FTRLProximal¶
FTRL-Proximal optimizer.
Parameters¶
-
alpha – defaults to
0.05 -
beta – defaults to
1.0 -
l1 – defaults to
0.0 -
l2 – defaults to
1.0
Attributes¶
-
z (collections.defaultdict)
-
n (collections.defaultdict)
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()
>>> optimizer = optim.FTRLProximal()
>>> model = (
... preprocessing.StandardScaler() |
... linear_model.LogisticRegression(optimizer)
... )
>>> metric = metrics.F1()
>>> evaluate.progressive_val_score(dataset, model, metric)
F1: 87.66%
Methods¶
look_ahead
Updates a weight vector before a prediction is made.
Parameters: w (dict): A dictionary of weight parameters. The weights are modified in-place. Returns: The updated weights.
Parameters
- w ('dict')
step
Updates a weight vector given a gradient.
Parameters
- w ('dict | VectorLike')
- g ('dict | VectorLike')
Returns
dict | VectorLike: The updated weights.
References¶
-
McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D. and Chikkerur, S., 2013, August. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230) ↩