Create app.py

app.py

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+# ======================== (A) 导入库和配置环境 ========================
+import pandas as pd
+import numpy as np
+import time
+import warnings
+import os
+import logging
+
+# 数据分析与建模
+from scipy import stats
+from statsmodels.tsa.stattools import adfuller
+from statsmodels.tsa.seasonal import STL
+from statsmodels.stats.diagnostic import acorr_ljungbox
+from prophet import Prophet
+import pmdarima as pm
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from joblib import Parallel, delayed
+
+# 可视化
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Web UI
+import gradio as gr
+
+# --- 全局设置 ---
+warnings.filterwarnings("ignore")
+logging.getLogger('prophet').setLevel(logging.ERROR)
+logging.getLogger('cmdstanpy').setLevel(logging.ERROR)
+
+# 配置中文字体 (使用 packages.txt 安装的字体)
+plt.rcParams['font.sans-serif'] = ['WenQuanYi Zen Hei']
+plt.rcParams['axes.unicode_minus'] = False
+
+# --- 输出文件夹设置 ---
+OUTPUT_DIR = 'outputs'
+if not os.path.exists(OUTPUT_DIR):
+    os.makedirs(OUTPUT_DIR)
+
+# ======================== (B) 辅助函数 ========================
+
+def calculate_metrics(actual, predicted):
+    # (此函数来自您的原始代码)
+    metrics_df = pd.DataFrame({'actual': actual, 'predicted': predicted}).dropna()
+    if metrics_df.empty:
+        return {'MAE': np.nan, 'RMSE': np.nan, 'MAPE': np.nan, 'sMAPE': np.nan}
+    clean_actual, clean_predicted = metrics_df['actual'], metrics_df['predicted']
+    mae = mean_absolute_error(clean_actual, clean_predicted)
+    rmse = np.sqrt(mean_squared_error(clean_actual, clean_predicted))
+    actual_safe = np.where(clean_actual == 0, 1e-6, clean_actual)
+    mape = np.mean(np.abs((clean_actual - clean_predicted) / actual_safe)) * 100
+    smape = 200 * np.mean(np.abs(clean_actual - clean_predicted) / (np.abs(clean_actual) + np.abs(clean_predicted)))
+    return {'MAE': mae, 'RMSE': rmse, 'MAPE': mape, 'sMAPE': smape}
+
+# ======================== (C) 主分析函数 (Gradio核心) ========================
+
+def run_full_analysis(progress=gr.Progress(track_tqdm=True)):
+    """
+    这个主函数封装了所有的分析步骤，并通过 yield 返回结果来实时更新Gradio界面。
+    """
+    # --- 1. 初始化 ---
+    log_lines = ["## 🚀 数据分析流程已启动..."]
+    figures = []
+    final_report_text = ""
+    report_file_path = None
+    
+    # 定义一个辅助函数来更新界面状态
+    def update_ui(new_log_line=None):
+        if new_log_line:
+            log_lines.append(new_log_line)
+        # 返回当前所有输出的状态
+        # [log, gallery, final_report, download_button]
+        return "\n\n".join(log_lines), figures, final_report_text, report_file_path
+    
+    yield update_ui() # 立即显示启动信息
+
+    try:
+        # --- 2. 数据清洗 ---
+        log_lines.append("### 1. 数据清洗")
+        df = pd.read_excel('gmqrkl.xlsx')
+        df['Date'] = pd.to_datetime(df['Date'])
+        df = df.drop_duplicates(subset=['Date']).sort_values('Date').reset_index(drop=True)
+        log_lines.append(f"✅ 数据读取并去重成功，共 {len(df)} 行。")
+        
+        df['Value'] = df['Value'].replace(0, np.nan)
+        df['Value'].interpolate(method='linear', limit_direction='both', inplace=True)
+        log_lines.append("✅ 零值替换与线性插值完成。")
+        df.set_index('Date', inplace=True)
+        ts_data = df['Value']
+        yield update_ui()
+
+        # --- 3. 平稳性检验 ---
+        log_lines.append("### 2. 平稳性检验与差分")
+        # (代码与您提供的一致)
+        diff_order = 0
+        current_data = ts_data.dropna()
+        adf_result = adfuller(current_data)
+        p_value = adf_result[1]
+        if p_value < 0.05:
+            msg = f"✅ 序列在 d=0 阶差分后达到平稳 (p={p_value:.4f})。"
+            log_lines.append(msg)
+            d_order = 0
+        else:
+            current_data_diff = current_data.diff().dropna()
+            adf_result_diff = adfuller(current_data_diff)
+            p_value_diff = adf_result_diff[1]
+            if p_value_diff < 0.05:
+                msg = f"✅ 序列在 d=1 阶差分后达到平稳 (p={p_value_diff:.4f})。"
+                log_lines.append(msg)
+                d_order = 1
+                current_data = current_data_diff
+            else:
+                 msg = f"⚠️ 1阶差分后仍未平稳 (p={p_value_diff:.4f})，将使用 d=1 继续分析。"
+                 log_lines.append(msg)
+                 d_order = 1
+                 current_data = current_data_diff
+        ts_stationary = current_data
+        yield update_ui()
+
+        # --- 4. 白噪声检验 ---
+        log_lines.append("### 3. 白噪声检验")
+        # (代码与您提供的一致)
+        lags = min(10, len(ts_stationary) // 5)
+        lb_test_result = acorr_ljungbox(ts_stationary, lags=[lags], return_df=True)
+        lb_p_value = lb_test_result['lb_pvalue'].iloc[0]
+        if lb_p_value > 0.05:
+            log_lines.append(f"⚠️ 序列可能是白噪声(p-value = {lb_p_value:.4f})，模型可能无效。")
+        else:
+            log_lines.append(f"✅ 通过白噪声检验 (p-value = {lb_p_value:.4f})，可以进行后续建模。")
+        yield update_ui()
+        
+        # --- 5. 季节性检验与分解 ---
+        log_lines.append("### 4. 季节性检验与STL分解")
+        # (简化了您的季节性检验逻辑，直接检查周期性并设定参数)
+        period = 365
+        seasonal_enabled = len(ts_data) > 2 * 7 # 简化判断，若数据多于两周则开启周季节性
+        m_period = 7 if seasonal_enabled else 1
+        log_lines.append(f"✅ 季节性参数设定: m={m_period}, seasonal={seasonal_enabled}")
+        
+        if len(ts_data) >= 2 * period:
+            stl = STL(ts_data, period=period, seasonal=period+1)
+            res = stl.fit()
+            fig, axes = plt.subplots(4, 1, figsize=(12, 8), sharex=True)
+            res.plot(axes=axes)
+            fig.suptitle('STL 分解图 (周期=365)', fontsize=16)
+            plt.tight_layout(rect=[0, 0, 1, 0.96])
+            figures.append(fig)
+            log_lines.append("✅ STL分解图已生成。")
+        else:
+            log_lines.append("⚠️ 数据长度不足以进行年度季节性分解。")
+        yield update_ui()
+
+        # --- 6. 混合策略回测优化窗口大小 ---
+        log_lines.append("\n### 5. 优化训练窗口大小")
+        log_lines.append("⏳ **此步骤计算量大，可能需要5-15分钟，请耐心等待...**")
+        yield update_ui()
+
+        def evaluate_window_hybrid(window_size, time_series, d, m, seasonal):
+            # (此函数来自您的代码)
+            errors = []
+            series_values = time_series.values
+            backtest_length = 100 # 减少回测长度以加速
+            if len(series_values) <= window_size + backtest_length: return {'window_size': window_size, 'mae': np.inf}
+            end_index = len(series_values)
+            start_index = end_index - backtest_length
+            for i in range(start_index, end_index):
+                train_window = pd.Series(series_values[i-window_size:i], index=time_series.index[i-window_size:i])
+                test_point = series_values[i]
+                use_seasonal = seasonal and (len(train_window) >= 2 * m)
+                try:
+                    model = pm.auto_arima(train_window, d=d, seasonal=use_seasonal, m=m, max_p=2, max_q=2,
+                                          stepwise=True, trace=False, error_action='ignore', suppress_warnings=True)
+                    forecast = model.predict(n_periods=1)[0]
+                    errors.append(test_point - forecast)
+                except Exception: continue
+            if not errors: return {'window_size': window_size, 'mae': np.inf}
+            return {'window_size': window_size, 'mae': np.mean(np.abs(errors))}
+        
+        window_sizes_to_test = np.arange(70, 211, 14) # 增大步长以加速
+        with Parallel(n_jobs=-1) as parallel:
+            results = parallel(
+                delayed(evaluate_window_hybrid)(ws, ts_data, d_order, m_period, seasonal_enabled) for ws in window_sizes_to_test
+            )
+        
+        window_results_df = pd.DataFrame(results).sort_values('mae').set_index('window_size')
+        if not window_results_df.empty and np.isfinite(window_results_df['mae'].min()):
+            best_window_size = window_results_df['mae'].idxmin()
+            log_lines.append(f"✅ **窗口优化完成!** 基于MAE，最佳训练窗口大小为: **{best_window_size}** 天。")
+        else:
+            best_window_size = 90
+            log_lines.append(f"⚠️ 窗口优化失败，使用默认窗口大小: {best_window_size} 天。")
+
+        fig, ax = plt.subplots(figsize=(12, 6))
+        ax.plot(window_results_df.index, window_results_df['mae'], marker='o', label='MAE')
+        ax.set_title('训练窗口大小对预测误差的影响')
+        ax.set_xlabel('训练窗口天数'); ax.set_ylabel('误差值'); ax.legend(); ax.grid(True)
+        figures.append(fig)
+        yield update_ui()
+
+        # --- 7 & 8. 动态滚动预测与评估 ---
+        log_lines.append("\n### 6. 动态滚动预测与评估")
+        log_lines.append("⏳ **此步骤同样耗时，正在进行模型滚动预测...**")
+        yield update_ui()
+
+        split_point_roll = int(len(ts_data) * 0.8)
+        test_rolling_target = ts_data.iloc[split_point_roll:]
+        rolling_predictions = {}
+        
+        # SARIMA 滚动
+        sarima_rolling_preds = []
+        for i in range(len(test_rolling_target)):
+            train_window = ts_data.iloc[split_point_roll + i - best_window_size : split_point_roll + i]
+            try:
+                model = pm.auto_arima(train_window, d=d_order, m=m_period, seasonal=seasonal_enabled,
+                                      stepwise=True, trace=False, error_action='ignore', suppress_warnings=True)
+                sarima_rolling_preds.append(model.predict(n_periods=1)[0])
+            except:
+                sarima_rolling_preds.append(np.nan)
+        rolling_predictions['Auto-SARIMA'] = pd.Series(sarima_rolling_preds, index=test_rolling_target.index).ffill()
+        log_lines.append("✅ Auto-SARIMA 滚动预测完成。")
+        yield update_ui()
+
+        # Prophet 滚动 (简化策略)
+        prophet_rolling_preds = []
+        prophet_model = None
+        for i, (date, value) in enumerate(test_rolling_target.items()):
+            if i % 14 == 0 or prophet_model is None: # 每 14 天重训练
+                train_upto_date = ts_data.loc[:date - pd.Timedelta(days=1)]
+                prophet_train_df = train_upto_date.reset_index().rename(columns={'Date': 'ds', 'Value': 'y'})
+                prophet_model = Prophet(yearly_seasonality='auto', weekly_seasonality=seasonal_enabled, daily_seasonality=False).fit(prophet_train_df)
+            future_df = pd.DataFrame({'ds': [date]})
+            forecast = prophet_model.predict(future_df)
+            prophet_rolling_preds.append(forecast['yhat'].iloc[0])
+        rolling_predictions['Prophet'] = pd.Series(prophet_rolling_preds, index=test_rolling_target.index)
+        log_lines.append("✅ Prophet 滚动预测完成。")
+
+        # 评估
+        rolling_evaluation_results = {name: calculate_metrics(test_rolling_target, preds) for name, preds in rolling_predictions.items()}
+        rolling_evaluation_df = pd.DataFrame(rolling_evaluation_results).T.sort_values(by='MAE')
+        best_rolling_model_name = rolling_evaluation_df.index[0]
+        log_lines.append("\n**滚动预测性能对比:**")
+        log_lines.append(f"```\n{rolling_evaluation_df.to_markdown()}\n```")
+        log_lines.append(f"\n==> ✅ 最佳滚动预测模型是: **{best_rolling_model_name}**")
+        yield update_ui()
+        
+        # --- 9. 滚动预测可视化 ---
+        log_lines.append("\n### 7. 生成结果图表")
+        fig, ax = plt.subplots(figsize=(15, 8))
+        ax.plot(ts_data, label='历史数据', color='gray', alpha=0.5)
+        ax.plot(test_rolling_target, label='真实值 (测试集)', color='blue', linewidth=2)
+        for model_name, preds in rolling_predictions.items():
+            is_best = ' (最佳)' if model_name == best_rolling_model_name else ''
+            ax.plot(preds, label=f'{model_name} 预测{is_best}', linestyle='--')
+        ax.set_title('滚动预测结果对比'); ax.legend(); ax.grid(True)
+        figures.append(fig)
+        yield update_ui()
+        
+        # --- 10. 最终未来预测 ---
+        forecast_horizon = 90
+        log_lines.append(f"\n### 8. 使用最佳模型 `{best_rolling_model_name}` 预测未来 {forecast_horizon} 天")
+        
+        # 训练最终模型
+        if 'Auto-SARIMA' in best_rolling_model_name:
+            final_train_data = ts_data.iloc[-best_window_size:]
+            final_model = pm.auto_arima(final_train_data, d=d_order, m=m_period, seasonal=seasonal_enabled,
+                                      stepwise=True, trace=False, error_action='ignore', suppress_warnings=True)
+            final_forecast_values, conf_int = final_model.predict(n_periods=forecast_horizon, return_conf_int=True)
+        else: # Prophet
+            final_train_data = ts_data
+            final_prophet_train_df = final_train_data.reset_index().rename(columns={'Date': 'ds', 'Value': 'y'})
+            final_model = Prophet(yearly_seasonality='auto', weekly_seasonality=seasonal_enabled, daily_seasonality=False).fit(final_prophet_train_df)
+            future_df = final_model.make_future_dataframe(periods=forecast_horizon, freq='D')
+            forecast_obj = final_model.predict(future_df)
+            final_forecast_values = forecast_obj['yhat'].iloc[-forecast_horizon:].values
+            conf_int = np.column_stack((forecast_obj['yhat_lower'].iloc[-forecast_horizon:].values, forecast_obj['yhat_upper'].iloc[-forecast_horizon:].values))
+
+        future_dates = pd.date_range(start=ts_data.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon)
+        final_forecast_series = pd.Series(final_forecast_values, index=future_dates)
+        
+        # 可视化最终预测
+        fig, ax = plt.subplots(figsize=(15, 8))
+        ax.plot(ts_data.tail(365), label='近期历史数据', color='blue')
+        ax.plot(final_forecast_series, label=f'未来 {forecast_horizon} 天预测', color='red', linestyle='--')
+        ax.fill_between(future_dates, conf_int[:, 0], conf_int[:, 1], color='red', alpha=0.2, label='95% 置信区间')
+        ax.set_title(f'最终未来用量预测 (基于 {best_rolling_model_name})'); ax.legend(); ax.grid(True)
+        figures.append(fig)
+        
+        # 生成最终报告
+        final_report_text = f"""
+# 药品用量预测分析报告
+
+## 1. 数据概览
+- **数据时间范围**: {ts_data.index.min().strftime('%Y-%m-%d')} to {ts_data.index.max().strftime('%Y-%m-%d')}
+- **总数据点**: {len(ts_data)}
+- **平均用量**: {ts_data.mean():.2f}
+
+## 2. 分析与建模参数
+- **平稳性差���阶数 (d)**: {d_order}
+- **季节性周期 (m)**: {m_period}
+- **最佳训练窗口**: {best_window_size} 天
+
+## 3. 模型评估 (基于动态滚动预测)
+通过在历史数据上进行滚动预测，我们能更真实地评估模型在实际应用中的表现。
+
+{rolling_evaluation_df.to_markdown()}
+
+## 4. 最终结论与未来预测
+- **最佳模型**: **{best_rolling_model_name}** 被选为最终预测模型，因为它在滚动预测中表现最佳（MAE最低）。
+- **未来预测**: 已使用 `{best_rolling_model_name}` 模型对未来 **{forecast_horizon}** 天的用量进行预测。
+- **预测摘要**:
+  - 未来一周平均日用量: **{final_forecast_series.head(7).mean():.2f}**
+  - 未来一月平均日用量: **{final_forecast_series.head(30).mean():.2f}**
+        """
+        report_file_path = os.path.join(OUTPUT_DIR, 'final_analysis_report.txt')
+        with open(report_file_path, 'w', encoding='utf-8') as f:
+            f.write(final_report_text)
+            
+        log_lines.append("\n## 🎉 全部分析流程完成！请查看最终报告和图表。")
+        yield update_ui()
+
+    except Exception as e:
+        log_lines.append(f"\n\n❌ **分析过程中断，出现错误:**\n`{str(e)}`")
+        import traceback
+        log_lines.append(f"\n**Traceback:**\n```{traceback.format_exc()}```")
+        yield update_ui()
+
+
+# ======================== (D) Gradio 界面构建 ========================
+
+with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
+    gr.Markdown(
+        """
+        # 📈 智能时序预测与分析平台 (动态回测版)
+        欢迎使用！请确保您的数据文件 `gmqrkl.xlsx` 已上传至本 Space 的文件库中。
+        然后，点击下方按钮，启动包含 **窗口优化** 和 **动态滚动预测** 的完整分析流程。
+        **注意：完整流程计算量大，可能需要10-20分钟。请耐心等待，并观察下方日志区的实时进度。**
+        """
+    )
+    
+    start_button = gr.Button("🚀 点击这里，开始完整分析", variant="primary")
+
+    gr.Markdown("---")
+    
+    with gr.Tabs():
+        with gr.TabItem("📊 可视化图表", id=0):
+            gallery_output = gr.Gallery(label="分析图表", elem_id="gallery", columns=[1], height="auto")
+        with gr.TabItem("📝 实时分析日志", id=1):
+            log_output = gr.Markdown("点击按钮后，分析日志将实时显示在这里...")
+        with gr.TabItem("📋 最终报告与下载", id=2):
+            final_report_output = gr.Markdown("分析完成后，最终报告将显示在这里。")
+            download_output = gr.File(label="下载报告文件")
+
+    start_button.click(
+        fn=run_full_analysis,
+        inputs=None,
+        outputs=[log_output, gallery_output, final_report_output, download_output]
+    )
+    
+    gr.Markdown("<p style='text-align: center; font-size: 12px; color: grey;'>Powered by Gradio and Hugging Face Spaces.</p>")
+
+if __name__ == "__main__":
+    demo.launch()