Pickle model using sunshine example ensemble #136
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pschork
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May 26, 2023
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| params = { | ||
| "n_estimators": 2000, | ||
| "learning_rate": 0.1, | ||
| "max_depth": 5, | ||
| "num_leaves": 2**5, | ||
| "colsample_bytree": 0.1, | ||
| } |
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WIP These params are intentionally low to get things working
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use 0.01 learning rate,
5 max depth
2**5 num_leaves
for 2000 trees
and for 20k or 60k trees
0.001 lr
6 max depth
2**6 num_leaves
pschork
commented
May 26, 2023
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| ERA_COL = "era" | ||
| TARGET_COL = "target_cyrus_v4_20" | ||
| DATA_TYPE_COL = "data_type" | ||
| MODEL_FOLDER = "models" | ||
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| def load_model(name): | ||
| path = Path(f"{MODEL_FOLDER}/{name}.pkl") | ||
| if path.is_file(): | ||
| model = pd.read_pickle(f"{MODEL_FOLDER}/{name}.pkl") | ||
| else: | ||
| model = False | ||
| return model | ||
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| def neutralize( | ||
| df, columns, neutralizers=None, proportion=1.0, normalize=True, era_col="era", verbose=False |
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I had to flatten utils.py in order for the pickle to be unserializable with numerai_predict in our execution enclave
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| # export pickle | ||
| p = cloudpickle.dumps(predict) | ||
| model_pkl = f"sunshine_{'_'.join(sys.version.split('.')[:2])}.pkl" |
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maybe some comment about this naming scheme?
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Very rough