File size: 4,817 Bytes
e74a796 | 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 | #!/usr/bin/env python3
import argparse
import json
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.metrics import confusion_matrix
STRUCT_KEYS = [
"json_parse_rate",
"required_field_complete_rate",
"current_behavior_accuracy",
"current_behavior_macro_f1",
"next_possible_behavior_accuracy",
"next_possible_behavior_macro_f1",
"is_transition_accuracy",
"is_transition_macro_f1",
"stage_index_accuracy",
"total_stages_accuracy",
"sequence_exact_match",
"sequence_last_label_accuracy",
"sequence_prefix_label_match",
]
MAE_KEYS = [
"elapsed_seconds_in_current_behavior_mae",
"estimated_remaining_seconds_mae",
"full_remaining_seconds_mae",
"expected_end_time_mae",
]
QA_KEYS = [
"json_parse_rate",
"required_field_complete_rate",
"occupied_accuracy",
"occupied_f1",
"is_abnormal_accuracy",
"is_abnormal_f1",
"used_areas_micro_f1",
]
def load_metric(path):
payload = json.loads(Path(path).read_text(encoding="utf-8"))
return payload["run_name"], payload["task_type"], payload["metrics"]
def save_bar(df, x, y, hue, title, path):
plt.figure(figsize=(max(9, len(df[x].unique()) * 0.8), 5))
sns.barplot(data=df, x=x, y=y, hue=hue)
plt.title(title)
plt.xticks(rotation=35, ha="right")
plt.ylim(0, max(1.0, df[y].max() * 1.15 if len(df) else 1.0))
plt.tight_layout()
plt.savefig(path, dpi=180)
plt.close()
def prediction_rows(path):
rows = []
with open(path, encoding="utf-8") as f:
for line in f:
rec = json.loads(line)
pred = rec.get("prediction") if isinstance(rec.get("prediction"), dict) else {}
target = rec.get("target") if isinstance(rec.get("target"), dict) else {}
rows.append((target, pred))
return rows
def save_confusion(prediction_path, title, out_path, top_n=20):
rows = prediction_rows(prediction_path)
y_true = [t.get("current_behavior") for t, p in rows if p.get("current_behavior") is not None]
y_pred = [p.get("current_behavior") for t, p in rows if p.get("current_behavior") is not None]
if not y_true:
return
labels = pd.Series(y_true).value_counts().head(top_n).index.tolist()
cm = confusion_matrix(y_true, y_pred, labels=labels)
plt.figure(figsize=(11, 9))
sns.heatmap(cm, annot=False, cmap="Blues", xticklabels=labels, yticklabels=labels)
plt.title(title)
plt.xlabel("Predicted")
plt.ylabel("True")
plt.xticks(rotation=45, ha="right")
plt.yticks(rotation=0)
plt.tight_layout()
plt.savefig(out_path, dpi=180)
plt.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--metrics", nargs="+", required=True)
parser.add_argument("--predictions", nargs="*", default=[])
parser.add_argument("--out-dir", default="outputs/figures")
args = parser.parse_args()
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
rows = []
for metric_path in args.metrics:
run_name, task_type, metrics = load_metric(metric_path)
keys = STRUCT_KEYS + MAE_KEYS if task_type == "struct" else QA_KEYS + ["time_to_free_minutes_mae"]
for key in keys:
if key in metrics and metrics[key] is not None:
rows.append({"run": run_name, "task": task_type, "metric": key, "value": metrics[key]})
df = pd.DataFrame(rows)
df.to_csv(out_dir / "metric_comparison.csv", index=False)
if not df.empty:
struct_score = df[(df["task"] == "struct") & (df["metric"].isin(STRUCT_KEYS))]
if not struct_score.empty:
save_bar(struct_score, "metric", "value", "run", "Structural Metrics: Base vs Fine-tuned", out_dir / "struct_scores.png")
struct_mae = df[(df["task"] == "struct") & (df["metric"].isin(MAE_KEYS))]
if not struct_mae.empty:
save_bar(struct_mae, "metric", "value", "run", "Time MAE: Base vs Fine-tuned", out_dir / "struct_time_mae.png")
qa_score = df[(df["task"] == "qa") & (df["metric"].isin(QA_KEYS))]
if not qa_score.empty:
save_bar(qa_score, "metric", "value", "run", "QA Metrics: Base vs Fine-tuned", out_dir / "qa_scores.png")
qa_mae = df[(df["task"] == "qa") & (df["metric"] == "time_to_free_minutes_mae")]
if not qa_mae.empty:
save_bar(qa_mae, "metric", "value", "run", "QA Time-to-Free MAE", out_dir / "qa_time_mae.png")
for pred_path in args.predictions:
path = Path(pred_path)
if "struct" in path.name:
save_confusion(path, path.stem, out_dir / f"{path.stem}_current_behavior_confusion.png")
print(f"wrote figures and comparison CSV to {out_dir}")
if __name__ == "__main__":
main()
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