import pandas as pd import numpy as np # 导入数据 df = pd.read_csv("E:\data2.csv") # 根据label列分组并计数 df_label = df.groupby("LBDSCHSI").size() # 转化为作图所需要的array格式，如：([[ 0, 37536],[ 1, 12322]]) datas = list(zip(df_label.index.to_list(), df_label.to_list())) datas= np.array([[0,2.83],[2.83,5.20],[5.20,6.45],[6.45,8.0]]) # 绘制饼图 import matplotlib.pyplot as plt # 设置字体和编码格式 plt.rcParams['font.sans-serif'] = 'simhei' plt.rcParams['axes.unicode_minus'] = False # 添加数据，标签，比例 plt.pie(datas[:, 1],labels=datas[:, 0],autopct="%.2f%%") # 在右上方添加图例 plt.legend(datas[:, 0], loc="upper right") # 添加标题 plt.title("LBDSCHSI(胆固醇)含量分析") # 展示饼图 plt.show()

分析错误并改正：import os import pandas as pd from scipy.io import loadmat import numpy as np import torch from torch.utils.data import Dataset, DataLoader from sklearn.model_selection import train_test_split # 加载标签文件 labels = pd.read_csv("label.csv", index_col=0)['label'].to_dict()

df = pd.read_csv("label.csv") labels = df.set_index('sample_id')['label'].to_dict() 这里假设'sample_id'是作为键的列名，而'label'是目标列。如果用户原来的index_col=0是正确的，那可能没问题，否则需要调整...

import tensorflow as tfimport numpy as npimport pandas as pd# 加载预训练模型model = tf.keras.models.load_model('model.h5')# 加载标签label_df = pd.read_csv('labels.csv', header=None)label_dict = label_df.to_dict()[0]def recognize_audio(): audio = record_audio() text = recognize_speech(audio) text = process_text(text) # 将文本转换为数字序列 text_seq = [label_dict.get(char, 0) for char in text] # 填充序列 text_seq = np.pad(text_seq, (0, 16000 - len(text_seq))) # 预测结果 result = model.predict(np.array([text_seq])) result = np.argmax(result) return label_dict[result]这个模型我没有你可以重新书写一份吗

首先，我们需要安装必要的库，包括tensorflow、numpy和pandas。然后，我们需要准备训练数据和测试数据。训练数据应包含音频文件和相应的文本标签。测试数据只需要包含音频文件。接下来，我们需要进行数据预处理和...

检查我的代码，数据库大小为408张，因为8类别最多到8-3-8，其余到x-6-8,代码如下：import os import pandas as pd import numpy as np import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader from torchvision import transforms from PIL import Image from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split # ==================== # 数据预处理模块（无需CSV） # ==================== class FingerprintDataset(Dataset): def init(self, data_root, transform=None): self.data_root = data_root self.transform = transform or transforms.Compose([ transforms.Resize((64, 64)), # 统一缩放尺寸 transforms.Grayscale(), transforms.ToTensor() ]) # 自动扫描文件并解析标签 self.file_list = [] self.labels = [] self.label_encoder = LabelEncoder() # 遍历文件夹获取所有图片 for file_name in os.listdir(data_root): if file_name.lower().endswith(('.bmp', '.png', '.jpg')): # 从文件名解析标签（假设文件名为 1-1-1.bmp 格式） label_str = os.path.splitext(file_name)[0] self.file_list.append(os.path.join(data_root, file_name)) self.labels.append(label_str) # 生成标签编码 self.encoded_labels = self.label_encoder.fit_transform(self.labels) self.num_classes = len(self.label_encoder.classes_) def len(self): return len(self.file_list) def getitem(self, idx): img_path = self.file_list[idx] image = Image.open(img_path) if self.transform: image = self.transform(image) label = self.encoded_labels[idx] return image.view(-1), torch.tensor(label, dtype=torch.long) # ==================== # 忆阻器权重加载模块 # ==================== def load_memristor_weights(excel_path): df = pd.read_excel(excel_path) ltp = torch.tensor(df['LTP'].values, dtype=torch.float32) ltd = torch.tensor(df['LTD'].values, dtype=torch.float32) return ltp, ltd # ==============

好的，我现在需要处理用户关于指纹数据集代码审查和分类逻辑的请求。用户提到有一个包含408张图像的数据集，分类逻辑是“x-6-8”，使用PyTorch和LabelEncoder。首先，我需要理解用户的具体需求。首先，用户的请求...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings("ignore") file_path='train.csv' # 读取数据 df = pd.read_csv(file_path, encoding='gbk') print(df) # 数据清洗 print("开始对文件数据进行清洗：") print("1.数据清洗--删除重复值") df_unique_all= df.drop_duplicates(keep=False) print("将df_unique_all写入train.csv中") df_unique_all.to_csv("train1.csv", index=False) print("将df_unique_all写入train1.csv中") df_unique_all.to_csv("train1.csv", index=False) 在我代码的基础上进行补充，生成骑行距离分布的直方图，要用hist()函数

import numpy as np import matplotlib.pyplot as plt from scipy.stats import gaussian_kde # 设置中文字体支持 plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei'] plt.rcParams['axes.unicode_...

import pandas as pd import numpy as np from sklearn.preprocessing import OrdinalEncoder, StandardScaler from sklearn.impute import SimpleImputer from sklearn.model_selection import train_test_split from factor_analyzer import FactorAnalyzer from factor_analyzer.factor_analyzer import calculate_kmo from xgboost import XGBClassifier import lightgbm as lgb from sklearn.metrics import classification_report # ====================== # 1. 数据读取与清洗 # ====================== def load_and_clean(data_path): # 读取数据 df = pd.read_csv(data_path) # 缺失值处理 num_imputer = SimpleImputer(strategy='median') cat_imputer = SimpleImputer(strategy='most_frequent') # 数值型字段 numeric_cols = ['付费金额', '活跃时长', '广告收入', '留存'] df[numeric_cols] = num_imputer.fit_transform(df[numeric_cols]) # 分类型字段 categorical_cols = ['设备价值档位', '用户初始广告档位'] df[categorical_cols] = cat_imputer.fit_transform(df[categorical_cols]) # 异常值处理 df['活跃时长'] = np.where(df['活跃时长'] > 24, 24, df['活跃时长']) df['付费金额'] = np.where( df['付费金额'] > df['付费金额'].quantile(0.99), df['付费金额'].quantile(0.95), df['付费金额'] ) return df # ====================== # 2. 特征工程 # ====================== def feature_engineering(df): # 构造复合特征 df['ARPU密度'] = df['付费金额'] / (df['活跃天数'] + 1) df['广告展示率'] = df['广告曝光次数'] / df['短剧观看次数'] df['内容互动指数'] = np.log1p(df['收藏数']1 + df['分享数']2 + df['评论数']*3) # 分类变量编码 encoder = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1) cat_cols = ['设备价值档位', '用户初始广告档位'] df[cat_cols] = encoder.fit_transform(df[cat_cols]) return df # ====================== # 3. 特征筛选与降维 # ====================== def feature_selection(df, target_col='付费意愿档位'): # 划分特征和目标 X = df.drop(columns=[target_col]) y = df[target_col] # 计算IV值筛选 from sklearn.feature_selection import mutual_info_classif iv_values = mutual_info_classif(X, y) iv_df = pd.DataFrame({'feature': X.columns, 'iv': iv_values}) selected_features = iv_df[iv_df['iv'] > 0.02]['feature'].tolist() X_selected = X[selected_features] # 因子分析降维 kmo_all, kmo_model = calculate_kmo(X_selected) if kmo_model > 0.6: fa = FactorAnalyzer(n_factors=5, rotation='varimax') fa.fit(X_selected) factor_scores = fa.transform(X_selected) factor_cols = [f'Factor_{i}' for i in range(1,6)] X_factors = pd.DataFrame(factor_scores, columns=factor_cols) else: X_factors = X_selected.copy() return X_factors, y # ====================== # 4. XGBoost特征筛选 # ====================== def xgb_feature_importance(X, y): # 训练XGBoost模型 model = XGBClassifier( objective='multi:softmax', eval_metric='mlogloss', use_label_encoder=False ) model.fit(X, y) # 获取特征重要性 importance = pd.DataFrame({ 'feature': X.columns, 'importance': model.feature_importances_ }).sort_values('importance', ascending=False) top10_features = importance.head(10)['feature'].tolist() return X[top10_features] # ====================== # 5. LightGBM建模预测 # ====================== def lgb_modeling(X, y): # 数据划分 X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, stratify=y, random_state=42 ) # 模型训练 model = lgb.LGBMClassifier( num_leaves=31, max_depth=5, learning_rate=0.1, n_estimators=300, class_weight='balanced' ) model.fit(X_train, y_train) # 模型评估 y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) return model # ====================== # 主流程执行 # ====================== if name == "main": # 数据路径 DATA_PATH = "user_data.csv" # 执行流程 df = load_and_clean(DATA_PATH) df = feature_engineering(df) X, y = feature_selection(df) X_top10 = xgb_feature_importance(X, y) final_model = lgb_modeling(X_top10, y) # 模型保存 final_model.booster_.save_model('user_value_model.txt')

嗯，用户想优化他们的机器学习代码，里面用到了Pandas、NumPy、Scikit-learn、XGBoost和LightGBM。首先，我需要回忆一下这些库的常见优化方法。比如，Pandas的内存优化，可能涉及到数据类型转换，比如把category类型...

"""用户价值分层预测全流程示例""" import pandas as pd import numpy as np from sklearn.preprocessing import OrdinalEncoder, StandardScaler from sklearn.impute import SimpleImputer from sklearn.model_selection import train_test_split from factor_analyzer import FactorAnalyzer from factor_analyzer.factor_analyzer import calculate_kmo from xgboost import XGBClassifier import lightgbm as lgb from sklearn.metrics import classification_report # ====================== # 1. 数据读取与清洗 # ====================== def load_and_clean(data_path): # 读取数据 df = pd.read_csv(data_path) # 缺失值处理 num_imputer = SimpleImputer(strategy='median') cat_imputer = SimpleImputer(strategy='most_frequent') # 数值型字段 numeric_cols = ['付费金额', '活跃时长', '广告收入', '留存'] df[numeric_cols] = num_imputer.fit_transform(df[numeric_cols]) # 分类型字段 categorical_cols = ['设备价值档位', '用户初始广告档位'] df[categorical_cols] = cat_imputer.fit_transform(df[categorical_cols]) # 异常值处理 df['活跃时长'] = np.where(df['活跃时长'] > 24, 24, df['活跃时长']) df['付费金额'] = np.where( df['付费金额'] > df['付费金额'].quantile(0.99), df['付费金额'].quantile(0.95), df['付费金额'] ) return df # ====================== # 2. 特征工程 # ====================== def feature_engineering(df): # 构造复合特征 df['ARPU密度'] = df['付费金额'] / (df['活跃天数'] + 1) df['广告展示率'] = df['广告曝光次数'] / df['短剧观看次数'] df['内容互动指数'] = np.log1p(df['收藏数'] * 1 + df['分享数'] * 2 + df['评论数'] * 3) # 分类变量编码 encoder = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1) cat_cols = ['设备价值档位', '用户初始广告档位'] df[cat_cols] = encoder.fit_transform(df[cat_cols]) return df # ====================== # 3. 特征筛选与降维 # ====================== def feature_selection(df, target_col='付费意愿档位'): # 划分特征和目标 X = df.drop(columns=[target_col]) y = df[target_col] # 计算IV值筛选 from sklearn.feature_selection import mutual_info_classif iv_values = mutual_info_classif(X, y) iv_df = pd.DataFrame({'feature': X.columns, 'iv': iv_values}) selected_features = iv_df[iv_df['iv'] > 0.02]['feature'].tolist() X_selected = X[selected_features] # 因子分析降维 kmo_all, kmo_model = calculate_kmo(X_selected) if kmo_model > 0.6: fa = FactorAnalyzer(n_factors=5, rotation='varimax') fa.fit(X_selected) factor_scores = fa.transform(X_selected) factor_cols = [f'Factor_{i}' for i in range(1, 6)] X_factors = pd.DataFrame(factor_scores, columns=factor_cols) else: X_factors = X_selected.copy() return X_factors, y # ====================== # 4. XGBoost特征筛选 # ====================== def xgb_feature_importance(X, y): # 训练XGBoost模型 model = XGBClassifier( objective='multi:softmax', eval_metric='mlogloss', use_label_encoder=False ) model.fit(X, y) # 获取特征重要性 importance = pd.DataFrame({ 'feature': X.columns, 'importance': model.feature_importances_ }).sort_values('importance', ascending=False) top10_features = importance.head(10)['feature'].tolist() return X[top10_features] # ====================== # 5. LightGBM建模预测 # ====================== def lgb_modeling(X, y): # 数据划分 X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, stratify=y, random_state=42 ) # 模型训练 model = lgb.LGBMClassifier( num_leaves=31, max_depth=5, learning_rate=0.1, n_estimators=300, class_weight='balanced' ) model.fit(X_train, y_train) # 模型评估 y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) return model # ====================== # 主流程执行 # ====================== if name == "main": # 数据路径 DATA_PATH = "new.csv" # 执行流程 df = load_and_clean(DATA_PATH) df = feature_engineering(df) X, y = feature_selection(df) X_top10 = xgb_feature_importance(X, y) final_model = lgb_modeling(X_top10, y) # 模型保存 final_model.booster_.save_model('user_value_model.txt')

df = pd.read_csv(data_path) # 处理缺失值 df['last_purchase_days'] = df['last_purchase_days'].fillna(365*3) # 填充长期未购买用户 df = df.dropna(subset=['total_spend']) # 删除关键字段缺失记录 #...

执行了以下代码： import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score, classification_report, confusion_matrix from pgmpy.estimators import TreeSearch from pgmpy.models import BayesianNetwork from pgmpy.estimators import BayesianEstimator # ====================================== # 1. 数据加载与预处理 # ====================================== file_paths = ['H.csv', 'L.csv', 'LL.csv'] labels = ["H", "L", "LL"] dfs = [] for file, label in zip(file_paths, labels): df = pd.read_csv(file, header=None, names=['Fs', 'Fa']) df['Label'] = label dfs.append(df) data = pd.concat(dfs, ignore_index=True) X = data[['Fs', 'Fa']] y = data['Label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # ====================================== # 2. 训练朴素贝叶斯分类器 # ==================================

model.fit(data=pd.DataFrame(X_train, columns=data.feature_names), estimator=MaximumLikelihoodEstimator, state_names={col: data.target_names for col in model.nodes()}) ### 三、关键优化策略 1. **...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from sklearn.preprocessing import StandardScaler from torch.utils.data import Dataset, DataLoader # 加载数据集 data = pd.read_csv('pfyh.csv') df = pd.DataFrame(data) dataset = df.iloc[:, 2:].to_numpy() df.head() # 可视化数据 # 简单数据可视化 plt.plot(df.iloc[:, 2]) plt.title("Data Visualization") plt.show() # 提取特征和标签 X = np.array(dataset[:, :-1]) y = np.array(dataset[:, -1]) # 数据标准化和归一化 scaler = StandardScaler() X = scaler.fit_transform(X) y = y / 1000 # 划分训练集和测试集（90%训练，10%测试） split_index = int(len(X) * 0.9) X_train, X_test = X[:split_index], X[split_index:] y_train, y_test = y[:split_index], y[split_index:] # 自定义PyTorch数据集类 class TimeSeriesDataset(Dataset): def init(self, x, y, sequence_length): self.x = x self.y = y self.sequence_length = sequence_length def len(self): return len(self.x) - self.sequence_length def getitem(self, idx): return ( torch.tensor(self.x[idx:idx + self.sequence_length], dtype=torch.float), torch.tensor(self.y[idx + self.sequence_length], dtype=torch.float) ) # 创建数据集和数据加载器 sequence_length = 14 train_dataset = TimeSeriesDataset(X_train, y_train, sequence_length) train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) test_dataset = TimeSeriesDataset(X_test, y_test, sequence_length) test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) # 定义LSTM模型 class LSTMModel(nn.Module): def init(self, input_size, hidden_size, num_layers, output_size): super(LSTMModel, self).init() self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) self.init_weights() def forward(self, x): out, _ = self.lstm(x) out = self.fc(out[:, -1, :]) return out def init_weights(self): torch.manual_seed(42)

嗯，用户的问题是关于如何优化基于PyTorch的LSTM时间序列预测模型的代码，包括数据预处理、自定义Dataset类以及模型结构设计的最佳实践。首先，我需要回忆一下LSTM模型在时间序列预测中的常见优化方法。数据预处理...

import os import numpy as np import pandas as pd from PIL import Image from torch.optim.lr_scheduler import StepLR from sklearn.metrics import r2_score from torch.utils.data import Dataset, DataLoader from torchvision import transforms import torch import torch.nn as nn import random import torch.optim as optim def RMSE_funnc(actual, predicted): #actual = actual.detach().numpy() predicted = predicted diff = np.subtract(actual, predicted) square = np.square(diff) MSE = square.mean() RMSE = np.sqrt(MSE) return RMSE def seed_torch(seed=16): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) #torch.cuda.manual_seed_all(seed) # if you are using multi-GPU. torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True #torch.backends.cudnn.enabled = False seed_torch() # 数据路径 train_folder = r"E:\xyx\GASF_images\PG\train" pre_folder = r"E:\xyx\GASF_images\PG\pre" val_folder = r"E:\xyx\GASF_images\PG\val" train_excel = r"E:\xyx\随机数据集\PG\798\train_data_PG.csv" pre_excel = r"E:\xyx\随机数据集\PG\798\test_data_PG.csv" val_excel = r"E:\xyx\随机数据集\PG\798\val_data_PG.csv" class FruitSugarDataset(Dataset): def init(self, image_folder, excel_file, transform=None): self.data = pd.read_csv(excel_file, header=None) self.image_folder = image_folder # self.transform = transform def len(self): return len(self.data) def getitem(self, idx): row = self.data.iloc[idx] img_name = f"{int(row[0])}.png" # 第一列是图像文件名 label = row.iloc[-1] # 标签为最后一列 img_path = os.path.join(self.image_folder, img_name) if not os.path.exists(img_path): raise FileNotFoundError(f"Image file {img_name} not found in {self.image_folder}") # img = Image.open(img_path).convert("RGB") # if self.transform: # img = self.transform(im

labels_df = pd.read_csv("labels.csv") # 假设包含"image_path"和"target_value"列 #### 2. 继承Dataset类 python from torch.utils.data import Dataset from PIL import Image class CustomImageDataset...

import numpy as np import pandas as pd import matplotlib. pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from linear_model_03.closed_form_sol.LinearRegression_CFSol import LinearRegressionClosedFormSol # 数据2，读取数据，先进行数据转换 df = pd.read_table('../data/mpg.data', header=0) # data换成txt,即可转化txt格式 df.to_csv('../data/mpg.csv', index=False) mpg = pd. read_csv("../data/mpg.csv"). dropna() # ..表示上两级目录 X, y = np. asarray(mpg. loc[:, "horsepower"]), np. asarray(mpg. loc[:, "displacement"]) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, shuffle=True) lr_cfs = LinearRegressionClosedFormSol(fit_intercept=True, normalized=True) # 默认训练偏置项和进行标准化 lr_cfs.fit(X_train, y_train) # 训练模型 theta = lr_cfs.get_params() print(theta) y_test_pred = lr_cfs.predict(x_test=X_test) mse, r2, r2c = lr_cfs.cal_mse_r2(y_test, y_test_pred) print("均方误差：%.5f,判决系数：%.5f,修正判决系数：%.5f" % (mse, r2, r2c)) # lr_cfs. plt_predict(y_test, y_test_pred, is_sort=False) lr_cfs.plt_predict(y_test, y_test_pred, is_sort=True) plt. figure(figsize=(7,5)) plt. plot(X_test, y_test, "ro", label="Test Samples") plt. plot(X_test, y_test_pred, "k-", lw=1.5, label="Fit Model") plt. legend(frameon=False) plt. show()

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from linear_model_03.closed_form_...

import os os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" import torch import numpy as np import pandas as pd import torch.nn as nn from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler from sklearn.metrics import accuracy_score, \ classification_report # 1. 数据准备 # 载入数据 data_train = pd.read_csv('train.csv') data_test = pd.read_csv('test.csv') # 只取前500条训练样本和前50条测试样本，为了快速调试 X_train = data_train.text[0:500] y_train = data_train.label[0:500] X_test = data_test.text[0:50] y_test = data_test.label[0:50] # 动态获取类别信息，统计类别数量和每个类别的样本数。 NUM_CLASSES = len(np.unique(y_train)) class_counts = dict(zip(*np.unique(y_train, return_counts=True))) print(f"训练样本数: {len(X_train)}") print(f"测试样本数: {len(X_test)}") print(f"检测到{NUM_CLASSES}个类别:

df = pd.read_csv("text_data.csv") # 划分训练集和测试集 train_df, test_df = train_test_split(df, test_size=0.2, stratify=df['label']) # 统计训练集类别分布 train_counts = train_df['label'].value_...

import tkinter as tk from tkinter import ttk, filedialog, messagebox, scrolledtext import pandas as pd import numpy as np import os import re class ExcelProcessor: def init(self, root): self.root = root self.root.title("Excel数据处理器") self.root.geometry("900x600") self.df = None self.create_widgets() def create_widgets(self): # 顶部控制区域 control_frame = ttk.Frame(self.root, padding=10) control_frame.pack(fill=tk.X) # 文件选择按钮 ttk.Button(control_frame, text="选择Excel文件", command=self.load_excel).grid(row=0, column=0, padx=5) # 处理方式选择 self.process_var = tk.StringVar(value="点击选择") ttk.Label(control_frame, text="处理方式:").grid(row=0, column=1, padx=5) process_combo = ttk.Combobox(control_frame, textvariable=self.process_var, width=15) process_combo['values'] = ( '统计', '预处理','数据排序', '数据切分','保存结果') process_combo.grid(row=0, column=2, padx=5) # 执行按钮 ttk.Button(control_frame, text="执行", command=self.process_data).grid(row=0, column=3, padx=5) # 数据展示区域 self.notebook = ttk.Notebook(self.root) self.notebook.pack(fill=tk.BOTH, expand=True, padx=10, pady=10) # 预览标签页 self.preview_frame = ttk.Frame(self.notebook) self.notebook.add(self.preview_frame, text="数据展示") # 统计标签页 self.stats_frame = ttk.Frame(self.notebook) self.notebook.add(self.stats_frame, text="统计信息") # 状态栏 self.status_var = tk.StringVar(value="就绪") ttk.Label(self.root, textvariable=self.status_var, relief=tk.SUNKEN, anchor=tk.W).pack(fill=tk.X, side=tk.BOTTOM) def load_excel(self): """加载Excel文件""" file_path = filedialog.askopenfilename( title="选择Excel文件", filetypes=[("Excel文件", ".xlsx .xls"), ("所有文件", ".")] ) if not file_path: return try: self.status_var.set(f"正在加载: {os.path.basename(file_path)}...") self.root.update() # 更新界面显示状态 # 读取Excel文件 self.df = pd.read_excel(file_path) # 显示预览数据 self.show_preview() self.status_var.set( f"已加载: {os.path.basename(file_path)} | 行数: {len(self.df)} | 列数: {len(self.df.columns)}") except Exception as e: messagebox.showerror("加载错误", f"无法读取Excel文件:\n{str(e)}") self.status_var.set("加载失败") def preprocess_data(self): """数据预处理对话框 - 整合全部预处理功能""" if self.df is None: messagebox.showwarning("警告", "请先选择Excel文件") return preprocess_window = tk.Toplevel(self.root) preprocess_window.title("数据预处理") preprocess_window.geometry("650x800") main_frame = ttk.Frame(preprocess_window) main_frame.pack(fill=tk.BOTH, expand=True, padx=10, pady=10) # ================ 新增：功能启用复选框 ================ enable_frame = ttk.LabelFrame(main_frame, text="启用功能", padding=10) enable_frame.pack(fill=tk.X, pady=5) # 创建启用变量 self.enable_missing = tk.BooleanVar(value=True) self.enable_outlier = tk.BooleanVar(value=True) self.enable_datetime = tk.BooleanVar(value=True) self.enable_lag = tk.BooleanVar(value=True) ttk.Checkbutton(enable_frame, text="执行缺失值处理", variable=self.enable_missing).pack(anchor=tk.W) ttk.Checkbutton(enable_frame, text="执行异常值处理", variable=self.enable_outlier).pack(anchor=tk.W) ttk.Checkbutton(enable_frame, text="执行时间列转换", variable=self.enable_datetime).pack(anchor=tk.W) ttk.Checkbutton(enable_frame, text="添加滞后特征", variable=self.enable_lag).pack(anchor=tk.W) # ================================================= # 1. 缺失值处理部分 missing_frame = ttk.LabelFrame(main_frame, text="缺失值处理", padding=10) missing_frame.pack(fill=tk.X, pady=5) # 缺失值统计显示 missing_stats = self.df.isnull().sum() missing_text = scrolledtext.ScrolledText(missing_frame, height=4) missing_text.pack(fill=tk.X) for col, count in missing_stats.items(): if count > 0: missing_text.insert(tk.END, f"{col}: {count}个缺失值\n") missing_text.config(state=tk.DISABLED) # 缺失值处理方法选择 ttk.Label(missing_frame, text="处理方法:").pack(anchor=tk.W) missing_method_var = tk.StringVar(value="fill") missing_method_frame = ttk.Frame(missing_frame) missing_method_frame.pack(fill=tk.X, pady=5) ttk.Radiobutton(missing_method_frame, text="删除缺失行", variable=missing_method_var, value="drop").pack( side=tk.LEFT) ttk.Radiobutton(missing_method_frame, text="固定值填充", variable=missing_method_var, value="fill").pack( side=tk.LEFT) ttk.Radiobutton(missing_method_frame, text="插值法", variable=missing_method_var, value="interpolate").pack( side=tk.LEFT) # 填充选项 fill_options_frame = ttk.Frame(missing_frame) fill_options_frame.pack(fill=tk.X, pady=5) ttk.Label(fill_options_frame, text="填充值:").pack(side=tk.LEFT) fill_value_entry = ttk.Entry(fill_options_frame, width=10) fill_value_entry.pack(side=tk.LEFT, padx=5) fill_value_entry.insert(0, "0") ttk.Label(fill_options_frame, text="或选择:").pack(side=tk.LEFT, padx=5) fill_type_var = tk.StringVar(value="fixed") ttk.Radiobutton(fill_options_frame, text="前值填充", variable=fill_type_var, value="ffill").pack(side=tk.LEFT) ttk.Radiobutton(fill_options_frame, text="后值填充", variable=fill_type_var, value="bfill").pack(side=tk.LEFT) ttk.Radiobutton(fill_options_frame, text="均值填充", variable=fill_type_var, value="mean").pack(side=tk.LEFT) # 2. 异常值处理部分 outlier_frame = ttk.LabelFrame(main_frame, text="异常值处理", padding=10) outlier_frame.pack(fill=tk.X, pady=5) # 异常值检测方法 ttk.Label(outlier_frame, text="检测方法:").pack(anchor=tk.W) outlier_method_var = tk.StringVar(value="3sigma") outlier_method_frame = ttk.Frame(outlier_frame) outlier_method_frame.pack(fill=tk.X) ttk.Radiobutton(outlier_method_frame, text="3σ原则", variable=outlier_method_var, value="3sigma").pack( side=tk.LEFT) ttk.Radiobutton(outlier_method_frame, text="IQR方法", variable=outlier_method_var, value="iqr").pack( side=tk.LEFT) # 异常值处理方式 ttk.Label(outlier_frame, text="处理方式:").pack(anchor=tk.W) outlier_action_var = tk.StringVar(value="remove") outlier_action_frame = ttk.Frame(outlier_frame) outlier_action_frame.pack(fill=tk.X) ttk.Radiobutton(outlier_action_frame, text="删除", variable=outlier_action_var, value="remove").pack( side=tk.LEFT) ttk.Radiobutton(outlier_action_frame, text="用中位数替换", variable=outlier_action_var, value="median").pack( side=tk.LEFT) ttk.Radiobutton(outlier_action_frame, text="用前后均值替换", variable=outlier_action_var, value="neighbor").pack(side=tk.LEFT) # 3. 数据类型转换部分 type_frame = ttk.LabelFrame(main_frame, text="数据类型转换", padding=10) type_frame.pack(fill=tk.X, pady=5) # 时间列转换 ttk.Label(type_frame, text="时间列转换:").pack(anchor=tk.W) time_col_var = tk.StringVar() time_col_combo = ttk.Combobox(type_frame, textvariable=time_col_var, width=20) time_col_combo['values'] = tuple(self.df.columns) time_col_combo.pack(anchor=tk.W, pady=5) # === 新增：时间单位选择 === time_units_frame = ttk.Frame(type_frame) time_units_frame.pack(fill=tk.X, pady=5) ttk.Label(time_units_frame, text="提取时间单位:").pack(side=tk.LEFT) # 创建时间单位变量 self.extract_year = tk.BooleanVar(value=True) self.extract_month = tk.BooleanVar(value=True) self.extract_day = tk.BooleanVar(value=True) self.extract_hour = tk.BooleanVar(value=False) self.extract_minute = tk.BooleanVar(value=False) self.extract_second = tk.BooleanVar(value=False) # 添加复选框 ttk.Checkbutton(time_units_frame, text="年", variable=self.extract_year).pack(side=tk.LEFT, padx=5) ttk.Checkbutton(time_units_frame, text="月", variable=self.extract_month).pack(side=tk.LEFT, padx=5) ttk.Checkbutton(time_units_frame, text="日", variable=self.extract_day).pack(side=tk.LEFT, padx=5) ttk.Checkbutton(time_units_frame, text="时", variable=self.extract_hour).pack(side=tk.LEFT, padx=5) ttk.Checkbutton(time_units_frame, text="分", variable=self.extract_minute).pack(side=tk.LEFT, padx=5) ttk.Checkbutton(time_units_frame, text="秒", variable=self.extract_second).pack(side=tk.LEFT, padx=5) # === 修改时间转换逻辑 === if self.enable_datetime.get(): time_col = time_col_var.get() if time_col and time_col in self.df.columns: try: # 统一处理不同日期格式 self.df[time_col] = self.df[time_col].apply( lambda x: pd.to_datetime(x, errors='coerce', format='mixed') ) # 强制显示完整时间格式 pd.set_option('display.datetime_format', '%Y-%m-%d %H:%M:%S') # 根据用户选择提取时间单位 if self.extract_year.get(): self.df['year'] = self.df[time_col].dt.year if self.extract_month.get(): self.df['month'] = self.df[time_col].dt.month if self.extract_day.get(): self.df['day'] = self.df[time_col].dt.day if self.extract_hour.get(): self.df['hour'] = self.df[time_col].dt.hour if self.extract_minute.get(): self.df['minute'] = self.df[time_col].dt.minute if self.extract_second.get(): self.df['second'] = self.df[time_col].dt.second # 新增：确保时间部分显示 self.df['full_datetime'] = self.df[time_col].dt.strftime('%Y-%m-%d %H:%M:%S') # 时间周期特征 if self.extract_hour.get() or self.extract_minute.get(): self.df['time_of_day'] = self.df[time_col].dt.hour + self.df[time_col].dt.minute / 60.0 if self.extract_second.get(): self.df['time_of_day'] += self.df[time_col].dt.second / 3600.0 except Exception as e: messagebox.showerror("时间转换错误", f"时间列转换失败: {str(e)}") # 4. 特征工程部分 feature_frame = ttk.LabelFrame(main_frame, text="特征工程", padding=10) feature_frame.pack(fill=tk.X, pady=5) # 添加滞后特征 ttk.Label(feature_frame, text="滞后特征:").pack(anchor=tk.W) lag_frame = ttk.Frame(feature_frame) lag_frame.pack(fill=tk.X) ttk.Label(lag_frame, text="选择列:").pack(side=tk.LEFT) lag_col_var = tk.StringVar() lag_col_combo = ttk.Combobox(lag_frame, textvariable=lag_col_var, width=15) lag_col_combo['values'] = tuple(self.df.select_dtypes(include=['number']).columns) lag_col_combo.pack(side=tk.LEFT, padx=5) ttk.Label(lag_frame, text="滞后步数:").pack(side=tk.LEFT) lag_steps_entry = ttk.Entry(lag_frame, width=5) lag_steps_entry.pack(side=tk.LEFT) lag_steps_entry.insert(0, "1") # 执行预处理按钮 def apply_preprocessing(): try: original_shape = self.df.shape # 1. 处理缺失值 (如果启用) if self.enable_missing.get(): missing_method = missing_method_var.get() if missing_method == "drop": self.df = self.df.dropna() elif missing_method == "fill": fill_type = fill_type_var.get() if fill_type == "fixed": fill_value = fill_value_entry.get() self.df = self.df.fillna( float(fill_value) if self.df.select_dtypes(include=['number']).shape[ 1] > 0 else fill_value) elif fill_type == "ffill": self.df = self.df.ffill() elif fill_type == "bfill": self.df = self.df.bfill() elif fill_type == "mean": self.df = self.df.fillna(self.df.mean()) elif missing_method == "interpolate": self.df = self.df.interpolate() # 2. 处理异常值 (如果启用) if self.enable_outlier.get(): outlier_method = outlier_method_var.get() outlier_action = outlier_action_var.get() numeric_cols = self.df.select_dtypes(include=['number']).columns for col in numeric_cols: if outlier_method == "3sigma": mean, std = self.df[col].mean(), self.df[col].std() lower, upper = mean - 3 * std, mean + 3 * std else: # iqr q1, q3 = self.df[col].quantile(0.25), self.df[col].quantile(0.75) iqr = q3 - q1 lower, upper = q1 - 1.5 * iqr, q3 + 1.5 * iqr if outlier_action == "remove": self.df = self.df[(self.df[col] >= lower) & (self.df[col] <= upper)] elif outlier_action == "median": self.df.loc[(self.df[col] < lower) | (self.df[col] > upper), col] = self.df[col].median() elif outlier_action == "neighbor": mask = (self.df[col] < lower) | (self.df[col] > upper) self.df.loc[mask, col] = self.df[col].rolling(2, min_periods=1).mean()[mask] # 3. 时间列转换 (如果启用) if self.enable_datetime.get(): time_col = time_col_var.get() if time_col and time_col in self.df.columns: try: self.df[time_col] = pd.to_datetime(self.df[time_col]) self.df['year'] = self.df[time_col].dt.year self.df['month'] = self.df[time_col].dt.month self.df['day'] = self.df[time_col].dt.day except Exception as e: messagebox.showwarning("时间转换警告", f"时间列转换失败: {str(e)}") # 4. 添加滞后特征 (如果启用) if self.enable_lag.get(): lag_col = lag_col_var.get() if lag_col and lag_col in self.df.columns: try: lag_steps = int(lag_steps_entry.get()) self.df[f'{lag_col}_lag{lag_steps}'] = self.df[lag_col].shift(lag_steps) except Exception as e: messagebox.showwarning("滞后特征警告", f"创建滞后特征失败: {str(e)}") # ========================================================= # 更新显示 self.show_preview() preprocess_window.destroy() new_shape = self.df.shape self.status_var.set(f"预处理完成 | 原形状: {original_shape} | 新形状: {new_shape}") except Exception as e: messagebox.showerror("预处理错误", f"预处理过程中发生错误:\n{str(e)}") ttk.Button(main_frame, text="执行预处理", command=apply_preprocessing).pack(pady=10) def show_preview(self): """在表格中分页显示数据预览""" # 清除旧内容 for widget in self.preview_frame.winfo_children(): widget.destroy() # 创建主容器 container = ttk.Frame(self.preview_frame) container.pack(fill=tk.BOTH, expand=True) # 创建表格 columns = list(self.df.columns) self.tree = ttk.Treeview(container, columns=columns, show="headings") # 添加列标题 for col in columns: self.tree.heading(col, text=col) self.tree.column(col, width=100, anchor=tk.W) # 添加滚动条 scrollbar = ttk.Scrollbar(container, orient=tk.VERTICAL, command=self.tree.yview) self.tree.configure(yscroll=scrollbar.set) # 布局表格和滚动条 self.tree.pack(side=tk.LEFT, fill=tk.BOTH, expand=True) scrollbar.pack(side=tk.RIGHT, fill=tk.Y) # 创建分页控制面板 pagination_frame = ttk.Frame(self.preview_frame) pagination_frame.pack(fill=tk.X, pady=5) # 分页参数 self.current_page = 1 self.rows_per_page = 1000 # 每页显示的行数 self.total_pages = max(1, (len(self.df) + self.rows_per_page - 1) // self.rows_per_page) # 分页标签 self.page_label = ttk.Label(pagination_frame, text=f"第 {self.current_page} 页 / 共 {self.total_pages} 页") self.page_label.pack(side=tk.LEFT, padx=10) # 分页按钮 ttk.Button(pagination_frame, text="首页", command=lambda: self.change_page(1)).pack(side=tk.LEFT) ttk.Button(pagination_frame, text="上一页", command=lambda: self.change_page(self.current_page - 1)).pack( side=tk.LEFT) ttk.Button(pagination_frame, text="下一页", command=lambda: self.change_page(self.current_page + 1)).pack( side=tk.LEFT) ttk.Button(pagination_frame, text="末页", command=lambda: self.change_page(self.total_pages)).pack(side=tk.LEFT) # 跳转输入框 ttk.Label(pagination_frame, text="跳转到:").pack(side=tk.LEFT, padx=(10, 0)) self.page_entry = ttk.Entry(pagination_frame, width=5) self.page_entry.pack(side=tk.LEFT) ttk.Button(pagination_frame, text="跳转", command=self.jump_to_page).pack(side=tk.LEFT, padx=(5, 10)) # 显示第一页数据 self.load_page_data() def load_page_data(self): """加载当前页的数据""" # 清空现有数据 for item in self.tree.get_children(): self.tree.delete(item) # 计算起始和结束索引 start_idx = (self.current_page - 1) * self.rows_per_page end_idx = start_idx + self.rows_per_page # 添加当前页的数据行 for i, row in self.df.iloc[start_idx:end_idx].iterrows(): self.tree.insert("", tk.END, values=list(row)) # 更新分页标签 self.page_label.config(text=f"第 {self.current_page} 页 / 共 {self.total_pages} 页") self.page_entry.delete(0, tk.END) self.page_entry.insert(0, str(self.current_page)) def change_page(self, new_page): """切换页面""" # 确保新页码在有效范围内 new_page = max(1, min(new_page, self.total_pages)) if new_page != self.current_page: self.current_page = new_page self.load_page_data() def jump_to_page(self): """跳转到指定页码""" try: page_num = int(self.page_entry.get()) self.change_page(page_num) except ValueError: messagebox.showerror("错误", "请输入有效的页码数字") def process_data(self): """根据选择的处理方式处理数据""" if self.df is None: messagebox.showwarning("警告", "请先选择Excel文件") return process_type = self.process_var.get() if process_type == "统计": self.show_statistics() self.notebook.select(1) elif process_type == "数据排序": self.sort_data() elif process_type == "保存结果": self.save_data() elif process_type == "预处理": self.preprocess_data() elif process_type == "数据切分": self.divide_data() def show_statistics(self): """显示数据统计信息""" # 清除旧内容 for widget in self.stats_frame.winfo_children(): widget.destroy() # 计算统计信息 stats = self.df.describe(include='all').fillna('-') # 创建表格显示统计信息 columns = ['统计项'] + list(stats.columns) tree = ttk.Treeview(self.stats_frame, columns=columns, show="headings") # 添加列标题 for col in columns: tree.heading(col, text=col) tree.column(col, width=100, anchor=tk.W) # 添加数据行 for index, row in stats.iterrows(): tree.insert("", tk.END, values=[index] + list(row)) # 添加滚动条 scrollbar = ttk.Scrollbar(self.stats_frame, orient=tk.VERTICAL, command=tree.yview) tree.configure(yscroll=scrollbar.set) # 布局 tree.pack(side=tk.LEFT, fill=tk.BOTH, expand=True) scrollbar.pack(side=tk.RIGHT, fill=tk.Y) # 添加数据类型信息 type_frame = ttk.LabelFrame(self.stats_frame, text="数据类型") type_frame.pack(fill=tk.X, padx=5, pady=5) type_text = scrolledtext.ScrolledText(type_frame, height=5) type_text.pack(fill=tk.BOTH, expand=True, padx=5, pady=5) dtypes = self.df.dtypes.apply(lambda x: x.name).to_dict() type_info = "\n".join([f"{col}: {dtype}" for col, dtype in dtypes.items()]) type_text.insert(tk.END, type_info) type_text.config(state=tk.DISABLED) def sort_data(self): """数据排序对话框""" sort_window = tk.Toplevel(self.root) sort_window.title("数据排序") sort_window.geometry("400x300") ttk.Label(sort_window, text="选择排序列:").pack(pady=10) # 列选择 col_var = tk.StringVar() col_combo = ttk.Combobox(sort_window, textvariable=col_var, width=20) col_combo['values'] = tuple(self.df.columns) col_combo.pack(pady=5) # 排序方式 ttk.Label(sort_window, text="排序方式:").pack(pady=10) order_var = tk.StringVar(value="ascending") ttk.Radiobutton(sort_window, text="升序", variable=order_var, value="ascending").pack() ttk.Radiobutton(sort_window, text="降序", variable=order_var, value="descending").pack() def apply_sort(): if not col_var.get(): messagebox.showwarning("警告", "请选择排序列") return try: ascending = (order_var.get() == "ascending") self.df = self.df.sort_values(by=col_var.get(), ascending=ascending) self.show_preview() sort_window.destroy() self.status_var.set(f"数据已按 {col_var.get()} {'升序' if ascending else '降序'} 排序") except Exception as e: messagebox.showerror("排序错误", f"排序失败:\n{str(e)}") ttk.Button(sort_window, text="应用排序", command=apply_sort).pack(pady=20) def save_data(self): """保存处理结果""" if self.df is None or self.df.empty: messagebox.showwarning("警告", "没有可保存的数据") return save_path = filedialog.asksaveasfilename( defaultextension=".xlsx", filetypes=[("Excel文件", ".xlsx"), ("CSV文件", ".csv")] ) if not save_path: return try: if save_path.endswith('.xlsx'): self.df.to_excel(save_path, index=False) else: self.df.to_csv(save_path, index=False) self.status_var.set(f"文件已保存至: {os.path.basename(save_path)}") messagebox.showinfo("保存成功", f"文件已成功保存至:\n{save_path}") except Exception as e: messagebox.showerror("保存错误", f"保存文件失败:\n{str(e)}") # 创建并运行程序 if name == "main": root = tk.Tk() app = ExcelProcessor(root) root.mainloop() 请你用相同的风格也一个excel处理器类的一个方法：可以对数据进行筛选

我们将添加一个名为filter_data的方法，用于数据筛选。该方法将创建一个对话框，允许用户选择筛选条件。筛选条件包括：选择列、选择运算符（如等于、大于、包含等）、输入值。同时，我们将允许用户添加多个...

修改如下代码使其能够运行。import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn.preprocessing import MinMaxScaler import cv2 import open3d as o3d from skimage import color import colour from scipy.spatial import ConvexHull import pyntcloud def convert_data(data): res=[] data=data.tolist() for d in data: res.append(tuple(d)) # print(res) return res def load_data_and_plot_scatter(path1="1号屏srgb+rgb16预热10分钟切换0.5s.csv"): df1 = pd.read_csv(path1)[["X", "Y", "Z", "R", "G", "B"]] X1 = df1["X"].values Y1 = df1["Y"].values Z1 = df1["Z"].values df1_c = df1[["R", "G", "B"]].values / 255.0 XYZT1 = np.array([X1,Y1,Z1]) XYZ1 = np.transpose(XYZT1) LAB1 = colour.XYZ_to_Lab(XYZ1) cloud = pyntcloud.PyntCloud(LAB1) cloud.estimate_normals() surface = cloud.extract_surface() ax.scatter(surface[:,1], surface[:,2], surface[:,0], c=df1_c) # 创建一个 3D 坐标 fig = plt.figure() # ax = Axes3D(fig) ax = plt.axes(projection='3d') ax.scatter(LAB1[:,1], LAB1[:,2], LAB1[:,0], c=df1_c) # # 设置坐标轴标签 ax.set_xlabel('a* Label') ax.set_ylabel('b* Label') ax.set_zlabel('L Label') # 显示图形 plt.show() if name == "main": load_data_and_plot_scatter()

import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn.preprocessing import MinMaxScaler import cv2 import open3d as o3d from ...

帮我检查代码：import pandas as pd import numpy as np import lightgbm as lgb from lightgbm import early_stopping, log_evaluation from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score import chardet def detect_encoding(file_path): with open(file_path, 'rb') as f: result = chardet.detect(f.read(10000)) return result['encoding'], result['confidence'] def load_all_data(days=32): see_list, click_list, play_list = [], [], [] dtypes = {'did': 'category', 'vid': 'category'} for i in range(1, days + 1): day = f"{i:02d}" # 加载 see 数据 see = pd.read_csv(f'see_{day}.csv', encoding='latin1', dtype=dtypes) if 'did' not in see.columns or 'vid' not in see.columns: raise ValueError(f"see_{day}.csv 缺少必要字段") see['day'] = day see_list.append(see) # 加载 click 数据 click = pd.read_csv( f'click_{day}.csv', encoding='ISO-8859-1', on_bad_lines='skip', dtype=dtypes ) if 'click_time' not in click.columns: raise ValueError(f"click_{day}.csv 缺少 click_time 字段") click['date'] = pd.to_datetime(click['click_time']).dt.date click_list.append(click[['did', 'vid', 'date']]) # 加载 play 数据 play = pd.read_csv( f'playplus_{day}.csv', engine='python', encoding_errors='ignore', dtype=dtypes ) if 'play_time' not in play.columns: raise ValueError(f"playplus_{day}.csv 缺少 play_time 字段") play_list.append(play[['did', 'vid', 'play_time']]) all_see = pd.concat(see_list).drop_duplicates(['did', 'vid']) all_click = pd.concat(click_list).drop_duplicates(['did', 'vid']) all_play = pd.concat(play_list).groupby(['did', 'vid'], observed=True).sum().reset_index() return all_see, all_click, all_play def prepare_samples(all_see, all_click, all_play): video_info = pd.read_csv('vid_info_table.csv', encoding='gbk', dtype={'vid': 'category'}) # 合并基础数据 samples = all_see.merge(all_play, on=['did', 'vid'], how='left').fillna({'play_time': 0}) samples = samples.merge(video_info, on='vid', how='left') # 计算完成率（仅用于分析，不用于预测） samples['completion_rate'] = (samples['play_time'] / samples['item_duration']).clip(0, 1).astype(np.float32) # 点击标记 click_flag = all_click.groupby(['did', 'vid']).size().reset_index(name='clicked') click_flag['clicked'] = 1 samples = samples.merge(click_flag, on=['did', 'vid'], how='left').fillna({'clicked': 0}) samples['clicked'] = samples['clicked'].astype(np.int8) # 标签定义 samples['label'] = np.select( [ (samples['completion_rate'] > 0.9), (samples['clicked'] == 1) ], [2, 1], # 2=完成, 1=点击 default=0 # 0=曝光未点击 ) # 二分类目标（点击或完成为正类） samples['binary_label'] = samples['label'].apply(lambda x: 1 if x >= 1 else 0).astype(int) # 计算用户点击率（修正版） user_exposure = all_see.groupby('did').size().rename('exposure_count') user_click_count = all_click.groupby('did').size().rename('click_count') user_click_rate = (user_click_count / user_exposure).fillna(0).astype(np.float32) # 视频流行度 video_popularity = all_click.groupby('vid').size().rename('video_popularity') # 映射特征 samples['user_click_rate'] = samples['did'].map(user_click_rate).fillna(0) samples['video_popularity'] = samples['vid'].map(video_popularity).fillna(0) # 修复：保存唯一用户点击率（关键修复点） user_click_rate_df = pd.DataFrame({ 'did': user_click_rate.index, 'user_click_rate': user_click_rate.values }).drop_duplicates('did') # 修复：保存唯一视频流行度 video_popularity_df = pd.DataFrame({ 'vid': video_popularity.index, 'video_popularity': video_popularity.values }).drop_duplicates('vid') # 保存特征 user_click_rate_df.to_csv('user_click_rate.csv', index=False) video_popularity_df.to_csv('video_popularity.csv', index=False) return samples, user_click_rate, video_popularity def train_model(samples): # 仅使用可复现的特征 features = ['user_click_rate', 'video_popularity'] X = samples[features] y = samples['binary_label'] X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) lgb_train = lgb.Dataset(X_train, y_train) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) params = { 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'num_leaves': 31, 'learning_rate': 0.05, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': -1 } model = lgb.train( params, lgb_train, num_boost_round=100, valid_sets=[lgb_train, lgb_eval], callbacks=[ early_stopping(stopping_rounds=20), log_evaluation(period=50) ] ) y_pred = model.predict(X_test) auc_score = roc_auc_score(y_test, y_pred) print(f"Validation AUC: {auc_score:.4f}") return model, features, auc_score def predict_new_data(model, feature_columns, test_file): # 读取测试数据 test_data = pd.read_csv(test_file, dtype={'did': 'category', 'vid': 'category'}) # 修复：正确读取特征映射 user_click_rate_df = pd.read_csv('user_click_rate.csv') video_popularity_df = pd.read_csv('video_popularity.csv') # 计算全局均值用于填充新用户/新视频 global_user_rate = user_click_rate_df['user_click_rate'].mean() global_video_pop = video_popularity_df['video_popularity'].mean() # 创建映射字典 user_click_map = user_click_rate_df.set_index('did')['user_click_rate'].to_dict() video_pop_map = video_popularity_df.set_index('vid')['video_popularity'].to_dict() # 映射特征 test_data['user_click_rate'] = test_data['did'].map(user_click_map).fillna(global_user_rate) test_data['video_popularity'] = test_data['vid'].map(video_pop_map).fillna(global_video_pop) # 预测 test_data['click_prob'] = model.predict(test_data[feature_columns]) # 生成结果 top_predictions = test_data.sort_values('click_prob', ascending=False).groupby('did').head(1) result = top_predictions[['did', 'vid', 'click_prob']].copy() result.columns = ['did', 'vid', 'click_prob'] result.to_csv('prediction_result.csv', index=False) return result if name == 'main': encoding, confidence = detect_encoding('see_01.csv') print(f"编码: {encoding}, 置信度: {confidence:.2f}") all_see, all_click, all_play = load_all_data() samples, _, _ = prepare_samples(all_see, all_click, all_play) model, features, auc_score = train_model(samples) result = predict_new_data(model, features, 'testA_did_show.csv')

另外，为了避免数据泄露，所有用户和视频的历史统计量必须仅使用该样本之前的数据（但在我们的数据中，所有数据都是历史数据，没有时间顺序，所以可以整体计算，但在实际生产环境中需要按时间划分）。由于原代码...

if name == 'main': parser = argparse.ArgumentParser() parser.add_argument('--path', type=str, default=r"data/UCI HAR Dataset/UCI HAR Dataset", help='UCI dataset data path') parser.add_argument('--save', type=str, default='data/UCI_Smartphone_Raw.csv', help='save file name') args = parser.parse_args() data_path = args.path # read train subjects train_subjects = pd.read_csv(os.path.join(data_path, 'train/subject_train.txt'), header=None, names=['subject']) # read test subjects test_subjects = pd.read_csv(os.path.join(data_path, 'test/subject_test.txt'), header=None, names=['subject']) # concat subjects = pd.concat([train_subjects, test_subjects], axis=0) # read train labels train_labels = pd.read_csv(os.path.join(data_path, 'train/y_train.txt'), header=None, names=['label']) # read train labels test_labels = pd.read_csv(os.path.join(data_path, 'test/y_test.txt'), header=None, names=['label']) # labels labels = pd.concat([train_labels, test_labels], axis=0) final_dataframe = pd.concat([subjects, labels], axis=1) data = [] for name in COLUMNS: final_dataframe = pd.concat([final_dataframe, read_txt(name)], axis=1) final_dataframe.to_csv(args.save,index=False) 如何将文中txt文件改成mnist数据集数据，其他不做大修改

import numpy as np import pandas as pd from sklearn.datasets import fetch_openml 2. 加载MNIST数据集： python mnist = fetch_openml('mnist_784', version=1, cache=True) 3. 将图像矩阵和标签...

请帮我检查优化代码，并完整输出结果：import pandas as pd import numpy as np import lightgbm as lgb from lightgbm import early_stopping, log_evaluation from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score import chardet def detect_encoding(file_path): with open(file_path, 'rb') as f: result = chardet.detect(f.read(10000)) return result['encoding'], result['confidence'] def load_all_data(days=32): see_list, click_list, play_list = [], [], [] dtypes = {'did': 'category', 'vid': 'category'} for i in range(1, days + 1): day = f"{i:02d}" # 加载 see 数据 see = pd.read_csv(f'see_{day}.csv', encoding='latin1', dtype=dtypes) if 'did' not in see.columns or 'vid' not in see.columns: raise ValueError(f"see_{day}.csv 缺少必要字段") see['day'] = day see_list.append(see) # 加载 click 数据 click = pd.read_csv( f'click_{day}.csv', encoding='ISO-8859-1', on_bad_lines='skip', dtype=dtypes ) if 'click_time' not in click.columns: raise ValueError(f"click_{day}.csv 缺少 click_time 字段") click['date'] = pd.to_datetime(click['click_time']).dt.date click_list.append(click[['did', 'vid', 'date']]) # 加载 play 数据 play = pd.read_csv( f'playplus_{day}.csv', engine='python', encoding_errors='ignore', dtype=dtypes ) if 'play_time' not in play.columns: raise ValueError(f"playplus_{day}.csv 缺少 play_time 字段") play_list.append(play[['did', 'vid', 'play_time']]) all_see = pd.concat(see_list).drop_duplicates(['did', 'vid']) all_click = pd.concat(click_list).drop_duplicates(['did', 'vid']) all_play = pd.concat(play_list).groupby(['did', 'vid'], observed=True).sum().reset_index() return all_see, all_click, all_play def prepare_samples(all_see, all_click, all_play): video_info = pd.read_csv('vid_info_table.csv', encoding='gbk', dtype={'vid': 'category'}) # 合并基础数据 samples = all_see.merge(all_play, on=['did', 'vid'], how='left').fillna({'play_time': 0}) samples = samples.merge(video_info, on='vid', how='left') # 计算完成率（仅用于分析，不用于预测） samples['completion_rate'] = (samples['play_time'] / samples['item_duration']).clip(0, 1).astype(np.float32) # 点击标记 click_flag = all_click.groupby(['did', 'vid']).size().reset_index(name='clicked') click_flag['clicked'] = 1 samples = samples.merge(click_flag, on=['did', 'vid'], how='left').fillna({'clicked': 0}) samples['clicked'] = samples['clicked'].astype(np.int8) # 标签定义 samples['label'] = np.select( [ (samples['completion_rate'] > 0.9), (samples['clicked'] == 1) ], [2, 1], # 2=完成, 1=点击 default=0 # 0=曝光未点击 ) # 二分类目标（点击或完成为正类） samples['binary_label'] = samples['label'].apply(lambda x: 1 if x >= 1 else 0).astype(int) # 计算用户点击率（修正版） user_exposure = all_see.groupby('did').size().rename('exposure_count') user_click_count = all_click.groupby('did').size().rename('click_count') user_click_rate = (user_click_count / user_exposure).fillna(0).astype(np.float32) # 视频流行度 video_popularity = all_click.groupby('vid').size().rename('video_popularity') # 映射特征 samples['user_click_rate'] = samples['did'].map(user_click_rate).fillna(0) samples['video_popularity'] = samples['vid'].map(video_popularity).fillna(0) # 修复：保存唯一用户点击率（关键修复点） user_click_rate_df = pd.DataFrame({ 'did': user_click_rate.index, 'user_click_rate': user_click_rate.values }).drop_duplicates('did') # 修复：保存唯一视频流行度 video_popularity_df = pd.DataFrame({ 'vid': video_popularity.index, 'video_popularity': video_popularity.values }).drop_duplicates('vid') # 保存特征 user_click_rate_df.to_csv('user_click_rate.csv', index=False) video_popularity_df.to_csv('video_popularity.csv', index=False) return samples, user_click_rate, video_popularity def train_model(samples): # 仅使用可复现的特征 features = ['user_click_rate', 'video_popularity'] X = samples[features] y = samples['binary_label'] X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) lgb_train = lgb.Dataset(X_train, y_train) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) params = { 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'num_leaves': 31, 'learning_rate': 0.05, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': -1 } model = lgb.train( params, lgb_train, num_boost_round=100, valid_sets=[lgb_train, lgb_eval], callbacks=[ early_stopping(stopping_rounds=20), log_evaluation(period=50) ] ) y_pred = model.predict(X_test) auc_score = roc_auc_score(y_test, y_pred) print(f"Validation AUC: {auc_score:.4f}") return model, features, auc_score def predict_new_data(model, feature_columns, test_file): # 读取测试数据 test_data = pd.read_csv(test_file, dtype={'did': 'category', 'vid': 'category'}) # 修复：正确读取特征映射 user_click_rate_df = pd.read_csv('user_click_rate.csv') video_popularity_df = pd.read_csv('video_popularity.csv') # 计算全局均值用于填充新用户/新视频 global_user_rate = user_click_rate_df['user_click_rate'].mean() global_video_pop = video_popularity_df['video_popularity'].mean() # 创建映射字典 user_click_map = user_click_rate_df.set_index('did')['user_click_rate'].to_dict() video_pop_map = video_popularity_df.set_index('vid')['video_popularity'].to_dict() # 映射特征 test_data['user_click_rate'] = test_data['did'].map(user_click_map).fillna(global_user_rate) test_data['video_popularity'] = test_data['vid'].map(video_pop_map).fillna(global_video_pop) # 预测 test_data['click_prob'] = model.predict(test_data[feature_columns]) # 生成结果 top_predictions = test_data.sort_values('click_prob', ascending=False).groupby('did').head(1) result = top_predictions[['did', 'vid', 'click_prob']].copy() result.columns = ['did', 'vid', 'click_prob'] result.to_csv('prediction_result.csv', index=False) return result if name == 'main': encoding, confidence = detect_encoding('see_01.csv') print(f"编码: {encoding}, 置信度: {confidence:.2f}") all_see, all_click, all_play = load_all_data() samples, _, _ = prepare_samples(all_see, all_click, all_play) model, features, auc_score = train_model(samples) result = predict_new_data(model, features, 'testA_did_show.csv')

- 使用pd.read_csv时，指定dtype为类别类型（category）可以减少内存。 - 对于大文件，可以使用chunksize分批读取，但这里我们假设数据量不是特别大，所以直接读取。 - 在加载多个文件时，可以使用列表推导...

新能源车电机控制器：基于TI芯片的FOC算法源代码与实际应用

内容概要：本文详细介绍了基于TI芯片的FOC（场向量控制）算法在新能源车电机控制器中的应用。文章首先阐述了新能源车电机控制器的重要性及其对车辆性能的影响，接着深入探讨了FOC算法的工作原理，强调其在提高电机控制精度和能效方面的优势。随后，文章展示了完整的源代码资料，涵盖采样模块、CAN通信模块等多个关键部分，并指出这些代码不仅限于理论演示，而是来自实际量产的应用程序。此外，文中还特别提到代码遵循严格的规范，有助于读者理解和学习电机控制软件的最佳实践。适合人群：从事新能源车研发的技术人员、电机控制工程师、嵌入式系统开发者以及对电机控制感兴趣的电子工程学生。使用场景及目标：① 学习并掌握基于TI芯片的FOC算法的具体实现；② 理解电机控制器各模块的功能和交互方式；③ 提升实际项目开发能力，减少开发过程中遇到的问题。其他说明：本文提供的源代码资料来源于早期已量产的新能源车控制器，因此具有较高的实用价值和参考意义。

相关推荐

用Pandas作图

python数据分析与可视化常用函数介绍与使用.docx

Python：雷达图的实现 的 air_data

新能源车电机控制器：基于TI芯片的FOC算法源代码与实际应用

大家在看

华为逆变器SUN2000-(33KTL, 40KTL) MODBUS接口定义描述

BCM 56XX SDK 编程手册

Gurobi 生产计划调度学习案例（含代码实现）

FPGA数字信号处理设计教程--system generator 入门与提高随书光盘源码

SPP Workshop.pdf

最新推荐

新能源车电机控制器：基于TI芯片的FOC算法源代码与实际应用

中证500指数成分股历年调整名单2007至2023年 调入调出

基于28335的高精度旋变软解码技术及其应用 - 电机控制

langchain4j-embeddings-bge-small-en-1.0.0-beta5.jar中文文档.zip

苹果内购（IAP）人民币价格明细表

掌握XFireSpring整合技术：HELLOworld原代码使用教程

【Unity2018汉化大揭秘】：一步到位优化中文用户体验

iPhone

驾校一点通软件：提升驾驶证考试通过率

【DFLauncher自动化教程】：简化游戏启动流程，让游戏体验更流畅

Python：雷达图的实现的 air_data

中证500指数成分股历年调整名单2007至2023年调入调出