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alirezaaminzadeh/solverport-selection-model
solverport-selection-model is a machine learning model from alirezaaminzadeh. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for solverport. The card lists the license as apache-2.0.
Multi-head meta-model for optimization solver portfolio selection — predicts best solver, configuration, runtime, optimality gap, and feasibility probability from 18 instance features.
Downloads · 30 days
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13% of all-time downloads
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From the Hugging Face model README
Multi-head meta-model for optimization solver portfolio selection — predicts best solver, configuration, runtime, optimality gap, and feasibility probability from 18 instance features.
Ensemble calibrated scoring (LightGBM / CatBoost / Random Forest / Learning-to-Rank style) with expert-calibrated weights. No training required at inference time.
| Head | Output |
|---|---|
| Solver rank | Top-1 solver recommendation + full ranking |
| Runtime | Predicted solve time (seconds) |
| Gap | Predicted optimality gap (%) |
| Feasible | Probability of finding feasible solution |
| Config | Auto-tuned solver parameters |
General (7) + Graph (5) + Problem-specific (6) — see instance feature dataset.
cp_sat, highs, cbc, scip, glpk, gurobi, minizinc, alns
time_limit_sec, num_search_workers, presolve, cut_strategy, search_branching, heuristic_frequency, neighborhood_size, initial_solution
from huggingface_hub import hf_hub_download
import json
weights = json.load(open(hf_hub_download(
"alirezaaminzadeh/solverport-selection-model",
"selection_weights.json",
)))
config = json.load(open(hf_hub_download(
"alirezaaminzadeh/solverport-selection-model",
"config.json",
)))
Apache 2.0