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Browse files- README.md +70 -2
- fusion/biomarker_scaler.json +205 -0
- fusion/final_multiclass_sgl/final_multiclass_sgl_stream_only.pkl +3 -0
- fusion/final_multiclass_sgl/final_multiclass_sgl_with_demos.pkl +3 -0
- fusion/final_regression_fc2fs/final_regression_fc2fs_stream_only.pkl +3 -0
- fusion/final_regression_fc2fs/final_regression_fc2fs_with_demos.pkl +3 -0
- fusion/stacking/stacking_binary_stream_only.pkl +3 -0
- fusion/stacking/stacking_binary_stream_only_config.json +11 -0
- fusion/stacking/stacking_binary_stream_only_scaler.pkl +3 -0
- fusion/stacking/stacking_binary_with_demos.pkl +3 -0
- fusion/stacking/stacking_binary_with_demos_config.json +14 -0
- fusion/stacking/stacking_binary_with_demos_scaler.pkl +3 -0
- fusion/streams/biomarkers/binary_biomarkers_LGBMClassifier.pkl +3 -0
- fusion/streams/embeddings/binary_embeddings_XGBClassifier.pkl +3 -0
- fusion/streams/emotion/binary_emotion_LogisticRegression.pkl +3 -0
- fusion/streams/vision/binary_vision_LGBMClassifier.pkl +3 -0
- reference/biomarker_cn_stats.json +434 -0
- reference/biomarker_scaler.json +205 -0
- reference/emotion_cn_reference.json +1030 -0
- reference/umap_pitt_clouds.json +5146 -0
- reference/umap_reference.npz +3 -0
- reference/vlad_codebook.pkl +3 -0
README.md
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---
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license: other
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license_name:
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-
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---
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---
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license: other
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license_name: research-use-dementiabank
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tags:
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- speech
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- audio
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- acoustic-biomarkers
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- dementia
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- alzheimers
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library_name: acophenotype
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---
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# acophenotype: Pitt-trained models
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Frozen models and reference data released with the PhD thesis *[thesis title]*
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(Marek Svidersky, [University], 2026). They are downloaded automatically by the
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[`acophenotype`](https://github.com/msvidersky/acophenotype) Python packages; you do not
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need to fetch them by hand.
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```bash
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pip install acophenotype
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```
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```python
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from acophenotype import AcousticProfile
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profile = AcousticProfile.from_audio("recording.wav", task="binary")
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```
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## Intended use and limitations
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**Research use only. Not a medical device and not for diagnosis or any clinical decision.**
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- Trained on the Pitt Corpus (DementiaBank): English, predominantly picture-description
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speech from a single study's recording setup.
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- Performance has not been established for other languages, elicitation tasks, recording
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conditions or clinical populations.
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- A single recording is a noisy observation; outputs should not be interpreted in isolation.
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## Contents
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| Path | What it is |
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|---|---|
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| `fusion/stacking/` | Late-fusion stacking (binary): logistic meta-model over per-stream P(AD) |
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| `fusion/final_multiclass_sgl/` | Sparse-group-lasso one-vs-rest model (multiclass) |
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| `fusion/final_regression_fc2fs/` | Correlation-filtered LightGBM regressor (regression) |
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| `fusion/streams/` | The per-stream base models used by the binary stacker |
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| `fusion/biomarker_scaler.json` | Pitt z-score parameters applied to biomarkers before scoring |
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| `reference/vlad_codebook.pkl` | 16-cluster VLAD codebook for the Whisper embedding stream |
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| `reference/*.json`, `umap_reference.npz` | Control-cohort statistics and UMAP layout for the report |
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Each model has `stream_only` and `with_demos` (sex, age, education) variants.
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## Training data
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Pitt Corpus, DementiaBank (TalkBank). Access to the corpus is governed by DementiaBank's
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terms; these files contain trained models and summary statistics, not audio or transcripts.
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## Environment
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The models are serialized with pickle/joblib and are sensitive to library versions. They
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were saved with scikit-learn 1.7.2; the packages pin compatible versions. Staged with:
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- python: 1.0.8
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- scikit-learn: 1.7.2
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- lightgbm: 4.7.0
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- xgboost: 3.2.0
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- numpy: 2.4.6
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Pickle files execute code when loaded; load them only from this repository.
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## Citation
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Please cite the thesis and the software repository (see `CITATION.cff` there).
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fusion/biomarker_scaler.json
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{
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"_meta": {
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"description": "Per-feature z-score parameters used to standardize the Pitt biomarker stream (biomarkers_stream/2_pre_processing.py: scipy.stats.zscore, ddof=0) before training every fusion model. Apply (x - mean) / std to raw voxmarkers output.",
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"source": "biomarkers_stream/results/raw/tier1_core_features.csv",
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"n_recordings": 1284,
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"ddof": 0,
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"n_features": 48,
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"verified_max_abs_diff_vs_training_parquet": 0.0,
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"created": "2026-09-26"
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},
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"features": {
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"temp_syllable_rate": {
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"mean": 0.19068904887322866,
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"std": 0.20152788791789208
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},
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"temp_speaking_rate": {
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"mean": 0.06710527416382421,
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"std": 0.08212098329545407
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},
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"temp_pause_rate": {
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"mean": 0.32346738450192974,
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"std": 0.20531543519521156
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},
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"temp_phonation_ratio": {
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+
"mean": 0.485376985581585,
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"std": 0.2668197721905779
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+
},
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"temp_speech_pause_ratio": {
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+
"mean": 3.770874190173395,
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"std": 30.868998598402328
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+
},
|
| 32 |
+
"temp_mean_pause_dur": {
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+
"mean": 1.7386835491681645,
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+
"std": 2.2383440013307006
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+
},
|
| 36 |
+
"temp_pause_variability": {
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+
"mean": 1.783166975543538,
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| 38 |
+
"std": 1.6620581888602548
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| 39 |
+
},
|
| 40 |
+
"pros_f0_variability": {
|
| 41 |
+
"mean": 90.46994349940678,
|
| 42 |
+
"std": 43.10799271803868
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| 43 |
+
},
|
| 44 |
+
"pros_f0_range": {
|
| 45 |
+
"mean": 189.85960587995154,
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| 46 |
+
"std": 132.5704294428999
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| 47 |
+
},
|
| 48 |
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"pros_f0_slope": {
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| 49 |
+
"mean": 42.55658785258858,
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| 50 |
+
"std": 157.38070987937257
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| 51 |
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},
|
| 52 |
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"pros_loudness_variability": {
|
| 53 |
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"mean": 11.051262371858854,
|
| 54 |
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"std": 4.210381309777116
|
| 55 |
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},
|
| 56 |
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"pros_energy_slope": {
|
| 57 |
+
"mean": -0.006972617428326624,
|
| 58 |
+
"std": 0.8053655266745545
|
| 59 |
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},
|
| 60 |
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"art_fcr": {
|
| 61 |
+
"mean": 1.0015263647127193,
|
| 62 |
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"std": 0.10415861217943961
|
| 63 |
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},
|
| 64 |
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"art_vai": {
|
| 65 |
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"mean": 1.0106597842496157,
|
| 66 |
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"std": 0.11559752795363626
|
| 67 |
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},
|
| 68 |
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"art_f1_bw": {
|
| 69 |
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"mean": 333.33799855829466,
|
| 70 |
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"std": 74.41210836879945
|
| 71 |
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},
|
| 72 |
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"art_f2_bw": {
|
| 73 |
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"mean": 726.3454771852487,
|
| 74 |
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"std": 115.97506873898045
|
| 75 |
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"art_f2_slope": {
|
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"mean": 36082.2622712608,
|
| 78 |
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"std": 8735.186368092942
|
| 79 |
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},
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| 80 |
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"phon_hnr": {
|
| 81 |
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"mean": -66.66666666666667,
|
| 82 |
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"std": 47.14045207910317
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| 83 |
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},
|
| 84 |
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"spec_centroid": {
|
| 85 |
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"mean": 812.9493489701537,
|
| 86 |
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"std": 222.3863803401385
|
| 87 |
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},
|
| 88 |
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"spec_flux": {
|
| 89 |
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"mean": 1.2551230519080998,
|
| 90 |
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"std": 0.33245144349891276
|
| 91 |
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},
|
| 92 |
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"spec_entropy": {
|
| 93 |
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"mean": 12.013055550700935,
|
| 94 |
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"std": 0.9451386621773802
|
| 95 |
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},
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"spec_rpde": {
|
| 97 |
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"mean": -80.55158260341825,
|
| 98 |
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| 99 |
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"spec_mfcc1_mean": {
|
| 101 |
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| 103 |
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|
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"spec_mfcc4_mean": {
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|
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|
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|
| 204 |
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|
| 205 |
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|
fusion/final_multiclass_sgl/final_multiclass_sgl_stream_only.pkl
ADDED
|
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size 2901022
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fusion/final_multiclass_sgl/final_multiclass_sgl_with_demos.pkl
ADDED
|
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fusion/final_regression_fc2fs/final_regression_fc2fs_stream_only.pkl
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 2935165
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fusion/final_regression_fc2fs/final_regression_fc2fs_with_demos.pkl
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 2935374
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fusion/stacking/stacking_binary_stream_only.pkl
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 879
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fusion/stacking/stacking_binary_stream_only_config.json
ADDED
|
@@ -0,0 +1,11 @@
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{
|
| 2 |
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"task": "binary",
|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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],
|
| 9 |
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|
| 10 |
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"model_type": "LogisticRegression"
|
| 11 |
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|
fusion/stacking/stacking_binary_stream_only_scaler.pkl
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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fusion/stacking/stacking_binary_with_demos.pkl
ADDED
|
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size 911
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fusion/stacking/stacking_binary_with_demos_config.json
ADDED
|
@@ -0,0 +1,14 @@
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{
|
| 2 |
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"task": "binary",
|
| 3 |
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"features": [
|
| 4 |
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|
| 5 |
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"embeddings_proba",
|
| 6 |
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"emotion_proba",
|
| 7 |
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"vision_proba",
|
| 8 |
+
"sex",
|
| 9 |
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"age",
|
| 10 |
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"educ"
|
| 11 |
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],
|
| 12 |
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|
| 13 |
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"model_type": "LogisticRegression"
|
| 14 |
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|
fusion/stacking/stacking_binary_with_demos_scaler.pkl
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 767
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fusion/streams/biomarkers/binary_biomarkers_LGBMClassifier.pkl
ADDED
|
@@ -0,0 +1,3 @@
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size 150916
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fusion/streams/embeddings/binary_embeddings_XGBClassifier.pkl
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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fusion/streams/emotion/binary_emotion_LogisticRegression.pkl
ADDED
|
@@ -0,0 +1,3 @@
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fusion/streams/vision/binary_vision_LGBMClassifier.pkl
ADDED
|
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version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 737972
|
reference/biomarker_cn_stats.json
ADDED
|
@@ -0,0 +1,434 @@
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|
| 1 |
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{
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| 2 |
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| 3 |
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|
| 434 |
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|
reference/biomarker_scaler.json
ADDED
|
@@ -0,0 +1,205 @@
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|
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|
|
|
| 1 |
+
{
|
| 2 |
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"_meta": {
|
| 3 |
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|
| 4 |
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|
| 187 |
+
},
|
| 188 |
+
"spec_mfcc12_mean": {
|
| 189 |
+
"mean": -0.1947531587245327,
|
| 190 |
+
"std": 3.9540796120221176
|
| 191 |
+
},
|
| 192 |
+
"spec_mfcc12_std": {
|
| 193 |
+
"mean": 7.890369485981309,
|
| 194 |
+
"std": 2.675874492821634
|
| 195 |
+
},
|
| 196 |
+
"spec_mfcc13_mean": {
|
| 197 |
+
"mean": 0.34501662584532705,
|
| 198 |
+
"std": 3.512230696875546
|
| 199 |
+
},
|
| 200 |
+
"spec_mfcc13_std": {
|
| 201 |
+
"mean": 6.8180544846573214,
|
| 202 |
+
"std": 1.944272661967959
|
| 203 |
+
}
|
| 204 |
+
}
|
| 205 |
+
}
|
reference/emotion_cn_reference.json
ADDED
|
@@ -0,0 +1,1030 @@
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reference/umap_pitt_clouds.json
ADDED
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@@ -0,0 +1,5146 @@
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reference/umap_reference.npz
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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reference/vlad_codebook.pkl
ADDED
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@@ -0,0 +1,3 @@
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|
|
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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