Adriano
d30f981421
Aggiunge expanding_walk_forward e dataclass Split per generare fold walk-forward con train che cresce e test sulla finestra successiva. Rispetta min_train_days e ritorna lista vuota su fold troppo piccoli. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
import pandas as pd
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import pytest
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from multi_swarm.data.splits import expanding_walk_forward
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@pytest.fixture
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def daily_index():
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return pd.date_range("2024-01-01", "2024-12-31", freq="D", tz="UTC")
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def test_expanding_split_count(daily_index: pd.DatetimeIndex):
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splits = expanding_walk_forward(
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daily_index, train_ratio=0.7, n_folds=4, min_train_days=30
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)
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assert len(splits) == 4
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def test_expanding_split_train_grows(daily_index: pd.DatetimeIndex):
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splits = expanding_walk_forward(
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daily_index, train_ratio=0.7, n_folds=4, min_train_days=30
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)
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train_lengths = [len(s.train_idx) for s in splits]
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assert train_lengths == sorted(train_lengths)
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assert train_lengths[0] < train_lengths[-1]
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def test_no_overlap_train_test(daily_index: pd.DatetimeIndex):
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splits = expanding_walk_forward(
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daily_index, train_ratio=0.7, n_folds=4, min_train_days=30
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)
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for s in splits:
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assert s.train_idx[-1] < s.test_idx[0]
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def test_min_train_days_respected():
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idx = pd.date_range("2024-01-01", "2024-02-15", freq="D", tz="UTC")
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splits = expanding_walk_forward(idx, train_ratio=0.7, n_folds=2, min_train_days=20)
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for s in splits:
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assert len(s.train_idx) >= 20
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