PAClassifier¶
Passive-aggressive learning for classification.
Parameters¶
-
C – defaults to
1.0 -
mode – defaults to
1 -
learn_intercept – defaults to
True
Examples¶
The following example is taken from this blog post.
>>> from river import linear_model
>>> from river import metrics
>>> from river import stream
>>> import numpy as np
>>> from sklearn import datasets
>>> from sklearn import model_selection
>>> np.random.seed(1000)
>>> X, y = datasets.make_classification(
... n_samples=5000,
... n_features=4,
... n_informative=2,
... n_redundant=0,
... n_repeated=0,
... n_classes=2,
... n_clusters_per_class=2
... )
>>> X_train, X_test, y_train, y_test = model_selection.train_test_split(
... X,
... y,
... test_size=0.35,
... random_state=1000
... )
>>> model = linear_model.PAClassifier(
... C=0.01,
... mode=1
... )
>>> for xi, yi in stream.iter_array(X_train, y_train):
... y_pred = model.learn_one(xi, yi)
>>> metric = metrics.Accuracy() + metrics.LogLoss()
>>> for xi, yi in stream.iter_array(X_test, y_test):
... metric = metric.update(yi, model.predict_proba_one(xi))
>>> print(metric)
Accuracy: 88.46%, LogLoss: 0.325727
Methods¶
learn_one
Update the model with a set of features x and a label y.
Parameters
- x
- y
Returns
self
predict_one
Predict the label of a set of features x.
Parameters
- x (dict)
- kwargs
Returns
typing.Union[bool, str, int, NoneType]: The predicted label.
predict_proba_one
Predict the probability of each label for a dictionary of features x.
Parameters
- x
Returns
A dictionary that associates a probability which each label.