Upload 13 files

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/1.IA.Aug.2022.wav filter=lfs diff=lfs merge=lfs -text
+examples/1.IA.Dec.2022.wav filter=lfs diff=lfs merge=lfs -text
+examples/51.NZ.Jan.2023.wav filter=lfs diff=lfs merge=lfs -text
+examples/64.NZ.Jan.2023.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,109 @@
 ---
-title: Bee Ai
-emoji: 📊
-colorFrom: red
-colorTo: pink
+title: Bee Colony Survival Predictor
+emoji: 🐝
+colorFrom: yellow
+colorTo: green
 sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.13'
+sdk_version: 5.49.1
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🐝 Bee Colony Survival — Multi-modal Predictor
+
+Upload a hive **audio recording** and the app predicts whether the colony is a
+**Survivor (S)** or **Terminal / died (T)** over the next **0–3 months** and
+**4–6 months**, and shows the prediction next to the ground-truth label.
+
+## What the model uses (multi-modal)
+
+| Modality | Features |
+|---|---|
+| **Audio** (the `.wav`) | 191 `librosa` acoustic features — MFCCs (+Δ), log-mel bands, spectral centroid/bandwidth/rolloff/flatness/contrast, ZCR, RMS, bee-band relative energies (0–100 … 2000–4000 Hz), dominant frequency, spectral entropy |
+| **Metadata** | Country (USA/NZ), Month (cyclic), Colony |
+| **Viral loads** | CBPV, DWV, KBV (log-transformed) |
+
+Colony / Country / Month / Year are parsed from the file name
+(`Colony.CountryCode.Month.Year.wav`, e.g. `1.IA.Aug.2022.wav`; `IA`→USA, `NZ`→NZ).
+The lab-measured viral loads and the ground-truth labels are looked up from the
+bundled reference table (`model/lookup.csv`) by file name. If you upload a file
+that is **not** in the reference dataset, viral loads default to 0, ground truth is
+reported as *not available*, and the model still predicts from audio + parsed metadata.
+
+## The model
+
+**Fixed-weight late fusion** of two class-balanced base learners, one per horizon:
+
+```
+P(Terminal) = 0.8 · P_tabular  +  0.2 · P_audio
+
+  P_tabular : HistGradientBoosting on the 7 tabular features
+  P_audio   : StandardScaler → PCA(16) → LogisticRegression on the 191 audio features
+```
+
+A **weight scan under GroupKFold** showed `w_tab ≈ 0.8` is the point where audio
+*improves* both ranking (ROC-AUC) and average precision without letting its 191
+dimensions swamp the concentrated metadata signal. A naive `concat` of all 198
+features, and a learned meta-learner that over-weighted audio, both did worse.
+
+**Domain constraint enforced:** a colony predicted Terminal in 0–3 months is always
+Terminal in 4–6 months (`pred_46 = pred_46 OR pred_03`).
+
+## How good is it? (honest evaluation)
+
+Positives are rare (45 Terminal in 0–3 mo; 69 in 4–6 mo, out of 1,131 recordings
+from 112 colonies). We evaluate with **nested, repeated GroupKFold grouped by
+colony** — no colony ever appears in both train and test — so numbers reflect
+generalisation to *unseen colonies*, not memorised hive signatures.
+
+| Horizon | Model | ROC-AUC | Avg. Precision |
+|---|---|---|---|
+| 0–3 mo | tabular-only | 0.745 ± 0.010 | 0.349 |
+| 0–3 mo | **multi-modal (this app)** | **0.776 ± 0.018** | **0.352** |
+| 4–6 mo | tabular-only | 0.725 ± 0.006 | 0.285 |
+| 4–6 mo | **multi-modal (this app)** | **0.756 ± 0.008** | **0.296** |
+
+The multi-modal model beats the metadata-only baseline on **both** metrics for
+**both** horizons. Because the positive class is rare, the S/T decision threshold is
+tuned to maximise F1 on out-of-fold data (favours **precise** "Terminal" calls). The
+app also shows the raw `P(Terminal)` and a **risk band** (🟢/🟡/🟠/🔴) so
+elevated-but-below-threshold colonies are still visible.
+
+> Note: the strongest single signal is the country base-rate (USA colonies died at
+> ~6.7% vs NZ ~1.1% within 0–3 months); audio and viral loads add on top of it.
+
+## Files
+
+```
+app.py              Gradio app (upload → predict + ground truth)
+bee_features.py     shared audio feature extractor + filename parser (train == inference)
+bee_model.py        StackedMultimodal class (needed to unpickle the models)
+model/model_03.joblib, model_46.joblib   trained models (one per horizon)
+model/config.json   feature order, thresholds, validation report
+model/lookup.csv    per-recording viral loads + ground-truth labels
+examples/           four sample recordings (2 survivor, 2 terminal; USA + NZ)
+requirements.txt
+```
+
+## Run locally
+
+```bash
+pip install -r requirements.txt
+python app.py            # opens http://127.0.0.1:7860
+```
+
+## Deploy to Hugging Face Spaces
+
+```bash
+# 1) create a Space (SDK: Gradio) on huggingface.co, then:
+git clone https://huggingface.co/spaces/<user>/<space-name>
+cp -r hf_space/* <space-name>/
+cd <space-name>
+git lfs install
+git lfs track "*.wav" "*.joblib"
+git add . && git commit -m "Bee colony survival multi-modal predictor" && git push
+```
+
+The `.wav` examples and `.joblib` models are large — track them with **git-lfs**
+(as above) before pushing.

app.py

@@ -0,0 +1,164 @@
+"""
+Bee Colony Survival — multi-modal predictor (Hugging Face Space).
+
+Upload a hive audio recording (e.g. `1.IA.Aug.2022.wav`). The app:
+  1. parses Colony / Country / Month / Year from the file name,
+  2. looks up the lab-measured viral loads (CBPV, DWV, KBV) and the ground-truth
+     survival labels for that recording from the bundled dataset,
+  3. extracts 191 acoustic features from the audio,
+  4. runs the multi-modal model and predicts Terminal (T) / Survivor (S) for the
+     0-3 month and 4-6 month horizons (enforcing: died in 0-3 => died in 4-6),
+  5. shows the prediction next to the ground truth.
+
+If an uploaded file is not part of the reference dataset, viral loads default to 0
+and ground truth is reported as "not available", but the model still predicts.
+"""
+import os
+import json
+import numpy as np
+import pandas as pd
+import gradio as gr
+
+import bee_features as bf
+import bee_model  # noqa: F401  (needed so joblib can unpickle StackedMultimodal)
+import joblib
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+MODELDIR = os.path.join(HERE, "model")
+
+CFG = json.load(open(os.path.join(MODELDIR, "config.json")))
+M03 = joblib.load(os.path.join(MODELDIR, "model_03.joblib"))
+M46 = joblib.load(os.path.join(MODELDIR, "model_46.joblib"))
+LUT = pd.read_csv(os.path.join(MODELDIR, "lookup.csv"))
+LUT["key"] = LUT["sound_file"].str.strip().str.lower()
+LUT_BY_KEY = LUT.set_index("key")
+
+AUDIO_NAMES = CFG["audio_names"]
+TAB_ORDER = CFG["tabular_order"]
+THR03 = CFG["threshold_03"]
+THR46 = CFG["threshold_46"]
+
+# spreadsheet year-typo fixes, mirrored from training
+FILENAME_FIXES = {
+    "51.nz.nov.2022.wav": "51.nz.nov.2023.wav",
+    "100.nz.dec.2023.wav": "100.nz.dec.2022.wav",
+}
+
+LABEL = {"S": "Survivor", "T": "Terminal (died)"}
+
+
+def risk_band(p, thr):
+    """Map P(Terminal) to a human risk level (thr = hard Terminal cut-off)."""
+    if p >= thr:
+        return "🔴 High (predicted Terminal)"
+    if p >= 0.40:
+        return "🟠 Elevated"
+    if p >= 0.15:
+        return "🟡 Moderate"
+    return "🟢 Low"
+
+
+def _lookup(fname):
+    key = os.path.basename(str(fname)).strip().lower()
+    key = FILENAME_FIXES.get(key, key)
+    if key in LUT_BY_KEY.index:
+        r = LUT_BY_KEY.loc[key]
+        if isinstance(r, pd.DataFrame):
+            r = r.iloc[0]
+        return dict(found=True, cbpv=float(r["cbpv"]), dwv=float(r["dwv"]),
+                    kbv=float(r["kbv"]), y03=str(r["y03"]), y46=str(r["y46"]),
+                    colony=str(r["colony"]), month=str(r["month"]), country=str(r["country"]))
+    return dict(found=False)
+
+
+def predict(audio_path):
+    if not audio_path:
+        return "### ⚠️ Please upload or select a `.wav` recording first."
+    fname = os.path.basename(audio_path)
+    parsed = bf.parse_filename(fname)
+    lut = _lookup(fname)
+
+    # metadata: prefer authoritative dataset values, else parsed-from-filename
+    colony = lut.get("colony") or parsed["colony"] or "?"
+    country = lut.get("country") or parsed["country"] or "?"
+    month = lut.get("month") or parsed["month"] or "?"
+    year = parsed["year"] or "?"
+    cbpv = lut.get("cbpv", 0.0)
+    dwv = lut.get("dwv", 0.0)
+    kbv = lut.get("kbv", 0.0)
+
+    # ---- features ----
+    tabd = bf.build_tabular_dict(country, month, cbpv, dwv, kbv)
+    try:
+        afeat = bf.extract_features_from_file(audio_path)
+    except Exception as e:  # noqa
+        return f"### ❌ Could not read audio: {e}"
+    Xtab = np.array([[tabd[k] for k in TAB_ORDER]], dtype=float)
+    Xaud = np.array([[afeat.get(k, 0.0) for k in AUDIO_NAMES]], dtype=float)
+
+    # ---- predict both horizons + enforce constraint ----
+    p03, pt03, pa03 = M03.predict_proba(Xtab, Xaud)
+    p46, pt46, pa46 = M46.predict_proba(Xtab, Xaud)
+    p03, p46 = float(p03[0]), float(p46[0])
+    pred03 = "T" if p03 >= THR03 else "S"
+    pred46 = "T" if (p46 >= THR46 or pred03 == "T") else "S"
+
+    # ---- ground truth ----
+    if lut["found"]:
+        gt03, gt46 = lut["y03"], lut["y46"]
+        ok03 = "✅" if gt03 == pred03 else "❌"
+        ok46 = "✅" if gt46 == pred46 else "❌"
+        gt_line = (
+            f"| **Ground truth** | **{LABEL[gt03]}** ({gt03}) | **{LABEL[gt46]}** ({gt46}) |\n"
+            f"| Prediction correct? | {ok03} | {ok46} |")
+    else:
+        gt_line = "| **Ground truth** | not available (file not in reference dataset) | — |"
+
+    md = f"""
+### 🐝 Recording: `{fname}`
+
+**Parsed metadata**   Colony **{colony}** · Country **{country}** · Month **{month}** · Year **{year}**
+
+**Viral loads (lab-measured{' — from dataset' if lut['found'] else ', defaulted to 0'})**  
+CBPV `{cbpv:.3f}` · DWV `{dwv:.3f}` · KBV `{kbv:.3f}`
+
+| Survival horizon | 0–3 months | 4–6 months |
+|---|---|---|
+| **Prediction** | **{LABEL[pred03]}** ({pred03}) | **{LABEL[pred46]}** ({pred46}) |
+| P(Terminal) | {p03:.3f}  *(thr {THR03:.2f})* | {p46:.3f}  *(thr {THR46:.2f})* |
+| Risk level | {risk_band(p03, THR03)} | {risk_band(p46, THR46)} |
+|   ↳ tabular / audio sub-model | {pt03[0]:.2f} / {pa03[0]:.2f} | {pt46[0]:.2f} / {pa46[0]:.2f} |
+{gt_line}
+
+<sub>Positive class = Terminal (colony death). Fusion = 0.8·tabular + 0.2·audio.
+Constraint enforced: a colony predicted Terminal in 0–3 months is Terminal in 4–6 months.</sub>
+"""
+    return md
+
+
+EXAMPLES_DIR = os.path.join(HERE, "examples")
+examples = ([[os.path.join(EXAMPLES_DIR, f)] for f in sorted(os.listdir(EXAMPLES_DIR))]
+            if os.path.isdir(EXAMPLES_DIR) else None)
+
+with gr.Blocks(title="Bee Colony Survival Predictor") as demo:
+    gr.Markdown(
+        "# 🐝 Bee Colony Survival — Multi-modal Predictor\n"
+        "Upload a hive **audio recording** named like `Colony.Country.Month.Year.wav` "
+        "(e.g. `1.IA.Aug.2022.wav`). The model fuses the **sound** with the recording's "
+        "**metadata** (colony, month, country) and **viral loads** (CBPV, DWV, KBV) to predict "
+        "whether the colony is a **Survivor (S)** or **Terminal (T)** over the next 0–3 and 4–6 "
+        "months, and compares the prediction with the ground truth when the file is in the "
+        "reference dataset.")
+    with gr.Row():
+        with gr.Column(scale=1):
+            audio = gr.Audio(type="filepath", label="Hive recording (.wav)")
+            btn = gr.Button("Predict", variant="primary")
+            if examples:
+                gr.Examples(examples=examples, inputs=audio, label="Example recordings")
+        with gr.Column(scale=2):
+            out = gr.Markdown()
+    btn.click(predict, inputs=audio, outputs=out)
+    audio.change(predict, inputs=audio, outputs=out)
+
+if __name__ == "__main__":
+    demo.launch()

bee_features.py

@@ -0,0 +1,199 @@
+"""
+Shared feature extraction + metadata parsing for the Bee colony survival model.
+
+This exact module is used BOTH during training and in the Hugging Face Space so
+that audio features are computed identically in both places.
+
+Audio design notes:
+  - Honey-bee acoustic activity lives at low frequencies (wing-beat fundamental
+    ~180-250 Hz with harmonics extending to ~2 kHz). We therefore resample to
+    8 kHz (Nyquist 4 kHz) which preserves all bee-relevant energy while making
+    feature extraction ~5x faster than at 44.1 kHz.
+  - We aggregate frame-level features over the whole clip (mean/std) to obtain a
+    single fixed-length vector per recording, suitable for classical/MLP fusion.
+"""
+import os
+import re
+import numpy as np
+
+SR = 8000
+N_FFT = 1024
+HOP = 512
+N_MFCC = 20
+N_MELS = 40
+MAX_SECONDS = 90            # cap very long clips for speed/consistency
+BEE_BANDS_HZ = [(0, 100), (100, 200), (200, 300), (300, 500),
+                (500, 1000), (1000, 2000), (2000, 4000)]
+
+# ---- month normalisation ---------------------------------------------------
+_MONTH_MAP = {
+    'jan': 'January', 'january': 'January',
+    'feb': 'February', 'february': 'February',
+    'mar': 'March', 'march': 'March',
+    'apr': 'April', 'april': 'April',
+    'may': 'May',
+    'jun': 'June', 'june': 'June',
+    'jul': 'July', 'july': 'July',
+    'aug': 'August', 'august': 'August',
+    'sep': 'September', 'sept': 'September', 'september': 'September',
+    'oct': 'October', 'october': 'October',
+    'nov': 'November', 'november': 'November',
+    'dec': 'December', 'december': 'December',
+}
+MONTH_ORDER = {m: i for i, m in enumerate(
+    ['January', 'February', 'March', 'April', 'May', 'June', 'July',
+     'August', 'September', 'October', 'November', 'December'], start=1)}
+
+_COUNTRY_CODE = {'IA': 'USA', 'NZ': 'NZ'}
+
+
+def normalize_month(raw):
+    """Map any month spelling (e.g. 'Sept', 'October2023', 'January ') -> canonical."""
+    if raw is None:
+        return None
+    s = str(raw).strip().lower()
+    s = re.sub(r'20\d{2}', '', s).strip()        # drop trailing year e.g. october2023
+    s = re.sub(r'[^a-z]', '', s)
+    return _MONTH_MAP.get(s, None)
+
+
+def parse_filename(fname):
+    """Parse 'Colony.CountryCode.Month.Year.wav' -> dict of metadata.
+
+    Example: '1.IA.Aug.2022.wav' -> {colony:'1', country:'USA', month:'August', year:2022}
+    Returns whatever can be parsed; missing pieces are None.
+    """
+    base = os.path.basename(str(fname))
+    stem = re.sub(r'\.wav$', '', base, flags=re.IGNORECASE)
+    parts = stem.split('.')
+    out = {'colony': None, 'country': None, 'month': None, 'year': None}
+    if len(parts) >= 1:
+        out['colony'] = parts[0].strip()
+    if len(parts) >= 2:
+        out['country'] = _COUNTRY_CODE.get(parts[1].strip().upper(), parts[1].strip())
+    if len(parts) >= 3:
+        out['month'] = normalize_month(parts[2])
+    if len(parts) >= 4:
+        m = re.search(r'20\d{2}', parts[3])
+        out['year'] = int(m.group()) if m else None
+    # year sometimes appended to the month token (october2023)
+    if out['year'] is None:
+        m = re.search(r'20\d{2}', stem)
+        if m:
+            out['year'] = int(m.group())
+    return out
+
+
+# ---- audio feature extraction ---------------------------------------------
+def _agg(name, arr):
+    """mean/std of a (n_features, n_frames) or (n_frames,) array -> flat dict."""
+    arr = np.atleast_2d(arr)
+    out = {}
+    mean = np.nanmean(arr, axis=1)
+    std = np.nanstd(arr, axis=1)
+    for i in range(arr.shape[0]):
+        suffix = f'{i}' if arr.shape[0] > 1 else ''
+        out[f'{name}{suffix}_mean'] = float(mean[i])
+        out[f'{name}{suffix}_std'] = float(std[i])
+    return out
+
+
+def extract_features_from_audio(y, sr):
+    """Compute the fixed-length bee feature dict from a waveform."""
+    import librosa
+    if sr != SR:
+        y = librosa.resample(y, orig_sr=sr, target_sr=SR)
+        sr = SR
+    if y.ndim > 1:                      # to mono
+        y = np.mean(y, axis=0) if y.shape[0] < y.shape[1] else np.mean(y, axis=1)
+    y = y.astype(np.float32)
+    if MAX_SECONDS is not None:
+        y = y[: MAX_SECONDS * sr]
+    # normalise amplitude so loudness/mic-gain differences don't dominate
+    peak = np.max(np.abs(y)) if y.size else 0.0
+    if peak > 0:
+        y = y / peak
+
+    feats = {}
+    S = np.abs(librosa.stft(y, n_fft=N_FFT, hop_length=HOP)) ** 2  # power spectrogram
+    mel = librosa.feature.melspectrogram(S=S, sr=sr, n_mels=N_MELS)
+    logmel = librosa.power_to_db(mel + 1e-10)
+    mfcc = librosa.feature.mfcc(S=librosa.power_to_db(mel + 1e-10), n_mfcc=N_MFCC)
+    dmfcc = librosa.feature.delta(mfcc)
+
+    feats.update(_agg('mfcc', mfcc))
+    feats.update(_agg('dmfcc', dmfcc))
+    feats.update(_agg('logmel', logmel))
+    feats.update(_agg('centroid', librosa.feature.spectral_centroid(S=np.sqrt(S), sr=sr)))
+    feats.update(_agg('bandwidth', librosa.feature.spectral_bandwidth(S=np.sqrt(S), sr=sr)))
+    feats.update(_agg('rolloff', librosa.feature.spectral_rolloff(S=np.sqrt(S), sr=sr)))
+    feats.update(_agg('flatness', librosa.feature.spectral_flatness(S=np.sqrt(S))))
+    feats.update(_agg('contrast', librosa.feature.spectral_contrast(
+        S=np.sqrt(S), sr=sr, fmin=100.0, n_bands=4)))
+    feats.update(_agg('zcr', librosa.feature.zero_crossing_rate(y, frame_length=N_FFT, hop_length=HOP)))
+    feats.update(_agg('rms', librosa.feature.rms(S=np.sqrt(S), frame_length=N_FFT, hop_length=HOP)))
+
+    # bee-band relative energies from the mean power spectrum
+    freqs = librosa.fft_frequencies(sr=sr, n_fft=N_FFT)
+    mean_spec = np.mean(S, axis=1)
+    total = mean_spec.sum() + 1e-12
+    for lo, hi in BEE_BANDS_HZ:
+        band = mean_spec[(freqs >= lo) & (freqs < hi)].sum()
+        feats[f'bandfrac_{lo}_{hi}'] = float(band / total)
+    feats['dominant_freq'] = float(freqs[int(np.argmax(mean_spec))])
+    feats['spectral_entropy'] = float(
+        -np.sum((mean_spec / total) * np.log(mean_spec / total + 1e-12)))
+    return feats
+
+
+def extract_features_from_file(path):
+    """Load an audio file and return its feature dict (mono, resampled)."""
+    import librosa
+    y, sr = librosa.load(path, sr=SR, mono=True)
+    return extract_features_from_audio(y, sr)
+
+
+# canonical, sorted feature-name list so training and inference agree on order
+def audio_feature_names():
+    dummy = np.zeros(SR * 2, dtype=np.float32)
+    return sorted(extract_features_from_audio(dummy, SR).keys())
+
+
+# ---- unified model-input construction (shared by training & deployment) -----
+# Tabular block, in fixed order. Audio block is appended in audio_feature_names()
+# order. build_feature_vector() below is the SINGLE source of truth for the model
+# input, guaranteeing identical preprocessing at train and inference time.
+TABULAR_ORDER = ['is_usa', 'is_nz', 'month_sin', 'month_cos',
+                 'log_cbpv', 'log_dwv', 'log_kbv']
+
+
+def build_tabular_dict(country, month, cbpv, dwv, kbv):
+    """Turn raw metadata into the numeric tabular feature dict."""
+    country = (str(country).strip().upper() if country is not None else '')
+    is_usa = 1.0 if country in ('USA', 'US', 'IA') else 0.0
+    is_nz = 1.0 if country in ('NZ', 'NEW ZEALAND') else 0.0
+    mnum = MONTH_ORDER.get(normalize_month(month) or month, 0)
+    ang = 2.0 * np.pi * (mnum / 12.0)
+    def _log(x):
+        try:
+            return float(np.log1p(max(0.0, float(x))))
+        except (TypeError, ValueError):
+            return 0.0
+    return {
+        'is_usa': is_usa, 'is_nz': is_nz,
+        'month_sin': float(np.sin(ang)) if mnum else 0.0,
+        'month_cos': float(np.cos(ang)) if mnum else 0.0,
+        'log_cbpv': _log(cbpv), 'log_dwv': _log(dwv), 'log_kbv': _log(kbv),
+    }
+
+
+def build_feature_vector(tabular_dict, audio_dict, audio_names, use_audio=True):
+    """Concatenate tabular + audio into a single ordered numpy vector."""
+    vec = [tabular_dict.get(k, 0.0) for k in TABULAR_ORDER]
+    if use_audio:
+        vec += [audio_dict.get(k, 0.0) for k in audio_names]
+    return np.asarray(vec, dtype=np.float32)
+
+
+def full_feature_order(audio_names, use_audio=True):
+    return list(TABULAR_ORDER) + (list(audio_names) if use_audio else [])

bee_model.py

@@ -0,0 +1,97 @@
+"""
+StackedMultimodal — the final multi-modal model (used for training AND deployment).
+
+Architecture (per survival horizon):
+  base_tabular : HistGradientBoosting (class-balanced) on 7 tabular features
+                 (country, cyclic month, log viral loads CBPV/DWV/KBV)
+  base_audio   : StandardScaler -> PCA(16) -> LogisticRegression (balanced) on the
+                 191 librosa acoustic features
+  meta         : LogisticRegression on [p_tabular, p_audio]  (learned late fusion)
+
+fit() trains the meta on inner GroupKFold out-of-fold base predictions (no leakage),
+then refits both bases on all supplied data and tunes the decision threshold on the
+inner-OOF meta probabilities. Because fit() is self-contained, the same object can
+be honestly evaluated with an outer GroupKFold and then fit on all data for release.
+"""
+import numpy as np
+from sklearn.model_selection import GroupKFold
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.decomposition import PCA
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.metrics import f1_score
+
+RNG = 42
+
+
+def make_tabular():
+    return Pipeline([
+        ('imp', SimpleImputer(strategy='median')),
+        ('clf', HistGradientBoostingClassifier(
+            max_iter=400, learning_rate=0.05, max_leaf_nodes=31,
+            l2_regularization=1.0, class_weight='balanced', random_state=RNG))])
+
+
+def make_audio(k=16):
+    return Pipeline([
+        ('imp', SimpleImputer(strategy='median')),
+        ('sc', StandardScaler()),
+        ('pca', PCA(n_components=k, random_state=RNG)),
+        ('clf', LogisticRegression(max_iter=2000, class_weight='balanced', C=0.5))])
+
+
+def tune_threshold(y, p):
+    ts = np.clip(np.unique(np.round(p, 4)), 1e-3, 1 - 1e-3)
+    best_t, best_f1 = 0.5, -1.0
+    for t in ts:
+        f = f1_score(y, (p >= t).astype(int), zero_division=0)
+        if f > best_f1:
+            best_f1, best_t = f, t
+    return float(best_t)
+
+
+class StackedMultimodal:
+    """Fixed-weight late fusion: p = w_tab * p_tabular + (1 - w_tab) * p_audio.
+
+    A weight scan under GroupKFold showed w_tab ~= 0.8 dominates tabular-only on
+    BOTH ROC-AUC and average precision for both horizons (audio helps rank
+    borderline cases without diluting the concentrated metadata signal). A learned
+    meta-learner over-weighted audio and hurt precision, so we keep the robust,
+    transparent fixed weight.
+    """
+    def __init__(self, pca_k=16, inner_splits=5, w_tab=0.8):
+        self.pca_k = pca_k
+        self.inner_splits = inner_splits
+        self.w_tab = w_tab
+
+    def fit(self, Xtab, Xaud, y, groups):
+        y = np.asarray(y)
+        n = len(y)
+        oof_t = np.zeros(n)
+        oof_a = np.zeros(n)
+        gkf = GroupKFold(n_splits=self.inner_splits)
+        for tr, te in gkf.split(Xtab, y, groups):
+            mt = make_tabular().fit(Xtab[tr], y[tr])
+            ma = make_audio(self.pca_k).fit(Xaud[tr], y[tr])
+            oof_t[te] = mt.predict_proba(Xtab[te])[:, 1]
+            oof_a[te] = ma.predict_proba(Xaud[te])[:, 1]
+        # refit bases on ALL data
+        self.tab_ = make_tabular().fit(Xtab, y)
+        self.aud_ = make_audio(self.pca_k).fit(Xaud, y)
+        # threshold on inner-OOF fused probs (honest, no test leakage)
+        self.oof_meta_ = self.w_tab * oof_t + (1 - self.w_tab) * oof_a
+        self.threshold_ = tune_threshold(y, self.oof_meta_)
+        self.base_oof_ = (oof_t, oof_a)
+        return self
+
+    def predict_proba(self, Xtab, Xaud):
+        pt = self.tab_.predict_proba(Xtab)[:, 1]
+        pa = self.aud_.predict_proba(Xaud)[:, 1]
+        p = self.w_tab * pt + (1 - self.w_tab) * pa
+        return p, pt, pa
+
+    def predict(self, Xtab, Xaud):
+        p, _, _ = self.predict_proba(Xtab, Xaud)
+        return (p >= self.threshold_).astype(int)

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model/config.json

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+{
+  "tabular_order": [
+    "is_usa",
+    "is_nz",
+    "month_sin",
+    "month_cos",
+    "log_cbpv",
+    "log_dwv",
+    "log_kbv"
+  ],
+  "audio_names": [
+    "bandfrac_0_100",
+    "bandfrac_1000_2000",
+    "bandfrac_100_200",
+    "bandfrac_2000_4000",
+    "bandfrac_200_300",
+    "bandfrac_300_500",
+    "bandfrac_500_1000",
+    "bandwidth_mean",
+    "bandwidth_std",
+    "centroid_mean",
+    "centroid_std",
+    "contrast0_mean",
+    "contrast0_std",
+    "contrast1_mean",
+    "contrast1_std",
+    "contrast2_mean",
+    "contrast2_std",
+    "contrast3_mean",
+    "contrast3_std",
+    "contrast4_mean",
+    "contrast4_std",
+    "dmfcc0_mean",
+    "dmfcc0_std",
+    "dmfcc10_mean",
+    "dmfcc10_std",
+    "dmfcc11_mean",
+    "dmfcc11_std",
+    "dmfcc12_mean",
+    "dmfcc12_std",
+    "dmfcc13_mean",
+    "dmfcc13_std",
+    "dmfcc14_mean",
+    "dmfcc14_std",
+    "dmfcc15_mean",
+    "dmfcc15_std",
+    "dmfcc16_mean",
+    "dmfcc16_std",
+    "dmfcc17_mean",
+    "dmfcc17_std",
+    "dmfcc18_mean",
+    "dmfcc18_std",
+    "dmfcc19_mean",
+    "dmfcc19_std",
+    "dmfcc1_mean",
+    "dmfcc1_std",
+    "dmfcc2_mean",
+    "dmfcc2_std",
+    "dmfcc3_mean",
+    "dmfcc3_std",
+    "dmfcc4_mean",
+    "dmfcc4_std",
+    "dmfcc5_mean",
+    "dmfcc5_std",
+    "dmfcc6_mean",
+    "dmfcc6_std",
+    "dmfcc7_mean",
+    "dmfcc7_std",
+    "dmfcc8_mean",
+    "dmfcc8_std",
+    "dmfcc9_mean",
+    "dmfcc9_std",
+    "dominant_freq",
+    "flatness_mean",
+    "flatness_std",
+    "logmel0_mean",
+    "logmel0_std",
+    "logmel10_mean",
+    "logmel10_std",
+    "logmel11_mean",
+    "logmel11_std",
+    "logmel12_mean",
+    "logmel12_std",
+    "logmel13_mean",
+    "logmel13_std",
+    "logmel14_mean",
+    "logmel14_std",
+    "logmel15_mean",
+    "logmel15_std",
+    "logmel16_mean",
+    "logmel16_std",
+    "logmel17_mean",
+    "logmel17_std",
+    "logmel18_mean",
+    "logmel18_std",
+    "logmel19_mean",
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+    "logmel1_std",
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+    "logmel23_mean",
+    "logmel23_std",
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+    "logmel24_std",
+    "logmel25_mean",
+    "logmel25_std",
+    "logmel26_mean",
+    "logmel26_std",
+    "logmel27_mean",
+    "logmel27_std",
+    "logmel28_mean",
+    "logmel28_std",
+    "logmel29_mean",
+    "logmel29_std",
+    "logmel2_mean",
+    "logmel2_std",
+    "logmel30_mean",
+    "logmel30_std",
+    "logmel31_mean",
+    "logmel31_std",
+    "logmel32_mean",
+    "logmel32_std",
+    "logmel33_mean",
+    "logmel33_std",
+    "logmel34_mean",
+    "logmel34_std",
+    "logmel35_mean",
+    "logmel35_std",
+    "logmel36_mean",
+    "logmel36_std",
+    "logmel37_mean",
+    "logmel37_std",
+    "logmel38_mean",
+    "logmel38_std",
+    "logmel39_mean",
+    "logmel39_std",
+    "logmel3_mean",
+    "logmel3_std",
+    "logmel4_mean",
+    "logmel4_std",
+    "logmel5_mean",
+    "logmel5_std",
+    "logmel6_mean",
+    "logmel6_std",
+    "logmel7_mean",
+    "logmel7_std",
+    "logmel8_mean",
+    "logmel8_std",
+    "logmel9_mean",
+    "logmel9_std",
+    "mfcc0_mean",
+    "mfcc0_std",
+    "mfcc10_mean",
+    "mfcc10_std",
+    "mfcc11_mean",
+    "mfcc11_std",
+    "mfcc12_mean",
+    "mfcc12_std",
+    "mfcc13_mean",
+    "mfcc13_std",
+    "mfcc14_mean",
+    "mfcc14_std",
+    "mfcc15_mean",
+    "mfcc15_std",
+    "mfcc16_mean",
+    "mfcc16_std",
+    "mfcc17_mean",
+    "mfcc17_std",
+    "mfcc18_mean",
+    "mfcc18_std",
+    "mfcc19_mean",
+    "mfcc19_std",
+    "mfcc1_mean",
+    "mfcc1_std",
+    "mfcc2_mean",
+    "mfcc2_std",
+    "mfcc3_mean",
+    "mfcc3_std",
+    "mfcc4_mean",
+    "mfcc4_std",
+    "mfcc5_mean",
+    "mfcc5_std",
+    "mfcc6_mean",
+    "mfcc6_std",
+    "mfcc7_mean",
+    "mfcc7_std",
+    "mfcc8_mean",
+    "mfcc8_std",
+    "mfcc9_mean",
+    "mfcc9_std",
+    "rms_mean",
+    "rms_std",
+    "rolloff_mean",
+    "rolloff_std",
+    "spectral_entropy",
+    "zcr_mean",
+    "zcr_std"
+  ],
+  "threshold_03": 0.8568,
+  "threshold_46": 0.7063,
+  "sr": 8000,
+  "positive_class": "T (Terminal)",
+  "horizons": [
+    "0_to_3_months",
+    "4_to_6_months"
+  ],
+  "validation": {
+    "0_to_3": {
+      "tabular": {
+        "roc_auc": [
+          0.7453311506718504,
+          0.01010441269467074
+        ],
+        "ap": [
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+          0.006790023162079211
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+          0.400074094655923,
+          0.005445075059652479
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+        "recall_T": [
+          0.28148148148148144,
+          0.020951312035156967
+        ],
+        "precision_T": [
+          0.7223809523809525,
+          0.12287006207451431
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+        "bal_acc": [
+          0.6381317781870268,
+          0.00874744069399812
+        ]
+      },
+      "multimodal": {
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+          0.7760111861400997,
+          0.017524655788385357
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+        "recall_T": [
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+        "bal_acc": [
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+    "4_to_6": {
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+        "bal_acc": [
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+          0.007782928784327134
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+      },
+      "multimodal": {
+        "roc_auc": [
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+        "ap": [
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+        "bal_acc": [
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+          0.0077576674833323535
+        ]
+      }
+    }
+  }
+}

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