diff options
Diffstat (limited to 'python-autoailib.spec')
| -rw-r--r-- | python-autoailib.spec | 288 |
1 files changed, 288 insertions, 0 deletions
diff --git a/python-autoailib.spec b/python-autoailib.spec new file mode 100644 index 0000000..8c8d6d0 --- /dev/null +++ b/python-autoailib.spec @@ -0,0 +1,288 @@ +%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.nju.edu.cn/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 +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1 +- Package Spec generated |