From 23c187f36e44e167c2505f136ae3eee5acf83bae Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Mon, 15 May 2023 05:23:09 +0000 Subject: automatic import of python-autoailib --- python-autoailib.spec | 288 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 288 insertions(+) create mode 100644 python-autoailib.spec (limited to 'python-autoailib.spec') 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 +

AutoAI

+

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/ +
$ pip install autoailib
+This library was developed for and used with keras convolutional neural networks. They do however work with other keras models, besides image test obviously.

+
+

AutoAiLib.general_tester(model path or object, labels, preprocessor)

+ Class Video Demo +

AutoAiLib.general_tester.predict_single(example)

+ +

AutoAiLib.general_tester.predict_many(container=None, testing_folder = None, csv_dir)

+ + +
+ +
+

AutoAi.convnet_tester(model path or object, labels)

+ Class Video Demo +

AutoAi.image_predict(model_path, image_path, labels)

+
This function takes 3 arguments: a path to a keras model, a path to an image, and a list of labels.
+
Demo:
+ Given a the correct arguments, we get the following output, as well as this image saved to our model directory. + +

AutoAi.manual_test(model, testing_dir, labels)

+
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.
+ +
Example csv:
+ +
+ +
+

Update! This has now been packaged in the AutoAI.data_compiler class. + AutoAi.data_compiler(self,src, dest, **kwargs)
+ AutoAi.data_compiler.run() will compile the data based on the constructor parameters.

+
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.
+ +
Demo:
+ Given the a path to the following folder: + + If augmentation used the following results will be yielded: + + Then these images will be copied to the dest folder with copied file structure, but an added upper layer: + + Example showing the images made it: + +
+ +
+

Homeless Methods:

+

model_to_img(model_path)

+ +

plot(history=None, file=None, min_=0, max_=1)

+ +
+ +%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 +

AutoAI

+

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/ +
$ pip install autoailib
+This library was developed for and used with keras convolutional neural networks. They do however work with other keras models, besides image test obviously.

+
+

AutoAiLib.general_tester(model path or object, labels, preprocessor)

+ Class Video Demo +

AutoAiLib.general_tester.predict_single(example)

+ +

AutoAiLib.general_tester.predict_many(container=None, testing_folder = None, csv_dir)

+ + +
+ +
+

AutoAi.convnet_tester(model path or object, labels)

+ Class Video Demo +

AutoAi.image_predict(model_path, image_path, labels)

+
This function takes 3 arguments: a path to a keras model, a path to an image, and a list of labels.
+
Demo:
+ Given a the correct arguments, we get the following output, as well as this image saved to our model directory. + +

AutoAi.manual_test(model, testing_dir, labels)

+
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.
+ +
Example csv:
+ +
+ +
+

Update! This has now been packaged in the AutoAI.data_compiler class. + AutoAi.data_compiler(self,src, dest, **kwargs)
+ AutoAi.data_compiler.run() will compile the data based on the constructor parameters.

+
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.
+ +
Demo:
+ Given the a path to the following folder: + + If augmentation used the following results will be yielded: + + Then these images will be copied to the dest folder with copied file structure, but an added upper layer: + + Example showing the images made it: + +
+ +
+

Homeless Methods:

+

model_to_img(model_path)

+ +

plot(history=None, file=None, min_=0, max_=1)

+ +
+ +%package help +Summary: Development documents and examples for AutoAiLib +Provides: python3-AutoAiLib-doc +%description help +

AutoAI

+

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/ +
$ pip install autoailib
+This library was developed for and used with keras convolutional neural networks. They do however work with other keras models, besides image test obviously.

+
+

AutoAiLib.general_tester(model path or object, labels, preprocessor)

+ Class Video Demo +

AutoAiLib.general_tester.predict_single(example)

+ +

AutoAiLib.general_tester.predict_many(container=None, testing_folder = None, csv_dir)

+ + +
+ +
+

AutoAi.convnet_tester(model path or object, labels)

+ Class Video Demo +

AutoAi.image_predict(model_path, image_path, labels)

+
This function takes 3 arguments: a path to a keras model, a path to an image, and a list of labels.
+
Demo:
+ Given a the correct arguments, we get the following output, as well as this image saved to our model directory. + +

AutoAi.manual_test(model, testing_dir, labels)

+
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.
+ +
Example csv:
+ +
+ +
+

Update! This has now been packaged in the AutoAI.data_compiler class. + AutoAi.data_compiler(self,src, dest, **kwargs)
+ AutoAi.data_compiler.run() will compile the data based on the constructor parameters.

+
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.
+ +
Demo:
+ Given the a path to the following folder: + + If augmentation used the following results will be yielded: + + Then these images will be copied to the dest folder with copied file structure, but an added upper layer: + + Example showing the images made it: + +
+ +
+

Homeless Methods:

+

model_to_img(model_path)

+ +

plot(history=None, file=None, min_=0, max_=1)

+ +
+ +%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 - 1.1.0-1 +- Package Spec generated -- cgit v1.2.3