File size: 3,210 Bytes
94e0fc9 | 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 | import numpy as np
import pandas as pd
# def rank_results(
# results,
# metric="f1",
# clf_average="mean",
# weights=None
# ):
# """
# 对 FSExecutor 输出结果进行排行榜排序
# Parameters
# ----------
# results : list of dict
# 每个 dict 是一个算法在一个数据集上的结果
# metric : str
# 使用的指标: 'f1' or 'auc'
# clf_average : str
# 'mean' 或 'max'，表示跨分类器如何聚合
# weights : dict or None
# 多指标加权，例如 {'f1':0.5, 'auc':0.5}
# Returns
# -------
# ranked_df : pd.DataFrame
# """
# rows = []
# for res in results:
# algo = res["algorithm"]
# metrics = res["metrics"]
# # --------- 单指标 ----------
# if weights is None:
# vals = []
# for clf, m in metrics.items():
# if metric in m:
# vals.append(m[metric])
# if not vals:
# raise ValueError(f"No metric {metric} for {algo}")
# score = np.mean(vals) if clf_average == "mean" else np.max(vals)
# # --------- 多指标加权 ----------
# else:
# score = 0.0
# for m_name, w in weights.items():
# vals = [
# metrics[clf][m_name]
# for clf in metrics
# if m_name in metrics[clf]
# ]
# score += w * np.mean(vals)
# rows.append({
# "algorithm": algo,
# "score": score,
# "num_features": res["num_features"],
# "time": res.get("time", None)
# })
# df = pd.DataFrame(rows)
# # --------- 按 score 排序 ----------
# df = df.sort_values(
# by="score",
# ascending=False
# ).reset_index(drop=True)
# df["rank"] = df.index + 1
# return df
def aggregate_metrics(metrics, w_f1=0.7, w_auc=0.3):
"""
metrics:
{
"nb": {"f1": x, "auc": y},
"svm": {"f1": x, "auc": y},
"rf": {"f1": x, "auc": y},
}
"""
f1s = [m["f1"] for m in metrics.values()]
aucs = [m["auc"] for m in metrics.values()]
mean_f1 = sum(f1s) / len(f1s)
mean_auc = sum(aucs) / len(aucs)
return w_f1 * mean_f1 + w_auc * mean_auc,mean_f1,mean_auc
def rank_results(
results,
):
"""
results: list[dict]
return: 排序后的 list[dict]（每个 dict 会新增 score 字段）
"""
ranked = []
for r in results:
# 1. 性能融合
perf_score,mean_f1,mean_auc = aggregate_metrics(r["metrics"])
# 2. 惩罚项
# feature_penalty = alpha * r["num_features"]
# time_penalty = beta * r["time"]
# final_score = perf_score - feature_penalty - time_penalty
final_score = perf_score
ranked.append({
**r,
"mean_f1":mean_f1,
"mean_auc":mean_auc,
"score": final_score,
"perf_score": perf_score
})
# 3. 排序（score 越大越好）
ranked.sort(key=lambda x: x["score"], reverse=True)
return ranked
|