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multilabel_metrics_fn crashes when a thresholded metric comes after "ddi" in the metrics list

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@Om-singhaI 已经在做这个了。

开始于 2026年9月22日。

  • #1243 来自 @Om-singhaI —— 未关闭

评估

难度
2/5
预计耗时
1-3 小时
新手友好度
25/100
Issue 类型
缺陷
描述清晰度
描述清楚
活跃度
停滞
技术栈
numpy, python, scikit-learn
领域
machine-learning

调研方向

Start with multilabel_metrics_fn in pyhealth.metrics.multilabel, especially the ddi branch and the metrics loop; the report includes a reproduction script and expected results. Check the existing tests for this function, then verify that reversing metric order produces the same scores and that the documented threshold default and example output match the function. A pull request is already open for this issue.

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描述

Where "ddi" sits in the metrics list changes whether multilabel_metrics_fn returns at all. If a metric that reads the thresholded predictions comes after it, the call raises instead of returning scores.

This lands on drug recommendation, where the DDI rate is the headline safety number, so listing it first is the natural way to write the call. Trainer hands the list to the metrics function untouched, so Trainer(metrics=["ddi", "jaccard_samples", "f1_samples"]) fails inside evaluate(). The function's own docstring lists the accepted names with ddi ahead of hamming_loss, and that order fails too.

Steps to reproduce

On b96c3f5, with python 3.13.15, scikit-learn 1.7.2 and numpy 2.2.6:

import os
import tempfile
from unittest.mock import patch

import numpy as np

from pyhealth.metrics import multilabel_metrics_fn

y_true = np.array([[1, 0, 1], [0, 1, 1], [1, 1, 0], [0, 0, 1]])
y_prob = np.array(
    [[0.9, 0.4, 0.8], [0.2, 0.7, 0.6], [0.8, 0.9, 0.1], [0.1, 0.2, 0.7]]
)

tmp = tempfile.mkdtemp()
np.save(
    os.path.join(tmp, "ddi_adj.npy"),
    np.array([[0, 1, 0], [1, 0, 0], [0, 0, 0]], dtype=float),
)

with patch("pyhealth.metrics.multilabel.CACHE_PATH", tmp):
    print(multilabel_metrics_fn(y_true, y_prob, metrics=["jaccard_samples", "ddi"]))
    print(multilabel_metrics_fn(y_true, y_prob, metrics=["ddi", "jaccard_samples"]))
What I get
{'jaccard_samples': 0.9166666666666666, 'ddi_score': 0.4}
ValueError: Classification metrics can't handle a mix of multilabel-indicator and unknown targets
What I expected

Both lines print the same numbers, ddi_score 0.4 and jaccard_samples 0.9166666666666666. The position of a metric in the list should not change the result.

Notes

The "ddi" branch turns the thresholded y_pred matrix into a list of label indices and assigns it back to y_pred, so the next metric in the loop is handed a ragged list. f1_*, precision_*, recall_*, jaccard_* and hamming_loss all raise the ValueError above. accuracy raises AttributeError: 'list' object has no attribute 'flatten'. Metrics that read y_prob only still work, so metrics=["ddi", "pr_auc_samples"] returns {'ddi_score': 0.4, 'pr_auc_samples': 1.0}.

The error text names neither ddi nor y_pred, so there's nothing in it that points at the list order.

Two smaller docstring mistakes in the same function: the signature is threshold: float = 0.3 while the Args block says Default is 0.5, and the Examples block shows {'accuracy': 0.5} where the function returns {'accuracy': 0.6666666666666666}.

I have a fix and can open a PR.

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环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

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  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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