Package: ml-proj-init
ML-PROJ-INITA lightweight tool to create machine learning project structure and add neaty code snipt quickly to your projectml-proj-initis a python package to help you to do your machine learning experiment in python in a faster way by creating well defined project structure just by a few commands that works out of the box for most of the machine learning projects. You can focus on building the solution of your problem rather writing all the basic stuffs that are tedious but can be added to your project easily and in a faster way.2. Installation2.1 Install in Linux, MacOS and WindowsThe installation process is OS independent. If you have a working python 3.5 or newer version in your computer and pip3 as the package manager, you can easilty install the package just by following instructions.2.1.0 DependencyPython 3.x2.1.1 InstallationInstall using the following command-pipinstallml-proj-init2.1.2 UninstallationUninstall using the following commandpipuninstallml-proj-initProvide "y" if you are prompt for approval to remove some config files.See more info here:ml-proj-init in pypi.org4. Getting StartedML-PROJ-INIT has two modeCreate structure modeAdd class/code snipt mode4.1 Creating project structureIn create mode, you can create a well defined project structure for your machine learning or deep learning project by passing few arguments. The create structure mode supports the following parameters -Creation mode parameters:ParameterAccepted ValuesFormatUsageDefault Values-mor--modec-m=cor--mode=cor-m cor--mode cc for creating structureRequired, no default-nor--namestring, a valid project name-n=proj_nameor--name=proj_nameor-n proj_nameor--name proj_nameProvide the project nameRequired, no default-por--pathstring, a valid path-p pathor--path pathPath to create the projectOptional, if not given use the current working directory as the path-tor--typemlordl-t=mlor--type=mlor-t dlor--type dlproject type to create ml project or dl project structureOptional, if not given use "ml" as the default valueExample:To create project strucure for a project namedhello-worldin/home/user/Desktop/location and if the project type isdeep learning, then run the following command -ml-proj-init-m=c-n=hello-world-p=/home/user/Desktop/-t=dlorml-proj-init-mc-nhello-world-p/home/user/Desktop/-tdl4.2 Appending class/code sniptIn append mode, you can easily generate required class implementation or code snipt for loading different data source or building few common machine learning or deep learning model architecture.Append mode parameters:ParameterAccepted ValuesFormatUsageDefault Values-mor--modea-m=aor--mode=aor-m aor--mode aa for appending required class or code sniptRequired, no default-por--pathstring, a valid path-p pathor--path pathPath to create the projectOptional, if not given use the current working directory as the path-lor--loaderimgortextorcsv-l=imgor--loader=imgor-l textor--loader csvData loader type to generate instant code sniptOptional, no default-netor--networkcnnorlstmornn-net=cnnor--network=lstmor-net nnNetwork type to generate code snipt for common architectureOptional, no defaultExample:To add a data loader forimagetype data and or a simplecnnarchitecture to classify the image run the following command-ml-proj-init-m=a-l=img-net=cnn-p=/home/user/Desktop/orml-proj-init-ma-limg-netcnn-p/home/user/Desktop/5. Features5.1 Features avaiableCreating machine learning project structure based on popular ml library scikit-learn and others.Creating deep learning project structure using popular dl library tensorflow, keras and others.Adding necessary code snipts for data loader class like image data loader or text data loader.Adding different neural network implementation skeleton in popular deep learning library TF/KERAS.5.2 Features in QueueAdding frequently used data vizualization code sniptsAdding feature to generate quick documentation for project, like tables, visualizations etc.Adding more frequently used implementation for quick experimentAdding more implementatioin of NN architecture for experimentAdding more flexible and dynamic code snipt generation.6. Report IssuesBefore you report an issue in github, please make sure you are in the same pace with the up to date commit in our github repo and the latest release from pypi.7. How to ContributeYou can contribute in the one or either way of the following-7.1 Bug Reporting-- You can report a bug by adding issue in github
-- Or you can contribute by sharing how you solved an issue7.2 Requesting a Feature-- If you came accross any new idea that can be added as feature7.3 Adding Feature, Pull Request-- If you come with new idea of a feature and add it
-- Send us pull request7.4 Adding Nofification Resource-- Adding latest neural network architecture to make available for use
-- Adding more frequently used code snipt for machine learning and deep learning project.