mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
9488e92597
* feature(MetaLabeling): added hacky prototype * fix(MetaLabeling): drop index until first valid X & y * fix(MetaLabeling): transform both X & y before feature selection * fix(MetaLabeling): got feature selection to work * fix(MetaLabeling): correct values for meta_y * feat(MetaLabeling): created predictions multiplied by bet sizes * feat(Pipeline): print out averaged result * fix(Evaluation): correctly deal with non-discretized data * fix(Pipeline): use the right column names * refactor(Pipeline): move out meta-labeling * refactor(Pipeline): complete refactoring * feat(CI): post results to PR * fix(Pipeline): use the correct filename * chore(Config): removed now redundant feature_selection flag * feat(Models): added SVC * fix(Pipeline): accidentally switched two return values * feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file * fix(Pipeline): wrong function name * fix(Sweep): yaml + run_sweep * fix(Sweep): typo in name * fix(Reporting): only save averaged results * feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging * feat(Reporting): print out sharpe improvement in meta-labeling step * fix(Sweep): adjusted config, defaulted to good defaults * fix(Sweep): adjusted sweep
41 lines
2.2 KiB
Python
41 lines
2.2 KiB
Python
from reporting.wandb import send_report_to_wandb
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import pandas as pd
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from config.preprocess import get_model_name
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from utils.helpers import weighted_average
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def report_results(results:pd.DataFrame, all_predictions:pd.DataFrame, model_config:dict, wandb, sweep: bool, project_name:str):
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level1_columns = results[[column for column in results.columns if 'lvl1' in column]]
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level2_columns = results[[column for column in results.columns if 'lvl2' in column]]
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# Only send the results of the final model to wandb
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results_to_send = level2_columns if level2_columns.shape[1] > 0 else level1_columns
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send_report_to_wandb(results_to_send, wandb, project_name, get_model_name(model_config))
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results.to_csv('results.csv')
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level1_predictions = all_predictions[[column for column in all_predictions.columns if 'lvl1' in column]]
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level2_predictions = all_predictions[[column for column in all_predictions.columns if 'lvl2' in column]]
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predictions_to_save = level2_predictions if level2_predictions.shape[1] > 0 else level1_predictions
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predictions_to_save.to_csv('predictions.csv')
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print("\n--------\n")
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all_avg_results = weighted_average(results, 'no_of_samples')
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lvl1_avg_results = weighted_average(level1_columns, 'no_of_samples')
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lvl2_avg_results = weighted_average(level2_columns, 'no_of_samples')
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print("Benchmark buy-and-hold sharpe: ", round(all_avg_results.loc['benchmark_sharpe'], 3))
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print("Level-1: Number of samples evaluated: ", level1_columns.loc['no_of_samples'].sum())
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print("Mean Sharpe ratio for Level-1 models: ", round(lvl1_avg_results.loc['sharpe'], 3))
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print("Mean Probabilistic Sharpe ratio for Level-1 models: ", round(lvl1_avg_results.loc['prob_sharpe'].mean(), 3))
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if model_config['level_2_model'] is not None:
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print("Level-2 (Ensemble): Number of samples evaluated: ", level2_columns.loc['no_of_samples'].sum())
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print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", round(lvl2_avg_results.loc['sharpe'].mean(), 3))
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print("Mean Probabilistic Sharpe ratio for Level-2 (Ensemble) models: ", round(lvl2_avg_results.loc['prob_sharpe'].mean(), 3))
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lvl2_avg_results.to_csv('results_level2.csv')
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if sweep:
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if wandb.run is not None:
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wandb.finish() |