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| from __future__ import annotations | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from src.charts import bubble_quality, heatmap | |
| from src.ui import callout, section_title | |
| class QualityMapViewMixin: | |
| """Quality maps across domain, difficulty, and risk slices.""" | |
| def _page_quality_map(self) -> None: | |
| section_title("Quality Map", "Where quality is strong, unstable, or worth deeper review.") | |
| domain_diff = self._domain_difficulty_table(self.ctx.filtered_eval) | |
| left, right = st.columns(2, gap="large") | |
| with left: | |
| self._plot(heatmap(domain_diff, "difficulty", "domain", "correct_rate", "Correctness · domain × difficulty"), "quality_correctness") | |
| with right: | |
| self._plot(heatmap(domain_diff, "difficulty", "domain", "hallucination_rate", "Hallucination · domain × difficulty"), "quality_hallucination") | |
| self._plot(bubble_quality(self.ctx.risk_slices, "Risk slice map"), "quality_risk_bubble") | |
| if not self.ctx.demand_coverage.empty: | |
| callout( | |
| "info", | |
| "Coverage interpretation", | |
| "Positive demand-minus-corpus means the domain receives more evaluation demand than its corpus document share.", | |
| ) | |
| def _domain_difficulty_table(df: pd.DataFrame) -> pd.DataFrame: | |
| needed = {"domain", "difficulty"} | |
| if df.empty or not needed.issubset(df.columns): | |
| return pd.DataFrame() | |
| aggs: dict[str, tuple[str, str]] = {"n": ("domain", "size")} | |
| if "is_correct" in df.columns: | |
| aggs["correct_rate"] = ("is_correct", "mean") | |
| if "hallucination_flag" in df.columns: | |
| aggs["hallucination_rate"] = ("hallucination_flag", "mean") | |
| if "recall_at_10" in df.columns: | |
| aggs["recall_at_10"] = ("recall_at_10", "mean") | |
| out = df.groupby(["domain", "difficulty"], dropna=False).agg(**aggs).reset_index() | |
| for col in ["correct_rate", "hallucination_rate", "recall_at_10"]: | |
| if col not in out.columns: | |
| out[col] = np.nan | |
| return out | |