Dtw algo (#9)
* feat: implement Dynamic Time Warping (DTW) functionality - Added DTW distance computation and optimal warping path functions in Rust. - Introduced corresponding Python bindings for DTW, DTW_DISTANCE, and BATCH_DTW. - Enhanced WASM support with a new dtw_distance function. - Included comprehensive unit tests for DTW functionality, validating against the dtaidistance library and ensuring mathematical properties. * chore: update ferro-ta version to 1.1.4 - Bumped version number of ferro-ta to 1.1.4 in uv.lock and Cargo.lock files. - Ensured consistency across package dependencies for the updated version.
This commit is contained in:
Pratik Bhadane
@@ -1,10 +1,14 @@
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"""Unit tests for ferro_ta.indicators.statistic"""
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import numpy as np
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import pytest
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from ferro_ta.indicators.statistic import (
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BATCH_DTW,
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BETA,
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CORREL,
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DTW,
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DTW_DISTANCE,
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LINEARREG,
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LINEARREG_ANGLE,
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LINEARREG_INTERCEPT,
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@@ -308,3 +312,177 @@ class TestTSF:
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expected = _naive_linearreg(_A, timeperiod=14, x_value=14.0)
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result = TSF(_A, timeperiod=14)
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np.testing.assert_allclose(result, expected, equal_nan=True)
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# ---------------------------------------------------------------------------
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# DTW — Dynamic Time Warping
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# ---------------------------------------------------------------------------
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dtai = pytest.importorskip("dtaidistance", reason="dtaidistance not installed")
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_DTW_RNG = np.random.default_rng(42)
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class TestDTW:
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# --- Validation against dtaidistance (SOTA reference) ---
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def test_distance_matches_dtaidistance_random(self):
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"""Core correctness: our distance == dtaidistance on 20 random pairs."""
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for _ in range(20):
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n = int(_DTW_RNG.integers(5, 50))
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a = _DTW_RNG.random(n)
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b = _DTW_RNG.random(n)
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expected = dtai.dtw.distance(a, b)
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actual = DTW_DISTANCE(a, b)
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np.testing.assert_allclose(
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actual, expected, rtol=1e-9, err_msg=f"Mismatch on series length {n}"
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)
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def test_distance_matches_dtaidistance_unequal_length(self):
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"""Handles unequal-length series correctly."""
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for _ in range(10):
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a = _DTW_RNG.random(int(_DTW_RNG.integers(5, 30)))
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b = _DTW_RNG.random(int(_DTW_RNG.integers(5, 30)))
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expected = dtai.dtw.distance(a, b)
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actual = DTW_DISTANCE(a, b)
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np.testing.assert_allclose(actual, expected, rtol=1e-9)
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def test_path_distance_matches_dtaidistance(self):
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"""DTW() path variant: returned distance matches dtaidistance."""
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a = _DTW_RNG.random(20)
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b = _DTW_RNG.random(25)
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expected = dtai.dtw.distance(a, b)
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dist, _ = DTW(a, b)
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np.testing.assert_allclose(dist, expected, rtol=1e-9)
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def test_path_matches_dtaidistance_warping_path(self):
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"""Warping path matches dtaidistance.dtw.warping_path() on same-length series."""
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for _ in range(10):
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n = int(_DTW_RNG.integers(5, 20))
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a = _DTW_RNG.random(n)
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b = _DTW_RNG.random(n)
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expected_path = dtai.dtw.warping_path(a, b)
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_, actual_path = DTW(a, b)
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actual_pairs = [tuple(int(x) for x in row) for row in actual_path]
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assert actual_pairs == expected_path, (
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f"Path mismatch for n={n}:\n ours={actual_pairs}\n dtai={expected_path}"
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)
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def test_window_constrained_matches_dtaidistance(self):
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"""Sakoe-Chiba window matches dtaidistance window parameter."""
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a = _DTW_RNG.random(30)
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b = _DTW_RNG.random(30)
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for w in [3, 8, 15]:
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expected = dtai.dtw.distance(a, b, window=w)
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actual = DTW_DISTANCE(a, b, window=w)
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np.testing.assert_allclose(
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actual, expected, rtol=1e-9, err_msg=f"Mismatch at window={w}"
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)
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def test_batch_matches_dtaidistance(self):
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"""BATCH_DTW matches calling dtaidistance per-row."""
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ref = _DTW_RNG.random(20)
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matrix = _DTW_RNG.random((8, 20))
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batch_result = BATCH_DTW(matrix, ref)
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for i in range(8):
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expected = dtai.dtw.distance(matrix[i], ref)
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np.testing.assert_allclose(
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batch_result[i],
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expected,
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rtol=1e-9,
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err_msg=f"Batch mismatch at row {i}",
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)
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# --- Mathematical properties ---
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def test_identical_distance_is_zero(self):
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a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
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dist, _ = DTW(a, a)
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assert dist == pytest.approx(0.0, abs=1e-10)
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def test_symmetry(self):
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a, b = _DTW_RNG.random(20), _DTW_RNG.random(20)
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assert DTW_DISTANCE(a, b) == pytest.approx(DTW_DISTANCE(b, a), rel=1e-10)
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def test_triangle_inequality(self):
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a, b, c = _DTW_RNG.random(15), _DTW_RNG.random(15), _DTW_RNG.random(15)
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assert DTW_DISTANCE(a, c) <= DTW_DISTANCE(a, b) + DTW_DISTANCE(b, c) + 1e-9
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# --- Known hardcoded values ---
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def test_known_shifted_series(self):
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# [0,1,2] vs [1,2,3]: optimal path (0,0)→(1,0)→(2,1)→(2,2)
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# Squared costs: 1+0+0+1=2, sqrt(2). Verified against dtaidistance.
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a = np.array([0.0, 1.0, 2.0])
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b = np.array([1.0, 2.0, 3.0])
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np.testing.assert_allclose(DTW_DISTANCE(a, b), np.sqrt(2.0), rtol=1e-9)
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def test_known_single_element(self):
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# sqrt((3-7)^2) = sqrt(16) = 4.0
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np.testing.assert_allclose(
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DTW_DISTANCE(np.array([3.0]), np.array([7.0])), 4.0, rtol=1e-9
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)
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def test_known_constant_series(self):
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assert DTW_DISTANCE(np.full(10, 5.0), np.full(10, 5.0)) == pytest.approx(
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0.0, abs=1e-12
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)
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# --- Path structural guarantees ---
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def test_path_starts_at_origin(self):
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_, path = DTW(_DTW_RNG.random(10), _DTW_RNG.random(10))
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assert tuple(int(x) for x in path[0]) == (0, 0)
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def test_path_ends_at_corner(self):
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_, path = DTW(_DTW_RNG.random(7), _DTW_RNG.random(9))
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assert tuple(int(x) for x in path[-1]) == (6, 8)
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def test_path_is_monotone(self):
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_, path = DTW(_DTW_RNG.random(20), _DTW_RNG.random(20))
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for k in range(1, len(path)):
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assert path[k][0] >= path[k - 1][0]
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assert path[k][1] >= path[k - 1][1]
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def test_path_steps_unit_size(self):
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_, path = DTW(_DTW_RNG.random(15), _DTW_RNG.random(12))
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for k in range(1, len(path)):
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di = int(path[k][0]) - int(path[k - 1][0])
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dj = int(path[k][1]) - int(path[k - 1][1])
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assert di in (0, 1) and dj in (0, 1)
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assert not (di == 0 and dj == 0)
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# --- DTW_DISTANCE == DTW distance ---
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def test_distance_only_matches_full(self):
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a, b = _DTW_RNG.random(25), _DTW_RNG.random(25)
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d_full, _ = DTW(a, b)
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np.testing.assert_allclose(DTW_DISTANCE(a, b), d_full, rtol=1e-10)
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# --- Batch ---
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def test_batch_single_row(self):
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ref = np.array([1.0, 2.0, 3.0])
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result = BATCH_DTW(np.array([[1.0, 2.0, 3.0]]), ref)
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assert result[0] == pytest.approx(0.0, abs=1e-10)
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def test_batch_matches_single_calls(self):
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ref = _DTW_RNG.random(20)
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matrix = _DTW_RNG.random((8, 20))
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batch = BATCH_DTW(matrix, ref)
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for i in range(8):
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np.testing.assert_allclose(
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batch[i], DTW_DISTANCE(matrix[i], ref), rtol=1e-10
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)
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# --- Edge cases ---
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def test_empty_series_raises(self):
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with pytest.raises((ValueError, Exception)):
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DTW(np.array([]), np.array([1.0, 2.0]))
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def test_window_constrained_ge_unconstrained(self):
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a, b = _DTW_RNG.random(20), _DTW_RNG.random(20)
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d_full = DTW_DISTANCE(a, b)
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d_narrow = DTW_DISTANCE(a, b, window=2)
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assert d_narrow >= d_full - 1e-9
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