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Perceptron

Perceptron classifier.

In this implementation, the Perceptron is viewed as a special case of the logistic regression. The loss function that is used is the Hinge loss with a threshold set to 0, whilst the learning rate of the stochastic gradient descent procedure is set to 1 for both the weights and the intercept.

Parameters

  • l2 (mutable)

    Type → float

    Default → 0.0

    Amount of L2 regularization used to push weights towards 0.

  • clip_gradient (mutable)

    Type → float

    Default → 1000000000000.0

    Clips the absolute value of each gradient value.

  • initializer (mutable)

    Type → optim.initializers.Initializer | None

    Default → None

    Weights initialization scheme.

Attributes

  • weights

    The current weights.

Examples

from river import datasets
from river import evaluate
from river import linear_model as lm
from river import metrics
from river import preprocessing as pp

dataset = datasets.Phishing()

model = pp.StandardScaler() | lm.Perceptron()

metric = metrics.Accuracy()

evaluate.progressive_val_score(dataset, model, metric)

Accuracy: 85.84%

Methods

learn_many

Update the model with a mini-batch of features X and boolean targets y.

Parameters

  • X — IntoDataFrame
  • y — IntoSeries
  • w — float | IntoSeries — defaults to 1

learn_one

Update the model with a set of features x and a label y.

Parameters

  • x — dict[base.typing.FeatureName, typing.Any]
  • y — typing.Any
  • w — float — defaults to 1.0

predict_many

Predict the outcome for each given sample.

Parameters

  • X — IntoDataFrame

Returns

IntoSeries: The predicted labels.

predict_one

Predict the label of a set of features x.

Parameters

  • x — dict[base.typing.FeatureName, Any]
  • kwargs — Any

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_many

Predict the outcome probabilities for each given sample.

Parameters

  • X — IntoDataFrame

Returns

IntoDataFrame: A dataframe with probabilities of True and False for each sample.

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

  • x — dict[base.typing.FeatureName, typing.Any]
  • kwargs — typing.Any

Returns

dict[base.typing.ClfTarget, float]: A dictionary that associates a probability which each label.