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Description
import datetime as dt
from river import compose
from river import datasets
from river import feature_extraction
from river import linear_model
from river import metrics
from river import preprocessing
from river import stats
from river import stream
X_y = datasets.Bikes()
X_y = stream.simulate_qa(X_y, moment='moment', delay=dt.timedelta(minutes=30))
def add_time_features(x):
return {
**x,
'hour': x['moment'].hour,
'day': x['moment'].weekday()
}
model = add_time_features
model |= (
compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind') +
feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean()) +
feature_extraction.TargetAgg(by='station', how=stats.EWMean())
)
model |= preprocessing.StandardScaler()
model |= linear_model.LinearRegression()