%global _empty_manifest_terminate_build 0 Name: python-metriculous Version: 0.3.0 Release: 1 Summary: Very unstable library containing utilities to measure and visualize statistical properties of machine learning models. License: MIT URL: https://github.com/metriculous-ml/metriculous Source0: https://mirrors.nju.edu.cn/pypi/web/packages/64/5d/dae8ff38946fa2a97463b36e3b01fb908f650437c20d2f0c3ceaa6c52b94/metriculous-0.3.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scikit-learn Requires: python3-assertpy Requires: python3-pandas Requires: python3-bokeh Requires: python3-jupyter %description

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# __`metriculous`__ Measure, visualize, and compare machine learning model performance without the usual boilerplate. Breaking API improvements to be expected. # Installation ```console $ pip install metriculous ``` Or, for the latest unreleased version: ```console $ pip install git+https://github.com/metriculous-ml/metriculous.git ``` # Comparing Regression Models [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks%2Fquickstart_regression.py)
Click to see more code

```python import numpy as np # Mock the ground truth, a one-dimensional array of floats ground_truth = np.random.random(300) # Mock the output of a few models perfect_model = ground_truth noisy_model = ground_truth + 0.1 * np.random.randn(*ground_truth.shape) random_model = np.random.randn(*ground_truth.shape) zero_model = np.zeros_like(ground_truth) ```

```python import metriculous metriculous.compare_regressors( ground_truth=ground_truth, model_predictions=[perfect_model, noisy_model, random_model, zero_model], model_names=["Perfect Model", "Noisy Model", "Random Model", "Zero Model"], ).save_html("comparison.html").display() ``` This will save an HTML file with common regression metrics and charts, and if you are working in a [Jupyter notebook](https://github.com/jupyter/notebook) will display the output right in front of you: ![Screenshot of Metriculous Regression Metrics](./imgs/metriculous_regression_screen_shot_table.png) ![Screenshot of Metriculous Regression Figures](./imgs/metriculous_regression_screen_shot_figures.png) # Comparing Classification Models [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks%2Fquickstart_classification.py)
Click to see more code

```python import numpy as np def normalize(array2d: np.ndarray) -> np.ndarray: return array2d / array2d.sum(axis=1, keepdims=True) class_names = ["Cat", "Dog", "Pig"] num_classes = len(class_names) num_samples = 500 # Mock ground truth ground_truth = np.random.choice(range(num_classes), size=num_samples, p=[0.5, 0.4, 0.1]) # Mock model predictions perfect_model = np.eye(num_classes)[ground_truth] noisy_model = normalize( perfect_model + 2 * np.random.random((num_samples, num_classes)) ) random_model = normalize(np.random.random((num_samples, num_classes))) ```

```python import metriculous metriculous.compare_classifiers( ground_truth=ground_truth, model_predictions=[perfect_model, noisy_model, random_model], model_names=["Perfect Model", "Noisy Model", "Random Model"], class_names=class_names, one_vs_all_figures=True, ).display() ``` ![Screenshot of Metriculous Classification Table](./imgs/metriculous_classification_table.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_1.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_2.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_3.png) # Development ### Poetry This project uses [poetry](https://poetry.eustace.io/) to manage dependencies. Please make sure it is installed for the required python version. Then install the dependencies with `poetry install`. ### Makefile A Makefile is used to automate common development workflows. Type `make` or `make help` to see a list of available commands. Before commiting changes it is recommended to run `make format check test`. %package -n python3-metriculous Summary: Very unstable library containing utilities to measure and visualize statistical properties of machine learning models. Provides: python-metriculous BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-metriculous

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# __`metriculous`__ Measure, visualize, and compare machine learning model performance without the usual boilerplate. Breaking API improvements to be expected. # Installation ```console $ pip install metriculous ``` Or, for the latest unreleased version: ```console $ pip install git+https://github.com/metriculous-ml/metriculous.git ``` # Comparing Regression Models [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks%2Fquickstart_regression.py)
Click to see more code

```python import numpy as np # Mock the ground truth, a one-dimensional array of floats ground_truth = np.random.random(300) # Mock the output of a few models perfect_model = ground_truth noisy_model = ground_truth + 0.1 * np.random.randn(*ground_truth.shape) random_model = np.random.randn(*ground_truth.shape) zero_model = np.zeros_like(ground_truth) ```

```python import metriculous metriculous.compare_regressors( ground_truth=ground_truth, model_predictions=[perfect_model, noisy_model, random_model, zero_model], model_names=["Perfect Model", "Noisy Model", "Random Model", "Zero Model"], ).save_html("comparison.html").display() ``` This will save an HTML file with common regression metrics and charts, and if you are working in a [Jupyter notebook](https://github.com/jupyter/notebook) will display the output right in front of you: ![Screenshot of Metriculous Regression Metrics](./imgs/metriculous_regression_screen_shot_table.png) ![Screenshot of Metriculous Regression Figures](./imgs/metriculous_regression_screen_shot_figures.png) # Comparing Classification Models [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks%2Fquickstart_classification.py)
Click to see more code

```python import numpy as np def normalize(array2d: np.ndarray) -> np.ndarray: return array2d / array2d.sum(axis=1, keepdims=True) class_names = ["Cat", "Dog", "Pig"] num_classes = len(class_names) num_samples = 500 # Mock ground truth ground_truth = np.random.choice(range(num_classes), size=num_samples, p=[0.5, 0.4, 0.1]) # Mock model predictions perfect_model = np.eye(num_classes)[ground_truth] noisy_model = normalize( perfect_model + 2 * np.random.random((num_samples, num_classes)) ) random_model = normalize(np.random.random((num_samples, num_classes))) ```

```python import metriculous metriculous.compare_classifiers( ground_truth=ground_truth, model_predictions=[perfect_model, noisy_model, random_model], model_names=["Perfect Model", "Noisy Model", "Random Model"], class_names=class_names, one_vs_all_figures=True, ).display() ``` ![Screenshot of Metriculous Classification Table](./imgs/metriculous_classification_table.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_1.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_2.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_3.png) # Development ### Poetry This project uses [poetry](https://poetry.eustace.io/) to manage dependencies. Please make sure it is installed for the required python version. Then install the dependencies with `poetry install`. ### Makefile A Makefile is used to automate common development workflows. Type `make` or `make help` to see a list of available commands. Before commiting changes it is recommended to run `make format check test`. %package help Summary: Development documents and examples for metriculous Provides: python3-metriculous-doc %description help

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# __`metriculous`__ Measure, visualize, and compare machine learning model performance without the usual boilerplate. Breaking API improvements to be expected. # Installation ```console $ pip install metriculous ``` Or, for the latest unreleased version: ```console $ pip install git+https://github.com/metriculous-ml/metriculous.git ``` # Comparing Regression Models [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks%2Fquickstart_regression.py)
Click to see more code

```python import numpy as np # Mock the ground truth, a one-dimensional array of floats ground_truth = np.random.random(300) # Mock the output of a few models perfect_model = ground_truth noisy_model = ground_truth + 0.1 * np.random.randn(*ground_truth.shape) random_model = np.random.randn(*ground_truth.shape) zero_model = np.zeros_like(ground_truth) ```

```python import metriculous metriculous.compare_regressors( ground_truth=ground_truth, model_predictions=[perfect_model, noisy_model, random_model, zero_model], model_names=["Perfect Model", "Noisy Model", "Random Model", "Zero Model"], ).save_html("comparison.html").display() ``` This will save an HTML file with common regression metrics and charts, and if you are working in a [Jupyter notebook](https://github.com/jupyter/notebook) will display the output right in front of you: ![Screenshot of Metriculous Regression Metrics](./imgs/metriculous_regression_screen_shot_table.png) ![Screenshot of Metriculous Regression Figures](./imgs/metriculous_regression_screen_shot_figures.png) # Comparing Classification Models [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks%2Fquickstart_classification.py)
Click to see more code

```python import numpy as np def normalize(array2d: np.ndarray) -> np.ndarray: return array2d / array2d.sum(axis=1, keepdims=True) class_names = ["Cat", "Dog", "Pig"] num_classes = len(class_names) num_samples = 500 # Mock ground truth ground_truth = np.random.choice(range(num_classes), size=num_samples, p=[0.5, 0.4, 0.1]) # Mock model predictions perfect_model = np.eye(num_classes)[ground_truth] noisy_model = normalize( perfect_model + 2 * np.random.random((num_samples, num_classes)) ) random_model = normalize(np.random.random((num_samples, num_classes))) ```

```python import metriculous metriculous.compare_classifiers( ground_truth=ground_truth, model_predictions=[perfect_model, noisy_model, random_model], model_names=["Perfect Model", "Noisy Model", "Random Model"], class_names=class_names, one_vs_all_figures=True, ).display() ``` ![Screenshot of Metriculous Classification Table](./imgs/metriculous_classification_table.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_1.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_2.png) ![Screenshot of Metriculous Classification Figures](./imgs/metriculous_classification_figures_3.png) # Development ### Poetry This project uses [poetry](https://poetry.eustace.io/) to manage dependencies. Please make sure it is installed for the required python version. Then install the dependencies with `poetry install`. ### Makefile A Makefile is used to automate common development workflows. Type `make` or `make help` to see a list of available commands. Before commiting changes it is recommended to run `make format check test`. %prep %autosetup -n metriculous-0.3.0 %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-metriculous -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.3.0-1 - Package Spec generated