POC: duplicated runs fold caching for backtest and stackingensemble via joblib.Memory

[martins0n]

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[martins0n]

from etna.datasets import TSDataset, generate_ar_df
from etna.pipeline import Pipeline
from etna.ensembles import StackingEnsemble
from etna.models import CatBoostModelMultiSegment, LinearPerSegmentModel, NaiveModel
from etna.transforms import StandardScalerTransform, LagTransform, SegmentEncoderTransform, TimeSeriesImputerTransform, DateFlagsTransform
from etna.metrics import SMAPE, MAE
from etna.analysis import plot_backtest, plot_forecast
from copy import deepcopy
import warnings


warnings.filterwarnings("ignore")

df = generate_ar_df(periods=500, start_time="2021-01-01", n_segments=20)

ts = TSDataset.to_dataset(df[["target", "segment", "timestamp"]])

ts = TSDataset(ts, freq="D")

pipe_naive_one = Pipeline(model=NaiveModel(7), horizon=7)
pipe_naive_two = Pipeline(model=NaiveModel(1), horizon=7)

stack_pipe = StackingEnsemble(
    pipelines=[
        pipe_naive_one, pipe_naive_two
    ], n_jobs=1, joblib_params=dict(verbose=0, backend="multiprocessing", mmap_mode="c"), n_folds=2
)

stack_pipe_copy = deepcopy(stack_pipe)
import time
import shelve


with shelve.open('counter') as db:
    db['counter'] = 0

start = time.monotonic()

metrics, _ , _ = stack_pipe.backtest(ts, n_jobs=1, n_folds=2, metrics=[MAE()], joblib_params=dict(verbose=0, backend="multiprocessing", mmap_mode="c"))

print(time.monotonic() - start)

with shelve.open('counter') as db:
    print(db['counter'])
    
print(metrics.MAE.mean())

Run with enabled cache ETNA_CACHE=1 python script.py

[github-actions]

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