From 1e1d4b0d17e46d07af072665d2681e0b942cb0fd Mon Sep 17 00:00:00 2001 From: shenjianZ Date: Fri, 20 Mar 2026 17:01:30 +0800 Subject: [PATCH] fix:generate_evaluation_plots --- backend/core/generate_evaluation_plots.py | 330 ++++++++++++++++++++++ 1 file changed, 330 insertions(+) create mode 100644 backend/core/generate_evaluation_plots.py diff --git a/backend/core/generate_evaluation_plots.py b/backend/core/generate_evaluation_plots.py new file mode 100644 index 0000000..503e984 --- /dev/null +++ b/backend/core/generate_evaluation_plots.py @@ -0,0 +1,330 @@ +import json +import os +from pathlib import Path + +import joblib +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sklearn.metrics import confusion_matrix +from sklearn.model_selection import train_test_split + +import config +from core.deep_learning_model import ( + _build_sequence_arrays, + load_lstm_mlp_bundle, +) +from core.model_features import ( + NUMERICAL_OUTLIER_COLUMNS, + TARGET_COLUMN, + apply_outlier_bounds, + engineer_features, + fit_outlier_bounds, + make_target_bins, + normalize_columns, + prepare_modeling_dataframe, +) +from core.preprocessing import get_clean_data + + +matplotlib.rcParams['font.sans-serif'] = [ + 'Microsoft YaHei', + 'SimHei', + 'Noto Sans CJK SC', + 'Arial Unicode MS', + 'DejaVu Sans', +] +matplotlib.rcParams['axes.unicode_minus'] = False + +BASE_DIR = Path(config.BASE_DIR) +MODELS_DIR = Path(config.MODELS_DIR) +OUTPUT_DIR = BASE_DIR / 'outputs' / 'eval_figures' +PREDICTION_CSV = OUTPUT_DIR / 'lstm_predictions.csv' +SUMMARY_JSON = OUTPUT_DIR / 'evaluation_summary.json' + +MODEL_DISPLAY_NAMES = { + 'lstm_mlp': '时序注意力融合网络', + 'xgboost': 'XGBoost', + 'gradient_boosting': 'GBDT', + 'random_forest': '随机森林', + 'extra_trees': '极端随机树', + 'lightgbm': 'LightGBM', +} + + +def ensure_output_dir(): + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + + +def load_metrics(): + metrics_path = MODELS_DIR / 'model_metrics.pkl' + if not metrics_path.exists(): + raise FileNotFoundError(f'未找到模型评估文件: {metrics_path}') + metrics = joblib.load(metrics_path) + return dict(sorted(metrics.items(), key=lambda item: item[1].get('r2', -999), reverse=True)) + + +def get_test_split(): + raw_df = normalize_columns(get_clean_data()) + target_bins = make_target_bins(raw_df[TARGET_COLUMN].values) + raw_train_df, raw_test_df = train_test_split( + raw_df, + test_size=config.TEST_SIZE, + random_state=config.RANDOM_STATE, + stratify=target_bins, + ) + return raw_train_df.reset_index(drop=True), raw_test_df.reset_index(drop=True) + + +def classify_risk(values): + values = np.asarray(values, dtype=float) + return np.where(values < 4, '低风险', np.where(values <= 8, '中风险', '高风险')) + + +def load_lstm_predictions(): + model_path = MODELS_DIR / 'lstm_mlp_model.pt' + if not model_path.exists(): + raise FileNotFoundError(f'未找到深度学习模型文件: {model_path}') + + bundle = load_lstm_mlp_bundle(str(model_path)) + if bundle is None: + raise RuntimeError('无法加载深度学习模型，请确认 torch 环境和模型文件正常。') + + raw_train_df, raw_test_df = get_test_split() + fit_df = prepare_modeling_dataframe(raw_train_df) + test_df = prepare_modeling_dataframe(raw_test_df) + outlier_bounds = fit_outlier_bounds(fit_df, NUMERICAL_OUTLIER_COLUMNS) + fit_df = apply_outlier_bounds(fit_df, outlier_bounds) + test_df = apply_outlier_bounds(test_df, outlier_bounds) + + feature_layout = bundle['feature_layout'] + category_maps = bundle['category_maps'] + target_transform = bundle['target_transform'] + + _, _, _, _, _ = _build_sequence_arrays( + fit_df, + feature_layout, + category_maps, + target_transform, + ) + test_seq_num, test_seq_cat, test_static_num, test_static_cat, y_test = _build_sequence_arrays( + test_df, + feature_layout, + category_maps, + target_transform, + ) + + test_seq_num = ((test_seq_num - bundle['seq_mean']) / bundle['seq_std']).astype(np.float32) + test_static_num = ((test_static_num - bundle['static_mean']) / bundle['static_std']).astype(np.float32) + + import torch + + model = bundle['model'] + model.eval() + with torch.no_grad(): + predictions = model( + torch.tensor(test_seq_num, dtype=torch.float32), + torch.tensor(test_seq_cat, dtype=torch.long), + torch.tensor(test_static_num, dtype=torch.float32), + torch.tensor(test_static_cat, dtype=torch.long), + ).cpu().numpy() + + if target_transform == 'log1p': + y_true = np.expm1(y_test) + y_pred = np.expm1(predictions) + else: + y_true = y_test + y_pred = predictions + + y_true = np.asarray(y_true, dtype=float) + y_pred = np.clip(np.asarray(y_pred, dtype=float), a_min=0.0, a_max=None) + residuals = y_pred - y_true + + prediction_df = pd.DataFrame({ + '真实值': np.round(y_true, 4), + '预测值': np.round(y_pred, 4), + '残差': np.round(residuals, 4), + '真实风险等级': classify_risk(y_true), + '预测风险等级': classify_risk(y_pred), + }) + prediction_df.to_csv(PREDICTION_CSV, index=False, encoding='utf-8-sig') + return prediction_df + + +def plot_model_comparison(metrics): + model_names = [MODEL_DISPLAY_NAMES.get(name, name) for name in metrics.keys()] + r2_values = [metrics[name]['r2'] for name in metrics] + rmse_values = [metrics[name]['rmse'] for name in metrics] + mae_values = [metrics[name]['mae'] for name in metrics] + + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + bar_colors = ['#0f766e' if name == 'lstm_mlp' else '#94a3b8' for name in metrics.keys()] + + axes[0].bar(model_names, r2_values, color=bar_colors) + axes[0].set_title('模型R2对比') + axes[0].set_ylabel('R2') + axes[0].tick_params(axis='x', rotation=20) + + axes[1].bar(model_names, rmse_values, color=bar_colors) + axes[1].set_title('模型RMSE对比') + axes[1].set_ylabel('RMSE') + axes[1].tick_params(axis='x', rotation=20) + + axes[2].bar(model_names, mae_values, color=bar_colors) + axes[2].set_title('模型MAE对比') + axes[2].set_ylabel('MAE') + axes[2].tick_params(axis='x', rotation=20) + + fig.tight_layout() + fig.savefig(OUTPUT_DIR / '01_模型性能对比.png', dpi=220, bbox_inches='tight') + plt.close(fig) + + +def plot_actual_vs_pred(prediction_df): + y_true = prediction_df['真实值'].to_numpy() + y_pred = prediction_df['预测值'].to_numpy() + max_value = max(float(y_true.max()), float(y_pred.max())) + + fig, ax = plt.subplots(figsize=(7, 7)) + ax.scatter(y_true, y_pred, s=18, alpha=0.55, color='#0f766e', edgecolors='none') + ax.plot([0, max_value], [0, max_value], color='#dc2626', linestyle='--', linewidth=1.5) + ax.set_title('LSTM模型真实值与预测值对比') + ax.set_xlabel('真实缺勤时长（小时）') + ax.set_ylabel('预测缺勤时长（小时）') + fig.tight_layout() + fig.savefig(OUTPUT_DIR / '02_LSTM真实值_vs_预测值.png', dpi=220, bbox_inches='tight') + plt.close(fig) + + +def plot_residuals(prediction_df): + y_pred = prediction_df['预测值'].to_numpy() + residuals = prediction_df['残差'].to_numpy() + + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + axes[0].hist(residuals, bins=30, color='#2563eb', alpha=0.85, edgecolor='white') + axes[0].axvline(0, color='#dc2626', linestyle='--', linewidth=1.2) + axes[0].set_title('LSTM残差分布') + axes[0].set_xlabel('残差（预测值 - 真实值）') + axes[0].set_ylabel('样本数') + + axes[1].scatter(y_pred, residuals, s=18, alpha=0.55, color='#7c3aed', edgecolors='none') + axes[1].axhline(0, color='#dc2626', linestyle='--', linewidth=1.2) + axes[1].set_title('LSTM残差散点图') + axes[1].set_xlabel('预测缺勤时长（小时）') + axes[1].set_ylabel('残差') + + fig.tight_layout() + fig.savefig(OUTPUT_DIR / '03_LSTM残差分析.png', dpi=220, bbox_inches='tight') + plt.close(fig) + + +def plot_confusion_matrix(prediction_df): + labels = ['低风险', '中风险', '高风险'] + cm = confusion_matrix( + prediction_df['真实风险等级'], + prediction_df['预测风险等级'], + labels=labels, + ) + + fig, ax = plt.subplots(figsize=(6, 5)) + image = ax.imshow(cm, cmap='GnBu') + ax.set_title('LSTM风险等级混淆矩阵') + ax.set_xlabel('预测类别') + ax.set_ylabel('真实类别') + ax.set_xticks(range(len(labels))) + ax.set_xticklabels(labels) + ax.set_yticks(range(len(labels))) + ax.set_yticklabels(labels) + + for row in range(cm.shape[0]): + for col in range(cm.shape[1]): + ax.text(col, row, int(cm[row, col]), ha='center', va='center', color='#111827') + + fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04) + fig.tight_layout() + fig.savefig(OUTPUT_DIR / '04_LSTM风险等级混淆矩阵.png', dpi=220, bbox_inches='tight') + plt.close(fig) + + +def plot_feature_importance(): + candidate_files = [ + ('xgboost', MODELS_DIR / 'xgboost_model.pkl'), + ('random_forest', MODELS_DIR / 'random_forest_model.pkl'), + ('extra_trees', MODELS_DIR / 'extra_trees_model.pkl'), + ] + selected_features_path = MODELS_DIR / 'selected_features.pkl' + feature_names_path = MODELS_DIR / 'feature_names.pkl' + selected_features = joblib.load(selected_features_path) if selected_features_path.exists() else None + feature_names = joblib.load(feature_names_path) if feature_names_path.exists() else None + + for model_name, model_path in candidate_files: + if not model_path.exists(): + continue + model = joblib.load(model_path) + if not hasattr(model, 'feature_importances_'): + continue + importances = model.feature_importances_ + names = selected_features or feature_names or [f'feature_{idx}' for idx in range(len(importances))] + if len(names) != len(importances): + names = [f'feature_{idx}' for idx in range(len(importances))] + + top_items = sorted(zip(names, importances), key=lambda item: item[1], reverse=True)[:15] + top_items.reverse() + + fig, ax = plt.subplots(figsize=(8, 6)) + ax.barh( + [config.FEATURE_NAME_CN.get(name, name) for name, _ in top_items], + [float(value) for _, value in top_items], + color='#0f766e', + ) + ax.set_title(f'{MODEL_DISPLAY_NAMES.get(model_name, model_name)}特征重要性 Top15') + ax.set_xlabel('重要性') + fig.tight_layout() + fig.savefig(OUTPUT_DIR / '05_特征重要性_Top15.png', dpi=220, bbox_inches='tight') + plt.close(fig) + return model_name + + return None + + +def save_summary(metrics, prediction_df, feature_model_name): + residuals = prediction_df['残差'].to_numpy() + summary = { + 'best_model': next(iter(metrics.keys())), + 'metrics': metrics, + 'lstm_prediction_summary': { + 'prediction_count': int(len(prediction_df)), + 'residual_mean': round(float(residuals.mean()), 4), + 'residual_std': round(float(residuals.std()), 4), + 'risk_accuracy': round( + float((prediction_df['真实风险等级'] == prediction_df['预测风险等级']).mean()), + 4, + ), + }, + 'feature_importance_model': feature_model_name, + 'generated_files': sorted([file.name for file in OUTPUT_DIR.iterdir() if file.is_file()]), + } + SUMMARY_JSON.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding='utf-8') + + +def main(): + ensure_output_dir() + metrics = load_metrics() + prediction_df = load_lstm_predictions() + + plot_model_comparison(metrics) + plot_actual_vs_pred(prediction_df) + plot_residuals(prediction_df) + plot_confusion_matrix(prediction_df) + feature_model_name = plot_feature_importance() + save_summary(metrics, prediction_df, feature_model_name) + + print(f'评估图片已生成: {OUTPUT_DIR}') + print(f'LSTM预测明细: {PREDICTION_CSV}') + print(f'评估摘要: {SUMMARY_JSON}') + + +if __name__ == '__main__': + main()