This is a class containing a binary classifier for online machine learning. It employs approaches based on Random Bits Regression and the FTRL-Proximal algorithm
| Parameter | Description |
|---|---|
| alpha. | Float. Learning Rate. Default = 0.1 |
| beta. | Float. Smoothing parameter for adaptive learning rate. Default = 1. |
| l1. | Float. L1 Regularization. Default = 0.1 |
| l2. | Float. L2 Regularization. Default = 1.0 |
| nr_projections. | Int. Number of random linear projections to create. Default = 10000 |
| max_projections. | Int. Not implemented. |
| subsample_projections. | Float. Uses subsampling when making a first pass to create the random thresholds. This is more memory friendly for larger datasets. Default = 1. |
| size_projections. | Int. Number of (feature_value random_weight) to use in the random linear functions. Default = 3* |
| random_state. | Int. Seed for replication. Default = 0 |
| Verbose. | Int. Verbosity of classifier. Default = 0 |
clf = rftrl.RandomLeaderClassifier(nr_projections=50000, random_state=1, size_projections=3)
# Project data
clf.project(X_train)
# Train
loss = 0
for e, (x,y) in enumerate(zip(X_train,y)):
clf.fit(x,e,y)
pred = clf.predict()
loss += clf.logloss()
clf.update(pred)
# Test
y = 1 # Dummy label
for e, x in enumerate(X_test):
clf.fit(x,e,y)
pred = clf.predict()
print("%s,%s"%(e,pred))Random Bit Regression (RBR).
Random Bits Regression: a Strong General Predictor for Big Data
Yi Wang, Yi Li, Momiao Xiong, Li Jin
http://arxiv.org/abs/1501.02990
Follow the Regularized Leader (FTRL)
Ad Click prediction: A view from the trenches.
H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica.
https://research.google.com/pubs/archive/41159.pdf
Tinrtgu's Beat the Benchmark online FTRL proximal script's
Beat the benchmark with less then 200MB of memory.