Serendipity
d353133b31
- 新增强化学习个人项目报告，包含基于PyTorch从零实现的PPO算法 - 重构课程作业报告代码结构，提取运行时路径管理和notebook执行逻辑到独立模块 - 更新依赖文件requirements.txt，添加强化学习相关依赖 - 简化模型比较结果表格，仅保留基线逻辑回归模型数据
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
import warnings
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warnings.filterwarnings("ignore")
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as _real_mpl_plt
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_real_mpl_plt.show = lambda *a, **kw: None
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import os
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import sys
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import time
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import json
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import traceback
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay
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from sklearn.model_selection import cross_val_score
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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from sklearn.linear_model import LogisticRegression
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from sklearn.ensemble import RandomForestClassifier, VotingClassifier
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from sklearn.pipeline import Pipeline
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from sklearn.compose import ColumnTransformer
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.cluster import KMeans
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from sklearn.mixture import GaussianMixture
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from sklearn.metrics import silhouette_score
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from sklearn.decomposition import PCA
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import xgboost as xgb
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import optuna
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optuna.logging.set_verbosity(optuna.logging.WARNING)
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from src.notebook_runner import execute_notebook
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from src.runtime_paths import build_paths
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paths = build_paths()
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print(f"Project root : {paths.project_root}")
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print(f"Notebook : {paths.notebook}")
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print(f"Data dir : {paths.data_dir}")
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print(f"Output dir : {paths.output_dir}")
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ns = vars()
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result = execute_notebook(namespace=ns)
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print(f"\nExecution finished: {result['status']}")
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print(f"Cells run: {len([c for c in result['cells'] if c['status'] == 'ok'])}/{result['total']}")
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print(f"Output dir: {result['outputs']['output_dir']}") |