File size: 3,963 Bytes
c687548 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | # -*- coding: utf-8 -*-
# @Time : 2025/7/10 20:56
# @Author : Lukax
# @Email : Lukarxiang@gmail.com
# @File : Utils.py
# -*- presentd: PyCharm -*-
import os
import numpy as np
import pandas as pd
from Settings import Config
from inplemental import load_data
from Utils import set_seed, train2compare_outlier_strategy, print_strategy_comparison, analyze_outliers, train_mlp_model, create_multiple_submissions, save2csv
def flow():
Config.print_config_summary()
set_seed(Config.RANDOM_STATE)
train, test, submission = load_data()
print(f"\ntrain shape: {train.shape}\ntest shape: {test.shape}")
breakpoint()
# if train[Config.TARGET].isnull().any():
# print(f"target has {train[Config.TARGET].isnull().sum()} NA.")
# analyze_outliers(train) # 单纯的异常值数量检测
# ML single training
single_oof_pred, single_test_pred, single_strategy_res, single_best_strategy, single_best_combination = train2compare_outlier_strategy(train, test, mode = 'single')
print(f"{'='*50}\n\tsingle best: {single_best_combination}")
# ML ensemble training
ensemble_oof_pred, ensemble_test_pred, ensemble_strategy_res, ensemble_best_strategy, ensemble_best_combination = train2compare_outlier_strategy(train, test, mode = 'ensemble')
print(f"{'='*50}\n\tensemble best: {ensemble_best_combination}")
# strategy comparison
print_strategy_comparison(single_strategy_res, 'single', single_best_combination)
print_strategy_comparison(ensemble_strategy_res, 'ensemble', ensemble_best_combination)
single_best_score = single_strategy_res[single_best_strategy]['ensemble_score']
ensemble_best_score = ensemble_strategy_res[ensemble_best_strategy]['ensemble_score']
if ensemble_best_score > single_best_score: # 比较选出 单模型 和 集成模型 中更好的
final_ml_pred, final_ml_strategy = ensemble_test_pred, ensemble_best_combination
final_ml_score, strategy_type = ensemble_best_score, "ensemble ml"
else:
final_ml_pred, final_ml_strategy = single_test_pred, single_best_combination
final_ml_score, strategy_type = single_best_score, "single ml"
print(f"{'='*50}\n\tBest ML strategy: {strategy_type} - {final_ml_strategy}\nBest score: {final_ml_score:.6f}")
# DL mlp
mlp_predictions, mlp_score = train_mlp_model(train, test)
print(f"{'='*50}\n\tMLP score: {mlp_score:.5f}")
# generate submission
if mlp_predictions is not None: # mlp和最好的 ml模型进行集成制作 submission
best_predictions, best_filename = create_multiple_submissions(
train, final_ml_pred, mlp_predictions, submission,
final_ml_strategy.replace(' ', '_').lower(), final_ml_score, mlp_score)
else: # ML only
submission[Config.TARGET] = final_ml_pred
best_filename = f"submission_{final_ml_strategy.replace(' ', '_').lower()}_{final_ml_score:.6f}.csv"
best_filepath = os.path.join(Config.SUBMISSION_DIR, best_filename)
submission.to_csv(best_filepath, index = False)
print(f"ML submission saved to {best_filepath}")
best_predictions, final_score = final_ml_pred, final_ml_score
print(best_predictions, '\n', best_filename, '\n', final_score)
# summary analysis
results_summary = {
'ml_single_best': {'strategy': single_best_combination, 'score': single_best_score},
'ml_ensemble_best': {'strategy': ensemble_best_combination, 'score': ensemble_best_score},
'ml_final': {'strategy': final_ml_strategy, 'score': final_ml_score, 'type': strategy_type},
'mlp_score': mlp_score if mlp_predictions is not None else 'N/A',
'best_filename': best_filename
}
results_df = pd.DataFrame([results_summary])
summary_filepath = os.path.join(Config.RESULTS_DIR, 'comprehensive_results_summary.csv')
results_df.to_csv(summary_filepath, index = False)
if __name__ == "__main__":
flow()
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