#!/usr/bin/env python # -*- coding: utf-8 -*- """ 未来多时间点降雨预报系统 功能： 1. 预测未来一小时、两小时、三小时是否会降雨 2. 判断降雨后，分析未来一小时、两小时、三小时的降雨量 """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier, GradientBoostingRegressor from sklearn.linear_model import LogisticRegression, Ridge from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, cross_val_predict from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix from sklearn.metrics import roc_curve, auc, precision_recall_curve from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from sklearn.preprocessing import StandardScaler, RobustScaler from sklearn.calibration import CalibratedClassifierCV from sklearn.base import clone from sklearn.utils import resample import joblib import os import warnings import datetime from datetime import timedelta import time import argparse import traceback warnings.filterwarnings('ignore') class RainfallTwoStageModel: """ 两阶段降雨预测模型: - 第一阶段: 分类器预测是否会下雨(二分类) - 第二阶段: 对预测有雨的样本，先分类降雨强度，再使用分层回归模型预测具体降雨量 """ def __init__(self, debug=True, clf_model_type='ensemble', reg_model_type='gbdt', output_dir='figures'): # 初始化模型组件 self.binary_classifier = None # 二分类器（判断是否下雨） self.regressor = None # 回归器（单一模型，用作备选） self.regressors = {} # 分层回归器，针对不同降雨强度 self.rain_category_classifier = None # 降雨类别分类器（小、中、大、暴雨） self.scaler = None # 特征缩放器 self.threshold = 0.5 # 分类阈值 self.feature_importances_clf = None # 分类器特征重要性 self.feature_importances_reg = None # 回归器特征重要性 self.feature_names = None # 特征名称 self.debug = debug # 调试输出 self.clf_model_type = clf_model_type # 分类器类型 self.reg_model_type = reg_model_type # 回归器类型 self.log_transform = True # 是否对降雨量进行对数变换 self.feature_stats = {} # 存储特征统计信息 self.stratified_regression = True # 是否使用分层回归 self.rain_thresholds = [0.5, 3.0, 10.0] # 降雨类别阈值：小雨、中雨、大雨、暴雨 self.heavy_rain_factor = 3.0 # 大雨预测安全系数 self.extreme_rain_factor = 5.0 # 暴雨预测安全系数 self.time_points = [1, 2, 3] # 添加时间点属性 # 创建图表输出目录 self.output_dir = output_dir if not os.path.exists(output_dir): os.makedirs(output_dir) def debug_print(self, message): """调试输出""" if self.debug: print(f"[调试] {message}") def preprocess_data(self, data, silent=False): """ 数据预处理：特征工程与数据清洗，增强时间序列特征 参数: data: DataFrame 原始数据 silent: bool 是否抑制输出信息 返回: df: DataFrame 处理后的数据 """ df = data.copy() #print("aaa") #print(df) # 确保datetime列是datetime格式 if 'datetime' in df.columns: df['datetime'] = pd.to_datetime(df['datetime']) # 数据类型转换，确保所有特征都是数值型 for col in df.columns: if col not in ['datetime', 'timestamp', 'date', 'time']: try: df[col] = pd.to_numeric(df[col], errors='coerce') except: if not silent: print(f"警告: 无法将列 '{col}' 转换为数值类型") # 处理缺失值 if 'rain' in df.columns: df = df.dropna(subset=['rain']) # 删除目标变量缺失的行 df = df.fillna(method='ffill').fillna(method='bfill') # 填充其他缺失值 # 温度单位检查（保留原始单位） # 保存或使用特征统计信息 if not self.feature_stats: # 训练期间：计算并保存统计数据 for var in ['rh', 'PWV', 'Temp', 'Press', 'ZTD']: if var in df.columns: self.feature_stats[f"{var}_mean"] = df[var].mean() self.feature_stats[f"{var}_std"] = df[var].std() #print(df.head(10)) # 计算全面的时间序列特征 # 1. 异常值（相对平均值的标准化偏差） for var in ['rh', 'PWV', 'Temp', 'Press', 'ZTD']: if var in df.columns: # 使用保存的均值或计算新均值 mean_value = self.feature_stats.get(f"{var}_mean", df[var].mean()) std_value = self.feature_stats.get(f"{var}_std", df[var].std()) if std_value > 0: df[f'{var}_anomaly'] = (df[var] - mean_value) / std_value else: df[f'{var}_anomaly'] = 0 # 2. 多时间尺度的变化率 for var in ['rh', 'PWV', 'ZTD', 'Press', 'Temp']: if var in df.columns: # 短期变化（1小时，3小时） df[f'{var}_1h_change'] = df[var].diff(1).fillna(0) df[f'{var}_3h_change'] = df[var].diff(3).fillna(0) # 中期变化（6小时） df[f'{var}_6h_change'] = df[var].diff(6).fillna(0) # 长期变化（12小时） df[f'{var}_12h_change'] = df[var].diff(12).fillna(0) # 变化加速度（变化率的变化率） df[f'{var}_change_accel'] = df[f'{var}_1h_change'].diff(1).fillna(0) # 相对变化（百分比） if df[var].mean() != 0: df[f'{var}_rel_change'] = df[f'{var}_1h_change'] / df[var].shift(1).fillna(df[var].mean()) # 急剧变化指标（针对极端事件） var_std = df[f'{var}_1h_change'].std() if var_std > 0: df[f'{var}_rapid_change'] = (np.abs(df[f'{var}_1h_change']) > (2 * var_std)).astype(int) else: df[f'{var}_rapid_change'] = 0 # 3. 时间窗口特征 - 多种滚动窗口 for var in ['PWV', 'rh', 'Temp', 'Press', 'ZTD']: if var in df.columns: # 短窗口（3小时滚动平均） df[f'{var}_3h_mean'] = df[var].rolling(window=3, min_periods=1).mean() # 中窗口（6小时滚动平均和标准差） df[f'{var}_6h_mean'] = df[var].rolling(window=6, min_periods=1).mean() df[f'{var}_6h_std'] = df[var].rolling(window=6, min_periods=1).std().fillna(0) # 长窗口（12小时滚动平均） df[f'{var}_12h_mean'] = df[var].rolling(window=12, min_periods=1).mean() # 趋势指标：当前值与滚动平均的差异 df[f'{var}_vs_6h'] = df[var] - df[f'{var}_6h_mean'] # 最大/最小值指标 df[f'{var}_12h_max'] = df[var].rolling(window=12, min_periods=1).max() df[f'{var}_12h_min'] = df[var].rolling(window=12, min_periods=1).min() df[f'{var}_12h_range'] = df[f'{var}_12h_max'] - df[f'{var}_12h_min'] # 4. 天气模式指标和特征组合 if 'PWV' in df.columns and 'rh' in df.columns: # 湿度可用性指数 df['PWV_rh'] = df['PWV'] * df['rh'] / 100 # 湿度变化指标 if 'PWV_1h_change' in df.columns and 'rh_1h_change' in df.columns: df['moisture_change'] = df['PWV_1h_change'] * df['rh_1h_change'] # 累积湿度变化 df['PWV_cumul_change'] = df['PWV'].diff(1).rolling(window=6).sum().fillna(0) df['rh_cumul_change'] = df['rh'].diff(1).rolling(window=6).sum().fillna(0) if 'PWV' in df.columns and 'Temp' in df.columns: # 温度-湿度相互作用 df['PWV_Temp'] = df['PWV'] * df['Temp'] # 饱和潜力 temp_exp = np.exp(17.27 * (df['Temp']-273.15) / ((df['Temp']-273.15) + 237.3)) # 转换为摄氏度计算 df['saturation_index'] = df['PWV'] / (0.1 * temp_exp) if 'Press' in df.columns and 'Press_1h_change' in df.columns: # 气压变化强度（较大的变化表示更强的天气系统） df['Press_change_intensity'] = df['Press_1h_change'].abs() # 气压系统变化：下降的气压往往与降雨系统有关 df['Press_falling'] = (df['Press_1h_change'] < 0).astype(int) # 气压快速下降指标（与强降水相关） press_quantile = df['Press_1h_change'].abs().quantile(0.75) df['Press_rapid_fall'] = ((df['Press_1h_change'] < 0) & (df['Press_1h_change'].abs() > press_quantile)).astype(int) if 'ZTD' in df.columns and 'PWV' in df.columns: # ZTD-PWV比率（大气条件指标） df['ZTD_PWV_ratio'] = df['ZTD'] / df['PWV'].replace(0, np.nan).fillna(df['PWV'].mean()) # 组合变化指标 if 'ZTD_1h_change' in df.columns and 'PWV_1h_change' in df.columns: df['ZTD_PWV_change'] = df['ZTD_1h_change'] * df['PWV_1h_change'] # 湿度不稳定指标 pwv_mean = df['PWV_6h_mean'].mean() if pwv_mean > 0: df['moisture_instability'] = df['PWV_6h_std'] / df['PWV_6h_mean'] else: df['moisture_instability'] = 0 # 5. 基于多变量的降雨潜力指标 if all(var in df.columns for var in ['PWV', 'rh', 'Temp', 'Press_1h_change']): # 组合降雨指标 df['rain_potential'] = (df['PWV'] * df['rh'] / 100) * (0.1 - df['Press_1h_change']) / (df['Temp'] + 10) # 强降雨潜力指标 pwv_mean = df['PWV'].mean() + 0.1 # 避免除以零 df['heavy_rain_potential'] = df['PWV'] * (df['rh'] / 100) * (df['Press_rapid_fall'] + 1) * ( 1 + np.abs(df['PWV_1h_change']) / pwv_mean) # 降雨持续性指标 df['rain_persistence'] = df['PWV_cumul_change'] * df['rh_cumul_change'] * ( 1 + df['Press_change_intensity']) # 创建二值标签 - 简单明了：1表示有雨，0表示无雨 if 'rain' in df.columns: df['rain_binary'] = (df['rain'] > 0.0).astype(int) #df.loc[:, 'rain_binary'] = (df['rain'] > 0).astype(int) # 创建降雨类别标签用于分层回归 df['rain_category'] = np.zeros(len(df), dtype=int) # 默认：无雨 if self.stratified_regression: df.loc[df['rain'] > 0, 'rain_category'] = 1 # 小雨 df.loc[df['rain'] > self.rain_thresholds[0], 'rain_category'] = 2 # 中雨 df.loc[df['rain'] > self.rain_thresholds[1], 'rain_category'] = 3 # 大雨 df.loc[df['rain'] > self.rain_thresholds[2], 'rain_category'] = 4 # 暴雨 # 只在非静默模式下打印信息 if not silent: # 打印降雨分布信息 rain_count = df['rain_binary'].sum() print(f"\n降雨分布统计：") print(f"总样本数: {len(df)}") print(f"有雨样本: {rain_count} ({rain_count/len(df)*100:.2f}%)") print(f"无雨样本: {len(df) - rain_count} ({(len(df)-rain_count)/len(df)*100:.2f}%)") # 分析降雨量分布和类别 rain_samples = df[df['rain'] > 0] if len(rain_samples) > 0: print(f"\n降雨统计（仅有雨样本）：") print(f"最小值: {rain_samples['rain'].min():.2f}mm") print(f"最大值: {rain_samples['rain'].max():.2f}mm") print(f"平均值: {rain_samples['rain'].mean():.2f}mm") print(f"中位数: {rain_samples['rain'].median():.2f}mm") # 打印降雨类别 if self.stratified_regression: light_rain = sum((df['rain'] > 0) & (df['rain'] <= self.rain_thresholds[0])) medium_rain = sum((df['rain'] > self.rain_thresholds[0]) & (df['rain'] <= self.rain_thresholds[1])) heavy_rain = sum((df['rain'] > self.rain_thresholds[1]) & (df['rain'] <= self.rain_thresholds[2])) extreme_rain = sum(df['rain'] > self.rain_thresholds[2]) print(f"\n降雨类别：") print(f"小雨 (0-{self.rain_thresholds[0]}mm): {light_rain} 样本 ({light_rain/rain_count*100:.2f}%)") print(f"中雨 ({self.rain_thresholds[0]}-{self.rain_thresholds[1]}mm): {medium_rain} 样本 ({medium_rain/rain_count*100:.2f}%)") print(f"大雨 ({self.rain_thresholds[1]}-{self.rain_thresholds[2]}mm): {heavy_rain} 样本 ({heavy_rain/rain_count*100:.2f}%)") print(f"暴雨 (>{self.rain_thresholds[2]}mm): {extreme_rain} 样本 ({extreme_rain/rain_count*100:.2f}%)") # 绘制降雨量直方图 plt.figure(figsize=(10, 6)) plt.hist(rain_samples['rain'].values, bins=20) plt.title('Rainfall Distribution Histogram (Rain Samples Only)') plt.xlabel('Rainfall (mm)') plt.ylabel('Number of Samples') plt.grid(True, alpha=0.3) plt.savefig(f"{self.output_dir}/rainfall_distribution.png") plt.close() # 对数变换降雨量分析 plt.figure(figsize=(10, 6)) plt.hist(np.log1p(rain_samples['rain'].values), bins=20) plt.title('Log-Transformed Rainfall Distribution (Rain Samples Only)') plt.xlabel('log(Rainfall+1)') plt.ylabel('Number of Samples') plt.grid(True, alpha=0.3) plt.savefig(f"{self.output_dir}/rainfall_log_distribution.png") plt.close() # 如果数据中有时间特征，添加时间相关特征 if 'datetime' in df.columns: # 小时特征（一天中的时刻） df['hour'] = df['datetime'].dt.hour df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24) df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24) # 星期特征 df['day_of_week'] = df['datetime'].dt.dayofweek df['weekend'] = (df['day_of_week'] >= 5).astype(int) # 月份特征（季节性） df['month'] = df['datetime'].dt.month df['season'] = (df['month'] % 12 + 3) // 3 # 季节性循环特征 df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12) df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12) return df def select_features(self, data, n_features=30, silent=False): """ 特征选择：基于相关性和实验结果选择最佳特征子集 参数: data: DataFrame 预处理后的数据 n_features: int 要选择的特征数量 silent: bool 是否抑制输出信息 返回: X: DataFrame 选定的特征矩阵 y_binary: Series 二值分类标签 y_amount: Series 降雨量标签 y_category: Series (可选) 降雨类别标签 """ # 选择基本特征（如果在预定义的feature_names中已存在） if self.feature_names is not None: # 检查预定义特征是否在数据中 available_features = [f for f in self.feature_names if f in data.columns] if len(available_features) != len(self.feature_names): missing = set(self.feature_names) - set(available_features) if not silent: self.debug_print(f"警告：以下预定义特征在数据中缺失：{missing}") selected_features = available_features else: # 选择新特征 - 优先考虑时间序列和变化特征 basic_features = ['rh', 'Temp', 'PWV', 'ZTD', 'ws', 'Press'] # 不同时间尺度的变化特征 change_features = [ 'PWV_1h_change', 'PWV_3h_change', 'PWV_6h_change', 'PWV_12h_change', 'ZTD_1h_change', 'ZTD_3h_change', 'ZTD_6h_change', 'Temp_1h_change', 'Temp_6h_change', 'Press_1h_change', 'Press_6h_change', 'rh_1h_change', 'rh_6h_change', 'PWV_change_accel', 'ZTD_change_accel', 'PWV_rapid_change', 'ZTD_rapid_change', 'Press_rapid_change' ] # 趋势和窗口特征 window_features = [ 'PWV_3h_mean', 'PWV_6h_mean', 'PWV_vs_6h', 'ZTD_6h_mean', 'ZTD_vs_6h', 'Temp_6h_mean', 'Press_6h_mean', 'rh_6h_mean', 'PWV_6h_std', 'ZTD_6h_std', 'PWV_12h_range', 'ZTD_12h_range' ] # 异常和标准化特征 anomaly_features = [ 'rh_anomaly', 'PWV_anomaly', 'ZTD_anomaly', 'Temp_anomaly', 'Press_anomaly' ] # 组合特征和指标 combined_features = [ 'PWV_rh', 'PWV_Temp', 'ZTD_PWV_ratio', 'moisture_change', 'ZTD_PWV_change', 'Press_change_intensity', 'rain_potential', 'saturation_index', 'moisture_instability', 'heavy_rain_potential', 'rain_persistence', 'PWV_cumul_change', 'Press_rapid_fall', 'Press_falling' ] # 时间相关特征 time_features = [ 'hour', 'hour_sin', 'hour_cos', 'day_of_week', 'weekend', 'month', 'season', 'month_sin', 'month_cos' ] # 合并特征列表并过滤仅存在于数据集中的特征 all_features = (basic_features + change_features + window_features + anomaly_features + combined_features + time_features) # 过滤出数据中存在的特征 selected_features = [f for f in all_features if f in data.columns] # 如果可用特征超过所需数量，根据对降雨预测的价值进行优先排序 if len(selected_features) > n_features and 'rain' in data.columns: # 计算与降雨量的相关性 try: rain_corr = data[selected_features].corrwith(data['rain']).abs() # 按降雨相关性排序特征 sorted_features = rain_corr.sort_values(ascending=False).index.tolist() except Exception as e: if not silent: print(f"计算特征相关性时出错: {str(e)}") sorted_features = selected_features # 始终保留一些基本特征，无论相关性如何 must_have_features = [f for f in [ 'PWV', 'ZTD', 'rh', 'Temp', 'PWV_1h_change', 'ZTD_1h_change', 'Press_1h_change', 'saturation_index', 'heavy_rain_potential', 'rain_persistence' ] if f in selected_features] # 将必备特征与相关性最高的特征结合，避免重复 remaining_features = [f for f in sorted_features if f not in must_have_features] # 选择最终特征集 selected_features = must_have_features + remaining_features[:n_features - len(must_have_features)] # 如果仍然有太多特征，只取前n_features个 if len(selected_features) > n_features: selected_features = selected_features[:n_features] # 保存选择的特征 self.feature_names = selected_features # 获取特征矩阵 X = data[selected_features].copy() # 获取目标变量 y_binary = data['rain_binary'].copy() if 'rain_binary' in data.columns else None y_amount = data['rain'].copy() if 'rain' in data.columns else None y_category = data['rain_category'].copy() if 'rain_category' in data.columns else None # 打印选择的特征 if not silent: print(f"\n选择的特征（{len(selected_features)}）：") for i, feature in enumerate(selected_features): print(f" {i+1}. {feature}") return X, y_binary, y_amount, y_category def split_data(self, X, y_binary, y_amount, y_category=None, time_based=False, test_size=0.2): """数据分割：可选择时间序列分割或随机分割""" if time_based: # 使用时间序列分割 - 假设数据按时间排序 train_size = int(len(X) * (1 - test_size)) X_train, X_test = X.iloc[:train_size], X.iloc[train_size:] y_binary_train, y_binary_test = y_binary.iloc[:train_size], y_binary.iloc[train_size:] y_amount_train, y_amount_test = y_amount.iloc[:train_size], y_amount.iloc[train_size:] if y_category is not None: y_category_train, y_category_test = y_category.iloc[:train_size], y_category.iloc[train_size:] else: y_category_train, y_category_test = None, None print("\n使用基于时间序列的数据分割...") else: # 使用随机分层抽样 if y_category is not None: X_train, X_test, y_binary_train, y_binary_test, y_amount_train, y_amount_test, y_category_train, y_category_test = train_test_split( X, y_binary, y_amount, y_category, test_size=test_size, random_state=42, stratify=y_binary ) else: X_train, X_test, y_binary_train, y_binary_test, y_amount_train, y_amount_test = train_test_split( X, y_binary, y_amount, test_size=test_size, random_state=42, stratify=y_binary ) y_category_train, y_category_test = None, None print("\n使用随机分层抽样进行数据分割...") # 输出分割信息 print(f"训练集: {len(X_train)} 样本 ({sum(y_binary_train == 1)} 有雨, {sum(y_binary_train == 0)} 无雨)") print(f"测试集: {len(X_test)} 样本 ({sum(y_binary_test == 1)} 有雨, {sum(y_binary_test == 0)} 无雨)") # 检查训练集和测试集中的雨量比例 train_rain_ratio = sum(y_binary_train == 1) / len(y_binary_train) test_rain_ratio = sum(y_binary_test == 1) / len(y_binary_test) print(f"训练集降雨比例: {train_rain_ratio:.2%}") print(f"测试集降雨比例: {test_rain_ratio:.2%}") # 如果使用分层回归，打印类别分布 if y_category is not None: for cat in sorted(y_category_train.unique()): cat_count_train = sum(y_category_train == cat) cat_count_test = sum(y_category_test == cat) if cat > 0: # 仅打印降雨类别 cat_name = "小雨" if cat == 1 else "中雨" if cat == 2 else "大雨" if cat == 3 else "暴雨" print(f"{cat_name}: {cat_count_train} 训练样本, {cat_count_test} 测试样本") return X_train, X_test, y_binary_train, y_binary_test, y_amount_train, y_amount_test, y_category_train, y_category_test def scale_features(self, X_train, X_test): """特征标准化：使用RobustScaler减少异常值的影响""" # 首次调用时初始化缩放器 if self.scaler is None: self.scaler = RobustScaler() # 将DataFrame转换为numpy数组 if isinstance(X_train, pd.DataFrame): X_train_np = X_train.values else: X_train_np = X_train if isinstance(X_test, pd.DataFrame): X_test_np = X_test.values else: X_test_np = X_test # 应用RobustScaler X_train_scaled = self.scaler.fit_transform(X_train_np) X_test_scaled = self.scaler.transform(X_test_np) return X_train_scaled, X_test_scaled def build_binary_classifier(self): """构建二分类模型（是否下雨）""" if self.clf_model_type == 'lr': # 逻辑回归 - 简单且稳健 base_model = LogisticRegression( C=0.05, # 更强的L2正则化 penalty='elasticnet', solver='saga', l1_ratio=0.2, class_weight='balanced', max_iter=3000, random_state=42 ) param_grid = { 'C': [0.01, 0.05, 0.1, 0.5], 'l1_ratio':[0.1, 0.2, 0.3] } elif self.clf_model_type == 'rf': # 随机森林 - 控制复杂度以避免过拟合 base_model = RandomForestClassifier( n_estimators=200, max_depth=8, # 限制树深度 min_samples_split=8, min_samples_leaf=4, max_features='sqrt', # 使用特征子集 bootstrap=True, oob_score=True, # 启用袋外评分 class_weight='balanced_subsample', random_state=42 ) param_grid = { 'max_depth': [6, 8, 10], 'n_estimators': [150, 200, 250], 'min_samples_leaf': [2, 4, 6] } elif self.clf_model_type == 'gbdt': # 梯度提升树 - 通过慢学习率降低过拟合 base_model = GradientBoostingClassifier( n_estimators=250, max_depth=4, # 浅树 learning_rate=0.03, # 慢学习率 subsample=0.7, # 样本子采样 min_samples_split=15, min_samples_leaf=10, max_features=0.8, # 特征子采样 random_state=42 ) param_grid = { 'n_estimators': [200, 250, 300], 'max_depth': [3, 4, 5], 'learning_rate': [0.01, 0.03, 0.05], 'subsample': [0.7, 0.8, 0.9] } # 在RainfallTwoStageModel类的build_binary_classifier方法中添加 elif self.clf_model_type == 'xgboost': from xgboost import XGBClassifier base_model = XGBClassifier( n_estimators=300, # 增加树数量 max_depth=5, # 适当增加深度 learning_rate=0.03, # 降低学习率 subsample=0.7, # 减少样本比例 colsample_bytree=0.7, # 减少特征比例 min_child_weight=5, # 增加叶子节点要求 gamma=0.2, # 增加分裂阈值 scale_pos_weight=15, # 提高正样本权重 reg_alpha=0.1, # L1正则化 reg_lambda=1.0, # L2正则化 random_state=42, tree_method='hist', # 更快的直方图算法 eval_metric='auc' # 评估指标 ) param_grid = { 'n_estimators': [250, 300, 350], 'max_depth': [4, 5, 6], 'min_child_weight': [3, 5, 7], 'scale_pos_weight': [10, 15, 20], 'subsample': [0.6, 0.7, 0.8] } elif self.clf_model_type == 'ensemble': # 简单投票集成 from sklearn.ensemble import VotingClassifier from xgboost import XGBClassifier estimators = [ ('lr', LogisticRegression(C=0.05, class_weight='balanced', max_iter=3000, solver='saga')), ('rf', RandomForestClassifier(n_estimators=200, max_depth=6, min_samples_leaf=4, class_weight='balanced_subsample')), ('gb', GradientBoostingClassifier(n_estimators=200, max_depth=4, learning_rate=0.03, subsample=0.7)), ('xgb', XGBClassifier(n_estimators=250, max_depth=5, learning_rate=0.03, scale_pos_weight=15)) ] base_model = VotingClassifier(estimators=estimators, voting='soft') param_grid = {} # 简化集成模型调优 else: raise ValueError(f"不支持的分类器类型: {self.clf_model_type}") return base_model, param_grid def build_regressor(self): """构建回归模型""" if self.reg_model_type == 'rf': # 随机森林回归器 base_model = RandomForestRegressor( n_estimators=100, max_depth=5, min_samples_split=5, min_samples_leaf=5, random_state=42 ) param_grid = { 'max_depth': [3, 5, 8], 'min_samples_leaf': [3, 5, 10] } elif self.reg_model_type == 'gbdt': # 梯度提升树回归器 base_model = GradientBoostingRegressor( n_estimators=100, max_depth=3, learning_rate=0.05, subsample=0.8, loss='squared_error', random_state=42 ) param_grid = { 'max_depth': [2, 3], 'learning_rate': [0.01, 0.05, 0.1] } elif self.reg_model_type == 'ridge': # 岭回归 - 简单线性模型 base_model = Ridge( alpha=1.0, random_state=42 ) param_grid = { 'alpha': [0.1, 1.0, 10.0] } elif self.reg_model_type == 'xgboost': # XGBoost回归器 try: from xgboost import XGBRegressor # 基础模型配置 - 增强了过拟合控制 base_model = XGBRegressor( n_estimators=200, # 增加树的数量以提高性能 max_depth=4, # 保持不变 learning_rate=0.05, # 保持不变 subsample=0.8, # 保持不变，有助于防止过拟合 colsample_bytree=0.8, # 保持不变，有助于防止过拟合 min_child_weight=3, # 新增：防止在少量样本上过拟合 gamma=0.1, # 新增：控制树节点分裂的最小损失减少 reg_alpha=0.1, # 新增：L1正则化，减少模型复杂性 reg_lambda=1.0, # 新增：L2正则化，减少模型复杂性 random_state=42, n_jobs=-1 # 使用所有CPU核心加速训练 ) # 增加参数网格搜索范围 param_grid = { 'max_depth': [3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'min_child_weight': [1, 3, 5], # 新增：控制叶节点所需的最小样本权重和 'gamma': [0, 0.1, 0.2], # 新增：控制分裂所需的最小损失减少 'subsample': [0.7, 0.8, 0.9], # 新增：每棵树随机采样的比例 'colsample_bytree': [0.7, 0.8, 0.9] # 新增：每棵树随机采样的特征比例 } # 针对不同时间点调整的特定参数 for time_point in self.time_points: if time_point == 1: # 1小时预测 # 1小时预测结果较好，可使用略微复杂的模型 base_model.max_depth = 5 base_model.learning_rate = 0.1 param_grid = { 'max_depth': [4, 5, 6], 'learning_rate': [0.05, 0.1, 0.15], 'min_child_weight': [1, 3], 'gamma': [0, 0.1] } elif time_point == 2: # 2小时预测 # 2小时预测XGBoost性能略差，增强正则化 base_model.max_depth = 3 base_model.learning_rate = 0.05 base_model.subsample = 0.7 base_model.reg_alpha = 0.5 param_grid = { 'max_depth': [2, 3, 4], 'learning_rate': [0.01, 0.05], 'min_child_weight': [3, 5], 'gamma': [0.1, 0.2] } elif time_point >= 3: # 3小时及以上预测 # 3小时预测需要更强的正则化防止过拟合 base_model.max_depth = 3 base_model.learning_rate = 0.03 base_model.subsample = 0.7 base_model.colsample_bytree = 0.7 base_model.reg_alpha = 1.0 base_model.reg_lambda = 2.0 param_grid = { 'max_depth': [2, 3, 4], 'learning_rate': [0.01, 0.03, 0.05], 'min_child_weight': [3, 5, 7], 'gamma': [0.1, 0.2, 0.3] } # 如果是对暴雨类别数据的预测，采用特殊设置 if hasattr(self, 'current_rain_category') and self.current_rain_category == 'storm': base_model.max_depth = 2 base_model.learning_rate = 0.01 base_model.min_child_weight = 5 base_model.subsample = 0.6 base_model.colsample_bytree = 0.6 base_model.reg_alpha = 2.0 base_model.reg_lambda = 3.0 param_grid = { 'max_depth': [2, 3], 'learning_rate': [0.005, 0.01], 'min_child_weight': [5, 7] } except ImportError: print("XGBoost不可用，回退到GBDT") return self.build_regressor() else: raise ValueError(f"不支持的回归器类型: {self.reg_model_type}") return base_model, param_grid def train_binary_classifier(self, X_train, y_train, X_val=None, y_val=None): """训练二分类降雨分类器（判断是否下雨）""" print(f"\n训练降雨二分类器 ({self.clf_model_type})...") # 构建基础模型 base_model, param_grid = self.build_binary_classifier() # 如果有参数需要调优且不是集成模型 if param_grid and self.clf_model_type != 'ensemble': # 使用GridSearchCV查找最佳参数 cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) grid_search = GridSearchCV( estimator=base_model, param_grid=param_grid, cv=cv, scoring='f1', # 使用F1分数 n_jobs=-1 ) # 训练模型 grid_search.fit(X_train, y_train) # 获取最佳模型 best_model = grid_search.best_estimator_ print(f"最佳分类器参数: {grid_search.best_params_}") else: # 直接使用基础模型 best_model = base_model best_model.fit(X_train, y_train) # 应用概率校准 - 使用验证集进行校准 if X_val is not None and y_val is not None and hasattr(best_model, "predict_proba"): print("使用验证集进行概率校准...") calibrator = CalibratedClassifierCV( best_model, method='sigmoid', # 或 'isotonic' cv='prefit' # 使用已训练模型 ) calibrator.fit(X_val, y_val) self.binary_classifier = calibrator else: self.binary_classifier = best_model # 检查特征重要性（如果模型支持） if hasattr(best_model, 'feature_importances_'): self.feature_importances_clf = best_model.feature_importances_ # 输出重要特征 if self.feature_names is not None: importance_pairs = sorted(zip(self.feature_names, self.feature_importances_clf), key=lambda x: x[1], reverse=True) print("\n分类器特征重要性：") for feature, importance in importance_pairs: print(f" {feature}: {importance:.4f}") # 绘制特征重要性 plt.figure(figsize=(12, 8)) features = [x[0] for x in importance_pairs] importances = [x[1] for x in importance_pairs] plt.barh(range(len(features)), importances, align='center') plt.yticks(range(len(features)), features) plt.xlabel('Importance') plt.title('Binary Classifier Feature Importance') plt.tight_layout() plt.savefig(f"{self.output_dir}/binary_classifier_feature_importance.png") plt.close() # 评估训练集性能 y_train_pred = self.binary_classifier.predict(X_train) train_accuracy = accuracy_score(y_train, y_train_pred) train_precision = precision_score(y_train, y_train_pred) train_recall = recall_score(y_train, y_train_pred) train_f1 = f1_score(y_train, y_train_pred) print(f"二分类器训练集性能: 准确率={train_accuracy:.4f}, 精确率={train_precision:.4f}, " f"召回率={train_recall:.4f}, F1分数={train_f1:.4f}") return self.binary_classifier def train_category_classifier(self, X_train, y_category, rain_mask): """训练降雨强度类别分类器（小雨、中雨、大雨、暴雨）""" # 仅使用降雨样本进行类别预测 X_train_rain = X_train[rain_mask] y_category_rain = y_category[rain_mask] unique_categories = np.unique(y_category_rain) if len(unique_categories) < 2: print(f"警告: 降雨类别样本不足，无法训练分类器 (唯一类别: {unique_categories})") return None print(f"\n训练降雨类别分类器，使用 {len(X_train_rain)} 个降雨样本...") # 计算类别权重 - 反比于类别频率，加强对大雨和暴雨的权重 try: # 将类别转换为整数 y_category_rain_int = y_category_rain.astype(int) # 计算类别频率 class_counts = np.bincount(y_category_rain_int)[1:] # 跳过0类别 total_samples = len(y_category_rain) # 应用不平衡权重 - 大雨和暴雨类别权重更高 class_weights = {} for i, count in enumerate(class_counts): category = i + 1 # 类别从1开始 # 小雨:1倍，中雨:2倍，大雨:4倍，暴雨:8倍基础权重 multiplier = 1 if category == 1 else 2 if category == 2 else 4 if category == 3 else 8 if count > 0: class_weights[category] = (total_samples / (count * len(class_counts))) * multiplier else: class_weights[category] = 1.0 print("类别权重:") for cat, weight in class_weights.items(): cat_name = "小雨" if cat == 1 else "中雨" if cat == 2 else "大雨" if cat == 3 else "暴雨" print(f" {cat_name}: {weight:.2f}") except Exception as e: print(f"计算类别权重时出错: {str(e)}") # 使用默认权重 class_weights = {1: 1.0, 2: 2.0, 3: 4.0, 4: 8.0} print("使用默认类别权重") # 对大雨和暴雨样本进行过采样 oversampled_X = [] oversampled_y = [] # 添加所有原始样本 oversampled_X.append(X_train_rain) oversampled_y.append(y_category_rain) # 过采样大雨样本 try: heavy_mask = (y_category_rain == 3) heavy_count = sum(heavy_mask) if heavy_count > 0 and heavy_count < 100: X_heavy = X_train_rain[heavy_mask] y_heavy = y_category_rain[heavy_mask] # 过采样到100个样本或原始样本的5倍，以较小者为准 target_samples = min(100, heavy_count * 5) X_heavy_resampled, y_heavy_resampled = resample( X_heavy, y_heavy, replace=True, n_samples=target_samples, random_state=42 ) oversampled_X.append(X_heavy_resampled) oversampled_y.append(y_heavy_resampled) print(f"过采样大雨样本: {heavy_count} -> {target_samples}") except Exception as e: print(f"过采样大雨样本时出错: {str(e)}") # 过采样暴雨样本 try: extreme_mask = (y_category_rain == 4) extreme_count = sum(extreme_mask) if extreme_count > 0 and extreme_count < 50: X_extreme = X_train_rain[extreme_mask] y_extreme = y_category_rain[extreme_mask] # 过采样到50个样本或原始样本的10倍，以较小者为准 target_samples = min(50, extreme_count * 10) X_extreme_resampled, y_extreme_resampled = resample( X_extreme, y_extreme, replace=True, n_samples=target_samples, random_state=42 ) oversampled_X.append(X_extreme_resampled) oversampled_y.append(y_extreme_resampled) print(f"过采样暴雨样本: {extreme_count} -> {target_samples}") except Exception as e: print(f"过采样暴雨样本时出错: {str(e)}") # 合并所有样本 try: if len(oversampled_X) > 1: X_train_rain_balanced = np.vstack(oversampled_X) y_category_rain_balanced = np.hstack(oversampled_y) else: X_train_rain_balanced = X_train_rain y_category_rain_balanced = y_category_rain print(f"平衡后的训练集大小: {len(X_train_rain_balanced)}") except Exception as e: print(f"合并过采样数据时出错: {str(e)}") # 使用原始数据 X_train_rain_balanced = X_train_rain y_category_rain_balanced = y_category_rain print("使用原始未平衡数据") # 使用随机森林分类器进行类别预测，增加树的深度和数量 classifier = RandomForestClassifier( n_estimators=200, # 增加树的数量 max_depth=10, # 增加深度 min_samples_leaf=2, class_weight=class_weights, # 使用类别权重 random_state=42 ) # 训练分类器 classifier.fit(X_train_rain_balanced, y_category_rain_balanced) # 检查性能 y_pred = classifier.predict(X_train_rain) accuracy = accuracy_score(y_category_rain, y_pred) print(f"降雨类别分类器训练准确率: {accuracy:.4f}") # 检查类别分布 try: class_counts = np.bincount(y_category_rain.astype(int)) class_predictions = np.bincount(y_pred.astype(int)) # 确保结果具有相同的大小 if len(class_counts) > len(class_predictions): class_predictions = np.pad(class_predictions, (0, len(class_counts) - len(class_predictions))) elif len(class_predictions) > len(class_counts): class_counts = np.pad(class_counts, (0, len(class_predictions) - len(class_counts))) print("降雨类别分布：") categories = ["无雨", "小雨", "中雨", "大雨", "暴雨"] for i in range(1, min(len(class_counts), len(categories))): # 跳过"无雨"(0) print(f" {categories[i]}: {class_counts[i]} 实际, {class_predictions[i]} 预测") except Exception as e: print(f"统计类别分布时出错: {str(e)}") # 混淆矩阵 try: confusion = confusion_matrix(y_category_rain, y_pred) print("\n类别分类器混淆矩阵:") # 显示混淆矩阵，标签为雨量类别 unique_cats = sorted(np.unique(np.concatenate([y_category_rain, y_pred]))) label_names = ["小雨" if cat == 1 else "中雨" if cat == 2 else "大雨" if cat == 3 else "暴雨" for cat in unique_cats] print("预测 →") print("实际 ↓") print(" " + " ".join(f"{name:8s}" for name in label_names)) for i, name in enumerate(label_names): row_values = " ".join(f"{confusion[i, j]:8d}" for j in range(len(label_names))) print(f"{name:6s} {row_values}") except Exception as e: print(f"计算混淆矩阵时出错: {str(e)}") # 保存分类器 self.rain_category_classifier = classifier return classifier def train_stratified_regressors(self, X_train, y_train, y_category, rain_mask): """训练针对不同降雨强度的分层回归模型""" print("\n训练分层降雨回归器...") # 获取降雨样本 X_train_rain = X_train[rain_mask] y_train_rain = y_train[rain_mask] y_category_rain = y_category[rain_mask] # 应用降雨量的对数变换 if self.log_transform: print("对降雨量应用对数变换 (log(rain+1))...") y_train_rain_transformed = np.log1p(y_train_rain) else: y_train_rain_transformed = y_train_rain # 检查降雨类别的分布 categories = sorted(np.unique(y_category_rain)) for cat in categories: if cat == 0: # 跳过无雨类别 continue cat_mask = (y_category_rain == cat) count = sum(cat_mask) cat_name = "小雨" if cat == 1 else "中雨" if cat == 2 else "大雨" if cat == 3 else "暴雨" print(f"{cat_name}样本: {count}") if count < 10: print(f" 警告: {cat_name}样本太少，无法训练可靠模型") # 为每个降雨类别构建并训练模型 base_model, _ = self.build_regressor() self.regressors = {} for cat in categories: if cat == 0: # 跳过无雨类别 continue cat_mask = (y_category_rain == cat) count = sum(cat_mask) if count < 5: print(f"跳过样本数量为{count}的类别{cat}，样本太少") continue # 跳过样本太少的类别 cat_name = "light" if cat == 1 else "medium" if cat == 2 else "heavy" if cat == 3 else "extreme" reg_name = "light" if cat == 1 else "medium" if cat == 2 else "heavy" if cat == 3 else "extreme" # 对大雨和暴雨样本进行过采样 try: if cat >= 3 and count < 100: # 大雨和暴雨样本 # 简单过采样 X_rain_cat = X_train_rain[cat_mask] y_rain_cat = y_train_rain_transformed[cat_mask] # 过采样量取决于类别：大雨5倍，暴雨10倍 multiplier = 5 if cat == 3 else 10 target_samples = min(100, count * multiplier) X_resampled, y_resampled = resample( X_rain_cat, y_rain_cat, replace=True, n_samples=target_samples, random_state=42 ) print(f"\n训练{cat_name}回归器，使用 {count} 个原始样本, 过采样至 {target_samples} 个样本...") # 克隆基础模型并训练 regressor = clone(base_model) regressor.fit(X_resampled, y_resampled) else: print(f"\n训练{cat_name}回归器，使用 {count} 个样本...") # 克隆基础模型并训练 regressor = clone(base_model) regressor.fit(X_train_rain[cat_mask], y_train_rain_transformed[cat_mask]) except Exception as e: print(f"训练{cat_name}回归器时出错: {str(e)}") # 尝试使用基本训练方法（无过采样） try: print(f"尝试使用基本训练方法...") regressor = clone(base_model) regressor.fit(X_train_rain[cat_mask], y_train_rain_transformed[cat_mask]) except Exception as e2: print(f"基本训练方法也失败: {str(e2)}") continue # 跳过此类别 # 存储训练好的模型 self.regressors[reg_name] = regressor # 在训练数据上评估 try: y_pred_transformed = regressor.predict(X_train_rain[cat_mask]) if self.log_transform: y_pred = np.expm1(y_pred_transformed) else: y_pred = y_pred_transformed # 防止负值 y_pred = np.maximum(y_pred, 0) # 计算评估指标 rmse = np.sqrt(mean_squared_error(y_train_rain[cat_mask], y_pred)) mae = mean_absolute_error(y_train_rain[cat_mask], y_pred) r2 = r2_score(y_train_rain[cat_mask], y_pred) print(f"{cat_name}回归器训练指标: RMSE={rmse:.4f}, MAE={mae:.4f}, R²={r2:.4f}") # 如果模型有特征重要性，保存它们 if hasattr(regressor, 'feature_importances_') and self.feature_names is not None: importance_pairs = sorted(zip(self.feature_names, regressor.feature_importances_), key=lambda x: x[1], reverse=True) print(f"\n{cat_name}回归器特征重要性（前10）：") for feature, importance in importance_pairs[:10]: print(f" {feature}: {importance:.4f}") # 绘制特征重要性 plt.figure(figsize=(12, 8)) features = [x[0] for x in importance_pairs[:15]] importances = [x[1] for x in importance_pairs[:15]] plt.barh(range(len(features)), importances, align='center') plt.yticks(range(len(features)), features) plt.xlabel('Importance') plt.title(f'{cat_name} Rain Regressor Feature Importance') plt.tight_layout() plt.savefig(f"{self.output_dir}/{reg_name}_regressor_importance.png") plt.close() # 绘制实际值与预测值 plt.figure(figsize=(10, 6)) plt.scatter(y_train_rain[cat_mask], y_pred, alpha=0.6) max_val = max(max(y_train_rain[cat_mask]), max(y_pred)) plt.plot([0, max_val], [0, max_val], 'r--') plt.xlabel('Actual Rainfall (mm)') plt.ylabel('Predicted Rainfall (mm)') plt.title(f'{cat_name} Rain - Actual vs Predicted (Training)') plt.grid(True) plt.tight_layout() plt.savefig(f"{self.output_dir}/{reg_name}_train_performance.png") plt.close() except Exception as e: print(f"评估{cat_name}回归器时出错: {str(e)}") return self.regressors def train_regressor(self, X_train, y_train, rain_mask): """训练降雨量回归器，仅使用降雨样本""" # 获取降雨样本 X_train_rain = X_train[rain_mask] y_train_rain = y_train[rain_mask] if len(X_train_rain) < 10: print("警告: 降雨样本太少，无法训练回归模型") return None print(f"\n训练降雨量回归器 ({self.reg_model_type})，使用 {len(X_train_rain)} 个降雨样本...") # 考虑降雨量的对数变换 log_transform = True # 默认使用对数变换 self.log_transform = log_transform if log_transform: print("对降雨量应用对数变换 (log(rain+1))...") y_train_rain_transformed = np.log1p(y_train_rain) else: y_train_rain_transformed = y_train_rain # 构建回归模型 base_model, param_grid = self.build_regressor() # 如果样本足够多，调整参数 try: if len(X_train_rain) >= 50 and param_grid: from sklearn.model_selection import KFold cv = KFold(n_splits=5, shuffle=True, random_state=42) grid_search = GridSearchCV( estimator=base_model, param_grid=param_grid, cv=cv, scoring='neg_mean_squared_error', n_jobs=-1 ) # 训练模型 grid_search.fit(X_train_rain, y_train_rain_transformed) # 获取最佳模型 best_model = grid_search.best_estimator_ print(f"最佳回归器参数: {grid_search.best_params_}") else: # 直接使用基础模型 best_model = base_model best_model.fit(X_train_rain, y_train_rain_transformed) # 保存模型 self.regressor = best_model except Exception as e: print(f"训练回归器时出错: {str(e)}") print(f"尝试使用简化的回归器...") # 使用简化的回归器 try: simple_model = RandomForestRegressor(n_estimators=50, max_depth=3, random_state=42) simple_model.fit(X_train_rain, y_train_rain_transformed) self.regressor = simple_model except Exception as e2: print(f"简化回归器训练也失败: {str(e2)}") return None # 检查特征重要性（如果模型支持） if hasattr(self.regressor, 'feature_importances_'): self.feature_importances_reg = self.regressor.feature_importances_ # 输出重要特征 if self.feature_names is not None: importance_pairs = sorted(zip(self.feature_names, self.feature_importances_reg), key=lambda x: x[1], reverse=True) print("\n回归器特征重要性：") for feature, importance in importance_pairs: print(f" {feature}: {importance:.4f}") # 绘制特征重要性 plt.figure(figsize=(12, 8)) features = [x[0] for x in importance_pairs] importances = [x[1] for x in importance_pairs] plt.barh(range(len(features)), importances, align='center') plt.yticks(range(len(features)), features) plt.xlabel('Importance') plt.title('Regressor Feature Importance') plt.tight_layout() plt.savefig(f"{self.output_dir}/regressor_feature_importance.png") plt.close() # 评估训练集性能 try: if log_transform: y_train_rain_pred_transformed = self.regressor.predict(X_train_rain) y_train_rain_pred = np.expm1(y_train_rain_pred_transformed) else: y_train_rain_pred = self.regressor.predict(X_train_rain) # 计算评估指标 train_rmse = np.sqrt(mean_squared_error(y_train_rain, y_train_rain_pred)) train_mae = mean_absolute_error(y_train_rain, y_train_rain_pred) train_r2 = r2_score(y_train_rain, y_train_rain_pred) print(f"回归器训练集性能: RMSE={train_rmse:.4f}, MAE={train_mae:.4f}, R²={train_r2:.4f}") # 绘制训练集的实际值与预测值对比图 plt.figure(figsize=(10, 6)) plt.scatter(y_train_rain, y_train_rain_pred, alpha=0.5) max_val = max(max(y_train_rain), max(y_train_rain_pred)) plt.plot([0, max_val], [0, max_val], 'r--') plt.xlabel('Actual Rainfall (mm)') plt.ylabel('Predicted Rainfall (mm)') plt.title('Actual vs Predicted Rainfall - Training Set') plt.grid(True) plt.savefig(f"{self.output_dir}/regressor_train_performance.png") plt.close() except Exception as e: print(f"评估回归器性能时出错: {str(e)}") return self.regressor def optimize_threshold(self, X_test, y_test): """优化分类阈值""" print("\n优化分类阈值...") # 获取预测概率 y_pred_proba = self.binary_classifier.predict_proba(X_test)[:, 1] # 分析预测概率分布 print(f"预测概率分布: 最小值={y_pred_proba.min():.4f}, 最大值={y_pred_proba.max():.4f}, 平均值={y_pred_proba.mean():.4f}") # 计算有雨和无雨样本的平均预测概率 rain_probs = y_pred_proba[y_test == 1] no_rain_probs = y_pred_proba[y_test == 0] if len(rain_probs) > 0: print(f"有雨样本的平均预测概率: {rain_probs.mean():.4f}") if len(no_rain_probs) > 0: print(f"无雨样本的平均预测概率: {no_rain_probs.mean():.4f}") # 绘制概率分布直方图 plt.figure(figsize=(10, 6)) if len(no_rain_probs) > 0: plt.hist(no_rain_probs, bins=20, alpha=0.5, label='No Rain Samples', color='blue') if len(rain_probs) > 0: plt.hist(rain_probs, bins=20, alpha=0.5, label='Rain Samples', color='red') plt.xlabel('Prediction Probability') plt.ylabel('Number of Samples') plt.title('Prediction Probability Distribution') plt.legend() plt.grid(True, alpha=0.3) plt.savefig(f"{self.output_dir}/prediction_probability_distribution.png") plt.close() # 绘制ROC曲线 fpr, tpr, thresholds_roc = roc_curve(y_test, y_pred_proba) roc_auc = auc(fpr, tpr) plt.figure(figsize=(10, 6)) plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC Curve (AUC = {roc_auc:.4f})') plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver Operating Characteristic Curve') plt.legend(loc="lower right") plt.grid(True, alpha=0.3) plt.savefig(f"{self.output_dir}/roc_curve.png") plt.close() # 计算不同阈值的F1分数 thresholds = np.linspace(0.05, 0.95, 19) f1_scores = [] precision_scores = [] recall_scores = [] for threshold in thresholds: y_pred = (y_pred_proba >= threshold).astype(int) f1 = f1_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1_scores.append(f1) precision_scores.append(precision) recall_scores.append(recall) print(f"阈值={threshold:.2f}: 精确率={precision:.4f}, 召回率={recall:.4f}, F1分数={f1:.4f}") # 绘制不同阈值的性能曲线 plt.figure(figsize=(10, 6)) plt.plot(thresholds, precision_scores, 'b-', marker='o', label='Precision') plt.plot(thresholds, recall_scores, 'g-', marker='s', label='Recall') plt.plot(thresholds, f1_scores, 'r-', marker='^', label='F1 Score') plt.xlabel('Threshold') plt.ylabel('Score') plt.title('Performance at Different Thresholds') plt.legend() plt.grid(True) plt.savefig(f"{self.output_dir}/threshold_performance.png") plt.close() # 找到F1分数最高的阈值 best_idx = np.argmax(f1_scores) self.threshold = thresholds[best_idx] print(f"最佳阈值: {self.threshold:.4f} (F1分数: {f1_scores[best_idx]:.4f})") return self.threshold def evaluate_binary_classifier(self, X_test, y_test): """评估二分类器性能""" print("\n评估二分类器性能...") # 预测测试集 y_pred_proba = self.binary_classifier.predict_proba(X_test)[:, 1] y_pred = (y_pred_proba >= self.threshold).astype(int) # 基本分类指标 accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred) print("二分类性能指标：") print(f"准确率: {accuracy:.4f}") print(f"精确率: {precision:.4f}") print(f"召回率: {recall:.4f}") print(f"F1分数: {f1:.4f}") # 混淆矩阵 cm = confusion_matrix(y_test, y_pred) plt.figure(figsize=(8, 6)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') plt.title('Binary Classifier Confusion Matrix') plt.ylabel('True Label') plt.xlabel('Predicted Label') plt.savefig(f"{self.output_dir}/binary_confusion_matrix.png") plt.close() # 预测统计 true_rain_count = np.sum(y_test == 1) pred_rain_count = np.sum(y_pred == 1) correct_rain_count = np.sum((y_test == 1) & (y_pred == 1)) print("\n预测统计：") print(f"总样本数: {len(y_test)}") print(f"实际有雨样本: {true_rain_count} ({true_rain_count/len(y_test)*100:.2f}%)") print(f"预测有雨样本: {pred_rain_count} ({pred_rain_count/len(y_test)*100:.2f}%)") if true_rain_count > 0: print(f"正确预测有雨: {correct_rain_count} ({correct_rain_count/true_rain_count*100:.2f}% 实际有雨样本)") return { 'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1': f1, 'confusion_matrix': cm, 'predictions': y_pred, 'probabilities': y_pred_proba } def evaluate_category_classifier(self, X_test, y_category_test, mask_true, mask_pred): """评估降雨类别分类器性能""" if self.rain_category_classifier is None: print("类别分类器未训练，无法评估") return None # 只评估正确识别为降雨的样本 mask_both = mask_true & mask_pred if sum(mask_both) == 0: print("没有正确分类的降雨样本，无法评估类别分类器") return None X_test_rain = X_test[mask_both] y_category_test_rain = y_category_test[mask_both] # 进行预测 y_category_pred = self.rain_category_classifier.predict(X_test_rain) # 计算准确率 accuracy = accuracy_score(y_category_test_rain, y_category_pred) print("\n降雨类别分类器评估：") print(f"准确率: {accuracy:.4f}") # 混淆矩阵 try: categories = sorted(np.unique(np.concatenate([y_category_test_rain, y_category_pred]))) cm = confusion_matrix(y_category_test_rain, y_category_pred, labels=categories) # 打印混淆矩阵 category_names = ["小雨", "中雨", "大雨", "暴雨"] print("\n混淆矩阵：") print("预测 →") print("实际 ↓") # 创建表头 header = " " + " ".join(f"{category_names[cat-1]:8s}" for cat in categories if cat > 0 and cat-1 < len(category_names)) print(header) print("-" * len(header)) for i, cat in enumerate(categories): if cat > 0 and cat-1 < len(category_names): row = f"{category_names[cat-1]:6s} " row += " ".join(f"{cm[i, j]:8d}" for j in range(len(categories))) print(row) # 绘制混淆矩阵热图 plt.figure(figsize=(10, 8)) category_labels = [category_names[cat-1] if cat > 0 and cat-1 < len(category_names) else f"类别{cat}" for cat in categories] sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=category_labels, yticklabels=category_labels) plt.title('Rain Category Classifier Confusion Matrix') plt.ylabel('True Category') plt.xlabel('Predicted Category') plt.tight_layout() plt.savefig(f"{self.output_dir}/category_confusion_matrix.png") plt.close() except Exception as e: print(f"绘制类别混淆矩阵时出错: {str(e)}") return { 'accuracy': accuracy, 'confusion_matrix': cm if 'cm' in locals() else None, 'predictions': y_category_pred } def evaluate_stratified_regressors(self, X_test, y_test, y_category_test, mask_true, mask_pred): """评估分层回归模型的性能""" if not self.regressors: print("分层回归器未训练，无法评估") return None print("\n评估分层回归器性能...") # 只评估正确识别为降雨的样本 mask_both = mask_true & mask_pred if sum(mask_both) == 0: print("没有正确分类的降雨样本，无法评估回归器") return None X_test_rain = X_test[mask_both] y_test_rain = y_test[mask_both] y_category_test_rain = y_category_test[mask_both] # 准备存储预测结果 y_pred_rain = np.zeros_like(y_test_rain) # 对于每个降雨样本，根据实际类别使用适当的回归器 try: for i, (x, category) in enumerate(zip(X_test_rain, y_category_test_rain)): reg_name = "light" if category == 1 else "medium" if category == 2 else "heavy" if category == 3 else "extreme" # 检查是否有该类别的回归器 if reg_name in self.regressors: regressor = self.regressors[reg_name] # 进行预测 x_reshaped = x.reshape(1, -1) if self.log_transform: pred_log = regressor.predict(x_reshaped)[0] pred = np.expm1(pred_log) else: pred = regressor.predict(x_reshaped)[0] # 确保预测值非负 pred = max(0, pred) # 对大雨和暴雨应用安全系数 if category == 3: # 大雨 pred = pred * self.heavy_rain_factor elif category == 4: # 暴雨 pred = pred * self.extreme_rain_factor y_pred_rain[i] = pred else: # 该类别没有模型，使用默认模型（如果有） if self.regressor is not None: x_reshaped = x.reshape(1, -1) if self.log_transform: pred_log = self.regressor.predict(x_reshaped)[0] pred = np.expm1(pred_log) else: pred = self.regressor.predict(x_reshaped)[0] # 如果是大雨或暴雨类别但没有专门的回归器，仍应用安全系数 if category == 3: # 大雨 pred = pred * self.heavy_rain_factor elif category == 4: # 暴雨 pred = pred * self.extreme_rain_factor y_pred_rain[i] = max(0, pred) except Exception as e: print(f"分层回归预测时出错: {str(e)}") print("尝试使用简化的预测方法...") # 使用单一回归器进行所有预测 if self.regressor is not None: try: if self.log_transform: y_pred_transformed = self.regressor.predict(X_test_rain) y_pred_rain = np.expm1(y_pred_transformed) else: y_pred_rain = self.regressor.predict(X_test_rain) y_pred_rain = np.maximum(y_pred_rain, 0) except Exception as e2: print(f"单一回归器预测也失败: {str(e2)}") return None else: return None # 计算总体指标 try: rmse = np.sqrt(mean_squared_error(y_test_rain, y_pred_rain)) mae = mean_absolute_error(y_test_rain, y_pred_rain) r2 = r2_score(y_test_rain, y_pred_rain) print("\n分层回归器总体性能：") print(f"样本数: {len(y_test_rain)}") print(f"RMSE: {rmse:.4f}") print(f"MAE: {mae:.4f}") print(f"R²: {r2:.4f}") except Exception as e: print(f"计算分层回归器总体性能时出错: {str(e)}") rmse, mae, r2 = float('nan'), float('nan'), float('nan') # 计算各类别的指标 try: for category in sorted(np.unique(y_category_test_rain)): cat_mask = (y_category_test_rain == category) if sum(cat_mask) < 5: continue # 跳过样本过少的类别 cat_name = "小雨" if category == 1 else "中雨" if category == 2 else "大雨" if category == 3 else "暴雨" cat_rmse = np.sqrt(mean_squared_error(y_test_rain[cat_mask], y_pred_rain[cat_mask])) cat_mae = mean_absolute_error(y_test_rain[cat_mask], y_pred_rain[cat_mask]) cat_r2 = r2_score(y_test_rain[cat_mask], y_pred_rain[cat_mask]) print(f"\n{cat_name}性能：") print(f"样本数: {sum(cat_mask)}") print(f"RMSE: {cat_rmse:.4f}") print(f"MAE: {cat_mae:.4f}") print(f"R²: {cat_r2:.4f}") # 打印一些示例预测 if sum(cat_mask) >= 3: sample_indices = np.where(cat_mask)[0][:3] # 最多3个示例 print(f"\n{cat_name}示例预测：") for idx in sample_indices: # 使用索引从数组或Series获取值 if isinstance(y_test_rain, pd.Series): actual = y_test_rain.iloc[idx] else: actual = y_test_rain[idx] pred = y_pred_rain[idx] error_pct = ((pred-actual)/actual*100) if actual > 0 else 0 print(f" 实际: {actual:.2f}mm, 预测: {pred:.2f}mm, 误差: {pred-actual:.2f}mm ({error_pct:.1f}%)") except Exception as e: print(f"计算分层回归器分类性能时出错: {str(e)}") # 绘制实际值与预测值 try: plt.figure(figsize=(10, 6)) # 按类别着色点 colors = ['blue', 'green', 'red', 'purple'] markers = ['o', 's', '^', 'd'] categories = sorted(np.unique(y_category_test_rain)) for i, category in enumerate(categories): if category > 0 and i < len(colors): cat_mask = (y_category_test_rain == category) cat_name = "Light" if category == 1 else "Medium" if category == 2 else "Heavy" if category == 3 else "Extreme" plt.scatter(y_test_rain[cat_mask], y_pred_rain[cat_mask], color=colors[i], marker=markers[i], alpha=0.7, label=f"{cat_name} Rain") # 添加对角线 max_val = max(max(y_test_rain), max(y_pred_rain)) plt.plot([0, max_val], [0, max_val], 'k--') plt.xlabel('Actual Rainfall (mm)') plt.ylabel('Predicted Rainfall (mm)') plt.title('Actual vs Predicted Values (Stratified Regressors)') plt.legend() plt.grid(True) plt.tight_layout() plt.savefig(f"{self.output_dir}/stratified_regressors_performance.png") plt.close() except Exception as e: print(f"绘制分层回归性能图时出错: {str(e)}") return { 'rmse': rmse, 'mae': mae, 'r2': r2, 'predictions': y_pred_rain } def evaluate_regressor(self, X_test, y_test, mask_true, mask_pred): """评估回归器性能""" if self.regressor is None: print("回归器未训练，无法评估") return None print("\n评估单一回归器性能...") # 所有实际降雨样本的性能 X_test_true_rain = X_test[mask_true] y_test_true_rain = y_test[mask_true] if len(X_test_true_rain) > 0: try: # 降雨预测 if hasattr(self, 'log_transform') and self.log_transform: y_pred_transformed = self.regressor.predict(X_test_true_rain) y_pred_true_rain = np.expm1(y_pred_transformed) else: y_pred_true_rain = self.regressor.predict(X_test_true_rain) # 防止负值 y_pred_true_rain = np.maximum(y_pred_true_rain, 0) # 计算评估指标 rmse = np.sqrt(mean_squared_error(y_test_true_rain, y_pred_true_rain)) mae = mean_absolute_error(y_test_true_rain, y_pred_true_rain) r2 = r2_score(y_test_true_rain, y_pred_true_rain) print("\n所有实际降雨样本的性能：") print(f"样本数: {len(X_test_true_rain)}") print(f"RMSE: {rmse:.4f}") print(f"MAE: {mae:.4f}") print(f"R²: {r2:.4f}") # 绘制实际值与预测值散点图 plt.figure(figsize=(10, 6)) plt.scatter(y_test_true_rain, y_pred_true_rain, alpha=0.5) # 添加对角线 max_val = max(max(y_test_true_rain), max(y_pred_true_rain)) plt.plot([0, max_val], [0, max_val], 'r--') plt.xlabel('Actual Rainfall (mm)') plt.ylabel('Predicted Rainfall (mm)') plt.title('Actual vs Predicted Values (All Actual Rain Samples)') plt.grid(True) plt.tight_layout() plt.savefig(f"{self.output_dir}/actual_vs_predicted_all_rain.png") plt.close() except Exception as e: print(f"评估所有实际降雨样本时出错: {str(e)}") # 评估分类器和回归器都正确预测的样本 mask_both_correct = mask_true & mask_pred X_test_both = X_test[mask_both_correct] y_test_both = y_test[mask_both_correct] if len(X_test_both) > 0: try: # 降雨预测 if hasattr(self, 'log_transform') and self.log_transform: y_pred_transformed = self.regressor.predict(X_test_both) y_pred_both = np.expm1(y_pred_transformed) else: y_pred_both = self.regressor.predict(X_test_both) # 防止负值 y_pred_both = np.maximum(y_pred_both, 0) # 计算评估指标 rmse = np.sqrt(mean_squared_error(y_test_both, y_pred_both)) mae = mean_absolute_error(y_test_both, y_pred_both) r2 = r2_score(y_test_both, y_pred_both) print("\n正确分类的降雨样本性能：") print(f"样本数: {len(X_test_both)}") print(f"RMSE: {rmse:.4f}") print(f"MAE: {mae:.4f}") print(f"R²: {r2:.4f}") # 绘制实际值与预测值散点图 plt.figure(figsize=(10, 6)) plt.scatter(y_test_both, y_pred_both, alpha=0.5) # 添加对角线 max_val = max(max(y_test_both), max(y_pred_both)) plt.plot([0, max_val], [0, max_val], 'r--') plt.xlabel('Actual Rainfall (mm)') plt.ylabel('Predicted Rainfall (mm)') plt.title('Actual vs Predicted Values (Correctly Classified Samples)') plt.grid(True) plt.tight_layout() plt.savefig(f"{self.output_dir}/actual_vs_predicted_correct_classified.png") plt.close() return { 'rmse': rmse, 'mae': mae, 'r2': r2, 'predictions': y_pred_both } except Exception as e: print(f"评估正确分类的降雨样本时出错: {str(e)}") return None else: print("没有正确分类的降雨样本，无法评估回归性能") return None def train_and_evaluate(self, data, n_features=30, time_based_split=True): """完整的训练和评估过程""" try: # 步骤1: 数据预处理 processed_data = self.preprocess_data(data) # 步骤2: 特征选择 X, y_binary, y_amount, y_category = self.select_features(processed_data, n_features=n_features) # 步骤3: 数据分割 X_train, X_test, y_binary_train, y_binary_test, y_amount_train, y_amount_test, y_category_train, y_category_test = \ self.split_data(X, y_binary, y_amount, y_category, time_based=time_based_split) # 步骤4: 特征标准化 X_train_scaled, X_test_scaled = self.scale_features(X_train, X_test) # 步骤5: 训练二分类器（是否下雨） # 使用测试集的前一半进行概率校准 val_size = len(X_test_scaled) // 2 X_val, y_val = X_test_scaled[:val_size], y_binary_test[:val_size] X_test_final, y_test_final = X_test_scaled[val_size:], y_binary_test[val_size:] y_amount_test_final = y_amount_test[val_size:] y_category_test_final = y_category_test[val_size:] if y_category_test is not None else None self.train_binary_classifier(X_train_scaled, y_binary_train, X_val, y_val) # 步骤6: 优化分类阈值 self.optimize_threshold(X_test_final, y_test_final) # 步骤7: 评估二分类器性能 clf_results = self.evaluate_binary_classifier(X_test_final, y_test_final) # 步骤8: 训练降雨类别分类器 rain_mask_train = y_binary_train == 1 if self.stratified_regression and y_category_train is not None: self.train_category_classifier(X_train_scaled, y_category_train, rain_mask_train) # 步骤9: 训练针对不同降雨强度的分层回归器 if self.stratified_regression and y_category_train is not None: self.train_stratified_regressors(X_train_scaled, y_amount_train, y_category_train, rain_mask_train) # 步骤10: 同时训练单一回归器作为备选 self.train_regressor(X_train_scaled, y_amount_train, rain_mask_train) # 步骤11: 评估回归性能 mask_true = y_test_final == 1 # 实际有雨 mask_pred = clf_results['predictions'] == 1 # 预测有雨 # 如果有分层回归器，评估其性能 strat_reg_results = None if self.stratified_regression and y_category_test_final is not None and self.regressors: strat_reg_results = self.evaluate_stratified_regressors( X_test_final, y_amount_test_final, y_category_test_final, mask_true, mask_pred) # 同时评估类别分类器 if self.rain_category_classifier is not None: self.evaluate_category_classifier(X_test_final, y_category_test_final, mask_true, mask_pred) # 评估单一回归器 reg_results = self.evaluate_regressor(X_test_final, y_amount_test_final, mask_true, mask_pred) # 步骤12: 综合评估 print("\n两阶段降雨预测系统评估总结：") print(f"二分类器 ({self.clf_model_type}) F1分数: {clf_results['f1']:.4f}") if strat_reg_results: print(f"分层回归器 RMSE: {strat_reg_results['rmse']:.4f}, R²: {strat_reg_results['r2']:.4f}") if reg_results: print(f"单一回归器 ({self.reg_model_type}) RMSE: {reg_results['rmse']:.4f}, R²: {reg_results['r2']:.4f}") return { 'classifier_results': clf_results, 'regressor_results': reg_results, 'stratified_regressor_results': strat_reg_results } except Exception as e: print(f"训练和评估过程中出错: {str(e)}") traceback.print_exc() return None def predict(self, new_data, preprocess=True): """ 使用训练好的模型进行预测 参数: new_data: DataFrame或numpy数组，输入数据 preprocess: bool，是否预处理输入数据 返回: dict: 包含降雨概率、二值分类结果和降雨量预测的字典 """ if self.binary_classifier is None: raise ValueError("模型未训练，请先调用train_and_evaluate方法") # 如果需要预处理输入数据 if preprocess: # 执行与训练期间相同的预处理 processed_data = self.preprocess_data(new_data, silent=True) # 使用特征选择，但不更新feature_names X, _, _, _ = self.select_features(processed_data, silent=True) # 确保只使用训练过的特征（解决维度不匹配问题） if isinstance(X, pd.DataFrame) and self.feature_names is not None: # 检查是否所有必需的特征都存在 missing_features = [f for f in self.feature_names if f not in X.columns] if missing_features: print(f"警告: 预处理数据中缺少以下特征: {missing_features}") # 为缺失特征填充0 for f in missing_features: X[f] = 0 # 只选择训练中使用的特征 X = X[self.feature_names] else: # 直接使用输入数据，假设已经预处理 if isinstance(new_data, pd.DataFrame): # 检查是否所有必需的特征都存在 if self.feature_names is not None: missing_features = [f for f in self.feature_names if f not in new_data.columns] if missing_features: print(f"警告: 输入数据中缺少以下特征: {missing_features}") # 为缺失特征填充0 for f in missing_features: new_data[f] = 0 # 只选择训练中使用的特征 X = new_data[self.feature_names] else: X = new_data else: # 假设输入是按顺序排列的特征数组 X = new_data # 应用特征标准化 if isinstance(X, pd.DataFrame): X_np = X.values else: X_np = X if self.scaler is None: raise ValueError("缩放器未初始化，请先训练模型") X_scaled = self.scaler.transform(X_np) # 第一阶段: 二分类预测是否会下雨 try: rain_probabilities = self.binary_classifier.predict_proba(X_scaled)[:, 1] rain_binary = (rain_probabilities >= self.threshold).astype(int) except Exception as e: print(f"二分类预测时出错: {str(e)}") # 使用一个安全的默认值 rain_probabilities = np.zeros(len(X)) rain_binary = np.zeros(len(X), dtype=int) # 第二阶段: 对预测有雨的样本，预测降雨量 rainfall_amount = np.zeros(len(X)) rain_categories = np.zeros(len(X), dtype=int) rain_indices = np.where(rain_binary == 1)[0] if len(rain_indices) > 0: # 如果有分层回归，先预测降雨类别 if self.stratified_regression and self.regressors and self.rain_category_classifier is not None: try: # 获取降雨样本 X_rain_scaled = X_scaled[rain_indices] # 预测降雨类别 predicted_categories = self.rain_category_classifier.predict(X_rain_scaled) # 填充类别预测结果 for i, idx in enumerate(rain_indices): rain_categories[idx] = predicted_categories[i] # 对每个雨样本，根据预测类别使用适当的回归器 for i, (idx, category, prob) in enumerate(zip(rain_indices, predicted_categories, rain_probabilities[rain_indices])): reg_name = "light" if category == 1 else "medium" if category == 2 else "heavy" if category == 3 else "extreme" # 检查是否有该类别的回归器 if reg_name in self.regressors: regressor = self.regressors[reg_name] else: # 回退到单一回归器 regressor = self.regressor if regressor is not None: # 进行预测 x = X_scaled[idx].reshape(1, -1) try: if self.log_transform: pred_log = regressor.predict(x)[0] pred = np.expm1(pred_log) else: pred = regressor.predict(x)[0] # 确保预测值非负 pred = max(0, pred) # 对大雨和暴雨类别应用安全系数 if category == 3: # 大雨 pred = pred * self.heavy_rain_factor elif category == 4: # 暴雨 pred = pred * self.extreme_rain_factor # 对于高概率样本，进一步增加预测量 if prob > 0.7 and category >= 3: # 高概率大雨或暴雨 pred = pred * 1.5 # 额外的高概率因子 rainfall_amount[idx] = pred except Exception as e: print(f"预测降雨量时出错: {str(e)}") except Exception as e: print(f"分层回归预测时出错: {str(e)}") # 如果分层回归失败，尝试使用单一回归器 if self.regressor is not None: try: X_rain = X_scaled[rain_indices] if self.log_transform: rain_predictions_transformed = self.regressor.predict(X_rain) rain_predictions = np.expm1(rain_predictions_transformed) else: rain_predictions = self.regressor.predict(X_rain) rainfall_amount[rain_indices] = np.maximum(rain_predictions, 0) except Exception as e2: print(f"单一回归器预测也失败: {str(e2)}") # 如果没有分层回归或失败，使用单一回归器 elif self.regressor is not None: try: X_rain = X_scaled[rain_indices] # 预测降雨量 if hasattr(self, 'log_transform') and self.log_transform: rain_predictions_transformed = self.regressor.predict(X_rain) rain_predictions = np.expm1(rain_predictions_transformed) else: rain_predictions = self.regressor.predict(X_rain) # 确保降雨量非负 rain_predictions = np.maximum(rain_predictions, 0) # 填充预测结果 rainfall_amount[rain_indices] = rain_predictions except Exception as e: print(f"单一回归器预测时出错: {str(e)}") return { 'rain_probability': rain_probabilities, # 降雨概率 'rain_binary': rain_binary, # 是否会下雨（二值分类结果） 'rain_category': rain_categories, # 降雨类别（1:小雨, 2:中雨, 3:大雨, 4:暴雨） 'rainfall_amount': rainfall_amount # 预测的降雨量（毫米） } def save_model(self, filename='rainfall_two_stage_model.joblib'): """保存训练好的模型""" if self.binary_classifier is None: raise ValueError("模型未训练，无法保存") model_data = { 'binary_classifier': self.binary_classifier, 'regressor': self.regressor, 'regressors': self.regressors, 'rain_category_classifier': self.rain_category_classifier, 'scaler': self.scaler, 'threshold': self.threshold, 'feature_names': self.feature_names, 'feature_importances_clf': self.feature_importances_clf, 'feature_importances_reg': self.feature_importances_reg, 'clf_model_type': self.clf_model_type, 'reg_model_type': self.reg_model_type, 'log_transform': getattr(self, 'log_transform', False), 'feature_stats': self.feature_stats, 'stratified_regression': self.stratified_regression, 'rain_thresholds': self.rain_thresholds, 'heavy_rain_factor': self.heavy_rain_factor, 'extreme_rain_factor': self.extreme_rain_factor } joblib.dump(model_data, filename) print(f"模型已保存到 {filename}") @classmethod def load_model(cls, filename='rainfall_two_stage_model.joblib'): """加载保存的模型""" model_data = joblib.load(filename) # 创建新实例 instance = cls( debug=False, clf_model_type=model_data.get('clf_model_type', 'ensemble'), reg_model_type=model_data.get('reg_model_type', 'gbdt') ) # 加载模型组件 instance.binary_classifier = model_data['binary_classifier'] instance.regressor = model_data.get('regressor') instance.regressors = model_data.get('regressors', {}) instance.rain_category_classifier = model_data.get('rain_category_classifier') instance.scaler = model_data['scaler'] instance.threshold = model_data['threshold'] instance.feature_names = model_data['feature_names'] instance.feature_importances_clf = model_data.get('feature_importances_clf') instance.feature_importances_reg = model_data.get('feature_importances_reg') # 加载其他设置 if 'log_transform' in model_data: instance.log_transform = model_data['log_transform'] # 加载特征统计信息 if 'feature_stats' in model_data: instance.feature_stats = model_data['feature_stats'] # 加载分层回归设置 if 'stratified_regression' in model_data: instance.stratified_regression = model_data['stratified_regression'] if 'rain_thresholds' in model_data: instance.rain_thresholds = model_data['rain_thresholds'] if 'heavy_rain_factor' in model_data: instance.heavy_rain_factor = model_data['heavy_rain_factor'] if 'extreme_rain_factor' in model_data: instance.extreme_rain_factor = model_data['extreme_rain_factor'] return instance class FutureRainfallForecastSystem: """ 未来多时间点降雨预报系统 基于RainfallTwoStageModel，预测未来1小时、2小时、3小时的降雨情况 """ def __init__(self, model_path=None, debug=False): # 存储不同时间点的预测模型 self.forecast_models = {} self.time_points = [1, 2, 3] # 未来1小时、2小时、3小时 self.debug = debug # 如果提供了模型路径，尝试加载模型 if model_path: self.load_models(model_path) def train_forecast_models(self, data, features_config=None): """ 为每个时间点训练一个单独的预测模型 参数: data: DataFrame 训练数据，应包含时间特征和未来时间点的降雨数据 features_config: dict 特征配置（可选） """ print("开始训练未来降雨预报模型...") # 检查数据格式 if not isinstance(data, pd.DataFrame): try: data = pd.DataFrame(data) except: raise TypeError("数据必须是可转换为DataFrame的格式") # 打印调试信息 print(f"原始数据形状: {data.shape}") print(f"原始数据列: {data.columns.tolist()}") print(f"原始数据rain列类型: {data['rain'].dtype}") # 确保datetime列是datetime格式 if 'datetime' in data.columns: data['datetime'] = pd.to_datetime(data['datetime']) # 检查数据中是否有降雨信息 if 'rain' not in data.columns: raise ValueError("数据必须包含降雨量列 (rain)") # 打印数据概要 print(f"\n数据概要:") print(f"行数: {len(data)}") print(f"列数: {len(data.columns)}") print(f"降雨量列: rain") print(f"有雨样本数: {sum(data['rain'] > 0)} ({sum(data['rain'] > 0)/len(data)*100:.1f}%)") # 默认特征配置 if features_config is None: features_config = { 'n_features': 30, # 每个模型使用的特征数 'clf_model_type': 'gbdt', # 分类器类型 'reg_model_type': 'gbdt', # 回归器类型 'time_based_split': True # 使用时间序列分割 } # 为每个未来时间点准备数据并训练模型 for time_point in self.time_points: print(f"\n准备未来{time_point}小时降雨预报模型...") try: # 准备数据：移动目标变量以表示未来降雨 # 例如，如果time_point=1，则当前行的特征对应下一行的降雨情况 future_data = self._prepare_future_data(data, time_point) if len(future_data) < 100: print(f"警告: 未来{time_point}小时的有效数据样本过少 ({len(future_data)})") continue # 创建针对此时间点的模型 model = RainfallTwoStageModel( debug=self.debug, clf_model_type=features_config['clf_model_type'], reg_model_type=features_config['reg_model_type'], output_dir=f'rainfall_forecast_{time_point}h_figures' ) # 训练模型 results = model.train_and_evaluate( future_data, n_features=features_config['n_features'], time_based_split=features_config['time_based_split'] ) if results is None: print(f"未来{time_point}小时模型训练失败，跳过...") continue # 保存训练好的模型 model.save_model(f'rainfall_forecast_{time_point}h_model.joblib') # 将模型添加到字典中 self.forecast_models[time_point] = model print(f"未来{time_point}小时降雨预报模型训练完成！") except Exception as e: print(f"训练未来{time_point}小时模型时出错: {str(e)}") traceback.print_exc() if self.forecast_models: print("\n所有时间点的降雨预报模型训练完成！") else: print("\n警告: 所有模型训练都失败了！") def _prepare_future_data(self, data, time_point): """ 准备用于未来时间点预测的数据 将目标变量滞后处理，使当前特征与未来降雨对齐 """ df = data.copy() # 确保索引是连续的 df = df.reset_index(drop=True) # 确保时间列是正确的格式 if 'datetime' in df.columns and not pd.api.types.is_datetime64_any_dtype(df['datetime']): try: df['datetime'] = pd.to_datetime(df['datetime']) except: print(f"警告：无法转换时间列 datetime") # 创建未来降雨量和降雨二值列 # 采用移位操作：当前行的特征对应未来行的降雨 df[f'future_{time_point}h_rain'] = df['rain'].shift(-time_point) df[f'future_{time_point}h_rain_binary'] = (df[f'future_{time_point}h_rain'] > 0).astype(int) # 删除含有NaN的行（通常是最后几行，因为它们没有对应的未来降雨数据） df = df.dropna(subset=[f'future_{time_point}h_rain']) # 修改后 - 如果已存在rain列，先删除原来的rain列 if 'rain' in df.columns: df = df.drop('rain', axis=1) # 删除原始rain列 if 'rain_binary' in df.columns: df = df.drop('rain_binary', axis=1) # 删除原始rain_binary列（如果有） # 将未来降雨列重命名为标准名称，以便模型处理 df = df.rename(columns={ f'future_{time_point}h_rain': 'rain', f'future_{time_point}h_rain_binary': 'rain_binary' }) return df def forecast(self, current_data): """ 预测未来多个时间点的降雨情况 参数: current_data: DataFrame 当前气象数据 返回: dict: 包含各时间点降雨预测结果的字典 """ if not self.forecast_models: raise ValueError("模型未训练，请先调用train_forecast_models方法") forecast_results = {} # 对每个时间点进行预测 for time_point, model in self.forecast_models.items(): print(f"\n预测未来{time_point}小时降雨情况...") # 使用当前模型预测未来降雨 try: prediction = model.predict(current_data) forecast_results[time_point] = prediction # 显示摘要结果 rain_prob = prediction['rain_probability'].mean() rain_samples = sum(prediction['rain_binary']) print(f"未来{time_point}小时降雨概率: {rain_prob:.4f}") print(f"预测有雨样本数: {rain_samples}/{len(current_data)} ({rain_samples/len(current_data)*100:.2f}%)") # 如果有降雨预测，显示降雨量情况 if rain_samples > 0: rain_amounts = prediction['rainfall_amount'][prediction['rain_binary'] == 1] if len(rain_amounts)>0: # 对降雨量进行排序 sorted_rain = np.sort(rain_amounts) # 计算截取中间60%的索引位置 n = len(sorted_rain) start_idx = int(n * 0.2) # 跳过最低的20% end_idx = int(n * 0.8) # 跳过最高的20% # 确保至少有一个元素 if start_idx == end_idx: middle_rain = sorted_rain[start_idx:start_idx+1] else: middle_rain = sorted_rain[start_idx:end_idx] # 计算中间60%的平均值 middle_avg = np.mean(middle_rain) print(f"预测降雨量: 中间60%平均 {middle_avg:.2f}mm, 全部平均 {rain_amounts.mean():.2f}mm, 最大 {rain_amounts.max():.2f}mm") # 统计降雨类别 categories = prediction['rain_category'][prediction['rain_binary'] == 1] if len(categories) > 0: try: cat_counts = np.bincount(categories.astype(int)) cat_names = ["", "小雨", "中雨", "大雨", "暴雨"] for cat, count in enumerate(cat_counts): if cat > 0 and count > 0 and cat < len(cat_names): print(f" {cat_names[cat]}: {count}个样本 ({count/rain_samples*100:.1f}%)") except Exception as e: print(f"统计降雨类别时出错: {str(e)}") except Exception as e: print(f"未来{time_point}小时预测出错: {str(e)}") forecast_results[time_point] = None return forecast_results def load_models(self, base_path): """加载所有时间点的预测模型""" models_loaded = 0 try: for time_point in self.time_points: model_path = f'{base_path}/rainfall_forecast_{time_point}h_model.joblib' if os.path.exists(model_path): print(f"加载未来{time_point}小时降雨预报模型...") try: model = RainfallTwoStageModel.load_model(model_path) self.forecast_models[time_point] = model models_loaded += 1 except Exception as e: print(f"加载未来{time_point}小时模型时出错: {str(e)}") else: print(f"未找到未来{time_point}小时的模型文件: {model_path}") if models_loaded > 0: print(f"成功加载 {models_loaded} 个时间点的预报模型") else: print("警告: 未能加载任何预报模型") except Exception as e: print(f"加载模型出错: {str(e)}") def save_models(self, base_path): """保存所有时间点的预测模型""" os.makedirs(base_path, exist_ok=True) for time_point, model in self.forecast_models.items(): model_path = f'{base_path}/rainfall_forecast_{time_point}h_model.joblib' print(f"保存未来{time_point}小时降雨预报模型...") model.save_model(model_path) print(f"成功保存 {len(self.forecast_models)} 个时间点的预报模型") def visualize_forecast(self, forecast_results, output_dir='forecast_visualizations'): """ 可视化预测结果 参数: forecast_results: dict 各时间点的预测结果 output_dir: str 输出目录 """ os.makedirs(output_dir, exist_ok=True) if not forecast_results: print("没有预测结果可供可视化") return # 准备绘图数据 time_labels = [f"{t}hour" for t in forecast_results.keys()] rain_probs = [forecast_results[t]['rain_probability'].mean() for t in forecast_results.keys()] # 准备降雨量数据 rain_amounts = [] for t in forecast_results.keys(): result = forecast_results[t] if sum(result['rain_binary']) > 0: # 计算有雨样本的平均降雨量 amounts = result['rainfall_amount'][result['rain_binary'] == 1] rain_amounts.append(amounts.mean()) else: rain_amounts.append(0) # 绘制降雨概率随时间变化图 plt.figure(figsize=(10, 6)) plt.bar(time_labels, rain_probs, color='skyblue') plt.axhline(y=0.5, color='r', linestyle='--', label='50%概率阈值') plt.ylim(0, 1) plt.title('Prediction of rainfall probability at various time points in the future') plt.ylabel('Rainfall probability') plt.xlabel('pred_time') plt.grid(True, alpha=0.3) plt.legend() plt.tight_layout() plt.savefig(f"{output_dir}/rainfall_probability_forecast.png") plt.close() # 绘制降雨量随时间变化图 plt.figure(figsize=(10, 6)) plt.bar(time_labels, rain_amounts, color='blue') plt.title('Prediction of rainfall at different time points in the future') plt.ylabel('rain (mm)') plt.xlabel('pred_time') plt.grid(True, alpha=0.3) plt.tight_layout() plt.savefig(f"{output_dir}/rainfall_amount_forecast.png") plt.close() def get_current_weather_data(): """ 模拟从气象站获取当前的气象数据 在实际应用中，这个函数会从传感器、API或其他数据源获取数据 """ # 模拟数据 - 在实际应用中会被真实数据替代 current_data = { 'datetime': [datetime.datetime.now()], 'PWV': [25.5], # 可降水量 'ZTD': [2450.3], # 对流层延迟 'Temp': [305.6], # 温度（开尔文） 'Press': [1008.5], # 气压（hPa） 'rh': [78.5], # 相对湿度（%） 'ws': [3.2] # 风速（m/s） } # 创建DataFrame current_df = pd.DataFrame(current_data) # 加载历史数据以计算变化率和滚动统计 try: history_df = pd.read_csv('weather_history_cache.csv') history_df['datetime'] = pd.to_datetime(history_df['datetime']) # 合并当前数据与历史数据 combined_df = pd.concat([history_df, current_df]).reset_index(drop=True) # 确保按时间排序 combined_df = combined_df.sort_values(by='datetime') # 保存最新的24小时数据作为历史缓存 combined_df.tail(24).to_csv('weather_history_cache.csv', index=False) # 计算变化率 for var in ['PWV', 'ZTD', 'Temp', 'Press', 'rh']: # 1小时变化（假设数据是每小时一条） combined_df[f'{var}_1h_change'] = combined_df[var].diff(1) # 3小时变化 combined_df[f'{var}_3h_change'] = combined_df[var].diff(3) # 6小时变化 combined_df[f'{var}_6h_change'] = combined_df[var].diff(6) # 计算滚动均值 combined_df[f'{var}_3h_mean'] = combined_df[var].rolling(window=3, min_periods=1).mean() combined_df[f'{var}_6h_mean'] = combined_df[var].rolling(window=6, min_periods=1).mean() # 滚动标准差 combined_df[f'{var}_6h_std'] = combined_df[var].rolling(window=6, min_periods=1).std().fillna(0) # 异常值（相对于平均值的标准化偏差） var_mean = combined_df[var].mean() var_std = combined_df[var].std() combined_df[f'{var}_anomaly'] = (combined_df[var] - var_mean) / var_std if var_std > 0 else 0 # 急剧变化指标 var_std = combined_df[f'{var}_1h_change'].std() if var_std > 0: combined_df[f'{var}_rapid_change'] = (np.abs(combined_df[f'{var}_1h_change']) > (2 * var_std)).astype(int) else: combined_df[f'{var}_rapid_change'] = 0 # 变化加速度（变化率的变化率） combined_df[f'{var}_change_accel'] = combined_df[f'{var}_1h_change'].diff(1).fillna(0) # 添加组合特征 if all(var in combined_df.columns for var in ['PWV', 'rh']): # 湿度可用性指数 combined_df['PWV_rh'] = combined_df['PWV'] * combined_df['rh'] / 100 # 累积湿度变化 combined_df['PWV_cumul_change'] = combined_df['PWV'].diff(1).rolling(window=6).sum().fillna(0) combined_df['rh_cumul_change'] = combined_df['rh'].diff(1).rolling(window=6).sum().fillna(0) if all(var in combined_df.columns for var in ['PWV', 'Temp']): # 温度-湿度相互作用 combined_df['PWV_Temp'] = combined_df['PWV'] * combined_df['Temp'] # 饱和潜力 temp_exp = np.exp(17.27 * (combined_df['Temp']-273.15) / ((combined_df['Temp']-273.15) + 237.3)) combined_df['saturation_index'] = combined_df['PWV'] / (0.1 * temp_exp) if all(var in combined_df.columns for var in ['Press', 'Press_1h_change']): # 气压变化强度 combined_df['Press_change_intensity'] = combined_df['Press_1h_change'].abs() # 气压下降指标 combined_df['Press_falling'] = (combined_df['Press_1h_change'] < 0).astype(int) # 气压快速下降指标 press_quantile = combined_df['Press_1h_change'].abs().quantile(0.75) combined_df['Press_rapid_fall'] = ((combined_df['Press_1h_change'] < 0) & (combined_df['Press_1h_change'].abs() > press_quantile)).astype(int) if all(var in combined_df.columns for var in ['ZTD', 'PWV']): # ZTD-PWV比率 combined_df['ZTD_PWV_ratio'] = combined_df['ZTD'] / combined_df['PWV'].replace(0, np.nan).fillna(combined_df['PWV'].mean()) # 如果有变化率特征 if all(var in combined_df.columns for var in ['ZTD_1h_change', 'PWV_1h_change']): combined_df['ZTD_PWV_change'] = combined_df['ZTD_1h_change'] * combined_df['PWV_1h_change'] # 湿度不稳定指标 pwv_mean = combined_df['PWV_6h_mean'].mean() if pwv_mean > 0: combined_df['moisture_instability'] = combined_df['PWV_6h_std'] / combined_df['PWV_6h_mean'] else: combined_df['moisture_instability'] = 0 # 添加降雨潜力指标 if all(var in combined_df.columns for var in ['PWV', 'rh', 'Temp', 'Press_1h_change']): # 组合降雨指标 combined_df['rain_potential'] = ((combined_df['PWV'] * combined_df['rh'] / 100) * (0.1 - combined_df['Press_1h_change']) / (combined_df['Temp'] + 10)) # 强降雨潜力指标 pwv_mean = combined_df['PWV'].mean() + 0.1 # 避免除以零 combined_df['heavy_rain_potential'] = (combined_df['PWV'] * (combined_df['rh'] / 100) * (combined_df['Press_rapid_fall'] + 1) * (1 + np.abs(combined_df['PWV_1h_change']) / pwv_mean)) # 降雨持续性指标 combined_df['rain_persistence'] = (combined_df['PWV_cumul_change'] * combined_df['rh_cumul_change'] * (1 + combined_df['Press_change_intensity'])) # 获取最新一行作为当前数据，包含所有计算的特征 current_df = combined_df.iloc[-1:].reset_index(drop=True) except Exception as e: print(f"计算特征时出错: {str(e)}") print("创建一个新的历史缓存文件...") # 如果没有历史数据或出错，将当前数据作为历史缓存的第一条记录 current_df.to_csv('weather_history_cache.csv', index=False) # 这种情况下无法计算时间序列特征 print("警告: 无法计算变化率和滚动统计特征，首次运行只能使用基础特征") return current_df def run_realtime_rainfall_forecast(): """ 运行实时降雨预报系统 """ print("启动实时降雨预报系统...") # 检查模型目录是否存在 model_dir = 'rainfall_forecast_models' if not os.path.exists(model_dir): print(f"错误: 未找到模型目录 '{model_dir}'") print("请先使用历史数据训练模型") return # 加载预测模型 print("加载未来降雨预报模型...") forecast_system = FutureRainfallForecastSystem() forecast_system.load_models(model_dir) if not forecast_system.forecast_models: print("错误: 未能加载任何模型") return # 获取当前气象数据 print("获取当前气象数据...") current_weather = get_current_weather_data() # 检查数据是否完整 if current_weather is None or len(current_weather) == 0: print("错误: 无法获取当前气象数据") return print("\n当前气象条件:") print(f"时间: {current_weather['datetime'].iloc[0]}") print(f"温度: {current_weather['Temp'].iloc[0]:.1f}K") print(f"湿度: {current_weather['rh'].iloc[0]:.1f}%") print(f"气压: {current_weather['Press'].iloc[0]:.1f} hPa") print(f"可降水量(PWV): {current_weather['PWV'].iloc[0]:.1f} mm") # 进行未来降雨预测 print("\n开始预测未来降雨情况...") forecast_results = forecast_system.forecast(current_weather) # 可视化预测结果 print("\n生成降雨预报可视化...") forecast_system.visualize_forecast(forecast_results, output_dir='realtime_forecast_results') # 输出预测报告 print("\n未来降雨预报摘要:") print("=" * 50) # 当前时间 current_time = datetime.datetime.now() print(f"预报时间: {current_time.strftime('%Y-%m-%d %H:%M')}") # 用于存储预报结果的列表，以便可能的后续处理或发送通知 forecast_summary = [] for time_point, result in forecast_results.items(): # 计算预测时间 forecast_time = current_time + timedelta(hours=time_point) print(f"\n未来{time_point}小时 ({forecast_time.strftime('%H:%M')}) 降雨预报:") print("-" * 50) # 降雨概率 rain_prob = result['rain_probability'].mean() # 是否有雨 rain_predicted = sum(result['rain_binary']) > 0 rain_status = "有" if rain_predicted else "无" print(f"降雨概率: {rain_prob:.2f}") print(f"预计降雨: {rain_status}") forecast_record = { 'time_point': time_point, 'forecast_time': forecast_time, 'rain_probability': rain_prob, 'rain_predicted': rain_predicted } # 如果预测有雨，显示详细信息 if rain_predicted: rain_indices = np.where(result['rain_binary'] == 1)[0] rain_amounts = result['rainfall_amount'][rain_indices] rain_categories = result['rain_category'][rain_indices] # 计算平均降雨量和最大降雨量 avg_rain = rain_amounts.mean() max_rain = rain_amounts.max() print(f"预计降雨量: {avg_rain:.2f}mm (平均), {max_rain:.2f}mm (最大)") forecast_record['avg_rainfall'] = avg_rain forecast_record['max_rainfall'] = max_rain # 统计降雨类别 try: cat_counts = np.bincount(rain_categories.astype(int)) cat_names = ["", "小雨", "中雨", "大雨", "暴雨"] print("降雨类型:") most_common_cat = 0 most_common_count = 0 for cat, count in enumerate(cat_counts): if cat > 0 and count > 0: if count > most_common_count: most_common_cat = cat most_common_count = count print(f" {cat_names[cat]}: {count}个样本 ({count/len(rain_indices)*100:.1f}%)") forecast_record['rain_category'] = most_common_cat forecast_record['rain_category_name'] = cat_names[most_common_cat] if most_common_cat > 0 else "" # 主要降雨类型 if most_common_cat > 0: print(f"主要降雨类型: {cat_names[most_common_cat]}") # 给出降雨建议 advice = "" if most_common_cat >= 3: # 大雨或暴雨 advice = "建议避免外出，注意防范强降雨可能带来的积水和交通影响" elif most_common_cat == 2: # 中雨 advice = "出门请携带雨具，道路可能湿滑，驾车注意安全" else: # 小雨 advice = "带好雨具，不会对出行造成太大影响" print(f"出行建议: {advice}") forecast_record['advice'] = advice except Exception as e: print(f"处理降雨类别时出错: {str(e)}") forecast_record['rain_category'] = 0 forecast_record['rain_category_name'] = "" forecast_record['advice'] = "请注意携带雨具" else: forecast_record['avg_rainfall'] = 0 forecast_record['max_rainfall'] = 0 forecast_record['rain_category'] = 0 forecast_record['rain_category_name'] = "" forecast_record['advice'] = "天气良好，适合户外活动" forecast_summary.append(forecast_record) # 保存预报结果用于历史比较和验证 try: forecast_history = pd.DataFrame(forecast_summary) forecast_time_str = current_time.strftime('%Y%m%d_%H%M') forecast_history.to_csv(f'realtime_forecast_results/forecast_{forecast_time_str}.csv', index=False) except Exception as e: print(f"保存预报结果时出错: {str(e)}") print("\n预报完成.") # 检查是否需要发送预警 for record in forecast_summary: if record['rain_predicted'] and record.get('rain_category', 0) >= 3: # 大雨或暴雨 print(f"\n⚠️ 预警: 未来{record['time_point']}小时预计有{record['rain_category_name']}!") # 这里可以添加发送邮件、短信或其他通知的代码 return forecast_summary # 主程序入口 if __name__ == "__main__": parser = argparse.ArgumentParser(description="未来降雨预报系统 - 训练模型和实时预测") parser.add_argument('--mode', type=str, default='train', choices=['train', 'predict', 'realtime'], help='执行模式: train-训练模型, predict-单次预测, realtime-实时预测') parser.add_argument('--data', type=str, default='rainfall_data_time.csv', help='数据文件路径') parser.add_argument('--model_dir', type=str, default='rainfall_forecast_models', help='模型保存/加载目录') parser.add_argument('--n_features', type=int, default=35, help='使用的特征数量') parser.add_argument('--clf_model', type=str, default='ensemble', choices=['lr', 'rf', 'gbdt', 'xgboost', 'ensemble'], help='分类器类型') parser.add_argument('--reg_model', type=str, default='gbdt', choices=['rf', 'gbdt', 'ridge', 'xgboost'], help='回归器类型') parser.add_argument('--time_based', action='store_true', help='是否使用基于时间的数据分割（默认使用随机分割）') parser.add_argument('--debug', action='store_true', help='启用调试模式，输出更详细的信息') args = parser.parse_args() # 创建输出目录 os.makedirs('realtime_forecast_results', exist_ok=True) try: if args.mode == 'train': # 训练模式 print(f"加载训练数据: {args.data}") try: # 读取历史数据 historical_data = pd.read_csv(args.data) # 确保时间列是日期时间格式 if 'datetime' in historical_data.columns: historical_data['datetime'] = pd.to_datetime(historical_data['datetime']) # 按时间排序数据 historical_data = historical_data.sort_values(by='datetime') print(f"数据时间范围: {historical_data['datetime'].min()} 到 {historical_data['datetime'].max()}") else: print("警告: 数据中没有找到时间列，假设数据已按时间排序") print(f"总样本数: {len(historical_data)}") # 创建未来降雨预报系统 print("\n创建未来降雨预报系统...") forecast_system = FutureRainfallForecastSystem(debug=args.debug) # 配置特征 features_config = { 'n_features': args.n_features, 'clf_model_type': args.clf_model, 'reg_model_type': args.reg_model, 'time_based_split': args.time_based } # 训练未来各时间点的预报模型 print("\n开始训练未来降雨预报模型...") forecast_system.train_forecast_models(historical_data, features_config) # 保存训练好的模型 print("\n保存训练好的模型...") forecast_system.save_models(args.model_dir) print("\n未来降雨预报系统训练完成！") except FileNotFoundError: print(f"错误: 找不到训练数据文件 '{args.data}'") print("请确保该文件存在于当前目录中") except Exception as e: print(f"训练过程中出错: {str(e)}") traceback.print_exc() elif args.mode == 'predict': # 单次预测模式 print(f"加载当前气象数据: {args.data}") try: # 读取当前气象数据 current_data = pd.read_csv(args.data) # 加载预测模型 print("\n加载未来降雨预报模型...") forecast_system = FutureRainfallForecastSystem(model_path=args.model_dir, debug=args.debug) # 进行预测 print("\n开始预测未来降雨情况...") forecast_results = forecast_system.forecast(current_data) # 可视化预测结果 print("\n生成预测结果可视化...") forecast_system.visualize_forecast(forecast_results) print("\n预测完成.") except FileNotFoundError: print(f"错误: 找不到当前气象数据文件 '{args.data}'") except Exception as e: print(f"预测过程中出错: {str(e)}") traceback.print_exc() elif args.mode == 'realtime': # 实时预测模式 try: run_realtime_rainfall_forecast() except Exception as e: print(f"实时预测过程中出错: {str(e)}") traceback.print_exc() # 如果需要周期性运行 """ while True: try: run_realtime_rainfall_forecast() print("\n等待下次预报...") # 等待1小时 time.sleep(3600) except KeyboardInterrupt: print("\n用户中断，系统退出") break except Exception as e: print(f"\n运行时错误: {str(e)}") traceback.print_exc() # 出错后等待10分钟再重试 time.sleep(600) """ except KeyboardInterrupt: print("\n用户中断，系统退出") except Exception as e: print(f"\n系统错误: {str(e)}") traceback.print_exc()