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online-ml/river:

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

online-ml/river

开源软件地址(OpenSource Url):

https://github.com/online-ml/river

开源编程语言(OpenSource Language):

Python 97.9%

开源软件介绍(OpenSource Introduction):

river_logo

tests documentation roadmap pypi pepy mypy bsd_3_license


River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow.

⚡️ Quickstart

As a quick example, we'll train a logistic regression to classify the website phishing dataset. Here's a look at the first observation in the dataset.

>>> from pprint import pprint
>>> from river import datasets

>>> dataset = datasets.Phishing()

>>> for x, y in dataset:
...     pprint(x)
...     print(y)
...     break
{'age_of_domain': 1,
 'anchor_from_other_domain': 0.0,
 'empty_server_form_handler': 0.0,
 'https': 0.0,
 'ip_in_url': 1,
 'is_popular': 0.5,
 'long_url': 1.0,
 'popup_window': 0.0,
 'request_from_other_domain': 0.0}
True

Now let's run the model on the dataset in a streaming fashion. We sequentially interleave predictions and model updates. Meanwhile, we update a performance metric to see how well the model is doing.

>>> from river import compose
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing

>>> model = compose.Pipeline(
...     preprocessing.StandardScaler(),
...     linear_model.LogisticRegression()
... )

>>> metric = metrics.Accuracy()

>>> for x, y in dataset:
...     y_pred = model.predict_one(x)      # make a prediction
...     metric = metric.update(y, y_pred)  # update the metric
...     model = model.learn_one(x, y)      # make the model learn

>>> metric
Accuracy: 89.20%

Of course, this is just a contrived example. We welcome you to check the introduction section of the documentation for a more thorough tutorial.


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