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d13c106 9c763c6 d13c106 9c763c6 d13c106 9c763c6 d13c106 9c763c6 d13c106 9c763c6 d13c106 9c763c6 d13c106 | 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 | """
Helpers for reading notebook-generated artifacts and training metadata.
"""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Any, Dict, Optional
import numpy as np
from PIL import Image
from .model_registry import CalibrationResult
ASSIGNMENT_ROOT = Path(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)
ARTIFACTS_DIR = ASSIGNMENT_ROOT / "image" / "artifacts"
def _render_reliability_diagram_from_metrics(metrics: Dict[str, Any]) -> np.ndarray:
"""Render a reliability diagram directly from saved calibration metrics."""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
bin_accuracies = [float(x) for x in metrics["bin_accuracies"]]
bin_confidences = [float(x) for x in metrics["bin_confidences"]]
bin_counts = [int(x) for x in metrics["bin_counts"]]
ece = float(metrics["ece"])
n_bins = len(bin_accuracies)
bin_boundaries = np.linspace(0, 1, n_bins + 1)
bin_centers = [
(bin_boundaries[i] + bin_boundaries[i + 1]) / 2 for i in range(n_bins)
]
total = max(sum(bin_counts), 1)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
fig.patch.set_facecolor("#0d1117")
ax1.set_facecolor("#161b22")
width = 0.08
ax1.bar(
[c - width / 2 for c in bin_centers],
bin_accuracies,
width,
label="Accuracy",
color="#58a6ff",
alpha=0.9,
edgecolor="#58a6ff",
)
ax1.bar(
[c + width / 2 for c in bin_centers],
bin_confidences,
width,
label="Avg Confidence",
color="#f97583",
alpha=0.9,
edgecolor="#f97583",
)
ax1.plot(
[0, 1],
[0, 1],
"--",
color="#8b949e",
linewidth=2,
label="Perfect Calibration",
)
ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
ax1.set_xlabel("Confidence", color="white", fontsize=12)
ax1.set_ylabel("Accuracy / Confidence", color="white", fontsize=12)
ax1.set_title(
f"Reliability Diagram (ECE: {ece:.4f})",
color="white",
fontsize=14,
fontweight="bold",
pad=15,
)
ax1.legend(
facecolor="#161b22",
edgecolor="#30363d",
labelcolor="white",
fontsize=10,
)
ax1.tick_params(colors="white")
for spine in ax1.spines.values():
spine.set_edgecolor("#30363d")
ax1.grid(True, alpha=0.1, color="white")
ax2.set_facecolor("#161b22")
ax2.bar(
bin_centers,
[count / total for count in bin_counts],
0.08,
color="#56d364",
alpha=0.9,
edgecolor="#56d364",
)
ax2.set_xlim(0, 1)
ax2.set_xlabel("Confidence", color="white", fontsize=12)
ax2.set_ylabel("Fraction of Samples", color="white", fontsize=12)
ax2.set_title(
"Confidence Distribution",
color="white",
fontsize=14,
fontweight="bold",
pad=15,
)
ax2.tick_params(colors="white")
for spine in ax2.spines.values():
spine.set_edgecolor("#30363d")
ax2.grid(True, alpha=0.1, color="white")
plt.tight_layout(pad=3)
fig.canvas.draw()
rgba_buffer = fig.canvas.buffer_rgba()
diagram = np.array(rgba_buffer)[:, :, :3]
plt.close(fig)
return diagram
def get_best_accuracy_from_history(history: Optional[Dict[str, Any]]) -> Optional[float]:
"""Return the best validation accuracy found in a checkpoint history."""
if not history:
return None
val_acc = history.get("val_acc")
if isinstance(val_acc, list) and val_acc:
return float(max(val_acc))
return None
def load_precomputed_calibration_result(
model_tag: str,
sample_tag: str = "full",
) -> Optional[CalibrationResult]:
"""
Load notebook-generated calibration metrics and figure from image/artifacts/.
The function searches recursively so nested folders like artifacts/cnn and
artifacts/vit are both supported.
"""
if not ARTIFACTS_DIR.exists():
return None
metrics_name = f"{model_tag}_calibration_metrics_{sample_tag}.json"
metrics_path = next(ARTIFACTS_DIR.rglob(metrics_name), None)
image_name = f"{model_tag}_calibration_{sample_tag}.png"
image_path = next(ARTIFACTS_DIR.rglob(image_name), None)
if metrics_path is None:
return None
metrics = json.loads(metrics_path.read_text(encoding="utf-8"))
if image_path is not None:
reliability_diagram = np.array(Image.open(image_path).convert("RGB"))
else:
reliability_diagram = _render_reliability_diagram_from_metrics(metrics)
return CalibrationResult(
ece=float(metrics["ece"]),
bin_accuracies=[float(x) for x in metrics["bin_accuracies"]],
bin_confidences=[float(x) for x in metrics["bin_confidences"]],
bin_counts=[int(x) for x in metrics["bin_counts"]],
reliability_diagram=reliability_diagram,
source=f"Notebook artifact ({metrics_path.parent.name})",
)
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