File size: 4,381 Bytes
32f5a65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | from __future__ import annotations
import numpy as np
import pandas as pd
from sepsis_mcp.grud_data import (
build_patient_grud_samples,
fit_grud_scaler,
stack_grud_samples,
transform_grud_stacked,
)
def _patient_frame() -> pd.DataFrame:
return pd.DataFrame(
{
"HR": [80.0, 82.0, 84.0, 86.0],
"O2Sat": [95.0, np.nan, 93.0, 92.0],
"Age": [65.0] * 4,
"Gender": [1.0] * 4,
"Unit1": [1.0] * 4,
"Unit2": [0.0] * 4,
"HospAdmTime": [-5.0] * 4,
"ICULOS": [1.0, 2.0, 3.0, 4.0],
"SepsisLabel": [0, 0, 1, 1],
}
)
def test_build_patient_grud_samples_pads_left_and_aligns_future_targets() -> None:
samples = build_patient_grud_samples(
_patient_frame(),
patient_id="p000001",
dynamic_columns=["HR", "O2Sat"],
lookback_hours=3,
horizon_hours=1,
)
assert len(samples) == 3
first = samples[0]
assert first["patient_id"] == "p000001"
assert first["sample_index"] == 0
assert first["label"] == 0
assert first["values"].shape == (3, 2)
assert first["masks"].shape == (3, 2)
assert first["deltas"].shape == (3, 2)
assert first["static"].shape == (5,)
assert first["values"].tolist() == [
[0.0, 0.0],
[0.0, 0.0],
[80.0, 95.0],
]
assert first["masks"].tolist() == [
[0.0, 0.0],
[0.0, 0.0],
[1.0, 1.0],
]
def test_build_patient_grud_samples_computes_featurewise_deltas_for_missing_values() -> None:
samples = build_patient_grud_samples(
_patient_frame(),
patient_id="p000001",
dynamic_columns=["HR", "O2Sat"],
lookback_hours=3,
horizon_hours=1,
)
second = samples[1]
assert second["values"].tolist() == [
[0.0, 0.0],
[80.0, 95.0],
[82.0, 0.0],
]
assert second["masks"].tolist() == [
[0.0, 0.0],
[1.0, 1.0],
[1.0, 0.0],
]
assert second["deltas"].tolist() == [
[0.0, 0.0],
[0.0, 0.0],
[0.0, 1.0],
]
assert second["label"] == 1
assert np.isclose(second["global_missing_rate"], 0.5)
def test_build_patient_grud_samples_replaces_missing_static_values_with_zero() -> None:
frame = _patient_frame()
frame.loc[0, "Unit1"] = np.nan
frame.loc[0, "Unit2"] = np.nan
samples = build_patient_grud_samples(
frame,
patient_id="p000001",
dynamic_columns=["HR", "O2Sat"],
lookback_hours=3,
horizon_hours=1,
)
assert samples[0]["static"].tolist() == [65.0, 1.0, 0.0, 0.0, -5.0]
def test_transform_grud_stacked_standardizes_observed_values_and_preserves_missing_zero() -> None:
samples = build_patient_grud_samples(
_patient_frame(),
patient_id="p000001",
dynamic_columns=["HR", "O2Sat"],
lookback_hours=3,
horizon_hours=1,
)
stacked = stack_grud_samples(samples)
scaler = fit_grud_scaler(stacked)
transformed = transform_grud_stacked(stacked, scaler)
observed_hr = transformed["values"][stacked["masks"][:, :, 0] == 1, 0]
assert np.isclose(observed_hr.mean(), 0.0, atol=1e-5)
assert np.isclose(observed_hr.std(), 1.0, atol=1e-5)
assert transformed["values"][1, 2, 1] == 0.0
def test_transform_grud_stacked_standardizes_static_features_after_imputation() -> None:
samples = build_patient_grud_samples(
_patient_frame(),
patient_id="p000001",
dynamic_columns=["HR", "O2Sat"],
lookback_hours=3,
horizon_hours=1,
)
stacked = stack_grud_samples(samples)
scaler = fit_grud_scaler(stacked)
transformed = transform_grud_stacked(stacked, scaler)
assert np.all(np.isfinite(transformed["static"]))
assert np.isclose(transformed["static"][:, 0].mean(), 0.0, atol=1e-6)
def test_stack_grud_samples_preserves_sample_missing_rates() -> None:
samples = build_patient_grud_samples(
_patient_frame(),
patient_id="p000001",
dynamic_columns=["HR", "O2Sat"],
lookback_hours=3,
horizon_hours=1,
)
stacked = stack_grud_samples(samples)
assert "global_missing_rates" in stacked
assert stacked["global_missing_rates"].shape == (3,)
assert np.isclose(stacked["global_missing_rates"][0], 2.0 / 3.0)
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