---
license:
  - cc-by-4.0
  - mit
  - other
task_categories:
  - tabular-regression
tags:
  - metric-shift
  - benchmark
  - scientific-ml
  - cross-domain
  - cross-metric-prediction
size_categories:
  - 100K<n<1M
configs:
  - config_name: zinc250k
    data_files:
      - split: features
        path: zinc250k/features.csv
      - split: labels
        path: zinc250k/labels.csv
  - config_name: air_quality
    data_files:
      - split: features
        path: air_quality/features.csv
      - split: labels
        path: air_quality/labels.csv
  - config_name: jarvis_materials
    data_files:
      - split: features
        path: jarvis_materials/features.csv
      - split: labels
        path: jarvis_materials/labels.csv
  - config_name: protein_fitness_expanded
    data_files:
      - split: features
        path: protein_fitness_expanded/features.csv
      - split: labels
        path: protein_fitness_expanded/labels.csv
  - config_name: drug_admet
    data_files:
      - split: features
        path: drug_admet/features.csv
      - split: labels
        path: drug_admet/labels.csv
  - config_name: climate_stations
    data_files:
      - split: features
        path: climate_stations/features.csv
      - split: labels
        path: climate_stations/labels.csv
---

# Metric Shift Benchmark

A cross-domain benchmark for predicting expensive scientific measurements from
cheap surrogates, spanning **6 scientific fields** and **134 valid
(y1, y2) pairs** with a standardized evaluation protocol.

**Paper:** *Metric Shift: A Benchmark for Predicting Expensive Scientific
Measurements from Cheap Surrogates* (NeurIPS 2026 Evaluations & Datasets Track, under review)

## Benchmark Overview

| Dataset | Domain | Samples | Feat. dim | Labels | Valid pairs | License |
|---------|--------|---------|-----------|--------|-------------|---------|
| `zinc250k` | Drug Chemistry | 249,455 | 14 | 3 | 6 | ZINC academic-use, f... |
| `air_quality` | Environmental Science | 382,168 | 7 | 6 | 28 | CC-BY-4.0 (UCI ML Re... |
| `jarvis_materials` | Materials Science | 10,800 | 14 | 6 | 30 | Public domain / NIST... |
| `protein_fitness_expanded` | Protein Biology | 61,704 | 22 | 24 | 38 | MIT (ProteinGym aggr... |
| `drug_admet` | Pharmacology | 1,523 | 14 | 4 | 12 | CC-BY-4.0 (Polaris H... |
| `climate_stations` | Climate Science | 28,488 | 5 | 5 | 20 | CC-BY-4.0, dual attr... |
| **Total** | --- | **734,138** | --- | --- | **134** | --- |

## Problem: Metric Shift

Given a shared entity x (molecule, material, protein variant), a cheap source
metric y1, and an expensive target metric y2: can we use universally available
y1 to improve prediction of the sparsely labeled y2?

Key properties:
- y1 is **always available at test time** (cheap to measure for any new candidate)
- The input distribution p(x) is fixed; only the prediction target changes
- Unlike domain adaptation (shifts p(x)) or multi-task learning (co-predicts)

## Evaluation Protocol

- **Split:** 60% train / 20% val / 20% test at `split_seed=42`
- **Labeled ratio:** 20% of train (main setting); 1% and 5% for ablation
- **Seeds:** 5 model seeds per pair
- **Metrics:** R-squared and Spearman rho
- **Significance:** Paired t-test across seeds + Benjamini-Hochberg FDR at q=0.05
- **Aggregation:** Macro-median (per-dataset median, then cross-dataset median)
- **StandardScaler:** fit on labeled train only

## Usage

```python
import pandas as pd

# Load one sub-dataset
features = pd.read_csv("zinc250k/features.csv")
labels = pd.read_csv("zinc250k/labels.csv")

# Each (source, target) column pair in labels defines a Metric Shift task
# See metadata.json for the list of valid pairs with Spearman correlations
```

One-command reproduction of all tables and figures:
```bash
pip install metric-shift-benchmark
python -m metric_shift.run_all
```

## Dataset Details

### `zinc250k` — Drug Chemistry

249,455 drug-like molecules, 14 RDKit descriptors, 3 labels (logP, QED, SAS), 6 pairs

- **Source:** ZINC database (Irwin & Shoichet 2005; Sterling & Irwin 2015)
- **License:** ZINC academic-use, free redistribution with attribution
- **Features (14d):** `MolWt, HeavyAtomCount, NumHeteroatoms, NumValenceElectrons, TPSA, MolMR, HBA, HBD, NumRotatableBonds, RingCount, NumAromaticRings, FractionCSP3, BalabanJ, BertzCT`
- **Labels (3col):** `logP, QED, SAS`

### `air_quality` — Environmental Science

382,168 hourly records, 7 meteo features, 6 pollutants, 28 pairs

- **Source:** Beijing Multi-Site Air-Quality Dataset (Zhang et al. 2017)
- **License:** CC-BY-4.0 (UCI ML Repository)
- **Features (7d):** `TEMP, PRES, DEWP, RAIN, WSPM, wd_sin, wd_cos`
- **Labels (6col):** `PM25, PM10, SO2, NO2, CO, O3`

### `jarvis_materials` — Materials Science

10,800 inorganic crystals, 14 composition descriptors, 6 labels, 30 pairs

- **Source:** JARVIS-DFT 3D (Choudhary et al. 2020)
- **License:** Public domain / NIST (17 USC §105)
- **Features (14d):** `mean_Z, std_Z, mean_X, std_X, mean_row, std_row, mean_group, std_group, mean_atomic_mass, std_atomic_mass, density, volume_per_atom, n_sites, packing_fraction`
- **Labels (6col):** `formation_energy_peratom, optb88vdw_bandgap, bulk_modulus_kv, shear_modulus_gv, n_seebeck, p_seebeck`

### `protein_fitness_expanded` — Protein Biology

61,704 variants, 22-d mutation features, 24 DMS assays, 38 within-protein pairs

- **Source:** ProteinGym substitution benchmark (Notin et al. 2023)
- **License:** MIT (ProteinGym aggregation)
- **Features (22d):** `protein_id, n_mutations, AA_A_diff, AA_C_diff, AA_D_diff, AA_E_diff, AA_F_diff, AA_G_diff, AA_H_diff, AA_I_diff, AA_K_diff, AA_L_diff, AA_M_diff, AA_N_diff, AA_P_diff, AA_Q_diff, AA_R_diff, AA_S_diff, AA_T_diff, AA_V_diff, AA_W_diff, AA_Y_diff`
- **Labels (24col):** `p53_null_etoposide, p53_null_nutlin, p53_wt_nutlin, blat_deng_2012, blat_firnberg_2014, blat_jacquier_2013, blat_stiffler_2015, pten_matreyek_2021, pten_mighell_2018, cp2c9_amorosi_abundance_2021, cp2c9_amorosi_activity_2021, hsp82_flynn_2019, hsp82_mishra_2016, spike_starr_bind_2020, spike_starr_expr_2020, a0a2z5u3z0_doud_2016, a0a2z5u3z0_wu_2014, rl401_mavor_2016, rl401_roscoe_2013, rl401_roscoe_2014, ccdb_adkar_2012, ccdb_tripathi_2016, vkor1_chiasson_abundance_2020, vkor1_chiasson_activity_2020`

### `drug_admet` — Pharmacology

1,523 compounds, 14 RDKit descriptors, 4 ADME endpoints, 12 pairs

- **Source:** Biogen ADME-Fang v1 (Fang et al. 2023)
- **License:** CC-BY-4.0 (Polaris Hub)
- **Features (14d):** `MolWt, HeavyAtomCount, NumHBD, NumHBA, TPSA, MolLogP, NumRotatableBonds, RingCount, NumAromaticRings, FractionCSP3, MolMR, BertzCT, BalabanJ, NumHeteroatoms`
- **Labels (4col):** `LOG_HLM_CLint, LOG_RLM_CLint, LOG_SOLUBILITY, LOG_MDR1-MDCK_ER`

### `climate_stations` — Climate Science

28,488 daily records, 5 context features, 5 climate variables, 20 pairs

- **Source:** Open-Meteo Historical Weather API / ERA5 reanalysis
- **License:** CC-BY-4.0, dual attribution to Open-Meteo and Copernicus C3S/ERA5
- **Features (5d):** `lat, lon, day_sin, day_cos, year_norm`
- **Labels (5col):** `temp_max, temp_min, precip, windspeed, solar_radiation`


## Responsible AI

- **Personal / sensitive data:** None. All datasets contain scientific measurements
  on molecules, materials, proteins, pollutants, or climate variables. No human
  subjects, no personally identifiable information.
- **Intended use:** Benchmarking ML methods for the Metric Shift problem. Not
  intended for direct clinical, regulatory, or safety-critical deployment.
- **Known limitations:** (1) All six datasets are re-curations of existing public
  sources; our contribution is pair construction, validity filter, and protocol.
  (2) Domain coverage spans chemistry, biology, materials, environment, and
  climate --- not yet high-energy physics, astronomy, or social science.
  (3) Feature spaces are intentionally low-dimensional (5--22d) to isolate the
  contribution of y1; higher-dimensional encoders may change relative method
  rankings.
- **Potential misuse:** drug_admet contains ADME measurements that could
  theoretically inform adverse drug design; however, the 1,523-compound dataset
  is far too small and coarse for such purposes, and all data is already public.

## Maintenance

The authors commit to maintaining this repository for at least 2 years
post-publication, with semantic versioning (v1.0, v1.1, ...) and a CHANGELOG
for every split, filter, or protocol change.

## Citation

```bibtex
@inproceedings{metric_shift_2026,
  title={Metric Shift: A Benchmark for Predicting Expensive Scientific Measurements from Cheap Surrogates},
  author={Anonymous},
  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
  year={2026},
  note={Under review}
}
```