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

  Default → 0.0

  Amount of L2 regularization used to push weights towards 0.

• clip_gradient

  Default → 1000000000000.0

  Clips the absolute value of each gradient value.

• initializer

  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 — 'pd.DataFrame'
• y — 'pd.Series'
• w — 'float | pd.Series' — defaults to 1

learn_one

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

Parameters

• x — 'dict'
• y — 'base.typing.ClfTarget'
• w — defaults to 1.0

predict_many

Predict the outcome for each given sample.

Parameters

• X — 'pd.DataFrame'

Returns

pd.Series: The predicted labels.

predict_one

Predict the label of a set of features x.

Parameters

• x — 'dict'
• kwargs

Returns

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

predict_proba_many

Predict the outcome probabilities for each given sample.

Parameters

• X — 'pd.DataFrame'

Returns

pd.DataFrame: 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

Returns

A dictionary that associates a probability which each label.