mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
b1c04afb13
* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
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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primary_results = results[[column for column in results.columns if 'primary' in column]]
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ensemble_results = results[[column for column in results.columns if 'ensemble' in column]]
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# Only send the results of the final model to wandb
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results_to_send = ensemble_results if ensemble_results.shape[1] > 0 else primary_results
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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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primary_weights = all_predictions[[column for column in all_predictions.columns if 'primary' in column]]
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ensemble_weights = all_predictions[[column for column in all_predictions.columns if 'ensemble' in column]]
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predictions_to_save = ensemble_weights if ensemble_weights.shape[1] > 0 else primary_weights
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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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primary_avg_results = weighted_average(primary_results, 'no_of_samples')
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ensemble_avg_results = weighted_average(ensemble_results, '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: ", primary_results.loc['no_of_samples'].sum())
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print("Mean Sharpe ratio for Level-1 models: ", round(primary_avg_results.loc['sharpe'], 3))
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print("Mean Probabilistic Sharpe ratio for Level-1 models: ", round(primary_avg_results.loc['prob_sharpe'].mean(), 3))
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if len(model_config['meta_labeling_models']) > 0:
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print("Level-2 (Ensemble): Number of samples evaluated: ", ensemble_results.loc['no_of_samples'].sum())
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print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", round(ensemble_avg_results.loc['sharpe'].mean(), 3))
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print("Mean Probabilistic Sharpe ratio for Level-2 (Ensemble) models: ", round(ensemble_avg_results.loc['prob_sharpe'].mean(), 3))
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ensemble_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() |