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%global _empty_manifest_terminate_build 0
Name: python-AutoAiLib
Version: 1.1.0
Release: 1
Summary: The library that automates the silly ML things.
License: GNU GPLv3
URL: https://pypi.org/project/AutoAiLib/
Source0: https://mirrors.aliyun.com/pypi/web/packages/3e/1d/a6e70d607be8e88b01f0f5986f731f4b09b656006f5e45f22ab4988c8311/AutoAiLib-1.1.0.tar.gz
BuildArch: noarch
%description
<h1>AutoAI</h1>
<p>This repository is a compilation of scripts that I have created in my time working with machine learning. These scripts aim to automate the annoying and tedious parts of ML, allowing you to focus on what is important.
PyPi: https://pypi.org/project/AutoAILib/
</br> $ pip install autoailib </br>
This library was developed for and used with keras convolutional neural networks. They do however work with other keras models, besides image test obviously.</p>
<div class="entry">
<h1> AutoAiLib.general_tester(model path or object, labels, preprocessor)</h1>
<a href="https://youtu.be/TQisVhgUzWo"> Class Video Demo</a>
<h2> AutoAiLib.general_tester.predict_single(example)</h2>
<ul><li>example- If you have defined a preprocessor for your tester, this should comply with the preprocessor's argument. If
you have not defined a preprocessor, example must be in a form that your model will accept.</li></ul>
<h2> AutoAiLib.general_tester.predict_many(container=None, testing_folder = None, csv_dir)</h2>
<ul>
<li> container - This can be a container of test objects (any iterable). If preprocessor is defined, these objects must comply with the preprocessors parameter. Otherwise they must be in a form that your model will accept.</li>
<li> testing_dir - This can be a path to a testing folder which has sub folders of all classes. Again, must be preprocessed or have preprocessor defined.</li>
<li> csv_dir - This function compiles data into a csv folder to allow users to easily extract data from it, if not defined it will return a pandas data frame.</li>
</ul>
</div>
<div class="entry">
<h1> AutoAi.convnet_tester(model path or object, labels) </h1>
<a href="https://youtu.be/sM57JDasREk"> Class Video Demo </a>
<h2> AutoAi.image_predict(model_path, image_path, labels)</h2>
<h5> This function takes 3 arguments: a path to a keras model, a path to an image, and a list of labels.</h5>
<h5> Demo:</h5>
Given a the correct arguments, we get the following output, as well as this image saved to our model directory.
<img src="https://i.imgur.com/woiPdus.png"></img>
<h2> AutoAi.manual_test(model, testing_dir, labels) </h2>
<h5> This function tests a model given labels and testing data. It then compiles the results in a CSV file, and groups the results by class, and by correct and incorrect.</h5>
<ul>
<li> Model - Path of model that you want to test or model object.</li>
<li> Testing_dir - Path to the directory with your testing data.</li>
<li> Labels - Dictionary of the classes, in form (index:class_name)</li>
</ul>
<h5>Example csv:</h5>
<img src="https://i.imgur.com/g4gNQjS.png"></img>
</div>
<div class="entry">
<h2>Update! This has now been packaged in the AutoAI.data_compiler class.
AutoAi.data_compiler(self,src, dest, **kwargs)</br>
AutoAi.data_compiler.run() will compile the data based on the constructor parameters. </h2>
<h5> This function takes 2 required arguments, an original data source file, and a path to the desired data directory. Given just these two arguments, this function will create a new testing data folder at dest with training, validation, and testing folders, containing folders for each class. You can alter the ratio with the ratio arguments, as well as provide a number of img transforms to do if you are using images.</h5>
<ul>
<li> Src - Path to a folder that contains a folder for each class and then data examples in those class folders. </li>
<li> Dest - Path to a folder where you want the data to end up. </li>
<li> Num_imgs_per_class - This number of images will be added to the original set for each class through transforms. The theoretical limit for this would be 3! * original images per class </li>
</ul>
<h5> Demo:</h5>
Given the a path to the following folder:
<img src="https://i.imgur.com/SSpydEv.png"></img>
If augmentation used the following results will be yielded:
<img src="https://i.imgur.com/4okyMrN.png"></img>
Then these images will be copied to the dest folder with copied file structure, but an added upper layer:
<img src="https://i.imgur.com/TY7HvL4.png"</img>
Example showing the images made it:
<img src="https://i.imgur.com/3ily5dU.png"</img>
</div>
<div class="entry">
<h2>Homeless Methods:</h2>
<h4> model_to_img(model_path) </h4>
<ul>
<li>Returns an image form of your model.</li>
</ul>
<h4> plot(history=None, file=None, min_=0, max_=1)</h4>
<ul><li>history- numpy file (Keras callback)</li>
<li>file - path to a .npy file.</li>
<li>min_ - the minimum of accuracy/loss in the graph</li>
<li>max_ - the maximum of accuracy/loss in the graph, the closer the min and max, the more zoomed your graph will be</li>
</ul>
</div>
%package -n python3-AutoAiLib
Summary: The library that automates the silly ML things.
Provides: python-AutoAiLib
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-AutoAiLib
<h1>AutoAI</h1>
<p>This repository is a compilation of scripts that I have created in my time working with machine learning. These scripts aim to automate the annoying and tedious parts of ML, allowing you to focus on what is important.
PyPi: https://pypi.org/project/AutoAILib/
</br> $ pip install autoailib </br>
This library was developed for and used with keras convolutional neural networks. They do however work with other keras models, besides image test obviously.</p>
<div class="entry">
<h1> AutoAiLib.general_tester(model path or object, labels, preprocessor)</h1>
<a href="https://youtu.be/TQisVhgUzWo"> Class Video Demo</a>
<h2> AutoAiLib.general_tester.predict_single(example)</h2>
<ul><li>example- If you have defined a preprocessor for your tester, this should comply with the preprocessor's argument. If
you have not defined a preprocessor, example must be in a form that your model will accept.</li></ul>
<h2> AutoAiLib.general_tester.predict_many(container=None, testing_folder = None, csv_dir)</h2>
<ul>
<li> container - This can be a container of test objects (any iterable). If preprocessor is defined, these objects must comply with the preprocessors parameter. Otherwise they must be in a form that your model will accept.</li>
<li> testing_dir - This can be a path to a testing folder which has sub folders of all classes. Again, must be preprocessed or have preprocessor defined.</li>
<li> csv_dir - This function compiles data into a csv folder to allow users to easily extract data from it, if not defined it will return a pandas data frame.</li>
</ul>
</div>
<div class="entry">
<h1> AutoAi.convnet_tester(model path or object, labels) </h1>
<a href="https://youtu.be/sM57JDasREk"> Class Video Demo </a>
<h2> AutoAi.image_predict(model_path, image_path, labels)</h2>
<h5> This function takes 3 arguments: a path to a keras model, a path to an image, and a list of labels.</h5>
<h5> Demo:</h5>
Given a the correct arguments, we get the following output, as well as this image saved to our model directory.
<img src="https://i.imgur.com/woiPdus.png"></img>
<h2> AutoAi.manual_test(model, testing_dir, labels) </h2>
<h5> This function tests a model given labels and testing data. It then compiles the results in a CSV file, and groups the results by class, and by correct and incorrect.</h5>
<ul>
<li> Model - Path of model that you want to test or model object.</li>
<li> Testing_dir - Path to the directory with your testing data.</li>
<li> Labels - Dictionary of the classes, in form (index:class_name)</li>
</ul>
<h5>Example csv:</h5>
<img src="https://i.imgur.com/g4gNQjS.png"></img>
</div>
<div class="entry">
<h2>Update! This has now been packaged in the AutoAI.data_compiler class.
AutoAi.data_compiler(self,src, dest, **kwargs)</br>
AutoAi.data_compiler.run() will compile the data based on the constructor parameters. </h2>
<h5> This function takes 2 required arguments, an original data source file, and a path to the desired data directory. Given just these two arguments, this function will create a new testing data folder at dest with training, validation, and testing folders, containing folders for each class. You can alter the ratio with the ratio arguments, as well as provide a number of img transforms to do if you are using images.</h5>
<ul>
<li> Src - Path to a folder that contains a folder for each class and then data examples in those class folders. </li>
<li> Dest - Path to a folder where you want the data to end up. </li>
<li> Num_imgs_per_class - This number of images will be added to the original set for each class through transforms. The theoretical limit for this would be 3! * original images per class </li>
</ul>
<h5> Demo:</h5>
Given the a path to the following folder:
<img src="https://i.imgur.com/SSpydEv.png"></img>
If augmentation used the following results will be yielded:
<img src="https://i.imgur.com/4okyMrN.png"></img>
Then these images will be copied to the dest folder with copied file structure, but an added upper layer:
<img src="https://i.imgur.com/TY7HvL4.png"</img>
Example showing the images made it:
<img src="https://i.imgur.com/3ily5dU.png"</img>
</div>
<div class="entry">
<h2>Homeless Methods:</h2>
<h4> model_to_img(model_path) </h4>
<ul>
<li>Returns an image form of your model.</li>
</ul>
<h4> plot(history=None, file=None, min_=0, max_=1)</h4>
<ul><li>history- numpy file (Keras callback)</li>
<li>file - path to a .npy file.</li>
<li>min_ - the minimum of accuracy/loss in the graph</li>
<li>max_ - the maximum of accuracy/loss in the graph, the closer the min and max, the more zoomed your graph will be</li>
</ul>
</div>
%package help
Summary: Development documents and examples for AutoAiLib
Provides: python3-AutoAiLib-doc
%description help
<h1>AutoAI</h1>
<p>This repository is a compilation of scripts that I have created in my time working with machine learning. These scripts aim to automate the annoying and tedious parts of ML, allowing you to focus on what is important.
PyPi: https://pypi.org/project/AutoAILib/
</br> $ pip install autoailib </br>
This library was developed for and used with keras convolutional neural networks. They do however work with other keras models, besides image test obviously.</p>
<div class="entry">
<h1> AutoAiLib.general_tester(model path or object, labels, preprocessor)</h1>
<a href="https://youtu.be/TQisVhgUzWo"> Class Video Demo</a>
<h2> AutoAiLib.general_tester.predict_single(example)</h2>
<ul><li>example- If you have defined a preprocessor for your tester, this should comply with the preprocessor's argument. If
you have not defined a preprocessor, example must be in a form that your model will accept.</li></ul>
<h2> AutoAiLib.general_tester.predict_many(container=None, testing_folder = None, csv_dir)</h2>
<ul>
<li> container - This can be a container of test objects (any iterable). If preprocessor is defined, these objects must comply with the preprocessors parameter. Otherwise they must be in a form that your model will accept.</li>
<li> testing_dir - This can be a path to a testing folder which has sub folders of all classes. Again, must be preprocessed or have preprocessor defined.</li>
<li> csv_dir - This function compiles data into a csv folder to allow users to easily extract data from it, if not defined it will return a pandas data frame.</li>
</ul>
</div>
<div class="entry">
<h1> AutoAi.convnet_tester(model path or object, labels) </h1>
<a href="https://youtu.be/sM57JDasREk"> Class Video Demo </a>
<h2> AutoAi.image_predict(model_path, image_path, labels)</h2>
<h5> This function takes 3 arguments: a path to a keras model, a path to an image, and a list of labels.</h5>
<h5> Demo:</h5>
Given a the correct arguments, we get the following output, as well as this image saved to our model directory.
<img src="https://i.imgur.com/woiPdus.png"></img>
<h2> AutoAi.manual_test(model, testing_dir, labels) </h2>
<h5> This function tests a model given labels and testing data. It then compiles the results in a CSV file, and groups the results by class, and by correct and incorrect.</h5>
<ul>
<li> Model - Path of model that you want to test or model object.</li>
<li> Testing_dir - Path to the directory with your testing data.</li>
<li> Labels - Dictionary of the classes, in form (index:class_name)</li>
</ul>
<h5>Example csv:</h5>
<img src="https://i.imgur.com/g4gNQjS.png"></img>
</div>
<div class="entry">
<h2>Update! This has now been packaged in the AutoAI.data_compiler class.
AutoAi.data_compiler(self,src, dest, **kwargs)</br>
AutoAi.data_compiler.run() will compile the data based on the constructor parameters. </h2>
<h5> This function takes 2 required arguments, an original data source file, and a path to the desired data directory. Given just these two arguments, this function will create a new testing data folder at dest with training, validation, and testing folders, containing folders for each class. You can alter the ratio with the ratio arguments, as well as provide a number of img transforms to do if you are using images.</h5>
<ul>
<li> Src - Path to a folder that contains a folder for each class and then data examples in those class folders. </li>
<li> Dest - Path to a folder where you want the data to end up. </li>
<li> Num_imgs_per_class - This number of images will be added to the original set for each class through transforms. The theoretical limit for this would be 3! * original images per class </li>
</ul>
<h5> Demo:</h5>
Given the a path to the following folder:
<img src="https://i.imgur.com/SSpydEv.png"></img>
If augmentation used the following results will be yielded:
<img src="https://i.imgur.com/4okyMrN.png"></img>
Then these images will be copied to the dest folder with copied file structure, but an added upper layer:
<img src="https://i.imgur.com/TY7HvL4.png"</img>
Example showing the images made it:
<img src="https://i.imgur.com/3ily5dU.png"</img>
</div>
<div class="entry">
<h2>Homeless Methods:</h2>
<h4> model_to_img(model_path) </h4>
<ul>
<li>Returns an image form of your model.</li>
</ul>
<h4> plot(history=None, file=None, min_=0, max_=1)</h4>
<ul><li>history- numpy file (Keras callback)</li>
<li>file - path to a .npy file.</li>
<li>min_ - the minimum of accuracy/loss in the graph</li>
<li>max_ - the maximum of accuracy/loss in the graph, the closer the min and max, the more zoomed your graph will be</li>
</ul>
</div>
%prep
%autosetup -n AutoAiLib-1.1.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-AutoAiLib -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated
|
