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ebcde1f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 | from __future__ import annotations
import numpy as np
import polars as pl
import pytest
from microstructure.config import EvaluationConfig, ModelConfig
from microstructure.research.models import (
ModelEvaluationError,
SigmoidCalibrator,
block_bootstrap_metric,
build_model_candidates,
classification_metrics,
evaluate_model_ladder,
paired_block_bootstrap_difference,
)
from microstructure.research.splits import expanding_walk_forward_splits
def _evaluation_config() -> EvaluationConfig:
return EvaluationConfig(
min_train_events=20,
validation_events=8,
test_events=8,
step_events=8,
embargo_events=1,
bootstrap_samples=40,
calibration_bins=5,
)
def _model_config() -> ModelConfig:
return ModelConfig(
selection_metric="log_loss",
logistic_c_values=(1.0,),
tree_max_depth_values=(2,),
tree_min_samples_leaf=1,
)
def _model_frame() -> pl.DataFrame:
rows: list[dict[str, object]] = []
for decision in range(48):
positive = decision % 2
for symbol_offset, symbol in enumerate(("BTCUSDT", "ETHUSDT")):
censored = decision == 47
rows.append(
{
"symbol": symbol,
"decision_ts_ns": decision,
"label_information_end_ts_ns": None if censored else decision + 1,
"right_censored": censored,
"future_mid_up": None if censored else positive,
"future_mid_return": None if censored else (0.001 if positive else -0.001),
"feature_ready": True,
"spread_bps": 2.0 + 0.1 * symbol_offset,
"depth_total_l1": 20.0,
"queue_imbalance_l1": 0.8 if positive else -0.8,
"microprice_deviation_bps": 0.5 if positive else -0.5,
"ofi_l1": 2.0 if positive else -2.0,
"log_mid_return_1": 0.0001 if positive else -0.0001,
"ofi_w2": 3.0 if positive else -3.0,
"signed_trade_volume_w2": 4.0 if positive else -4.0,
"trade_volume_w2": 4.0,
"trade_count_w2": 1.0,
"trade_intensity_w2": 2.0,
"realized_volatility_w2": 0.001,
}
)
return pl.DataFrame(rows).sort(["decision_ts_ns", "symbol"])
def test_model_ladder_contains_required_transparent_families() -> None:
candidates = build_model_candidates(_model_config())
assert [candidate.family for candidate in candidates] == [
"baseline",
"logistic",
"logistic_l2",
"shallow_tree",
]
def test_out_of_time_ladder_is_deterministic_and_test_is_not_selected_on() -> None:
frame = _model_frame()
plan = expanding_walk_forward_splits(frame, _evaluation_config())
first = evaluate_model_ladder(
frame,
plan,
_model_config(),
seed=7,
calibration_bins=5,
)
second = evaluate_model_ladder(
frame,
plan,
_model_config(),
seed=7,
calibration_bins=5,
)
assert first.selected_model == second.selected_model
assert first.comparison.equals(second.comparison)
assert first.predictions.equals(second.predictions)
assert first.predictions.get_column("is_oos").all()
assert first.predictions.filter(
pl.col("fit_cutoff_ts_ns") >= pl.col("decision_ts_ns")
).is_empty()
assert set(first.comparison.get_column("split")) == {"validation", "test"}
assert first.comparison.get_column("instrument_scope").unique().to_list() == ["POOLED"]
assert first.comparison.filter(pl.col("split") == "test").get_column(
"period_start_ts_ns"
).unique().to_list() == [40]
assert {"sample_id", "decision_sequence"}.issubset(first.predictions.columns)
selected = first.comparison.filter(pl.col("selected_on_validation"))
assert selected.get_column("model").unique().to_list() == [first.selected_model]
def test_single_class_fallback_is_labeled_as_prior_and_cannot_win_selection() -> None:
frame = _model_frame().with_columns(
pl.when(pl.col("right_censored"))
.then(None)
.otherwise(1)
.cast(pl.Int8)
.alias("future_mid_up")
)
plan = expanding_walk_forward_splits(frame, _evaluation_config())
result = evaluate_model_ladder(
frame,
plan,
_model_config(),
seed=7,
calibration_bins=5,
)
fallback = result.comparison.filter(pl.col("requested_family") != "baseline")
assert fallback.get_column("family").unique().to_list() == ["baseline"]
assert fallback.get_column("model").str.ends_with("__prior_fallback").all()
assert fallback.get_column("fit_status").str.contains("single_class_prior_fallback").all()
assert result.selected_model == "historical_prior"
def test_label_columns_are_rejected_from_feature_allowlist() -> None:
frame = _model_frame()
plan = expanding_walk_forward_splits(frame, _evaluation_config())
with pytest.raises(ModelEvaluationError, match="cannot be model features"):
evaluate_model_ladder(
frame,
plan,
_model_config(),
seed=7,
calibration_bins=5,
features=("queue_imbalance_l1", "future_mid_return"),
)
def test_calibration_and_metrics_have_exact_small_values() -> None:
y_true = np.asarray([0, 1], dtype=np.int64)
probability = np.asarray([0.1, 0.9], dtype=np.float64)
metrics = classification_metrics(y_true, probability, calibration_bins=2)
assert metrics["accuracy"] == 1.0
assert metrics["brier_score"] == pytest.approx(0.01)
assert metrics["log_loss"] == pytest.approx(-np.log(0.9))
assert metrics["expected_calibration_error"] == pytest.approx(0.1)
calibrator = SigmoidCalibrator()
calibration_y = np.asarray([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=np.int64)
raw = np.linspace(0.2, 0.8, 10, dtype=np.float64)
calibrator.fit(calibration_y, raw)
transformed = calibrator.transform(raw)
assert calibrator.status == "sigmoid"
assert np.all((transformed >= 0.0) & (transformed <= 1.0))
assert np.all(np.diff(transformed) > 0)
def test_block_bootstrap_is_seeded_and_paired_identity_is_zero() -> None:
predictions = pl.DataFrame(
{
"row_id": list(range(8)),
"y_true": [0, 1, 0, 1, 1, 0, 1, 0],
"probability": [0.1, 0.8, 0.2, 0.7, 0.9, 0.3, 0.6, 0.4],
"block": ["a", "a", "a", "a", "b", "b", "b", "b"],
}
)
first = block_bootstrap_metric(
predictions,
metric="brier_score",
block_column="block",
n_bootstrap=40,
seed=11,
)
second = block_bootstrap_metric(
predictions,
metric="brier_score",
block_column="block",
n_bootstrap=40,
seed=11,
)
assert first == second
assert first.status == "ok"
assert first.n_blocks == 2
paired = paired_block_bootstrap_difference(
predictions,
predictions,
metric="brier_score",
block_column="block",
n_bootstrap=40,
seed=11,
)
assert paired.point_estimate == 0.0
assert paired.lower == 0.0
assert paired.upper == 0.0
assert set(paired.draws) == {0.0}
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