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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
f8af5641e8
* chore(Project): added output folder * chore(CI): updated path * fix(CI): correct path
41 lines
2.3 KiB
Python
41 lines
2.3 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('output/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('output/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('output/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() |