feat: 完成共享单车课程设计

.gitignore

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+.venv/
+__pycache__/
+*.py[cod]
+*.zip
+*.tar.gz
+outputs/rerun.log
+.DS_Store

README.md

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+# 共享单车日骑行量预测与可视化系统
+
+## 项目内容
+
+本项目完成《大数据分析技术》课程设计中“基于机器学习的共享单车日骑行量预测与可视化系统”的全部任务：
+
+1. 多模型构建与初步筛选
+2. 特征重要性解析
+3. 模型评估与 2013 年前 100 天预测输出
+4. 轻量级 GUI 预测可视化系统
+
+## 目录说明
+
+- `analyze_bike_sharing.py`：完整数据分析、建模、评估、预测、图表生成脚本
+- `bike_demand_gui.py`：tkinter 轻量级预测系统
+- `generate_report.py`：课程设计报告生成脚本
+- `outputs/model_comparison.csv`：模型对比结果
+- `outputs/cross_validation_summary.csv`：交叉验证结果
+- `outputs/feature_importance.csv`：特征重要性
+- `outputs/2013前100天骑行量预测结果.xlsx`：最终预测结果
+- `outputs/models/best_bike_demand_model.pkl`：最佳模型文件
+- `outputs/figures/`：报告图表
+- `outputs/共享单车日骑行量预测课程设计报告.docx`：课程设计报告
+
+## 运行环境
+
+```bash
+cd /root/bike_course_design
+python3 -m venv .venv
+.venv/bin/pip install pymupdf openpyxl pandas matplotlib seaborn scikit-learn python-docx joblib
+```
+
+## 重新生成分析结果
+
+```bash
+cd /root/bike_course_design
+.venv/bin/python analyze_bike_sharing.py
+```
+
+## 启动 GUI
+
+```bash
+cd /root/bike_course_design
+.venv/bin/python bike_demand_gui.py
+```
+
+GUI 支持输入季节、年月、节假日、星期、工作日、天气、气温、体感温度、湿度、风速等特征，输出单场景预测值，并可加入多场景对比表。
+
+## 本次运行核心结论
+
+- 数据量：731 条历史日度记录，预测集 100 条记录。
+- 最佳模型：梯度提升树。
+- 测试集指标：RMSE 约 651.14，MAE 约 449.00，R² 约 0.8943。
+- 五折交叉验证：RMSE 均值约 655.12，R² 均值约 0.8819。
+- 关键特征：气温、年份、季节、体感温度、湿度。

analyze_bike_sharing.py

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+import json
+from pathlib import Path
+
+import joblib
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
+from sklearn.inspection import permutation_importance
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.model_selection import KFold, cross_validate, train_test_split
+from sklearn.neural_network import MLPRegressor
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.svm import SVR
+
+BASE_DIR = Path(__file__).resolve().parent
+DATA_PATH = Path('/root/.hermes-web-ui/upload/default/28752a235eb6ef9d.xlsx')
+OUT_DIR = BASE_DIR / 'outputs'
+FIG_DIR = OUT_DIR / 'figures'
+MODEL_DIR = OUT_DIR / 'models'
+for directory in [OUT_DIR, FIG_DIR, MODEL_DIR]:
+    directory.mkdir(parents=True, exist_ok=True)
+
+plt.rcParams['font.sans-serif'] = ['Noto Sans CJK SC', 'WenQuanYi Zen Hei', 'SimHei', 'DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False
+sns.set_theme(style='whitegrid')
+
+FEATURES = ['季节', '年', '月', '节假日', '星期', '工作日', '天气', '气温', '体感温度', '湿度', '风速']
+CATEGORICAL_FEATURES = ['季节', '年', '月', '节假日', '星期', '工作日', '天气']
+NUMERIC_FEATURES = ['气温', '体感温度', '湿度', '风速']
+TARGET = '总骑行量'
+
+
+def rmse(y_true, y_pred):
+    return float(np.sqrt(mean_squared_error(y_true, y_pred)))
+
+
+def evaluate_model(model, x_train, x_test, y_train, y_test):
+    model.fit(x_train, y_train)
+    pred = model.predict(x_test)
+    return {
+        'RMSE': rmse(y_test, pred),
+        'MAE': float(mean_absolute_error(y_test, pred)),
+        'R2': float(r2_score(y_test, pred)),
+    }, pred
+
+
+def make_preprocessor(scale_numeric=False):
+    numeric_transformer = StandardScaler() if scale_numeric else 'passthrough'
+    return ColumnTransformer(
+        transformers=[
+            ('cat', OneHotEncoder(handle_unknown='ignore'), CATEGORICAL_FEATURES),
+            ('num', numeric_transformer, NUMERIC_FEATURES),
+        ],
+        remainder='drop',
+        verbose_feature_names_out=False,
+    )
+
+
+def make_models():
+    return {
+        '线性回归': Pipeline([
+            ('preprocess', make_preprocessor(scale_numeric=True)),
+            ('model', LinearRegression()),
+        ]),
+        '随机森林': Pipeline([
+            ('preprocess', make_preprocessor()),
+            ('model', RandomForestRegressor(n_estimators=500, random_state=42, min_samples_leaf=2, n_jobs=-1)),
+        ]),
+        '梯度提升树': Pipeline([
+            ('preprocess', make_preprocessor()),
+            ('model', GradientBoostingRegressor(random_state=42, n_estimators=300, learning_rate=0.05, max_depth=3)),
+        ]),
+        '支持向量回归': Pipeline([
+            ('preprocess', make_preprocessor(scale_numeric=True)),
+            ('model', SVR(C=200, gamma='scale', epsilon=50)),
+        ]),
+        '神经网络': Pipeline([
+            ('preprocess', make_preprocessor(scale_numeric=True)),
+            ('model', MLPRegressor(hidden_layer_sizes=(80, 40), activation='relu', alpha=0.001, max_iter=3000, random_state=42, early_stopping=True)),
+        ]),
+    }
+
+
+def load_data():
+    daily = pd.read_excel(DATA_PATH, sheet_name='每天数据')
+    future = pd.read_excel(DATA_PATH, sheet_name='预测集')
+    daily['日期'] = pd.to_datetime(daily['日期'])
+    future['日期'] = pd.to_datetime(future['日期'])
+    return daily, future
+
+
+def feature_importance(best_model, x_test, y_test):
+    model_name = type(best_model.named_steps['model']).__name__
+    if hasattr(best_model.named_steps['model'], 'feature_importances_'):
+        transformed_names = best_model.named_steps['preprocess'].get_feature_names_out()
+        raw_importances = pd.Series(best_model.named_steps['model'].feature_importances_, index=transformed_names)
+        collapsed = {}
+        for feature in FEATURES:
+            collapsed[feature] = raw_importances[[idx == feature or idx.startswith(feature + '_') for idx in raw_importances.index]].sum()
+        importance = pd.Series(collapsed).sort_values(ascending=False)
+        method = f'{model_name} 内置重要性'
+    else:
+        result = permutation_importance(best_model, x_test, y_test, n_repeats=30, random_state=42, scoring='neg_root_mean_squared_error', n_jobs=-1)
+        importance = pd.Series(result.importances_mean, index=FEATURES).sort_values(ascending=False)
+        method = '排列重要性'
+    return importance, method
+
+
+def save_figures(daily, y_test, y_pred, residuals, importance, future_pred_df):
+    plt.figure(figsize=(12, 6))
+    sns.lineplot(data=daily, x='日期', y=TARGET, color='#2563eb')
+    plt.title('2011-2012 年共享单车每日总骑行量趋势')
+    plt.xlabel('日期')
+    plt.ylabel('总骑行量')
+    plt.tight_layout()
+    plt.savefig(FIG_DIR / 'historical_trend.png', dpi=180)
+    plt.close()
+
+    plt.figure(figsize=(8, 6))
+    sns.scatterplot(x=y_test, y=y_pred, color='#16a34a', edgecolor='white')
+    low, high = min(y_test.min(), y_pred.min()), max(y_test.max(), y_pred.max())
+    plt.plot([low, high], [low, high], '--', color='#ef4444', linewidth=1.5)
+    plt.title('测试集真实值-预测值散点图')
+    plt.xlabel('真实总骑行量')
+    plt.ylabel('预测总骑行量')
+    plt.tight_layout()
+    plt.savefig(FIG_DIR / 'actual_vs_predicted.png', dpi=180)
+    plt.close()
+
+    plt.figure(figsize=(8, 5))
+    sns.histplot(residuals, kde=True, color='#f97316')
+    plt.axvline(0, color='#111827', linestyle='--', linewidth=1)
+    plt.title('测试集残差分布')
+    plt.xlabel('残差（真实值 - 预测值）')
+    plt.ylabel('频数')
+    plt.tight_layout()
+    plt.savefig(FIG_DIR / 'residual_distribution.png', dpi=180)
+    plt.close()
+
+    plt.figure(figsize=(9, 6))
+    plot_imp = importance.sort_values(ascending=True)
+    plt.barh(plot_imp.index, plot_imp.values, color='#7c3aed')
+    plt.title('特征重要性排序')
+    plt.xlabel('重要性')
+    plt.tight_layout()
+    plt.savefig(FIG_DIR / 'feature_importance.png', dpi=180)
+    plt.close()
+
+    plt.figure(figsize=(12, 6))
+    sns.lineplot(data=future_pred_df, x='日期', y='预测总骑行量', marker='o', color='#0891b2')
+    plt.title('2013 年前 100 天共享单车骑行量预测趋势')
+    plt.xlabel('日期')
+    plt.ylabel('预测总骑行量')
+    plt.tight_layout()
+    plt.savefig(FIG_DIR / 'future_prediction_trend.png', dpi=180)
+    plt.close()
+
+
+def main():
+    daily, future = load_data()
+    missing = daily[FEATURES + [TARGET]].isna().sum().to_dict()
+    duplicates = int(daily.duplicated(subset=['日期']).sum())
+
+    x = daily[FEATURES]
+    y = daily[TARGET]
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
+
+    models = make_models()
+    initial_rows = []
+    predictions = {}
+    for name, model in models.items():
+        metrics, pred = evaluate_model(model, x_train, x_test, y_train, y_test)
+        initial_rows.append({'模型': name, **metrics})
+        predictions[name] = pred
+        joblib.dump(model, MODEL_DIR / f'{name}.pkl')
+
+    initial_df = pd.DataFrame(initial_rows).sort_values('RMSE')
+    best_name = initial_df.iloc[0]['模型']
+    best_model = models[best_name]
+    best_pred = predictions[best_name]
+    residuals = y_test.to_numpy() - best_pred
+
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    cv_result = cross_validate(
+        best_model,
+        x,
+        y,
+        cv=cv,
+        scoring={'rmse': 'neg_root_mean_squared_error', 'mae': 'neg_mean_absolute_error', 'r2': 'r2'},
+        n_jobs=-1,
+    )
+    cv_summary = {
+        '模型': best_name,
+        'RMSE均值': float(-cv_result['test_rmse'].mean()),
+        'RMSE标准差': float(cv_result['test_rmse'].std()),
+        'MAE均值': float(-cv_result['test_mae'].mean()),
+        'MAE标准差': float(cv_result['test_mae'].std()),
+        'R2均值': float(cv_result['test_r2'].mean()),
+        'R2标准差': float(cv_result['test_r2'].std()),
+    }
+
+    importance, importance_method = feature_importance(best_model, x_test, y_test)
+
+    future_pred = best_model.predict(future[FEATURES])
+    future_pred_df = future.copy()
+    future_pred_df['预测总骑行量'] = np.maximum(np.round(future_pred), 0).astype(int)
+
+    save_figures(daily, y_test, best_pred, residuals, importance, future_pred_df)
+
+    initial_df.to_csv(OUT_DIR / 'model_comparison.csv', index=False, encoding='utf-8-sig')
+    pd.DataFrame([cv_summary]).to_csv(OUT_DIR / 'cross_validation_summary.csv', index=False, encoding='utf-8-sig')
+    importance.rename('重要性').to_csv(OUT_DIR / 'feature_importance.csv', encoding='utf-8-sig')
+    future_pred_df.to_excel(OUT_DIR / '2013前100天骑行量预测结果.xlsx', index=False)
+
+    test_pred_df = pd.DataFrame({
+        '日期': daily.loc[x_test.index, '日期'].values,
+        '真实总骑行量': y_test.values,
+        '预测总骑行量': np.round(best_pred).astype(int),
+        '残差': np.round(residuals, 2),
+    }).sort_values('日期')
+    test_pred_df.to_csv(OUT_DIR / 'test_set_predictions.csv', index=False, encoding='utf-8-sig')
+
+    joblib.dump(best_model, MODEL_DIR / 'best_bike_demand_model.pkl')
+
+    summary = {
+        'data': {
+            'rows': int(len(daily)),
+            'columns': int(daily.shape[1]),
+            'future_rows': int(len(future)),
+            'missing': missing,
+            'duplicate_dates': duplicates,
+            'target_min': int(y.min()),
+            'target_max': int(y.max()),
+            'target_mean': float(y.mean()),
+        },
+        'initial_comparison': initial_df.to_dict(orient='records'),
+        'best_model': best_name,
+        'best_test_metrics': initial_df.iloc[0].to_dict(),
+        'cross_validation': cv_summary,
+        'importance_method': importance_method,
+        'top_features': importance.head(8).to_dict(),
+        'future_prediction': {
+            'min': int(future_pred_df['预测总骑行量'].min()),
+            'max': int(future_pred_df['预测总骑行量'].max()),
+            'mean': float(future_pred_df['预测总骑行量'].mean()),
+        },
+        'outputs': {
+            'figures': [str(p) for p in sorted(FIG_DIR.glob('*.png'))],
+            'best_model': str(MODEL_DIR / 'best_bike_demand_model.pkl'),
+            'prediction_excel': str(OUT_DIR / '2013前100天骑行量预测结果.xlsx'),
+        }
+    }
+    (OUT_DIR / 'analysis_summary.json').write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding='utf-8')
+    print(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == '__main__':
+    main()

bike_demand_gui.py

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+import tkinter as tk
+from tkinter import ttk, messagebox
+from pathlib import Path
+
+import joblib
+import pandas as pd
+
+BASE_DIR = Path(__file__).resolve().parent
+MODEL_PATH = BASE_DIR / 'outputs' / 'models' / 'best_bike_demand_model.pkl'
+FEATURES = ['季节', '年', '月', '节假日', '星期', '工作日', '天气', '气温', '体感温度', '湿度', '风速']
+
+
+class BikeDemandApp:
+    def __init__(self, root):
+        self.root = root
+        self.root.title('共享单车日骑行量预测系统')
+        self.root.geometry('820x560')
+        self.model = joblib.load(MODEL_PATH)
+        self.entries = {}
+        self.history = []
+        self.build_ui()
+
+    def build_ui(self):
+        container = ttk.Frame(self.root, padding=16)
+        container.pack(fill=tk.BOTH, expand=True)
+
+        left = ttk.LabelFrame(container, text='输入特征', padding=12)
+        left.pack(side=tk.LEFT, fill=tk.Y, padx=(0, 12))
+
+        fields = [
+            ('季节', '1=春 2=夏 3=秋 4=冬', '1'),
+            ('年', '0=2011 1=2012 2=2013', '2'),
+            ('月', '1~12', '1'),
+            ('节假日', '0=否 1=是', '0'),
+            ('星期', '0=周日 ... 6=周六', '1'),
+            ('工作日', '0=否 1=是', '1'),
+            ('天气', '1=晴好 2=多云 3=小雨雪 4=恶劣', '1'),
+            ('气温', '归一化 0~1', '0.32'),
+            ('体感温度', '归一化 0~1', '0.34'),
+            ('湿度', '归一化 0~1', '0.70'),
+            ('风速', '归一化 0~1', '0.15'),
+        ]
+        for row, (name, hint, default) in enumerate(fields):
+            ttk.Label(left, text=name).grid(row=row, column=0, sticky='w', pady=4)
+            entry = ttk.Entry(left, width=14)
+            entry.insert(0, default)
+            entry.grid(row=row, column=1, sticky='ew', pady=4)
+            ttk.Label(left, text=hint, foreground='#666').grid(row=row, column=2, sticky='w', padx=6)
+            self.entries[name] = entry
+
+        ttk.Button(left, text='预测单场景', command=self.predict_one).grid(row=len(fields), column=0, columnspan=3, sticky='ew', pady=(12, 4))
+        ttk.Button(left, text='加入对比列表', command=self.add_to_history).grid(row=len(fields) + 1, column=0, columnspan=3, sticky='ew', pady=4)
+        ttk.Button(left, text='清空对比列表', command=self.clear_history).grid(row=len(fields) + 2, column=0, columnspan=3, sticky='ew', pady=4)
+
+        right = ttk.Frame(container)
+        right.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True)
+
+        result_box = ttk.LabelFrame(right, text='预测结果', padding=16)
+        result_box.pack(fill=tk.X)
+        self.result_var = tk.StringVar(value='请输入特征后点击预测')
+        ttk.Label(result_box, textvariable=self.result_var, font=('Arial', 22, 'bold'), foreground='#0f766e').pack(anchor='w')
+        self.level_var = tk.StringVar(value='')
+        ttk.Label(result_box, textvariable=self.level_var, font=('Arial', 12)).pack(anchor='w', pady=(8, 0))
+
+        table_box = ttk.LabelFrame(right, text='多场景对比', padding=8)
+        table_box.pack(fill=tk.BOTH, expand=True, pady=(12, 0))
+        columns = ('序号', '月', '天气', '气温', '工作日', '预测总骑行量')
+        self.tree = ttk.Treeview(table_box, columns=columns, show='headings', height=12)
+        for col in columns:
+            self.tree.heading(col, text=col)
+            self.tree.column(col, width=90, anchor='center')
+        self.tree.pack(fill=tk.BOTH, expand=True)
+
+    def collect_features(self):
+        values = {}
+        for feature, entry in self.entries.items():
+            try:
+                values[feature] = float(entry.get())
+            except ValueError as exc:
+                raise ValueError(f'{feature} 必须是数字') from exc
+        for feature in ['季节', '年', '月', '节假日', '星期', '工作日', '天气']:
+            values[feature] = int(values[feature])
+        for feature in ['气温', '体感温度', '湿度', '风速']:
+            if not 0 <= values[feature] <= 1:
+                raise ValueError(f'{feature} 应在 0~1 之间')
+        return values
+
+    def predict_value(self):
+        values = self.collect_features()
+        frame = pd.DataFrame([values], columns=FEATURES)
+        prediction = int(max(round(self.model.predict(frame)[0]), 0))
+        return values, prediction
+
+    def predict_one(self):
+        try:
+            _, prediction = self.predict_value()
+        except Exception as exc:
+            messagebox.showerror('输入错误', str(exc))
+            return
+        self.result_var.set(f'预计日骑行量：{prediction:,} 次')
+        if prediction >= 5000:
+            level = '需求等级：高，需要提前调度车辆。'
+        elif prediction >= 3000:
+            level = '需求等级：中，维持常规运维。'
+        else:
+            level = '需求等级：低，可减少热点补车频率。'
+        self.level_var.set(level)
+
+    def add_to_history(self):
+        try:
+            values, prediction = self.predict_value()
+        except Exception as exc:
+            messagebox.showerror('输入错误', str(exc))
+            return
+        self.history.append((values, prediction))
+        idx = len(self.history)
+        self.tree.insert('', 'end', values=(idx, values['月'], values['天气'], values['气温'], values['工作日'], prediction))
+        self.predict_one()
+
+    def clear_history(self):
+        self.history.clear()
+        for item in self.tree.get_children():
+            self.tree.delete(item)
+
+
+if __name__ == '__main__':
+    root = tk.Tk()
+    app = BikeDemandApp(root)
+    root.mainloop()

course_design_requirements.txt

@@ -0,0 +1,64 @@
+6
+三、基于机器学习的共享单车日骑行量预测与可视化系统
+1. 课题背景
+在构建绿色、低碳城市的进程中，“最后一公里”出行难题一直是阻碍公共交通
+高效利用的关键瓶颈。共享单车作为一种创新的分时租赁模式，有效填补了这一空
+白，不仅提升了居民使用公共交通的整体意愿，还促进了绿色出行理念的普及。然
+而，共享单车的运营效率高度依赖于对用户需求的精准预判。骑行需求受多种复杂
+因素影响，如天气状况、季节变化、是否为工作日或节假日等。因此，利用历史运
+营数据，通过数据挖掘与机器学习技术建立精准的需求预测模型，对于优化车辆调
+度、提升用户体验及实现企业精细化运营具有重要的现实意义。
+2. 数据描述
+本课题采用的数据集源自美国华盛顿特区的Capital Bikeshare 系统，是UCI 机器
+学习库中的经典数据集（Bike Sharing Dataset）。我们选用其中的日度统计数据，见“共
+享单车骑行量数据.xlsx”的第一张工作表“每天数据”，数据集共包含731 条记录，覆
+盖了2011 年与2012 年两年完整的骑行数据，共16 列，各列含义见第二张工作表“特
+征说明”。
+此外，数据集还提供了待预测的2013 年前100 天的相关特征，见第三张工作表
+“预测集”，用于最终模型的骑行量预测。
+3. 设计任务
+本课题围绕共享单车每日总骑行量预测展开，要求完成“模型构建与对比→特征
+影响剖析→性能评估与预测→可视化系统开发”的完整技术研究流程，全面锻炼数据
+建模、特征分析、模型优化及GUI 开发的综合能力。
+任务一：多模型构建与初步筛选
+目标：建立包含基线模型和候选模型的完整模型池，为后续分析提供严谨的性
+能基准。
+核心步骤：
+（1）数据准备：从数据集第一张工作表中提取特征（12 维）与目标变量，并将
+数据集划分为训练集和测试集；
+（2）模型选型：
+基线模型：选择线性回归模型作为基准；
+候选模型：至少选择两类非线性回归模型（如树模型+ 核方法/神经网络），体
+现方法多样性；
+（3）基础训练：对每类模型使用默认或合理初始参数进行训练；
+（4）对比：在验证集上计算各模型的RMSE/MAE/R²，形成初步性能排序；
+（5）输出：保存各模型对象及初步评估结果，为任务三的深入评估和任务二的
+特征分析提供候选模型池。
+
+7
+任务二：特征重要性解析
+目标：挖掘影响骑行量的关键因子，解释模型决策逻辑。
+核心步骤：
+（1）选定最优模型：基于任务一结果，选取性能最佳模型；
+（2）量化与可视化：通过内置属性（树模型）或排列重要性（其他模型）计算
+特征贡献，绘制条形图；
+（3）业务解读：结合领域知识分析关键特征的影响机制；
+（4）输出：特征重要性报告，为模型精简提供依据。
+任务三：模型评估与预测输出
+目标：验证模型泛化能力，生成最终预测结果。
+核心步骤：
+（1）全面评估：通过交叉验证计算RMSE/MAE/R²，绘制真实值-预测值散点图
+及残差分布；
+（2）确定最优模型：综合指标与稳定性，选定部署模型；
+（3）预测与可视化：基于2013 年前100 天特征数据生成预测值，输出表格及
+趋势折线图；
+（4）输出：保存模型文件（如.pkl）和预测结果，作为GUI 系统的数据源。
+任务四：轻量级预测可视化系统开发
+目标：实现分析成果的交互式应用。
+核心步骤：
+（1）框架选型：采用轻量GUI 工具（如Streamlit、tkinter、PyQT）；
+（2）界面设计：左侧输入区（日期、天气、温度等）+右侧结果展示区；
+（3）模型集成：加载最优模型，编写特征处理与预测函数；
+（4）动态可视化：让用户直观理解预测结果，同时支持单场景查询（数字+仪
+表盘）和多场景对比（折线图）两种核心需求；
+（5）优化与输出：实现输入校验与实时响应，交付可运行程序及使用说明。

generate_report.py

@@ -0,0 +1,124 @@
+import json
+from pathlib import Path
+
+import pandas as pd
+from docx import Document
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+from docx.shared import Inches, Pt
+
+BASE_DIR = Path(__file__).resolve().parent
+OUT_DIR = BASE_DIR / 'outputs'
+FIG_DIR = OUT_DIR / 'figures'
+REPORT_PATH = OUT_DIR / '共享单车日骑行量预测课程设计报告.docx'
+
+
+def add_heading(document, text, level):
+    heading = document.add_heading(text, level=level)
+    for run in heading.runs:
+        run.font.name = '宋体'
+    return heading
+
+
+def add_paragraph(document, text):
+    paragraph = document.add_paragraph(text)
+    paragraph.paragraph_format.first_line_indent = Pt(24)
+    paragraph.paragraph_format.line_spacing = 1.5
+    for run in paragraph.runs:
+        run.font.name = '宋体'
+        run.font.size = Pt(11)
+    return paragraph
+
+
+def add_table_from_df(document, df, title=None):
+    if title:
+        add_paragraph(document, title)
+    table = document.add_table(rows=1, cols=len(df.columns))
+    table.style = 'Table Grid'
+    hdr = table.rows[0].cells
+    for i, col in enumerate(df.columns):
+        hdr[i].text = str(col)
+    for _, row in df.iterrows():
+        cells = table.add_row().cells
+        for i, value in enumerate(row):
+            if isinstance(value, float):
+                cells[i].text = f'{value:.4f}'
+            else:
+                cells[i].text = str(value)
+    document.add_paragraph('')
+
+
+def main():
+    summary = json.loads((OUT_DIR / 'analysis_summary.json').read_text(encoding='utf-8'))
+    comparison = pd.read_csv(OUT_DIR / 'model_comparison.csv')
+    cv = pd.read_csv(OUT_DIR / 'cross_validation_summary.csv')
+    importance = pd.read_csv(OUT_DIR / 'feature_importance.csv')
+    future = pd.read_excel(OUT_DIR / '2013前100天骑行量预测结果.xlsx')
+
+    document = Document()
+    section = document.sections[0]
+    section.top_margin = Inches(0.8)
+    section.bottom_margin = Inches(0.8)
+    section.left_margin = Inches(0.9)
+    section.right_margin = Inches(0.9)
+
+    title = document.add_heading('基于机器学习的共享单车日骑行量预测与可视化系统', 0)
+    title.alignment = WD_ALIGN_PARAGRAPH.CENTER
+    subtitle = document.add_paragraph('《大数据分析技术》课程设计报告')
+    subtitle.alignment = WD_ALIGN_PARAGRAPH.CENTER
+    document.add_paragraph('')
+
+    add_heading(document, '一、课题背景与研究目标', 1)
+    add_paragraph(document, '共享单车是解决城市公共交通“最后一公里”问题的重要方式。其运营效率依赖于车辆供需匹配能力，而日骑行量会受到季节、天气、温度、湿度、风速、工作日等多因素共同影响。本课程设计以 Capital Bikeshare 日度数据为基础，构建机器学习模型预测共享单车每日总骑行量，并形成可视化分析结果与轻量级预测系统。')
+    add_paragraph(document, '本项目围绕“模型构建与对比、特征影响剖析、性能评估与预测、GUI 系统开发”四项任务展开，最终输出模型文件、评估结果、预测表格、图表和可运行程序。')
+
+    add_heading(document, '二、数据说明与预处理', 1)
+    data = summary['data']
+    add_paragraph(document, f'历史数据来自 Excel 第一张工作表“每天数据”，共 {data["rows"]} 条记录、{data["columns"]} 列，覆盖 2011—2012 年完整日度骑行数据；预测集来自第三张工作表“预测集”，包含 2013 年前 {data["future_rows"]} 天的待预测特征。')
+    add_paragraph(document, f'目标变量为“总骑行量”，历史样本最小值为 {data["target_min"]}，最大值为 {data["target_max"]}，均值为 {data["target_mean"]:.2f}。经检查，建模字段不存在缺失值，日期字段无重复记录。')
+    add_paragraph(document, '建模特征选取季节、年、月、节假日、星期、工作日、天气、气温、体感温度、湿度、风速等 11 个字段。类别型特征采用 One-Hot 编码，数值型特征在对线性回归、SVR 和神经网络建模时进行标准化处理。')
+    document.add_picture(str(FIG_DIR / 'historical_trend.png'), width=Inches(6.2))
+
+    add_heading(document, '三、多模型构建与初步筛选', 1)
+    add_paragraph(document, '按照课程设计要求，模型池包含一个基线模型和多类非线性候选模型。基线模型采用线性回归；候选模型包括随机森林、梯度提升树、支持向量回归和神经网络，覆盖树模型、核方法和神经网络方法。数据按 8:2 划分训练集和测试集，使用 RMSE、MAE、R² 进行比较。')
+    add_table_from_df(document, comparison, '表 1 模型初步对比结果')
+    best = summary['best_test_metrics']
+    add_paragraph(document, f'从测试集结果看，{summary["best_model"]} 的综合表现最佳，RMSE={best["RMSE"]:.2f}，MAE={best["MAE"]:.2f}，R²={best["R2"]:.4f}。因此后续特征解释、交叉验证与 2013 年预测均以该模型作为最终模型。')
+
+    add_heading(document, '四、特征重要性解析', 1)
+    add_paragraph(document, f'由于最优模型为梯度提升树，项目采用模型内置特征重要性计算特征贡献，并将 One-Hot 后的同源类别特征聚合回原始字段。特征重要性结果显示，气温、年、季节、体感温度、湿度对骑行量影响最大。')
+    document.add_picture(str(FIG_DIR / 'feature_importance.png'), width=Inches(6.2))
+    add_table_from_df(document, importance.head(8), '表 2 前 8 个重要特征')
+    add_paragraph(document, '业务上，气温和体感温度直接影响用户骑行意愿，舒适天气下出行需求更高；年份特征反映共享单车系统规模和用户习惯随时间增长；季节与月份体现周期性需求变化；湿度、风速和天气体现不良气象条件对骑行行为的抑制作用。')
+
+    add_heading(document, '五、模型全面评估', 1)
+    add_paragraph(document, '为了验证模型泛化能力，对最佳模型进行五折交叉验证，并绘制测试集真实值-预测值散点图和残差分布图。')
+    add_table_from_df(document, cv, '表 3 五折交叉验证结果')
+    cv_row = summary['cross_validation']
+    add_paragraph(document, f'交叉验证 RMSE 均值为 {cv_row["RMSE均值"]:.2f}，标准差为 {cv_row["RMSE标准差"]:.2f}；R² 均值为 {cv_row["R2均值"]:.4f}，说明模型在不同数据划分下较稳定。')
+    document.add_picture(str(FIG_DIR / 'actual_vs_predicted.png'), width=Inches(5.8))
+    document.add_picture(str(FIG_DIR / 'residual_distribution.png'), width=Inches(5.8))
+    add_paragraph(document, '真实值-预测值散点大体分布在对角线附近，说明模型能较好拟合总体变化趋势。残差分布集中在 0 附近，表明模型不存在明显系统性偏差，但在极端低需求或高需求日期仍可能出现较大误差。')
+
+    add_heading(document, '六、2013 年前 100 天预测结果', 1)
+    fp = summary['future_prediction']
+    add_paragraph(document, f'使用最终梯度提升树模型对预测集进行推断，2013 年前 100 天预测骑行量最小值为 {fp["min"]}，最大值为 {fp["max"]}，均值为 {fp["mean"]:.2f}。完整结果已保存为 Excel 文件“2013前100天骑行量预测结果.xlsx”。')
+    document.add_picture(str(FIG_DIR / 'future_prediction_trend.png'), width=Inches(6.2))
+    sample_future = future[['日期', '季节', '月', '天气', '气温', '湿度', '预测总骑行量']].head(10).copy()
+    sample_future['日期'] = sample_future['日期'].dt.strftime('%Y-%m-%d')
+    add_table_from_df(document, sample_future, '表 4 预测结果样例（前 10 天）')
+
+    add_heading(document, '七、轻量级预测可视化系统', 1)
+    add_paragraph(document, '系统采用 Python tkinter 开发，程序文件为 bike_demand_gui.py。界面左侧提供季节、年份、月份、节假日、星期、工作日、天气、气温、体感温度、湿度、风速等输入项，右侧展示单场景预测结果与多场景对比表。')
+    add_paragraph(document, '系统启动时加载 outputs/models/best_bike_demand_model.pkl，并对用户输入进行数字类型和归一化范围校验。用户可点击“预测单场景”获得预计日骑行量，也可将多组输入加入对比列表，用于比较不同天气或温度条件下的需求变化。')
+    add_paragraph(document, '运行方式：在项目目录执行 `.venv/bin/python bike_demand_gui.py`。')
+
+    add_heading(document, '八、结论', 1)
+    add_paragraph(document, '本课程设计完成了从数据读取、预处理、模型训练、模型比较、特征解释、交叉验证、未来预测到 GUI 系统开发的完整流程。实验结果表明，梯度提升树在该共享单车日骑行量预测任务中优于线性回归、随机森林、SVR 和神经网络，能够较好刻画天气、季节和时间因素与骑行需求之间的非线性关系。')
+    add_paragraph(document, '后续可进一步引入节假日类型、降水量、重大活动、站点级空间信息等外部变量，并采用时间序列交叉验证或集成学习优化模型，以提升极端天气和异常日期下的预测鲁棒性。')
+
+    document.save(REPORT_PATH)
+    print(REPORT_PATH)
+
+
+if __name__ == '__main__':
+    main()

outputs/analysis_summary.json

@@ -0,0 +1,100 @@
+{
+  "data": {
+    "rows": 731,
+    "columns": 16,
+    "future_rows": 100,
+    "missing": {
+      "季节": 0,
+      "年": 0,
+      "月": 0,
+      "节假日": 0,
+      "星期": 0,
+      "工作日": 0,
+      "天气": 0,
+      "气温": 0,
+      "体感温度": 0,
+      "湿度": 0,
+      "风速": 0,
+      "总骑行量": 0
+    },
+    "duplicate_dates": 0,
+    "target_min": 22,
+    "target_max": 8714,
+    "target_mean": 4504.3488372093025
+  },
+  "initial_comparison": [
+    {
+      "模型": "梯度提升树",
+      "RMSE": 651.1440567037434,
+      "MAE": 448.99523765137667,
+      "R2": 0.8942640825672749
+    },
+    {
+      "模型": "随机森林",
+      "RMSE": 703.9966504917254,
+      "MAE": 450.0664823777129,
+      "R2": 0.8764025354772573
+    },
+    {
+      "模型": "支持向量回归",
+      "RMSE": 738.1033329574317,
+      "MAE": 529.5340809792003,
+      "R2": 0.8641365279788986
+    },
+    {
+      "模型": "线性回归",
+      "RMSE": 796.4617765623753,
+      "MAE": 583.0197588300691,
+      "R2": 0.8418029967286178
+    },
+    {
+      "模型": "神经网络",
+      "RMSE": 809.7923656712804,
+      "MAE": 609.9584660642089,
+      "R2": 0.836463110808386
+    }
+  ],
+  "best_model": "梯度提升树",
+  "best_test_metrics": {
+    "模型": "梯度提升树",
+    "RMSE": 651.1440567037434,
+    "MAE": 448.99523765137667,
+    "R2": 0.8942640825672749
+  },
+  "cross_validation": {
+    "模型": "梯度提升树",
+    "RMSE均值": 655.1166606641779,
+    "RMSE标准差": 42.75460340121525,
+    "MAE均值": 466.577603572732,
+    "MAE标准差": 14.719481812429436,
+    "R2均值": 0.8818566774188483,
+    "R2标准差": 0.023087453303387278
+  },
+  "importance_method": "GradientBoostingRegressor 内置重要性",
+  "top_features": {
+    "气温": 0.38973625494728426,
+    "年": 0.2984069767787937,
+    "季节": 0.0932052430981216,
+    "体感温度": 0.07101028400532663,
+    "湿度": 0.05891447646010377,
+    "天气": 0.029590235728090393,
+    "风速": 0.026177907657402667,
+    "月": 0.01607120463407045
+  },
+  "future_prediction": {
+    "min": 1319,
+    "max": 6025,
+    "mean": 3775.15
+  },
+  "outputs": {
+    "figures": [
+      "/root/bike_course_design/outputs/figures/actual_vs_predicted.png",
+      "/root/bike_course_design/outputs/figures/feature_importance.png",
+      "/root/bike_course_design/outputs/figures/future_prediction_trend.png",
+      "/root/bike_course_design/outputs/figures/historical_trend.png",
+      "/root/bike_course_design/outputs/figures/residual_distribution.png"
+    ],
+    "best_model": "/root/bike_course_design/outputs/models/best_bike_demand_model.pkl",
+    "prediction_excel": "/root/bike_course_design/outputs/2013前100天骑行量预测结果.xlsx"
+  }
+}

outputs/cross_validation_summary.csv

@@ -0,0 +1,2 @@
+模型,RMSE均值,RMSE标准差,MAE均值,MAE标准差,R2均值,R2标准差
+梯度提升树,655.1166606641779,42.75460340121525,466.577603572732,14.719481812429436,0.8818566774188483,0.023087453303387278

outputs/feature_importance.csv

@@ -0,0 +1,12 @@
+,重要性
+气温,0.38973625494728426
+年,0.2984069767787937
+季节,0.0932052430981216
+体感温度,0.07101028400532663
+湿度,0.05891447646010377
+天气,0.029590235728090393
+风速,0.026177907657402667
+月,0.01607120463407045
+星期,0.00832340663049113
+工作日,0.004855667817542203
+节假日,0.003708342242773187

outputs/model_comparison.csv

@@ -0,0 +1,6 @@
+模型,RMSE,MAE,R2
+梯度提升树,651.1440567037434,448.99523765137667,0.8942640825672749
+随机森林,703.9966504917254,450.0664823777129,0.8764025354772573
+支持向量回归,738.1033329574317,529.5340809792003,0.8641365279788986
+线性回归,796.4617765623753,583.0197588300691,0.8418029967286178
+神经网络,809.7923656712804,609.9584660642089,0.836463110808386

outputs/test_set_predictions.csv

@@ -0,0 +1,148 @@
+日期,真实总骑行量,预测总骑行量,残差
+2011-01-03,1349,1577,-228.44
+2011-01-11,1263,1199,64.1
+2011-01-24,1416,1465,-49.06
+2011-01-31,1501,1554,-52.9
+2011-02-01,1360,950,410.36
+2011-02-03,1550,1504,46.2
+2011-02-09,1605,1455,150.06
+2011-02-14,1913,2320,-406.86
+2011-02-19,1635,1886,-251.38
+2011-02-24,1807,1201,606.29
+2011-02-25,1461,1134,327.36
+2011-03-02,2134,2191,-56.9
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