Python scikit learn pip

Библиотека SciKit Learn в Python

Проект scikit-learn стартовал, как проект Google Summer of Code (также известный как GSoC) Дэвида Курнапо, как scikits.learn. Он получил свое название от «Scikit», отдельного стороннего расширения для SciPy.

Scikit написан на Python (большая его часть), а некоторые из его основных алгоритмов написаны на Cython для еще большей производительности.

Scikit-learn используется для построения моделей, и не рекомендуется использовать его для чтения, обработки и суммирования данных, поскольку для этой цели доступны более подходящие фреймворки. Это открытый исходный код и выпущен под лицензией BSD.

Как установить Scikit Learn?

Scikit предполагает, что на вашем устройстве установлена платформа Python 2.7 или выше с пакетами NumPY (1.8.2 и выше) и SciPY (0.13.3 и выше). После того, как мы установили эти пакеты, мы можем продолжить установку.

Для установки pip выполните в терминале следующую команду:

Если вам нравится conda, вы также можете использовать ее

для установки пакета, выполните следующую команду:

conda install scikit-learn

Использование Scikit-Learn

После того, как вы закончите установку, вы можете легко использовать scikit-learn в своем коде Python, импортировав его как:

Загрузка набора данных

Начнем с загрузки набора данных для игры. Загрузим простой набор данных с именем Iris. Это набор данных о цветке, он содержит 150 наблюдений о различных размерах. Давайте посмотрим, как загрузить набор данных с помощью scikit-learn.

# Import scikit learn from sklearn import datasets # Load data iris= datasets.load_iris() # Print shape of data to confirm data is loaded print(iris.data.shape)

Мы печатаем форму данных для удобства, вы также можете распечатать данные целиком, если хотите, запуск кодов дает следующий результат:

Обучение и прогнозирование

Теперь мы загрузили данные, давайте попробуем поучиться на них и спрогнозировать новые данные. Для этого мы должны создать оценщик, а затем вызвать его метод соответствия.

from sklearn import svm from sklearn import datasets # Load dataset iris = datasets.load_iris() clf = svm.LinearSVC() # learn from the data clf.fit(iris.data, iris.target) # predict for unseen data clf.predict([[ 5.0, 3.6, 1.3, 0.25]]) # Parameters of model can be changed by using the attributes ending with an underscore print(clf.coef_ )

Вот что мы получаем, когда запускаем этот скрипт:

Линейная регрессия

Создавать различные модели с помощью scikit-learn довольно просто. Начнем с простого примера регрессии.

#import the model from sklearn import linear_model reg = linear_model.LinearRegression() # use it to fit a data reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2]) # Let's look into the fitted data print(reg.coef_)

Запуск модели должен вернуть точку, которую можно построить на той же линии:

k-Классификатор ближайшего соседа

Попробуем простой алгоритм классификации. Этот классификатор использует алгоритм для представления обучающих выборок.

from sklearn import datasets # Load dataset iris = datasets.load_iris() # Create and fit a nearest-neighbor classifier from sklearn import neighbors knn = neighbors.KNeighborsClassifier() knn.fit(iris.data, iris.target) # Predict and print the result result=knn.predict([[0.1, 0.2, 0.3, 0.4]]) print(result)

Запустим классификатор и проверим результаты, классификатор должен вернуть 0. Попробуем пример:

К-средство кластеризации

Это самый простой алгоритм кластеризации. Набор делится на » k’ кластеров, и каждое наблюдение назначается кластеру. Это делается итеративно до тех пор, пока кластеры не сойдутся.

Мы создадим одну такую модель кластеризации в следующей программе:

from sklearn import cluster, datasets # load data iris = datasets.load_iris() # create clusters for k=3 k=3 k_means = cluster.KMeans(k) # fit data k_means.fit(iris.data) # print results print( k_means.labels_[::10]) print( iris.target[::10])

При запуске программы мы увидим в списке отдельные кластеры. Вот результат для приведенного выше фрагмента кода:

Заключение

В этом руководстве мы увидели, что Scikit-Learn упрощает работу с несколькими алгоритмами машинного обучения. Мы видели примеры регрессии, классификации и кластеризации.

Источник

scikit-learn 1.3.0

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

It is currently maintained by a team of volunteers.

Installation

Dependencies

• Python (>= 3.8)
• NumPy (>= 1.17.3)
• SciPy (>= 1.5.0)
• joblib (>= 1.1.1)
• threadpoolctl (>= 2.0.0)

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with “Display”) require Matplotlib (>= 3.1.3). For running the examples Matplotlib >= 3.1.3 is required. A few examples require scikit-image >= 0.16.2, a few examples require pandas >= 1.0.5, some examples require seaborn >= 0.9.0 and plotly >= 5.14.0.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip :

pip install -U scikit-learn

conda install -c conda-forge scikit-learn

The documentation includes more detailed installation instructions.

Changelog

See the changelog for a history of notable changes to scikit-learn.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.

Important links

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Contributing

To learn more about making a contribution to scikit-learn, please see our Contributing guide.

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 7.1.2 installed):

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: https://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn .

Help and Support

Documentation

• HTML documentation (stable release): https://scikit-learn.org
• HTML documentation (development version): https://scikit-learn.org/dev/
• FAQ: https://scikit-learn.org/stable/faq.html

Communication

• Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
• Gitter: https://gitter.im/scikit-learn/scikit-learn
• Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
• Blog: https://blog.scikit-learn.org
• Calendar: https://blog.scikit-learn.org/calendar/
• Twitter: https://twitter.com/scikit_learn
• Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
• Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
• Website: https://scikit-learn.org
• LinkedIn: https://www.linkedin.com/company/scikit-learn
• YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
• Facebook: https://www.facebook.com/scikitlearnofficial/
• Instagram: https://www.instagram.com/scikitlearnofficial/
• TikTok: https://www.tiktok.com/@scikit.learn

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scikit-learn: machine learning in Python

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README.rst

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

It is currently maintained by a team of volunteers.

• Python (>= 3.8)
• NumPy (>= 1.17.3)
• SciPy (>= 1.5.0)
• joblib (>= 1.1.1)
• threadpoolctl (>= 2.0.0)

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with «Display») require Matplotlib (>= 3.1.3). For running the examples Matplotlib >= 3.1.3 is required. A few examples require scikit-image >= 0.16.2, a few examples require pandas >= 1.0.5, some examples require seaborn >= 0.9.0 and plotly >= 5.14.0.

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip :

pip install -U scikit-learn

conda install -c conda-forge scikit-learn

The documentation includes more detailed installation instructions.

See the changelog for a history of notable changes to scikit-learn.

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

To learn more about making a contribution to scikit-learn, please see our Contributing guide.

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 7.1.2 installed):

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: https://scikit-learn.org/stable/developers/index.html

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

• HTML documentation (stable release): https://scikit-learn.org
• HTML documentation (development version): https://scikit-learn.org/dev/
• FAQ: https://scikit-learn.org/stable/faq.html

• Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
• Gitter: https://gitter.im/scikit-learn/scikit-learn
• Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
• Blog: https://blog.scikit-learn.org
• Calendar: https://blog.scikit-learn.org/calendar/
• Twitter: https://twitter.com/scikit_learn
• Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
• Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
• Website: https://scikit-learn.org
• LinkedIn: https://www.linkedin.com/company/scikit-learn
• YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
• Facebook: https://www.facebook.com/scikitlearnofficial/
• Instagram: https://www.instagram.com/scikitlearnofficial/
• TikTok: https://www.tiktok.com/@scikit.learn

If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn

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scikit-learn: machine learning in Python

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