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.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.
-
intercept_init – defaults to
0.0
Initial intercept value.
-
intercept_lr (Union[float, optim.schedulers.Scheduler]) – defaults to
0.01
Learning rate scheduler used for updating the intercept. A
optim.schedulers.Constant
is used if afloat
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.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
>>> 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 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.