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 – defaults to
0.0
Amount of L2 regularization used to push weights towards 0.
-
clip_gradient – defaults to
1000000000000.0
Clips the absolute value of each gradient value.
-
initializer (optim.initializers.Initializer) – defaults to
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¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
learn_many
Update the model with a mini-batch of features X
and boolean targets y
.
Parameters
- X (pandas.core.frame.DataFrame)
- y (pandas.core.series.Series)
- w (Union[float, pandas.core.series.Series]) – defaults to
1
Returns
MiniBatchClassifier: self
learn_one
Update the model with a set of features x
and a label y
.
Parameters
- x (dict)
- y (Union[bool, str, int])
- w – defaults to
1.0
Returns
Classifier: self
predict_many
Predict the outcome for each given sample.
Parameters --------- X A dataframe of features.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
Series: The predicted labels.
predict_one
Predict the label of a set of features x
.
Parameters
- x (dict)
Returns
typing.Union[bool, str, int]: The predicted label.
predict_proba_many
Predict the outcome probabilities for each given sample.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
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.