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Mark Aron Szulyovszky
f5bbc266a4
* feat(Evaluation): added robustness/correlation test * feat(Reporting): saving correlations * fix(Reporting): record correlations properly * fix(Model): SVC's random seed * feat(CI): store artifacts
24 lines
944 B
Python
24 lines
944 B
Python
from run_pipeline import run_pipeline
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from config.config import get_default_ensemble_config, get_dev_config
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import pandas as pd
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all_results = []
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all_predictions = []
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for index in range(6):
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results_1, predictions_1, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config= get_default_ensemble_config)
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all_results.append(results_1)
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all_predictions.append(predictions_1)
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correlations = pd.Series(index = all_predictions[0].columns)
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for asset_name in all_predictions[0].columns:
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predictions_for_asset = pd.concat([preds[asset_name] for preds in all_predictions], axis=1)
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correlations[asset_name] = predictions_for_asset.corr().mean()[0]
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print("Correlation for asset ", asset_name, ": ", correlations[asset_name])
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correlations["Overall"] = correlations.mean()
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print("Average correlation across all assests: ", correlations.mean())
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correlations.to_csv("output/correlations.csv")
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