from tabnanny import verbose import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import xgboost from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.ensemble import RandomForestRegressor from xgboost import XGBRegressor from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, confusion_matrix, accuracy_score from 实验1 import param_grid # 解决中文显示问题 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False # ----------------------------- 1. 数据加载与预处理 ----------------------------- def load_and_preprocess_data(): """加载数据并执行预处理""" df = pd.read_excel('.che22.xlsx') # 处理缺失值（将'暂无'替换为NaN） df.replace('暂无', np.nan, inplace=True) # 字段格式转换 df['售价'] = df['售价'].str.replace('万', '').astype(float) df['新车指导价'] = df['新车指导价'].str.replace('万', '').astype(float) df['里程'] = df['里程'].str.replace('万公里', '').astype(float) df['过户次数'] = df['过户次数'].str.replace('次', '').astype(float) # 提取品牌前4字符（如“奔驰GLC” → “奔驰”） df['品牌'] = df['品牌'].str[:4] # 更合理的品牌提取方式 # 计算车龄（修复空值问题） current_year = pd.Timestamp.now().year df['上牌时间'] = pd.to_numeric(df['上牌时间'].str[:4], errors='coerce') # 转换为年份数字 df['车龄'] = current_year - df['上牌时间'] # 排量处理（正则提取数值 + 填充均值） df['排量'] = df['排量'].astype(str).str.extract(r'(\d+\.?\d*)', expand=False).astype(float) df['排量'].fillna(df['排量'].mean(), inplace=True) # 删除重复项 df.drop_duplicates(inplace=True) df.reset_index(drop=True, inplace=True) return df # 加载数据 df = pd.read_excel('./che22.xlsx') # ----------------------------- 2. 探索性数据分析（EDA） ----------------------------- # 箱线图：检测异常值 fig, ax = plt.subplots(1, 2, figsize=(16, 6)) df.boxplot(column=['里程'], ax=ax[0], flierprops={'marker': 'o', 'markerfacecolor': 'red', 'markersize': 4}) df.boxplot(column=['售价'], ax=ax[1], flierprops={'marker': 'o', 'markerfacecolor': 'red', 'markersize': 4}) plt.suptitle('里程与售价异常值检测') plt.show()

""" XGBoost房价预测模板适用于回归任务 """ # 基础库 import pandas as pd import numpy as np # 预处理和评估 from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from sklearn.preprocessing import StandardScaler # 可视化 import matplotlib.pyplot as plt import seaborn as sns # XGBoost import xgboost as xgb # 1. 数据加载 # 替换为你的数据路径 data = pd.read_csv('F:\房地产\删改.csv') # 显示数据前五行 print("数据预览：") print(data.head(3)) # 2. 数据预处理 # 删除包含缺失值的行（根据实际情况调整处理方式） data = data.dropna() # 分离特征和目标变量（假设目标列名为'price'） X = data.drop('房价', axis=1) y = data['房价'] # 处理分类特征（示例） categorical_cols = data['商业营业用房','办公楼','其他用房'] X = pd.get_dummies(X, columns=categorical_cols) # 数据标准化（根据需求选择） scaler = StandardScaler() X = scaler.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) # 3. 模型配置 model = xgb.XGBRegressor( objective='reg:squarederror', # 回归任务 n_estimators=1000, # 树的数量 learning_rate=0.01, # 学习率 max_depth=5, # 树的最大深度 subsample=0.8, # 样本采样比例 colsample_bytree=0.8, # 特征采样比例 reg_alpha=0.1, # L1正则化 reg_lambda=1, # L2正则化 random_state=42, early_stopping_rounds=50 # 早停法 ) # 4. 模型训练 model.fit( X_train, y_train, eval_set=[(X_test, y_test)], # 验证集 verbose=10 # 每10轮显示进度 ) # 5. 模型评估 # 预测结果 y_pred = model.predict(X_test) # 计算评估指标 mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) mae = mean_absolute_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print(f"\n模型评估：") print(f"MSE: {mse:.2f}") print(f"RMSE: {rmse:.2f}") print(f"MAE: {mae:.2f}") print(f"R² Score: {r2:.2f}") # 6. 特征重要性可视化 # feature_importance = model.feature_importances_ # sorted_idx = np.argsort(feature_importance)[::-1] # # plt.figure(figsize=(12, 6)) # plt.title("特征重要性") # sns.barplot(x=feature_importance[sorted_idx][:15], # 显示前15个重要特征 # y=X.columns[sorted_idx][:15] 这个代码疫情结果哪个地方出现了问题，怎样改正这个问题？

好的，我现在要解决用户提供的XGBoost房价预测模板中的特征重要性可视化部分的问题。首先，用户指出这段代码在运行时会出错，需要找出错误所在并改正。让我仔细看一下用户提供的代码。首先，用户的问题出现在代码...

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import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import tensorflow as tf import random from cv2 import resize from glob import glob import warnings warnings.filterwarnings("ignore")img_height = 244 img_width = 244 train_ds = tf.keras.utils.image_dataset_from_directory( 'D:/Faulty_solar_panel', validation_split=0.2, subset='training', image_size=(img_height, img_width), batch_size=32, seed=42, shuffle=True) val_ds = tf.keras.utils.image_dataset_from_directory( 'D:/Faulty_solar_panel', validation_split=0.2, subset='validation', image_size=(img_height, img_width), batch_size=32, seed=42, shuffle=True)class_names = train_ds.class_names print(class_names) train_dsbase_model = tf.keras.applications.VGG16( include_top=False, weights='imagenet', input_shape=(img_height, img_width, 3) ) base_model.trainable = False inputs = tf.keras.Input(shape=(img_height, img_width, 3)) x = tf.keras.applications.vgg16.preprocess_input(inputs) x = base_model(x, training=False) x = tf.keras.layers.GlobalAveragePooling2D()(x) x = tf.keras.layers.Dropout(0.3)(x) outputs = tf.keras.layers.Dense(90)(x) model = tf.keras.Model(inputs, outputs) model.summary()model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])epoch = 15 model.fit(train_ds, validation_data=val_ds, epochs=epoch, callbacks = [ tf.keras.callbacks.EarlyStopping( monitor="val_loss", min_delta=1e-2, patience=3, verbose=1, restore_best_weights=True ) ] )# fine tuning base_model.trainable = True for layer in base_model.layers[:14]: layer.trainable = False model.summary()model.compile(optimizer=tf.keras.optimizers.Adam(0.0001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])epoch = 15 history = model.fit(train_ds, validation_data=val_ds, epochs=epoch, callbacks = [ tf.keras.callbacks.EarlyStopping( monitor="val_loss", min_delta=1e-2, patience=3, verbose=1, ) ] )改进代码，使用更好的模型

super().__init__(name=name, **kwargs) self.precision = tf.keras.metrics.Precision() self.recall = tf.keras.metrics.Recall() def update_state(self, y_true, y_pred, sample_weight=None): self....

import numpy as np import pandas as pd import xgboost as xgb from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error, r2_score # 从Excel文件中读取数据 noisy_df = pd.read_excel('015.xlsx', engine='openpyxl') clean_df = pd.read_excel('000.xlsx', engine='openpyxl') # 取前50000行数据 noisy_df = noisy_df.iloc[:50000] clean_df = clean_df.iloc[:50000] # 只取前50000组数据 X_noisy = noisy_df.to_numpy().reshape(-1, 10) X_clean = clean_df.to_numpy().reshape(-1, 10) # 生成目标数据 y，预测序列中最后一个干净数据点 y = X_clean[:, -1] # 数据集划分（60%训练，20%验证，20%测试） X_train, X_temp, y_train, y_temp = train_test_split(X_noisy, y, test_size=0.4, random_state=42) X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) # XGBoost 训练 xgb_model = xgb.XGBRegressor( objective='reg:squarederror', n_estimators=500, learning_rate=0.05, max_depth=6, subsample=0.8, colsample_bytree=0.8, random_state=42, early_stopping_rounds=10 # 将 early_stopping_rounds 移至此处 ) xgb_model.fit( X_train, y_train, eval_set=[(X_valid, y_valid)], verbose=False ) # 预测 y_pred = xgb_model.predict(X_test) # 评估模型 mse = np.mean((y_pred - y_test) ** 2) mae = mean_absolute_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print(f'Test MSE: {mse:.6f}, MAE: {mae:.6f}, R² Score: {r2:.4f}')Test MSE: 0.015968, MAE: 0.100940, R² Score: -0.0048数值不太好如何修改模型

嗯，用户提供的代码在训练...import seaborn as sns correlation = pd.DataFrame(X_noisy).corrwith(pd.Series(y)) sns.heatmap(correlation.to_frame(), annot=True) # 若多数特征相关性接近0，说明数据质量差 ##...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold from sklearn.linear_model import LogisticRegression from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report, roc_curve, auc) from sklearn.preprocessing import StandardScaler from sklearn.feature_selection import SelectKBest, f_classif from imblearn.over_sampling import SMOTE # 处理不平衡数据 # 读取数据 file_path = '神经外科-sheet2_imputed.xlsx' data = pd.read_excel(file_path, sheet_name='Sheet1') # 数据预处理 feature_columns = ['年龄', '转运时间', '意识情况', '有无氧气枕', '有无氧气瓶', '有无心电监护仪', '有无微量泵', '有无PEEP简易呼吸气囊', '有无便携式指脉氧', '有无呼吸问题', '有无循环问题', 'P', 'R', 'BP收缩压', 'BP舒张压', 'SpO2', 'T', 'GCS'] X = data[feature_columns] y = data['病情变化'] # 标准化特征 scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 特征选择 - 选择最重要的10个特征 selector = SelectKBest(f_classif, k=10) X_selected = selector.fit_transform(X_scaled, y) # 处理不平衡数据 smote = SMOTE(random_state=42) X_res, y_res = smote.fit_resample(X_selected, y) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split( X_res, y_res, test_size=0.2, random_state=42, stratify=y_res ) # 构建逻辑回归模型 model = LogisticRegression( max_iter=1000, penalty='l2', # L2正则化防止过拟合 C=1.0, # 正则化强度 solver='lbfgs', random_state=42 ) # 训练模型 model.fit(X_train, y_train) # 在测试集上进行预测 y_pred = model.predict(X_test) y_prob = model.predict_proba(X_test)[:, 1] # 预测概率 # ========== 模型评估 ========== # 基础指标 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) roc_auc = roc_auc_score(y_test, y_prob) print(f'模型准确率为: {accuracy * 100:.2f}%') print(f'精确率: {precision * 100:.2f}%') print(f'召回率: {recall * 100:.2f}%') print(f'F1分数: {f1 * 100:.2f}%') print(f'AUC-ROC: {roc_auc * 100:.2f}%') # 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', xticklabels=['无变化', '有变化'], yticklabels=['无变化', '有变化']) plt.xlabel('预测值') plt.ylabel('真实值') plt.title('混淆矩阵') plt.show() # 分类报告 print("\n分类报告:") print(classification_report(y_test, y_pred, target_names=['无变化', '有变化'])) # ROC曲线 fpr, tpr, thresholds = roc_curve(y_test, y_prob) roc_auc = auc(fpr, tpr) plt.figure() plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC曲线 (AUC = {roc_auc:.2f})') 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('假阳性率') plt.ylabel('真阳性率') plt.title('接收者操作特征曲线(ROC)') plt.legend(loc="lower right") plt.show() # ========== 模型验证 ========== # 交叉验证 cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) cv_scores = cross_val_score(model, X_res, y_res, cv=cv, scoring='roc_auc') print(f'\n交叉验证AUC-ROC分数:') for i, score in enumerate(cv_scores): print(f'折叠 {i+1}: {score:.4f}') print(f'平均AUC-ROC: {np.mean(cv_scores):.4f} ± {np.std(cv_scores):.4f}') # 特征重要性分析 feature_scores = pd.DataFrame({ '特征': feature_columns, '重要性': selector.scores_, '选择状态': ['是' if x else '否' for x in selector.get_support()] }) print("\n特征重要性排序:") print(feature_scores.sort_values(by='重要性', ascending=False)) # 模型系数分析 if X_selected.shape[1] == 10: # 确保我们选择了10个特征 selected_features = [feature_columns[i] for i in selector.get_support(indices=True)] coef_df = pd.DataFrame({ '特征': selected_features, '系数': model.coef_[0] }).sort_values(by='系数', key=abs, ascending=False) print("\n模型系数分析:") print(coef_df) 这个模型是多元逻辑模型吗，还有其他的吗

from sklearn.model_selection import GridSearchCV # 初始化随机森林模型 rf_model = RandomForestClassifier(random_state=42) # 定义参数网格 param_grid = { 'n_estimators': [50, 100, 200], 'max_depth': ...

将 pandas 导入为 PD 将 numpy 导入为 NP 将 Seaborn 导入为 SNS 将 matplotlib.pyplot 导入为 PLT %matplotlib 内联将 TensorFlow 导入为 TF 导入随机从 cv2 import 调整大小 from glob import glob 导入警告 warnings.filterwarnings（“ignore”）img_height = 244 img_width = 244 train_ds = tf.keras.utils.image_dataset_from_directory（ 'D：/Faulty_solar_panel'， validation_split=0.2， subset='training'， image_size=（img_height， img_width）， batch_size=32， seed=42， shuffle=True） val_ds = tf.keras.utils.image_dataset_from_directory（ 'D：/Faulty_solar_panel'， validation_split=0.2， subset='validation'， image_size=（img_height， img_width）， batch_size=32， seed=42， shuffle=True）class_names = train_ds.class_names 打印（class_names） train_dsbase_model = tf.keras.applications.VGG16（ include_top=False、 weights='imagenet'， input_shape=（img_height、img_width、3） ) base_model.trainable = False inputs = tf.keras.Input（shape=（img_height， img_width， 3）） x = tf.keras.applications.vgg16.preprocess_input（输入） x = base_model（x， training=False） x = tf.keras.layers.GlobalAveragePooling2D（）（x） x = tf.keras.layers.Dropout（0.3）（x）输出 = tf.keras.layers.Dense（90）（x）模型 = tf.keras.Model（输入，输出） model.summary（）model.compile（optimizer=tf.keras.optimizers.Adam（0.001）， loss=tf.keras.losses.SparseCategoricalCrossentropy（from_logits=True）， metrics=['accuracy']）epoch = 15 model.fit（train_ds， validation_data=val_ds， epochs=纪元，回调 = [ tf.keras.callbacks.EarlyStopping（ monitor=“val_loss”， min_delta=1e-2，耐心 = 3， verbose=1， restore_best_weights=真 ) ] ）# 微调 base_model.trainable = 真对于 base_model.layers[：14] 中的 layer： layer.trainable = 假 model.summary（）model.compile（optimizer=tf.keras.optimizers.Adam（0.0001）， loss=tf.keras.losses.SparseCategoricalCrossentropy（from_logits=True）， metrics=['accuracy']）epoch = 15 历史 = model.fit（train_ds， validation_data=val_ds， epochs=epoch，回调 = [ tf.keras.callbacks.EarlyStopping（ monitor=“val_loss”， min_delta=1e-2，耐心 = 3， verbose=1， ) ] ) get_ac = history.history['准确性'] get_los = history.history['损失'] val_acc = history.history['val_accuracy'] val_loss = history.history['val_loss'] 纪元 = 范围（len（get_ac）） plt.plot（epochs， get_ac， 'g'， label='训练数据的准确性'） plt.plot（epochs， get_los， 'r'， label='训练数据丢失'） plt.title（'训练数据准确性和损失'） plt.legend（loc=0） plt.figure（） plt.plot（epochs， get_ac， 'g'， label='训练数据的准确性'） plt.plot（epochs， val_acc， 'r'， label='验证数据的准确性'） plt.title（'训练和验证准确性'） plt.legend（loc=0） plt.figure（） plt.plot（epochs， get_los， 'g'， label='训练数据丢失'） plt.plot（纪元， val_loss， 'r'， label='验证数据丢失'） plt.title（'训练和验证损失'） plt.legend（loc=0） plt.figure（） plt.show（）把这段代码使用的模型改为mobilenet模型并提升精度，给出修改后的完整代码

import matplotlib.pyplot as plt import tensorflow as tf import random import warnings from glob import glob from cv2 import resize %matplotlib inline warnings.filterwarnings("ignore") # 修改2：调整...

import os import time import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error, mean_squared_error import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader, TensorDataset from torch.optim.lr_scheduler import ReduceLROnPlateau # 设置随机种子确保结果可复现 torch.manual_seed(42) np.random.seed(42) sns.set_style('whitegrid') # 设备配置 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 1. 数据加载与预处理函数 # -------------------------------------------------- def load_and_preprocess_data(): """加载并预处理所有数据源""" print("开始数据加载与预处理...") start_time = time.time() # 加载EC气象数据 ec_df = pd.read_csv('阿拉山口风电场_EC_data.csv', parse_dates=['生成日期', '预测日期']) ec_df = ec_df[ec_df['场站名'] == '阿拉山口风电场'] # 计算EC风速和风向 ec_df['EC风速(m/s)'] = np.sqrt(ec_df['U风分量(m/s)']2 + ec_df['V风分量(m/s)']2) ec_df['EC风向(度)'] = np.degrees(np.arctan2(ec_df['V风分量(m/s)'], ec_df['U风分量(m/s)'])) % 360 # 添加EC数据可用时间（生成时间+12小时） ec_df['可用时间'] = ec_df['生成日期'] + pd.Timedelta(hours=12) # 选择关键特征 ec_features = [ '可用时间', '预测日期', 'EC风速(m/s)', 'EC风向(度)', '位势高度_850hPa(gpm)', '温度_850hPa(K)', '相对湿度_850hPa(%)', '位势高度_500hPa(gpm)', '温度_500hPa(K)' ] ec_df = ec_df[ec_features] # 加载风机数据 turbine_df = pd.read_csv('阿拉山口风电场风机数据.csv', encoding='gbk', parse_dates=[0]) turbine_df.columns = ['timestamp', 'wind_speed', 'active_power'] # 加载远动数据 scada_df = pd.read_csv('阿拉山口风电场远动数据.csv', encoding='gbk', parse_dates=[0]) scada_df.columns = ['timestamp', 'active_power_total'] # 合并风机和远动数据 power_df = pd.merge(turbine_df[['timestamp', 'wind_speed']], scada_df, on='timestamp', how='outer') # 按时间排序并填充缺失值 power_df.sort_values('timestamp', inplace=True) power_df['active_power_total'].ffill(inplace=True) power_df['wind_speed'].ffill(inplace=True) # 创建完整的时间序列索引（15分钟间隔） full_range = pd.date_range( start=power_df['timestamp'].min(), end=power_df['timestamp'].max(), freq='15T' ) power_df = power_df.set_index('timestamp').reindex(full_range).reset_index() power_df.rename(columns={'index': 'timestamp'}, inplace=True) power_df[['wind_speed', 'active_power_total']] = power_df[['wind_speed', 'active_power_total']].ffill() # 合并EC数据到主数据集 ec_data = [] for idx, row in power_df.iterrows(): ts = row['timestamp'] # 获取可用的EC预测（可用时间 <= 当前时间） available_ec = ec_df[ec_df['可用时间'] <= ts] if not available_ec.empty: # 获取最近发布的EC数据 latest_gen = available_ec['可用时间'].max() latest_ec = available_ec[available_ec['可用时间'] == latest_gen] # 找到最接近当前时间点的预测 time_diff = (latest_ec['预测日期'] - ts).abs() closest_idx = time_diff.idxmin() ec_point = latest_ec.loc[closest_idx].copy() ec_point['timestamp'] = ts ec_data.append(ec_point) # 创建EC数据DataFrame并合并 ec_ts_df = pd.DataFrame(ec_data) merged_df = pd.merge(power_df, ec_ts_df, on='timestamp', how='left') # 填充缺失的EC数据 ec_cols = [col for col in ec_ts_df.columns if col not in ['timestamp', '可用时间', '预测日期']] for col in ec_cols: merged_df[col] = merged_df[col].interpolate(method='time') # 添加时间特征 merged_df['hour'] = merged_df['timestamp'].dt.hour merged_df['day_of_week'] = merged_df['timestamp'].dt.dayofweek merged_df['day_of_year'] = merged_df['timestamp'].dt.dayofyear merged_df['month'] = merged_df['timestamp'].dt.month # 计算实际风向（如果有测风塔数据，这里使用EC风向） merged_df['风向(度)'] = merged_df['EC风向(度)'] # 移除包含NaN的行 merged_df.dropna(inplace=True) # 特征选择 feature_cols = [ 'wind_speed', 'active_power_total', 'EC风速(m/s)', '风向(度)', '位势高度_850hPa(gpm)', '温度_850hPa(K)', '相对湿度_850hPa(%)', '位势高度_500hPa(gpm)', '温度_500hPa(K)', 'hour', 'day_of_week', 'day_of_year', 'month' ] target_col = 'active_power_total' print(f"数据处理完成! 耗时: {time.time()-start_time:.2f}秒") print(f"数据集形状: {merged_df.shape}") print(f"特征数量: {len(feature_cols)}") return merged_df[feature_cols], merged_df[target_col], merged_df['timestamp'] # 2. 数据准备类 (PyTorch Dataset) # -------------------------------------------------- class WindPowerDataset(Dataset): """风功率预测数据集类""" def init(self, X, y, look_back, forecast_steps): """ :param X: 特征数据 (n_samples, n_features) :param y: 目标数据 (n_samples,) :param look_back: 回溯时间步长 :param forecast_steps: 预测步长 """ self.X = X self.y = y self.look_back = look_back self.forecast_steps = forecast_steps self.n_samples = len(X) - look_back - forecast_steps + 1 def len(self): return self.n_samples def getitem(self, idx): # 获取历史序列 x_seq = self.X[idx:idx+self.look_back] # 获取未来目标序列 y_seq = self.y[idx+self.look_back:idx+self.look_back+self.forecast_steps] # 转换为PyTorch张量 x_tensor = torch.tensor(x_seq, dtype=torch.float32) y_tensor = torch.tensor(y_seq, dtype=torch.float32) return x_tensor, y_tensor # 3. LSTM模型 (PyTorch实现) # -------------------------------------------------- class WindPowerLSTM(nn.Module): """风功率预测LSTM模型""" def init(self, input_size, hidden_size, num_layers, output_steps): """ :param input_size: 输入特征维度 :param hidden_size: LSTM隐藏层大小 :param num_layers: LSTM层数 :param output_steps: 输出步长 """ super(WindPowerLSTM, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.output_steps = output_steps # LSTM层 self.lstm = nn.LSTM( input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=0.2 if num_layers > 1 else 0 ) # 全连接层 self.fc = nn.Sequential( nn.Linear(hidden_size, 128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, 64), nn.ReLU(), nn.Dropout(0.2), nn.Linear(64, output_steps) ) def forward(self, x): # 初始化隐藏状态 h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) # 前向传播LSTM out, _ = self.lstm(x, (h0, c0)) # 只取最后一个时间步的输出 out = out[:, -1, :] # 全连接层 out = self.fc(out) return out # 4. 训练和评估函数 # -------------------------------------------------- def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs, model_name): """训练模型""" print(f"\n开始训练 {model_name} 模型...") start_time = time.time() best_val_loss = float('inf') history = {'train_loss': [], 'val_loss': []} for epoch in range(epochs): # 训练阶段 model.train() train_loss = 0.0 for inputs, targets in train_loader: inputs, targets = inputs.to(device), targets.to(device) # 前向传播 outputs = model(inputs) loss = criterion(outputs, targets) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() train_loss += loss.item() * inputs.size(0) # 验证阶段 model.eval() val_loss = 0.0 with torch.no_grad(): for inputs, targets in val_loader: inputs, targets = inputs.to(device), targets.to(device) outputs = model(inputs) loss = criterion(outputs, targets) val_loss += loss.item() * inputs.size(0) # 计算平均损失 train_loss = train_loss / len(train_loader.dataset) val_loss = val_loss / len(val_loader.dataset) history['train_loss'].append(train_loss) history['val_loss'].append(val_loss) # 更新学习率 if scheduler: scheduler.step(val_loss) # 保存最佳模型 if val_loss < best_val_loss: best_val_loss = val_loss torch.save(model.state_dict(), f'best_{model_name}_model.pth') # 打印进度 if (epoch + 1) % 5 == 0: print(f"Epoch [{epoch+1}/{epochs}] - Train Loss: {train_loss:.6f}, Val Loss: {val_loss:.6f}") print(f"训练完成! 耗时: {time.time()-start_time:.2f}秒") print(f"最佳验证损失: {best_val_loss:.6f}") return history def evaluate_model(model, test_loader, scaler, model_name): """评估模型性能""" model.eval() actuals = [] predictions = [] with torch.no_grad(): for inputs, targets in test_loader: inputs = inputs.to(device) # 预测 outputs = model(inputs) # 反归一化 outputs_np = outputs.cpu().numpy() targets_np = targets.numpy() # 反归一化 outputs_inv = scaler.inverse_transform(outputs_np) targets_inv = scaler.inverse_transform(targets_np.reshape(-1, 1)).flatten() # 收集结果 actuals.extend(targets_inv) predictions.extend(outputs_inv.flatten()) # 转换为numpy数组 actuals = np.array(actuals) predictions = np.array(predictions) # 计算性能指标 mae = mean_absolute_error(actuals, predictions) rmse = np.sqrt(mean_squared_error(actuals, predictions)) print(f"\n{model_name} 模型评估结果:") print(f"MAE: {mae:.2f} kW") print(f"RMSE: {rmse:.2f} kW") return actuals, predictions, mae, rmse # 5. 可视化函数 # -------------------------------------------------- def plot_predictions(actuals, predictions, timestamps, model_name, forecast_steps, mae): """可视化预测结果""" # 创建结果DataFrame results = pd.DataFrame({ 'timestamp': timestamps, 'actual': actuals, 'predicted': predictions }) # 设置时间索引 results.set_index('timestamp', inplace=True) # 选择一段代表性的时间序列展示 sample = results.iloc[1000:1300] plt.figure(figsize=(15, 7)) # 绘制实际值 plt.plot(sample.index, sample['actual'], label='实际功率', color='blue', alpha=0.7, linewidth=2) # 绘制预测值 plt.plot(sample.index, sample['predicted'], label='预测功率', color='red', alpha=0.7, linestyle='--', linewidth=2) plt.title(f'{model_name}风功率预测 (预测步长: {forecast_steps}步, MAE: {mae:.2f} kW)', fontsize=14) plt.xlabel('时间', fontsize=12) plt.ylabel('有功功率 (kW)', fontsize=12) plt.legend(fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) plt.tight_layout() plt.savefig(f'{model_name}_prediction_plot.png', dpi=300) plt.show() return results def plot_training_history(history, model_name): """绘制训练过程中的损失曲线""" plt.figure(figsize=(12, 6)) # 绘制训练损失 plt.plot(history['train_loss'], label='训练损失') # 绘制验证损失 if 'val_loss' in history: plt.plot(history['val_loss'], label='验证损失') plt.title(f'{model_name} 训练过程', fontsize=14) plt.xlabel('训练轮次', fontsize=12) plt.ylabel('损失 (MSE)', fontsize=12) plt.legend(fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) plt.tight_layout() plt.savefig(f'{model_name}_training_history.png', dpi=300) plt.show() # 6. 主函数 # -------------------------------------------------- def main(): # 加载数据 X, y, timestamps = load_and_preprocess_data() # 定义预测配置 ULTRA_SHORT_CONFIG = { 'name': '超短期', 'look_back': 24, # 6小时历史 (2415min) 'forecast_steps': 16, # 4小时预测 (1615min) 'batch_size': 64, 'hidden_size': 128, 'num_layers': 2, 'epochs': 100, 'lr': 0.001 } SHORT_TERM_CONFIG = { 'name': '短期', 'look_back': 96, # 24小时历史 (9615min) 'forecast_steps': 288, # 72小时预测 (28815min) 'batch_size': 32, 'hidden_size': 256, 'num_layers': 3, 'epochs': 150, 'lr': 0.0005 } # 准备超短期预测数据集 print("\n准备超短期预测数据集...") # 数据标准化 X_scaler = StandardScaler() y_scaler = StandardScaler() X_scaled = X_scaler.fit_transform(X) y_scaled = y_scaler.fit_transform(y.values.reshape(-1, 1)).flatten() # 创建数据集 dataset = WindPowerDataset(X_scaled, y_scaled, ULTRA_SHORT_CONFIG['look_back'], ULTRA_SHORT_CONFIG['forecast_steps']) # 划分数据集 train_size = int(0.8 * len(dataset)) val_size = int(0.1 * len(dataset)) test_size = len(dataset) - train_size - val_size train_dataset, val_dataset, test_dataset = torch.utils.data.random_split( dataset, [train_size, val_size, test_size] ) # 创建数据加载器 train_loader = DataLoader(train_dataset, batch_size=ULTRA_SHORT_CONFIG['batch_size'], shuffle=True) val_loader = DataLoader(val_dataset, batch_size=ULTRA_SHORT_CONFIG['batch_size']) test_loader = DataLoader(test_dataset, batch_size=ULTRA_SHORT_CONFIG['batch_size']) # 创建模型 model_ultra = WindPowerLSTM( input_size=X.shape[1], hidden_size=ULTRA_SHORT_CONFIG['hidden_size'], num_layers=ULTRA_SHORT_CONFIG['num_layers'], output_steps=ULTRA_SHORT_CONFIG['forecast_steps'] ).to(device) # 损失函数和优化器 criterion = nn.MSELoss() optimizer = optim.Adam(model_ultra.parameters(), lr=ULTRA_SHORT_CONFIG['lr']) scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=5, verbose=True) # 训练模型 history_ultra = train_model( model_ultra, train_loader, val_loader, criterion, optimizer, scheduler, ULTRA_SHORT_CONFIG['epochs'], 'ultra_short' ) # 评估模型 actuals_ultra, preds_ultra, mae_ultra, rmse_ultra = evaluate_model( model_ultra, test_loader, y_scaler, '超短期' ) # 可视化结果 plot_training_history(history_ultra, '超短期模型') # 获取对应的时间戳 ultra_timestamps = timestamps[ULTRA_SHORT_CONFIG['look_back']:][:len(actuals_ultra)] results_ultra = plot_predictions( actuals_ultra, preds_ultra, ultra_timestamps, '超短期', ULTRA_SHORT_CONFIG['forecast_steps'], mae_ultra ) # 准备短期预测数据集 print("\n准备短期预测数据集...") # 创建数据集 dataset_short = WindPowerDataset(X_scaled, y_scaled, SHORT_TERM_CONFIG['look_back'], SHORT_TERM_CONFIG['forecast_steps']) # 划分数据集 train_size_short = int(0.8 * len(dataset_short)) val_size_short = int(0.1 * len(dataset_short)) test_size_short = len(dataset_short) - train_size_short - val_size_short train_dataset_short, val_dataset_short, test_dataset_short = torch.utils.data.random_split( dataset_short, [train_size_short, val_size_short, test_size_short] ) # 创建数据加载器 train_loader_short = DataLoader(train_dataset_short, batch_size=SHORT_TERM_CONFIG['batch_size'], shuffle=True) val_loader_short = DataLoader(val_dataset_short, batch_size=SHORT_TERM_CONFIG['batch_size']) test_loader_short = DataLoader(test_dataset_short, batch_size=SHORT_TERM_CONFIG['batch_size']) # 创建模型 model_short = WindPowerLSTM( input_size=X.shape[1], hidden_size=SHORT_TERM_CONFIG['hidden_size'], num_layers=SHORT_TERM_CONFIG['num_layers'], output_steps=SHORT_TERM_CONFIG['forecast_steps'] ).to(device) # 损失函数和优化器 optimizer_short = optim.Adam(model_short.parameters(), lr=SHORT_TERM_CONFIG['lr']) scheduler_short = ReduceLROnPlateau(optimizer_short, mode='min', factor=0.5, patience=10, verbose=True) # 训练模型 history_short = train_model( model_short, train_loader_short, val_loader_short, criterion, optimizer_short, scheduler_short, SHORT_TERM_CONFIG['epochs'], 'short_term' ) # 评估模型 actuals_short, preds_short, mae_short, rmse_short = evaluate_model( model_short, test_loader_short, y_scaler, '短期' ) # 可视化结果 plot_training_history(history_short, '短期模型') # 获取对应的时间戳 short_timestamps = timestamps[SHORT_TERM_CONFIG['look_back']:][:len(actuals_short)] results_short = plot_predictions( actuals_short, preds_short, short_timestamps, '短期', SHORT_TERM_CONFIG['forecast_steps'], mae_short ) # 最终报告 print("\n" + "="50) print("风功率预测模型训练完成!") print("="50) print(f"超短期模型 (4小时预测):") print(f" - 回溯步长: {ULTRA_SHORT_CONFIG['look_back']} (6小时)") print(f" - 预测步长: {ULTRA_SHORT_CONFIG['forecast_steps']} (4小时)") print(f" - 测试集MAE: {mae_ultra:.2f} kW") print(f" - 测试集RMSE: {rmse_ultra:.2f} kW") print(f"\n短期模型 (72小时预测):") print(f" - 回溯步长: {SHORT_TERM_CONFIG['look_back']} (24小时)") print(f" - 预测步长: {SHORT_TERM_CONFIG['forecast_steps']} (72小时)") print(f" - 测试集MAE: {mae_short:.2f} kW") print(f" - 测试集RMSE: {rmse_short:.2f} kW") print("="*50) # 保存预测结果 results_df = pd.DataFrame({ 'timestamp': short_timestamps, '实际功率': actuals_short, '超短期预测': results_ultra['predicted'].values[:len(actuals_short)], '短期预测': preds_short }) results_df.to_csv('风功率预测结果.csv', index=False) print("预测结果已保存到 '风功率预测结果.csv'") if name == "main": main() 请在上述的代码基础上修改，本次修改要求只使用"阿拉山口风电场_EC_data"和"阿拉山口风电场风机数据"这两个数据集进行训练和预测。

import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_...

--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[1], line 22 20 from sklearn.preprocessing import StandardScaler 21 from sklearn.feature_extraction.image import extract_patches_2d ---> 22 from skimage.feature import hog 23 from scipy.ndimage import gaussian_filter, map_coordinates 24 import seaborn as sns ModuleNotFoundError: No module named 'skimage'

import matplotlib.pyplot as plt import pandas as pd import tkinter as tk from tkinter import ttk, filedialog, messagebox, simpledialog from PIL import Image, ImageDraw, ImageOps import cv2 import os ...

超参数调优完成，耗时 2488.94 秒最佳参数: {'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200, 'subsample': 0.8} 最佳交叉验证准确率: 0.7202 模型已保存至 'water_quality_classification_results/models/xgboost_2022_model.pkl' 评估模型: xgboost_2022 分类报告: precision recall f1-score support 0 0.99 0.21 0.35 41943 1 0.42 0.99 0.59 41943 2 0.99 0.51 0.67 41943 3 0.99 0.93 0.96 41943 4 0.99 0.96 0.97 41943 accuracy 0.72 209715 macro avg 0.88 0.72 0.71 209715 weighted avg 0.88 0.72 0.71 209715 整体指标: 准确率: 0.7201 精确率: 0.8754 召回率: 0.7201 F1分数: 0.7089 ROC AUC: 0.9134 为 xgboost_2022 创建可视化图表... 为 xgboost_2022 执行SHAP分析... Traceback (most recent call last): File "D:\Users\ASUS\PycharmProjects\ML paper prediction\XGBoost.py", line 499, in <module> File "D:\Users\ASUS\PycharmProjects\ML paper prediction\XGBoost.py", line 428, in main model, X_test, y_test, feature_names, f"xgboost_{year}" ^^^^^^^^^^^^^^^^ File "D:\Users\ASUS\PycharmProjects\ML paper prediction\XGBoost.py", line 235, in evaluate_model File "D:\Users\ASUS\PycharmProjects\ML paper prediction\XGBoost.py", line 277, in plot_evaluation_results File "D:\Users\ASUS\PycharmProjects\ML paper prediction\XGBoost.py", line 320, in plot_shap_analysis plt.figure(figsize=(10, 6)) ^^^^^^^^^^^^^ TypeError: only integer scalar arrays can be converted to a scalar index

import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn....

class DistanceAnalyzer: def init(self, max_history=1000): # 初始化统计数据存储 self.distance_history = deque(maxlen=max_history) # 历史距离数据 self.filter_ratios = deque(maxlen=max_history) # 历史过滤比例 self.frame_count = 0 # 处理帧数计数器 self.stats_data = [] # 统计信息存储 def analyze_distances(self, distances, min_threshold, max_threshold): """ 分析距离分布并计算过滤比例 :param distances: 当前帧的距离数组 :param min_threshold: 最小距离阈值 :param max_threshold: 最大距离阈值 :return: 过滤比例和统计信息字典 """ # 更新历史数据 self.distance_history.extend(distances) self.frame_count += 1 # 转换为NumPy数组 dist_array = np.array(distances) # 基本统计量 mean = np.mean(dist_array) median = np.median(dist_array) std = np.std(dist_array) min_val = np.min(dist_array) max_val = np.max(dist_array) # 百分位数 percentiles = np.percentile(dist_array, [5, 25, 50, 75, 95]) # 过滤比例计算 filter_flag = (dist_array >= min_threshold) & (dist_array <= max_threshold) filtered_count = np.sum(filter_flag) total_count = len(dist_array) filter_ratio = filtered_count / total_count if total_count > 0 else 1.0 self.filter_ratios.append(filter_ratio) # 距离分布直方图数据 hist, bin_edges = np.histogram(dist_array, bins=20, range=(0, max(100, max_val))) # 创建统计信息字典 stats_info = { 'frame': self.frame_count, 'total_points': total_count, 'filtered_points': filtered_count, 'filter_ratio': filter_ratio, 'mean_distance': mean, 'median_distance': median, 'std_distance': std, 'min_distance': min_val, 'max_distance': max_val, 'percentiles': { '5th': percentiles[0], '25th': percentiles[1], '50th': percentiles[2], '75th': percentiles[3], '95th': percentiles[4] }, 'histogram': { 'counts': hist.tolist(), 'bins': bin_edges.tolist() }, 'thresholds': { 'min': min_threshold, 'max': max_threshold } } # 保存统计信息 self.stats_data.append(stats_info) return filter_ratio, stats_info def print_current_stats(self, stats_info): """打印当前帧的统计信息""" print("\n" + "=" * 50) print(f"帧 #{stats_info['frame']} 距离分布统计") print("=" * 50) print(f"总点数: {stats_info['total_points']}") print(f"过滤后点数: {stats_info['filtered_points']}") print(f"过滤比例: {stats_info['filter_ratio'] * 100:.2f}%") print(f"平均距离: {stats_info['mean_distance']:.2f}m") print(f"中位数距离: {stats_info['median_distance']:.2f}m") print(f"标准差: {stats_info['std_distance']:.2f}m") print(f"最小距离: {stats_info['min_distance']:.2f}m") print(f"最大距离: {stats_info['max_distance']:.2f}m") print(f"距离阈值: {stats_info['thresholds']['min']:.2f}m - {stats_info['thresholds']['max']:.2f}m") print(f"5%分位数: {stats_info['percentiles']['5th']:.2f}m") print(f"95%分位数: {stats_info['percentiles']['95th']:.2f}m") def visualize_distribution(self, stats_info): """可视化当前帧的距离分布""" plt.figure(figsize=(15, 10)) # 直方图 plt.subplot(2, 2, 1) bins = stats_info['histogram']['bins'] counts = stats_info['histogram']['counts'] plt.bar(bins[:-1], counts, width=np.diff(bins), align='edge', alpha=0.7) plt.axvline(stats_info['thresholds']['min'], color='r', linestyle='--', label=f'最小阈值: {stats_info["thresholds"]["min"]:.1f}m') plt.axvline(stats_info['thresholds']['max'], color='g', linestyle='--', label=f'最大阈值: {stats_info["thresholds"]["max"]:.1f}m') plt.axvline(stats_info['mean_distance'], color='b', linestyle=':', label=f'平均值: {stats_info["mean_distance"]:.1f}m') plt.xlabel('距离 (米)') plt.ylabel('点数') plt.title(f'帧 #{stats_info["frame"]} 距离分布') plt.legend() # 箱线图 plt.subplot(2, 2, 2) sns.boxplot(x=np.array(self.distance_history)) plt.axvline(stats_info['thresholds']['min'], color='r', linestyle='--') plt.axvline(stats_info['thresholds']['max'], color='g', linestyle='--') plt.xlabel('距离 (米)') plt.title('距离分布箱线图') # 过滤比例趋势 plt.subplot(2, 2, 3) frames = [s['frame'] for s in self.stats_data] ratios = [s['filter_ratio'] for s in self.stats_data] plt.plot(frames, ratios, 'bo-', label='过滤比例') plt.axhline(0.9, color='r', linestyle='--', label='目标比例(90%)') plt.xlabel('帧号') plt.ylabel('过滤比例') plt.title('过滤比例趋势') plt.legend() plt.ylim(0, 1.1) # 距离阈值变化 plt.subplot(2, 2, 4) min_ths = [s['thresholds']['min'] for s in self.stats_data] max_ths = [s['thresholds']['max'] for s in self.stats_data] plt.plot(frames, min_ths, 'r-', label='最小阈值') plt.plot(frames, max_ths, 'g-', label='最大阈值') plt.xlabel('帧号') plt.ylabel('阈值 (米)') plt.title('距离阈值变化趋势') plt.legend() plt.tight_layout() plt.show() def generate_report(self, last_n_frames=100): """生成综合统计报告""" if not self.stats_data: return "无统计数据" # 获取最近的统计数据 recent_data = self.stats_data[-last_n_frames:] # 创建DataFrame用于分析 df = pd.DataFrame(recent_data) # 计算整体统计量 total_points = df['total_points'].sum() filtered_points = df['filtered_points'].sum() overall_ratio = filtered_points / total_points if total_points > 0 else 0 # 距离统计 all_distances = np.array(list(self.distance_history)) distance_stats = { 'mean': np.mean(all_distances), 'median': np.median(all_distances), 'std': np.std(all_distances), 'min': np.min(all_distances), 'max': np.max(all_distances) } # 阈值统计 min_th_mean = df['thresholds'].apply(lambda x: x['min']).mean() max_th_mean = df['thresholds'].apply(lambda x: x['max']).mean() # 打印报告 report = f"\n{'=' * 50}\n距离分析报告 (最近{last_n_frames}帧)\n{'=' * 50}" report += f"\n总点数: {total_points}" report += f"\n过滤后点数: {filtered_points}" report += f"\n整体过滤比例: {overall_ratio * 100:.2f}%" report += f"\n平均过滤比例: {df['filter_ratio'].mean() * 100:.2f}%" report += f"\n平均距离: {distance_stats['mean']:.2f}m ± {distance_stats['std']:.2f}m" report += f"\n距离范围: {distance_stats['min']:.2f}m - {distance_stats['max']:.2f}m" report += f"\n平均阈值范围: {min_th_mean:.2f}m - {max_th_mean:.2f}m" report += f"\n过滤比例标准差: {df['filter_ratio'].std() * 100:.2f}%" # 识别异常帧 low_ratio_frames = df[df['filter_ratio'] < 0.7] if not low_ratio_frames.empty: report += "\n\n警告: 以下帧过滤比例低于70%:" for _, row in low_ratio_frames.iterrows(): report += f"\n - 帧 #{row['frame']}: {row['filter_ratio'] * 100:.1f}%" return report把这个类整合进车辆过滤函数中

import matplotlib.pyplot as plt import seaborn as sns from collections import deque import pandas as pd from scipy import stats class VehicleFilter: def __init__(self, min_threshold=1.0, max_...

merged_df = merged_df.set_index('timestamp').reindex(full_range).reset_index() File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\frame.py", line 5378, in reindex return super().reindex( File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\generic.py", line 5610, in reindex return self._reindex_axes( File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\generic.py", line 5633, in _reindex_axes new_index, indexer = ax.reindex( File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\indexes\base.py", line 4429, in reindex raise ValueError("cannot reindex on an axis with duplicate labels") ValueError: cannot reindex on an axis with duplicate labels

import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_...

Traceback (most recent call last): File "D:\python\负荷预测测试版.py", line 207, in <module> stacking_model = StackingRegressor( ^^^^^^^^^^^^^^^^^^ TypeError: StackingRegressor.init() got an unexpected keyword argument 'weights'

import matplotlib.pyplot as plt import matplotlib import seaborn as sns from scipy import stats from scipy.stats import randint, uniform, spearmanr from sklearn.model_selection import train_test_split...

File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\frame.py", line 778, in init mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager) File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\internals\construction.py", line 503, in dict_to_mgr return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy) File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\internals\construction.py", line 114, in arrays_to_mgr index = _extract_index(arrays) File "D:\Anaconda\envs\gpu\lib\site-packages\pandas\core\internals\construction.py", line 690, in _extract_index raise ValueError(msg) ValueError: array length 4304 does not match index length 1802

results_df = pd.DataFrame({ 'timestamp': short_timestamps, '实际功率': actuals_short, '超短期预测': results_ultra['predicted'].values[:len(actuals_short)], '短期预测': preds_short }) 我们需要...

聚类特征提取: 100%|██████████| 11246/11246 [02:26<00:00, 77.00it/s] 聚类分布统计: cluster 2 5701 0 4766 1 302 3 85 7 42 6 8 5 4 4 2 Name: count, dtype: int64 股票聚类完成，共分为 8 个类别聚类结果已保存到: stock_prediction_model_clusters.csv 准备训练数据集... 准备训练数据集... 处理股票数据: 0%| | 0/23 [00:00<?, ?it/s]处理股票 shsh000001 失败: "['ROC', 'WILLR'] not in index" 处理股票 shsh000002 失败: "['ROC', 'WILLR'] not in index" 处理股票 shsh000003 失败: "['ROC', 'WILLR'] not in index" 处理股票 shsh000004 失败: "['ROC', 'WILLR'] not in index" 处理股票 shsh000005 失败: "['ROC', 'WILLR'] not in index" 处理股票 shsh000006 失败: "['ROC', 'WILLR'] not in index" ......所有的股票都报错

import matplotlib.pyplot as plt import seaborn as sns from tqdm import tqdm from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans from sklearn.ensemble import ...

seaborn绘图展示p值

import matplotlib.pyplot as plt #### 创建样本数据集 python np.random.seed(0) data = { 'group': ['A'] * 50 + ['B'] * 50, 'value': list(np.random.normal(loc=0, scale=1, size=50)) + list(np....

更换模型为XGBoost，给出完整代码

import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold from sklearn.preprocessing import LabelEncoder, StandardScaler...

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Traceback (most recent call last): File "D:\python\负荷预测测试版.py", line 207, in <module> stacking_model = StackingRegressor( ^^^^^^^^^^^^^^^^^^ TypeError: StackingRegressor.__init__() got an unexpected keyword argument 'weights'

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Traceback (most recent call last): File "D:\python\负荷预测测试版.py", line 207, in <module> stacking_model = StackingRegressor( ^^^^^^^^^^^^^^^^^^ TypeError: StackingRegressor.init() got an unexpected keyword argument 'weights'