River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River’s ambition is to be the go-to library for doing machine learning on streaming data.
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%