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2020美赛周末赛题及数据集解析

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根据给定文件信息，可以解析出以下知识点： 标题知识点： 1. 文件命名规范与识别：标题中的“2020_Weekend1_Problems.zip”表示这是一个关于2020年某个周末（初步推断为数学建模竞赛的第一个周末）相关问题的压缩包文件。文件命名采用了“年份_活动类型_时间周期_主题”的结构化命名方式，这种命名方式便于在大量文件中快速识别内容。 描述知识点： 2. 数学建模竞赛介绍：描述中提到的“2020年美赛”，指的是美国大学生数学建模竞赛（Mathematical Contest in Modeling，简称MCM）。这是全球大学生规模最大的数学建模竞赛之一，每年举办，分春季和秋季两轮，通常被称为A/B题和C题轮次。 3. 数学建模题目类型：描述中提到的A、D、E三个题目分别代表了数学建模竞赛中的不同类型的题目。数学建模竞赛通常包含理论建模、数据分析、优化问题等，A、D、E题目可能分别对应不同领域的具体问题。 4. 文件内容：描述明确指出压缩包内含有三个题目的PDF文件，以及D题的数据表。PDF文件通常用于发布文档，便于阅读和打印。数据表则暗示D题涉及数据驱动的问题，需要参赛者通过分析数据表中的信息来解决问题。 标签知识点： 5. 算法重要性：标签“算法”表明在数学建模过程中，算法的应用非常重要。算法是解决问题、进行数据分析和模型建立的基础工具，尤其在处理数据集、优化模型等方面扮演核心角色。它要求参赛者具备编程能力，比如使用Python、R、MATLAB等语言进行算法实现。 文件名称列表知识点： 6. 压缩文件格式理解：文件名称列表中的“2020_Weekend1_Problems.zip”直接显示了文件是经过ZIP格式压缩的。ZIP是一种流行的文件压缩格式，可以将多个文件压缩为一个文件包，便于传输和存储。压缩包在数学建模竞赛中非常常见，因为参赛材料往往包含多个文件，压缩后有利于下载和解压。 7. 文件内容组织：由于文件名列表只列出一个文件，可能说明该压缩包内有多个子文件夹或文件结构，每个子文件夹或文件对应不同的题目。例如，可能有一个文件夹专门存储A题的PDF和相关数据，其他文件夹同理。在处理此类压缩文件时，需要了解如何合理地组织和管理文件，确保在解压后能高效地找到所需资料。 综上所述，文件提供了数学建模竞赛相关的重要信息，涵盖了数学建模的基本概念、文件格式和命名规范、以及数据分析等知识点。对于准备参与数学建模竞赛的学生来说，这些知识点非常重要，可以帮助他们更好地组织材料、理解问题，并运用算法解决问题。

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文件选择部分 file_frame = ttk.LabelFrame(control_frame, text="数据文件", padding=10) file_frame.pack(fill=tk.X, pady=5) # 训练集选择 ttk.Label(file_frame, text="训练集:").grid(row=0, column=0, sticky=tk.W, pady=5) self.train_file_var = tk.StringVar() ttk.Entry(file_frame, textvariable=self.train_file_var, width=30, state='readonly').grid( row=0, column=1, padx=5) ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("train")).grid(row=0, column=2) # 测试集选择 ttk.Label(file_frame, text="测试集:").grid(row=1, column=0, sticky=tk.W, pady=5) self.test_file_var = tk.StringVar() ttk.Entry(file_frame, textvariable=self.test_file_var, width=30, state='readonly').grid(row=1, column=1, padx=5) ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("test")).grid(row=1, column=2) # PINNs参数设置 param_frame = ttk.LabelFrame(control_frame, text="PINNs参数", padding=10) param_frame.pack(fill=tk.X, pady=10) # 验证集切分比例 ttk.Label(param_frame, text="验证集比例:").grid(row=0, column=0, sticky=tk.W, pady=5) self.split_ratio_var = tk.DoubleVar(value=0.2) ttk.Spinbox(param_frame, from_=0, to=1, increment=0.05, textvariable=self.split_ratio_var, width=10).grid(row=0, column=1, padx=5) # 隐藏层数量 ttk.Label(param_frame, text="网络层数:").grid(row=1, column=0, sticky=tk.W, pady=5) self.num_layers_var = tk.IntVar(value=4) ttk.Spinbox(param_frame, from_=2, to=8, increment=1, textvariable=self.num_layers_var, width=10).grid(row=1, column=1, padx=5) # 每层神经元数量 ttk.Label(param_frame, text="神经元数/层:").grid(row=2, column=0, sticky=tk.W, pady=5) self.hidden_units_var = tk.IntVar(value=32) ttk.Spinbox(param_frame, from_=16, to=128, increment=4, textvariable=self.hidden_units_var, width=10).grid(row=2, column=1, padx=5) # 训练轮次 ttk.Label(param_frame, text="训练轮次:").grid(row=3, column=0, sticky=tk.W, pady=5) self.epochs_var = tk.IntVar(value=500) ttk.Spinbox(param_frame, from_=100, to=2000, increment=100, textvariable=self.epochs_var, width=10).grid(row=3, column=1, padx=5) # 物理损失权重 ttk.Label(param_frame, text="物理损失权重:").grid(row=4, column=0, sticky=tk.W, pady=5) self.physics_weight_var = tk.DoubleVar(value=0.5) ttk.Spinbox(param_frame, from_=0.1, to=1.0, increment=0.1, textvariable=self.physics_weight_var, width=10).grid(row=4, column=1, padx=5) # 控制按钮 btn_frame = ttk.Frame(control_frame) btn_frame.pack(fill=tk.X, pady=10) ttk.Button(btn_frame, text="训练模型", command=self.train_model).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="预测结果", command=self.predict).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="保存结果", command=self.save_results).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="重置", command=self.reset).pack(side=tk.RIGHT, padx=5) # 状态栏 self.status_var = tk.StringVar(value="就绪") status_bar = ttk.Label(control_frame, textvariable=self.status_var, relief=tk.SUNKEN, anchor=tk.W) status_bar.pack(fill=tk.X, side=tk.BOTTOM) # 右侧结果显示区域 result_frame = ttk.Frame(main_frame) result_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True, padx=5, pady=5) # 创建标签页 self.notebook = ttk.Notebook(result_frame) self.notebook.pack(fill=tk.BOTH, expand=True) # 损失曲线标签页 self.loss_frame = ttk.Frame(self.notebook) self.notebook.add(self.loss_frame, text="训练损失") # 预测结果标签页 self.prediction_frame = ttk.Frame(self.notebook) self.notebook.add(self.prediction_frame, text="预测结果") # 指标显示 self.metrics_var = tk.StringVar() metrics_label = ttk.Label( self.prediction_frame, textvariable=self.metrics_var, font=('TkDefaultFont', 10, 'bold'), relief='ridge', padding=5 ) metrics_label.pack(fill=tk.X, padx=5, pady=5) # 初始化绘图区域 self.fig, self.ax = plt.subplots(figsize=(10, 6)) self.canvas = FigureCanvasTkAgg(self.fig, master=self.prediction_frame) self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) # 损失曲线画布 self.loss_fig, self.loss_ax = plt.subplots(figsize=(10, 4)) self.loss_canvas = FigureCanvasTkAgg(self.loss_fig, master=self.loss_frame) self.loss_canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) def select_file(self, file_type): """选择Excel文件并计算时间步长""" try: file_path = filedialog.askopenfilename( title=f"选择{file_type}集Excel文件", filetypes=[("Excel文件", "*.xlsx *.xls"), ("所有文件", "*.*")] ) if not file_path: return df = pd.read_excel(file_path) # 验证必需列是否存在 required_cols = ['year', 'month', 'day', '水位'] missing_cols = [col for col in required_cols if col not in df.columns] if missing_cols: messagebox.showerror("列名错误", f"缺少必需列: {', '.join(missing_cols)}") return # 时间特征处理 time_features = ['year', 'month', 'day'] missing_time_features = [feat for feat in time_features if feat not in df.columns] if missing_time_features: messagebox.showerror("列名错误", f"Excel文件缺少预处理后的时间特征列: {', '.join(missing_time_features)}") return # 创建时间戳列 (增强兼容性) time_cols = ['year', 'month', 'day'] if 'hour' in df.columns: time_cols.append('hour') if 'minute' in df.columns: time_cols.append('minute') if 'second' in df.columns: time_cols.append('second') # 填充缺失的时间单位 for col in ['hour', 'minute', 'second']: if col not in df.columns: df[col] = 0 df['datetime'] = pd.to_datetime(df[time_cols]) # 设置时间索引 df = df.set_index('datetime') # 计算相对时间（天） df['days'] = (df.index - df.index[0]).days # 新增：计算时间步长dt（单位：天） df['dt'] = df.index.to_series().diff().dt.total_seconds() / 86400 # 精确到秒级 # 处理时间步长异常值 if len(df) > 1: # 计算有效时间步长（排除<=0的值） valid_dt = df['dt'][df['dt'] > 0] if len(valid_dt) > 0: avg_dt = valid_dt.mean() else: avg_dt = 1.0 else: avg_dt = 1.0 # 替换非正值 df.loc[df['dt'] <= 0, 'dt'] = avg_dt # 填充缺失值 df['dt'] = df['dt'].fillna(avg_dt) # 保存数据 if file_type == "train": self.train_df = df self.train_file_var.set(os.path.basename(file_path)) self.status_var.set(f"已加载训练集: {len(self.train_df)}条数据") else: self.test_df = df self.test_file_var.set(os.path.basename(file_path)) self.status_var.set(f"已加载测试集: {len(self.test_df)}条数据") except Exception as e: error_msg = f"文件读取失败: {str(e)}\n\n请确保：\n1. 文件不是打开状态\n2. 文件格式正确\n3. 包含必需的时间和水位列" messagebox.showerror("文件错误", error_msg) def calculate_metrics(self, y_true, y_pred): """计算评估指标""" from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score mse = mean_squared_error(y_true, y_pred) rmse = np.sqrt(mse) mae = mean_absolute_error(y_true, y_pred) non_zero_idx = np.where(y_true != 0)[0] if len(non_zero_idx) > 0: mape = np.mean(np.abs((y_true[non_zero_idx] - y_pred[non_zero_idx]) / y_true[non_zero_idx])) * 100 else: mape = float('nan') r2 = r2_score(y_true, y_pred) return { 'MSE': mse, 'RMSE': rmse, 'MAE': mae, 'MAPE': mape, # 修正键名 'R2': r2 } def train_model(self): """训练PINNs模型（带早停机制+训练指标监控）""" if self.train_df is None: messagebox.showwarning("警告", "请先选择训练集文件") return try: self.status_var.set("正在预处理数据...") self.root.update() # 从训练集中切分训练子集和验证子集（时间顺序切分） split_ratio = 1 - self.split_ratio_var.get() split_idx = int(len(self.train_df) * split_ratio) train_subset = self.train_df.iloc[:split_idx] valid_subset = self.train_df.iloc[split_idx:] # 检查数据量是否足够 if len(train_subset) < 2 or len(valid_subset) < 2: messagebox.showerror("数据错误", "训练集数据量不足（至少需要2个时间步）") return # 数据预处理 - 分别归一化不同特征 # 归一化时间特征 t_train = train_subset['days'].values[1:].reshape(-1, 1) self.scaler_t.fit(t_train) t_train_scaled = self.scaler_t.transform(t_train).astype(np.float32) # 归一化水位特征 h_train = train_subset['水位'].values[:-1].reshape(-1, 1) self.scaler_h.fit(h_train) h_train_scaled = self.scaler_h.transform(h_train).astype(np.float32) # 归一化时间步长特征 dt_train = train_subset['dt'].values[1:].reshape(-1, 1) self.scaler_dt.fit(dt_train) dt_train_scaled = self.scaler_dt.transform(dt_train).astype(np.float32) # 归一化标签（下一时刻水位） h_next_train = train_subset['水位'].values[1:].reshape(-1, 1) h_next_train_scaled = self.scaler_h.transform(h_next_train).astype(np.float32) # 准备验证数据（同样进行归一化） t_valid = valid_subset['days'].values[1:].reshape(-1, 1) t_valid_scaled = self.scaler_t.transform(t_valid).astype(np.float32) h_valid = valid_subset['水位'].values[:-1].reshape(-1, 1) h_valid_scaled = self.scaler_h.transform(h_valid).astype(np.float32) dt_valid = valid_subset['dt'].values[1:].reshape(-1, 1) dt_valid_scaled = self.scaler_dt.transform(dt_valid).astype(np.float32) h_next_valid_scaled = self.scaler_h.transform( valid_subset['水位'].values[1:].reshape(-1, 1) ).astype(np.float32) # 原始值用于指标计算 h_next_train_true = h_next_train h_next_valid_true = valid_subset['水位'].values[1:].reshape(-1, 1) # 创建模型和优化器 self.model = PINNModel( num_layers=self.num_layers_var.get(), hidden_units=self.hidden_units_var.get() ) optimizer = Adam(learning_rate=0.001) # 在训练循环中，使用归一化后的数据 train_dataset = tf.data.Dataset.from_tensor_slices( ((t_train_scaled, h_train_scaled, dt_train_scaled), h_next_train_scaled) ) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(32) valid_dataset = tf.data.Dataset.from_tensor_slices( ((t_valid_scaled, h_valid_scaled, dt_valid_scaled), h_next_valid_scaled) ) valid_dataset = valid_dataset.batch(32) # 初始化训练历史记录列表 train_data_loss_history = [] physics_loss_history = [] valid_data_loss_history = [] train_metrics_history = [] valid_metrics_history = [] # 早停机制参数 patience = int(self.epochs_var.get() / 3) min_delta = 1e-4 best_valid_loss = float('inf') wait = 0 best_epoch = 0 best_weights = None start_time = time.time() # 自定义训练循环 for epoch in range(self.epochs_var.get()): # 训练阶段 epoch_train_data_loss = [] epoch_physics_loss = [] # 收集训练预测值（归一化后） train_pred_scaled = [] for step, ((t_batch, h_batch, dt_batch), h_next_batch) in enumerate(train_dataset): with tf.GradientTape() as tape: # 预测下一时刻水位 h_pred = self.model([t_batch, h_batch, dt_batch]) data_loss = tf.reduce_mean(tf.square(h_next_batch - h_pred)) # 动态调整物理损失权重 current_physics_weight = tf.minimum( self.physics_weight_var.get() * (1.0 + epoch / self.epochs_var.get()), 0.8 ) # 计算物理损失（传入时间步长dt） physics_loss = self.model.physics_loss(t_batch, h_batch, dt_batch) loss = data_loss + current_physics_weight * physics_loss grads = tape.gradient(loss, self.model.trainable_variables) optimizer.apply_gradients(zip(grads, self.model.trainable_variables)) epoch_train_data_loss.append(data_loss.numpy()) epoch_physics_loss.append(physics_loss.numpy()) train_pred_scaled.append(h_pred.numpy()) # 保存训练预测值（归一化） # 合并训练预测值（归一化后） train_pred_scaled = np.concatenate(train_pred_scaled, axis=0) # 反归一化得到原始预测值 train_pred_true = self.scaler_h.inverse_transform(train_pred_scaled) # 计算训练集指标（使用原始真实值和预测值） train_metrics = self.calculate_metrics( y_true=h_next_train_true.flatten(), y_pred=train_pred_true.flatten() ) train_metrics_history.append(train_metrics) # 验证阶段 epoch_valid_data_loss = [] valid_pred_scaled = [] for ((t_v_batch, h_v_batch, dt_v_batch), h_v_next_batch) in valid_dataset: h_v_pred = self.model([t_v_batch, h_v_batch, dt_v_batch]) valid_data_loss = tf.reduce_mean(tf.square(h_v_next_batch - h_v_pred)) epoch_valid_data_loss.append(valid_data_loss.numpy()) valid_pred_scaled.append(h_v_pred.numpy()) # 保存验证预测值（归一化） # 合并验证预测值（归一化后） valid_pred_scaled = np.concatenate(valid_pred_scaled, axis=0) # 反归一化得到原始预测值 valid_pred_true = self.scaler_h.inverse_transform(valid_pred_scaled) # 计算验证集指标（使用原始真实值和预测值） valid_metrics = self.calculate_metrics( y_true=h_next_valid_true.flatten(), y_pred=valid_pred_true.flatten() ) valid_metrics_history.append(valid_metrics) # 计算平均损失 avg_train_data_loss = np.mean(epoch_train_data_loss) avg_physics_loss = np.mean(epoch_physics_loss) avg_valid_data_loss = np.mean(epoch_valid_data_loss) # 记录损失 train_data_loss_history.append(avg_train_data_loss) physics_loss_history.append(avg_physics_loss) valid_data_loss_history.append(avg_valid_data_loss) # 早停机制逻辑 current_valid_loss = avg_valid_data_loss if current_valid_loss < best_valid_loss - min_delta: best_valid_loss = current_valid_loss best_epoch = epoch + 1 wait = 0 best_weights = self.model.get_weights() else: wait += 1 if wait >= patience: self.status_var.set(f"触发早停！最佳轮次: {best_epoch}，最佳验证损失: {best_valid_loss:.4f}") if best_weights is not None: self.model.set_weights(best_weights) break # 更新状态 if epoch % 1 == 0: # 提取当前训练/验证的关键指标 train_rmse = train_metrics['RMSE'] valid_rmse = valid_metrics['RMSE'] train_r2 = train_metrics['R2'] valid_r2 = valid_metrics['R2'] elapsed = time.time() - start_time self.status_var.set( f"训练中 | 轮次: {epoch + 1}/{self.epochs_var.get()} | " f"训练RMSE: {train_rmse:.4f} | 验证RMSE: {valid_rmse:.4f} | " f"训练R²: {train_r2:.4f} | 验证R²: {valid_r2:.4f} | " f"k1: {self.model.k1.numpy():.6f}, k2: {self.model.k2.numpy():.6f} | 时间: {elapsed:.1f}秒 | 早停等待: {wait}/{patience}" ) self.root.update() # 绘制损失曲线 self.loss_ax.clear() epochs_range = range(1, len(train_data_loss_history) + 1) self.loss_ax.plot(epochs_range, train_data_loss_history, 'b-', label='训练数据损失') self.loss_ax.plot(epochs_range, physics_loss_history, 'r--', label='物理损失') self.loss_ax.plot(epochs_range, valid_data_loss_history, 'g-.', label='验证数据损失') self.loss_ax.set_title('PINNs训练与验证损失') self.loss_ax.set_xlabel('轮次') self.loss_ax.set_ylabel('损失', rotation=0) self.loss_ax.legend() self.loss_ax.grid(True, alpha=0.3) self.loss_ax.set_yscale('log') self.loss_canvas.draw() # 训练完成提示 elapsed = time.time() - start_time if wait >= patience: completion_msg = ( f"早停触发 | 最佳轮次: {best_epoch} | 最佳验证损失: {best_valid_loss:.4f} | " f"最佳验证RMSE: {valid_metrics_history[best_epoch - 1]['RMSE']:.4f} | " f"总时间: {elapsed:.1f}秒" ) else: completion_msg = ( f"训练完成 | 总轮次: {self.epochs_var.get()} | " f"最终训练RMSE: {train_metrics_history[-1]['RMSE']:.4f} | " f"最终验证RMSE: {valid_metrics_history[-1]['RMSE']:.4f} | " f"最终训练R²: {train_metrics_history[-1]['R2']:.4f} | " f"最终验证R²: {valid_metrics_history[-1]['R2']:.4f} | " f"总时间: {elapsed:.1f}秒" ) # 保存训练历史 self.train_history = { 'train_data_loss': train_data_loss_history, 'physics_loss': physics_loss_history, 'valid_data_loss': valid_data_loss_history, 'train_metrics': train_metrics_history, 'valid_metrics': valid_metrics_history } # 保存学习到的物理参数 self.learned_params = { "k1": self.model.k1.numpy(), "k2": self.model.k2.numpy(), "alpha": self.model.alpha.numpy(), "beta": self.model.beta.numpy() } self.status_var.set(completion_msg) messagebox.showinfo("训练完成", f"PINNs模型训练成功完成！\n{completion_msg}") except Exception as e: messagebox.showerror("训练错误", f"模型训练失败:\n{str(e)}") self.status_var.set("训练失败") def predict(self): """使用PINNs模型进行递归预测（自回归预测）""" if self.model is None: messagebox.showwarning("警告", "请先训练模型") return if self.test_df is None: messagebox.showwarning("警告", "请先选择测试集文件") return try: self.status_var.set("正在生成预测...") self.root.update() # 预处理测试数据 - 归一化 # 归一化时间特征 t_test = self.test_df['days'].values.reshape(-1, 1) t_test_scaled = self.scaler_t.transform(t_test).astype(np.float32) # 归一化时间步长特征 dt_test = self.test_df['dt'].values.reshape(-1, 1) dt_test_scaled = self.scaler_dt.transform(dt_test).astype(np.float32) # 初始水位（归一化） h_test = self.test_df['水位'].values.reshape(-1, 1) h_initial_scaled = self.scaler_h.transform(h_test[0:1]).astype(np.float32)[0] # 递归预测（自回归）带误差修正 n = len(t_test) # 初始化预测序列（归一化），第一个点使用真实值 predicted_scaled = np.zeros((n, 1), dtype=np.float32) predicted_scaled[0] = h_initial_scaled # 第一个点使用真实值 # 误差累积修正因子 error_correction_factor = 0.3 # 从第二个时间点开始预测 for i in range(1, n): # 使用上一个时间点的特征（归一化后） t_prev = t_test_scaled[i - 1:i] h_prev = predicted_scaled[i - 1:i] dt_i = dt_test_scaled[i:i + 1] # 预测当前时间点的水位 h_pred = self.model([t_prev, h_prev, dt_i]) predicted_value = h_pred.numpy()[0][0] # 误差修正：混合真实值和预测值 if i < n - 1 and i % 5 == 0: # 每5步校正一次 # 获取当前真实值（归一化后） current_actual_scaled = self.scaler_h.transform( np.array([[h_test[i][0]]]) ).astype(np.float32)[0][0] correction = current_actual_scaled - predicted_value predicted_scaled[i] = predicted_value + error_correction_factor * correction else: predicted_scaled[i] = predicted_value # 反归一化预测结果 predictions = self.scaler_h.inverse_transform(predicted_scaled) actual_values = h_test # 创建时间索引 test_time = self.test_df.index # 清除现有图表 self.ax.clear() # 绘制结果 self.ax.plot(test_time, actual_values, 'b-', label='真实值') self.ax.plot(test_time, predictions, 'r--', label='预测值') self.ax.set_title('大坝渗流水位预测结果(PINNs)') self.ax.set_xlabel('时间') self.ax.set_ylabel('测压管水位', rotation=0) self.ax.legend() # 优化时间轴刻度 self.ax.xaxis.set_major_locator(mdates.YearLocator()) self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) self.ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=2)) self.ax.grid(which='minor', axis='x', linestyle=':', color='gray', alpha=0.3) self.ax.grid(which='major', axis='y', linestyle='-', color='lightgray', alpha=0.5) self.ax.tick_params(axis='x', which='major', rotation=0, labelsize=9) self.ax.tick_params(axis='x', which='minor', length=2) # 计算评估指标（排除第一个点） eval_actual = actual_values[1:].flatten() eval_pred = predictions[1:].flatten() self.evaluation_metrics = self.calculate_metrics(eval_actual, eval_pred) metrics_text = ( f"MSE: {self.evaluation_metrics['MSE']:.4f} | " f"RMSE: {self.evaluation_metrics['RMSE']:.4f} | " f"MAE: {self.evaluation_metrics['MAE']:.4f} | " f"MAPE: {self.evaluation_metrics['MAPE']:.2f}% | " f"R²: {self.evaluation_metrics['R2']:.4f}" ) self.metrics_var.set(metrics_text) # 在图表上添加指标 self.ax.text( 0.5, 1.05, metrics_text, transform=self.ax.transAxes, ha='center', fontsize=9, bbox=dict(facecolor='white', alpha=0.8) ) params_text = ( f"物理参数: k1={self.learned_params['k1']:.4f}, " f"k2={self.learned_params['k2']:.4f}, " f"alpha={self.learned_params['alpha']:.4f}, " f"beta={self.learned_params['beta']:.4f}" ) self.ax.text( 0.5, 1.12, params_text, # 显示在指标上方 transform=self.ax.transAxes, ha='center', fontsize=9, bbox=dict(facecolor='white', alpha=0.8) ) # 调整布局 plt.tight_layout(pad=2.0) self.canvas.draw() # 保存预测结果 self.predictions = predictions self.actual_values = actual_values self.test_time = test_time self.status_var.set("预测完成，结果已显示") except Exception as e: messagebox.showerror("预测错误", f"预测失败:\n{str(e)}") self.status_var.set("预测失败") # 记录详细错误信息 import traceback traceback.print_exc() def save_results(self): """保存预测结果和训练历史数据""" if not hasattr(self, 'predictions') or not hasattr(self, 'train_history'): messagebox.showwarning("警告", "请先生成预测结果并完成训练") return # 选择保存路径 save_path = filedialog.asksaveasfilename( defaultextension=".xlsx", filetypes=[("Excel文件", "*.xlsx"), ("所有文件", "*.*")], title="保存结果" ) if not save_path: return try: # 1. 创建预测结果DataFrame result_df = pd.DataFrame({ '时间': self.test_time, '实际水位': self.actual_values.flatten(), '预测水位': self.predictions.flatten() }) # 2. 创建评估指标DataFrame metrics_df = pd.DataFrame([self.evaluation_metrics]) # 3. 创建训练历史DataFrame history_data = { '轮次': list(range(1, len(self.train_history['train_data_loss']) + 1)), '训练数据损失': self.train_history['train_data_loss'], '物理损失': self.train_history['physics_loss'], '验证数据损失': self.train_history['valid_data_loss'] } # 添加训练集指标 for metric in ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']: history_data[f'训练集_{metric}'] = [item[metric] for item in self.train_history['train_metrics']] # 添加验证集指标 for metric in ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']: history_data[f'验证集_{metric}'] = [item[metric] for item in self.train_history['valid_metrics']] history_df = pd.DataFrame(history_data) # 保存到Excel with pd.ExcelWriter(save_path) as writer: result_df.to_excel(writer, sheet_name='预测结果', index=False) metrics_df.to_excel(writer, sheet_name='评估指标', index=False) history_df.to_excel(writer, sheet_name='训练历史', index=False) # 保存图表 chart_path = os.path.splitext(save_path)[0] + "_chart.png" self.fig.savefig(chart_path, dpi=300) # 保存损失曲线图 loss_path = os.path.splitext(save_path)[0] + "_loss.png" self.loss_fig.savefig(loss_path, dpi=300) self.status_var.set(f"结果已保存至: {os.path.basename(save_path)}") messagebox.showinfo("保存成功", f"预测结果和图表已保存至:\n" f"主文件: {save_path}\n" f"预测图表: {chart_path}\n" f"损失曲线: {loss_path}") except Exception as e: messagebox.showerror("保存错误", f"保存结果失败:\n{str(e)}") def reset(self): # 重置归一化器 self.scaler_t = MinMaxScaler(feature_range=(0, 1)) self.scaler_h = MinMaxScaler(feature_range=(0, 1)) self.scaler_dt = MinMaxScaler(feature_range=(0, 1)) """重置程序状态""" self.train_df = None self.test_df = None self.model = None self.train_file_var.set("") self.test_file_var.set("") # 清除训练历史 if hasattr(self, 'train_history'): del self.train_history # 清除图表 if hasattr(self, 'ax'): self.ax.clear() if hasattr(self, 'loss_ax'): self.loss_ax.clear() # 重绘画布 if hasattr(self, 'canvas'): self.canvas.draw() if hasattr(self, 'loss_canvas'): self.loss_canvas.draw() # 清除状态 self.status_var.set("已重置，请选择新数据") # 清除预测结果 if hasattr(self, 'predictions'): del self.predictions # 清除指标文本 if hasattr(self, 'metrics_var'): self.metrics_var.set("") messagebox.showinfo("重置", "程序已重置，可以开始新的分析") if __name__ == "__main__": root = tk.Tk() app = DamSeepageModel(root) root.mainloop() 帮我增强一下模型的特征工程

df['is_weekend'] = df['day_of_week'].isin([5,6]).astype(int) # 2. 添加滞后特征 df['h_lag1'] = df['水位'].shift(1) # 前1期水位 df['h_lag2'] = df['水位'].shift(2) # 前2期水位 df['h_lag3'] = df['...
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import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense from sklearn.metrics import mean_squared_error import matplotlib.pyplot as plt # 将 starttime 转换为 datetime 类型 data['starttime'] = pd.to_datetime(data['starttime']) # 提取特征和目标变量 features = data[['starttime', 'hour', 'is_weekend']] target = data['volume'] # 将 starttime 转换为时间戳 features['starttime'] = features['starttime'].map(pd.Timestamp.timestamp) # 归一化特征和目标变量 scaler_features = MinMaxScaler(feature_range=(0, 1)) scaled_features = scaler_features.fit_transform(features) scaler_target = MinMaxScaler(feature_range=(0, 1)) scaled_target = scaler_target.fit_transform(target.values.reshape(-1, 1)) # 准备时间序列数据 def create_sequences(data, seq_length): X = [] y = [] for i in range(len(data) - seq_length): X.append(data[i:i + seq_length, :]) y.append(data[i + seq_length, 0]) return np.array(X), np.array(y) seq_length = 10 X, y = create_sequences(np.concatenate((scaled_features, scaled_target), axis=1), seq_length) # 划分训练集和测试集 train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # 构建 LSTM 模型 model = Sequential() model.add(LSTM(50, activation='relu', input_shape=(seq_length, X_train.shape[2]))) model.add(Dense(1)) model.compile(optimizer='adam', loss='mse') # 训练模型 model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=1) # 进行预测 y_pred = model.predict(X_test) # 反归一化预测结果和真实值 y_pred = scaler_target.inverse_transform(y_pred) y_test = scaler_target.inverse_transform(y_test.reshape(-1, 1)) # 计算均方误差 mse = mean_squared_error(y_test, y_pred) print(f'均方误差: {mse}') # 设置图片清晰度 plt.rcParams['figure.dpi'] = 300 # 绘制预测结果和真实值的对比图 plt.plot(y_test, label='真实值') plt.plot(y_pred, label='预测值') plt.title('交通流量预测') plt.xlabel('时间步') plt.ylabel('交通流量') plt.legend() plt.show()这是造成以上错误的代码，请提供具体的解决方案

1. **版本不兼容**：新版本`numpy`（>=1.24）移除了`numpy.int`等别名类型，而旧版`scikit-learn`或依赖库仍尝试从`numpy`导入`int`[^4][^2]。 2. **依赖冲突**：环境中可能同时安装了多个版本的`numpy`或`scikit-...
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catboost-spark_2.11-0.25-rc1-javadoc.jar

catboost-spark_2.11-0.25-rc1-javadoc.jar
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sparkling-water-repl_2.11-3.32.1.6-1-2.2-sources.jar

sparkling-water-repl_2.11-3.32.1.6-1-2.2-sources.j......
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