def test_drift_adaptation_hatr():
dataset = synth.Friedman(seed=7).take(500)
model = tree.HoeffdingAdaptiveTreeRegressor(
leaf_prediction="model",
grace_period=50,
split_confidence=0.1,
adwin_confidence=0.1,
drift_window_threshold=10,
seed=7,
max_depth=3,
)
for i, (x, y) in enumerate(dataset):
y_ = y
if i > 250:
# Emulate an abrupt drift
y_ = 3 * y
model.learn_one(x, y_)
assert model._n_alternate_trees > 0
def get_regression_data():
return iter(synth.Friedman(seed=42).take(200))
from river import evaluate
from river import metrics
from river import neighbors
from river import preprocessing
from river import synth
# from radius_neighbors import RadiusNeighborsRegressor
from k_nearest_neighbors import KNNRegressor
dataset = iter(synth.Friedman(seed=1).take(50000))
# model = (
# preprocessing.StandardScaler() |
# neighbors.KNNRegressor(window_size=1000)
# )
# model = (
# preprocessing.StandardScaler() |
# RadiusNeighborsRegressor(max_size=1000, r=1, aggregation='distance',
# delta=0.1, seed=42, k=4)
# )
model = (preprocessing.StandardScaler()
| KNNRegressor(n_neighbors=3,
window_size=1000,
r=1,
c=10,
aggregation_method='mean',
delta=0.1,
seed=42,
k=4))
def get_regression_data():
return synth.Friedman(seed=42).take(500)
from river import synth
from river import metrics
from sgt import StreamingGradientTreeRegressor
metric = metrics.MAE()
dataset = iter(synth.Friedman(seed=42).take(1000))
tree = StreamingGradientTreeRegressor(delta=0.1)
for x, y in dataset:
metric.update(y, tree.predict_one(x))
tree.learn_one(x, y)
print(metric)
print('Tree depth:', tree.depth)
print('Number of nodes:', tree.n_nodes)
# 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
#
# from sgt import StreamingGradientTreeClassifier
#
# dataset = datasets.Phishing()
# model = (
