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请帮我检查并完善代码：#步骤1：数据读取与预处理，如果内存不足，可以考虑分批处理或使用Dask等工具。 import pandas as pd import numpy as np import lightgbm as lgb from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, mean_absolute_error # 读取数据 did_features = pd.read_csv('did_features_table.csv') # 用户特征 vid_info = pd.read_csv('vid_info_table.csv') # 视频信息 # 历史32天曝光数据see_list（用于构建负样本） # 历史32天点击数据click_list（正样本） # 历史32天播放数据play_list（用于训练完播率模型） 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}" # 检查文件是否存在 for file_type in [f'see_{day}.csv', f'click_{day}.csv', f'playplus_{day}.csv']: if not os.path.exists(file_type): print(f"⚠️ 警告: 文件 {file_type} 不存在，跳过该天数据") continue try: # 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: print(f"⚠️ 警告: see_{day}.csv 缺少必要字段，跳过该天数据") continue see['day'] = day see_list.append(see) del see gc.collect() # 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: print(f"⚠️ 警告: click_{day}.csv 缺少 click_time 字段，跳过该天数据") continue click['date'] = pd.to_datetime(click['click_time'], errors='coerce').dt.date click_list.append(click[['did', 'vid', 'date']]) del click gc.collect() # 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: print(f"⚠️ 警告: playplus_{day}.csv 缺少 play_time 字段，跳过该天数据") continue play_list.append(play[['did', 'vid', 'play_time']]) del play gc.collect() except Exception as e: print(f"⚠️ 加载第 {day} 天数据时出错: {str(e)}") continue # 处理所有天都没有数据的情况 if not see_list: raise ValueError("错误: 未找到任何有效数据，请检查输入文件") all_see = pd.concat(see_list).drop_duplicates(['did', 'vid']) all_click = pd.concat(click_list).drop_duplicates(['did', 'vid']) to_predict_users = pd.read_csv('testA_pred_did.csv') # 待预测用户 to_predict_exposure = pd.read_csv('testA_did_show.csv') # 待预测用户的曝光视频 # 注意：弹幕数据暂不使用 #步骤2：构建点击预测模型的训练集，如果内存不足，可以考虑分批处理或使用Dask等工具。 # 正样本：历史点击数据，标记为1 positive_samples = hist_click[['did', 'vid']].copy() positive_samples['label'] = 1 # 负样本：从历史曝光数据中，排除出现在历史点击数据中的（did, vid） # 注意：同一个用户可能曝光了多个视频，但只点击了部分，所以未点击的就是负样本 # 合并同一个用户的所有曝光和点击，然后取差集 # 方法：对每个用户，曝光视频中不在点击视频列表中的作为负样本 # 注意：数据量可能很大，需要分组操作 # 先获取每个用户点击了哪些视频（did, vid集合） user_clicked_vids = hist_click.groupby('did')['vid'].apply(set).reset_index(name='clicked_set') # 将历史曝光数据与user_clicked_vids合并 hist_exposure_with_click = hist_exposure.merge(user_clicked_vids, on='did', how='left') # 对于每个曝光记录，如果vid不在clicked_set中，则作为负样本 # 注意：如果用户没有点击记录，则clicked_set为NaN，使用空集 hist_exposure_with_click['clicked_set'] = hist_exposure_with_click['clicked_set'].apply(lambda x: x if isinstance(x, set) else set()) hist_exposure_with_click['is_clicked'] = hist_exposure_with_click.apply(lambda row: row['vid'] in row['clicked_set'], axis=1) # 负样本：未点击的曝光记录 negative_samples = hist_exposure_with_click[~hist_exposure_with_click['is_clicked']][['did', 'vid']] negative_samples['label'] = 0 # 合并正负样本 click_train_data = pd.concat([positive_samples, negative_samples], axis=0, ignore_index=True) # 合并用户特征和视频特征 click_train_data = click_train_data.merge(did_features, on='did', how='left') click_train_data = click_train_data.merge(vid_info, on='vid', how='left') # 注意：这里可能会有缺失值（比如视频信息表中没有某个视频的信息），需要填充 # 填充策略：对于数值特征，用中位数或均值；对于类别特征，用众数或特殊值（如-1） #步骤3：特征工程（点击预测模型） video_click_count = hist_click.groupby('vid').size().reset_index(name='video_click_count') click_train_data = click_train_data.merge(video_click_count, on='vid', how='left') click_train_data['video_click_count'].fillna(0, inplace=True) # 对于新视频，用0填充 #步骤4：训练点击预测模型 # 划分训练集和验证集 X = click_train_data.drop(columns=['did', 'vid', 'label']) y = click_train_data['label'] # 将类别特征转换为类别类型（LightGBM可以处理类别特征） categorical_features = ['item_cid', 'item_type', 'item_assetSource', 'item_classify', 'item_isIntact', 'sid', 'stype'] for col in categorical_features: if col in X.columns: X[col] = X[col].astype('category') X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) # 训练LightGBM分类器 params = { 'objective': 'binary', 'metric': 'binary_logloss', 'boosting_type': 'gbdt', 'num_leaves': 31, 'learning_rate': 0.05, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': 0 } train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_features, free_raw_data=False) val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_features, free_raw_data=False) model_click = lgb.train(params, train_data, valid_sets=[train_data, val_data], num_boost_round=1000, early_stopping_rounds=50, verbose_eval=10) # 保存模型 model_click.save_model('click_model.txt') #步骤5：构建完播率预测模型的训练集 # 使用历史播放数据（有播放时长），需要合并视频信息表获取视频时长，然后计算完播率 # 注意：播放时长可能大于视频时长，所以完播率最大为1 play_data = hist_play.merge(vid_info[['vid', 'item_duration']], on='vid', how='left') play_data['completion_rate'] = play_data['play_time'] / play_data['item_duration'] play_data['completion_rate'] = play_data['completion_rate'].clip(upper=1.0) # 超过1的设为1 # 合并用户特征和视频特征 play_train_data = play_data.merge(did_features, on='did', how='left') play_train_data = play_train_data.merge(vid_info.drop(columns=['item_duration']), on='vid', how='left') # 同样，构造统计特征（如用户平均完播率、视频平均完播率等） # 示例：用户平均完播率 user_avg_completion = play_train_data.groupby('did')['completion_rate'].mean().reset_index(name='user_avg_completion') play_train_data = play_train_data.merge(user_avg_completion, on='did', how='left') # 视频平均完播率 video_avg_completion = play_train_data.groupby('vid')['completion_rate'].mean().reset_index(name='video_avg_completion') play_train_data = play_train_data.merge(video_avg_completion, on='vid', how='left') # 填充缺失值 # ... # 特征矩阵 X_play = play_train_data.drop(columns=['did', 'vid', 'play_time', 'item_duration', 'completion_rate']) y_play = play_train_data['completion_rate'] #步骤6：训练完播率预测模型 # 划分训练集和验证集 X_train_play, X_val_play, y_train_play, y_val_play = train_test_split(X_play, y_play, test_size=0.2, random_state=42) # 训练LightGBM回归模型 params_reg = { 'objective': 'regression', 'metric': 'mae', 'boosting_type': 'gbdt', 'num_leaves': 31, 'learning_rate': 0.05, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': 0 } train_data_play = lgb.Dataset(X_train_play, label=y_train_play, categorical_feature=categorical_features, free_raw_data=False) val_data_play = lgb.Dataset(X_val_play, label=y_val_play, categorical_feature=categorical_features, free_raw_data=False) model_play = lgb.train(params_reg, train_data_play, valid_sets=[train_data_play, val_data_play], num_boost_round=1000, early_stopping_rounds=50, verbose_eval=10) # 保存模型 model_play.save_model('play_model.txt')

from sklearn.metrics import accuracy_score, mean_absolute_error from tqdm import tqdm # 添加进度条 # 步骤1：数据读取与预处理（优化内存管理） def load_data_safely(file_path, usecols=None, dtype=None):...

train() got an unexpected keyword argument 'early_stopping_rounds'报错：请修复下列代码并完整输出：import pandas as pd import numpy as np import lightgbm as lgb from lightgbm import early_stopping, log_evaluation import gc import os import chardet from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, mean_absolute_error from tqdm import tqdm # 添加进度条 # 步骤1：数据读取与预处理（优化内存管理） def load_data_safely(file_path, usecols=None, dtype=None): """安全加载大型CSV文件""" try: # 自动检测编码 with open(file_path, 'rb') as f: encoding = chardet.detect(f.read(10000))['encoding'] encoding = encoding if encoding else 'latin1' # 分批读取 chunks = [] for chunk in tqdm(pd.read_csv(file_path, encoding=encoding, usecols=usecols, dtype=dtype, chunksize=100000, low_memory=False), desc=f"Loading {os.path.basename(file_path)}"): chunks.append(chunk) return pd.concat(chunks, ignore_index=True) except Exception as e: print(f"⚠️ 加载 {file_path} 失败: {str(e)}") return pd.DataFrame() # 定义内存优化的数据类型 dtypes = { 'did': 'category', 'vid': 'category', 'item_cid': 'category', 'item_type': 'category', 'item_assetSource': 'category', 'item_classify': 'category', 'item_isIntact': 'category', 'sid': 'category', 'stype': 'category', 'play_time': 'float32' } # 加载核心数据 did_features = load_data_safely('did_features_table.csv', dtype={dtypes, {f'f{i}': 'float32' for i in range(88)}}) vid_info = load_data_safely('vid_info_table.csv', dtype=dtypes) # 加载历史数据（分批处理） def load_historical_data(days=32, sample_frac=0.3): """分批加载历史数据并采样""" see_list, click_list, play_list = [], [], [] for day in tqdm(range(1, days + 1), desc="加载历史数据"): day_str = f"{day:02d}" # 加载曝光数据（采样减少内存） see_path = f'see_{day_str}.csv' if os.path.exists(see_path): see = load_data_safely(see_path, usecols=['did', 'vid'], dtype=dtypes) if not see.empty: see = see.sample(frac=sample_frac) # 采样减少数据量 see['day'] = day_str see_list.append(see) del see # 加载点击数据 click_path = f'click_{day_str}.csv' if os.path.exists(click_path): click = load_data_safely(click_path, usecols=['did', 'vid', 'click_time'], dtype=dtypes) if not click.empty and 'click_time' in click.columns: click['date'] = pd.to_datetime(click['click_time'], errors='coerce').dt.date click_list.append(click[['did', 'vid', 'date']]) del click # 加载播放数据 play_path = f'playplus_{day_str}.csv' if os.path.exists(play_path): play = load_data_safely(play_path, usecols=['did', 'vid', 'play_time'], dtype=dtypes) if not play.empty and 'play_time' in play.columns: play_list.append(play) del play gc.collect() return ( pd.concat(see_list).drop_duplicates(['did', 'vid']) if see_list else pd.DataFrame(), pd.concat(click_list).drop_duplicates(['did', 'vid']) if click_list else pd.DataFrame(), pd.concat(play_list).drop_duplicates(['did', 'vid']) if play_list else pd.DataFrame() ) # 加载历史数据（采样30%减少内存） hist_exposure, hist_click, hist_play = load_historical_data(days=32, sample_frac=0.3) # 加载预测数据 to_predict_users = load_data_safely('testA_pred_did.csv', dtype=dtypes) to_predict_exposure = load_data_safely('testA_did_show.csv', dtype=dtypes) # 步骤2：构建点击预测训练集（优化内存使用） def build_click_dataset(hist_exposure, hist_click, sample_ratio=0.1): """构建点击数据集，优化内存使用""" # 标记正样本 hist_click['label'] = 1 # 高效标记负样本（避免创建大集合） merged = hist_exposure.merge( hist_click[['did', 'vid']].assign(is_clicked=True), on=['did', 'vid'], how='left' ) merged['is_clicked'] = merged['is_clicked'].fillna(False) # 负样本采样 negative_samples = merged[~merged['is_clicked']].sample(frac=sample_ratio) negative_samples['label'] = 0 # 合并数据集 click_data = pd.concat([ hist_click[['did', 'vid', 'label']], negative_samples[['did', 'vid', 'label']] ], ignore_index=True) # 释放内存 del merged, negative_samples gc.collect() return click_data click_train_data = build_click_dataset(hist_exposure, hist_click, sample_ratio=0.1) # 步骤3：特征工程（点击预测模型） def add_click_features(df, did_features, vid_info, hist_click, hist_play): """添加关键特征，避免内存溢出""" # 基础特征 df = df.merge(did_features, on='did', how='left') df = df.merge(vid_info, on='vid', how='left') # 用户行为统计（使用聚合避免大表连接） user_stats = pd.concat([ hist_click.groupby('did').size().rename('user_click_count'), hist_play.groupby('did')['play_time'].sum().rename('user_total_play') ], axis=1).reset_index() df = df.merge(user_stats, on='did', how='left') # 视频热度统计 video_stats = pd.concat([ hist_click.groupby('vid').size().rename('video_click_count'), hist_play.groupby('vid')['play_time'].mean().rename('avg_play_time') ], axis=1).reset_index() df = df.merge(video_stats, on='vid', how='left') # 填充缺失值（冷启动处理） fill_values = { 'user_click_count': 0, 'user_total_play': 0, 'video_click_count': df['video_click_count'].median(), 'avg_play_time': df['avg_play_time'].median() } for col, value in fill_values.items(): df[col] = df[col].fillna(value) # 添加时间相关特征 if 'date' in df: df['day_of_week'] = pd.to_datetime(df['date']).dt.dayofweek.astype('category') df['hour'] = pd.to_datetime(df['date']).dt.hour.astype('category') return df # 添加特征 click_train_data = add_click_features( click_train_data, did_features, vid_info, hist_click, hist_play ) # 步骤4：训练点击预测模型（优化类别特征处理） categorical_features = [ 'item_cid', 'item_type', 'item_assetSource', 'item_classify', 'item_isIntact', 'sid', 'stype', 'day_of_week', 'hour' ] # 明确指定分类特征 for col in categorical_features: if col in click_train_data.columns: click_train_data[col] = click_train_data[col].astype('category').cat.as_ordered() # 准备训练数据 X = click_train_data.drop(columns=['did', 'vid', 'label', 'date'], errors='ignore') y = click_train_data['label'] # 内存优化：删除不需要的列后立即释放 del click_train_data gc.collect() # 划分数据集 X_train, X_val, y_train, y_val = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) # 训练模型（优化参数） params = { 'objective': 'binary', 'metric': 'binary_logloss', 'boosting_type': 'gbdt', 'num_leaves': 63, # 增加复杂度 'learning_rate': 0.05, 'feature_fraction': 0.8, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'min_child_samples': 100, # 防止过拟合 'verbosity': -1 } train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_features) val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_features) model_click = lgb.train( params, train_data, num_boost_round=1500, # 增加轮次 valid_sets=[train_data, val_data], early_stopping_rounds=100, # 更宽松的早停 verbose_eval=50 ) # 步骤5：构建完播率训练集（优化特征工程） def build_play_dataset(hist_play, vid_info, did_features, hist_click): """构建完播率数据集""" # 基础数据 play_data = hist_play.merge( vid_info[['vid', 'item_duration']], on='vid', how='left' ) # 计算完播率 play_data['completion_rate'] = play_data['play_time'] / play_data['item_duration'] play_data['completion_rate'] = play_data['completion_rate'].clip(upper=1.0) # 添加用户特征 play_data = play_data.merge(did_features, on='did', how='left') # 添加视频特征 play_data = play_data.merge( vid_info.drop(columns=['item_duration']), on='vid', how='left' ) # 添加统计特征 # 用户平均完播率 user_stats = play_data.groupby('did')['completion_rate'].agg(['mean', 'count']).reset_index() user_stats.columns = ['did', 'user_avg_completion', 'user_play_count'] play_data = play_data.merge(user_stats, on='did', how='left') # 视频平均完播率 video_stats = play_data.groupby('vid')['completion_rate'].agg(['mean', 'std']).reset_index() video_stats.columns = ['vid', 'video_avg_completion', 'video_completion_std'] play_data = play_data.merge(video_stats, on='vid', how='left') # 用户-视频互动特征 user_video_stats = hist_click.groupby(['did', 'vid']).size().reset_index(name='user_vid_clicks') play_data = play_data.merge(user_video_stats, on=['did', 'vid'], how='left') # 填充缺失值 play_data['user_avg_completion'].fillna(play_data['completion_rate'].mean(), inplace=True) play_data['user_play_count'].fillna(1, inplace=True) play_data['video_avg_completion'].fillna(play_data['completion_rate'].median(), inplace=True) play_data['video_completion_std'].fillna(0, inplace=True) play_data['user_vid_clicks'].fillna(0, inplace=True) return play_data play_train_data = build_play_dataset(hist_play, vid_info, did_features, hist_click) # 步骤6：训练完播率模型（添加正则化） X_play = play_train_data.drop(columns=['did', 'vid', 'play_time', 'item_duration', 'completion_rate']) y_play = play_train_data['completion_rate'] # 划分数据集 X_train_play, X_val_play, y_train_play, y_val_play = train_test_split( X_play, y_play, test_size=0.2, random_state=42 ) # 训练参数 params_reg = { 'objective': 'regression', 'metric': 'mae', 'boosting_type': 'gbdt', 'num_leaves': 63, 'learning_rate': 0.03, # 降低学习率 'feature_fraction': 0.8, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'lambda_l1': 0.1, # 添加L1正则化 'lambda_l2': 0.1, # 添加L2正则化 'min_data_in_leaf': 50, 'verbosity': -1 } train_data_play = lgb.Dataset(X_train_play, label=y_train_play, categorical_feature=categorical_features) val_data_play = lgb.Dataset(X_val_play, label=y_val_play, categorical_feature=categorical_features) model_play = lgb.train( params_reg, train_data_play, num_boost_round=2000, valid_sets=[train_data_play, val_data_play], early_stopping_rounds=100, verbose_eval=50 ) # 保存模型 model_click.save_model('click_model.txt') model_play.save_model('play_model.txt')

from sklearn.metrics import accuracy_score, mean_absolute_error from tqdm import tqdm # 添加进度条 # 步骤1：数据读取与预处理（优化内存管理） def load_data_safely(file_path, usecols=None, dtype=None):...

param = {'num_leaves': 31, 'min_data_in_leaf': 20, 'objective': 'binary', 'learning_rate': 0.06, "boosting": "gbdt", "metric": 'None', "verbosity": -1} trn_data = lgb.Dataset(trn, trn_label) val_data = lgb.Dataset(val, val_label) num_round = 666 # clf = lgb.train(param, trn_data, num_round, valid_sets=[trn_data, val_data], verbose_eval=100, # early_stopping_rounds=300, feval=win_score_eval) clf = lgb.train(param, trn_data, num_round) # oof_lgb = clf.predict(val, num_iteration=clf.best_iteration) test_lgb = clf.predict(test, num_iteration=clf.best_iteration)thresh_hold = 0.5 oof_test_final = test_lgb >= thresh_hold print(metrics.accuracy_score(test_label, oof_test_final)) print(metrics.confusion_matrix(test_label, oof_test_final)) tp = np.sum(((oof_test_final == 1) & (test_label == 1))) pp = np.sum(oof_test_final == 1) print('accuracy1:%.3f'% (tp/(pp)))test_postive_idx = np.argwhere(oof_test_final == True).reshape(-1) # test_postive_idx = list(range(len(oof_test_final))) test_all_idx = np.argwhere(np.array(test_data_idx)).reshape(-1) stock_info['trade_date_id'] = stock_info['trade_date'].map(date_map) stock_info['trade_date_id'] = stock_info['trade_date_id'] + 1tmp_col = ['ts_code', 'trade_date', 'trade_date_id', 'open', 'high', 'low', 'close', 'ma5', 'ma13', 'ma21', 'label_final', 'name'] stock_info.iloc[test_all_idx[test_postive_idx]] tmp_df = stock_info[tmp_col].iloc[test_all_idx[test_postive_idx]].reset_index() tmp_df['label_prob'] = test_lgb[test_postive_idx] tmp_df['is_limit_up'] = tmp_df['close'] == tmp_df['high'] buy_df = tmp_df[(tmp_df['is_limit_up']==False)].reset_index() buy_df.drop(['index', 'level_0'], axis=1, inplace=True)buy_df['buy_flag'] = 1 stock_info_copy['sell_flag'] = 0tmp_idx = (index_df['trade_date'] == test_date_min+1) close1 = index_df[tmp_idx]['close'].values[0] test_date_max = 20220829 tmp_idx = (index_df['trade_date'] == test_date_max) close2 = index_df[tmp_idx]['close'].values[0]tmp_idx = (stock_info_copy['trade_date'] >= test_date_min) & (stock_info_copy['trade_date'] <= test_date_max) tmp_df = stock_info_copy[tmp_idx].reset_index(drop=True)from imp import reload import Account reload(Account) money_init = 200000 account = Account.Account(money_init, max_hold_period=20, stop_loss_rate=-0.07, stop_profit_rate=0.12) account.BackTest(buy_df, tmp_df, index_df, buy_price='open')tmp_df2 = buy_df[['ts_code', 'trade_date', 'label_prob', 'label_final']] tmp_df2 = tmp_df2.rename(columns={'trade_date':'buy_date'}) tmp_df = account.info tmp_df['buy_date'] = tmp_df['buy_date'].apply(lambda x: int(x)) tmp_df = tmp_df.merge(tmp_df2, on=['ts_code', 'buy_date'], how='left')最终的tmp_df是什么？tmp_df[tmp_df['label_final']==1]又选取了什么股票？

根据代码逐行分析，tmp_df 是一个 DataFrame，包含了股票的信息以及回测结果。其中，选取了 label_final 为 1 的股票，也就是模型预测为涨的股票，并且过滤掉了当天涨停的股票。最终买入的股票信息保存在 buy_df 中...

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