统一代理评分与自动拟合误差计算
- 按物理时间重叠区对齐目标曲线和预测曲线 - 使用与C++真实求解一致的压力和导数误差公式 - 统一在线评分服务、trace回放和排序验证逻辑 - 增加固定时间轴和有效掩码回归测试feature/Model-20260625
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from __future__ import annotations
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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import h5py
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import joblib
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import numpy as np
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import torch
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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from src.common.config import Config
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from src.data.curve_processing import resample_curve_to_features_with_time_and_mask
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from src.data.preprocess import MaskedStandardScaler
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from src.data.preprocess import preprocess_dataset
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from src.evaluation.autofit_objective import (
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prediction_curve_time_from_meta,
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timed_dual_log_objective,
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)
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from src.training.train_forward import (
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CurveStats,
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LossConfig,
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LossContext,
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SampleReweightConfig,
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compute_weighted_loss,
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load_processed_dataset,
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smooth_l1_per_sample,
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)
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def _write_minimal_raw_h5(
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path: Path,
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curve_time: np.ndarray,
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*,
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include_mask: bool,
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) -> None:
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n_samples, n_time_points = curve_time.shape
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with h5py.File(path, "w") as f:
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f.create_dataset("params", data=np.ones((n_samples, 7), dtype=np.float32))
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f.create_dataset("schedule", data=np.zeros((n_samples, 4), dtype=np.float32))
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f.create_dataset(
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"curve",
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data=np.ones((n_samples, 2 * n_time_points), dtype=np.float32),
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)
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f.create_dataset("curve_time", data=curve_time.astype(np.float32))
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if include_mask:
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f.create_dataset(
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"curve_valid_mask",
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data=np.ones((n_samples, n_time_points), dtype=np.uint8),
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)
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class FixedTimePipelineTests(unittest.TestCase):
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def setUp(self) -> None:
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self.cfg = Config(ROOT / "configs" / "data_gen_family_random_v2_q.yaml")
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def test_all_curves_use_one_physical_time_grid(self) -> None:
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grids = []
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masks = []
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for end_time in (12.0, 30.0, 72.0, 120.0, 140.0):
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time = np.geomspace(1.6e-3, end_time, 240)
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pressure = 10.0 + np.log1p(time)
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derivative = 0.1 + np.sqrt(time)
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features, grid, valid_mask = resample_curve_to_features_with_time_and_mask(
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self.cfg,
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time,
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pressure,
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derivative,
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)
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self.assertEqual(features.shape, (320,))
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grids.append(grid)
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masks.append(valid_mask)
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for grid in grids[1:]:
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np.testing.assert_array_equal(grid, grids[0])
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self.assertAlmostEqual(float(grids[0][0]), 1.6e-3, places=8)
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self.assertAlmostEqual(float(grids[0][-1]), 139.0, places=5)
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self.assertLess(int(masks[0].sum()), int(masks[-1].sum()))
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def test_fixed_grid_point_before_curve_start_is_invalid(self) -> None:
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time = np.geomspace(1.61e-3, 139.0, 240)
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_, grid, valid_mask = resample_curve_to_features_with_time_and_mask(
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self.cfg,
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time,
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np.ones_like(time),
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np.ones_like(time),
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)
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self.assertAlmostEqual(float(grid[0]), 1.6e-3, places=8)
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self.assertEqual(int(valid_mask[0]), 0)
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self.assertEqual(int(valid_mask[1]), 1)
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def test_masked_scaler_ignores_placeholder_values(self) -> None:
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rng = np.random.RandomState(7)
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values = rng.normal(size=(12, 8))
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mask = np.ones_like(values, dtype=bool)
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mask[1:, 5:] = False
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changed = values.copy()
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changed[~mask] = 1.0e9
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scaler_a = MaskedStandardScaler().fit(values, mask)
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scaler_b = MaskedStandardScaler().fit(changed, mask)
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np.testing.assert_allclose(scaler_a.mean_, scaler_b.mean_, atol=0.0, rtol=0.0)
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np.testing.assert_allclose(scaler_a.scale_, scaler_b.scale_, atol=0.0, rtol=0.0)
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def test_masked_loss_has_no_gradient_outside_coverage(self) -> None:
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pred = torch.tensor([[0.2, 0.4, 3.0, 4.0]], dtype=torch.float32, requires_grad=True)
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target = torch.zeros_like(pred)
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mask = torch.tensor([[1.0, 1.0, 0.0, 0.0]], dtype=torch.float32)
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loss = smooth_l1_per_sample(pred, target, beta=0.05, valid_mask=mask).mean()
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loss.backward()
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self.assertGreater(float(pred.grad[0, 0]), 0.0)
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self.assertGreater(float(pred.grad[0, 1]), 0.0)
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self.assertEqual(float(pred.grad[0, 2]), 0.0)
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self.assertEqual(float(pred.grad[0, 3]), 0.0)
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def test_composite_training_loss_respects_time_mask(self) -> None:
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pred = torch.zeros((2, 320), dtype=torch.float32, requires_grad=True)
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target = torch.ones_like(pred)
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valid_mask = torch.zeros((2, 160), dtype=torch.float32)
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valid_mask[:, :80] = 1.0
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context = LossContext(
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slices={"log_pressure": slice(0, 160), "log_derivative": slice(160, 320)},
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curve_stats=CurveStats(
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mean_raw=torch.zeros(320, dtype=torch.float32),
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scale_raw=torch.ones(320, dtype=torch.float32),
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),
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loss_cfg=LossConfig(),
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reweight_cfg=SampleReweightConfig(enabled=False),
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)
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losses = compute_weighted_loss(pred, target, valid_mask, context)
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losses["loss"].backward()
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self.assertTrue(torch.isfinite(losses["loss"]))
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self.assertGreater(float(pred.grad[:, :80].abs().sum()), 0.0)
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self.assertGreater(float(pred.grad[:, 160:240].abs().sum()), 0.0)
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self.assertEqual(float(pred.grad[:, 80:160].abs().sum()), 0.0)
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self.assertEqual(float(pred.grad[:, 240:].abs().sum()), 0.0)
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def test_perfect_fit_composite_loss_has_finite_gradient(self) -> None:
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pred = torch.zeros((1, 4), dtype=torch.float32, requires_grad=True)
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target = torch.zeros_like(pred)
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valid_mask = torch.ones((1, 2), dtype=torch.float32)
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context = LossContext(
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slices={"log_pressure": slice(0, 2), "log_derivative": slice(2, 4)},
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curve_stats=CurveStats(
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mean_raw=torch.zeros(4, dtype=torch.float32),
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scale_raw=torch.ones(4, dtype=torch.float32),
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),
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loss_cfg=LossConfig(),
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reweight_cfg=SampleReweightConfig(enabled=False),
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)
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losses = compute_weighted_loss(pred, target, valid_mask, context)
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losses["loss"].backward()
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self.assertTrue(torch.isfinite(losses["loss"]))
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self.assertTrue(torch.isfinite(pred.grad).all())
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self.assertEqual(float(pred.grad.abs().sum()), 0.0)
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def test_surrogate_metadata_requires_fixed_time(self) -> None:
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layout = {
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"parts": [
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{"name": "log_pressure", "start": 0, "end": 3},
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{"name": "log_derivative", "start": 3, "end": 6},
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]
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}
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with self.assertRaises(ValueError):
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prediction_curve_time_from_meta({"curve_time_mode": "missing"}, layout)
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with self.assertRaises(ValueError):
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prediction_curve_time_from_meta({"curve_time_mode": "per_sample"}, layout)
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result = prediction_curve_time_from_meta(
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{
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"curve_time_mode": "fixed",
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"prediction_curve_time": [0.1, 1.0, 10.0],
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},
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layout,
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)
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np.testing.assert_array_equal(result, np.asarray([0.1, 1.0, 10.0]))
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def test_fixed_grid_objective_aligns_on_physical_time(self) -> None:
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prediction_time = np.asarray([0.1, 1.0, 2.0, 4.0], dtype=np.float64)
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prediction_pressure = 2.0 + 3.0 * prediction_time
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prediction_derivative = 1.0 + 0.5 * prediction_time
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prediction_curve = np.concatenate(
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[np.log(prediction_pressure), np.log(prediction_derivative)]
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)
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target_time = np.asarray([0.3, 0.8, 1.5, 2.3, 3.0], dtype=np.float64)
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layout = {
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"parts": [
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{"name": "log_pressure", "start": 0, "end": 4},
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{"name": "log_derivative", "start": 4, "end": 8},
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]
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}
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objective = timed_dual_log_objective(
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target_time=target_time,
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target_pressure=2.0 + 3.0 * target_time,
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target_derivative=1.0 + 0.5 * target_time,
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pred_time=prediction_time,
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pred_curve=prediction_curve,
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curve_layout=layout,
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)
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self.assertLess(objective["dual_log_objective"], 1.0e-12)
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def test_preprocess_preserves_fixed_grid_and_masks(self) -> None:
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rng = np.random.RandomState(11)
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n_groups = 10
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n = n_groups * 5
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grid = np.geomspace(1.6e-3, 139.0, 160).astype(np.float32)
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params = np.column_stack(
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[
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rng.uniform(1.0e-3, 1.0, n),
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rng.uniform(-2.0, 2.0, n),
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rng.uniform(1.0e-4, 0.1, n),
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rng.uniform(0.01, 0.2, n),
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rng.uniform(2.0, 20.0, n),
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rng.uniform(1.0e-4, 2.0e-3, n),
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np.tile(np.arange(1, 6, dtype=np.float32), n_groups),
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]
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).astype(np.float32)
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schedule = rng.normal(size=(n, 12)).astype(np.float32)
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curve = rng.normal(size=(n, 320)).astype(np.float32)
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mask = np.zeros((n, 160), dtype=np.uint8)
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for row in range(n):
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mask[row, : 80 + 20 * (row % 5)] = 1
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with tempfile.TemporaryDirectory() as temp_dir:
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h5_path = Path(temp_dir) / "fixed.h5"
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pkl_path = Path(temp_dir) / "fixed.pkl"
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with h5py.File(h5_path, "w") as f:
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f.create_dataset("params", data=params)
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f.create_dataset("schedule", data=schedule)
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f.create_dataset("curve", data=curve)
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f.create_dataset("curve_time", data=np.tile(grid, (n, 1)))
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f.create_dataset("curve_valid_mask", data=mask)
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f.create_dataset("group_id", data=np.repeat(np.arange(n_groups), 5))
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f.attrs["param_names"] = np.asarray(
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["k", "skin", "wellboreC", "phi", "h", "Cf", "solverType"],
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dtype="S",
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)
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preprocess_dataset(h5_path, pkl_path, test_size=0.2, val_size=0.2, random_seed=3)
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processed = joblib.load(pkl_path)
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self.assertEqual(processed["meta"]["curve_time_mode"], "fixed")
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self.assertEqual(processed["meta"]["curve_valid_mask_mode"], "explicit")
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np.testing.assert_array_equal(
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np.asarray(processed["meta"]["prediction_curve_time"], dtype=np.float32),
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grid,
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)
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train_mask = processed["curve_valid_mask_train"].astype(bool)
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full_train_mask = np.concatenate([train_mask, train_mask], axis=1)
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self.assertTrue(np.all(processed["Y_curve_train"][~full_train_mask] == 0.0))
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self.assertTrue(np.all(np.asarray(processed["scaler_curve"].n_samples_seen_) > 0))
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def test_preprocess_keeps_240_dim_two_channel_curve(self) -> None:
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fixed_grid = np.geomspace(1.6e-3, 139.0, 120)
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with tempfile.TemporaryDirectory() as temp_dir:
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h5_path = Path(temp_dir) / "two_channel_240.h5"
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pkl_path = Path(temp_dir) / "two_channel_240.pkl"
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_write_minimal_raw_h5(
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h5_path,
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np.tile(fixed_grid, (20, 1)),
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include_mask=True,
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)
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preprocess_dataset(h5_path, pkl_path, random_seed=5)
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processed = joblib.load(pkl_path)
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self.assertEqual(processed["meta"]["curve_dim"], 240)
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self.assertEqual(processed["meta"]["curve_layout"]["n_time_points"], 120)
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self.assertFalse(processed["meta"]["dropped_legacy_slope"])
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self.assertEqual(processed["Y_curve_train"].shape[1], 240)
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self.assertEqual(processed["curve_valid_mask_train"].shape[1], 120)
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np.testing.assert_allclose(
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processed["meta"]["prediction_curve_time"],
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fixed_grid,
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rtol=1.0e-6,
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atol=1.0e-10,
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)
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def test_preprocess_rejects_per_sample_time_grid(self) -> None:
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fixed_grid = np.geomspace(1.6e-3, 139.0, 160)
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other_grid = np.geomspace(1.6e-3, 120.0, 160)
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with tempfile.TemporaryDirectory() as temp_dir:
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h5_path = Path(temp_dir) / "per_sample.h5"
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_write_minimal_raw_h5(
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h5_path,
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np.stack([fixed_grid, other_grid], axis=0),
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include_mask=True,
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)
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with self.assertRaisesRegex(ValueError, "one fixed curve_time grid"):
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preprocess_dataset(h5_path, Path(temp_dir) / "unused.pkl")
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def test_preprocess_rejects_missing_curve_mask(self) -> None:
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fixed_grid = np.geomspace(1.6e-3, 139.0, 160)
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with tempfile.TemporaryDirectory() as temp_dir:
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h5_path = Path(temp_dir) / "missing_mask.h5"
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_write_minimal_raw_h5(
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h5_path,
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np.tile(fixed_grid, (2, 1)),
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include_mask=False,
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)
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with self.assertRaisesRegex(ValueError, "requires curve_valid_mask"):
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preprocess_dataset(h5_path, Path(temp_dir) / "unused.pkl")
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def test_training_rejects_legacy_processed_dataset(self) -> None:
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legacy = {}
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for split in ("train", "val", "test"):
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legacy[f"X_params_{split}"] = np.zeros((1, 1), dtype=np.float32)
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legacy[f"X_schedule_{split}"] = np.zeros((1, 1), dtype=np.float32)
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legacy[f"Y_curve_{split}"] = np.zeros((1, 4), dtype=np.float32)
|
||||||
|
|
||||||
|
with tempfile.TemporaryDirectory() as temp_dir:
|
||||||
|
path = Path(temp_dir) / "legacy.pkl"
|
||||||
|
joblib.dump(legacy, path)
|
||||||
|
with self.assertRaisesRegex(ValueError, "curve_time_mode='fixed'"):
|
||||||
|
load_processed_dataset(path)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in New Issue