Scaling the data

# mkdocs_gallery_thumbnail_path = 'images/example_thumnail.png'
from sklearn.ensemble import RandomForestRegressor

from fold.composites import TransformTarget
from fold.loop import train_evaluate
from fold.splitters import ExpandingWindowSplitter
from fold.transformations import AddWindowFeatures, StandardScaler
from fold.utils.dataset import get_preprocessed_dataset

X, y = get_preprocessed_dataset(
    "weather/historical_hourly_la", target_col="temperature", shorten=1000
)

splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=0.1)
pipeline = [
    TransformTarget(
        [
            StandardScaler(),
            AddWindowFeatures([("temperature", 14, "mean")]),
            RandomForestRegressor(),
        ],
        StandardScaler(),
    )
]

scorecard, prediction, trained_pipelines = train_evaluate(pipeline, X, y, splitter)