Get Started

Evaluate and train a Hoeffding Tree Classifier from a stream of events:

import rx
import rx.operators as ops

import rxsci as rs
import rxsci_river as rsr
from river import synth
from river import tree
from river import metrics


source = synth.ConceptDriftStream(
    stream=synth.SEA(seed=42, variant=0),
    drift_stream=synth.SEA(seed=42, variant=1),
    seed=1, position=500, width=50,
)


rx.from_(source).pipe(
    ops.take(10000),
    #ops.do_action(print),
    ops.map(lambda i: rsr.Utterance(i[0], i[1])),
    rsr.evaluate.prequential(
        tree.HoeffdingAdaptiveTreeClassifier(
            grace_period=100,
            split_confidence=1e-5,
            leaf_prediction='nb',
            nb_threshold=10,
            seed=0,
        )
    ),
    rsr.compute_metric(metrics.Accuracy())
).subscribe(
    on_next=print,
    on_completed=lambda: print("Done!"),
)