automatic import of python-metriculousopeneuler20.03

3 files changed, 539 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..8f841d3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/metriculous-0.3.0.tar.gz
diff --git a/python-metriculous.spec b/python-metriculous.spec
new file mode 100644
index 0000000..3090a65
--- /dev/null
+++ b/python-metriculous.spec
@@ -0,0 +1,537 @@
+%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
+<p align="center">
+    <a href="https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks">
+        <img 
+            src="https://mybinder.org/badge_logo.svg"
+            alt="Launch Binder"
+        />
+    </a>
+    <a href="https://github.com/metriculous-ml/metriculous/actions">
+        <img 
+            src="https://github.com/metriculous-ml/metriculous/workflows/CI/badge.svg?branch=master"
+            alt="Current GitHub Actions build status" 
+        />
+    </a>
+    <a href="http://mypy-lang.org/">
+        <img
+            src="https://img.shields.io/badge/mypy-checked-blue"
+            alt="Checked with mypy" 
+        />
+    </a>
+    <a href="https://badge.fury.io/py/metriculous">
+        <img
+            src="https://badge.fury.io/py/metriculous.svg"
+            alt="PyPI version"
+        />
+    </a>
+    <img 
+        src="https://img.shields.io/pypi/pyversions/metriculous"
+        alt="PyPI - Python Version"
+    >
+    <img 
+        src="https://img.shields.io/github/license/metriculous-ml/metriculous"
+        alt="License MIT"
+    >
+    <a href="https://luminovo.ai/">
+        <img
+            src="https://img.shields.io/badge/friends%20with-luminovo.AI-green"
+            alt="Friends with Luminovo.AI"
+        >
+    </a>
+</p>
+
+# __`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)
+<details><summary>Click to see more code</summary>
+<p>
+
+```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)
+```
+</p>
+</details>
+
+```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)
+<details><summary>Click to see more code</summary>
+<p>
+
+```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)))
+```
+
+</p>
+</details>
+
+```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
+<p align="center">
+    <a href="https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks">
+        <img 
+            src="https://mybinder.org/badge_logo.svg"
+            alt="Launch Binder"
+        />
+    </a>
+    <a href="https://github.com/metriculous-ml/metriculous/actions">
+        <img 
+            src="https://github.com/metriculous-ml/metriculous/workflows/CI/badge.svg?branch=master"
+            alt="Current GitHub Actions build status" 
+        />
+    </a>
+    <a href="http://mypy-lang.org/">
+        <img
+            src="https://img.shields.io/badge/mypy-checked-blue"
+            alt="Checked with mypy" 
+        />
+    </a>
+    <a href="https://badge.fury.io/py/metriculous">
+        <img
+            src="https://badge.fury.io/py/metriculous.svg"
+            alt="PyPI version"
+        />
+    </a>
+    <img 
+        src="https://img.shields.io/pypi/pyversions/metriculous"
+        alt="PyPI - Python Version"
+    >
+    <img 
+        src="https://img.shields.io/github/license/metriculous-ml/metriculous"
+        alt="License MIT"
+    >
+    <a href="https://luminovo.ai/">
+        <img
+            src="https://img.shields.io/badge/friends%20with-luminovo.AI-green"
+            alt="Friends with Luminovo.AI"
+        >
+    </a>
+</p>
+
+# __`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)
+<details><summary>Click to see more code</summary>
+<p>
+
+```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)
+```
+</p>
+</details>
+
+```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)
+<details><summary>Click to see more code</summary>
+<p>
+
+```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)))
+```
+
+</p>
+</details>
+
+```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
+<p align="center">
+    <a href="https://mybinder.org/v2/gh/metriculous-ml/metriculous/master?filepath=notebooks">
+        <img 
+            src="https://mybinder.org/badge_logo.svg"
+            alt="Launch Binder"
+        />
+    </a>
+    <a href="https://github.com/metriculous-ml/metriculous/actions">
+        <img 
+            src="https://github.com/metriculous-ml/metriculous/workflows/CI/badge.svg?branch=master"
+            alt="Current GitHub Actions build status" 
+        />
+    </a>
+    <a href="http://mypy-lang.org/">
+        <img
+            src="https://img.shields.io/badge/mypy-checked-blue"
+            alt="Checked with mypy" 
+        />
+    </a>
+    <a href="https://badge.fury.io/py/metriculous">
+        <img
+            src="https://badge.fury.io/py/metriculous.svg"
+            alt="PyPI version"
+        />
+    </a>
+    <img 
+        src="https://img.shields.io/pypi/pyversions/metriculous"
+        alt="PyPI - Python Version"
+    >
+    <img 
+        src="https://img.shields.io/github/license/metriculous-ml/metriculous"
+        alt="License MIT"
+    >
+    <a href="https://luminovo.ai/">
+        <img
+            src="https://img.shields.io/badge/friends%20with-luminovo.AI-green"
+            alt="Friends with Luminovo.AI"
+        >
+    </a>
+</p>
+
+# __`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)
+<details><summary>Click to see more code</summary>
+<p>
+
+```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)
+```
+</p>
+</details>
+
+```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)
+<details><summary>Click to see more code</summary>
+<p>
+
+```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)))
+```
+
+</p>
+</details>
+
+```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 <Python_Bot@openeuler.org> - 0.3.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..65796d8
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8265f9d0168e759a62e306b86cdca510  metriculous-0.3.0.tar.gz