWS resources as CSV files classified by AWS regionEnable filter to exclude AWS resources not in questionOutput the list of AWS resources as CSV files classified by AWS regionYou can check the states of AWS resources by AWS region. It's also useful to compare the states of AWS resources between AWS regions by using text diff tools likeVisual Studio Code.Enable filter to exclude AWS resources not in questionYou can exclude AWS resources not in question by usingconfig.yml. It's useful to reduce the number of AWS resources to be checked, such like the one AWS created by default. You can focus on AWS resources in question.Quickstart1.Installclassifyawsetsjsonbyregionwith pip:pip install classifyawsetsjsonbyregion2.Create following directories:inputoutputintermediate3.Put AWSets JSON files in it and rename it asawsets.json:your_working_directory/
+---input/
    +---awsets.json
+---intermediate/
+---output/4.Runclassify-awsets-json-by-region:classify-awsets-json-by-regionHow do I...Use intermediate directory for?This directory is for intermediate files. These files are useful for debugging. The contents of these files are the classifying result of AWSets JSON before filtering AWS resources. Note that the file for excluded AWS regions is not created.CreditsThis package was created withCookiecutterand theyukihiko-shinoda/cookiecutter-pypackageproject template.
Package: classify-imports
classify-importsUtilities for refactoring imports in python-like syntax.installationpip install classify-importsexamplessplitting an import object>>>fromclassify_importsimportimport_obj_from_str>>>obj=import_obj_from_str('import foo, bar, baz')>>>[str(i)foriinobj.split()]['import foo\n', 'import bar\n', 'import baz\n']sorting import objects# Or to partition into blocks (even with mixed imports)>>>importpprint>>>fromclassify_importsimportimport_obj_from_str,sort>>>partitioned=sort([import_obj_from_str('from classify_imports import sort'),import_obj_from_str('import sys'),import_obj_from_str('from pyramid.view import view_config'),import_obj_from_str('import cached_property'),],)>>>pprint.pprint(partitioned)((import_obj_from_str('import sys\n'),),(import_obj_from_str('import cached_property\n'),import_obj_from_str('from pyramid.view import view_config\n'),),(import_obj_from_str('from classify_imports import sort\n'),),)classify a module>>>fromclassify_importsimportclassify_base,import_obj_from_str,Classified>>>classify_base('__future__')'FUTURE'>>>classify_base('classify_imports')'APPLICATION'>>>classify_base('pyramid')'THIRD_PARTY'>>>classify_base('os')'BUILTIN'>>>classify_base(import_obj_from_str('import os.path').module_base)'BUILTIN'>>>Classified.APPLICATION'APPLICATION'>>>Classified.order('FUTURE', 'BUILTIN', 'THIRD_PARTY', 'APPLICATION')
Package: classin
ClassIn Python SDK。【阅读文档】。安装目前 classin-sdk 支持的 Python 环境有 2.7, 3.4, 3.5, 3.6, 3.7 和 pypy。为了简化安装过程，推荐使用 pip 进行安装pipinstallclassin升级 classin 到新版本:pip install -U classin如果需要安装 GitHub 上的最新代码:pip install https://github.com/007gzs/classin/archive/master.zip调用示例from classin import ClassInClient

client = ClassInClient('<SID>', '<SECRET>')

folders = client.cloud.get_folder_list()
Package: classInfo
classInfoSimple information writing moduleUsageimportclassInfoabout=classInfo.information(name='Dave',age='23',job='Developer',salary='$12300',co_worker='Mark')print(about)###Return a dict of informationThere's only one function(not anymore)ReadInfo and dict in information(New in1.0.1)importclassInfoabout=classInfo.information(name={'name1':'ali','name2':'ali'},age='23',job='poor',wife='None')print(classInfo.read_info(about))
Package: class-interference
class-interferenceMonkey patching utilities for classes.Installationpipinstallclass-interferenceUsage examplefromclass_interferenceimportExtension,inject,apply_extensionsclassLibraryClass:deflibrary_method(self,*args,**kwargs):returnNoneclassLibraryClassExtension(LibraryClass,Extension):@injectdeflibrary_method(self,*args,**kwargs):original_value=self.super_ext.library_method(*args,**kwargs)iforiginal_valueisNone:raiseValueErrorreturnoriginal_valueapply_extensions(LibraryClassExtension)if__name__=="__main__":library_class_instance=LibraryClass()library_class_instance.library_method()# raises ValueError