| """ |
| Shared helpers for the synthetic-rater sampling analyses (scripts 19-21). |
| |
| These live in their own module because the analysis scripts carry numeric |
| filename prefixes (`19_...py`), which are not importable as Python modules. |
| Anything more than one script needs goes here. |
| |
| human_panel per prompt x rater signed preference, plus the panel gold |
| synth_gaps per prompt mean synthetic margin and its absolute gap |
| prompt_matrix per-prompt rater-preference vectors, aligned to a prompt list |
| """ |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
|
|
|
|
| def human_panel(long_df): |
| """ |
| Per prompt x rater signed preference (clip0 - clip1), plus panel gold. |
| |
| Returns (wide, gold). `wide` has one row per (prompt, rater) with the |
| signed preference in `pref`; `gold` has one row per prompt with the |
| full-panel mean margin, the within-prompt rater SD, the panel size, and |
| the gold winner (sign of the mean margin). |
| """ |
| df = long_df[long_df["IsAttentionCheck"] == False].copy() |
| df["ClipIndex"] = df["AudioClip"].str.extract(r"_(\d+)\.mp3$")[0].astype(int) |
|
|
| wide = df.pivot_table( |
| index=["PromptID", "ParticipantID"], |
| columns="ClipIndex", values="Rating", aggfunc="first", |
| ).reset_index() |
| wide.columns.name = None |
| wide = wide.rename(columns={0: "r0", 1: "r1"}).dropna(subset=["r0", "r1"]) |
| wide["pref"] = wide["r0"] - wide["r1"] |
|
|
| gold = wide.groupby("PromptID")["pref"].agg( |
| gold_margin="mean", sd_within="std", n_raters="size" |
| ).reset_index() |
| gold["gold_winner"] = np.sign(gold["gold_margin"]) |
| return wide, gold |
|
|
|
|
| def synth_gaps(synth_df): |
| """Per prompt mean synthetic margin over repeated samples, and its gap.""" |
| s = synth_df.copy() |
| s["ClipIndex"] = s["AudioClip"].str.extract(r"_(\d+)\.(?:mp3|wav)$")[0].astype(int) |
|
|
| wide = s.pivot_table( |
| index=["PromptID", "synthNo"], |
| columns="ClipIndex", values="Rating", aggfunc="first", |
| ).reset_index() |
| wide.columns.name = None |
| wide = wide.rename(columns={0: "s0", 1: "s1"}).dropna(subset=["s0", "s1"]) |
| wide["margin"] = wide["s0"] - wide["s1"] |
|
|
| out = wide.groupby("PromptID")["margin"].agg( |
| synth_margin="mean", n_synth="size" |
| ).reset_index() |
| out["synth_gap"] = out["synth_margin"].abs() |
| out["synth_winner"] = np.sign(out["synth_margin"]) |
| return out |
|
|
|
|
| def prompt_matrix(panel, prompt_ids): |
| """List of per-prompt rater-preference vectors, aligned to prompt_ids.""" |
| by_prompt = {p: g["pref"].to_numpy() for p, g in panel.groupby("PromptID")} |
| return [by_prompt[p] for p in prompt_ids] |
|
|
