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37aeb1f | 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 | """Reference "dumb humanizer" baselines the learned model must beat (design §7)."""
from __future__ import annotations
import numpy as np
import pandas as pd
from ..data.features import N_PHASE_BINS
def _phase_bin(phase_beat: pd.Series) -> pd.Series:
return np.minimum((phase_beat * N_PHASE_BINS).astype(int), N_PHASE_BINS - 1)
class GlobalMeanBaseline:
def fit(self, df: pd.DataFrame):
self.mean_ = float(df["velocity"].mean())
return self
def predict(self, df: pd.DataFrame) -> np.ndarray:
return np.full(len(df), self.mean_, dtype=float)
class LookupTableBaseline:
"""Mean velocity per (voice, genre, phase_bin), backing off to coarser keys."""
def fit(self, df: pd.DataFrame):
d = df.copy()
d["phase_bin"] = _phase_bin(d["phase_beat"])
self.global_ = float(d["velocity"].mean())
self.by_voice_ = d.groupby("voice")["velocity"].mean().to_dict()
self.by_vg_ = d.groupby(["voice", "genre"])["velocity"].mean().to_dict()
self.by_vgp_ = d.groupby(["voice", "genre", "phase_bin"])["velocity"].mean().to_dict()
return self
def predict(self, df: pd.DataFrame) -> np.ndarray:
pb = _phase_bin(df["phase_beat"]).to_numpy()
v = df["voice"].to_numpy()
g = df["genre"].to_numpy()
out = np.empty(len(df), dtype=float)
for i in range(len(df)):
out[i] = self.by_vgp_.get((v[i], g[i], pb[i]),
self.by_vg_.get((v[i], g[i]),
self.by_voice_.get(v[i], self.global_)))
return out
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