LogisticRegression

Logistic regression.

This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: learn_many, predict_many, predict_proba_many. Each method takes as input a pandas.DataFrame where each column represents a feature.

It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a preprocessing.StandardScaler.

Parameters

• optimizer (optim.base.Optimizer) – defaults to None

  The sequential optimizer used for updating the weights. Note that the intercept is handled separately.

• loss (optim.losses.BinaryLoss) – defaults to None

  The loss function to optimize for. Defaults to optim.losses.Log.

• l2 – defaults to 0.0

  Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.

• l1 – defaults to 0.0

  Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.

• intercept_init – defaults to 0.0

  Initial intercept value.

• intercept_lr (Union[float, optim.base.Scheduler]) – defaults to 0.01

  Learning rate scheduler used for updating the intercept. A optim.schedulers.Constant is used if a float is provided. The intercept is not updated when this is set to 0.

• clip_gradient – defaults to 1000000000000.0

  Clips the absolute value of each gradient value.

• initializer (optim.base.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
>>> from river import metrics
>>> from river import optim
>>> from river import preprocessing

>>> dataset = datasets.Phishing()

>>> model = (
...     preprocessing.StandardScaler() |
...     linear_model.LogisticRegression(optimizer=optim.SGD(.1))
... )

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 88.96%

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 (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.