Saravanan666/pypi-org huggingface dataset:
Package: housemodel
A Python package for modelling the thermal behaviour of a building with HVAC installations.
The code is being developed in the Research Group Balanced Energy Systems (BES) at HAN University of Applied Sciences,
Thehousemodelpackage can be used under the conditions of the GPLv3 license.FeaturesBasic classes for thermal network representation of buildings.Additional classes for HVAC installations.Conductive and convective heat transfer.Matrix formalism for dynamic evaluation of temperature distributions.Heat rate balance equations including solar irradiance and human presence.ODE solver evaluating building dynamics and HVAC control.InstallationTo use the packagehousemodel, install it in a Python environment:(env)pip install housemodelor(env)conda install housemodelThis should
automatically install the dependency packagesmatplotlib,scipyandpandas, if they haven’t been installed already. If you are
installing by hand, ensure that these packages are installed as well.Example use"""Example Python script using the housemodel package."""fromhousemodel.buildings.buildingimportBuilding# create Building objecth=Building("MyHouse")section=param["Building"]# read nodes attribute from dictionary and create capacity matrixh.nodes_from_dict(section["nodes"])h.fill_c_inv()h.edges_from_dict(section["edges"])# read FixedNode objects (external nodes);h.boundaries_from_dict(param["boundaries"])# function selects "outdoor" as ambienth.make_k_ext_and_add_ambient()# initialize k_ext_mat and add diagonal elementsb=StratifiedBufferNew.from_dict(param["Buffer"])b.generate_nodes()b.fill_c_inv()b.generate_edges()b.generate_ambient()b.make_k_ext_and_add_ambient()total=TotalSystem("HouseWithBuffervessel",[h,b])total.sort_parts()# compose c-1-matrix from parts and merge tag_liststotal.merge_c_inv()total.merge_tag_lists()...housemodel pagesPyPi: housemodel Python packageGithub: housemodel source
codeReadTheDocs: housemodel
documentationAuthor and licenseAuthor: Paul J.M. van KanContact:paul.vankan@han.nlLicense:GPLv3References…
Package: housenumparser
A small library for merging and splitting sequences of Belgian house numbers.DescriptionSplits ranges of Belgian house numbers into individual ones and vice versa.Building documentationAfter installing dev-requirements, execute the following in the docs folder.sphinx-apidoc -f --separate --module-first --implicit-namespaces -o source/ ../housenumparser
sphinx-build -a -b html source buildAfter these 2 commands the docs folder should now contain a build folder which
will have an index.html that you can open in a browser.
Package: housepaint
housepaintWhat is it?housepaintis a demonstration library for a talk on publishing Python packages. A silly little thing with limited
utility, it allows you to recolor an entire function's worth of text with beautiful, simple decorators.Requirementshousepaintrequires Python >= 3.6.As of version 0.0.1 it has no dependencies outside the Python standard library, except forpytest in dev.InstallationViapoetry:poetryaddhousepaintViapip:pipinstallhousepaintUsagefromhousepaintimportBG,FG,paint,Style@paint(foreground=FG.GREEN,background=BG.BLACK,styles=Style.BOLD)defsuccess_example()->None:print("This")print("Should")print("All")print("Be")print("Green")success_example()Will result in all of the print output of the functionsuccess_examplebeing in bold, green font on a black background.LimitationsThis library relies on ANSI escape codes to recolour text. If your printed text has ANSI codes there's a good chance it
won't work out well for you.Can I use this in my project?Yes, please do! The code is all open source and BSD-3-Clause licensed.
Package: house-sales
House SalesFeaturesTODORequirementsTODOInstallationYou can installHouse SalesviapipfromPyPI:$pipinstallhouse-salesUsagePlease see theCommand-line Referencefor details.LicenseDistributed under the terms of theMIT license,House Salesis free and open source software.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: houses-client
HOUSES ClientA Python Client SDK for theMayo Clinic HOUSES APIRequirementsPython 3.9+Installationpipinstallhouses-clientExample Usagefromhouses_clientimportclient#Create a client passing in your API client id and client secretclient=client.HousesClient(api_endpoint="https://houses.konfidential.io",client_id="my oidc client id",client_secret="my oidc client secret",log_level="INFO")# Submit batch request reading from myinput.csv and writing results to myoutput.csv# year is applied to records if the csv format doesn not include yearclient.batch(input_path="myinput.csv",output_path="myoutput.csv",year=2021,delimiter=',')Input Data SchemaSingle Address Formatid: user defined unique idaddress: single address string; e.g. 123 main st anycity NY 12345. The address should contain one of the following combinations:city and statecitystatezipcodeyear: the requested year. This is an optional field and if provided overrides the request level yearreferenceAddress: the requested reference address. This is an optional field and if provided overrides the request level reference addressExample:id,address,year
1,"123 main st anytown ny 5555",2019
2,"456 main st anytown ny 55555",2018Example With referenceAddress:id,address,year,referenceAddress
1,"123 main st anytown ny 5555",2019,"789 main st anytown y 55555"
2,"456 main st anytown ny 55555",2018,"678 main st anytown y 55555"Full Component Address Formatid: user defined unique idaddress: Street Addresssecondary: Optional Secondary Street Address; e.g. Apartment or Suite numbercitystatezipyear: the requested year. This is an optional field and if provided overrides the request level yearreferenceAddress: the requested reference address. This is an optional field and if provided overrides the request level reference addressBuildpython3-mpipinstall--upgradebuildpython3-mbuild
Package: houses-pipeline
Houses Data Science PipelineThis is a Houses Data Science pipeline produced from an analysis you can see as
steps in notebooks/RequirementscondaInstallationRun the following to install the project as a python packagepipinstallhouses_pipelineStarting and exploring the projectcondacreate-fenvironment.yml
condaactivatehousesBuilding the packagepythonsetup.pybdist_wheelPipeline stepsFetch the dataset./houses_pipeline/fetch/fetch_dataset.shdata/rawOr simply a one-liner ofkagglecompetitionsdownload-chouse-prices-advanced-regression-techniques-pdata/raw;unzip-odata/raw/*.zip-ddata/raw/Preprocesspython houses_pipeline/preprocess data/raw/train.csv data/interim/train.csvData SplittingNot Yet ImplementedModel TrainingNot Yet ImplementedRunning testscondadevelop.
pytestUsageContributing\Developingpipinstall-e.[dev]
Package: housing
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >
Package: housing-5513
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython3 nonstandardcode.py
Package: housing-assignment2.1-5505
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython3 nonstandardcode.py
Package: housing-library-5490
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py ># mle-training
Package: housing-library-5492
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py ># mle-training
mle-training
Package: housing-library-5493
MLE-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >
Package: housing-library-5494
README.mdMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To execute the scriptpython nonstandardcode.pyTo activate conda environmentconda activate mle-devTo create environment from yml fileconda env create --name mle-dev --file=env.ymlCommand to install isort in conda environmentconda install isortCommand to install black in conda environmentconda install blackCommand to install flake8 in conda environmentconda install flake8Command to refactor python code with isortisort nonstandardcode.pyCommand to refactor python code with blackblack nonstandardcode.pyCommand to refactor python code with flake8flake8 nonstandardcode.py
Package: housing-library-5495
mle-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To Create a conda environment from new_env.yml fileconda env create -f new_env.ymlTo activate the created environmentconda activate mle-devTo excute the scriptpythonnonstandardcode.py
Package: housing-library-5496
mle-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.Command to create Virtual Enviornemnt:conda --version
conda create --name mle-dev biopython
conda activate mle-devinstall the necessary librabries like numpy,pandas , matplotlib and scikit learnconda install numpy
conda install pandas
conda install matplotlibTo excute the scriptpython < scriptname.py >
python nonstandardcode.pyExporting the enviormentconda export --name MLE-training >env.yml
Package: housing-library-5497
mle-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptconda create --name  biopythonscript for creating a new environmentconda activateactivating the new environmentinstalling necessary packagespython < scriptname.py >command to run the scriptconda env export  > <filename.yml>exporting environmentconda activatechanging the environment to default base
Package: housing-library-5500
#This Submission consists of soulutions for Tamlep-Assignment-2.1Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptSteps to reproduce:unzip dist/housing_library_5500-0.1-py3-none-any.whl -d wheel_contentscd wheel_contentspython3 house_price_prediction/score.pyTo activate the environmentconda activate <environment-name>To export the environmentconda env export > environment.ymlTo import the environmentconda env create -f environment.ymlLICENSEThis project is licensed under the MIT license -  see the (LICENSE) file for details
Package: housing-library-5503
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >sklearn pypi account registered and authenticated-done adding the functional and unit tests.
-test installation and test training cases are added to functional tests and executed.
-test data ingestion from unit tests is performed.
Package: housing-library-5506
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython3 nonstandard.pyCommand to create environment from env.yml fileconda env create -f env.ymlCommand to activate the environmentconda activate mle-dev
Package: housing-library-5507
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >
Package: housingLibrary-5508
#This change is for the Pull requestMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.##Packaging python code and test it using score.pypython -m build (Create a .tgz and .whl files of our project for the packing)
twine upload dist.* (Upload our packaging files to the pypi to make it globally accesable to all users)
-> Inorder to do upload the files to pypi we need to create an account in the pypi and generate and token.How to install the package and import the methodsInstall the package name = housing-library-5512==0.1pip install housing-library-5512==0.1In code file use these statements to import the required methods.from src import fetch_housing_data, load_housing_dataTesting with sample code for the package-> We tested it by running score.py file to test the package
Package: housing-library-5509
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptCreate conda environmentInstall necessary packagescondaactivatemle-devcondaenvexport--namemle-dev>>env.ymlpythonnonstandardcode.py
Package: housing-library-5510
mle-trainingMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython nonStandardCode.py
Package: housing-library-5511
Median housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython3 nonstandard.pymle-training
Package: housing-library-5512
#This change is for the Pull requestMedian housing value predictionThe housing data can be downloaded fromhttps://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.The following techniques have been used:Linear regressionDecision TreeRandom ForestSteps performedWe prepare and clean the data. We check and impute for missing values.Features are generated and the variables are checked for correlation.Multiple sampling techinuqies are evaluated. The data set is split into train and test.All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.To excute the scriptpython < scriptname.py >