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Merge pull request #75 from Project-Resilience/torch-config
Updated training script for torch prescriptors to allow config
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{ | ||
"predictor_path": "predictors/neural_network/trained_models/no_overlap_nn", | ||
"evolution_params": { | ||
"pop_size": 100, | ||
"n_generations": 100, | ||
"p_mutation": 0.2, | ||
"candidate_params": { | ||
"in_size": 12, | ||
"hidden_size": 16, | ||
"out_size": 5 | ||
}, | ||
"seed_dir": "prescriptors/nsga2/seeds/small_sample" | ||
}, | ||
"save_path": "prescriptors/nsga2/trained_prescriptors/test" | ||
} |
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""" | ||
Script to train the NSGA-II prescriptors. | ||
Script used to train NSGA-II prescriptors. | ||
Requires a config file with the same fields as shown in the | ||
test.json file in prescriptors/nsga2/configs | ||
""" | ||
import argparse | ||
import json | ||
from pathlib import Path | ||
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from data import constants | ||
from data.eluc_data import ELUCData | ||
from prescriptors.nsga2.torch_prescriptor import TorchPrescriptor | ||
from predictors.neural_network.neural_net_predictor import NeuralNetPredictor | ||
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if __name__ == "__main__": | ||
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# Load config | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument("--config_path", type=str, required=True) | ||
args = parser.parse_args() | ||
with open(Path(args.config_path), "r", encoding="utf-8") as f: | ||
config = json.load(f) | ||
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print("Loading dataset...") | ||
dataset = ELUCData() | ||
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print("Loading predictor...") | ||
# TODO: We need to make it so you can load any predictor here | ||
nnp = NeuralNetPredictor() | ||
nnp.load("predictors/neural_network/trained_models/no_overlap_nn") | ||
nnp_path = Path(config["predictor_path"]) | ||
nnp.load(nnp_path) | ||
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print("Initializing prescription...") | ||
candidate_params = {"in_size": len(constants.CAO_MAPPING["context"]), | ||
"hidden_size": 16, | ||
"out_size": len(constants.RECO_COLS)} | ||
if "seed_dir" in config["evolution_params"].keys(): | ||
config["evolution_params"]["seed_dir"] = Path(config["evolution_params"]["seed_dir"]) | ||
tp = TorchPrescriptor( | ||
pop_size=100, | ||
n_generations=100, | ||
p_mutation=0.2, | ||
eval_df=dataset.train_df.sample(frac=0.001, random_state=42), | ||
encoder=dataset.encoder, | ||
predictor=nnp, | ||
batch_size=4096, | ||
candidate_params=candidate_params, | ||
seed_dir=Path("prescriptors/nsga2/seeds/small_sample") | ||
**config["evolution_params"] | ||
) | ||
print("Training prescriptors...") | ||
save_path = Path("prescriptors/nsga2/trained_prescriptors/test") | ||
save_path = Path(config["save_path"]) | ||
final_pop = tp.neuroevolution(save_path) | ||
print("Done!") |