Saravanan666/pypi-org huggingface dataset:
Package: feature-store-utils
feature store utilsA light-weight package that allows you express ML features in simple yaml, build a training data set and then write them to a feature store.some general thoughts on building a training datasethttps://docs.google.com/presentation/d/1tVkrwCLVwFp8cZC7CmAHSNFhsJrcTdC20MlZfptkSBE/edit?usp=sharingoptions for useclone this repo.  create features.yaml.  follow demo notebook. do not check back in.create you own repo and install as package (currently in testpypi).  Seehttps://github.com/BenMacKenzie/churn_model_demoas an example.  Note that you must create a .env file in folder which contains the features.yaml fileNotesCurrent version is experimental.  Not clear that Jinja is the right way to write parameterized SQL. Might be better to do in Python.Current version is not optimized. Each feature is calculated individually, whereas if table, filters and time windows are identical, multiple aggregation features can be calculated simultaneously.I believe there are around a dozen standard feature types.  The most common have been implemented.  Note that views can fill in a lot of gaps if encountered.  missing:type 1 lookup.1st order aggregations over time series (e.g., just treat it like a fact table)2nd order aggregations over time series e.g., max monthly job dbu over 6 month window.time in state,  e.g., how long was a ticket open.  based on a type 2 table.time to event in fact table, e.g., time since last call to customer supportscalar functions of two or more features, e.g, time in days between two datenum state changes over interval (rare)functions of features (e.g., ratio of growth in job dbu to interactive dbu).  Arguably this is not needed for boosted trees. Might be useful for neural nets...but why use a nueral net on heterogeneous data? (actually this kind of thing can be good for model explainability)Need to illustrate adding features from a related dimension table (using a foreign key...machinery is in place to do so.)Current version illustrates creating a pipeline which uses the api.  But it would be nice just to generate the code and write it to a notebook so that the package is invisible in production (like bamboolib)The demo repo (https://github.com/BenMacKenzie/churn_model_demo) illustrates 'hyper-features' which are features with variable parameters.Connecting 'hyper-features' to feature store needs to be worked out.  Currently the option is to add all of them or specify individual version by their (generated) nameFix feature store feature gen observation dates.  Align with grain of feature, e.g., if grain is monthly make sure feature store contains an observation on first of month.Buildingpython3 -m build  
python3 -m twine upload --repository testpypi dist/*

python3 -m twine upload dist/*Running unit tests on databricksinstall the databricks extension for vscodeuse this repo as a template.  Note the following:remote_test_harness/pytest_databricks.py.vscode/launch.jsonwrite tests as usual (see tests/time_series/time_series_test.py as an example)
Package: featuretoggles
Feature TogglesThis repo provides functionality for feature toggles in python code.What does this package do?It allows to access the configured toggles liketoggles.feature1It logs all uses of the toggleIt logs a warning when toggles have exceeded their maximum lifetimeHow to useConfigure the togglesThe toggles are configured in yaml.feature1:value:truename:New Thing Togglerdescription:This toggles the new thing onjira:DAN-123creation_date:2020-10-12max_lifetime:14Required fields:value: a boolean setting the value of the togglename: a human readable name for the togglejira: the JIRA ticket that this toggle is linked to.creation_date: the date this toggle is introduced.Optional fields:description: A description of the purpose and use of the togglemax_lifetime: The number of days this toggle is supposed to be in the code.
A warning will be logged when this lifetime is exceeded. The default is 14 days.Declare the togglesTo use the configured toggles they need to be declared in the code like this:fromfeaturetogglesimportTogglesListclassReleaseToggles(TogglesList):feature1:boolfeature2:booltoggles=ReleaseToggles('toggles.yaml')The toggle can be used to gate particular bits of code like this:iftoggles.feature1:print("Feature One Activated")else:print("Feature One Deactivated")DemoSeedemofor a working example
Package: featuretools4s
## FeatureTools for Spark (featuretools4s)### 1. What's FeatureTools?FeatureTools is a Python library open-sourced by MIT'sFeatureLab aiming to automatethe process of feature engineering in Machine Learningapplications.Please visit the [official website](https://docs.featuretools.com/index.html)for more details about FeatureTools.*FeatureTools4S* is a Python library written by me aiming to scaleFeatureTools with **Spark**, making it capable of generatingfeatures for billions of rows of data, which is usuallyconsidered impossible to process on single machine usingoriginal FeatureTools library with Pandas.*FeatureTools4S* provides **almost the same** API as originalFeatureTools, which make its users completely free of transferringbetween FeatureTools and FeatureTools4S. **Hence we suggest the readersfirst to learn FeatureTools and then you can easily work on FeatureTools4S.**### 2. How to use FeatureTools4S?First install *featuretools4s* through pip:```bashpip3 install featuretools4s```Then a simple example of using *featuretools4s* is as follows:```pythonimport featuretools4s as ftsfrom pyspark.sql import SparkSessionimport osimport pandas as pdos.environ["SPARK_HOME"] = "C:\Python36\Lib\site-packages\pyspark"os.environ["PATH"] = "C:\Python36;" + os.environ["PATH"]pd.set_option('display.expand_frame_repr', False)spark = SparkSession.builder.master("local[*]").getOrCreate()order_df = spark.read.csv("resources/order.csv", header=True, inferSchema=True).sort("sales_tax")customer_df = spark.read.csv("resources/customer.csv", header=True, inferSchema=True)es = fts.EntitySetSpark(id="test")es.entity_from_dataframe("order", order_df, index="order_num", time_index="wo_timestamp")es.entity_from_dataframe("customer", customer_df, index="cust_num")es.add_relationship(fts.Relationship(es["customer"]["cust_num"], es["order"]["cust_num"]))features = fts.dfs(spark, entityset=es, target_entity="customer", primary_col="cust_num", num_partition=5)features.show()```
Package: featuretools-sklearn-transformer
featuretools-sklearn-transformerFeaturetools' DFS as a scikit-learn transformerInstallpipinstallfeaturetools_sklearn_transformerUseTo use the transformer in a pipeline, initialize an instance of the transformer by passing in
the parameters you would like to use for calculating features. To fit the model and generate features for
the training data, pass in an entityset or list of dataframes and relationships containing only the relevant
training data as theXinput, along with the training targets as theyinput. To generate a feature matrix from test data, pass in
an entityset containing only the relevant test data as theXinput.The input supplied forXcan take several formats:To use a Featuretools EntitySet without cutoff times, simply pass in the EntitySetTo use a Featuretools EntitySet with a cutoff times DataFrame, pass in a tuple of the form (EntitySet, cutoff_time_df)To use a list DataFrames and Relationships without cutoff times, pass a tuple of the form (dataframes, relationships)To use a list of DataFrames and Relationships with a cutoff times DataFrame, pass a tuple of the form ((dataframes, relationships), cutoff_time_df)Note that because this transformer requires a Featuretools EntitySet or dataframes and relationships as input, it does not currently work
with certain methods such assklearn.model_selection.cross_val_scoreorsklearn.model_selection.GridSearchCVwhich expect theXvalues
to be an iterable which can be split by the method.The example below shows how to use the transformer with an EntitySet, both with and without a cutoff time DataFrame.importfeaturetoolsasftimportpandasaspdfromfeaturetools.wrappersimportDFSTransformerfromsklearn.pipelineimportPipelinefromsklearn.ensembleimportExtraTreesClassifier# Get example datatrain_es=ft.demo.load_mock_customer(return_entityset=True,n_customers=3)test_es=ft.demo.load_mock_customer(return_entityset=True,n_customers=2)y=[True,False,True]# Build pipelinepipeline=Pipeline(steps=[('ft',DFSTransformer(target_dataframe_name="customers",max_features=2)),('et',ExtraTreesClassifier(n_estimators=100))])# Fit and predictpipeline.fit(X=train_es,y=y)# fit on customers in training entitysetpipeline.predict_proba(test_es)# predict probability of each class on test entitysetpipeline.predict(test_es)# predict on test entityset# Same as above, but using cutoff timestrain_ct=pd.DataFrame()train_ct['customer_id']=[1,2,3]train_ct['time']=pd.to_datetime(['2014-1-1 04:00','2014-1-2 17:20','2014-1-4 09:53'])pipeline.fit(X=(train_es,train_ct),y=y)test_ct=pd.DataFrame()test_ct['customer_id']=[1,2]test_ct['time']=pd.to_datetime(['2014-1-4 13:48','2014-1-5 15:32'])pipeline.predict_proba((test_es,test_ct))pipeline.predict((test_es,test_ct))Built at Alteryx Innovation Labs
Package: featuretools-sql
Automated creation of EntitySets from relational data stored in SQL databasesThefeaturetools_sqllibrary allows you to directly import your relational data intoFeaturetoolsto run automated feature engineering.InstallationInstall with pip:python-mpipinstall"featuretools[sql]"or from the Conda-forge channel onconda:conda install -c conda-forge featuretoolsExampleSimply pass in the database connection information:fromfeaturetools_sql.connectorimportDBConnectorsql_connector=DBConnector(system_name="mysql",host="127.0.0.1:3306"user="root",password="password",database="db")entityset=sql_connector.get_entityset()Theentitysetobject will have therelationshipsandDataFramesalready populated, allowing you to call featuretools.DFS and run automated feature generation.importfeaturetoolsasftfeature_defs,feature_matrix=ft.dfs(entityset=entityset,target_entity='target_table_name')We currently supports importing data from the following relational database systems:MySQLPostgreSQLSnowflakeSupportThe Featuretools community is happy to provide support to users. Project support can be found in four places depending on the type of question:For usage questions, useStack Overflowwith thefeaturetoolstag.For bugs, issues, or feature requests start aGithub issue.For discussion regarding development, useSlack.For everything else, the core developers can be reached by email atopen_source_support@alteryx.comBuilt at Alteryxfeaturetools_sqlis an open source project maintained byAlteryx. To see the other open source projects we’re working on, visitAlteryx Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.
Package: featuretools-tsfresh-primitives
TSFresh PrimitivesInstallationInstall with pip:python-mpipinstall"featuretools[tsfresh]"Calculating FeaturesIntsfresh, this is how you can calculate a feature.fromtsfresh.feature_extraction.feature_calculatorsimportagg_autocorrelationdata=list(range(10))param=[{'f_agg':'mean','maxlag':5}]agg_autocorrelation(data,param=param)[('f_agg_"mean"__maxlag_5',0.1717171717171717)]With tsfresh primtives infeaturetools, this is how you can calculate the same feature.fromfeaturetools.tsfreshimportAggAutocorrelationdata=list(range(10))AggAutocorrelation(f_agg='mean',maxlag=5)(data)0.1717171717171717Combining PrimitivesInfeaturetools, this is how to combine tsfresh primitives with built-in or other installed primitives.importfeaturetoolsasftfromfeaturetools.tsfreshimportAggAutocorrelation,Meanentityset=ft.demo.load_mock_customer(return_entityset=True)agg_primitives=[Mean,AggAutocorrelation(f_agg='mean',maxlag=5)]feature_matrix,features=ft.dfs(entityset=entityset,target_dataframe_name='sessions',agg_primitives=agg_primitives)feature_matrix[['MEAN(transactions.amount)','AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',]].head()MEAN(transactions.amount)  AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
session_id
1                           76.813125                                           0.044268
2                           74.696000                                          -0.053110
3                           88.600000                                           0.007520
4                           64.557200                                          -0.034542
5                           70.638182                                          -0.100571Notice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.feature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head()customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
session_id
1                                                    0.011102
2                                                   -0.001686
3                                                   -0.010679
4                                                    0.011204
5                                                   -0.010679Built at Alteryx Innovation Labs
Package: featuretools-update-checker
Featuretools Update CheckerFeaturetools update checker is a python library to automatically check that you have the latest version of Featuretools.InstallationInstall with pippython-mpipinstall"featuretools[update_checker]"Disable CheckerYou can disable the update checker by changing your environment variables to include the following:export FEATURETOOLS_UPDATE_CHECKER=FalseUninstallTo uninstall the update checker, you can do the following:python-mpipuninstallfeaturetools_update_checkerFeature LabsFeaturetools update checker is an open source project created byFeature Labs. To see the other open source projects we're working on visit Feature LabsOpen Source. If building impactful data science pipelines is important to you or your business, pleaseget in touch.
Package: featurevectormatrix
Python class to encapsulate different representations of large datasetsFree software: BSD licenseDocumentation:https://featurevectormatrix.readthedocs.org.FeaturesPython class to encapsulate different representations of large datasetsRows can be inserted as python dictionaries or arraysThey can be pulled out as dictionaries or arraysHistory0.1.1 (2014-09-12)Improve import path0.1.0 (2014-09-12)First release on PyPI.
Package: featureview
featureview 使用说明文档一、软件介绍名称：featureview用途：打开录制的 * .MF4 文件，读取并显示里面的变量信息。二、 安装说明2.1 测试电脑是否已安装 python3首先需要确定电脑是否已经安装了 python3，请按如下步骤测试：打开 cmd输入: python -V查看上一步的结果是否是 python2.* 。因为公司有的软件依赖于python2版本，所以这里必须显示为 python 版本为 2. *输入： pip -V查看上一步的结果是否显示关联到 python2. * 版本输入：python3 -V查看上一步的结果是否显示是python3.* ，如果正常，则说明已安装了python3版本输入：pip3 -V查看上一步的结果是否显示关联到 python3.* ，如果正常，则说明已安装了 pip3并且关联到python3解释： pip 是python安装外部模块的管理程序。如果已上都显示正确，可直接跳到 2.5，否则向下依次安装2.2 安装 python3注：不支持3.8版本，请下载3.7.4以上的子版本即可。前往网址：https://www.python.org/downloads/下载python3.7.4的win10安装包。（其它版本未测试）双击下载下来的安装包 python-3.7.4.exe 进行安装，安装过程中请勾选以下选项：
将python可执行程序添加到路径（add to PATH)；安装路径选择本用户，不要使用全局或重新选定路径；其它默认即可。2.3 解决python2 与 python3 的兼容性问题进入 python3 的安装路径文件夹中：C:\Users\AppData\Local\Programs\Python\Python37-32， 其中 user-name 是使用帐户名复制本文件夹内的 python.exe，并粘贴在本文件夹内，并修改粘贴后文件名为 python3.exe.2.4 测试 python3 安装是否成功重新按照 2.1中的指令进行测试，如果一切正常则跳到2.5, 否则请联系我。2.5 安新 featureview 模块如果电脑联网的话，打开 cmd， 输入指令： pip3 install featureview如果未联网，可联系我离线安装。如果网络不好，导致pip3安装模块速度太慢，可以更换国内的清华开源镜像源后安装，参考网址：https://mirrors.tuna.tsinghua.edu.cn/help/pypi/如果安装正常，可在路径：C:\Users\AppData\Local\Programs\Python\Python37\Scripts 下找到一个文件名为：featureview* .exe ，双击即可打开软件，也可以将此文件创建桌面快捷方式使用。三、软件打包指令python3 -m pip install --user --upgrade setuptools wheelpython3 setup.py sdist bdist_wheel打包后的文件在dist文件夹下四、本文档说明版本： v1.0作者： Andy.Yang部门： Radar.Feature五、版本更新说明1.0.3 将install_requires中的依赖项版本固定1.0.2 修改了setup.py中install_requires，增加了版本要求1.0.1 修复了一个Bug。由于上一个版本中 requirements.txt 未指定依赖包的版本，导致依赖包更新时某些API被弃用。本次在此文件中指定了开发时的版本1.0.0 在当前版本下测试正常
Package: featurExtract
OverviewThe featurExtract is a python package for genome feature extraction in bioinformatics.The package contains two useful executable command programs. The first executable program
is featurExtract including ten subroutines termed create, gene, promoter, UTR, uORF,  CDS,
dORF, exon, intron, intergenic. The second executable program is create that is used for
creating database. The promoter subroutine is used for extracting promoter sequence. The
uORF subroutine is used for extracting upstream open reading frames sequence. The UTR
subroutine is used for extracting untranslated region sequence. The CDS subroutine is used
for extracting coding sequence. The intergenic subroutine is used for extracting intergenic
sequence between two genes. The second executable program is genBankExtract includingfour subroutines termed gene, CDS, rRNA, tRNA.Brief introduction of featurExtract packageInstallTwo way offer to install featurExtract module.install command linepipinstallfeaturExtract# othergitclonehttps://github.com/SitaoZ/featurExtract.gitcdfeaturExtract
pythonsetup.pyinstallRequirementspython >= 3.7.6pythonpandas >= 1.2.4pandasgffutils >= 0.10.1gffutilssetuptools >= 49.2.0setuptoolsbiopython >= 1.78biopythonUsagefeaturExtract is designed for GFF and GTF fileand GenBankExtract is suited for GenBank file.featurExtract# gff or gtf databasewhichfeaturExtract
featurExtract-hfeaturExtractcreate-hfeaturExtractpromoter-hfeaturExtractUTR-hfeaturExtractuORF-hfeaturExtractCDS-hfeaturExtractdORF-h
featurExtractexon-h
featurExtractintron-h
featurExtractintergenic-hgenBankExtract# GenBank databasewhichgenBankExtract
genBankExtract-h
genBankExtractgene-h
genBankExtractCDS-h
genBankExtractrRNA-h
genBankExtracttRNA-hExamplesfeaturExtract# step 1 create databasefeaturExtractcreate-fGFF-gath.gff3-oath# step 2 command# promoter whole genomefeaturExtractpromoter-dath.GFF-fath.fa-l200-u100-opromoter.csv--output_formatfasta# promoter one gene to stdoutfeaturExtractpromoter-dath.GFF-fath.fa-l200-u100-gAT1G01010-p--output_formatfasta
featurExtractUTR-dath.GFF-fath.fa-oUTR.csv-sGFF
featurExtractuORF-dath.GFF-fath.fa-ouORF.csv-sGFF
featurExtractCDS-dath.GFF-fath.fa-oCDS.csv-sGFF
featurExtractmRNA-dath.GFF-fath.fa-omRNA.fasta-sGFF--output_formatfasta
featurExtractexon-dath.GFF-fath.fa-tAT1G01010.1-p-sGFF
featurExtractintron-dath.GFF-fath.fa-tAT1G01010.1-p-sGFFgenBankExtract# GenBank step 3genBankExtractgene-gNC_000932.gb-fdna-pgenBankExtractCDS-gNC_000932.gb-fdna-pgenBankExtractrRNA-gNC_000932.gb-fdna-p
genBankExtracttRNA-gNC_000932.gb-fdna-p
Package: featurize-jupyterlab
featurize-jupyterlabA JupyterLab extension.PrerequisitesPython >= 3.7Node.js >= 8It's recommended to usenvmto manage your node and npm versions.Installationjupyterlabextensioninstallfeaturize-jupyterlabDevelopmentFor the first timeClone this repogit clone git@github.com:louis-she/featurize-jupyterlab.gitCreate virtual environmentcd featurize-jupyterlab
python -m venv .venv
source .venv/bin/activateInstall Python dependenciesbrew install postgresql
pip3 install -r requirements.txtInstall frontend dependenciesnpm installInstall Typescriptnpm install -g typescriptAdd jupyter extensionpip3 install -e .
./.venv/bin/jupyter serverextension enable --py featurize_jupyterlab
./.venv/bin/jupyter labextension install . --no-buildAdd featurize extensionpip3 install featurize-package
./.venv/bin/featurize_server package:add ftpkg.demoStart development file watchingnpm run devFor the regular timesource .venv/bin/activatenpm run devWriting testsWe usepytestto write the tests case. All tests goes in thefeaturize_jupyterlab/testsdirectory.Both the test file and method should be started bytest_, using the following command to run all the tests,pytest featurize_jupyterlab/tests
Package: febelfin-coda
febelfin-coda is a parser forCODA files.NutshellImport:>>> import os
>>> from coda import CODAInstantiate:>>> coda = CODA('coda/CODA.txt')The statements:>>> len(coda.statements)
1
>>> statement, = coda.statements
>>> statement.account
'435000000080'
>>> statement.account_currency
'EUR'
>>> statement.old_balance
Decimal('0')
>>> statement.old_balance_date
datetime.date(2006, 12, 6)
>>> statement.new_balance
Decimal('9405296.99')
>>> statement.new_balance_date
datetime.date(2006, 12, 7)The transactions:>>> len(statement.moves)
59
>>> move = statement.moves[0]
>>> move.value_date
datetime.date(2006, 12, 6)
>>> move.entry_date
datetime.date(2006, 12, 6)
>>> move.amount
Decimal('-2578.25')
>>> move.bank_reference
'EPIB00048 AWIUBTKAPUO'
>>> move.transaction_code
'00799000'
>>> move.communication
"BORDEREAU DE DECOMPTE AVANCES    015 NUMERO D'OPERATION 495953"To report issues please visit thecoda bugtracker.
Package: febhd-clustering
hd-clusteringAuthors: Alejandro Hernández Cano, Mohsen Imani.InstallationIn order to install the package, simply run the following:pip install febhd_clusteringVisit the PyPIproject pagefor
more information about releases.DocumentationRead thedocumentationof this project.Quick startThe following code generates dummy data and trains a FebHD clustering model
with it.>>>importfebhd_clustering>>>dim=10000>>>n_samples=1000>>>features=100>>>clusters=5>>>x=torch.randn(n_samples,features)# dummy data>>>model=febhd_clustering.FebHD(clusters,features,dim=dim)>>>iftorch.cuda.is_available():...print('Training on GPU!')...model=model.to('cuda')...x=x.to('cuda')...TrainingonGPU!>>>model.fit(x,epochs=10)>>>ypred=model(x)>>>ypred.size()torch.Size([1000])For more examples, see theexamples/directory.Citation requestIf you use hd-clustering, please cite the following papers:Alejandro Hernández-Cano, Yeseong Kim, Mohsen Imani. "A Framework for
Efficient and Binary Clustering in High-Dimensional Space". IEEE/ACM Design
Automation and Test in Europe Conference (DATE), 2021.Mohsen Imani, et al. "DUAL: Acceleration of Clustering Algorithms using
Digital-based Processing In-Memory"r IEEE/ACM International Symposium on
Microarchitecture (MICRO), 2020.
Package: febraban
GlobalBefore deployment, change the projectversiononsetup.pyfile and
set the correctdownloadurl_. Then make sure you have the file~/.pypircwith the content below and the correct credentials instead
of the provided placeholders:[distutils]
index-servers =
    pypi
    pypitest

[pypi]
repository:https://upload.pypi.org/legacy/
username:myusername
password:mypassword

[pypitest]
repository:https://test.pypi.org/legacy/
username:myusername
password:mypasswordTest environmentRunpython setup.py sdist upload-rpypitestinside the project
directory.Live environmentRunpython setup.py sdist upload-rpypiinside the project
directory.